data-management

The HSLS Update has published numerous articles about preprints over the years. Here we introduce another iteration of the preprint movement—Research Square, a multidisciplinary platform that helps researchers share their work early, gather feedback, and improve their manuscripts prior to (or in parallel with) journal submission.

So what differentiates Research Square from other preprint servers? The focus is on “added value” features such as:

• Increased discoverability and readability due to indexed and machine-readable full text in HTML
• Commenting via a custom-built system or the hypothesis annotation tool
• Figure rendering with a lightbox, which allows for zooming and downloading
• Full metrics, including Altmetrics and Dimensions data
• Research Square Badges to indicate preprint quality
• Editing services to improve the manuscript prior to journal submission
• Video and infographic services to help communicate the research

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I recently attended a workshop from the Data Curation Network, a collaboration of institutions that have developed specific guidelines to help their researchers share research data. Though the workshop was aimed at librarians, the DCN’s process is useful to any researcher preparing data for sharing in a repository. If you are interested in making your research more reproducible, I encourage you to consider these simple steps.

Imagine that you have a dataset—a package of data files, documentation such as codebooks or READMEs, and perhaps analysis code—that you wish to (or are required to) deposit in a repository such as Figshare or OpenNeuro. The files you have probably require some cleanup before you share them with the world, but there may be other actions you can take that would have a big usability payoff for minimal investment. The steps below form the Data Curation Network’s “CURATE” model, paraphrased here but available in full online: Data Curation Network: A Cross-Institutional Staffing Model for Curating Research Data. Continue reading →

In a follow-up to last year’s request for input on updates to its 2003 Data Sharing Policy, the National Institutes of Health (NIH) is soliciting public feedback on a draft policy for data management and sharing activities related to public access and open science. Regarding the necessity of such a policy, the NIH states:

“Validation and progress in biomedical research—the cornerstone of developing new prevention strategies, treatments, and cures—is dependent on access to scientific data. Sharing scientific data helps validate research results, enables researchers to combine data types to strengthen analyses, facilitates reuse of hard to generate data or data from limited sources, and accelerates ideas for future research inquiries. Central to sharing scientific data is the recognized need to make data as available as possible while ensuring that the privacy and autonomy of research participants are respected, and that confidential/proprietary data are appropriately protected.”

The draft policy would apply to all NIH-funded or conducted research resulting in the generation of scientific data and requires: Continue reading →