NSF: EAGER: Improving scientific innovation by linking funding and scholarly literature

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Investigator:

• Daniel E. Acuña

Abstract

This project identifies scientists and organizations and their topical interests, enabling the tracking of past productivity and impact. By linking scholarly literature and grants, this project creates a unified dataset that captures diverse scientific disciplines and federal grant award types. A web-based levels the playing field for scientists lacking knowledge about research and funding programs. Users are expected to spend less time searching the literature and more time evaluating significance and impact.

This project consolidates disparate repositories of publications and grants, disambiguates and enriches information about scientists and organizations, and builds a web-based tool to help navigate this information. This project solves many of these issues by modeling the relationship approximately 2.6 million grants from the Federal RePORTER, and a consolidated, multi-source dataset of millions of articles from Microsoft Academic Graph (83 M), MEDLINE (25 M), PubMed Open Access Subset (1 M), ArXiv (0.6 M), and the National Bureau of Economic Research [NBER] (14K). The project creates a web-based tool that generates instantaneous reports about publications, grants, scientists, and organizations related to users’ interests. The unified dataset and web tool could revolutionize how Program Officers evaluate proposals and how researchers find fundable ideas, making science faster, more accurate, and less biased.

Articles (7)

• Achakulvisut, T, Acuna, DE, Bassett. DS, Kording, KP, Unique subfields of neuroscience exhibit more diverse language Link
• Líenard, JF, Achakulvisut, T, Acuna, DE, David, SV, Intellectual Synthesis in Mentorship Determines Success in Academic Careers Link
• Harandi, M, Acuna, DE, Differences in productivity patterns for junior and senior NSF grantees
• Teplitskiy, M, Acuna, DE, Elamrani-Raoult, A, Körding, K, Evans, J The Social Structure of Consensus in Scientific Review Link
• Acuna, DE, Brooks, P, Kording, P (2018) Bioscience-scale automated detection of figure element reuse (2018) BioArXiv, Link
• Shema, A, Acuna, DE (2017) Show Me Your App Usage and I Will Tell Who Your Close Friends Are: Predicting User’s Context from Simple Cellphone Activity, CHI 2017, Pages 2929-2935, Denver, Colorado Link
• Achakulvisut T, Acuna DE, Ruangrong T, Kording K (2016) Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications. PLoS ONE 11(7): e0158423. doi:10.1371/journal.pone.0158423 Link

Web service and software

• eileen.io
  • Data ingestion pipeline
  • Front end (soon)
  • Back end (soon)