Top notch training for our young scientists!

EQIPD E-Learning Modules


  • Learning objectives: upon completion of training, participants should have a basic understanding of…
  • Learning outcomes: upon completion of training, participants will be able to…
Module 1: Scientific integrity
Learning objectives Learning outcomes
The importance of research integrity and good data quality in researchExplain why research integrity and good data quality is important in non regulated research
Different risk areas related to (lack of) scientific integrity, such as patient safety, intellectual property and decision makingExplain which different risk areas are related to
Common principles of research integrity, e.g. Scrupulousness, Reliability, Verifiability, Impartiality and Independence(lack of) scientific integrity, including examples
The current debate about the reproducibility crisis in relation to scientific integrityName a number of common principles of research integrity, give examples of best practice and describe dilemma’s for each principle.
Training materials:
coming soon
Module 2: Experimental design
Learning objectives Learning outcomes
Confirmatory versus exploratory researchExplain the difference between exploratory and confirmatory research
The need for well-defined, a priori, hypotheses Formulate a well-defined hypothesis for an interventional study
Internal controls and external controls List different types of formal experimental designs (e.g. completely randomised, randomised block, repeated measures) and explain their advantages and disadvantages
Study designs, e.g. parallel groups, repeated measures, cross-over, multi-centerChoose the most appropriate study design for their planned study
Sample size and unit of measurement considerations Identify the experimental unit and recognise issues of non-independence (pseudoreplication).
Perform a sample size calculation for a study with a parallel group design
Explain the implications of study design on the required sample size
Training materials:
NC3Rs digital research assistant
Module 3: Validity
Learning objectives Learning outcomes
Internal validity and risks of bias in various study designs, measures to reduce bias (randomisation, blinding)Explain what bias is and how it differs from variability and confounding
External validity, predictive validity and indirectnessAssess the risk of bias in published a study, using an appropriate tool
Construct validityImplement measures to reduce bias in an experimental set-up
Explain what external validity is and how this is influenced by indirectness
Explain what construct validity is
Explain the importance of internal, external and construct validity in translational research
Training materials:
Sketchy EBM videoscribe on attrition bias
Module 4: Ethics and animal welfare
Learning objectives Learning outcomes
Ethics related to animal useDemonstrate a comprehensive understanding of the principles of replacement, reduction and refinement
The principles of replacement, reduction and refinementIdentify, assess and minimise all of the welfare costs to animals throughout the animals’ lifetime, including adverse effects relating to transport, housing, husbandry, handling, procedures and euthanasia methods
Potential effects of transport, housing, husbandry, handling, procedures and euthanasia methods on the experimental results
Ethical implications of need for larger sample sizes
Training materials:
coming soon
Module 5: Data handling
Learning objectives Learning outcomes
What constitutes an outlier and how to deal with themDescribe various methods for dealing with outliers and explain when these methods are appropriate
What missing data are and how to deal with themDescribe various methods for dealing with missing data and explain when these methods are appropriate
Methods for data documentation, e.g. electronic notebooks.Correctly document data and describe procedures used for data handling, e.g. how missing data were imputed or how outliers were determined
Training materials:
Coming soon
Module 6: Statistics
Learning objectives Learning outcomes
The purpose of statisticsExplain and decide when it is useful / appropriate to use statistics and when not
Importance of pre-specificationExplain the importance of pre-specification of the statistical approach of a study
Unit of measurementExplain the concepts of power calculation, p-values and corrections for multiple testing
Pre- vs. post-hocDemonstrate an understanding of the need to take expert advice and use appropriate
P-hackingStatistical methods,
PowerDesign a detailed statistical analysis plan for their study, including (but not limited to) a power calculation, in collaboration with a statistician
P-values, multiple testing and False Discovery Rate (q-values)
Training materials:
Coming soon
Module 7: Transparent reporting
Learning objectives Learning outcomes
Open science approaches (open data, open reporting, protocol registration)Identify several open science approaches and related platforms (e.g., open science framework, PROSPERO, data repositories etc.)
Reporting guidelinesIdentify and use reporting guidelines relevant for their field of research (e.g. ARRIVE, CONSORT or PRISMA)
Granularity of Methods and ResultsExplain the concept of publication bias and how this impacts translational research
Publication bias and dealing with significant (negative and positive) and neutral or controversial results
Training materials:
Coming soon
Module 8: Systematic review of published literature / existing data (SR)
Learning objectives Learning outcomes
Rationale of SR methodologyDraft a systematic review protocol
Formulating a PICO SR questionDraft a comprehensive search
Building a comprehensive search strategyUnder expert supervision, as member of a review team, perform the basic steps of a systematic review
Selecting studies using inclusion and exclusion criteriaAssess the quality of an existing systematic review, using an appropriate tool
Extracting study characteristics
Assessing bias in primary studies
Basics of meta-analysis
Interpretation of the SR results
Assessing the quality of a systematic review
Training materials:
SYRCLE’s E-learning on systematic reviews of animal studies(N.B. Use “SYRCLE” as your registration code)
Module 9: Data governance and data integrity
Learning objectives Learning outcomes
Data governance at a discovery research levelIdentify the risks relating to data governance and data integrity
Data integrity at a discovery research levelMitigate data integrity risks, having conducted data integrity risk assessments.
Training materials:
Coming soon
Module 10: Set up of industry / academia collaborations
Learning objectives Learning outcomes
Recommendations for content of research agreements with respect to:Explain the strength of collaborative research
Terminology & definitionsPrepare a research agreement
Legal requirements (litigation)Review a research agreement
IP requirementsUnderstand the risks of not having a robust research agreement
Scientific requirements
Quality expectations
Records Management
Training materials:
Coming soon
Module 11: Implementing QMS in discovery research environments
Learning objectives Learning outcomes
Components of a quality management system (QMS) in drug discovery researchConduct a gap analysis, in readiness for the introduction of a QMS in discovery research
Conduct a gap analysis
Understanding risks and limitations, and identify possible mitigations
Training materials:
Coming soon