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Knowledge Sharing: Don’t Be Stingy with Your Data

Researchers are changing the environment to be more collaborative: Supporters fund cross-team research and cross-disciplined individuals work together. Collaborations with those in other countries and partners in complementary industries are more commonplace today than in the past. Collaboration benefits — whether with the co-worker in the next cubicle or a company across the globe — are numerous. However, collaborations aren’t without challenges.

Collaboration benefits

By working collaboratively with people from other backgrounds and exchanging scientific viewpoints, you broaden your own understanding of a field of study — and improve your own research. Other benefits include

  • resource sharing;
  • multiplied efforts, which can result in greater accomplishments than those in each group working on their own could ever hope to achieve; and
  • ensuring efforts and services aren’t unnecessarily duplicated.

Data-sharing trends

Few things are as frustrating for researchers as having to recreate experimental data because other researchers don’t share raw data. The good news is that’s changing. Those in the science and scientific publishing field are becoming more open. It’s becoming increasingly common for researchers to share preprints, data sets, and scientific codes.

To facilitate this data-sharing trend, open-source platforms and tools are becoming increasingly available for those in specific scientific fields and industries. rOpenSci is a platform and repository that houses dozens of open-source data-and-analysis packages serving a broad range of scientific fields. Other platforms that facilitate data sharing include figshare, GitHub, arXiv, Dryad, and Mendeley.

Researchers are also sharing work via preprint servers. For example, Research Papers in Economics (RePEc), for economists, and arXiv, for physicists, are collections of working papers, journal articles, books, and software components that these scientists can share and use for further research or other services.

Data-sharing requirements 

The internet’s ability to host numerous collaborative tools has inspired scientific society members, university leaders, and academic publishers to push for more open science. Although open science raises concerns over data ownership, receiving proper credit for research, and the tenure system, members of the National Science Foundation (NSF), National Institutes of Health (NIH), and Public Library of Science (PLOS) have all enacted data-sharing requirements. They require authors to archive the raw data they use in their research papers.

“Data availability allows validation, replication, reanalysis, new analysis, reinterpretation, or inclusion into meta-analyses, and facilitates reproducibility of research,” according to a PLOS article, and sharing could provide “better ‘bang for the buck’” for scientific research.

A researcher’s work includes products, not just publications: According to an NSF article, “products may include, but are not limited to, publications, data sets, software, patents, and copyrights.”

Governments are also taking up the data-sharing movement. As part of the Human Genome Project, researchers are required to share the data and related code they generate through NIH-supported research.

Sharing incentives

Although more researchers see value in sharing their data, they tend to “undervalue shared data sets, methods, and analytical tools,” according to the Science article.

Some journal publishers are discussing rewarding data contributions — yet inherent benefits already exist. Sharing data and tools that other researchers use can lead to valuable citations, which in turn can help secure grants and job offers.

Miscellaneous considerations

Insiders recommended not posting your data on your personal website. You might change institutions or shut down the site, causing you to lose valuable data. You can safely post relevant data in any of the hundreds of repositories where others can easily discover it.

Metadata — information about your data — is crucial for colleagues to be able to find and use your data in their research. Don’t wait until after you complete your experiments: Create it as you go and link your methods, data, code, and the paper. rOpenSci offers a tool that can create automated workflows to continually update metadata.

It’s also important to create a data-sharing agreement that outlines use regulations and required citations.

Collaboration and data sharing can advance science faster and further than isolating data. Collaboration opportunities can give scientists access to additional funding opportunities across disciplines. How can you find collaboration opportunities? Start by networking with teams and those in other disciplines at conferences and events to find opportunities to communicate across disciplines. Some very successful endeavors have evolved from simple across-the-table conversations, so don’t be afraid to reach across the aisle when the opportunity presents itself.

Want more information on data sharing? Looking for tips on how to start collaborating? Contact your Sheridan representative to learn more.

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