Research data management and publication are growing concerns for researchers in a number of disciplines. With advances in computational techniques, the possibilities for data reuse have grown, while expectations for data sharing among grant funding agencies and within research communities have also increased. The Digital Scholarship and Communications Office is here to help you find the tools you need to develop a data management plan, follow best practices for data management throughout the life of your research project, and publish your datasets in an appropriate repository.
Grant funding agencies are increasingly requiring applicants to submit a data management plan as part of written proposals. Data management, however, goes beyond the plan submitted to a funding body to encompass the steps taken throughout the research process to ensure the longevity and usefulness of a data set.
There are a number of units around Vanderbilt that provide resources to aid in research data management. Following are descriptions of those units (based on information provided during a panel session hosted by the Digital Commons on 2021-11-01). To view videos from that session, visit this page
Provides workshops and tutorials on data management best practices and tools such as GitHub, R, Python, Open Science Framework (OSF). Operates Vanderbilt’s Institutional Repository. Provides consultations on data management tools, data management plans, and on using the DMPTool. Can assist with the selection of an appropriate repository for your data. Link for more information.
Research IT can assist with development of Data Use Agreements (DUA) in coordination with the Sponsored Programs Administration and Vanderbilt Information Technology. Can assist with access to dedicated work stations, on-premises storage through the Advanced Computing Center for Research and Education (ACCRE) facility, and cloud storage via Amazon Web Services (AWS). Link for more information.
Faculty, staff and students can schedule a one-hour consultation with DSI Data Scientists to discuss research questions with Data Science Institute consultants. Areas of focus: machine learning, deep learning, big data, data science. Provides periodic workshops on data science tools and techniques. The DSI can also assist with setting up a public-facing machine-learning model. Link for more information.
Provides resources for public data visualization and analysis. Link for more information.
Secure survey and storage system free to all Vanderbilt and Meharry users. Link for more information.
The datasets underlying research findings are increasingly acknowledged as valuable scholarly products. Through data publication, researchers can give more visibility to their work while making their research data available to others for reuse. There are a number of great reasons for publishing data, including meeting funding agency requirements, advancing knowledge within your discipline, and increasing exposure to your research.
The benefits of data sharing to advance knowledge are becoming increasingly clear, and data intensive research has come to be known as the Fourth Paradigm of Discovery (along with empirical, theoretical, and computational modes). By performing new analyses and meta-analyses of datasets, researchers can use existing data sources to answer new questions.
Sharing the datasets that support research findings also makes the research process more open, permitting others to replicate the findings of a study. To advance this goal, a number of journals (e.g. Nature and the Public Library of Science [PLOS]) require authors to submit datasets along with articles for peer review, and to describe how they will make those datasets publicly available.
Scholarly repositories preserve and dissiminate scholarly research in many forms. By uploading papers to scholarly repositories, professors can immediately share them with colleagues and other interested parties around the world. Scholarly repositories are essential components of the so-called “green” open access model.
On February 22, 2013, the Office of Science and Technology Policy (OSTP) in the Executive Office of the President released the memo Increasing Access to the Results of Federally Funded Scientific Research directing federal agencies to act to ensure that “the direct results of federally funded scientific research” [including peer-reviewed publications and digital data] “are made available to and useful for the public, industry, and the scientific community.”
In response, grant funding agencies in the United States, including the National Institutes of Health (NIH) and the National Science Foundation (NSF) now require grant applicants to submit data management plans, describing how research data will be managed during the course of a study and shared at the study’s conclusion.
All National Institutes of Healty (NIH) applications submitted after January 25, 2023 will need to comply with the NIH Data Management and Sharing Policy. This policy requires researchers to plan for how data will be preserved and shared. This expands the previous policy from requiring a data sharing plan to requiring a plan for both sharing and management of data. For detailed information about the new policy, visit this page.
Researchers who share datasets are also seeing increased citations to their work. One recent study found that
“cancer clinical trials which share their microarray data were cited about 70% more frequently than clinical trials which do not. This result held even for lower-profile publications and thus is relevant to authors of all trials.”
While these findings are specific to cancer clinical trials, we can expect to see more studies addressing citation rates for datasets in a variety of disciplines.
Piwowar HA, Day RS, Fridsma DB (2007) Sharing Detailed Research Data Is Associated with Increased Citation Rate. PLoS ONE 2(3): e308. http://dx.doi.org/10.1371/journal.pone.0000308
Strategy for Cultural Change (Center for Open Science) - discussion of steps to increase openness, integrity, and reproducibility of research
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