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Research Data Management

This guide introduce the essentials of good research data management and how the library can support researchers in managing their research data.

Finding Data

Part of why sharing data is so important is that it enables other researchers to fine and reuse that data, allowing for new discoveries and accelerating the pace of research. See the links below for a list of search engines and repositories you can use to find datasets for your own research. In addition, a couple of large and important datasets in the health sciences are linked to directly for your reference.

Of course, you can always reach out to your liaison librarian if you would like the librarians to search for a dataset on your behalf.

Data Repositories and Search Engines

Key Datasets and Data Tools

PubMed Data Filter

Data Filters in PubMed and PubMed Central (PMC) allow you to locate journal articles with associated data sets.

For PubMed, conduct a search using your keywords.  Then add in data[filter].
Example of a search string: (diabetes AND data[filter])

The results will be citations with related data links in either the Secondary Source ID field or the LinkOut - Other Literature Resources field.  Both fields are located below the abstract.  These data links may be to records in other NLM databases or external data repositories.

For PubMed Central, you have three different methods for finding specific types of associated datas.

  • Use has suppdata[filter] to find articles with associated supplementary material.
  • Use has data avail[filter] to find articles that include a data availability or data accessibility statement.
  • ​Use has data citations[filter] to find articles that include data citation(s).

Alternatively, use has associated data[filter] to find all articles with any type of data section described above

Evaluating Data

Evaluating data quality is an important part of reusing data, as it is important to check that the data is high quality, and, more importantly, is fit for your research need. See the checklist below for some guiding questions to help you evaluate research data.

Data Credibility

  • Who created the data?
  • Who published the data?
  • Who contributed to the data?
  • Is contact information available?


  • Are data outdated?
  • When are the data captured and updated?
  • Is version control implemented to track revisions of a data set?

Information Completeness

  • Are there data typos?
  • Are data formats correct?
  • Are there data outliers which may not be recorded accurately?
  • Do data represent the information you want to capture?

Data Consistency

  • Are data formats consistent?
  • Are data units measurements consistent?
  • Are types of data consistent?
  • Are data synched within and across platforms?

Data Deduplication

  • Are there repeated data records?
  • Are data entered more than once?