Since the early days of the Data Catalog, we have experimented with different ways to locate institutional datasets suitable for indexing. Recently, with the help of the folks at the National Library of Medicine (NLM), we were able to create a new workflow for locating data. In a series of blog posts, we will be writing about our experiences using the “has data avail” filter on PubMed Central (PMC) to identify a wide range of institutional datasets as well as what we learned about our institution’s data sharing practices from this exercise.
This series will be split into five blog posts:
This introductory post
An examination of data found in supporting information files
An examination of data available through a consortium
An examination of data available through a repository
Additional findings about our researchers and their data
In April 2018, NLM announced new search filters for PubMed and PMC. The “has data avail” filter allows users to narrow their search to journal articles that have data availability statements. Using that filter, we were able to limit our search to journal articles that included data availability statements and had at least one author affiliated with NYU. Our search strategy is listed below:
has data avail[filter] AND ((nyu langone school of medicine[ad]) OR (new york university langone school of medicine[ad]) OR (langone school of medicine[ad]) OR (New york univ School of Medicine[ad]) OR (Nyu School of Medicine[ad]) OR (New York University School of Medicine[ad]) OR (langone medical center[ad]) OR (nyu medical center[ad]) OR (new york university medical center[ad]) OR (new york university langone[ad]) OR (langone health[ad]) OR (NYU Langone[ad]) OR (nyulmc[ad]) OR (nyumc[ad]) OR (NYU Medical School[ad]) OR (New York University Medical School[ad]) OR (hospital for joint disease[ad]) OR (hospital for joint diseases[ad]) OR (harkness center for dance[ad]))
Once we identified the total number of articles that fit our criteria, our developer (whose other work you can read about here) pulled all 517 results into a spreadsheet for us to review. Together, we worked to identify articles that included viable datasets that could be indexed in the Data Catalog.
The purpose of this exercise was to explore what we could learn about our institution, our researchers, and their data. By examining each data availability statement and investigating the information provided, we were able to categorize data availability statements into four discrete groups:
Data available by emailing the author
Data available by applying to a consortium
Data available through a repository
Data available in supporting information files
This approach also allowed us to locate researchers who had not, or were no longer, complying with publishers’ open data requirements.
This exploration of the “has data available” filter demonstrates the heterogeneity of data practices in biomedical research which in turn demonstrates with the flexibility of the Data Catalog is imperative. By pointing researchers to other resources and not requiring them to upload their data, the Data Catalog can accommodate the wide variety of ways that researchers choose to make their data available.