The improvement of research reproducibility is not only about process implementation but also about promoting quality culture. The research community needs to join force to build a harmonized and recognized quality culture in research, providing tools, guidelines and policies to ensure data quality and research reproducibility.
4.1 Promoting Quality Culture
A process might be far easier for building systems than building a culture of quality. Very often goals are set around cost, speed and productivity. But what is the cost of working on poor processes and with low quality?
In the Oxford dictionary, culture is defined as “The ideas, customs and social behaviour of a particular people or society” and quality as “The standard of something as measured against other things of a similar kind; the degree of excellence of something” (Oxford-Dictionary 2019). So what are the building blocks, which could allow the research community to build a strong quality culture and which elements could influence scientist’s behaviours to strive for research excellence?
4.1.1 Raising Scientist Awareness, Training and Mentoring
In order to embark on the quality journey, researchers should understand the benefits of embracing robust quality:
First Benefit: Help Ensure Their Sustained Success
Great science can lead to patents, publications, key portfolio management decisions, scientific advances and drug submissions. Robust processes position researcher for sustained success, preserving their scientific credibility and enabling, for example, to defend their patent against litigation, make the right decisions, answer regulator’s questions.
Second Benefit: Serve Patients and Advance Scientific Knowledge
The main researcher focus, which fuels their motivation to innovate and go forward, is to advance scientific knowledge and discover new pathways, new drugs and new treatment. Efficient processes enhance research effectiveness and lead to scientific discoveries. Data integrity supports good science, drug safety, products and treatment development for patients and customers.
Once awareness is raised, researchers need to be trained on basic documentation processes and good scientific practices to ensure data integrity and quality. Targeted training should be added on new guidelines, processes and regulations applied to their specific activities (e.g. human tissue use, natural products, pharmacogenomics activities).
4.1.2 Empowering of Associates
The best way to engage researchers is to empower them to perform some changes in order to improve processes and systems. These changes need to be documented, fit for purpose and organized within the quality framework, managed and governed by the senior management. Managers should lead by example, embrace the change in quality culture and interact more with their staff during study planning or laboratory meetings. They should also encourage people to speak up when they observe inaccuracies in the results or potential fraud.
4.1.3 Incentives for Behaviours Which Support Research Quality
A culture that emphasizes research quality can be fostered by providing appropriate incentives for certain behaviours that are aligned with the quality objectives. Such incentives can come in form of promotions, monetary rewards or public recognition. Awards for best practices to ensure data integrity could be a start. Not all incentives must be endured. Some are only necessary to introduce or change a certain practice. Incentives permit an uptake to be measured and the more visible incentives within an institution improve the reach. There is a great variability in effectiveness of a certain incentive. Questionnaires are a useful instrument to find out which incentives are effective for a certain target research population. Any incentives that do not promote quality need to be critically evaluated by the management (Lesmeister 2018; Finkel 2019).
4.1.4 Promoting a Positive Error Culture
“Error is human” and errors will happen in any research laboratory environment, no matter what precautions are taken. However, errors can be prevented from reoccurring and serve as teaching examples for quality assurance and risk management. For this to happen, a positive error culture needs to be created by leaders that embrace learning and do not punish reported errors. The possibility of anonymous reporting is a crucial element as a seed for community trust, so error reporting is not used for blaming and shaming. Next, a guided discussion of reported errors with the laboratory personnel needs to take place, and potential consequences can be discussed. Such a community effort empowers laboratory workers and makes them part of the solution.
An example of a system to manage errors is the free “Laboratory Critical Incident and Error Reporting System” (LabCIRS) software which permits to record all incidents anonymously and to analyse, discuss and communicate them (Dirnagl and Bernard 2018).
4.2 Creating a Recognized Quality Standard in Research: IMI Initiative – EQIPD
Large pharmaceutical companies, service providers and academia are facing the same challenges. They need to manage budget and portfolio, keep credibility and serve customers and patients. Research reproducibility, accuracy and integrity are a benefit to all. For the first time, an Innovative Medicines Initiative (IMI 2008) project on quality was launched in October 2017, named European Quality In Preclinical Data (EQIPD 2017). EQIPD is a 3-year project co-funded by the EU’s Innovative Medicines Initiative (IMI 2008) and the European Federation of Pharmaceutical Industries and Associations (EFPIA).
Pharmaceutical companies and academia joined forces to foster a quality culture and develop a “unified non-GxP research quality standard”, which is expect to be released in 2020 (Steckler et al. 2018; Macleod and Steckler 2019).
The aim of this project is to establish best practices, primarily in the preclinical neuroscience field but also applicable to the overall non-GxP research, that are harmonized across the pharmaceutical industry to improve data quality and reproducibility in discovery and exploratory research. The EQIPD members are working together to develop simple and sustainable solutions to facilitate implementation of robust research quality systems and expansion of knowledge on principles necessary to address robustness and quality.
4.3 Funders Plan to Enhance Reproducibility and Transparency
The NIH proposed first to implement a mandatory training regarding result reproducibility and transparency and good experimental design. Starting in 2019, the NIH research grant applications now have to include components that address reproducibility, rigor and transparency. Applications must include measures to ensure robust and unbiased experimental design, methodology, analysis, interpretation and reporting of results. More relevant biological models should be considered, and the rigor of prior research that the application is based on should be reviewed. NIH asked publishers to get more involved, promote peer-review and data disclosure. In addition, the whole research community is encouraged to work together in order to improve research reproducibility (National-Institutes-of-Health-NIH 2019).
European funders as well aim to enhance reproducibility, mainly by increased transparency and public data availability of research results. The most prominent EU project with that goal is the European Open Science Cloud (EOSC 2018). A key feature of the EOSC is that the shared data conforms to the FAIR criteria: findable, accessible, interoperable and reusable (Wilkinson et al. 2016, 2019). Also at the national funder level, more calls of applications emerge that specifically address scientific rigor and robustness in non-GLP research (German-Federal-Ministry-of-Education-and-Research 2018).