LibGuides: Research Data Management: Basics of Data Management

What is research data?

Research data is:

Digital objects: such as text files, image files, sound files
Databases : structured collection of records or data stored in a computer system

The Office of Research Integrity defines research data as:

Any information or observations that are associated with a particular project, including experimental specimens, technologies, and products related to the inquiry.

Following best practices for managing research data are essential throughout the data lifecycle.

Data Management & Sharing SNAFU (a video in 3 short acts)

This short video, created by the librarians at NYU's Ehrman Health Sciences Library, tells a data management cautionary tale of what should not happen when a researcher makes a data sharing request.

Why manage your research data?

Data management is an essential part of the research process. Many funders, publishers, and institutions have data management and sharing policies. When data is managed well, it is easier to find, analyze, validate, and share. Good data management practices are key for responsible research and reproducibility. When data is managed well, it supports published research. In addition, data sets can be cited, which can enhance your research impact.

NIH has issued a new Data Management and Sharing (DMS) policy, effective January 25, 2023, to promote the sharing of scientific data. This Policy requires researchers to:

Plan and budget for the managing and sharing of data
Submit a DMS plan for review when applying for funding
Comply with the approved DMS plan

Additional information is available on Einstein's Data Management and Sharing website.

NNLM Toolkit for the NIH Data Management and Sharing Policy
The Network of the National Library of Medicine (a division of NIH) has developed a toolkit to help researchers comply with the new Data Management and Sharing Policy. The toolkit includes; policy information and updates; data management and sharing plans, data sharing; costs; classes and workshops; glossaries.
NIH Data Sharing Policies by Institute
NIH Data Sharing Repositories
This site lists NIH-supported domain-specific and generalist data repositories that accept submissions of appropriate data from NIH-funded investigators. The table can be sorted by name or NIH Institute or Center, and can be searched by keyword.

NSF Data Sharing Policy states that "investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants."

AHRQ's public access policy states that "digitally formatted scientific data resulting from unclassified research supported wholly or in part by Federal funding should be stored and publicly accessible to search, retrieve, and analyze."
In addition all AHRQ-funded researchers will be "required to include a data management plan for sharing final research data in digital format, or state why data sharing is not possible."

Many journals require data be made available to readers. Here are a few examples.

BMJ's Open Data Policy As of January 2013, the BMJ no longer publishes any trial of drugs or devices where the authors do not commit to making the relevant anonymized patient level data available, upon reasonable request.
Nature's Data Standards & Policy A condition of publication in a Nature Portfolio journal is that authors are required to make materials, data, code, and associated protocols promptly available to readers without undue qualifications.
PLoS One's Data Availability Statement PLOS journals require authors to make all data necessary to replicate their study’s findings publicly available without restriction at the time of publication

FAIR Principles

FAIR Principles
FAIR guidelines promote research data stewardship. The acronym stands for: Findable, Accessible, Interoperable, and Reusable. The combination of these concepts will help researchers create datasets and research findings that will be widely available and usable to enhance future research studies.

Findable
- Unique and persistant identifiers ( e.g. DOIs, ORCIDs) and metadata allow data to be located quickly and efficiantly
Accessible
- Protocol is universally implementable (avoid using proprietary software and platforms)
- Clearly stated conditions and requirments under which the data is accessible
- Metadata are accessible even after data are no longer available
Interoperable
- Common data elements to enable combining and sharing datasets (e.g. NIH CDE Repository)
- Commonly used controlled vocabularies, ontologies, thesauri (e.g. MeSH)
- Data dictionary to describe data
Reusable
- Clear and accessible data usage license (e.g. Creative Commons)
- Documentation of software, code, and similar files
- Clear description of authorship and provenance

FAIR Data Self Assessment Tool
The Australian Research Data Commons' data self assessment tool to assess how FAIR your research dataset is and get practical tips on how to enhance its FAIRness.