Explore the Principles of FAIR and Reproducible Data

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Reproducibility and FAIR Data Challenge

The scientific ideals of creating reproducible research can sometimes feel in competition or conflict with the necessity of keeping health data private and the fast pace of publication and job cycles. Luckily there are a lot of resources to help you figure out how well your procedures follow best practices.

As you are reading up on these sets of guidelines:

• Think about your research practices and whether you are already following their recommendations (or in the case of the NIH, requirements).
• Are there things you want to improve in your research after reading these guidelines?
• Have you benefited from the practices of others who have followed these guidelines? Or been frustrated when trying to replicate work didn't?

FAIR Data

What makes data FAIR? FAIR is an acronym standing for

• Findable
• Accessible
• Interoperable
• Reusable.

Take a look at this discussion of FAIR data practices and take the quiz at the bottom of the page. For a much more in-depth discussion of FAIR data, check out the FAIR Data 101 course offered by the Australian Research Data Commons.

Reproducibility

Trying to reproduce research (even research you did yourself) can be one of the great frustrations of science. What is the problem, and how do we work to fix it?

• Watch this short video (under 5 minutes) about the frustrations that result when data is not managed with reproducibility in mind. You may have already seen this video in DART's module on reproducibility.
• Take the PLOS reproducibility assessment to determine how reproducible your research is.
• Learn about the Center for Open Science’s Reproducibility Project: Cancer Biology.

NIH Data Sharing Rules

The NIH instituted new data sharing rules in January 2023. How well do you know them?

• Read through the FAQs on the NIH website.
• Did your lab have to change any of their practices to comply with these rules?
• How FAIR is your data sharing plan?