PREMIER QMS - Wiki - User contributions [en]

Premier Experimental Design

2024-08-30T10:26:29Z

Admin:

<h2>Introduction</h2>This template should help you to plan your project in such a way that all possible difficulties, risks and systematic errors that may occur are considered and minimized in advance. By means of specific questions the ever present bias in experiments shall be reduced and project specific topics shall be brought into the awareness of your experimental design. It should be noted that you do not necessarily have to / can answer all points, which depends on your type of project (exploratory / confirmatory etc.). If you can't say anything about single points, please briefly explain why.

Explainer videos for technical use can be found [https://premier-qms.org/premier/planning-of-experiments/template-usage here].

<h2>Project details</h2>'''Project name:'''

'''Project Manager:'''

'''Planned project duration:'''

'''Short project description:''' (What is your project about? What is the goal?)

(If required, click the above edit button and upload an image.)

<h2>Search</h2>[https://premier-qms.org/premier/planning-of-experiments/search support]

'''Literature Databases / Other sources:'''

'''What is (was) the research strategy?'''

<h2>Hypothesis / Counterhypothesis</h2>[https://premier-qms.org/premier/planning-of-experiments/hypothesis-counter-hypothesis support]

'''Set up hypothesis / counter hypothesis: Have you formulated your hypothesis and the corresponding counter and/or null hypothesis?'''

'''Counterhypothesis: Have you searched for literature that argues against the hypothesis?'''

<h2>Target Parameter</h2>[https://premier-qms.org/premier/planning-of-experiments/target-parameters support]

'''Definition of the target parameters: Which primary and secondary target parameters have you defined?'''

<h2>Sample Size Calculation</h2>[https://premier-qms.org/premier/planning-of-experiments/sample-size-calculation support]

'''Need for experimental units: How was the need for experimental units (number of animals, organs, organ sections or cultured cells) determined?'''

'''Which reference was used for the estimation? groups and units: Name the experimental groups with the exact number of units and identify test and control groups.'''

<h2>Experimental Design / Model Planning</h2>[https://premier-qms.org/premier/planning-of-experiments/model-planning-study-design support]

'''Definition of criteria: Have you defined the following criteria for your project: - method selection - influencing factors - requirements - control groups - validations?'''

<h2>Feasibility Check</h2>[https://premier-qms.org/premier/planning-of-experiments/feasibility-study support]

'''Feasibility of the project: At the end of the feasibility study there is a plausibility check. Is it positive for your project?'''

<h2>Nesting and Pseudoreplication</h2>[https://premier-qms.org/premier/planning-of-experiments/nesting-and-pseudoreplication support]

'''Identification of the problem: Does the biological unit used for statistical evaluation correspond to the one used for randomisation? If not, check if pseudoreplication is present and how it can be avoided in the evaluation.'''

<h2>Randomization and Blinding</h2>[https://premier-qms.org/premier/planning-of-experiments/randomisation-and-blinding support]

'''Implementation of R+B: Which methods were used to randomize and blind your experiments?'''

'''If you did not perform randomization and blinding, please explain why!'''

<h2>Resource Plan</h2>[https://premier-qms.org/premier/planning-of-experiments/resource-plan-financing-capacities-personnel support]

'''Resources and infrastructure: Is the financing of your project secured and is there enough personnel, material and a suitable infrastructure available?'''

'''Alternatives: If this is not the case, are there planned strategies to overcome the possible challenges?'''

<h2>Timetable</h2>[https://premier-qms.org/premier/planning-of-experiments/schedule support]

'''Preparation of the timetable: Have you created a detailed, realistic timetable for your project?'''

'''Is a project management tool available?'''

'''How do you manage, review and update this timetable?'''

(If required, click the above edit button and upload a timetable Excel file.)

<h2>Accompanying training courses</h2>[https://premier-qms.org/premier/planning-of-experiments/accompanying-training-and-courses support]

'''Determine training needs: Are there any special trainings, courses etc. that are absolutely necessary in order to carry out the project?'''

'''Attend training courses: Do you attend training courses that can be helpful for your project?'''

<h2>Planning of Data Preparation / Analysis</h2>[https://premier-qms.org/premier/planning-of-experiments/planning-of-data-preparation-analysis support]

'''Data analysis: How did you plan the aggregation and preparation of the data?'''

'''Please explain and document (in the ELN) the step from primary to secondary data!'''

'''Archiving the data: Have you ensured the archiving of primary and secondary data?'''

<h2>Data Storage</h2>[https://premier-qms.org/premier/planning-of-experiments/data-storage support]

'''Location: Where did you store the primary and secondary data?'''

'''Backups: Are you planning additional backups?'''

<h2>Clarification of Authorships</h2>[https://premier-qms.org/premier/planning-of-experiments/clarification-of-authorship support]

'''Requirements for first authorship: How are the authorships for your project clarified?'''

'''Documentation: Has the agreement been documented? If so, where?'''

<h2>Preregistration</h2>[https://premier-qms.org/premier/planning-of-experiments/pre-registration support]

'''Implementation: Where did you pre register your project?'''

'''Please explain in the ELN if you have not pre registered your project!'''

Premier Experimental Design

2024-08-30T10:25:16Z

Admin:

==Introduction==
This template should help you to plan your project in such a way that all possible difficulties, risks and systematic errors that may occur are considered and minimized in advance. By means of specific questions the ever present bias in experiments shall be reduced and project specific topics shall be brought into the awareness of your experimental design. It should be noted that you do not necessarily have to / can answer all points, which depends on your type of project (exploratory / confirmatory etc.). If you can't say anything about single points, please briefly explain why.

Explainer videos for technical use can be found [https://premier-qms.org/premier/planning-of-experiments/template-usage here].

==Project details==
'''Project name:'''

'''Project Manager:'''

'''Planned project duration:'''

'''Short project description:''' (What is your project about? What is the goal?)

(If required, click the above edit button and upload an image.)

==Search==
[https://premier-qms.org/premier/planning-of-experiments/search support]

'''Literature Databases / Other sources:'''

'''What is (was) the research strategy?'''

==Hypothesis / Counterhypothesis==
[https://premier-qms.org/premier/planning-of-experiments/hypothesis-counter-hypothesis support]

'''Set up hypothesis / counter hypothesis: Have you formulated your hypothesis and the corresponding counter and/or null hypothesis?'''

'''Counterhypothesis: Have you searched for literature that argues against the hypothesis?'''

==Target Parameter==
[https://premier-qms.org/premier/planning-of-experiments/target-parameters support]

''''''

'''Definition of the target parameters: Which primary and secondary target parameters have you defined?'''

==Sample Size Calculation==
[https://premier-qms.org/premier/planning-of-experiments/sample-size-calculation support]

'''Need for experimental units: How was the need for experimental units (number of animals, organs, organ sections or cultured cells) determined?'''

'''Which reference was used for the estimation? groups and units: Name the experimental groups with the exact number of units and identify test and control groups.'''

==Experimental Design / Model Planning==
[https://premier-qms.org/premier/planning-of-experiments/model-planning-study-design support]

'''Definition of criteria: Have you defined the following criteria for your project: - method selection - influencing factors - requirements - control groups - validations?'''

==Feasibility Check==
[https://premier-qms.org/premier/planning-of-experiments/feasibility-study support]

'''Feasibility of the project: At the end of the feasibility study there is a plausibility check. Is it positive for your project?'''

==Nesting and Pseudoreplication==
[https://premier-qms.org/premier/planning-of-experiments/nesting-and-pseudoreplication support]

'''Identification of the problem: Does the biological unit used for statistical evaluation correspond to the one used for randomisation? If not, check if pseudoreplication is present and how it can be avoided in the evaluation.'''

==Randomization and Blinding==
[https://premier-qms.org/premier/planning-of-experiments/randomisation-and-blinding support]

'''Implementation of R+B: Which methods were used to randomize and blind your experiments?'''

'''If you did not perform randomization and blinding, please explain why!'''

==Resource Plan==
[https://premier-qms.org/premier/planning-of-experiments/resource-plan-financing-capacities-personnel support]

'''Resources and infrastructure: Is the financing of your project secured and is there enough personnel, material and a suitable infrastructure available?'''

'''Alternatives: If this is not the case, are there planned strategies to overcome the possible challenges?'''

==Timetable==
[https://premier-qms.org/premier/planning-of-experiments/schedule support]

'''Preparation of the timetable: Have you created a detailed, realistic timetable for your project?'''

'''Is a project management tool available?'''

'''How do you manage, review and update this timetable?'''

(If required, click the above edit button and upload a timetable Excel file.)

==Accompanying training courses==
[https://premier-qms.org/premier/planning-of-experiments/accompanying-training-and-courses support]

'''Determine training needs: Are there any special trainings, courses etc. that are absolutely necessary in order to carry out the project?'''

'''Attend training courses: Do you attend training courses that can be helpful for your project?'''

==Planning of Data Preparation / Analysis==
[https://premier-qms.org/premier/planning-of-experiments/planning-of-data-preparation-analysis support]

'''Data analysis: How did you plan the aggregation and preparation of the data?'''

'''Please explain and document (in the ELN) the step from primary to secondary data!'''

'''Archiving the data: Have you ensured the archiving of primary and secondary data?'''

==Data Storage==
[https://premier-qms.org/premier/planning-of-experiments/data-storage support]

'''Location: Where did you store the primary and secondary data?'''

'''Backups: Are you planning additional backups?'''

==Clarification of Authorships==
[https://premier-qms.org/premier/planning-of-experiments/clarification-of-authorship support]

'''Requirements for first authorship: How are the authorships for your project clarified?'''

'''Documentation: Has the agreement been documented? If so, where?'''

==Preregistration==
[https://premier-qms.org/premier/planning-of-experiments/pre-registration support]

'''Implementation: Where did you pre register your project?'''

'''Please explain in the ELN if you have not pre registered your project!'''

PREMIER Quality Assurance

2021-12-02T21:36:31Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
Quality assurance is intended to ensure that PREMIER's requirements as well as the own requirements that the organization / laboratory has set itself, are implemented and continuously improved.

== Background ==
PREMIER is a conceptual framework of modules with minimum requirements that can be adapted to the needs and resources of any organization. An essential building block in PREMIER is quality assurance with its specific quality measures. Quality assurance in preclinical research is based on measurements and continuous improvements in order to assess the effectiveness of the applied quality measures.

== Tasks / Actions ==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== PDCA Cycle ===
The foundation of quality assurance is measurement and continuous improvement in order to be able to assess the effectiveness of the quality measures you use. To maintain a high level of performance in research work, it is essential to react to changes in internal and external requirements and to create new solutions. Foundation is Deming's PDCA cycle (Plan-Do-Check-Act) which is the most tested and accepted base for continuous improvement on a wide range of organizations including clinical research. This makes PREMIER comparable to other Quality Management Systems.
[[File:PDCA Zyclus nach Deming.png|none|frame|PDCA Zyclus nach Deming]]

=== Key Performance Indicators ===
The success of research is complex and difficult to measure; therefore, only indirect methods can be used to approach the topic. One possibility is to use key performance indicators. Indicators are specific measures or quality criteria defined to measure achievable milestones or to enable clear, valid conclusions about specific areas. The organization or laboratory has to define its own key performance indicators for the working environment. To be effective they need to be collected and analyzed at regular intervals (usually yearly) or when needed. All key performance indicators should be monitored, i.e. they are modified and adjusted accordingly to the requirements of the research work. This makes it possible to identify developments and trends in the core areas and take appropriate countermeasures if necessary.

A laboratory should define its specific key performance indicators depending on the field of research, which are collected for example in a table by a dedicated person and evaluated by the management. Some sample are listed below:

* number of open access publications
* number of registered reports
* number of studies clearly documented in laboratory notebook
* the raising of funds
* the number of used ORCID IDs
* the number of entries in an electronic laboratory notebook
* the number of master and doctoral theses completed
* the number of participants in mandatory training courses etc.

The results should be discussed with all project leaders and involved employees and, if necessary, countermeasures should be taken to counteract a negative development.

=== Impact Analysis ===
A second method of indirectly measuring quality is impact analysis. This is about whether and to what extent effects have been achieved with specific measures. The impact analysis asks four questions:

# How many resources flow into the project? '''Input'''
# What results / services does the project deliver and what is achieved? '''Output'''
# What changes does this bring about for researchers and to what extent? '''Outcome'''
# To which developments in research does the project / results contribute? '''Impact'''

'''Chain of effects: INPUT -> OUTPUT -> OUTCOME -> IMPACT'''

Impact analysis means the recording, investigation and evaluation of all expected and unexpected effects of a project. It thus enables active guidance of the project.

It allows:

* react promptly to unwanted changes / results and
* make more precise conclusions about which changes in the project lead to which consequences.

[[File:Wirkungstreppe.jpg|none|thumb|Effect stairs]]

=== Monitoring and Evaluation ===
Two instruments of impact analysis are monitoring and evaluation. Both follow different questions and objectives (see table).

{| class="wikitable"
|+ Table Monitoring and Evaluation
|-
| || '''Monitoring''' || '''Evaluation'''
|-
| '''What do you like to know?''' || What happens in the project? || Why does something happen in what quality and with what consequences? (impacts)
|-
| '''Why?''' ||
* To check the progress of the project.
* To make informed decisions.
* To be able to make adjustments.
* To create a basis for further analysis (e.g. evaluation).
||
* To describe and evaluate progress and results.
* To provide conclusions and recommendations.
|-
| '''Who?''' || internal, employees working on this project || internal and external
|-
| '''When?''' || continuously, during the entire project || during the project, at the end of the project or sometime after the end of the project
|-
| '''For which stage in the impact logic is this important?''' || Focus on inputs and outputs and easily identifiable effects (outcomes) || Focus on impacts (outcome and impact)
|}

==== Monitoring ====
Monitoring means collecting information on a regular basis to observe the progress of the project and to check whether quality standards are being met.

* Monitoring is suitable for documenting inputs, outputs and easy-to-understand impacts.
* Systematically carried out, monitoring also allows statements to be made later on about the entire project.
* With the help of the data obtained in monitoring, it can be determined that a project is not running as planned.

==== Evaluation ====
* An evaluation examines and assesses processes, results and impacts and can be -carried out at different times in the project.
* The focus is on outcomes and impact.
An evaluation can be used to determine why a project is not running as planned.

=== Internal Validation ===
The internal validation should help us to answer the following questions:

* Where do we stand? Where is our baseline?
* Are the introduced quality-measures being implemented?
* Have we taken the right measures (according to our requirements) or have we overstepped the target?
* Are our quality-measures effective?
* Are our milestones being achieved? (transparency, knowledge sharing, open access / open data / publishing null results, data storage, documentation, study design, statistical evaluation, randomization + blinding, pre-registration etc.).

To be able to answer the questions, various forms of internal validation (accompanying research), such as self-assessment and internal audits, could be carried out to answer the questions. Thereby:

* to determine the status quo of implemented measures,
* identifies the gaps / recognizes problems and
* processes / procedures / measures can be readjusted.

=== External Validation ===
The external validation are 3rd party assessments, i.e. audits, on-site visits or peer audits, which are carried out by external third parties. These can be auditors or cooperation partners who check specific processes for their feasibility and effectiveness. Here it is checked whether the laboratories with their processes and work flows meet their own requirements. A view from outside, from a neutral person, is often helpful against work blindness, which often arises after years in the laboratory.

Peer audits as a special form for external validation are a promising novel tool to solicit external feedback and foster professional exchange of ongoing projects at eye-level. They are very effective in fostering the improvement of methods or processes.

PREMIER recommend conducting at least one peer audit during a project's lifetime, especially if specific methodological challenges need to be solved and an exchange with colleagues is desired.

Auditing contributes to transparency. In general, and peer audits in particular, auditing might serve as fundamental processes of open and transparent science in the future.

=== Audits ===
Internal and external audits are further quality assurance measures. They are part of the PDCA cycle (plan-do-check-act) at the "check" level. The measures identified in the audits are implemented and the continuous improvement process is set in motion ("act"). Audits in PREMIER meet the following requirements:

Not to focus on bureaucratic processes, but
carry out content validation of the research processes and
accepted by personnel at all levels in the organization.
Based on our experience with various forms of audits, we recommend in PREMIER:

Error management with LabCIRS (Self-Assessment) (see [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2000705 Plos Biology])
Internal audits in variable form (depending on need), such as method, document, project, process and data audits etc.
Peer Audits as external audit (professional exchange, expertise at eye level).
These forms of audits have shown to be effective in our setting and are accepted by scientists. (see [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240719 Plos One])

Audits carried out, run through a predefined workflow.

==== Auditworkflow ====

The results of the audits are qualitatively evaluated. At each final meeting of an audit you should ask the participating employees for feedback and document this in the audit reports. On the basis of these reports, the follow-up audits can be further developed and improved accordingly.

A checklist for carrying out audits and a template for the audit plan can be found [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240719 here].
[[File:Auditworkflow.jpg|alternativtext=Auditworkflow|none|thumb|600x600px|Auditworkflow]]

=== Risk Assessment ===
In PREMIER, risk assessment takes place indirectly at various levels:

* with the help of specific, very focused audits (internal and peer audits)
* with an anonymous error reporting system (LabCIRS)
* with specific key performance indicators that cover all research areas
* with accompanying research to monitor the status quo of measures introduced
* with a specific design of experiments, which considers risks in advance - before the start of the project - and tries to avoid them with targeted countermeasures: experimental design template

With the help of the listed measures, risks, errors and gaps are identified using various methods:

* identified
* analyzed
* rated
* reduced / avoided / solved
* monitored
* documented

In summary, all measures result in an overall view that allows developments, trends and risks to be identified and, if necessary, counteracted.

=== References ===
* Deming W E; Out of the crisis; Massachusetts Institute of Technology Press, 1986 Deming, W. Edwards (1993). The New Economics for Industry, Government, and Education. Boston, Ma: MIT Press. p. 132. ISBN 0262541165)
* Kurreck C, Castanos-Velez, Bernard R, Dirnagl U. PREMIER: Structured quality assurance from and for academic preclinical biomedicine. [https://www.osf.io/xw75z/ Pre-registration]
* Dirnagl U, Kurreck C, Castaños-Vélez E, Bernard R. Quality management for academic laboratories: burden or boon? Professional quality management could be very beneficial for academic research but needs to overcome specific caveats. EMBO Rep. 2018 Nov;19(11). pii: e47143. doi: 10.15252/embr.201847143. Epub 2018 Oct 19. PubMed PMID: 30341068; [https://www.embopress.org/doi/pdf/10.15252/embr.201847143 EMBO]
* Kurreck C, Castanos-Velez E., Freyer D., Blumenau S., Przesdzing I., Bernard R, Dirnagl U.; Improving quality of preclinical academic research through auditing: A feasibility study. [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240719 PLOS ONE]
* Dirnagl U, Przesdzing I, Kurreck C, Major S. A Laboratory Critical Incident and Error Reporting System for Experimental Biomedicine. PLoS Biol. 2016;14(12): e2000705. Epub 2016/12/03. doi: 10.1371/journal.pbio.2000705. PubMed PMID: 27906976; [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2000705 Plos Biology]
* Bewertung qualitativer Forschung - Springer [https://link.springer.com/content/pdf/10.1007%2F978-3-540-95936-6_8.pdf Link]
* [https://forum.premier-qms.org/t/measures-improvement Discuss at PREMIER forum]

PREMIER Laboratory Organization

2021-12-02T21:36:13Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
The objective is to ensure an effective and good laboratory organization in a way that ultimately generates robust and reproducible research results.

== Background ==
Crucial factors to achieve a successful research laboratory organization in our experience are a team effort approach and the assignment of individual responsibilities.

In order to be able to implement novel and standardized processes (SOPs) in each workflow, responsibilities in the laboratory must be defined, a safe working environment and functioning, preferably maintained and calibrated equipment must be available. Irregularities and errors should be reported preferentially using tools such as [https://github.com/major-s/labcirs LABCIRS] and regularly analyzed, evaluated and corrected.

Although PREMIER strives for an uncomplicated, non-bureaucratic approach, there are some points that need always be taken care of.

== Tasks / Actions ==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Responsibilities of the laboratory management ===
The organization chart of an organization / laboratory and the quality assurance requirements from the PREMIER model result in specific responsibilities for different areas. Specific tasks and additional responsibilities, e.g. for laboratory areas and equipment, should be defined separately. The responsibilities within the specific work processes should be regulated in the corresponding SOPs.

Laboratory management must have the authority and formal responsibility for the organization and functioning of the laboratory.
This person does not need to be the principal investigator or the head of the department but needs unrestricted support from the upper management of the organization.

Other responsibilities of the laboratory manager(s) include:

* ensure that all personnel working in the lab clearly understand the tasks they are to perform and, where necessary, provide training for these functions
* ensure that appropriate and technically valid Standard Operating Procedures are established and followed
* ensure that the laboratory supplies meet requirements appropriate to their use in a study
* ensure that test and reference items are appropriately characterized
* establish procedures to ensure that data recording systems are suitable for their intended purpose, and are validated, operated and maintained

=== Resource Management ===
Continuous maintenance should ensure the reliability and trust in the research results obtained with the laboratory equipment. Resource management is not limited to equipment, but also includes chemicals and biomaterials, as they also play a very important role in the laboratory and influence research results.

==== Human Resources ====
The staff working in academic research laboratories typically includes a wide range of individuals with different educational, cultural, etc. backgrounds and with different goals and span of research activities.

All personnel should be made aware of the importance of training and its impact on the quality of the results. The management should ensure that those assigned to perform research activities have or get the appropriate combination of education, experience and training to be competent with their assignments. When personnel in training is involved in research activities, appropriate supervision should be provided. Job descriptions, and training records should be maintained.

Training should be carried out by personnel with appropriate skills, qualifications and experience.

The following paragraphs follow the structure given in the Best quality practices for biomedical research in drug development ASQ TR1 – 2012 (2).

It is recommended that new laboratory members are registered using a form, if possible, online (faster, less error prone, etc.). Here an example of such a form: [https://premier-qms.org/fileadmin/user_upload/New_Members__Registration__Form.pdf download]

This form initiates the process of registrations in the various facilities where the particular activities will be realized, speeding up the training processes and including the necessary laboratory tour. The areas of the main laboratory should be shown. At this point, the most important organizational and work safety regulations can be explained directly on the application site.

Especially important is an initial instruction on occupational safety.

Only after completing the training, the new member gets access to the physical and virtual facilities.

Explanation of the principles of Good Scientific Practice must be part of the initial training of each laboratory member and a commitment to follow them must be required. Here an example: download

In a similar way,when the staff member finishes permanently the activities at the department, an analogous reverse process should be followed. The main objective of this procedure is to ensure that the institution expertise is kept in a transparent manner.

The primary and secondary original data of the experiments should be kept at the institution, preferentially in an electronic laboratory notebook or in any other type of secure data storage device.

==== Facility and infrastructure ====
The research institution should have facilities and equipment sufficient for the conduct of the studies and to maintain the infrastructure.

Infrastructure can include:

* Buildings and workspace suitable for the research activities. In particular, lighting, temperature, humidity and ventilation should be appropriate and such that they do not adversely affect, directly or indirectly, either the test conditions, the accurate functioning of equipment or the safety of the staff.
* Utilities, such as controlled temperature storage facilities, purified water, steam and compressed air, bottled gases.
* Storage areas and test and control mixtures should be separate from areas housing the test systems. It should be adequate to preserve the identity and stability of the reagents and mixtures should be ensured.
* Computer and communications networking
* Equipment to ensure the safety of staff and the integrity of test systems and reference standards
* Premises designed and equipped to afford protection against the entry of insects or other animals
* Limited/restricted access to prevent the entry of unauthorized people.

Premises should be maintained, ensuring that repair and maintenance operations do not affecting the integrity of the testing. They should be cleaned and where applicable, disinfected according to written procedures.

==== Equipment ====
===== Equipment design =====
Equipment used in the conduct of research activities should conform to the following:

* Equipment should be designed, located and maintained to suit its intended purpose.
* Equipment should be constructed so that surfaces that contact components, in-process materials, or chemical substances should not be reactive, additive, or absorptive so as to affect the test method or test results.
* Equipment should be designed so that it can be easily and thoroughly cleaned. It should be cleaned according to detailed and written procedures. Cleaning agents should be effective at removing test residues to eliminate the potential for cross contamination. In addition, cleaning agents should not to be a source of contamination.
* Defective equipment should, if possible, be removed from active use, or at least be clearly labelled as defective to avoid usage until repairs are completed and the equipment is brought back into service.
* Repair and maintenance operations should not negatively affect test results.
* Equipment should be installed in such a way as to avoid contamination.

===== Equipment calibration =====
Measuring, weighing, recording and control equipment should be calibrated and checked by appropriate methods for accuracy and precision to ensure valid results. Where necessary, equipment should be:

* Calibrated, at specific intervals, and/or prior to use, against measurement standards traceable to known international or national measurement standards.
* Labelled with the unit's calibration status and date of recalibration
* Safeguarded from adjustments that may invalidate the measurement results
* Protected from damage and deterioration during handling, maintenance and storage.

Data generated by the equipment should be considered suspect should the equipment be found out of calibration at any time. In such cases, an investigation should be initiated to evaluate the validity of data generated during the out of calibration interval.

Records and results of calibration at the time of use should be maintained.

===== Equipment validation =====
Although equipment used in research activities typically does not need to be validated to the extent necessary for GLP studies or GMP production, equipment should function as expected so as not to introduce unknowns into the research that may render the results irreproducible. Every user should make sure that the equipment is working properly before using it. The laboratory manager or deputy should be informed in case a piece of equipment does not operate as expected.

===== List of Laboratory Equipment =====
Laboratory equipment and infrastructure equipment should be documented in an equipment list. This list should be updated at regular intervals or when necessary. Here an example for such a list:
{| class="wikitable"
|+ Equipment List
|-
| '''Location''' || '''Instrument Name''' || '''Inventory number''' || '''Responsible person''' || '''Verified (DD.MM.YY) by (Name)''' || '''Use only after Instruction'''
|-
| Laboratory 2 || Leica confocal Microscope SP8 || C-258432 || J. Mustermann || 16.01.2020 LEICA || Yes
|-
| Laboratory 3 || U-HPLC amino acids || C-124582 || M. Beispiel || 01.03.2020 M. Beispiel || Yes
|}

=== Methods (Method validation) ===
Validation is the confirmation by examination and the provision of objective evidence that the particular requirements for a specific intended use are fulfilled (3). Validation of methods used in a research institution is critical for the integrity and authenticity of the results.

Validation, e.g. by internal method audits, should demonstrate that the test methods have an adequate level of precision, accuracy, repeatability and robustness. See the [[PREMIER Quality Assurance|Quality Assurance]] module for the performance of internal audits.

The Laboratory should implement new methods or change standard procedures only after verification and approval by the competent staff. Feasibility assessments are helpful in making this process transparent and understandable. Complete documentation in the form of SOPs.

=== Materials ===
Factors such as the handling, storage and quality of materials used in the experiments can affect the outcome. Ordering, handling, labeling and storage of any materials should be clearly defined, described and documented. Keeping the required of documentation is the responsibility of laboratory management. Legal requirements and institution-specific hazardous material regulations must always be observed.

The professional and safe handling of reagents and chemicals and their safe storage should be subject of the initial instructions and training and it should be addressed with regularly (e.g., a yearly meeting) mandatory instruction on occupational safety and health of all employees of institution.

It is advisable that the institution/laboratory names a qualified person to serve as safety and hazardous substances responsible to support employees and management in complying with the legal and internal guidelines and regulations. This person can point out possible risks and necessary preventive measures. It is recommended that all hazardous substances are recorded in a central hazardous substances register.

Procedures should be in place for the receiving, handling and storage of these supplies to ensure:

* The identity and purity of the material received meets method specifications
* Manufacturer identifier, such as lot or batch number, is recorded and material is not expired
* In-house labeling is used to provide traceability
* Materials are stored under the appropriate conditions
* Labeling clearly identifies the material
* Usage of the materials is tracked
* All secondary containers have labelling that is traceable back to the reference standard or material and at minimum includes:
** Identity
** Preparation date
** Preparer
** Expiry or retest date
** Storage requirements

=== Ordering ===
The entire process of purchasing (ordering, requesting offers, procurement information, incoming goods inspections, logistics, supplier evaluation, monitoring, invoice verification) as well as the strategic planning and central control of the institution relationships with suppliers might be the responsibility of a superior central level specially in big research institutions. However, the lab should have a procedure in place guiding a transparent and objective purchase of items used in research.

The sampling process should address the factors that need to be controlled to ensure the validity of the test and results. Sampling plans, procedures and records generated, should be documented and permanently archived.

The research facility should have in place chain of custody procedures to ensure that collected samples are properly identified, stored and analyzed in a manner that the original sample identification remains and the history of the sample from collection to analysis can be traced if necessary.

=== Occupational Safety ===
It is usually a highly regulated issue. The laboratory should have procedures in place to keep all employees informed about the applicable regulations for hazardous substances, pathogens, fire protection, internal emergency services, etc. This may be achieved either by online or in person regular trainings e.g., once a year. Some items are mentioned above.

=== Note ===
There is a growing awareness in the scientific community of the need for standardized reporting on key resources, which has become mandatory for publication in high-level scientific journals (e.g. https://star-methods.com/). A well-structured laboratory will make it easier to meet these requirements, as the information will be readably available.

=== References ===
* Dirnagl U, Przesdzing I, Kurreck C, Major S. A Laboratory Critical Incident and Error Reporting System for Experimental Biomedicine. PLoS Biol. 2016;14(12):e2000705. Epub 2016/12/03. doi: 10.1371/journal.pbio.2000705. PubMed PMID: 27906976; PubMed Central PMCID: PMCPMC5131907.
* Trotter AM, Calabrese R, Huang LC, Krumenaker A, Palm U. Best quality practices for biomedical research in drug development. http://asqprinceton.org/wordpress/archives/1300 (accessed on 10.06.2020)
* ISO 9000:2015(en) Quality management systems — Fundamentals and vocabulary https://www.iso.org/obp/ui/#iso:std:iso:9000:ed-4:v1:en (accessed on 10.06.2020)
* [https://forum.premier-qms.org/t/laboratory-maintenance Discuss at PREMIER forum]

PREMIER Legal Requirements and Guidelines

2021-12-02T21:35:45Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
Working in the laboratory requires compliance with applicable guidelines and legal regulations at any time. Every organization / laboratory / scientist must clarify in advance which legal regulations are valid for the respective work and ensure compliance with them accordingly.

== Background ==
According to the [https://council.science/ International Science council], academic freedom and scientific autonomy are vital for science to progress and best serve to society, as confirmed by numerous legislative texts.

At the individual level, the independence granted to scientists obligates them to behavior that is ethical, honest, open-minded and objective. Such integrity in the conduct and communication of research is a critical component ensuring the individual right to academic freedom. While scientists should be free in their research and teaching activities, it is their responsibility to take into account the legal and regulatory framework in which they work.

See: ICSU [https://council.science/wp-content/uploads/2017/04/Academic_freedom_ICSU_CFRS_principle_document.pdf The Principle of Universality of Science and Academic Freedom]

== Tasks / Actions
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Legal, Normative Conditions and Internal Requirements ===
Depending on the location where the research institution or laboratory operates, multiple laws, norms and guidelines may apply. For instance, in Europe, the European Code of Conduct for Research Integrity of the European Science Foundation published by [https://allea.org/ ALLEA] is to be considered. Other common guidance is the [http://www.icmje.org/recommendations/ ICMJE] recommendations developed to review best practice and ethical standards in the conduct and reporting of research and other material published in medical journals. It also help authors, editors, and others involved in peer review and biomedical publishing create and distribute medical journal articles.

Each researcher together with other instances at the organization is responsible for the compliance of applicable legal regulations. The researcher has to ensure that the regulations that apply at the personal level and to the research area are observed. As a rule, '''lack of knowledge of the applicable law and regulations does not excuse from responsibility.'''

The heads of the working groups are responsible for ensuring that the principles and requirements of Good Scientific Practice (GSP) are observed by all staff including themselves and that legal requirements are met. A synonym for GSP frequently used outside Germany is “Responsible Conduct of Research”.

International, national or regional regulations or requirements also apply to specific topics related to the particular scientific activity. Moreover, for entities handling human materials for research purposes many clinical standards applied. The applicable laws and regulations, e.g. Animal Welfare Protection Laws and the organization's internal statutes '''must be observed without exceptions.''' Failing to do so may result in illegal conduct.

The verification of the compatibility of the research with legal regulations, self-regulatory measures and ethical principles is initially the responsibility of the project leaders. Particularly within the framework of the legally required supervisory duty and in the sense of the GSP - the scientist's supervisors, i.e. the heads of the working group, are responsible for the scientific integrity and legal conformity of the research work of their working group.

=== Animal Welfare Law ===
People who work with animals in the laboratory are strictly obliged to observe the Animal Protection Law. It is therefore highly recommended to provide evidence of appropriate training in the handling of animals. Depending on national and international regulations, the government regularly checks compliance with the Animal Welfare Law on site.

==== Arrive Guidelines ====
Researchers working with animals in the laboratory should familiarize themselves with the Arrive Guidelines. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) are a checklist of recommendations to improve the reporting of research involving animals – maximizing the quality and reliability of published research, and enabling others to better scrutinize, evaluate and reproduce it.

See: [https://arriveguidelines.org/ Arrive Guidelines]

=== Good Scientific Practice (GSP) ===
All universities and non-university research institutions are obliged to implement Good Scientific Practice (GSP) in a legally binding manner. Good scientific practice is a guideline for researchers to implement reliable research.

Most universities have their own rules for implementing GSP. They are based on the guidelines of the Code of Good Scientific Practice of the German Research Foundation (DFG).

The aim of the Code is to enable the addressees - the scientists and academics and the management of universities and non-university research institutions - to align their internal structures, processes and actions with the guidelines for good scientific practice. A culture of scientific integrity is to be established in the scientific institutions, which is not so much based on violations of good scientific practice as on the professional ethics of scientists and academics.

Deutsche Forschungsgemeinschaft (DFG): [https://www.dfg.de/download/pdf/foerderung/rechtliche_rahmenbedingungen/gute_wissenschaftliche_praxis/kodex_gwp_en.pdf Good Scientific Practice]

=== Declaration of Independence and Confidentiality ===
Every academic research institution should provide a declaration of independence and confidentiality in order to define what science is commited to its purposes. This should, for example, include a binding declaration of scientific freedom and the right to strive for knowledge independently and without external influences.

Or also that behavior which systematically fails to meet the claim to scientific integrity, deliberately falls behind or violates applicable law, cannot claim to be protected by the constitutionally protected freedom of science.

=== References ===
* Deutsche Forschungsgemeinschaft (DFG): [https://www.dfg.de/download/pdf/foerderung/rechtliche_rahmenbedingungen/gute_wissenschaftliche_praxis/kodex_gwp_en.pdf Good Scientific Practice]
* [http://www.icmje.org/conflicts-of-interest/ ICMJE] (Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals; Author Responsibilities - Conflicts of Interest)
* [https://dsgvo-gesetz.de/ Datenschutzgrundverordnung]
* [https://www.nc3rs.org.uk/sites/default/files/documents/Guidelines/NC3Rs%20ARRIVE%20Guidelines%202013.pdf Arrive Guidelines]
* [https://forum.premier-qms.org/t/support-processes Discuss at PREMIER forum]

PREMIER Error Management

2021-12-02T21:35:37Z

Admin:

[[QM House|← QM House]]
__NOTOC__
'''"The biggest mistake you can make in life is to always be afraid to make a mistake."'''

Dietrich Bonhoeffer

== Objectives ==
Basic reporting of errors and critical incidents is important to provide transparency about the experimental work performed in a laboratory. The aim is to learn from mistakes and avoid them in the future.

== Background ==
In a complex environment, as it is the case in basic experimental research, a not to be underestimated number of errors occur every day, which can negatively influence the quality of the work, waste materials and working time and put people at risk.

The reporting of critical incidents in clinical medicine has long been an internationally recognized instrument for improving patient safety, and is required by law in many countries. The basic principle of CIR (Critical Incident Reporting) in clinical medicine is that safety can be improved by learning from incidents that could have harmed or have harmed patients rather than ignoring such incidents.

In basic biomedical research, such a concept was completely lacking until now, although a number of critical incidents and errors can occur in the complex environment of a research laboratory with its state-of-the-art machinery, multi-professional and often international staff with different expertise, complicated assays and potentially harmful chemicals. These have the potential to negatively impact data integrity, test results, animal welfare, personnel safety, or the integrity of expensive reagents or machinery. Unfortunately, these errors and critical incidents of variable severity are currently reported only sporadically or are not reported at all. Sometimes such events are even covered up for fear of negative consequences.

To create a transparent error culture it is therefore essential to communicate such incidents properly and to create an awareness of how to deal with errors or critical incidents. To achieve this, there are different possibilities, for example with the help of an error reporting list or using an open source tool, the LabCIRS.

== Tasks / Actions ==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Error reporting list ===
When introducing error management, many employees are afraid of being punished for errors. The head of an organization / laboratory must counteract this fear by ensuring that a transparent error culture is important. To ensure that errors are reported by employees, the preservation of anonymity is the top priority.

The entries in an error list, which can be posted in the laboratory, need to be anonymous. It must be ensured that the errors reported are regularly analysed and discussed, for example in meetings, in order to take appropriate countermeasures to avoid the reported error in the future.

{| class="wikitable"
|+ Example for an error reporting list
|-
|rowspan="2"|'''No.''' ||rowspan="2"|'''Date''' ||rowspan="2"|'''Error / Critical Incident''' ||rowspan="2"|'''Cause of error''' ||rowspan="2"|'''(Counter-) Measure''' ||colspan="3"| '''Evaluation'''
|-
| Measure || Responsibility || Date
|-
| 1. || || || || || || ||
|-
| 2. || || || || || || ||
|-
| 3. || || || || || || ||
|}

=== LabCIRS (Critical Incident Reporting System for Laboratories) ===
The LabCIRS is an anonymous error reporting system developed by the Department of Experimental Neurology at the Charité together with the QUEST Center at the BIH and is made available to the Charité - and to the entire scientific community.

With the development of the LabCIRS, we provide researchers with structured measures for recording, analyzing, reporting and ultimately avoiding critical incidents in experimental biomedicine. The software is administered in a standardized manner on an internal server at the Charité, so that the program can be accessed via any computer within the Charité intranet using a web browser. Every interested laboratory facility of the Charité can apply for its own access, which is managed by the facility itself. Thus LabCIRS functions in every laboratory or department as an independent error reporting system, in which the reported critical events and errors are processed only within the respective organization (e.g. working group or department).

The success of the LabCIRS depends largely on the safeguarding of anonymity and confidentiality. This concerns both the identity of the reporting party and the identity of the persons involved in the specific CIRS case. The protection of the identities of these persons is therefore an absolute priority. For this reason, each participating institution is assigned a general user account, which all employees use equally for registration and for writing error reports. The institution's own LabCIRS area is managed by a person, the "reviewer"; this person can be a scientific employee, laboratory manager or another person. The reviewer will be notified of incoming error reports via e-mail. He or she evaluates the incoming error reports and, if necessary, initiates appropriate (immediate) measures.

=== LabCIRS for laboratory facilities at the Charité ===
Access to LabCIRS for laboratory facilities at the Charité:

LabCIRS is available on the Charité intranet. Employees of the Charité can request access by clicking on the "Register" button.

The QUEST Center at BIH is responsible for the service.

Questions can be directed to: labcirs-admin@charite.de.

=== LabCIRS for the scientific community ===
LabCIRS is an open-source software and can be used by every laboratory. The source code is available on [https://github.com/major-s/labcirs GitHub].

=== Recommendation ===
A structured error management with a laboratory critical incidence system is an effective self-assessment activity, which can be implemented relatively easily. Beyond helping in the recurrence of reported errors, it is a powerful tool to introduce a non-punitive error culture.

=== References ===
* [http://expneuropedia.charite.de/mw/index.php/LabCIRS-English LabCIRS tool] for reporting critical incidents
* [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2000705 Plos Biology article about the LabCIRS]
* [http://www.laborjournal-archiv.de/epaper/LJ_19_01/22/index.html Article in the laboratory journal] by Ulrich Dirnagl
* LabCIRS on [https://github.com/major-s/labcirs GitHub]
* [https://forum.premier-qms.org/t/support-processes Discuss at PREMIER forum]

PREMIER Education / Training

2021-12-02T21:35:28Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
The maintenance of a certain qualification level and the promotion of the qualification and education measures for new and permanent laboratory members is vital for every laboratory. Trainings need to be adapted to the conditions of the laboratory and need to take into account the prior knowledge of the person to be trained. It is thereby important that training be provided in a consistent manner and that a transparent training record for lab members exist. Training courses have the following objectives:

* Establishing an appropriate training plan and providing the appropriate materials to the training participant.
* Providing evidence that the training participant has the required knowledge/skills/competencies.
* Identifying which processes/devices need to be trained.
* The encouragement of voluntary training.

== Background ==
The need for training results from the qualification level of the employees, their function within the department, the introduction of new processes, procedures, measuring instruments and legal regulations as well as potential performance deficits that have become visible in quality development.

== Tasks / Actions ==

=== How to plan a training concept ===
Before you start a training program in your organization there are several items to consider for the implementation. These are:

* What are the conditions for specific trainings.
* What is to be achieved by the training (competencies).
* What qualifications / experiences need the trainer for your program.
* A briefly described process of how the training will be conducted is helpful.
* Training / refresher training should be conducted on a regular basis (for whom and under what conditions).
* Shadowing or mentoring for newly trained staff is required to give feedback and to improve their skills.

Preparations:

* Make alist of legally required, quality-required, and all other trainings.
* Setup a system to document:
** when trainings take place
** who was trained
** brief content of the training
** who trained
* For which training does material exist and for which does it need to be produced (manual, instruction list, training video)? Do trainees have access to the training material at all time? Is there training material current and up-to-date?

Who has the competence to be a training provider? List the names of these people. They need to work out details of the training (concept, objectives, material, training procedure) to which they will adhere and determine may also determine when a well-trained person can become a training provider.

=== Need for training ===
It is important to determine the need for training. Entry point(s) to set up trainings (aside from legally required trainings) could be:

* to start with an introduction management for new lab members
* to start with a review of the device list (what device requires training – who could be responsible)
* to start with competence level of new or existing lab member:
** never worked in a lab before
** < 1 year
** 2-4 years
** >5 years
* List of skills/expertise from CV.
* to start with a list of procedures / techniques / protocols - do they require training?

=== Performance of the training ===
It is important to create an open atmosphere during the training so that participants are encouraged to ask questions. Training should always be motivating, the training participants should always be involved, if possible. Many different techniques exist for active participation in training sessions to encourage discussion and approaches to solutions.

Training can also be provided in a variety of online formats. The training topics and the target audience are key factors here.

=== Validation of the training ===
It makes sense to include validation techniques for training in order to be able to verify whether the training method / technique used has been applied correctly (self-monitoring). For example, a known standard could be included and a standard curve created.

If the lab is large enough, regular refresher courses should be offered. Consideration should then be given to whether these should be voluntary or mandatory, applicable to all or selected personnel. The concept of such a course is important; should the refresher course include another demonstration by the trainer or should the trainer observe the training / people and correct as needed.
* [https://forum.premier-qms.org/t/support-processes Discuss at PREMIER forum]

PREMIER Documentation and Data Storage

2021-12-02T21:35:17Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
The aim is to ensure the traceability and integrity of the research data so that the reported results can be documented and verified.

== Background ==
To ensure experimental recording, experiments should be documented. The entries should include all data and relevant details in a way that other researchers can trace and, if needed, repeat the experiment.

To ensure the traceability, the source of the data (primary and secondary), including the identity of the scientist involved in the generation of the information should be available to authorized personnel, ensuring the personal data protection rights of the people involved.

== Tasks / Actions ==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Data Collection ===
Both electronic and paper data can be collected. Electronic data are collected on a device and/or its software application. The information required to verify and/or analyze electronic data may include metadata such as machine settings, software type and version, etc. Paper data are generated, for example, by using a paper notebook or the map record.

Experimental data consist of two components:
# '''Primary data (raw data):''' These are original data which are the result of the original measurements, observations and experimental activities.
# '''Secondary data (derived data):''' This are raw data that have been analyzed and processed.

All data should be recorded immediately after generation and permanently and safely stored.

In PREMIER, protocols can be recorded as SOPs (standard protocols) or WIs (work instructions). When generating data, the researcher documents which protocol was used and whether there were any changes, including description of the deviations if any. Any changes to data already entered should be clearly described and explained. Subsequent changes to source data should not obscure or explain the original data. The changes should include the identification of the person making the change and the date on which the change was done. In general, it is recommended to ensure that the protocols are assignable, readable, simultaneous, original, accurate and complete (commonly referred to as ALCOA):

* Assignable: The author(s), all persons who participated and/or contributed to the experiment, including the recorders if applicable, must be uniquely identified so that the data can be traced back to each individual contribution by name and date.
* Readable: The record must be legible and recorded in and/or on a permanent medium (paper or electronic).
* Simultaneous: Newly obtained/collected data and new scientific discussions and ideas should be recorded at the time of observation.
* Original: The initial recording of the data should be retained.
* Accurate: The recorded observations must be true and accurate.
* Complete: Records should be complete to ensure traceability, immediate and accurate retrieval and exact reconstruction or review of the work described.

In order to minimize the probability of loss or damage to experimental data, they should be entered directly in the standard system used in the lab, e.g. ELN. For computer applications used to collect, analyze, plot, summarize or otherwise characterize experimental data, the following information should be provided: name, version and provider of the application and where in the experiment or protocol the application was used.

Each organization / laboratory must implement rules regarding the recording of raw data. Raw data and other records should be sufficiently detailed and complete to ensure study traceability and reconstruction. If computers are used to acquire, modify or archive data, the raw data must be clearly labeled as such.

=== Documentation ===
Document of research data can be done on paper or even better, if the local conditions allow, in an electronic laboratory notebook (ELN). The electronic documentation has multiple advantages such as to facilitate the cooperation within and between teams ("Team Science"), where projects, templates and stocks can be exchanged electronically. All entries are visible and allow a real-time workflow. Furthermore, electronic documentation increases the quality and reliability of research documentation and is an essential element not only in the digitization process but also in increasing the robustness and efficiency of research.

The [https://www.go-fair.org/fair-principles/ FAIR Guiding Principles] for scientific data management and stewardship intend to provide guidelines to improve the '''F'''indability, '''A'''ccessibility, '''I'''nteroperability, and '''R'''euse of digital assets. Their emphasis lies in the capacity of computational systems with none or minimal human intervention, as humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.

There are multiple possibilities beside an ELN for documentation management. Examples include an open and guided [https://en.wikipedia.org/wiki/Wiki Wiki], a SharePoint® or a LIMS for all team members granting free access to documents such as SOPs, protocols and templates. The content of SOPs and protocols need to be regularly checked to keep them actualized. (See PREMIER module „[[PREMIER Conducting Experiments|Conducting Experiments]]“).

=== Electronic Lab Notebooks ===
An ELN (electronic laboratory notebook) is a software tool that in its simple form replicates an interface similar to a page in a traditional paper laboratory notebook. Raw data, protocols, observations, notes and other data can be entered into this electronic notebook via computer or mobile device. Especially, the sharing of data and the joint work on projects, even across work groups, offers clear advantages compared to the traditional paper laboratory notebook.

The number of available ELN tools is growing rapidly and the functions of individual tools are changing rapidly. Therefore, it can be difficult to evaluate all the advantages and limitations when searching for the best solution for your project. This constantly updated overview of [https://docs.google.com/spreadsheets/d/1ar8fgwagOh30E31EAPL-Gorwn_g6XNf81g3VDQnQ_I8/edit#gid=0 existing ELNs] can be helpful in the evaluation and selection process. Here is an approach to [https://www.nature.com/articles/d41586-018-05895-3 selecting suitable ELNs].

=== Traceability of Data ===
The traceability and integrity of the data ensures that the reported results can be reproduced. Traceability is the ability to identify the source of the data (raw and/or analyzed) and any person with a relevant influence on the data sets mentioned in a publication.

Each experimental record should contain the following important references, if possible:

* Names of all persons involved in the experiment.
* Specific experimental (research) plan (see module „Planning of Experiments“) with hypothesis and counter-hypothesis to which the experiment should refer.
* Any protocols, standard procedures, test methods, statistical tools (and/or data analysis software) used.
* Description of all materials and equipment used.
* Date on which each experiment was performed.
* Location of records and materials.
* All raw data generated, processed and reported in the experiment.
* An appropriate reference should be added to the path where the raw data are stored or if the raw data come from other researchers conducting supporting experiments.

It is recommended that all associated experiment records must be referenced in the experiment. The raw data obtained in an experiment should be stored in a separate archive system and referenced in the experiment recording. The laboratory should establish conventions for the file names of all data files and experimental records to ensure consistency and traceability including, a unique identifier assigned to each experimental data record in accordance with the applicable SOPs.

The responsibility for the creation of the trial records and the documentation of the resulting data lies with the researcher who creates the data. Where several researchers collaborate on data generation, they should be identified as collaborators.

=== Data Storage ===
Due to a steady increase in information technologies, a large part of the data collected in research today is available in a digital way. It is therefore essential to comprehensively handling the data, especially its archiving. According to Good Scientific Practice (GSP) guidelines, all primary and secondary data must be securely stored or retained for at least 10 years after their creation.

Every organization or laboratory should define an internal policy ensuring the integrity of the research data. All research data must be stored in the Electronic Laboratory Notebook (ELN) as soon as possible after they have been generated.

If a larger amount of files is involved or the file size exceeds the limit allowed in the ELN, the primary data must be stored on the archive storage, the secondary data on the standard storage.

For further information, see module [[PREMIER Data Storage|Planning of Experiments - Data storage]].

=== References ===
Recommended readings:

* [https://oir.nih.gov/sites/default/files/uploads/sourcebook/documents/ethical_conduct/guidelines-scientific_recordkeeping.pdf Guidelines for SCIENTIFIC RECORD KEEPING in the Intramural Research Program at the NIH]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
* [https://docs.google.com/spreadsheets/d/1ar8fgwagOh30E31EAPL-Gorwn_g6XNf81g3VDQnQ_I8/edit#gid=0 Existing ELNs]
* [https://www.nature.com/articles/d41586-018-05895-3 Selecting suitable ELNs]
* [https://cdn2.hubspot.net/hubfs/149400/docs/ALCOA-C_checklist.pdf?__hstc=&__hssc=&hsCtaTracking=5d7c54eb-4ed9-4b0e-a1dd-b8ea8a59c3db%7C315641aa-4671-43a7-bcce-e255448cab2c ALCOA C - Checklist]
* [https://www.dfg.de/foerderung/grundlagen_rahmenbedingungen/gwp/ Good Scientific Praxis DFG]
* [https://forum.premier-qms.org/t/support-processes Discuss at PREMIER forum]

PREMIER Communication and Dissemination

2021-12-02T21:35:09Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
In order to be effective and efficient with PREMIER, either as a QM system or by using the modular application, it is important to involve all employees in the corresponding processes. The aim is therefore to develop a suitable communication and dissemination strategy for the laboratory and/or the entire organisation so that all participants are informed at all times.
== Background ==
High-quality research needs an effective, targeted and comprehensible communication and dissemination structure. The size of the organisation determines which team, collaboration and project management tool is most appropriate and effective. The documentation of the meetings must include an action plan in order to define comprehensible responsibilities / accountabilities for actions. It is necessary to work transparently and to involve all interested employees in the development of a QM system or quality tools. For this reason, PREMIER has created the conditions for a transparent and open knowledge base.
==Tasks / Actions==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Internal Communication ===
The internal communication within a laboratory / organization should be clearly defined.

Due to the size and the different areas of expertise and working groups, different forms of communication are possible. They can take the form of general meetings or team meetings.It is recommended that relevant meetings be established and an overview table created.
{| class="wikitable"
|+Meeting Table
|-
| class="text-center"|'''Meetings'''
| class="text-center"|'''Frequency'''
| class="text-center"|'''Participants'''
| class="text-center"|'''Purpose'''
|-
| |
| |
| |
| |
|-
| |
| |
| |
| |
|}
The team meetings should be protocolled and archived on a server or in a QM folder. It might be useful to design a standard form for some meetings that is made available to all users.

Here are examples for internal communication which can be supported with the following tools:
* Forum:
Intranet-based communication platform as a central information platform to which all employees have access. It serves to improve and facilitate cooperation, promotes social exchange between staff members and serves to provide information, announcements and notices.
* Wiki system
[https://www.mediawiki.org/wiki/MediaWiki/de MediaWiki] as an internal platform. This structure of an open Wiki system guarantees a continuous exchange of knowledge. All information and documents about processes and internal regulations of a laboratory can be stored here and are available to all employees. Document control, e.g. writing and updating SOPs, could also carried out via MediaWiki. Each employee has an own Wiki account.
* Electronic project management tool
An other possibility is to use special project management tools like e.g. "[https://www.openproject.org/de/ Open Project]" to manage projects. This tool helps to keep an overview of the project and provides a place where all necessary information can be found. When changes and edits are made to the project, the system automatically sends e-mails to all participants.
* Microsoft Teams
Microsoft Teams is a platform that combines chats, meetings, notes and attachments. The service is integrated in the[https://www.office.com/ Office 365 Package]. Microsoft Teams is used to work on specific topics.

=== Internal Communication ===
* Collaborations
Depending on the project, collaborations take place within the research community and also with other stakeholders. External communication is possible via seminars, symposia, face-to-face meetings and telephone conferences.

Other possibilities for external communication are:
* Interactive Websites
* Social media / Twitter
* Blogs

===References===
* Media library BIH-Quest: Videos, webinars, potcasts and more can be found on the Media Library page of the Quest Center [https://www.bihealth.org/de/forschung/quest-center/mediathek/ Mediathek].
* [https://www.office.com/ Office 365 Package]
* Communication - [https://de.wikipedia.org/wiki/Kommunikation Wikipedia]
* [https://forum.premier-qms.org/t/support-processes Discuss at PREMIER forum]

PREMIER Reporting

2021-12-02T21:34:49Z

Admin:

[[QM House|← QM House]]
__NOTOC__
==Objectives==
The aim of reporting is to make data and research results transparent, provide them to the scientific community and foster the required exchange and cooperation.

==Background==
Reporting of research results should preferably follow the principles of open science. Open Science combines the guiding principles of '''Open Access''', '''Open Data''' and '''Open Source'''. Although all Open Principles can exist independently, there are synergies of the principles which can also be applied in a meaningful way in everyday scientific life.
==Tasks / Actions==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.
===Open Science===
''Open Access'' involves long-term and unrestricted access to scientific results in the form of publications / papers and scientific literature.
''Open Data'' is publicly accessible data that can be freely used, reused and shared by anyone; the only restriction is the obligation to name the author of the data.
''Open Source'' is software with publicly available source code that is freely available and subject to a recognized open source software license. Furthermore, open source means freely available knowledge and information in general.
Basic research should be committed to all these principles of openness, scientific transparency and the possibility of unlimited reuse of its post-publication data.
Through Open Science it is possible to guarantee full transparency of research services, including the validity of used models and the reproducibility of the data, e.g. through replication studies. This allows a time-independent, public, further evaluation, also by secondary or meta-analyses of the data far beyond a limited readership. In addition, research processes can be accelerated through better data sharing. The informal added value of each publication is increased if raw data is also available. These public raw research data are stored in suitable and recognized online repositories. Repositories are managed repositories for the storage of ordered documents that are accessible to the public or a restricted group of users.
For researchers it is important to pursue a consistent publication practice. This means that all results, data and procedures of the research are made available to the scientific community and the general public as openly as possible, i.e. without access restrictions and in complete form. In all processes, it is important to observe conformity with applicable law (animal protection, occupational safety; genetic engineering, etc.), the principles of Good Scientific Practice (GSP), and commit yourselves to observing internationally recognized rules (e.g. ARRIVE Guidelines for animal experiments, and International Committee of Medical Journal Editors (ICMJE) recommendations for publications).
===Open Access===
====Basic principles====
There are several models for Open Access:
* Golden Way
Immediate free availability in an Open Access journal after successful peer review. All work in such journals is Open Access, e.g. journals of the PLoS series. Incomplete List of open-access journals:[https://en.wikipedia.org/wiki/List_of_open-access_journals List of open-access journals]
* Green Way
Secures the author the right to parallel publication or self-archiving of an original work, in addition to publication by the publisher. The author grants the publisher the simple right of publication, but there is no transfer of all exploitation rights (no copyright transfer to the publisher). According to the publisher's regulations, the author may make either the content of the article or the PDF of the article available to the public via:
# institutional repositories (e.g. document server of a university (for the Charité:[https://refubium.fu-berlin.de/?locale-attribute=en refubium.fu-berlin.de/]at the FU Berlin)
# scientific repositories (servers of a thematic orientation)
# own homepage (recommended with a Creative Commons license)
A list of scientific Open Access repositories can be found at [http://v2.sherpa.ac.uk/opendoar/ OpenDOAR] and in the Registry of Open Access Repositories [http://roar.eprints.org/ (ROAR)].
* Postprint
Many publishers or journals have conditions according to which the content of a peer-reviewed publication may be archived by the authors themselves. The conditions for this are sometimes more relaxed (e.g. the publisher's PDF may be used) or sometimes more restrictive (only the accepted manuscript may be published). Sometimes embargo times have to be taken into account. The exact conditions under which accepted manuscripts may be published can be found in the[http://sherpa.ac.uk/romeo/search.php SHERPA / RoMEO]directory at the entry of the respective journal.
* Hybrid Model
The non-Open Access publisher grants Open Access rights to self-archive the publisher's version upon payment of a fee by the submitting author. Hybrid is this model because OA is the exception rather than the rule and an additional source of income in such journals, in addition to the parallel subscription. The publisher thus collects twice: once from the author, who wants to make his publication accessible to all, and once from the institution, the fee for institutional access to all articles. This model is supported by fewer and fewer founders.
* Green Way through Funders Mandate
Some non-Open Access journals grant Open Access rights to original works funded by certain large public financiers. BMBF and DFG are not among them.

'''PREMIER recommends both, the golden and the green path.'''The decision as to which path to take lies with the publishing author or the working group.
* Bronze Way
Journals offers delayed open access, i.e. after an embargo period.
* Diamond / Platinum Way
Journals do not charge either readers or authors but require funding from external sources.
====Publishing Open Access====
Even before writing the manuscript of the original work, the corresponding author should choose a journal that allows Open Access publishing. For journals that support the golden path, the QUEST Center of the BIH has developed a curated[https://s-quest.bihealth.org/OAPositiveList/ Open Access Journal Whitelist]. It contains journals with high quality standards in order to protect against publications in so-called predatory journals. In addition, the list, which can be sorted by subject area, contains information on the review times, publication costs and whether the DFG Publication.

Alternative sources of information for Open Access journal selection are:[http://sherpa.ac.uk/romeo/search.php Sherpa/Romeo]and[https://doaj.org/ Directory of Open Access Journals].

After acceptance of the original work, the author must apply to the publisher for Open Access publication via the green or hybrid way. The journal usually sends the necessary forms to the corresponding author, otherwise this must be requested.
* „Predatory“ / Junk Journals
Open Access has come into disrepute with the emergence of so-called "predatory" or "junk" journals. These Predatory Publishers often invite you via e-mail to submit manuscripts for which they charge Open Access fees, which at first glance seem relatively low. However, these manuscripts often do not undergo an orderly and recognized peer review process, nor do the publications appear later in PubMed, making them less visible and often qualitatively worthless.
* Higher-level open access specifications
The[https://www.parlament-berlin.de/ados/17/IIIPlen/vorgang/d17-2512.pdf Berliner Senat]calls for at least 60% Open Access from all public scientific institutions in 2020. The BMBF is primarily committed to the green path and sets Open Access as the standard in its own funding programs. The DFG also recommends and supports efforts towards Open Access publications. For EU-funded Horizon 2020 projects, Open Access is mandatory for all publications (including books and monographs). An overview of the Open Access policies of various funding organizations can be found in the[http://www.sherpa.ac.uk/juliet/index.php SHERPA/][http://v2.sherpa.ac.uk/juliet/ Juliet Database]
* Subsequent secondary publication for existing publications (Postprint)
There is a legally secure way to make the contents of publications subsequently accessible to the public. First, you should check whether the article has free full text access after all. (e.g. via Text availability --> "free full text" in the[https://www.ncbi.nlm.nih.gov/books/NBK3827/#pubmedhelp.How_to_Get_the_Journal_Articl Side bar filter in PubMed)]) or in the[https://www.ncbi.nlm.nih.gov/pmc/ PMC]catalogue, which only contains openly accessible articles. Uploading a PDF article, for which the publisher has not expressly permitted self-publication, to public social platforms such as''Research Gate''or''Academia.eu''constitutes a copyright violation, which may result in criminal prosecution for the uploader. However, sending a private copy by e-mail is permitted. The German secondary publication law[https://irights.info/wp-content/uploads/2017/09/zweitveroeffentlichungsrecht-20150425.pdf (deutsche Zweitveröffentlichungsrecht)]also permits the publication of the accepted manuscript version 12 months after the first publication on institutional repositories for projects that are funded mostly with public resources. Documents posted there are indexed by[https://scholar.google.de/ Google Scholar]and Open Access search engines such as[https://www.base-search.net/ base-search.net]and[https://unpaywall.org/ unpaywall]and thus, permanently searchable. Alternatively, the EU's own[https://zenodo.org/ Zenodo]repository offers the possibility of publishing on the green path of Open Access. The[http://sherpa.ac.uk/romeo/search.php Sherpa-RoMEO]Database provides journal-specific information on which publication form (Postprint PDF, Publisher PDF or Preprint PDF) may be used for the repository upload after the embargo period.
* Pre-publication of manuscripts (Preprints)
It often takes months after the submission of a finished manuscript for publication, sometimes even years for rejection and new submission. This is remedied by a new means of communication that is becoming more and more popular: The purpose of preprints is to make publication content public immediately, e.g. before or during the submission of the manuscript for peer review to a journal. More and more publishers and journals are accepting this approach ([https://en.wikipedia.org/wiki/List_of_academic_journals_by_preprint_policy List of Academic Journals by Preprint policy]). Preprints are uploaded to special preprint servers. The following preprint servers are recommended for biomedical manuscripts:
# [https://www.biorxiv.org/ bioRxiv]
# [https://www.medrxiv.org/ medRxiv]
# [https://osf.io/preprints/ OSF]
Preprints offer many advantages:
* The manuscripts can be uploaded to the preprint servers free of charge.
* The published content is up to date. Especially with scientifically hot topics, a preprint can give you a head start in the community.
* Since all preprints receive a[https://www.doi.org/ Digital Object Identifier (DOI)], preprints can also be permanently quoted and found.
* Many publishing houses (e.g. Elsevier, Springer-Nature, PLoS) allow cited preprints in manuscripts submitted to them to be officially listed as references in the bibliography.
* Many traditional journals allow that manuscripts submitted to them for peer review are already available as preprints.[https://en.wikipedia.org/wiki/List_of_academic_journals_by_preprint_policy#Individual_journals (List of journals that accept preprints]).
* Even if the resulting peer-reviewed publication is not Open Access, the content remains accessible worldwide via the preprint.
* Important funding organizations such as[https://www.sciencemag.org/news/2017/03/nih-enables-investigators-include-draft-preprints-grant-proposals NIH],[http://openaccess.ox.ac.uk/2017/02/08/wellcome-trust-and-medical-research-council-support-preprints/ Wellcome und MRC]recognize preprints in funding applications as citable works.
Disadvantages of preprints:
* Contents are not peer reviewed; the significance of preprints is therefore limited.
* not listed in PubMed
Qualitatively poor works find a way to reach scientifically oriented audiences.
===Open Data===
====Basic principles====
Open Data in scientific research means reliable access to the underlying original data for each interested person and in the sense of maximum transparency, quality assurance of possible reproducibility and cost savings through possible secondary analyzes. The provision of raw data for subsequent use is to be carried out promptly and completely. Anyone may use, share or modify the disclosed data for any purpose.

A growing number of journals demand transparency in the publication and the disclosure of the raw data, and increasingly, sponsors demand this as well. It is to be expected that from 2020 onwards, research grants will no longer be awarded unless a data management plan is included. The DFG now requires''"making research data available as soon as possible," immediately after completion of the research or after a few months… "Research data should be accessible at an early stage, allowing meaningful and further use by third parties.''

What should be observed before the data are published?
# Who created or commissioned the creation of the data?
# Is there any personal data in the data set? If so, anonymization must be ensured, in accordance with the Berlin Data Protection Act.
# Is the data usable for others? Exact captions and descriptions in separate files are essential.
# Are the data only available in proprietary formats? In this case, a file copy (for example, by export) should also be made public into a generally accessible open format.
How does Open Data work?Each staff member is free to use a repository of choice for Open Data. An overview of existing repositories (> 1700) can be found in the DFG-funded project'''''re3data.org.'''''For general, multidisciplinary data, Experimental Neurology recommends three international online repositories (Zenodo, Figshare, Mendeley Data), in which:
* Raw data (data of all types, tables, diagrams, pictures, but also posters) can be permanently identified and citable by a permanent Internet address DOI (Digital Object Identifier).
* The data volumes for freely available data are unlimited.
* An embargo can be placed on data deposited before it becomes visible to the public.
* It is possible to link the uploader with ORCID.
{| class="table" border="1" cellpadding="8"
|-
|
|[http://zenodo.org/ Zenodo.org]
|[http://figshare.com/ Figshare.com]
|[http://data.mendeley.com/ data.mendeley.com]
|-
|'''maximum file size'''
|50 GB/dataset
|5 GB
|?
|-
|'''Upload size private'''
|50 GB
|20 GB
|?
|-
|'''embargo for public data'''
|yes, selfset limit
|up to 12 months
|6, 12 month or selfset limit
|-
|'''data findable in search engines'''
|[https://share.osf.io/ https://share.osf.io];[http://google.com/ google.com];[http://opendoar.org/ opendoar.org];[http://www.base-search.net/ www.base-search.net]
|[https://share.osf.io/ https://share.osf.io];[http://google.com/ google.com];[http://opendoar.org/ opendoar.org];[http://www.base-search.net/ www.base-search.net]
|[http://google.com/ google.com];[http://www.base-search.net/ www.base-search.net]
|-
|'''re3data.org registry'''
|[http://www.re3data.org/repository/r3d100010468 www.re3data.org/]<br />[http://www.re3data.org/repository/r3d100010468 repository/r3d100010468]
|[http://www.re3data.org/repository/r3d100010066 www.re3data.org/]<br />[http://www.re3data.org/repository/r3d100010066 repository/r3d100010066]
|[http://www.re3data.org/repository/r3d100011868 www.re3data.org/]<br />[http://www.re3data.org/repository/r3d100011868 repository/r3d100011868]
|-
|'''Data upload'''
|no instructions
|[https://support.figshare.com/support/solutions/articles/6000102511-how-to-upload-your-data https://support.figshare.com/]<br />[https://support.figshare.com/support/solutions/articles/6000102511-how-to-upload-your-data support/solutions/articles/]<br />[https://support.figshare.com/support/solutions/articles/6000102511-how-to-upload-your-data 6000102511-how-to-upload-your-data]
|no instructions
|-
|'''Data publishing'''
|no instructions
|[https://support.figshare.com/support/solutions/articles/6000091698-how-to-publish-your-data https://support.figshare.com/]<br />[https://support.figshare.com/support/solutions/articles/6000091698-how-to-publish-your-data support/solutions/articles/]<br />[https://support.figshare.com/support/solutions/articles/6000091698-how-to-publish-your-data 6000091698-how-to-publish-your-data]
|no instructions
|}
To use one of these three services, a previous registration is necessary.
====Special Data====
These include, for example, -omics, open fMRI, RNA / DNA and protein sequence data. These are stored in special databases, which have their own requirements for data formats and metadata. A selection of suitable databases can be found at:[https://biosharing.org/databases/ Fairsharing]
====Personal data====
In the event that Open Data with personal data is considered, the document "Basic information on Open Data publications in studies with personal data" provides an overview of the steps to be taken if the free availability of personal data is planned. This requires, among other things, a positive vote by the ethics committee and a correspondingly effective consent. In addition, some basic concepts such as de facto anonymization are explained, exemplary standard formulations for patient consent are given, and alternatives are described for cases where the complete disclosure of data is not possible.Some personal data (records) cannot be made openly available without further explanation due to legal or contractual provisions, for data or other protection reasons or for ethical reasons. However, it should be checked whether it is possible to make this data at least accessible in a limited way, i.e. non-critical, anonymizable data that is not subject to any obligation of secrecy.
====Open Data Principles====
The'''''FAIR'''''principles define minimum standards of data to make them easier to find, accessible, fully compatible, and reusable ('''F'''indable,'''A'''ccessible,'''I'''nteroperable,'''R'''eusable). The following section describes how this requirement can be implemented.''Open Data: General Approach''Simple, undocumented upload of the original data to one of the repositories is a procedure no longer recommended. One of the main objectives of Open Data is that data in repositories can be comprehensible and human as well as machine-readable. Usually, only one container file (Zip / Archive) is stored in the repository. This contains all necessary files, including an explanatory metafile. A unique DOI is created and the work is thus clearly identified.Proprietary formatsAny proprietary formats, such as raw image files or output files from devices that are readable only with licensed and non-open accessible software, should be avoided or converted into open, license-free formats by means of converted copies. For proprietary formats, the specification of the software, version, manufacturer and operating system is essential. Examples of non-proprietary open formats are:
* CSV - comma separated value (tables)
* XML - Extensible Markup Language (documents, texts with a hierarchical structure
* Gzip / ZIP compression file format
{| class="table" style="width: 1026px;" border="1" cellpadding="8" data-mce-style="width: 1026px;"
|-
| style="width: 742px;" data-mce-style="width: 742px;"|'''DO'''
| style="width: 240px;" data-mce-style="width: 240px;"|'''DO NOT'''
|-
| style="width: 742px;" data-mce-style="width: 742px;"|Exact and concrete title
| style="width: 240px;" data-mce-style="width: 240px;"|Merge cells
|-
| style="width: 742px;" data-mce-style="width: 742px;"|Each column should have a heading and be limited to one cell
| style="width: 240px;" data-mce-style="width: 240px;"|Use special characters or spaces
|-
| style="width: 742px;" data-mce-style="width: 742px;"|The first cell of a data sheet (A1) must not be free
| style="width: 240px;" data-mce-style="width: 240px;"|Use formatting or color-coding
|-
| style="width: 742px;" data-mce-style="width: 742px;"|The file name should reflect the content as precisely as possible
| style="width: 240px;" data-mce-style="width: 240px;"|Merge cells
|-
| style="width: 742px;" data-mce-style="width: 742px;"|Each file may contain only one data sheet
| style="width: 240px;" data-mce-style="width: 240px;"|
|-
| style="width: 742px;" data-mce-style="width: 742px;"|In each data sheet only one table
| style="width: 240px;" data-mce-style="width: 240px;"|
|-
| style="width: 742px;" data-mce-style="width: 742px;"|Each cell should be occupied, missing values are marked with NA (not available) and undefined values (e.g. division by zero) with NaN (not a number)
| style="width: 240px;" data-mce-style="width: 240px;"|
|-
| style="width: 742px;" data-mce-style="width: 742px;"|All acronyms used, units are transferred to the metafile and defined there
| style="width: 240px;" data-mce-style="width: 240px;"|
|-
| style="width: 742px;" data-mce-style="width: 742px;"|General table rule: Variables as columns, samples / observation as rows
| style="width: 240px;" data-mce-style="width: 240px;"|
|-
| style="width: 742px;" data-mce-style="width: 742px;"|Use of unique identifiers for samples / observation
| style="width: 240px;" data-mce-style="width: 240px;"|
|-
| style="width: 742px;" data-mce-style="width: 742px;"|Exact and concrete title
| style="width: 240px;" data-mce-style="width: 240px;"|
|}

====Images====
Image files of digital cameras or microscopes are often stored as raw image files. Although these are raw data, they are usually proprietary software (''see raw filename extensions and the camera manufacturers''). The inclusion of information about, the manufacturer / software / version / operating system is mandatory when storing these files. Including an additional export in TIFF format, which makes minimal compression changes, is an even better complement. Associated image sequences are stored in folders, named in a way that describes the included files accurately. Further descriptions (e.g. origin, reference, etc.) of the files / folders should be included in the metafile.
====Source Code====
GitHub is the best-suited Internet hosting service for Open Data. A GitHub link with another repository (e.g. Zenodo) is easy to set up, as it is often useful to store the records and the corresponding code together. The following procedure is recommended to make quotable a public GitHub repository via Zenodo:
# Log in to Zenodo using the GitHub access data
# Set-up the[https://zenodo.org/account/settings/github/ Zenodo-GitHub]Synchronization. Zenodo can only archive public repositories.
# A new Zenodo entry is automatically triggered after the release of the GitHub Repository.
# Take the DOI badge from Zenodo and add it to the readme file of the GitHub repository.

====Metadata====
Metadata is collected text information about a data set that describes its specific characteristics in a sufficiently comprehensible manner, so that independent reuse is ensured. Parts of these can also be used in the respective data description fields of the repositories. We recommend a minimum standard consisting of:
* Title: a descriptive title of the data and all associated work in the file store
* Author: Name (s) of the data author
* Creation date: as dd/mm/yyyy
* Method: a description of how the data was created (for example, devices that provided the data used experimental method)
* Description: A description of each data record (including description of the variables and their units, acronyms, etc.)
* Discipline, Keywords, Reference Quotes
* Validation: All necessary information, using tools and instruments that validate the published results (e.g. formulas, valid measuring ranges, analysis protocols, algorithms, software code, etc.)

====Open licenses====
In order to ensure a subsequent use of the data, the data must have an open license. This is usually done by the repository, but sometimes it is asked. Recommended open licenses are CC0 or CCBY.
====Citation and link of records in articles====
A data set that stored in one of the recommended repositories is given a permanent DOI and can be quoted as such. The DOI of the data set should not appear in the text of a publication or as a supplement, but rather be referenced in the bibliography. The following information is mandatory: authors, publication date, title, publisher of the record, to the record. The generated reference could then look as follows:First Author, Second Author ... Last Author (2016) Data from "Dataset title". [https://doi.org/10.5281/zenodo.253652 Zenodo repository]. The prefix: [https://doi.org/ https://doi.org/] ensures that the journal or the publication recognizes this address as an active link. During the review process, reviewers can get confidential access to the stored data, even before the data is published. When publishing or accepting the manuscript, the data must then be openly disclosed, and the restriction actively canceled. If the final URL of the article is determined, this should be included in the expanded properties of the record.Data Management Plan Research sponsors such as Horizon 2020 of the EU, are increasingly calling for data management plans (DMP). As a helpful reference are the documents of the Charité Research Group. Moreover, there is a [https://dmptool.org/ tool for creating DMP]. The [https://dmptool.org/ DMP Tool] is a web-based tool that helps you construct data management plans using templates that address specific funder requirements.

===References===

* [https://en.wikipedia.org/wiki/List_of_open-access_journals List of open-access journals]
* [http://v2.sherpa.ac.uk/opendoar/ OpenDOAR]
* [http://s-quest.bihealth.org:3838/OAWhitelist/ BIH Open-Access-Journal-Whitelist]
* [http://www.sherpa.ac.uk/juliet/index.php SHERPA/][http://v2.sherpa.ac.uk/juliet/ Juliet Database]
* [http://sherpa.ac.uk/romeo/search.php Sherpa / Romeo]
* [https://doaj.org/ Directory of Open Access Journals]
* [https://www.protocols.io/ Protocols.io]
* [https://guides.github.com/activities/citable-code/ Github Guides]
* [https://www.casrai.org/credit.html CRediT](Contributor Roles Taxonomy) to describe each author's individual contributions to the work.
* Author identification - Open Researcher and Contributor Identifier ([https://orcid.org/ ORCID])
* [https://publicationethics.org/ COPE](Committee on Publication Ethics)
* [https://dmptool.org/ Data Management Tool (DMP)]
* [https://forum.premier-qms.org/t/key-processes Discuss at PREMIER forum]

PREMIER Evaluation

2021-12-02T21:34:39Z

Admin:

[[QM House|← QM House]]
__NOTOC__
==Objectives==
The aim is to find the most suitable statistical method, to deliver a meaningful result from a well-planned experiment / study.

==Background==
In an experiment, the credibility of the inferred causal relationship between treatment and outcome is dependent upon on the statistical power and internal validity ([https://pubmed.ncbi.nlm.nih.gov/24549183/ Ref 1]).

There are different statistical methods to analyze a given experiment / data set. The choice of these methods and the way a particular method is implemented can significantly influence the conclusions drawn from the experiment.

The principal features of the eventual statistical analysis of the data are described, before analyzing the data and even before performing the experiments.

The [https://biometrie.charite.de/en/about_the_institute/ Institute of Biometry and Clinical Epidemiology] at the Charité has developed a series of easy understandable, freely-available presentations in English and German about the most important topics for the correct [https://biometrie.charite.de/en/service_unit_biometry/after_works_statistics/ statistical planning and evaluation] in biomedical research.

==HARKING==
At PREMIER, the focus is on transparent and scientific work. Therefore, it is essential that the first working hypothesis is documented and included in the study protocol or pre-registration. In this way, so-called HARKing can also be prevented.

"HARKing" means "formulating hypotheses after the results are known": a hypothesis based on the interpretation of the data is presented as if it had already existed before the data collection. HARKing can also occur when a researcher tests an a-priori hypothesis, but then omits this hypothesis from his research report after he has learned the results of his test.

The protocol or pre-registration is preferably stored in an electronic laboratory journal / platform (e.g. OSF), so that all steps are traceable at any time (see also module “Planning of Experiments” – [[PREMIER Pre-registration|Pre-registration]]).

==Tasks / Actions==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

===Primary Analysis and Evaluation of Raw Data===
Primary analysis of raw data is the data processing necessary to derive (secondary) data that are shared, presented and/or subjected to statistical analysis ([https://jpet.aspetjournals.org/content/351/1/200.short Ref. 2]).

Information about the primary analysis of raw data is crucial for establishing a link between raw data and published results and is therefore an essential part of data traceability.

Primary analysis of raw data should be:

* performed blindly (e.g. by an experimenter who does not know the pharmacological treatment) -> For confirmatory research this is a requirement.
* retain the original randomization scheme (if applicable)
* follow a pre-defined analysis plan, which can be part of the curriculum
* Include data verification (even for data generated by automatic systems, there is usually additional data that is generated manually. Examples may include body weight, volume of medication administered, and unplanned observations during an experiment, such as abnormal behavior).
* include a data validity check, i.e. in relation to the acceptance criteria predefined in the study protocol.

===Statistical Analysis===
The following recommendations are based on Motulsky ([https://jpet.aspetjournals.org/content/351/1/200.short Ref. 2]):

* Statistical analysis should be performed exactly as described in the study plan.
* Any changes (e.g. in steps used to process and analyze the data or changes to study hypothesis) must be documented; the reason for a change must be explained and the study conclusion may need to be labeled as “preliminary”.
* As the p-value provides no information about the actual size of the observed effect, it is recommended to calculate, document and present the effect size as difference, percent difference, ratio, or correlation coefficient along with its confidence interval.
* It is strongly recommended to report statistical hypothesis testing (and place significance asterisks on figures) only if a decision is to be based on that one analysis.
* It is strongly advised against the use of the word “significant” in a report or a publication; in plain English "significant" means "relevant" or "important", but a p-value provides no basis for the importance of a finding. If statistical hypothesis testing is used to decide, it is recommended to state the p-value, a preset p-value threshold (statistical alpha), and the decision.
* Once the statistical analysis is conducted, it is recommended to plot figures that show the distribution of data (scatter plot; box & whiskers; violin plot). However, if the data have to be presented as a mean (e.g. in a table), display results as a mean and the standard deviation (mean ± SD or median with inter-quartile ranges if normal distribution is not assumed).
* It is recommended not to plot the mean with error bars that represent the standard error (mean ± SEM) because SEM is not an indicator of variability but of precision and as such less informative than confidence intervals.

It is strongly recommended to report all details when describing statistical methods.
===Visualization of Data===
The implementation of projects concentrates on the steps after data collection, especially statistical analysis and visualization.

The execution of projects focuses on the steps that happen after data collection, especially statistical analysis and visualization.

Several templates or instructions are provided here for the easy creation of scatterplots: (Ref.<span></span>[https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128 3]<span></span>–<span></span>[https://www.jbc.org/content/292/50/20592.full.pdf 4])
# Excel template for creating univariate scatterplots for independent data<span></span>[https://doi.org/10.1371/journal.pbio.1002128.s002 https://doi.org/10.1371/journal.pbio.1002128.s002]
# Excel templates for creating univariate scatterplots for paired or matched data<span></span>[https://doi.org/10.1371/journal.pbio.1002128.s003 https://doi.org/10.1371/journal.pbio.1002128.s003]
# GraphPad PRISM instruction for creating univariate scatterplots for independent data<span></span>[https://doi.org/10.1371/journal.pbio.1002128.s004 https://doi.org/10.1371/journal.pbio.1002128.s004]
# GraphPad PRISM instruction for creating univariate scatterplots for paired or matched data (one group, two conditions)<span></span>[https://doi.org/10.1371/journal.pbio.1002128.s005 https://doi.org/10.1371/journal.pbio.1002128.s005]
# GraphPad PRISM instruction for creating univariate scatterplots for paired or matched data (two groups, two conditions)<span></span>[https://doi.org/10.1371/journal.pbio.1002128.s006 https://doi.org/10.1371/journal.pbio.1002128.s006]
Online interactive tools for creating better data presentations
# Interactive<span></span>[http://statistika.mfub.bg.ac.rs/interactive-dotplot/ Dotblot]<span></span>with instruction<span></span>[https://1825021.mediaspace.kaltura.com/media/InteractiveDotplot_3.mp4/0_kf4at1x8 video]
# Interactive<span></span>[http://statistika.mfub.bg.ac.rs/interactive-linegraph/ line graphs]
# Plots of Data - interactive<span></span>[https://huygens.science.uva.nl/PlotsOfData/ web app]<span></span>for visualizing data together with their summaries

====Analysis Code====

* Git
* Notebooks with R and RStudio
* Jupyter Nootebooks
Data generated by the primary analysis of raw data should be securely stored. Alternatively, tools, algorithms, scripts and related analysis-related information that would be sufficient to restore the analysis may be stored. If the last approach is chosen, two requirements apply:
* It should be possible for any researcher with the necessary qualifications to repeat the analysis.
* The technical feasibility of such reanalysis should be ensured for the entire period during which raw data are stored (e.g. the ability to reanalyze should not be affected by software updates or the readability of guidance information).
Important:
* Label and store all primary analysis files in a way that ensures traceability of the data.
* Outside the specified criteria, the exclusion of data points and observations is only possible as long as the primary analysis is performed blind (i.e. before unblinding).
* All decisions on the exclusion of data must be transparent.
* Consider including this topic in a training programme for new staff or in a refresher training course (if applicable).

===References===
* Sena ES, Currie GL, McCann SK, Macleod MR, Howells DW. Systematic reviews and meta-analysis of preclinical studies: why perform them and how to appraise them critically.<span></span>[https://pubmed.ncbi.nlm.nih.gov/24549183/ J Cereb Blood Flow Metab. 2014 May; 34(5):737-42. doi: 10.1038/jcbfm.2014.28. PMCID: PMC4013765.]
* Motulsky HJ. Common misconceptions about data analysis and statistics.<span></span>[https://jpet.aspetjournals.org/content/351/1/200.short Pharmacol Res Perspect. 2015; 3(1). doi:10.1002/prp2.93]
* Weissgerber T, Milic N, Winham S, Garovic VD. Beyond Bar Graphs: Time for a New Data Presentation Paradigm.<span></span>[http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128 PLoS Biol. 2015;13: e1002128].
* Weissgerber, T.L., et al. Data visualization, bar naked: A free tool for creating interactive graphics.<span></span>[http://www.jbc.org/content/292/50/20592.full.pdf The Journal of biological chemistry 292, 20592-20598 (2017)].
* Percie du Sert N, Hurst V, Ahluwalia A, et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.<span></span>[https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000410 PLoS Biol. 2020;18(7). doi:10.1371/journal.pbio.3000410]
* Rauch, G., Neumann, K., Grittner, U. / Herrmann, C., Kruppa, J. 2020. Medizinische Statistik für Dummies. ISBN: 978-3-527-71584-8 Wiley-VCH, Weinheim
* G*Power:<span></span>[https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html Tool for Statistical Power Analyses]
* [https://forum.premier-qms.org/t/key-processes Discuss at PREMIER forum]

PREMIER Conducting Experiments

2021-12-02T21:34:20Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
The aim of conducting experiments that follow a specific experimental or study design is to prove a hypothesis, avoiding bias and errors.

== Background ==
The experimental design should ensure compliance with the research plan or the previous experimental design as well as the safe and timely data collection and application of appropriate SOPs.

== Tasks / Actions ==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Bias ===

Bias is a distortion or an unintentional influence on results and, in the worst case, a systematic error, e.g. caused by inadequate experimental design (e.g. non-representative sample sizes). Researcher are able to reduce and counteract the omnipresent bias with different tools and methods, such as the described steps of experimental design, adequate statistics in the evaluation of experiments and also the application of suitable standard protocols in the execution of experiments.

Further information can also be found in the manual for bias assessment of the Cochrane and AWMF: https://www.cochrane.de/sites/cochrane.de/files/public/uploads/manual_biasbewertung.pdf

=== Standard Operation Procedures (SOPs) ===

The key processes of basic research in PREMIER are defined in standardized processes (SOPs) and Workinstructions (WIs). They describe the methods and process flows that are necessary for the research work. They regulate how the requirements placed on research can be translated into results and how new methods and models can be developed from them. The Standard Operating Procedures are intended to ensure and document a uniform quality standard of the work performed in academic biomedical research. The purpose is to define organisational processes and procedures for the implementation of research-related processes which, due to their complexity or importance, require standardisation and written specification.

The SOPs and WIs must be continuously reviewed and updated in order to continuously improve the content of these protocols and to ensure their compliance and realization. SOPs follow a defined life cycle: writing, approval, distribution, updating and reset.

The SOPs are binding implementing rules or standardised protocols. We differentiate between organizational and methodical SOPs. The first are specific instructions for action, which are subdivided into overarching SOPs (cross-group and cross-departmental) and specialized area SOPs (assigned to a specialized area in terms of content). Methodical SOPs are specific instructions that describe the implementation of standardized research methods and techniques and/or procedures on equipment.

All SOPs have a uniform layout, which is maintained by using a format template as the basis for the creation of a new SOP.

The content of the SOPs is divided into the following sub-items:

# Changes from the previous version
# Goal / Background
# Scope
# Implementation (process description)
# Evaluation / Control (if applicable)
# Other applicable documents

Note:

SOPs should always be described concisely and should not be too long. Too much detail should be avoided or should be provided as background information via a link. Regulatory requirements and standards (laws, regulations, guidelines) should be taken into account. If possible, the work process should be presented together with the names of the responsible group of people or functional areas (e.g. employees, department heads, AG heads) (clear responsibility).

Each research organisation / laboratory must establish appropriate SOPs covering the activities of the research organisation and the research studies performed.

* The content of the SOPs should follow a standard format defined by the research organization.
* The organization must implement a system for managing SOPs. This includes writing, reviewing, releasing, amending, withdrawing and archiving SOPs.

The successful implementation of SOPs requires:

* Sustained support from all levels of management with commitment to establishing SOPs as an essential and transparent element in the organization and culture of the laboratory.
* An effective SOP management system to ensure that current SOPs are available in the right place. This could be a MediaWiki System, Sharepoint, Lims or others.

WIs (Work Instructions) are non-standardised protocols that can be modified or further developed as required. WIs include, for example, protocols / instructions / recipes / information (can be anything that does not have the binding force of an SOP or the manual). WIs are coordinated by the author with the respective project leader released by them in terms of content. A dedicated person (researcher or QM personel) initiates the document control.

=== Responsibilities ===

It is essential to define and document the responsibilities at each personnel level regarding laboratory activities in order to determine the role of each individual within the organization. Binding roles and responsibilities are defined by using an organization chart (Organigram) and/or specific responsibility tables.

''Responsibilities, roles and activities in basic biomedical research:''

{| class="wikitable"
|-
| || Organisational Role || Responsibility
|-
|rowspan="4"|Responsibility for research activities || Director / head of organization || Policy, provision of resources of all types, budget, supervision of activities; use of resources, advice and support, compliance with institutional policy.
|-
| Project leader || Conduct of study / projects and advice, scientific interpretation of results, verification of data.
|-
| Researcher || Conduct of experiments and study / projects as required in study design / protocol, generating and analyzing data
|-
| Technician || Performance of procedures as required in study plan and SOPs, or other documented instruction.
|-
| Responsibility for review of research activities || Quality assurance personnel or qualified researcher || Assist in implementation and maintenance of quality practices. Help to assure the authenticity, traceability, and consistency of data, and compliance with quality practices, conduct of audits
|-
| Legal, ethical und quality requirements || All personnel || Compliance with guidelines, policy, legal, ethical and quality requirements
|}

=== References ===

* Biasbewertung: [https://www.cochrane.de/sites/cochrane.de/files/public/uploads/manual_biasbewertung.pdf Cochrane and AWMF]
* Susanne Hollmann, Marcus Frohme, Christoph Endrullat, Andreas Kremer, Domenica D’Elia, Babette Regierer, Alina Nechyporenko: Ten simple rules on how to write a standard operating procedure; [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008095 Plos Computational Biology]
* [https://forum.premier-qms.org/t/key-processes Discuss at PREMIER forum]

PREMIER Planning of Experiments

2021-12-02T21:34:11Z

Admin:

[[QM House|← QM House]]
__NOTOC__
== Objectives ==
PREMIER's objective is to support researchers in the detailed planning of projects / experiments before the beginning in order to implement a suitable test procedure in which all risks are considered and avoided in advance and feasibility is implemented with a suitable experimental design.

== Background ==
In recent years, Meta-Research has identified significant deficits in the planning, implementation, analysis and reporting of results from biomedical research. The lack of reproducibility, even of findings published in well-known journals, has brought up the term "replication crisis" and is probably at least partly responsible for the low success rate in translating often spectacular findings from preclinical research into clinically effective therapies. To overcome this phenomenon, experimental neurology has developed structured processes to make projects transparent and comprehensible in their planning, implementation, analysis and reporting.

Accomplishment of projects/experiments requires careful planning as the first step, since a large number of variables can influence the project. The PREMIER approach to this challenge is described in detail below.

== Tasks / Actions ==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Issues ===
The design of experiments is carried out in several steps and starts with following questions.

* What is the goal of the research project?
* Is it realistically achievable?
* Is the research question relevant in the research context?
* Who will benefit from the project/results?

===Two Study Modes: Exploration vs. Confirmation===
In the exploratory investigation, researchers should aim to develop robust pathophysiological theories of diseases.

In the confirmatory investigation (confirmation of hypotheses), researchers should collect strong and reproducible treatment effects in relevant animal models.

We should separate these two modes and adapt the design and reporting guidelines for each mode to the requirements.

The table gives a first overview of how the distinction between exploratory and confirmatory studies can lead to different study designs.

{| class="wikitable"
|-
|
| style="text-align: center;" data-mce-style="text-align: center;"|'''Exploratory (Discovery)'''
| style="text-align: center;" data-mce-style="text-align: center;"|'''Confirmatory'''
|-
| Hypothesis
| style="text-align: center;" data-mce-style="text-align: center;"|(+)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|-
| Establish pathophysiology
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
| style="text-align: center;" data-mce-style="text-align: center;"|(+)
|-
| Sequence and detail of experiments at onset
| style="text-align: center;" data-mce-style="text-align: center;"|(+)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|-
| Defined primary and point
| style="text-align: center;" data-mce-style="text-align: center;"|(-)
| style="text-align: center;" data-mce-style="text-align: center;"|(++)
|-
| Sample size calculation
| style="text-align: center;" data-mce-style="text-align: center;"|(+)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|-
| Blinding
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|-
| Randomization
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|-
| External validity (aging, comorbidities)
| style="text-align: center;" data-mce-style="text-align: center;"|(-)
| style="text-align: center;" data-mce-style="text-align: center;"|(++)
|-
| Predefined inclusion/exclusion criteria
| style="text-align: center;" data-mce-style="text-align: center;"|(++)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|-
| Test statistics
| style="text-align: center;" data-mce-style="text-align: center;"|(+)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|-
| Preregistration
| style="text-align: center;" data-mce-style="text-align: center;"|(-)
| style="text-align: center;" data-mce-style="text-align: center;"|(++)
|-
| High sensitivity (high type I error rate, low type II error rate): find what might work
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
| style="text-align: center;" data-mce-style="text-align: center;"|(+)
|-
| High specifity (low type I error rate, high type II error rate): weed out false-positives
| style="text-align: center;" data-mce-style="text-align: center;"|(+)
| style="text-align: center;" data-mce-style="text-align: center;"|(+++)
|}

== Process of Experimental Design ==
Depending on the type of project, the following steps must be considered and answered for a comprehensive and complete design of experiments:
# [[PREMIER Search|Search]]
# [[PREMIER Hypothesis / Counter-Hypothesis|Hypothesis / Counter (null)-Hypothesis]]
# [[PREMIER Target Parameter|Target Parameters]]
# [[PREMIER Sample Size Calculation|Sample Size Calculation]]
# [[PREMIER Study Design|Model Planning / Study Design]]
# [[PREMIER Feasibility Study|Feasibility Study]]
# [[Nesting and Pseudoreplication]]
# [[Randomization and blinding of biomedical experiments|Randomisation and Blinding]]
# [[PREMIER Resource Plan|Resource Plan (financing, capacities, personnel)]]
# [[PREMIER Schedule|Schedule]]
# [[PREMIER Accompanying Training and Courses|Accompanying Training and Courses]]
# [[PREMIER Planning of Data Preparation / Analysis|Planning of Data Preparation / Analysis]]
# [[PREMIER Data Storage|Data Storage]]
# [[PREMIER Clarification of Authorship|Clarification of Authorship]]
# [[PREMIER Pre-registration|Pre-registration]]

== Template Experimental Design ==
This background information is also linked to a template created included here. Thus, it is possible for every scientist to enter, edit and save the project directly in the template. If this template is included in any form of electronic laboratory notebook, all information will be located in one place and stored safely (at least 10 years) according to the Good Scientific Practice guidelines.

A video tutorial on the use of templates for design of experiments and the export as PDF can be found under [[Premier Experimental Design|Template]]

== Changes ==
Changes that have occurred during the design of the experiment and during the project must always be documented and explained in the ELN, e.g. if something has changed in the progress of the project with regard to the previously agreed authorship or the core method. Transparency and traceability of the entire research process are absolutely necessary and mandatory in order to generate robust and reliable results.

== Sustainability ==
Every researcher should finally think about how and with what means the project can achieve sustainability. Are there factors that could affect the project and its success in the long run? If so, how can these factors be reduced in advance?

== References ==
* Experimental Design Assistant (EDA) Website at the NC3Rs https://eda.nc3rs.org.uk/
* Experimental Design Assistan EDA [https://www.youtube.com/watch?v=caAoTSAuEtk&t=+ Video] ('''Youtube video. Only accessible if you agree to Google conditions''')
* IACUC: Resources and Links for Animal Subjects: Experimental Design and Statistical Analysis in Animal Studies (PDF) http://blink.ucsd.edu/_files/sponsor-tab/iacuc/Guidelines.pdf
* Dirnagl U. Thomas Willis Lecture: Is Translational Stroke Research Broken, and if So, How Can We Fix It? Stroke. 2016 Aug;47(8):2148-53. doi:10.1161/STROKEAHA.116.013244. PubMed PMID: 27354221.[https://www.ahajournals.org/doi/full/10.1161/STROKEAHA.116.013244?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed]
* Kimmelman J, Mogil JS, Dirnagl U. Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol. 2014 May 20;12(5):e1001863. doi: 10.1371/journal.pbio.1001863. PubMed PMID: 24844265. [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001863]
* Lindner MD, Torralba KD, Khan NA. Scientific productivity: An exploratory study of metrics and incentives. PLoS One. 2018 Apr 3;13(4):e0195321. doi: 10.1371/journal.pone.0195321. PubMed PMID: 29614101. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882165/]
* [https://forum.premier-qms.org/t/key-processes Discuss at PREMIER forum]

PREMIER Policy

2021-12-02T21:33:42Z

Admin:

[[QM House|← QM House]]
__NOTOC__
What is the quality policy of an organization?

It is the expression of the aspirations, values and aims of the management regarding quality of its processes and results. The policy should show the engagement to achieve continual improvement and it must be shared or at least accepted by all staff in the research organization.

== Objectives ==
A policy defines the objectives of the quality and therefore the objectives of the organization.

== Background ==
The policy is defined by a general guiding principle consisting of the mission and vision, the scope and commitment to implement and maintain the self-defined standards.

The entire policy with mission and vision must be communicated to all employees, as they need to identify themselves with the quality objectives in general and their specific tasks.

== Tasks / Actions ==
In order to create a lab specific action plan, the first step is an assessment, which will be carried out by the PREMIER team. The assessment will determine the status quo of the laboratory in regard to existing quality tools. Here you find the general tasks / actions that are necessary to implement the module.

=== Scope ===
PREMIER is to be implemented in a research environment where roles and responsibilities are clearly defined. First, the laboratory or organization should be defined where PREMIER should be applied either as a system or only as individual modules. The use of PREMIER is then mandatory for this research unit. All employees must be informed about PREMIER implementation so that the new rules can be followed in accordance with the requirements. The visualization of this process is made possible through an organization chart (Organigram).

=== What to do ===
* define the guiding principles / mission of the organization
* include commitment to good scientific practice

=== Mission ===
The mission statement should include the following aspects:

* A general statement on research quality, describing what quality means in a given context, where quality is most important and what the goal of your research unit or organization is in terms of research quality.
* Risks that arise from non-compliance with the policy.

Quality objectives that are to be achieved in order to improve research. Goals are defined which are specific, measurable, achievable, realistic and traceable (SMART rule).

=== Self-commitment ===
The quality policy represents a voluntary commitment of the management and all employees of the organization to fulfil the established quality standards and to continuously improve the effectiveness of the system.

The organization supports each employee to familiarize with the basic principles of the PREMIER system or its single modules, as well as the more detailed documents relevant to every employee.

=== References ===
* Principles for Good Scientific Practice: Deutsche Forschungsgemeinschaft (DFG) https://www.dfg.de/en/research_funding/principles_dfg_funding/good_scientific_practice/
* Conflicts of interest in research: looking out for number one means keeping the primary interest front and center: Paul L. Romain; PMID: [https://www.ncbi.nlm.nih.gov/pubmed/25851417 25851417]; doi: [https://dx.doi.org/10.1007%2Fs12178-015-9270-2 10.1007/s12178-015-9270-2] [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4596167/]
* [https://forum.premier-qms.org/t/governance Discuss at PREMIER forum]

QM House

2021-12-02T21:33:18Z

Admin:

This structure is the basis and visualization of our new research QM. Officially, the QM House is called PREMIER ('''P'''roductiveness and '''R'''obustness through '''M'''odular '''I'''mprovement of '''E'''xperimental '''R'''esearch) and is a three-year project financed by the Volkswagen Foundation.

The aim of PREMIER is the development of a structured quality assurance, consisting of modular elements, in which high-quality preclinical research is feasible and practicable. We want to demonstrate the principle feasibility of structured quality assurance (proof of concept) in the field of preclinical academic biomedicine, provide indirect proof of the effectiveness of the measures and lay a foundation for the scientific community to further develop and improve such an open system.

The individual modules of the QM House can be clicked on and lead to further information.

<imagemap>
File:QM-House.png|640px
desc none
rect 0 0 1043 99 [[PREMIER Policy]]
rect 0 100 1043 186 [[PREMIER Planning of Experiments]]
rect 0 187 1043 280 [[PREMIER Conducting Experiments]]
rect 0 281 1043 367 [[PREMIER Evaluation]]
rect 0 368 1043 458 [[PREMIER Reporting]]
rect 0 459 566 596 [[PREMIER Communication and Dissemination]]
rect 573 459 1043 596 [[PREMIER Documentation and Data Storage]]
rect 0 603 320 714 [[PREMIER Education / Training]]
rect 334 603 679 714 [[PREMIER Error Management]]
rect 689 603 1043 714 [[PREMIER Legal Requirements and Guidelines]]
rect 0 723 1043 820 [[PREMIER Laboratory Organization]]
rect 0 827 1043 912 [[PREMIER Quality Assurance]]
</imagemap>

The PREMIER concept (QM House) is pre-registered with OSF at https://osf.io/xw75z/

'''You can find detailed information on the [https://premier-qms.org/ PREMIER Website].'''

[https://forum.premier-qms.org Discuss at PREMIER forum]

The PREMIER Explainer Video describes the objectives of such quality assurance and why PREMIER should be used as a basis for biomedical research.
[[File:PREMIER Explainer Video.mp4|left|miniatur|PREMIER Explainer Video]]

PREMIER

2021-12-02T21:32:54Z

Admin:

Welcome to the PREMIER Wiki platform. This platform was developed to transparently share, store and further develop knowledge within a department / laboratory / institute.

This structure of an open wiki system guarantees a continuous exchange of knowledge. All information and documents about processes and internal regulations of a laboratory can be stored here and are available to all employees. Document control, e.g. writing and updating SOPs, can also be done via the PREMIER Wiki. The Wiki is password protected so that each employee has his or her own Wiki account. Reading and writing rights can be assigned variably, depending on existing needs. This means that the wiki can be adapted precisely to any organization.

On this PREMIER Wiki platform you will find the modular clickable PREMIER QMS (the QM house) with all contents. These contents are not editable, they should rather serve as an orientation for your laboratory and help with the introduction of the individual modules. Additionally you will find on this platform the PREMIER template for the experimental design of your research project.

All other content of the wiki platform is up to you. You decide which content should be shared, maintained and passed on to your colleagues.

[https://forum.premier-qms.org/ New PREMIER forum available!]

MediaWiki:Sidebar

2021-06-18T20:11:19Z

Admin:

Create New Category

2021-05-25T19:55:33Z

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Legal

2021-04-23T07:27:36Z

Admin:

__NOTOC__
Information in accordance with Section 5 TMG

Department of Experimental Neurology, Charité - Universitätsmedizin Berlin<br/>
Charitéplatz 1<br/>
10117 BERLIN

Germany

== Represented by ==
Prof. Dr. Ulrich Dirnagl

== Contact Information ==
E-Mail: ulrich.dirnagl@charite.de<br/>
Internet address: https://premier-qms.org

== Disclaimer ==
=== Accountability for content ===
The contents of our pages have been created with the utmost care. However, we cannot guarantee the contents' accuracy, completeness or topicality. According to statutory provisions, we are furthermore responsible for our own content on these web pages. In this matter, please note that we are not obliged to monitor the transmitted or saved information of third parties, or investigate circumstances pointing to illegal activity. Our obligations to remove or block the use of information under generally applicable laws remain unaffected by this as per §§ 8 to 10 of the Telemedia Act (TMG).

=== Accountability for links ===
Responsibility for the co ntent of external links (to web pages of third parties) lies solely with the operators of the linked pages. No violations were evident to us at the time of linking. Should any legal infringement become known to us, we will remove the respective link immediately.

=== Copyright ===
Our web pages and their contents are subject to German copyright law. Unless expressly permitted by law, every form of utilizing, reproducing or processing works subject to copyright protection on our web pages requires the prior consent of the respective owner of the rights. Individual reproductions of a work are only allowed for private use. The materials from these pages are copyrighted and any unauthorized use may violate copyright laws.

[https://creativecommons.org/licenses/by/4.0/ CC BY 4.0]

=== Release Date ===
1. September 2020

Create New Page

2021-03-05T13:50:29Z

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PREMIER Pre-registration

2021-02-23T08:50:40Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Background ==
Transparency and independent replication are of central value to science. However, scientists are sometimes "forced" to publish what is most likely to lead to positive results, even if this is at the expense of transparent and reproducible research. For this reason, reported positive results often do not reflect the original hypothesis.

When you preregister your research, you're simply specifying your research plan in advance of your study and submitting it to a registry.

Preregistration separates hypothesis-generating (exploratory) from hypothesis-testing (confirmatory) research. Both are important. But the same data cannot be used to generate and test a hypothesis, which can happen unintentionally and reduce the credibility of your results. Addressing this problem through planning improves the quality and transparency of your research. This helps you clearly report your study and helps others who may wish to build on it (COS).

Preregistration is a time-stamped freezing of a certain document. It is available to others and cannot be deleted. If an update of the document is necessary, a new version can be uploaded that is clearly marked as such with a new time stamp. Preregistation is useful for depositing an experimental plan that is about to executed in the described manner. It publicly permits the comparison between planned experiment and a final report of the outcome. Since a preregistration is always a public deposit, setting an embargo when information becomes available is possible.

=== Where can you pre-register? ===
Preregistration is currently possible at:
# Open Science Framework ([http://help.osf.io/m/registrations/l/524205-register-your-project Link])
# AsPredicted ([https://aspredicted.org/ Link])
# PRECLINICALTRIALS.EU ([https://preclinicaltrials.eu/ Link])

=== Registered Report ===
As special form of preregistration is a registered report which is offered by certain journals. Registered reports outline the proposed experimental designs and protocols, which are then peer reviewed prior to data collection. After successful peer review, experiments can be conducted as agreed upon. Then final report containing the results and discussion is drafted and submitted to the journal where the design was registered and the second round of peer review starts.

The advantage is that a publication cannot be denied on the reason of poor experimental design, making it very likely that a carefully planned and registered study leads to a publication. Outcome is not a factor for publication.
[[File:The registered report work flow .png|thumb|Image credit: Center for Open Science CC 4.0|none]]

Registered Reports are possible at these journals:
# BMJ OpenScience ([https://openscience.bmj.com/ Link])
# BMJ Medicine ([https://www.bmj.com/ Link])
# F1000Research - submitted as study protocol ([https://f1000research.com/for-authors/article-guidelines/study-protocols Link])

=== Animal Study Registry ===
The Animal Study Registry is an online registry for scientific studies with animals conducted worldwide. The Registry was established in response to the reproducibility crisis and provides a platform for scientists to register a detailed study plan before starting experiments to prevent selective reporting. This allows reviewers or other scientists to compare the originally registered content with the final publication. This promotes transparency, reproducibility and animal welfare.

[https://www.animalstudyregistry.org/asr_web/index.action;jsessionid=2FAEA48A107FF4CDF1F04FA2C8514723 Animal Study Registry] at German Federal Institute for Risk Assessment (BfR).

=== Preclinical Trials ===
Preclinical trials aims to provide a comprehensive listing of preclinical animal study protocols. Preferably registered at inception in order to increase transparency, help avoid duplication, and reduce the risk of reporting bias by enabling comparison of the completed study with what was planned in the protocol.

[https://preclinicaltrials.eu/ Preclinical Trials]

=== References ===
* [https://www.cos.io/our-services/prereg Center of Open Science (COS)]
* [https://www.pnas.org/content/115/11/2600 The Preregistration Revolution]
* [https://www.psychologicalscience.org/observer/research-preregistration-101#.WR3GyFPyvOT Research Preregistration 101]
* [https://www.cos.io/blog/preregistration-plan-not-prison Preregistration: A Plan, Not a Prison]
* [https://www.nature.com/articles/s41562-016-0021 A manifesto for reproducible science]
* [https://www.theguardian.com/science/blog/2013/jun/05/trust-in-science-study-pre-registration Trust in science would be improved by study pre-registration]
* [https://www.nature.com/news/let-s-think-about-cognitive-bias-1.18520 Let’s think about cognitive bias]
* [https://www.psychologicalscience.org/observer/seven-selfish-reasons-for-preregistration#.WR3HblMrLOS Seven Selfish Reasons for Preregistration]
* [https://www.animalstudyregistry.org/asr_web/index.action;jsessionid=2FAEA48A107FF4CDF1F04FA2C8514723 Animal Study Registry]
* [https://preclinicaltrials.eu/ Preclinical Trials]

PREMIER Clarification of Authorship

2021-02-23T08:44:22Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Authorships ==
Scientific publications are the primary medium of reporting by scientists on their work. With a publication, an author (or group of authors) announces a scientific result, identifies with it and assumes responsibility for the content of the publication. At the same time, the author thereby acquires documented rights (copyright, copyright etc.). In connection with this, the date of publication has acquired a significant importance in the sense of documenting scientific priority; many journals report when a manuscript has been received and when it has been accepted (usually after review by reviewers).

Because of their great importance as proof of performance, publications or authorships are the subject of many conflicts and disagreements. However, they have given rise to generally accepted rules for the most common conflict issues, namely the originality and autonomy of content and authorship, which are summarized below.

Publications should be:

* describe the results completely and comprehensibly,
* provide complete and correct evidence of their own and other people's preparatory work (quotations),
* previously published results only in clearly identified form

and repeat only to the extent necessary to understand the context. Many journals require a written declaration in their author guidelines that the content of a manuscript has not already been published or submitted for publication in whole or in part elsewhere.

Only those authors of an original scientific publication should be named who have made a substantial contribution to the study or experimental design, to the elaboration, analysis and interpretation of the data and to the drafting of the manuscript itself, and who have agreed to its publication, i.e. who are responsible contributors. Some journals require this to be evidenced by the signatures of all authors, while others require the corresponding author, as the person responsible for all details of a publication, to provide appropriate insurance. In the event that not all co-authors can provide a guarantee for the entire content, some journals recommend that the individual contributions be identified.

In order to avoid conflicts over authorship, it is recommended that, the greater the number of people involved in the development of the results, clear agreements should be made early on (if possible before the start of the project and at the latest before the publication is produced) to provide orientation in the event of dissent. It is against the rules of good scientific practice to terminate the collaboration without sufficient reason or to prevent the publication of the results as co-author, on whose consent the publication is dependent, without urgent reason. Publication refusals must be justified by verifiable criticism of data, methods or results.

Table 1: Inclusion and exclusion criteria for co-authorship according to DFG Guidelines

{| class="wikitable"
!authorship is justified if
has made a significant contribution to:
!authorship as a rule not
justified if only the following

contributions were made:
|-
|Study Design
|Responsibility for the recruitment of the
Funding
|-
|Development, analysis, interpretation
of the data
|Contribution more important
test materials
|-
|Wording of the manuscript
was made
|management of an institution or
organizational unit, in which the

publication has been produced

Organisationseinheit, in der die

Publikation entstanden ist
|}

== References ==
* [https://intranet.charite.de/fileadmin/user_upload/portal/forschung/Information_Fakultaet/Affiliation_Charite_final_150617_2.pdf Affiliationsrichtlinie der Charité]
* [http://www.icmje.org/ ICMJE]
* [http://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html Defining the Role of Authors and Contributors]
* Moore HM, Kelly A, Jewell SD, et al. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169291/ Biospecimen Reporting for Improved Study Quality] (BRISQ)

PREMIER Data Storage

2021-02-23T08:41:28Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
According to Good Scientific Practice (GSP) guidelines, all primary and secondary data must be securely stored or retained for at least 10 years after their creation.

== Tasks/Actions ==
=== Regulations on the storage location ===
* All research data must be stored in the Electronic Laboratory Notebook as soon as they are created. If it is a larger amount of files or if the file size exceeds the limit allowed in the ELN, the primary data must instead be stored on the archive storage, the secondary data on the standard storage.
* Due to the risk of data loss, the data may be stored additionally, but not exclusively, on USB sticks / external hard disks or even local hard disks.
* Research data (e.g. personal data) may only be additionally stored on cloud or sharing systems (e.g. dropbox, external email providers) if it complies with the Data Protection Law.
* Data records that are included in a publication / dissertation must be collected, structured and stored in the archive data storage.

=== What must be available in each working group? ===
* It is recommended that each workgroup designate a data administrator. He/she makes sure that the members of this group store the primary and secondary data according to a defined concept.
* A standard storage to which all members of the group can have read and write access and which complies with the "storage location policy". Read and write access should be defined by the working group on a project-specific basis and managed by the data administrator (administrator).
* An archive storage.

A concept for the clear identification of the data to the project, the experiment, the creator of the file. Here, reference can be made to the Electronic Laboratory Notebook, if the exact location of the data is referenced in the respective entries.

=== Proposals for folder structures ===
Data submitted in publications or in papers for obtaining academic degrees or diplomas should be ordered or named according to the following structure:

Example of an archive folder structure:

* Primary data
** Project name and name of the person responsible for the project
* Publication dates
** Working title and name of the person responsible for the project
* Raw data (only link from primary data folders)
* Evaluations and data preparation
* Statistics
* Tables and figures
* Manuscript

In addition, a file should be used as a table of contents, indicating the software used to process the files.

=== Emergency plan ===
It is recommended to have an emergency plan in case the data archive or the standard storage should not be accessible to ensure access to all stored data.

For further information, see module [[PREMIER Documentation and Data Storage|Documentation/Data storage]].

PREMIER Planning of Data Preparation / Analysis

2021-02-23T08:37:33Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Objective ==
The aim of data preparation and data analysis is to increase or enhance data quality.

== Background ==
Why Data Preparation?

In science, working with data of questionable quality leads to false results, which not only prevent the gain of knowledge, but can also have undesirable practical consequences. Therefore data preparation and analysis are absolutely necessary. Data preparation includes all well-founded and documented processing or changes to the raw data material that increase the validity and (re)usability of the data and prepare the data analysis. This includes:

* the creation of structured data sets from the raw data
* the commentary
* the anonymisation of the data records
* the data correction
* the data transformation

== Tasks / Actions ==
=== Criteria for Data Quality ===
Data quality can be specified for quantitative data by a number of criteria, including:

# completeness
# uniformity (e.g. of dates and currencies, use of acronyms)
# exclusion of double values/multiple data rows
# proper handling of missing values
# detection and treatment of outlier values, whereby this often only takes place during data analysis
# plausibility of the response patterns

=== Functions of Data Preparation ===
The data preparation fulfills several functions:

==== Avoidance of incorrect results ====
An evaluation of incomplete or incorrect data leads to incorrect results (so-called garbage-in-garbage-out principle). The analysis of an uncorrected data set, which contains e.g. duplications, typing errors or implausible answers, can distort the entire result and lead to wrong conclusions regarding content. Such problems can be avoided if the data quality is checked and ensured from the beginning. The drawback of suboptimal data quality is that it is only recognized the moment it is checked. If poor data quality is discovered late in a research process - for example, only during or at the end of the data analysis - all previously performed analysis steps are often in vain.

==== Avoidance of difficulties and delays in data analysis ====
The aim is to ensure that current and subsequent data analysis is carried out smoothly by the researcher, research partners or other collaborators who wish to perform re-analysis or secondary analysis of the data. This requires a strict organisation and sufficient commentary of the data sets by means of metadata (e.g. exact details of when, where and by whom the data were collected, what variable names and measured values mean, etc.) In addition, uniform forms of presentation and compatible formatting are necessary for the exchange and evaluation of data. A poorly prepared data set can become unusable, for example if a lack of commentary or a missing code plan means that it is not possible - or only at great expense - to reconstruct at a later date what certain measured values actually mean in terms of content.

==== Avoidance of ethical problems ====
Especially in qualitative research, the execution of clinical studies, failures to anonymise the raw data material can make the participating persons identifiable. Identifiability is - unless there is explicit consent of the study participants (e.g. permitted naming in an expert interview) - not only a violation of the GSP, but also of data protection laws. This applies regardless of whether or not the identification actually results in noticeable adverse effects for an individual participant.

=== Procedure ===
Since data preparation represents an intervention in the data, the procedure for data preparation (step from raw to secondary data) must be justified and documented accordingly in the ELN / results report. It is therefore absolutely necessary to write down a defined algorithm how the data must be prepared.

Need to:

* justify deviations
* an evaluation schema is stored and
* requirements are established that data must be available.

In order to be able to understand how data is evaluated, an openly designed analysis plan should be stored in the ELN.

While for simple evaluations, spreadsheet programs (e.g. Excel) are used, statistical programs such as SPSS are preferred for more complicated evaluations.

For more information see module „[[PREMIER Evaluation|Evaluation]]“.

== References ==
* Sittampalam GS, Coussens NP, Brimacombe K, et al., editors. Assay Guidance Manual [Internet]. Bethesda (MD): Eli Lilly & Company and the National Center for Advancing Translational Sciences; 2004-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK53196/
* Statistical experiment design for animal research: C.O.S. Sorzano and M. Parkinson; 09/2019
* Motulsky HJ (2015) Common misconceptions about data analysis and statistics. Pharmacol Res Perspect. 3(1). [https://www.ncbi.nlm.nih.gov/pubmed/25692012 PubMed]
* Guidelines on reporting of statistical analysis (in vivo research): [https://paasp.sharepoint.com/sites/EQIPD/EQIPD%20QS/ARRIVE%20Essential%20-%20Statistical%20methods.aspx ARRIVE 2.0]

PREMIER Accompanying Training and Courses

2021-02-23T08:33:04Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
Accompanying education and training are essential as supporting measures for the design of experiments. These include instruction in the specialist areas, methods and equipment as well as specific training courses that are project-dependent. The lab manager and project leader are responsible for defining the training needs. They must set priorities and define requirements for content, enforcement and documentation based on the quality criteria.

It is important to cover the following topics:

* Good Scientific Practice (GSP)
* Training on handling the electronic laboratory book (ELN)
* Experimental Design
* Training of Methods
* How to reduce bias (randomizing / blinding / nesting etc.)
* Instructions of Equipment
* Training for new employees
* Ethics and animal welfare (e.g. Felasa course, principles of 3Rs etc.)
* Data Handling / Data Storage
* Statistics
* Transparent Reporting (open access / open data)

All training courses carried out must be documented and securely stored electronically or in an appropriate folder. If, for example, there are inspections by the animal welfare authority, it must be possible to present the relevant documentation.

Finally it is important to consider refresher trainings.

PREMIER Schedule

2021-02-23T08:32:18Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== General Information ==
It is essential for the design of experiments to establish a suitable time schedule. Especially for medical doctoral students with a very limited time window for their experimental work, a realistic schedule is essential. Important time points or milestones should be defined.

Statements on experiments requiring approval should be planned in advance.

== Template Overview Plan ==
[[File:Schedule.png|500px|none]]

You can download the timetable as [[File:Versuchsplanung 20180718.xlsx|Excel]] file.

== Alternative: Electronic Project Management Tool ==
User accounts and new projects can be applied for on the following page: Apply for [https://exneuro13.charite.de/form/?page_id=649 Open Project account and Project].

File:Schedule.png

2021-02-23T08:27:56Z

Admin:

PREMIER Resource Plan

2021-02-23T08:25:55Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== General Information ==
The resources of a project include equipment and reagents as well as personnel, facilities, IT-services and funding among other. The following questions should be answer '''before''' starting the project:

* Are all required resources available, including personnel with right skills?
* Are resources being shared with other projects? If yes, when are they available?
* Is there a preliminary time table for use of the different resources?
* Is the process for ordering of goods within the organization understood/followed?

== Possible Structuring of Resource Types ==

[[File:Resources EN.png|thumb|600x600px|Resources|none]]

There are some open source project management software tools easy to use such as “Open Project” that can help in the process or planning resources for a particular project.

== Reference ==
Diagram modified from: "[https://www.projektmagazin.de/glossarterm/ressource Projektmagazin]"

Randomization and blinding of biomedical experiments

2021-02-23T08:24:02Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Background ==
One of the largest sources of experimental bias that influences outcome measures derives from studies in which experimental conditions are not fully or only partly known to the observer. The second largest source of bias arises from experiments in which the samples/ subjects are not randomly allocated to the different experimental conditions. While it is not always possible to control all forms of known or unknown bias, simple adjustments in the experimental design can greatly reduce its impact.

=== Randomization ===
Randomization refers to the practice of using chance methods (random number tables, flipping a coin, etc.) to assign subjects to treatments.

==== Determine the number of blocks needed ====
Every experiment tests and compares at least 2 different conditions, for instance drug vs. control. In addition various drug concentrations, drug antagonists, positive and negative controls of extend the number of testable conditions. Power calculations can project how many samples each conditions needs to contain to answer the research question with statistically solid confidence. This forms a matrix of x number of samples times y numbers of conditions. Then x*y need to be arranged in experimental groups (blocks) that are similar to one another. An example for a block are number of animals undergoing MCAO surgery on a given day or the number of cell culture plates undergoing OGD. Sometime other nuisance variables have to be taken into account such as sex or genetic variants.

==== Two Different Designs ====
# ''Simple randomization:'' Randomized allocation of study subjects or sample to the different treatment groups based on one single sequence of random assignments. While this leads to a completely randomized sequence, the breakup into experimental units may either lead to imbalanced groups and group sizes.
# ''Stratified randomization or Block randomization:'' Allocation of study subjects or samples to blocks that share similar baseline characteristics followed by randomized allocation of the subjects/samples of each block to the different treatment groups.

This method adjusts for potential covariates (nuisance variables) across treatment groups and is therefore preferred.

=== Tools for randomization ===
''QuickCalc:'' [http://www.graphpad.com/quickcalcs/randomize1.cfm link]

''Clinical Trial Randomization tool:'' [https://ctrandomization.cancer.gov/instructions/ link]

[[File:Simple Randomization.png|none|500px|Simple Randomization]]

[[File:Randomized Block Design.png|none|500px|Randomized Block Design]]

=== Blinding ===
Blinding is the continuous masking of the treatment allocation for the person(s) who perform the experiment, collect data and assess outcome. "Any process using a human as perceptor, rater, or interpreter should be as blind as possible for as long as possible" (Rosenthal, R., 1978. How often are our numbers wrong? American Psychologist 33, 1005–1008)

==== How to achieve effective blinding ====
Effective blinding requires at least 2 people, one blinded and one unblinded person. If different persons are involved in various steps of the research process, make sure that they are sufficiently blinded. Exchange about the experiment its goals and expectations should ideally be limited.

The unblinded person is the keeper the blinding scheme. Blinding must occur prior to the start of the experiment and unblinding shall not occur until final analysis. It is of utmost importance that the inclusion and exclusion criteria are applied in a blinded fashion.

==== What is a blinding scheme? ====
A blinding schema contains a chronological, stepwise workflow of the experiment. Every step contains information about the conductor and participants and their subjective blinding status, which can be unblended, partially blinded or fully blinded. It also mentions the step(s) at which inclusion and exclusion criteria are applied. There are multiple web-based tools that you can use, e.g. the [https://www.nc3rs.org.uk/experimental-design-assistant-eda Experimental Design Assistant - EDA] provided by NR3R or the [https://ctrandomization.cancer.gov/tool/ Trial Randomization Tool] available at the NCI.

==== Allocation concealment ====
The investigator should not be aware and/or have the choice to which treatment group a sample is allocated. Therefore the assignment to a specific group needs to be concealed and every sample should have the same chance to be assign to each of the groups. This can be achieved separating the assignment of identifiers and randomization of interventions into two independent processes and then merge the two.

==== Blinding Procedures ====
Blinding individual samples can be cumbersome because they usually part of a larger order system (cages, batches, or plates). But within these container systems allocation of treatments to all samples need to be randomized. It is utmost important that during measurement and analyses the executing person are unable to draw conclusions with regards to the conditions. However, this is sometimes not possible especially when certain cues cannot be blinded, such as skin color of a transgenic mice or color of a solution in a well.

Blinding requires at least 2 people, the blinded (BP) and the unblinded person (UP). The UP creates and holds on randomization and blinding key and masks the conditions appropriately. However, depending on the setup /duties several scenarios can occur, that require different workflows:

'''Scenario 1''': In the absence of the BP, the UP provides the usually drug solutions or other intervention and applies them to experimental samples. For the BP, which is the rater and analyzer, no clues are given. The BP also applies the inclusion and exclusion criteria and marks excluded samples clearly. The UP then provides information, which samples belong to the same group and the BP applies the statistical analysis from the study plan. In the final step, the BP becomes unblinded.

'''Scenario 2''': Similar to scenario 1, except that the BP, which is rater and analyzer, applies the intervention to the samples. Therefore, when providing the intervention, the UP must mask them. At this step, many experimenters often practice pseudoblinding by simply masking the common substance container. This provides grouping information to the BP, which now tends to harmonize rating of samples that belong to the same group. This can be avoided by the UP providing the substances or intervention in single units designated for one sample only. This requires special caution when labeling those samples and when they are applied.

'''Scenario 3''': There is no UP to help. '''This should be avoided at any cost.''' There are occasions, for instance, on weekends or late hours, no personnel is available to help with blinding. Planning in advance can avoid such situations. Drugs could be prepared and blinded earlier or on a Friday.

=== Hidden cues ===
Often measurements occur long after treatment is applied. This can be the case when tissue samples are taken and further analyzed and videos of behavioral experiments were taken, but are scored at a much alter time point. Often immediate measures are already analyzed and unblinded. It is important to maintain the blinding status for the rater as long as needed.

In addition many hidden clues exist that can partially unblind the rater. This could be an additional sample information on a glass slide or vial, a folder or file name on data storage system or even time /date of files that could give away temporal or sequential information or access to analyzed and unblinded parts of data, e.g. shared electronic laboratory notebook project. During experimental planning phase, such potential cues must be identified and prevented.
[[File:Hidden cues in sample names.png|500px|none|Hidden cues in sample names]]

=== Reporting blinding and randomizing ===
Most often publications contain the standard sentence like: "''All experiments were randomized and outcomes measurement were blinded.''" to satisfy publication guidelines. Even ARRIVE guidelines contain stricter requirements on reporting of blinding and randomization. In times of combating the replication crisis, reporting should contain:

* How was randomization achieved?
* What tool was used?
* Were randomized blocks used? - if yes, size of the blocks
* Was allocation concealment practiced? If no, explain why.
* How was ensured that the entire study was conducted in a blinded manner? If any, which parts were not blinded?

Were inclusion and exclusion criteria applied in a blinded manner? If not, explain why.

== References ==
* Rosenthal, R., 1978. How often are our numbers wrong? American Psychologist 33, 1005–1008
* [https://www.sciencedirect.com/science/article/pii/B9780123919113000062 Clinical trials : study design, endpoints and biomarkers, drug safety, FDA and ICH guidelines] Brody, Tom. 1st ed. Amsterdam : Elsevier/AP, 2012. NLM ID: 101575674.

Nesting and Pseudoreplication

2021-02-23T07:47:40Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Background ==
Biologists determine experimental effects by perturbing biological entities or units. When done appropriately, independent replication contributes to the sample size (N) and forms the basis of statistical inference. Pseudoreplication artificially inflates the sample size, and thus the evidence for a scientific claim, resulting in false positives [https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.2005282&type=printable [1<nowiki>]</nowiki>]. The term `replication' has several related meanings, and here it refers to the classic statistical definition of an intervention or treatment applied to multiple biological entities i.e. experimental units (EUs). It does not refer to researchers trying to reproduce or replicate their own or others' results [https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.2005282&type=printable [1<nowiki>]</nowiki>].

There are two types of replication:

# The first is replication that increases the sample size (N) and thus contributes to testing an experimental hypothesis. It is called '''true, genuine, or absolute replication'''.
# The second type is replication that does not increase the sample size and is called '''pseudoreplication'''. Confusing pseudoreplication for genuine replication artificially inflates the sample size, thereby inflating the apparent evidence supporting a scientific claim, and contributes to irreproducible results.

Optimization of experimental designs nearly always concerns collection of more truly independent observations, rather than more observations from one research object [https://www.nature.com/articles/nn.3648.pdf [2<nowiki>]</nowiki>].

== Tasks / Actions ==

=== The Problem of Nesting ===
Nesting refers to statistical term "nested data". Those occur when the observation of a dataset can be assigned to units of superordinate hierarchy. A leveled analysis is necessary to reveal these relationships. In biomedicine, nested data present a problem when they are counted as the true sample size. The following paragraphs are designed to prevent such pitfalls already at the stage of the study design.

Nested designs are designs in which multiple observations or measurements are collected in each research object (for example, animal, tissue sample or neuron/cell). Consider the following fictive, yet representative, research results. “The channel blocker significantly affected Ca2+ signals (n = 120 regions of interest (ROI) from 10 cells, P < 0.01).” “The number of vesicles docked at the active zone was smaller in presynaptic buttons in mutant neurons than in WT neurons (n = 20 and 25 synapses each from 3 neurons for mutant and WT, P < 0.01).” Both statements concern experimental designs involving nested (or clustered) data.

These nested designs are particularly common to neuroscience, as many research questions in neuroscience consider multiple layers of complexity: from protein complexes, synapses and neurons, to neuronal networks, connected systems in the brain and behavior. In such multiple layer–crossing designs, careful consideration of the issues that come with nesting is crucial to avoid incorrect inferences [https://www.nature.com/articles/nn.3648.pdf [2<nowiki>]</nowiki>].

=== Genuine and Pseudoreplication ===
True or genuine replication increases the samples size (N) and contributes to testing an experimental hypothesis. Pseudoreplication artificially inflates the samples size, which leads to more false positive results. Problem: Often pseudoreplication is mistaken for genuine replication. The problem is are multiple layers of biological organization. (DNA-->RNA-->protein-->tissue-->organ-->organism)

[[File:Biological organization levels in a mouse.png|thumb|Figure 1: Biological organisation Levels in a mouse|500x500px|none]]
Properties at one level of biological organization tend to be influenced by those above. In the same organism, two cells in same tissue tend to be more alike, then cells between different tissues. Research hypothesis, experimental manipulation and measurement can be assigned to different levels of biological organization.

Each study contains replication that is relevant to the hypothesis being tested. Therefore, it is important to define:

* '''Biological Unit (BU)''' of interest which is the entity about which inferences are made. The purpose of an experiment is to test a hypothesis, estimate a property, or draw a conclusion about BUs.
* '''Experimental Unit (EU)''' is the entity randomly and independently assigned to the experimental conditions. EUs must not influence each other, especially on the measured outcome variable. The sample size (N) is equal to the number of EUs.
* '''Observational Unit (OU)''' corresponds to the entity on which the actual measurements are made, which may be different from the EUs and BUs of interest. Increasing the number of OUs does not increase the sample size.

[[File:Definitions of BU EU OU.png|thumb|Figure 2: Definitions of units for experimental replication|600x600px|none]]

In every study design the conditions must be tested to determine whether the OU is the experimental unit or if there is another superordinate hierarchy. In following, the hierarchical characteristics for different experimental types and setups are laid out.

== In vivo Experiments ==
If treatments are randomly and independently applied to an entity other than the individual animal (e.g. pregnant females), then the sample size is not the number of animals. Offspring rarely meet the criteria for genuine replications.[[File:Figure 3 litters.png|thumb|Figure 3 Offsprings from one mother are pseudoreplicants|none|500x500px]]

A possibility is to apply treatment after offspring are born. They are then randomized by litter to the treatment groups. The problem with this design is that the variable litter is nested under the variable group. [[File:Figure 4 litters.png|thumb|Figure 4 - Littermates that treated individually can still be pseudoreplicants|none|500x500px]]

A solution can be a crossed arrangement which crossed litters and treatment groups: Individual animals are randomized to the treatment condition which removes the litter-to-litter variation. [[File:Figure 5 litters.png|thumb|500x500px|Figure 5 Genuine replication using littermates|none]]

This applies to all recognizable subgroups, such as transgenic and wild-type mice..
[[File:Three rules for genuine replication.png|thumb|500x500px|Figure 6: Rules for genuine replication in homegeneous and recognisable subgroups|none]]

If a treatment is applied cage-wise, then cages are the experimental unit. Only when the treatment is applied independently, animal act as EU.

In many animal experiments, the condition for genuine replication (treated animals do not influence each other) is often overlooked. In reality mandatory group housing violates this condition, because animals in the same cage influence each other on many relevant variables, from behaviour to microbiome. Even if the first two criteria for genuine replication are met, mutual influence of animals in the same cage may render them unsuitable to be an EU. A solution would be one animal housing per cage which is often not possible for animal-ethical reasons. A compromise could be to house 2 animals maximum per cage (which is then the EU) for all animals of the study.

=== Slice Preparations and Histological Samples ===
For some experiments, animals are randomized to treatment conditions and an intervention is applied to the animals. Then an organ or body part is examined, usually postmortem. Because of the large size or diversity of the body parts, multiple histological sections, neurons per section, spines per neuron are counted. All of these OU have been randomized together (I), the treatment is applied simultaneously (II) and treated neurons and spines within an area of interest may influence each other (III). Therefore animals are the EU in this case.
[[File:Figure 7 slices histo.png|thumb|500x500px|Figure 7 Pseudoreplication in body parts|none]]

If the body part is removed first, treatment is applied, and then observations are made, multiple body parts per animal can used , especially the one that come in pairs (brain hemispheres, kidneys, lungs, testes, ovaries) can be used and individually randomized and treated. This approach can also reduce also the number of animals in a study. Sample size is then the body part but it is strongly advised to use multiple animals to establish the robustness of the investigated effect.

=== In vitro Cell Culture Experiments ===
In cell culture experiments cells are often both, the BU and OU, but rarely the true EU.
[[File:Figure 8 cell culture1.png|thumb|500x500px|Figure 8 - Pseudoreplication in ''in vitro'' experiments|none]]

There is a lot of batch-to-batch variability in cell culture experiments because the experimental material needs to be created for every experiment. For this reason such in-vitro experiments are usually repeated on multiple days, and the number of wells, aliquots, or culture dishes within a given day are treated as subsamples. In order to test whether a phenomenon is robust, multiple replications of the entire experimental run or protocol are required. This cannot be done on a one day setup using a large number of samples. Multiple repetitions provide an estimate of the consistency of the effects across different experimental runs on different days
[[File:Figure 9 cell culture2.png|thumb|500x500px|Figure 9 Robust setup for genuine cell culture studies|none]]

=== References ===
* Lazic SE, Clarke-Williams CJ, Munafò MR. What exactly is 'N' in cell culture and animal experiments? http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005282 PLoS Biol. 2018 Apr 4;16(4):e2005282. doi: 10.1371/journal.pbio.2005282. PMID: 29617358;
* Aarts E, Verhage M, Veenvliet JV, Dolan CV, van der Sluis S. [https://www.nature.com/articles/nn.3648.pdf A solution to dependency: using multilevel analysis to accommodate nested data]. Nat Neurosci. 2014 Apr;17(4):491-6. doi: 10.1038/nn.3648. Epub 2014 Mar 26. PMID: 24671065.

PREMIER Feasibility Study

2021-02-22T18:55:28Z

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[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
A feasibility study checks the practicality of possible solutions for a project. Within the framework of a feasibility study:

* the approaches are analyzed
* the risks identified
* the prospects of success assessed.

It is checked whether the planned project results can be achieved under the given conditions with the respective solution approach.

This requires preliminary tests or re-tests in which the handling with the equipment, materials and methods is tested. In addition, an assessment of the risks that the model/method and the execution of the experiments entail should be carried out. With the final validation it is possible to identify a suitable solution approach.

With the help of the feasibility study an overview is obtained whether a method works and whether the hypothesis can be tested.

Questions to be answered when conducting a feasibility study include:

* Which '''options''' are available?
* Is there a '''need for the research question''' or the Project?
* Are there '''already studies/publications on the subject?'''
* Which are the '''challenges''' of the project and the possible '''risks'''?
* Which are the '''qualifications''' needed or are '''cooperation partners''' foreseen?
* Is there an '''existing method or a new method''' must be established?
* Are all '''equipment and materials''' available?
* Are all '''approvals''' (ethics committee, animal welfare, genetic, etc.) available?
* Where are the '''critical points/risks'''?
* Are the available '''resources''' sufficient (personnel, financial, infrastructure)?
* What are possible '''milestones'''?
* Is a '''plausibility check''' planned and done?

== References ==
* Whitehead AL, Sully BG, Campbell MJ. [https://www.sciencedirect.com/science/article/pii/S1551714414000494?via%3Dihub Pilot and feasibility studies: is there a difference from each other and from a randomized controlled trial?] Contemp Clin Trials. 2014 May;38(1):130-3. doi: 10.1016/j.cct.2014.04.001. Epub 2014 Apr 13. PubMed PMID: 24735841.
* Billingham SA, Whitehead AL, Julious SA. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3765378/pdf/1471-2288-13-104.pdf An audit of sample sizes for pilot and feasibility trials being undertaken in the United Kingdom registered in the United Kingdom Clinical Research Network database.] BMC Med Res Methodol. 2013 Aug 20;13:104. doi: 10.1186/1471-2288-13-104. PubMed PMID: 23961782;

PREMIER Study Design

2021-02-22T18:46:57Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Introduction ==
The functional and performance requirements for models and methods depend on the planned research project in which they are to be used, the research question and the neurological disease or pathomechanism to be investigated. The scientists involved determine the requirements and objectives of the model planning. The experimental design is defined as: A plan for the collection and use of data so that the desired information can be obtained with sufficient accuracy or so that a hypothesis can be tested properly [https://www.ncbi.nlm.nih.gov/mesh/?term=experimental%20design (ref. 1)][https://academic.oup.com/ilarjournal/article/55/3/457/643598 (ref. 2)].

A good experimental design is important to answer the research question of interest in an unbiased way, it can be generalized to the desired or intended audience to answer the question correctly. This includes determining the measurements to be performed, the timing, etc. It also includes identifying the relevant statistical analyses, determining the appropriate sample size and establishing a random system [https://www.ncbi.nlm.nih.gov/books/NBK53196/ (ref. 3)].

'''Requirements for good experimental design and analysis:'''
* An a priori explanation of the objectives/hypotheses.
* Appropriate experimental methods.
* Sufficient but not overly large sample size to ensure statistical significance of biologically relevant effects.
* A priori determination of appropriate statistical methods.

=== Experimental Design / Method Selection ===
First and most important is the choice of method (e.g. in vivo, in vitro).

A more detailed description of these two options is included below.

==== In ''vivo'' models ====
In vivo studies should be designed so that all meaningful biological effects are statistically significant. In an exploratory study, this "significant effect" could correspond to any pharmacologically relevant effect. The power and sample size analyses are particularly relevant for studies designed to address important endpoints. Biologically significant effects are not always known in advance, so that in this case a number of plausible effects should be considered. The design of experiments is largely about identifying and establishing a strategy for dealing with different types of variables. The types of variables that occur in research include:

* Manipulated variable ('''independent/explanatory variable''')
* Response variable ('''dependent/outcome variable''')
* Extraneous variables ('''uncontrolled/random''')

The manipulated variable is a targeted attempt to introduce variability into the experiment, for example by administering different doses of a drug. Extraneous variables can interfere with an experiment and alter the results in an undesirable way, or in a way we do not know about. Examples of extraneous variables could be inherent, such as animal variations, time of day, body weight, laboratory noise, etc. Design of experiments is largely about designing a strategy for dealing with extraneous variables. Ignoring them can lead to biased results and the demand for larger sample sizes. Fixing (keeping constant) or eliminating, e.g. by considering only a subgroup of animals, can reduce bias and sample sizes, but also reduce the general validity of the results to only those conditions considered in the experiment. (REF?) Another approach is to control them by integrating them into the experimental design, ideally at the design stage or, if this is not possible, in statistical analysis.

Other additional factors that should be considered are:
* Appropriate random allocation of animals to treatment groups
* Blinding of observers in the allocation of medication whenever possible, especially when subjective assessments are to be made by observers.
* Correct selection of dosages.
* Optimal selection of control groups.
* Optimal timing of sampling.
* Appropriate statistical methodology.

Different design strategies should be carefully considered to minimize variability and maximize information from the experiment. The above design issues should be addressed in the context of the main endpoints (or summary measures) of the study. Examples of such endpoints may include survival rate, glucose normalization, etc. If there are multiple endpoints of interest for a study, certain design questions such as the significance of the study should be evaluated in relation to the endpoints that the researcher and the project team consider to be the most important. [https://www.ncbi.nlm.nih.gov/books/NBK53196/ (ref. 3)]

===== Endpoints =====
The key endpoints of the study must first be identified, as all other design decisions should be based on these results. Typical results include:

Statistical significance from control using analysis of variance (ANOVA). ED50 (either absolute or relative) from a dose-response model, such as the 4-parameter logistic model (4PL).

===== Control Groups =====
Control groups serve three purposes:
* a comparison with the test groups,
* as a quality control marker and
* to normalize the response for comparison between studies.
A '''"positive" control''' is a combination that has a different response than the negative control and is usually the maximum response of a standard treatment. Positive controls are accompanying experiments in which a phenomenon (or effect) achieved with the main experiment is certain to occur. Positive controls are used to demonstrate that a method works with the known values of the variables (method validation) and can therefore exclude false negative results of an experiment.

'''Negative controls''' are accompanying experiments in which a phenomenon (or effect) achieved with the main experiment does not occur (zero value) or should not occur. This ensures that a positive result in the main experiment can only have been due to the change in the variables. In contrast, a positive result of the negative control indicates a lack of specificity of the respective main experiment, i.e. the effect achieved in the main experiment also occurs due to other influences. Negative controls serve to exclude other reasons ( error sources) than the hypothesis for a phenomenon and serve to avoid incorrect interpretations of false positive results. This reduces the possibility of a falsification of the hypothesis after publication. If the results of the experiment and the negative controls are negative, it can be concluded that the experiment output is independent of the variables, i.e. the change in the variables had no influence on the experiment output. In animal experiments, placebo administration is a typical negative control. Blind and double-blind experiments avoid the use of additional negative controls to investigate the influence of the level of knowledge of the subject and, in the latter case, of the experimenter on an experimental result.

=== in-vitro Cell Culture Models ===
If a question is to be processed in an in-vitro model, the following decision flow can be used.

==== Selection of cells ====
What kind of cells are of interest (neurons, endothelium, etc.)?

Should cell lines or primary cell cultures be used?

==== Cell lines ====
* Are the cell lines available in the institution? Is there a SOP about handling of Liquid Nitrogen and retrieval of samples from storage?
* Are the cell lines characterized (data sheets ATCC, publications, SOP) and tested for mycoplasma?
* Are genetically modified lines available from the selected cell line?

==== Primary cell cultures ====
* Are there protocols for the preparation of primary cells?
* Who can provide training?
* Where do you get the material from? Is it possible to use tissue from other projects?
* What are the quality criteria for the primary cultures?

==== Selection of the model ====
* Chemical hazard (toxicity of substances)
* Physical hazard (temperature, pH, flow)

Once the target parameters have been defined, the model can be selected:

==== Influencing Impact factors ====
The previous steps must be followed by considering the possible influencing factors (both known and unknown) and the resulting outcome.

There is a very informative [https://www.pmi.org/learning/library/characterizing-unknown-unknowns-6077 paper] on this topic.

Below are some factors that (may) influence the results of cell culture experiments:
* Cell density
* Age of the cells - especially in primary cultures
* Protocol of the passenger (distance to the experiment)
* Protocol of medium change (distance to experiment, complete medium change, partial medium change)
* Volume of the medium during the experiment
* CO2, bicarbonate, pH value
* Temperature

=== Reference groups ===
A reference group is a control group within a scientific experiment in which, experimental the independent variable or intervention is not changed or applied. Ideally, the group distribution is randomized, with identical conditions in the control and experimental groups. To achieve valid and robust results, control groups are essential in every experiment.

=== Validation ===
New methods must be exhaustively validated, i.e. it must be checked whether they meet the pre-set requirements and specifications and thus, fulfills the intended purpose.

=== Note ===
It is highly recommended to get professional advice well in advance of the starting of a project on the specific biometrics, recommended statistical software and analysis plan.

=== References ===
* MESH Definition: [https://www.ncbi.nlm.nih.gov/mesh/?term=experimental%20design Research Design].
* Fry DJ. Teaching experimental design. ILAR J. 2014;55(3):457-71. doi: 10.1093/ilar/ilu031. [https://academic.oup.com/ilarjournal/article/55/3/457/643598 Review. PubMed PMID: 25541547].
* Sittampalam GS, Coussens NP, Brimacombe K, et al., editors. Assay Guidance Manual [Internet]. Bethesda (MD): Eli Lilly & Company and the National Center for Advancing Translational Sciences; 2004-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK53196/
* Charité Institut für Biometrie und Klinische Epidemiologie ([https://biometrie.charite.de/service_unit_biometrie/kostenloses_beratungsangebot/ iBikE])

PREMIER Sample Size Calculation

2021-02-22T17:19:09Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Sample Size Calculation ==
In a sample size calculation, the sample size required to demonstrate a previously determined, relevant difference at a defined significance level, with a defined test strength is determined. With the help of the sample size calculation, effects that actually exist should be detected and at the same time guarantee that no effect exists if no statistical difference is measured.

The sample size must not be too small, otherwise no verifiable result regarding a relevant difference is possible after the study has been carried out. In principle, the smaller the expected effect or the greater the variance (standard deviation) of samples, the larger the number of samples required.

The program G*Power, which is available free of charge [https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html here] , is suitable for calculating the sample size. G*Power is a tool for calculating statistical power analyses for many different t-tests, F-tests, χ2 tests, z-tests and some exact tests. G*Power can also be used to calculate effect sizes and graphically display the results of power analyses. G*Power was developed by General Psychology and Work Psychology of the HHU. [http://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html (Ref.1)]

=== Example ===
A drug is said to increase the RotaRod test performance of a laboratory animal by 15%. The basic spread of results with the RotaRod is 20%. The same variation is assumed for the drug group. Which number of samples per group is necessary with a significance level of 0.05 and a power of 0.80?

[[File:G*Power Rechenbeispiel (t-test für 2 Gruppen mit unabhängigen Durchschnittswerten).jpg|thumb|800x800px|G*Power Rechenbeispiel (t-test für 2 Gruppen mit unabhängigen Durchschnittswerten)|none]]

==== Result ====
To test this hypothesis in 2 independent animal groups (1 control group, 1 drug group), 29 experimental animals are required in each case.

=== Pilot Studies ===
A pilot study can be defined as a small-scale study that helps to test the practicability and feasibility of the methods to be used in a later larger and more comprehensive study. As the conduct of a sufficiently powerful study often requires the involvement of a large number of participants and can therefore be very time consuming and expensive, the conduct of a pilot study on a smaller scale can help to identify unforeseen problems that might affect the quality or the conduct of the study [http://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html (Ref.1)].

Existing methods for calculating sample size typically focus on how to select an appropriate sample size for a pilot study so that various parameters of interest can be estimated with sufficient accuracy (e.g. the effect size, standard deviation of the outcome measure, its reliability, or adherence or wear rates). Such calculations can also play an important role in deciding whether to proceed with the primary study at all. These considerations have led to various guidelines for selecting an appropriate sample size for a pilot study. Further details and references are given in [https://www.sciencedirect.com/science/article/pii/S0895435615003030?via%3Dihub (Ref.2)].

=== References ===
* Heinrich-Heine-Universität Düsseldorf, Allgemeine Psychologie und Arbeitspsychologie. [http://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html G*Power: Statistical Power Analyses for Windows and Mac]
* Viechtbauer W, Smits L, Kotz D, et al. [https://www.sciencedirect.com/science/article/pii/S0895435615003030?via%3Dihub A simple formula for the calculation of sample size in pilot studies]. ''J Clin Epidemiol''. 2015;68(11):1375–1379].

PREMIER Target Parameter

2021-02-13T13:02:52Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Definition of the Target Parameters ==
Parameter is a characteristic that can help to define or classify a particular population. Target parameter is any parameter that we want to estimate. By defining suitable target parameters, the significance / quality and efficiency of the project can be ensured. A distinction is made between primary and secondary target parameters, which must be checked in advance for relevance and validity.

=== Relevance ===
* Do the target parameters match the question posed?
* Are the target parameters scientifically and clinically significant?

=== Validity ===
* Are there any target parameters at all?
* How many target parameters are there?
* How are the target parameters measured?
* How valid are the measuring instruments used?

The inclusion and exclusion criteria for the target parameters must be defined in the model planning..

=== Target Parameters / Target Size ===
By defining suitable measurement and target parameters, quality and efficiency of the research process can be controlled. Recording many parameters without reflection does not increase the quality of the experiment, but can lead to a bias if the results are evaluated afterwards.

Besides the definition of clear target parameters, their technical implementation suitable for everyday use is essential.

A target parameter must be validated so that a reliable and reproducible statement can be made for the research process. An ideal target parameter would be sensitive and robust. Preliminary tests should be carried out to validate the test setup. The use of "historical" results can complement or, under certain conditions, replace these preliminary tests.

== Primary / Secondary Target Parameters ==
The primary target parameter (main target variable) is the most important parameter and represents the criteria of the study objective. With its help it can be determined whether the applied experimental measures (e.g. a treatment or genetic factors) were successful. The effect of the experimental measures can also be reflected by two or more main outcome measures. In the experiment, the target parameters established before the start of the study are then compared in the treated group with those of the control group.

Secondary target parameters should be supportive for the primary target parameters. Several secondary target parameters can lead to more reliable results.

=== Target Parameters in vitro / in vivo ===

==== In vitro ====

===== Target parameter cell culture: =====
* cell death -> necrosis vs. apoptosis
* vitality: adhesion, migration, proliferation
* gene expression
* protein synthesis
* enzyme activation

How these are connected to each other is shown in this figure:

[[File:Zielparameter Zellkultur.jpg|thumb|Zielparameter Zellkultur|none]]

===== Quantification of the Target Parameters =====
Various methods are available for the quantification of the target parameters. Which method is used depends on:
* the measuring equipment that is available
* the desired measuring accuracy
* the amount of cells available
Some examples are given below:

===== Cell Death =====
* LDH assay: release of lactadehydrogenase into the cell culture supernatant
* MTT assay: measures the metabolic activity of the cell culture via the conversion of tetrazolium blue to formazan in the mitochondria
* Crystal violet staining: non-specific staining of intracellular proteins

===== Proliferation =====
* MTT assay: measures the metabolic activity of the cell culture via the metabolism of tetrazolium blue to formazan in the mitochondria
* BrdU: uptake of bromodeoxyuridine into the DNA of dividing cells

==== In vivo ====
Select target parameter in vivo: Target parameters that can be measured directly and objectively are preferably not relative scores but numerical units of measurement.

Important: Validation of the desired parameters to ensure a causal relationship with the method used.

==== State according to MCAO ====
Example:
* survival
* infarct size, histological and/or MRT
* body weight and body weight change according to MCAO

==== Functional Outcome ====
* General and stroke-related illness behaviour: Neuroscore
* Behavioural parameters, such as turns in the corner test or duration of stay on the Rota-Rod
* Parameters for inflammation or infection: Cytokine level, blood count

== References ==
* University of California, Davis Department of Statistics. [http://www.stat.ucdavis.edu/~ntyang/teaching/12SSII/lecture05.pdf 5.1 Identifying the Target Parameter. Lecture 5: Estimation with Confidence intervals].

PREMIER Hypothesis / Counter-Hypothesis

2021-02-13T12:57:17Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== Hypothesis ==
Hypothesis comes from Greek hypothesis = basis, assumption

* It can be general: presumption, assumption; opposite of knowledge
or a verifiable statement about a possible fact that goes beyond mere observation
* Hypotheses can be formulated about all, some or individual facts of a certain area.

A hypothesis is:

* a statement / assumption to be tested, between two or more variables
* a preliminary prediction concerning the relationship between two or more variables
* a description of the problem or the question predicting the expected results
* a claim without a question

There are different types of hypotheses:

* simple (bivariate) / multiple (multivariate)
* non directional (direction of the relationship)
* statistical / null hypothesis
* inductive (based on concrete experience) vs. deductive (derived from theory)

'''Hypotheses are the link between theory and research:'''

In PREMIER, transparent scientific work should be the focus of every organization. Therefore the first working hypothesis has to be included in the pre-registration. In this way, so-called HARKing can also be prevented.

"HARKing" means "formulating hypotheses after the results are known": a hypothesis based on the interpretation of the data is presented as if it had already existed before the data were obtained. HARKing may also occur when a researcher tests an a priori hypothesis but then omits that hypothesis from their research report after they find out the results of their test.

The pre-registration is preferably stored in an electronic laboratory journal, so that all steps can be traced at any time.

== Counter-Hypothesis ==
In order not to get caught up in a hypothesis and to try to prove it, even though it may be incorrect, it is necessary to make a counter-hypothesis or a null-hypothesis. The null hypothesis states that there is no effect or difference or that a certain connection does not exist. The testing of the counter-hypothesis helps to avoid false conclusions.

== Testing the Hypothesis ==
'''How can the hypothesis be tested?'''

The hypothesis is examined through a series of tests (study), collecting and analyzing the obtained data. Before starting the experiment, it is fundamental to define the number of test subjects (number of samples) needed to draw reliable, valid conclusions and the at least two groups to be compared. These groups can be independent of each other, or they can be matched pairs.

== References ==
* G*Power: Statistical Power Analyses for Windows and Mac: http://www.gpower.hhu.de/
* [https://www.sagepub.com/sites/default/files/upm-binaries/40007_Chapter8.pdf Introduction to Hypothesis Testing]
* [https://people.uwec.edu/piercech/ResearchMethods/Generating%20a%20research%20hypothesis/generating%20a%20research%20hypothesis%20index.htm Generating A Research Hypothesis]

PREMIER Search

2021-02-13T12:53:25Z

Admin:

[[PREMIER Planning of Experiments|← Planning of Experiments]]
__NOTOC__
== General Information ==
There is an enormous density of information about biomedical research. This presents a challenge to find the relevant information on a given topic and to collect reliable methods, results and conclusions in order to better define a specific project.

A systematic and open-ended research is carried out in [https://pubmed.ncbi.nlm.nih.gov/ Pubmed], libraries and databases, among others. Additionally are needed:
* suitable tools / algorithms
* search history / terms / keywords (also necessary for animal experiment applications)
* ev. meta-analysis
* new site search engine

== Tasks / Actions ==
=== Search ===
Every question requires detailed background knowledge and analysis. This should be balanced and detailed, because later in the publication the introduction and parts of the discussion should refer to it. It is important to find and study solid basic information on the topic. If you are not yet familiar with the topic, review articles are a first starting point. Afterwards, you should study the relevant original papers and review the content with regard to your own question.

A systematic approach is needed to be able to answer each research question in a well-founded and comprehensive way.
* Make sure that you fully understand at least the basic idea of your project.
* If you do not understand a part of the project, do not hesitate to ask questions directly. It is better to get an explanation about something than to assume that you know what it means and later find out that the assumption was incorrect.
* If you don't know much about your topic, look for a resource that provides a general overview. This information is usually freely available through PubMed in the form of reviews or books.
* The success of your research depends on finding the right keywords.

=== Bias ===
Even at this early stage, there is a risk of unbalanced research through various forms of bias.

* Hypothesis bias happens when the hypothesis is already pre-established at the beginning of the research and only literature that supports this hypothesis is considered.
* Literature bias can happen when only limited access to the primary literature of the subject due to restrictions such as subscription restrictions of the organization central library.

To avoid these bias aspects, it is recommended to read and consider all literature relevant to the topic. If a hypothesis is already given (e.g. by an application), the literature can be divided into hypotheses -proving, refuting and neutral literature, for example. If access to literature is limited, it is helpful to contact the library, search its online catalogue for alternative access to the journal or contact the corresponding author.

With tools such as the [https://library.csuchico.edu/help/source-or-information-good CRAAP test] (Currency, Relevance, Authority, Accuracy, Purpose) you can evaluate the quality of the references.

=== Traditional Search Systems ===
'''[https://www.ncbi.nlm.nih.gov/pubmed/ PubMed]''' is the search interface for the MEDLINE database and other resources, making it the world's largest and most important medical bibliographic database. It is produced by the National Library of Medicine (USA) and is accessible free of charge. PubMed documents over 23 million references from the biomedical field, from MEDLINE and from over 5,200 journals and e-books. The NCBI channel on Youtube offers numerous tutorials and [https://www.youtube.com/user/NCBINLM/search?query=PubMed webinars on PubMed] [https://www.umm.uni-heidelberg.de/bibliothek/kurse-beratung-tutorials-pruefungsvorbereitung/fitmedma/ <nowiki>[1]</nowiki>].

[https://www.ncbi.nlm.nih.gov/pmc/ '''PubMedCentral (PMC''')] is a freely accessible database containing full-text scientific literature from medicine, biology and related fields. PMC was established by the National Library of Medicine (USA) and is funded by the National Institutes of Health (NIH). Since 2008, it has been a legal requirement in the USA (H.R. 2764) that all research results obtained through funding by the NIH be published in original or copy form at PMC within twelve months [[https://de.wikipedia.org/wiki/PubMed_Central <nowiki>[2]</nowiki>].

'''Web of Science''' (ISI Web of Knowledge) is a paid service with several scientific online citation and literature databases. It was purchased and operated by Thomson Reuters in 1992. In 2016 Clarivate Analytics bought the Intellectual Property and Science business unit with the [https://bibliothek.charite.de/?id=16571 scientific databases]. [https://de.wikipedia.org/wiki/Web_of_Science [3]] Web of Science is particularly useful for finding out which publications cite a particular article for reference. Access to Web of Science can be found on the database page of the Charité Medical Library. Corresponding training and tutorials can be found [https://www.youtube.com/results?search_query=web+of+science+tutorial+training. here].

=== Newer General Search Systems ===
'''[https://web.archive.org/web/20090718141635/http://www.gopubmed.org// Gopubmed]''' is a knowledge-based search tool for biomedical texts. Gene Ontology is used as a "table of contents" to structure the millions of articles in the MEDLINE database. Gopubmed allows to find relevant search results faster [https://de.wikipedia.org/wiki/GoPubMed <nowiki>[4]</nowiki>].

'''[https://www.macinchem.org/mobsci/index.php/9-searchtool/35-pubchase PubChase]''' is a free search and recommendation tool for biomedical literature. Based on the search, and stored search results in the cloud-based PubChase library, suitable publications are recommended via an algorithm.

'''[https://www.sparrho.com/ Sparrho]''' is a non-traditional search engine that uses both expert recommendations and AI algorithms to find relevant and recent publications. It stands out for its Pinterest-like interface.

'''[https://www.dimdi.de/dynamic/de/startseite DIMDI]''' Information for all areas of health care: DIMDI is the publisher of official medical classifications such as ICD-10-GM and OPS (German Procedural Classification) and maintains medical terminologies, thesauri, nomenclatures and catalogues that are important for health telematics and other applications.

=== References ===
* ISI Web of [http://apps.webofknowledge.com/WOS_GeneralSearch_input.do?product=WOS&search_mode=GeneralSearch&SID=E5YuzA8HyAX9Vm4QED3&preferencesSaved= knowledge]
* PubMed Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2005-. [https://www.ncbi.nlm.nih.gov/books/NBK3827/ PubMed Help]. [Updated 2018 Mar 28].
* McDonagh M, Peterson K, Raina P, et al. Avoiding Bias in Selecting Studies. 2013 Feb 20. In: [https://www.ncbi.nlm.nih.gov/books/NBK126701/ Methods Guide for Effectiveness and Comparative Effectiveness Reviews] [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK126701/
* Institute of Medicine (US) Committee on Standards for Systematic Reviews of Comparative Effectiveness Research; Eden J, Levit L, Berg A, et al., editors. Finding What Works in Health Care: [http://www.nap.edu/catalog.php?record_id=13059 Standards for Systematic Reviews]. Washington (DC): National Academies Press (US); 2011.
* Jonas DE, Wilkins TM, Bangdiwala S, et al. Findings of Bayesian Mixed Treatment Comparison Meta-Analyses: [https://www.ncbi.nlm.nih.gov/books/NBK126109/ Comparison and Exploration Using Real-World Trial Data and Simulation] [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Feb.

MediaWiki:Mainpage

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PREMIER

Main Page

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Premier Experimental Design

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==Introduction==
This template should help you to plan your project in such a way that all possible difficulties, risks and systematic errors that may occur are considered and minimized in advance. By means of specific questions the ever present bias in experiments shall be reduced and project specific topics shall be brought into the awareness of your experimental design. It should be noted that you do not necessarily have to / can answer all points, which depends on your type of project (exploratory / confirmatory etc.). If you can't say anything about single points, please briefly explain why.

Explainer videos for technical use can be found [https://premier-qms.org/premier/planning-of-experiments/template-usage here].

== Project details ==

'''Project name:'''

'''Project Manager:'''

'''Planned project duration:'''

'''Short project description:''' (What is your project about? What is the goal?)

(If required, click the above edit button and upload an image.)

== Search ==
[https://premier-qms.org/premier/planning-of-experiments/search support]

'''Literature Databases / Other sources:'''

'''What is (was) the research strategy?'''

== Hypothesis / Counterhypothesis ==

[https://premier-qms.org/premier/planning-of-experiments/hypothesis-counter-hypothesis support]

'''Set up hypothesis / counter hypothesis: Have you formulated your hypothesis and the corresponding counter and/or null hypothesis?'''

'''Counterhypothesis: Have you searched for literature that argues against the hypothesis?'''

== Target Parameter ==

[https://premier-qms.org/premier/planning-of-experiments/target-parameters support]

Contact person for:

In vivo questions: André Rex

In vitro questions: Dorette Freyer

'''Definition of the target parameters: Which primary and secondary target parameters have you defined?'''

== Sample Size Calculation ==

[https://premier-qms.org/premier/planning-of-experiments/sample-size-calculation support]

'''Need for experimental units: How was the need for experimental units (number of animals, organs, organ sections or cultured cells) determined?'''

'''Which reference was used for the estimation? groups and units: Name the experimental groups with the exact number of units and identify test and control groups.'''

== Experimental Design / Model Planning ==

[https://premier-qms.org/premier/planning-of-experiments/model-planning-study-design support]

'''Definition of criteria: Have you defined the following criteria for your project: - method selection - influencing factors - requirements - control groups - validations?'''

== Feasibility Check ==

[https://premier-qms.org/premier/planning-of-experiments/feasibility-study support]

'''Feasibility of the project: At the end of the feasibility study there is a plausibility check. Is it positive for your project?'''

== Nesting and Pseudoreplication ==

[https://premier-qms.org/premier/planning-of-experiments/nesting-and-pseudoreplication support]

René Bernard will help you with this topic.

'''Identification of the problem: Does the biological unit used for statistical evaluation correspond to the one used for randomisation? If not, check if pseudoreplication is present and how it can be avoided in the evaluation.'''

== Randomization and Blinding ==

[https://premier-qms.org/premier/planning-of-experiments/randomisation-and-blinding support]

René Bernard will help you with this topic.

'''Implementation of R+B: Which methods were used to randomize and blind your experiments?'''

'''If you did not perform randomization and blinding, please explain why!'''

== Resource Plan ==

[https://premier-qms.org/premier/planning-of-experiments/resource-plan-financing-capacities-personnel support]

'''Resources and infrastructure: Is the financing of your project secured and is there enough personnel, material and a suitable infrastructure available?'''

'''Alternatives: If this is not the case, are there planned strategies to overcome the possible challenges?'''

==Timetable==
[https://premier-qms.org/premier/planning-of-experiments/schedule support]

'''Preparation of the timetable: Have you created a detailed, realistic timetable for your project?'''

'''Is a project management tool available?'''

'''How do you manage, review and update this timetable?'''

(If required, click the above edit button and upload a timetable Excel file.)

== Accompanying training courses ==

[https://premier-qms.org/premier/planning-of-experiments/accompanying-training-and-courses support]

'''Determine training needs: Are there any special trainings, courses etc. that are absolutely necessary in order to carry out the project?'''

'''Attend training courses: Do you attend training courses that can be helpful for your project?'''

== Planning of Data Preparation / Analysis ==

[https://premier-qms.org/premier/planning-of-experiments/planning-of-data-preparation-analysis support]

'''Data analysis: How did you plan the aggregation and preparation of the data?'''

'''Please explain and document (in the ELN) the step from primary to secondary data!'''

'''Archiving the data: Have you ensured the archiving of primary and secondary data?'''

== Data Storage ==

[https://premier-qms.org/premier/planning-of-experiments/data-storage support]

'''Location: Where did you store the primary and secondary data?'''

'''Backups: Are you planning additional backups?'''

== Clarification of Authorships ==

[https://premier-qms.org/premier/planning-of-experiments/clarification-of-authorship support]

'''Requirements for first authorship: How are the authorships for your project clarified?'''

'''Documentation: Has the agreement been documented? If so, where?'''

== Preregistration ==

[https://premier-qms.org/premier/planning-of-experiments/pre-registration support]

René Bernard and Claudia Kurreck also provide further assistance on this topic.

'''Implementation: Where did you pre register your project?'''

'''Please explain in the ELN if you have not pre registered your project!'''

BRISQ - Biospecimen Reporting for Improved Study Quality

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== BRISQ - Biospecimen Reporting for Improved Study Quality ==
In 2011, the NIH released a set of recommendations for the publication of studies using human biospecimens<sup>1</sup>. These guidelines are applicable to animal biospecimens as well. According to the authors, "biospecimens are subject to a number of different collection, processing, and storage factors that can significantly alter their molecular composition and consistency. These biospecimen preanalytical factors, in turn, influence experimental outcomes and the ability to reproduce scientific results. Currently, the extent and type of information specific to the biospecimen preanalytical conditions reported in scientific publications and regulatory submissions varies widely. To improve the quality of research utilizing human tissues it is critical that information regarding the handling of biospecimens be reported in a thorough, accurate, and standardized manner. The Biospecimen Reporting for Improved Study Quality (BRISQ) recommendations outlined herein are intended to apply to any study in which Biospecimens are used. The purpose of reporting these details is to supply others, from researchers to regulators, with more consistent and standardized information to better evaluate, interpret, compare, and reproduce the experimental results. The BRISQ guidelines are proposed as an important and timely resource tool to strengthen communication and publications around biospecimen-related research and help reassure contributors and the advocacy community that the contributions are valued and respected".

Similar guidelines have been published to address the need to integrate the 3Rs principles of animal studies and make systematic reviews feasible<sup>2</sup>.The recommended parameters cover a broader spectrum as BRISQ and go beyond the biospecimen, including information about the study design, the methods used, the description of results, etc. This approach is more suited for the final reporting of the study.

== References ==
1. Moore HM, Kelly A, Jewell SD, et al. Biospecimen Reporting for Improved Study Quality (BRISQ). Journal of proteome research. 2011;10(8):3429-3438. doi:10.1021/pr200021n.
[[File:Moore 2011 BB BRISQ.pdf|none|thumb|Moore 2011 BB BRISQ]]

2. Hooijmans CR, Leenaars M, Ritskes-Hoitinga M. A gold standard publication checklist to improve the quality of animal studies, to fully integrate the Three Rs, and to make systematic reviews more feasible. Altern Lab Anim. 2010 May;38(2):167-82. PMID:20507187
[[File:Hooijmans 2010 3R Gold standard guidelines.pdf|none|thumb|Hooijmans_2010_3R_Gold_standard_guidelines]]

==== BRISQ modified from Moore et al. 2011: ====

====== Quick-Reference BRISQ Summary/Checklist: Tier 1 Items to Report If Known and Applicable ======
{| class="wikitable"
!Spalte1
!Data Elements
!Description
!Examples
|-
|1
|Biospecimen type
|Solid tissue, whole blood, or another product
|Serum, Urine
|-
|2
|Anatomical site
|Organ of origin or site of blood draw
|Liver, Tail
|-
|3
|Disease status
|Controls or individuals with the disease of interest
|Diabetic, Healthy control
|-
|4
|Clinical characteristics
|Available information known or believed to be pertinent to the condition of the biospecimens
|K.O mouse
|-
|5
|Vital State
|Alive or deceased animal when biospecimens were obtained
|Postmortem
|-
|6
|Diagnosis
|Diagnoses (determined by history, physical examination, and analyses of the biospecimen) pertinent to the study
|Stroke
|-
|7
|Pathology diagnosis
|Pathology diagnoses (determined by macro and/or microscopic evaluation of the biospecimen at the time of diagnosis and/or prior to research use) pertinent to the study
|Breast Cancer
|-
|8
|Collection mechanism
|How the biospecimens were obtained
|Fine needle aspiration, Pre-operative blood draw
|-
|9
|Type of stabilization
|The initial process by which biospecimens were stabilized during collection
|Heparin, on ice
|-
|10
|Type of long-term preservation
|The process by which the biospecimens were sustained after collection
|Formalin fixation, freezing
|-
|11
|Constitution of preservative
|The make-up of any formulation used to maintain the biospecimens in a non-reactive state
|10% neutral-buffered formalin, 10 USP Heparin Units/mL
|-
|12
|Storage temperature
|The temperature or range thereof at which the biospecimens were kept until distribution/analysis.
| -80 °C, 20 to 25°C
|-
|13
|Storage duration
|The time or range thereof between biospecimen acquisition and distribution or analysis.
|8 days, 5 to 7 years
|-
|14
|Shipping temperature
|The temperature or range thereof at which biospecimens were kept during shipment or relocation.
| -170°C to -190°C
|-
|15
|Composition assessment & selection
|Parameters used to choose biospecimens for the study
|Minimum 80% tumor nuclei & maximum 50% necrosis
|}

File:Hooijmans 2010 3R Gold standard guidelines.pdf

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File:Moore 2011 BB BRISQ.pdf

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Data organization in spreadsheets

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== Purpose ==
Spreadsheet software (such as Microsoft Excel, Google Sheets and LibreOffice Calc) is commonly by scientists as tool to store, to analyze and display data. While this one-in-all package is convenient for many, there are many drawbacks and even danger to accidentally alter results or data without knowing. Second, having raw data, secondary data, analyses and graphs in one spreadsheet hinders the further aggregation and inter-compatibility with similar data in databases for instance. The purpose of this information is to give advice how to setup spreadsheets best for transparent and integer data management.

The second part is devoted to formulas in Excel that are often hidden and a potential source of errors, which could be carried to other data sheets without knowing. Here we present way to make existing formulas transparent and to display the source cells involved in formulas.

== Rules for better data organization in spreadsheets ==

=== Always be consistent ===
* consistent names for variables, e.g. for sex (<code>"male" "m" "Male" "M"</code>) or study ID (<code>"mouse153" "M153" "M-153" "Mouse-153F"</code>). Pick one and stick to it.

* consistent date format; preferably as 8-digit integer of the form <code>YYYYMMDD</code> or the global ISO8601 standard <code>YYYY-MM-DD</code>

* consistent phrases in notes columns (<code>"dead" "deceased" "Dead" "tot"</code>)
* check for hidden spaces in cells (<code>"male" "male " " male"</code>)
* use hypens or underscores but not space in a cell (<code>MCAO_60min MCAO-60min</code>) but avoid symbols that have meaning in programming languages (<code>$ @ % # & * ! , / \</code>)

=== Make sure that text stays text ===
* By default, Excel converts certain text entries into dates. This is problematic when using name of genes or transcripts, such as SEPT7 or OCT4. To avoid this:
*# Select the column, row, or cell(s) in questionn
*# in the Menu bar, select Formal --> Cells
*# Choose "Text" on the left
Alternatively, especially when entering text manually, begin with an apostrophe <code>'SEPT7</code> Whatever you decide on, be consistent.

=== No empty cells ===
* use "<code>NA"</code> or <code>"-"</code> for missing data
* when repeating conditions or values occur in neighboring cells, fill each cell with that value

=== One information per cell ===
* for instance, specification of a <code>well</code> postion on a certain cell culture plate - Do no use: <code>"13-A01"</code> instead have 3 separate columns <code>"plate" "well_row" "well_coumn"</code> with corresponding entries <code>"13" "A" "1"</code>
* do not put value and the corresponding unit into cell like <code>"45 g"</code>. Either use column name such as <code>"body_weight_g"</code> or <code>"body_weight"</code> and define units in a data dictionary and have only the information <code>"45"</code> in the cell.

=== Data entries should be made in a rectangle (or set of rectangles) ===
* first row should start with cell A1 and only contain variable names, <u>not more than one row</u> for variable name
* if row with variables gets too long and complex, consider breaking them into logical bins of your study; e.g. one file for week1 outcome measures; one file for week2 outcome measures
* even though tempting to use multiple worksheets for data storage, use only one sheet per file, to permit easy and smooth conversion into comma-separated-value (CSV) files.CSV files are so generic that any spreadsheet program can open them and any data analysis program can import them.
* Use only raw data entries, no calculations such as means, SDs, or fold change
* do not use merged cells anywhere, even if one heading term like <code>"week 4"</code> applies to subordinate columns <code>"weight" "rotarod_run1" "rotarod_run2" rotarod_run3"</code> . Instead use conventions like <code>"weight_w4" "rotarod_run1_w4" "rotarod_run2_w4" rotarod_run3_w4"</code>
[[File:Data Entry Rectangle.jpg|left|thumb|400x400px]]

=== Create a data dictionary ===
* separate file that explains variables, ideally in in rectangular shape for better overview
* Data dictionary might contain (as headers):
** the exact variable as in data file
** version of variable name as might be used in data visualization
** a longer explanation what the variable means
** the measurement unit
** expected minimum and maximum values
* in addition always include a <code>ReadMe</code>text file that gives an overview of the project and data or other descritptions that make data and project more accessible and understandable to others, or permanent link to where the project is pre-registered or published.

=== No calculations/analyses in raw data files ===
* instead add another column with an indicator variable, e.g. <code>"trusted"</code> with values <code>TRUE or FALSE</code>

=== Use data validation to avoid errors when entering data manually ===
* to avoid data entry errors to control the type of data or the values that users enter into a cell
** Select the column
** in the menu bar, select Data-->Validation
** Choose the appropriate validation criteria you want to set:
**# a whole number of range
**# a decimal number in some range
**# a list of positive values
**# text but with a limit on length

=== Save data in plain files and backup ===
* keep a copy of the data file in plain text format, such as CSV.
* be aware that German operating systems use <code>;</code> instead of <code>,</code> in CSV files. Conversions can easily be made in any text editor program (find & replace)

== Transparency of formulas in Excel ==
Formulas in Excel cells are not transparent and therefore a potential source of hidden errors. The most common errors with Excel formulas include
* operational error of a formula
* using the wrong cell(s) in a certain formula
The following procedures are easy check mechanisms to avoid such errors.

=== Show formulas instead of values ===
* Click on the <code>‘Formulas’</code> Tab in the ribbon
* Click on the <code>Show Formulas</code> option
* Upon clicking, all formulas in the worksheet will become visible.
* Click <code>Show Formulas</code> again to make the underlying calculated results visible again
[[File:Show-Formulas-in-Excel-Instead-of-the-Values-Formulas-Tab.png|left|thumb|500x500px]]

[[File:Show-Formulas-in-Excel-Instead-of-the-Values-Show-Formulas2.png|thumb|500x500px|none]]

=== Show cells that are part of a a formula ===
* Select the cell containing the formula
* Click on the <code>‘Formulas’</code> Tab in the ribbon
* Click <code>Trace Precedents</code> and all cells that are part of the formula are marked in blue and an arrow points to them.
* Click <code>Remove Arrows</code> to turn off the tracing
[[File:Trace Precedents.jpg|left|thumb|500x500px]]
[[File:Show predecessor.jpg|none|thumb|450x450px]]

== References ==
1. Karl W. Broman and Kara H Woo Data organization in spreadsheets The American Statistician Vol.72, No.1, pages 2-10, 2018 https://doi.org/10.1080/00031305.2017.1375989

2. [https://trumpexcel.com/show-formulas-in-excel/ Blog "Excel Tips" - Show Formulas in Excel instead of values https://trumpexcel.com/show-formulas-in-excel/]

3. Hadley Wickham Tidy Data Journal of Statistical Software Vol 59 issue 10 (2014) 10.18637/jss.v059.i10 https://www.jstatsoft.org/article/view/v059i10

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File:The registered report work flow .png

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File:Versuchsplanung 20180718.xlsx

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