Announcement | Winners of the Library Innovation Prizes and Carney Institute Brain Science Reproducible Paper Prize

This year the Brown University Library and the Carney Institute for Brain Science partnered to create new awards to recognize student innovations in research rigor, transparency, or reproducibility.

Andrew Creamer, Scientific Data Management Specialist and librarian for Computer Science and Cognitive, Linguistic & Psychological Sciences, and Dr. Jason Ritt, Scientific Director of the Carney Institute, collaborated to update the Library’s Innovation Prize, first awarded in 2015, to reflect Brown’s recent focus on open access and research rigor and transparency and to highlight innovations in student research. The Carney Institute Brain Science Reproducible Paper Prize was created this year to honor innovations in reproducibility as documented by students in their honors theses and/or publications with Brown faculty. 

Library Prizes for Innovations in Research Rigor, Transparency, or Reproducibility

The Library Innovation Prizes were awarded to publications and/or digital projects in three categories based on methods/discipline.

Humanities and Digital Humanities

Sara Mohr

PhD students Sara Mohr (Egyptology & Assyriology) and Shane M. Thompson (Religious Studies) were selected for their digital humanities project, “The Advanced Digitization and Archival Analysis for Preservation and Accessibility (ADAAPA) Project.” Sara and Shane worked with several subject experts, including Bill Monroe, Senior Scholarly Resources Librarian, and Lindsay Elgin, Senior Library Technologist. The team was able to digitally represent the cuneiform objects in the John Hay Library along with the translations of their texts and accompanying archival material elucidating their provenance. View the cuneiform in the Brown Digital Repository.

Shane M. Thompson

Life and Physical Sciences Category

Adam Spierer

Ecology and Evolutionary Biology PhD student Adam Spierer (Rand Lab) was selected for his contributions to the development of the FreeClimber research software and its use in both a forthcoming publication (and chapter of his dissertation), “The Genetic Architecture of Flight and Climbing Performance in Drosophila melanogaster.” This program was designed initially for replacing manual assessment of the climbing performance in Drosophila (fruit flies); however, the program employs several functions that may be of use beyond the initial design in his field. The program can be accessed from the source code available in the Brown Digital Repository and on GitHub.

Behavioral, Public Health, and Social Sciences Category

Joseph Heffner

CLPS PhD student Joseph Heffner (FeldmanHall Lab) was selected for his dissertation project and publication, “Emotional responses to prosocial messages increase willingness to self-isolate during the COVID-19 pandemic.” Joseph made use of the Library’s new Center for Open Science, supporting institutional membership, and utilizing its Open Science Framework (OSF) for pre-registering his and his co-author’s study as well as sharing its OSF preprint platform for sharing their publication.

We would like to thank the panel of volunteer judges:

  • Lydia Curliss, Native American and Indigenous Studies and Physical Sciences Librarian
  • Dr. Oludurotimi Adetunji, Associate Dean of the College for Undergraduate Research and Inclusive Science

Carney Institute inaugural Brain Science Reproducible Paper Prize

Logan Cho ’20

Logan Cho ’20 was selected to receive the Carney Institute inaugural Brain Science Reproducible Paper Prize for his contributions to the project and publication, “Automating Clinical Chart Review: An Open-Source Natural Language Processing Pipeline Developed on Free-Text Radiology Reports From Patients With Glioblastoma.” 

Thank you to the volunteer judges:

  • Dr. David Sheinberg, Professor of Neuroscience
  • PhD student Abdullah Rashed Ahmed (Serre Lab)

The judges commented that “Logan’s application nicely illustrates how open source tools for natural language processing can be used to mine information from clinical reports of patients with glioblastoma. The approach was innovative and, between the publication and the associated Python notebook made available through GitHub, the analysis pipeline was clearly presented.” 

Congratulations to these students for their innovations and for the positive impact they have made on their academic fields’ methodological rigor, transparency, or reproducibility!