Skip to main content

Bethel Libraries

Data Management: Data Archiving And Sharing

Learn about best practices for research data management and find links to external resources of help.

Data Re-Use: Background

Role of eScience and Computing

Advancements in computing, data storage, and network infrastructure are helping researchers to make sense of data on a previously unthinkable scale.

Some have called these computational possibilities a new "paradigm" in research. The terms eScience and "cyberinfrastructure" are often used to describe these new capabilities.

Data Sharing/Re-Use Project Examples

Data Sharing in Practice

Several fields of research have embraced the research benefits of making data more discoverable to other scientists and open for re-use. Large scale examples of intensive data sharing include:

Archiving Your Data

At the conclusion of a research project, researchers often report findings in journal articles, book chapters, conference papers, or other forms of scholarly communication. They many not thinking about the ongoing usefulness of their data.

Preserving data for long-term use and referral serves several purposes (as noted by the Digital Curation Centre): 

  • Data are often unique, and difficult to replace if lost or destroyed
  • Verifiable data adds strength to research findings 
  • Eases the researcher's compliance with contractual or legal guidelines for maintaing data

Johns Hopkins has a simple diagram showing the stages of data management both during and after a project is completed.

Important Archiving Considerations

Whether a researcher intends to share their data broadly or not, there are several things that he or she can do to make sure that data is well prepared for long-term storage.  Some data practices researchers can explore:

  • Indicate "version" information in file names (i.e. track any data transformations)
  • Consider non-proprietary or multi-platform file  formats, when possible (.csv and .txt files, for example)
  • Consider writing a "code book" document, explaining variable names, definitions, and use of abbreviations
  • If data was gathered from an instrument, include model specifications and calibration information
  • See if your field or research community has preferred standards for metadata
  • Have someone unfamiliar with the project look at your files to see if you've provided enough context

List of data repositiories

[INCOMPLETE: LIST IS STILL BEING DEVELOPED]

This list contains data repositories with a brief description, including the related discipline(s). Some of these repositories accept research dataset submissions from all researchers, while others require institutional affiliation for archiving your data.