Challenges in large-scale bioinformatics projects

Sarah Morrison-Smith, Christina Boucher, Aleksandra Sarcevic, Noelle Noyes, Catherine O’Brien, Nazaret Cuadros, Jaime Ruiz

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number125
JournalHumanities and Social Sciences Communications
Volume9
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This work is partially supported by National Science Foundation Grant Award #IIS-2013998. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect these agencies’ views. The authors would like to thank Isaac Wang, Courtney Sanchez, Megan Hofmann, Julia Chang, Ariel Goldman, Aditi Patil, Dipashreya Sur, Luiza Leschziner, and Christopher Dean for assisting in the data analysis and related works.

Publisher Copyright:
© 2022, The Author(s).

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