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Abstract This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data.When providing scientific computing support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data‐driven projects are provided.Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on‐demand specialized support to be the most impactful combination in helping scientists develop their technical skills.Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long‐lasting effects on the way researchers conduct science. We hope that other groups supporting team‐ and data‐driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error.
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Lessons Learned from a Comparative Study of Long-Term Action Research with Community Design of Infrastructural Systems
The question of how to develop and maintain appropriate, socially informed and sophisticated infrastructural systems is an ongoing concern for CSCW. Information infrastructure development efforts are usually large endeavors that involve many stakeholders, including several organizations that need to interoperate with legacy systems. Projects typically take several years to develop. The duration, variety, and sites of engagement in the development of information infrastructures can be challenging to approach with typical CSCW approaches. In this paper, we compare and analyze our varied experiences in order to generate lessons learned based on being embedded for three or more years as action researchers and ethnographers in infrastructure development projects in the domains of traffic engineering, vocational education, and ocean science. Drawing upon these experiences, as well as literature in infrastructure studies, design methodologies, and organizational studies, we extract guidance for researchers and practitioners seeking to understand and engage in long-term organizationally complex system development projects. Among these lessons, we encourage revisiting previously gathered data as scope and scale change, observing changes in the discursive reference public who will benefit from the system, and planning for different intellectual points of entry and exit. This paper lays groundwork for future developments in theory and method of collaborative design and development in and with complex systems.
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- Award ID(s):
- 1954620
- PAR ID:
- 10602370
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 7
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Format(s):
- Medium: X Size: p. 1-35
- Size(s):
- p. 1-35
- Sponsoring Org:
- National Science Foundation
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