Research data metrics
With increased emphasis placed by funders and policymakers on making available the underlying data behind one’s research, it follows that other researchers might then use this data in their own scholarly pursuits. Not only can that data be reused, but it can also be cited, downloaded, or mentioned on blogs and in tweets or other social media sites.
Raising your research data impact
The following measure can help ensure greater impact for your research data.
1. Deposit your data in a repository
Repositories offer stable locations that allow other researchers to cite the data. When choosing a repository consider the following:
- Is the repository well used and easily discoverable in your discipline?
- Does the repository offer a unique identifier, like a DOI, for your dataset.
- Are there usage statistics such as downloads or views. Or it is harvested by databases such as the Data Citation Index?
2. Describe your data
For other researchers to discover, understand, and reuse your data, it has to be well described. Providing basic information such as a title, abstract, keywords, geospatial and/or temporal coverage is a start. However, to be reusable, and even reproducible, data also need to be accompanied by more robust documentation.
3. Make your data open
If other researchers cannot access your data, it cannot be reused or cited. Notwithstanding ethical, legal, or commercial restrictions, data should be as open as possible. Note that access to open data can be controlled through registration prior to download or author requests for usage.
4. Apply a license to the data
Licenses define what others may or may not do with your data. They can run from no restrictions (public domain) to much narrower more restrictive licenses, such as limiting use to strictly non-commercial activities and requiring attribution to the data creator.
5. Promote and cite your data
To raise awareness about your data, consider the following:
- Cite it in your publications.
- Publish a data paper that describes your dataset.
- Reference the dataset in social networks such as Twitter, Academia.edu, or ResearchGate.
Sources of research data metrics
Research data metrics can be obtained either through citations, usage statistics, or altmetrics. Note that research data indicators and metrics can only be measured for data that has been deposited in a data repository.
1. Citation databases for datasets
Datasets can be cited like other research outputs such as articles and books. Although there is one commercially available data citation database, only the following free resource is currently available to Concordians:
- Google Dataset Search
Shows how many scholarly articles in Google Scholar cite datasets. Datasets are harvested from a variety of data repositories.
2. Usage statistics from data repositories
Most data repositories will provide some type of data metric, most often in the form of download statistics. Below are Concordia specific repositories. Consult the Re3data directory for a comprehensive list of data repositories around the world.
- Spectrum: Concordia University's Institutional Research Repository
- Includes many types of research outputs, including datasets
- Provides download statistics and altmetrics for each item in the repository
- Concordia Dataverse: Concordia University’s Research Data Repository
- Includes datasets deposited by Concordia researchers and graduate students
- Provides download metrics for datasets as well as for files within each dataset
The altmetrics available for your data will depend in large part, on where it is deposited and whether the dataset has been given a DOI.
- PlumX Metrics
- Tracks dataset DOIs from many different repositories such as Dryad and Figshare. It measures metrics for these datasets in over 50 sources (Twitter, Mendeley, Scopus, PLOS, etc.) along five categories: Usage, Captures, Mentions, Social Media and Citations.
- Impact Story
- Allows you to create an account (via Twitter) and track various metrics (Altmetric data, Mendeley saves, and more), for your research output, including datasets.