PAASP offers several formats of educating
How to recognize data likely to be irreproducible
How to generate reproducible data
How to comply with best documentation practices
Our lectures provide an introduction to the topic of Good Research Practice and create awareness about researchers bias.
The focus can be adapted to your requirements but usually provides a first insight into the topic of data robustness, reproducibility and data integrity. For examples, a focus can be set on statistics and solutions on how to increase data robustness.
Just like the lecture, PAASP also provide a webinar for informing researchers about the increasingly acknowledged topic of data integrity.
Some of our Webinars can be found HERE.
Our two-day workshop is dedicated for researchers of any level and touches on the following topics:
– Origins of poor data robustness in study design
– Beyond study design: Broad assessment of Risks of Bias
– The impact of the reproducibility crisis on drug discovery and beyond
– What needs to be done to enhance research rigor?
– How to make changes and enhance research rigorous
PAASP experts provide project support for research grant applications, e.g. as part of a work package. Planned activities will be introduced in four sequential steps:
Step 1 – General training
For each research partner, there will be an on-site two-day training on Good Research Practice hosted by the research partner and organized by PAASP. Curriculum of the training workshop will follow the program developed by the ECNP Network (link).
The primary objective of these workshop (open to everyone from students to PIs) is to make sure that the research teams (from PIs to students and laboratory associates) fully endorse the values of confirmatory mode of research.
Step 2 – Initial assessments and research rigor planning
Coupled timewise to the training workshops, for each research partner, PAASP team will conduct an on-site assessment of quality practices with, if needed, assistance in identifying, building and implementing required solutions.
Focus of these assessments is on the following:
– data management and documentation practices;
– internal validity of studies to be-conducted
– authentication of biological resources
– training qualification of scientists involved in the study design, conduct and analysis
This step is concluded by the development of a cross-lab study plan (with the feasibility assessment) that will be pre-registered on an online platform (Month 3)
Step 3 – Spot checks, critical accident and error management
For each research partner, PAASP will coordinate a time period during the conduct of the study when spot checks can be conducted (i.e. unannounced visits to research sites to test compliance with the pre-defined study protocol and research practices). The goal of these spot checks is to prevent:
– suboptimal handling of raw data
– inadequate control of risks of bias (such as performance bias)
– unplanned / unintended unblinding before the study analysis is completed
Further, operating mostly in a remote manner, PAASP team will provide support in managing and documenting the critical accidents, errors and deviations from the pre-agreed plan (by establishing a decision tree on how to handle errors and providing advice on specific cases to avoid unnecessary bureaucracy while maintaining maximum transparency).
Step 4 – Data QC and final report
For each research partner, PAASP will conduct a QC check on generated results and reports, and will produce a final report on compliance with the confirmatory research standards.