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Abstract Biological and biomedical research is increasingly conducted in large, interdisciplinary collaborations to address problems with significant societal impact, such as reducing antibiotic resistance, identifying disease sub-types, and identifying genes that control for drought tolerance in plants. Many of these projects are data driven and involve the collection and analysis of biological data at a large-scale. As a result, life-science projects, which are frequently diverse, large and geographically dispersed, have created unique challenges for collaboration and training. We examine the communication and collaboration challenges in multidisciplinary research through an interview study with 20 life-science researchers. Our results show that both the inclusion of multiple disciplines and differences in work culture influence collaboration in life science. Using these results, we discuss opportunities and implications for designing solutions to better support collaborative tasks and workflows of life scientists. In particular, we show that life science research is increasingly conducted in large, multi-institutional collaborations, and these large groups rely on “mutual respect” and collaboration. However, we found that the interdisciplinary nature of these projects cause technical language barriers and differences in methodology affect trust. We use these findings to guide our recommendations for technology to support life science. We also present recommendations for life science research training programs and note the necessity for incorporating training in project management, multiple language, and discipline culture.
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The Hitchhiker's Guide to Data in the History of Science
Every project in digital and computational history of science starts with the collection of data. Depending on the research project, subject of study, and other factors, data can be comprised of a variety of different types such as full-texts, images, audio, video, or bibliographic metadata. Often publications and project reports describe a project’s results and the employed methods and algorithms, but few publications discuss the challenges of the initial data collection process or how it fits into the overall research data life cycle. In this paper, we discuss a concrete research data life cycle and take a look at the difficulties it involves. Furthermore, we explore the strategies and challenges of data collection, and the question of comparability of datasets.
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- Award ID(s):
- 1734236
- PAR ID:
- 10093334
- Date Published:
- Journal Name:
- Isis
- Volume:
- 110
- Issue:
- 3
- ISSN:
- 0021-1753
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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