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NSF Doctoral Recipients Survey 2019 in Machine Readable form

We demonstrate the use of InDO by designing and ingesting NSF 2019 Doctoral Survey Recipients data (henceforth refered to as NSF results). We assume that this information could be beneficial for an aspiring graduate student to find US institutes that have prior experience in training and graduating doctoral students from their marginalized community. Figure. 5 illustrates the general process of converting the semi-structured NSF Tables to machine readable form as our knowledge graph instances. As a proof of concept, we have integrated 538 data points from

  • NSF results Table 1 - US graduate institutes' total number of doctoral recipients for the years 1958 to 2019
  • NSF results Table 3 - Top 50 doctorate granting institutes in the US ranked by total number of doctorate recipients and gender (M/F) demographics
  • a part of NSF results Table 4 - Top 20 doctorate-granting institutes in the US ranked by total number of doctoral recipients by broad field of study and sex (currently includes data point for Life Sciences and, Mathematics and Computer Sciences
with some additional information about the institute's location from the web.

Fig 5. An overview of the process of adding data to a knowledge graph using InDO.

Foley, D.: Survey of doctorate recipients, 2019. nsf 21-230.national center for sci-ence and engineering statistics (ncses), alexandria, va: National science foundation.available at https://ncses.nsf.gov/pubs/nsf21320/ (2021)