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Using Bibliometrics To Make Sense Of Research Proposals
Christina K. Pikas, BS, MLS, PhD
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Pikas using bibliometrics to make sense of research proposals

Poster for Bibliometrics and research assessment: a symposium for librarians and information professionals, NIH, October 31, 2016

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Pikas using bibliometrics to make sense of research proposals

  1. 1. Using Bibliometrics To Make Sense Of Research Proposals Christina K. Pikas, BS, MLS, PhD Christina.Pikas@jhuapl.edu ABSTRACT Grantmaking organizations use a number of different methods to make sense of, evaluate, and select proposals for further funding. A technical team reviews each proposal to determine if it is responsive to the call, if it proposes novel work that is likely to be successful, if the team is likely to be able to accomplish what they have proposed, and if the resource needs are reasonable. The funder may want to know what approaches or schools of thought are represented in the proposals. One way to show this is to extract the citations in the bibliography and perform a bibliographic analysis. This poster will describe approaches to extracting the bibliography and suggested analyses. BACKGROUND Funders may issue calls or solicitations that describe a general problem to be solved or phenomenon to be studied without providing guidance or requirements for how performers will address it. Submitters describe their proposed approach and provide evidence that they are likely to be successful in narrative text with citations to relevant literature. Individual proposals may cite anywhere from five to 200 sources. Proposals are often delivered in PDF format. We wanted to know if there were commonalities of approaches that could be ascertained from mapping the citations. OBJECTIVES • Develop a method to reliably extract citations from text • Compile a bibliography of all articles cited • Identify approaches • Group similar proposers METHODS Extracting Text • Used Adobe Acrobat Pro* to save as Word • Manually pasted bibliography sections to text editor Extracting Citations • Used Excel to reduce citations to an AuthorYear identifier for each citation • Used UCInet to build network Network Analysis • R (igraph) for community detection • NetDraw for visualization and analysis Inspection of the graph provided insights about different general approaches • Analysis revealed several highly central clusters related to key bodies of work for the research problem • Some proposals were outliers because they tapped into novel areas of the literature • No useful communities were evident using the various community detection techniques • The schools of thought identified by subject matter experts were cited by most proposals, even by some in order to contrast with chosen approach RESULTS CONCLUSIONS • Although not completely successful, the approach is promising and provided valuable insights • Using a database API to retrieve a parsed citation may be more effective than parsing from text. OCR Extract Text Identify Citations Graph Analyze RESULTS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Note: Product names are provided for reference. No endorsement implied UNSUCCESSFUL METHODS Extracting Text • Programmatically – too much variation in content Parsing Citations • ParsCit • FreeCite • ParaCite / ParaTools • AnyStyle.io Citation x Proposer Network Sized by betweenness centrality. Citations are blue boxes. Proposer x Proposer Network Sized by degree centrality.