Sci-Score, a tool to support rigor and transparency guidelines

Scicrunch, Inc., San Diego CA

Investigators

Linked publications, trials & patents

Abstract

Project Summary While  standards  in  reporting  of  scientific  methods  are  absolutely  critical  to  producing  reproducible  science,  meeting  such  standards  is  difficult.  Checklists  and  instructions  are  tough  to  follow  often  resulting  in  low  and inconsistent  compliance.  Scientific  journals  and  societies  as  well  as  the  National  Institutes  of  Health  are  now actively proposing general guidelines to address reproducibility issues, particularly in the reporting of methods  (e.g.,  http://www.cell.com/star-­methods),  but  the  trickier  part  will  be  to  train  the  biomedical  community  to  use these standards to effectively improve how scientific methods are communicated. To support new standards in methods reporting, specifically the RRID standard for Rigor and Transparency of  Key Biological Resources, we propose to build Sci-­Score a text mining based tool suite to help authors meet the  standard.  Sci-­Score  will  provide  an  automated  check  on  compliance  with  the  RRID  standard  already  implemented by over 100 journals including Cell, Journal of Neuroscience, and eLife. The innovation behind Sci-­ score is the provision of a score, which can be obtained by individual investigators, which reflects a numerical  validation of the quality of their methods reporting. We posit that the score will serve as a tool that investigators  and journals can use to compete with themselves and each other, or in the very least allow them to see how  close they are to the average in meeting quality requirements.   Recently, our group has developed a text mining algorithm that has now been successfully been used to detect software tools and databases from the SciCrunch Registry in published papers. Digital tools are one of four resource types that the RRID standard identifies. We propose to extend this approach to the other types of entities: antibodies, cell lines and model organisms. Resource identification along with other quality metrics twill be used to train an algorithm to score the overall quality of the methods document. If successful, the tool could be used by editors, reviewers, and investigators to improve the number of RRIDs, therefore the quality of descriptors of key biological resources in published papers. This SBIR project will build a set of algorithms similar to the resource finding pipeline and develop it into an industrial robust and reconfigurable software system. Our Phase I specific aims include to 1) creating gold sets of data for each resource type and training a set of algorithms for each resource type; 2) designing and evaluating the scoring system; 3) designing and evaluating a report generating system based on the previous aims. In Phase II, we will develop a scalable backend infrastructure to serve the needs of scientific publishers and research community.