Data Services

Data Sharing has multiple benefits which include increasing the visibility of your research, facilitate new discoveries by other researchers, and meet funding requirements.

Increase your visibility by including your data in open access repositories.  Raise your prominence by contributing to the field and keep your research relevant.

Facilitate new discoveries by allowing other researches to use your data sets.  Data sets can be re-analyzed to answer a research question and prevent duplication of effort.

Meet funding requirements as many federal grants require data sharing around the time of publication.  Individual journals and publishers also are requiring or encouraging data sharing in their editorial policies.

Make public assets available to the public through publishing data papers, research articles with your conclusions and submitting datasets to open repositories.

Other Incentives:

• Data repositories create DOIs allowing for citations of the data
• Data journals allow for data publications and full text articles
• NIH Biosketch allows for the citing of non-traditional research outputs such as datasets
• Data papers allow researchers to receive citations for a dataset like a publication
• Sharing data can result in increased citations for published studies
• Maximize transparency, accountability, and scrutiny of research findings

Not all data can be shared freely.

Restrictions on

• Threatened or endangered species
• National security and classified research
• Export controls (technologies)
• Personal Health Information
• Data involving children or prisoners

Ensure you can share your data easily:

• Use Open Source file formats (ASCII)
• Provide software information in metadata (software name, version, operating system)
• List number of files in file structure in metadata
• Select a consistent file format
• Select files that can be edited regardless of application
• Files should follow a documented standard
• File should stay unencrypted and uncompressed
• Upload datasets to open repositories for easy access
• Keep it clean by avoiding missing values or unclear column headers
• Understand funder/institution policies on data sharing
• Remove personal data

Find Additional Federal Grants

Findable- Data and metadata are easy to find by both humans and computers.
Accessible- Data use is open to the greatest extent, allowing others to query or copy data for their own use.
Interoperable- Data can be interpreted by a computer easily.
Re-useable- Data and metadata is well described for both humans and computers to replicate or combine data with other datasets.

More Info

FAIR Data Principles explained

Common Data Element (CDE) - A data element that is common to multiple data sets across different studies.

Certain types of CDEs are sometimes described:

• Universal - CDEs that may be used in studies, regardless of the specific disease or condition of interest, e.g., demographic information of study subjects or medical history
• Domain-specific - CDEs that are designed and intended for use in studies of a particular topic, disease or condition, body system, or other classification, e.g., Parkinson's disease, Alzheimer's disease, diabetes, ophthalmology. Some domains are broadly applicable to a wide range of studies, while others are more useful in specific fields of clinical research.
• Required - CDEs that are required or expected, as a matter of institutional policy (e.g., research funder or performer), to be collected for all subjects in studies of a particular type, e.g., NIH-funded studies of neurological disease
• Core - CDEs that are required or expected to be collected in particular classes of studies, e.g., any study of neurological disease or cancer, any genome-wide association study. Other, domain-specific common data elements may be suggested, expected, or required for collection, depending on the more specific focus of the study (e.g., Alzheimer's disease or ovarian cancer)

NIH Common Data Element Portal

NIH CDE Repository

Draft of U.S Core Data for Interoperability of Clinical Research

Data papers are an easy way to share data.  They are not traditional research articles, as their purpose is to simply describe your dataset.  

There are over 200 data journals, with 120 of those in biological, medical or health care fields.  They are mostly open access, indexed in widely used databases, peer reviewed, and some have impact factors.

The benefit to submitting a data paper is to double the publication output from a single research project.  Publishers consider data papers complementary to research articles, and not a prior publication.

The paper is easy to write as it is usually brief and you are provided an article template to follow.

Since data papers can be cited, it is easy to track reuse of your data and contribute to one's h-index.