Poster Session

Start Date

6-4-2016 12:30 PM

Description

This poster presents the results of a case study completed as part of the Simmons College course Scientific Research Data Management. The case study focuses on research conducted by a computer science laboratory on factors that influence patient mortality, using topic modeling and other computational techniques. Based on the research narrative, data management practices and needs specific to computer science research are mapped to the New England Collaborative Data Management Curriculum Modules for Managing Research Data. These areas of focus will help information professionals to identify the data management challenges presented by computer science research, as well as the tools and techniques recommended to create an effective data management plan. Overall, this case study demonstrates the importance of valuing and managing source code as data, in order to ensure reproducibility of results and open access to data.

Keywords

research data management, computer science, case study, data management plan, healthcare data

Creative Commons License

Creative Commons License
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Apr 6th, 12:30 PM

Modeling Mortality Data: A Case Study in Data Management for Computer Science Research

This poster presents the results of a case study completed as part of the Simmons College course Scientific Research Data Management. The case study focuses on research conducted by a computer science laboratory on factors that influence patient mortality, using topic modeling and other computational techniques. Based on the research narrative, data management practices and needs specific to computer science research are mapped to the New England Collaborative Data Management Curriculum Modules for Managing Research Data. These areas of focus will help information professionals to identify the data management challenges presented by computer science research, as well as the tools and techniques recommended to create an effective data management plan. Overall, this case study demonstrates the importance of valuing and managing source code as data, in order to ensure reproducibility of results and open access to data.

 

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