About this Course
- 0.1 Available course formats
1 Introduction
What is Data Sharing?
2 Data Sharing is Important
Effective Data Management
3 Defining reproducibility
4 Data Privacy
5 Creating Clear Documentation
6 Why Documentation is Worth the Time
- 6.1 The context of documentation in research
- 6.2 Why documentation is worth the time
7 Discussion on Benefits of Data Management and Sharing
- 7.1 Discussion
8 Why is there an NIH DMS policy?
Data sharing Expectations
9 Elements of the DMS Plan
- 9.1 Overall Takeaways
10 Organizing your project
11 Record keeping practices
12 Guidelines for Good Metadata
- 12.1 What are metadata?
- 12.2 How to create metadata?
  - 12.2.1 The goals in creating your metadata:
13 Data Storage and Data Repositories
14 Health care data sharing tools
- 14.0.1 REDCap (Research Electronic Data Capture)
About the Authors
15 References

Proactive Data Management and Sharing

Chapter 2 Data Sharing is Important

Sharing data is critical for optimizing the advancement of scientific understanding. Now that labs all over the world are producing massive amounts of data, there are many discoveries that can be made by simply re-using/re-analyzing this existing data.

The concept of data re-use is so important, that in January 2023, after years of development, the NIH released new guidance and began requiring new practices for data management and sharing. See the announcement here.

See this course about the NIH policy for more information about how to comply.

Note that many institutes and funding agencies or mechanisms have requirements about how your data can be shared. Typically, data sharing of protected data also requires Institutional Review Board (IRB) approval before the study is conducted. Ensure that you are following those requirements before you share your data. A later section in this course will cover data privacy.

There’s so many excellent reasons to put your data in a repository, whether or not a journal requires it:

Sharing your data…

Makes your project more transparent and thus more likely to be trusted and cited. In fact, one study found that articles with links to the data used (in a repository) were cited more than articles without such information or other forms of data sharing (Colavizza et al. 2020).

Another researcher is downloading the data from a repository and says ‘These insights are so exciting! I can’t wait to look into this data even more!’

Helps reduce your own workload so your email inbox isn’t overloaded by requests you probably don’t have time to respond to.

Ruby is reading a journal article with data she is interested in. The journal article says ‘Data are available upon request by email’. Ruby sends an email that says ‘ The email is going to an inbox with 999,999,565473 emails in it and it is labeled ‘the corresponding author’s inbox’.

Allows others to gain even more insights from your data which shows funders that your data will be used to its maximum potential.

Ruby has uploaded her data to a repository and now its being used by many other researchers. Ruby says to her funders, represented as a bank, ‘The data you funded is getting so much mileage!’

In summary, data sharing is the practice of making all appropriate data sources associated with a project available through systematic means. Broad data sharing is a critical step toward mitigating the reproducibility and replicability crisis. Not only that, but data sharing is economical, equitable, and frugal. New exciting findings can result from shared and reused data. Additionally, institutions and individuals with less resource availability can benefit from access to shared data.

References

Colavizza, Giovanni, Iain Hrynaszkiewicz, Isla Staden, Kirstie Whitaker, and Barbara McGillivray. 2020. “The Citation Advantage of Linking Publications to Research Data.” PLOS ONE 15 (4): e0230416. https://doi.org/10.1371/journal.pone.0230416.

All illustrations CC-BY.
All other materials CC-BY unless noted otherwise.