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J. P. Tennant et al., “A tale of two ’opens’: intersections between Free and Open Source Software and Open Scholarship,” 2020, doi: 10.34657/5085. https://hdl.handle.net/10495/29610

Abstract: There is no clear-cut boundary between Free and Open Source Software and Open Scholarship, and the histories, practices, and fundamental principles between the two remain complex. In this study, we critically appraise the intersections and differences between the two movements. Based on our thematic comparison here, we conclude several key things. First, there is substantial scope for new communities of practice to form within scholarly communities that place sharing and collaboration/open participation at their focus. Second, Both the principles and practices of FOSS can be more deeply ingrained within scholarship, asserting a balance between pragmatism and social ideology. Third, at the present, Open Scholarship risks being subverted and compromised by commercial players. Fourth, the shift and acceleration towards a system of Open Scholarship will be greatly enhanced by a concurrent shift in recognising a broader range of practices and outputs beyond traditional peer review and research articles. In order to achieve this, we propose the formulation of a new type of institutional mandate. We believe that there is substantial need for research funders to invest in sustainable open scholarly infrastructure, and the communities that support them, to avoid the capture and enclosure of key research services that would prevent optimal researcher behaviours. Such a shift could ultimately lead to a healthier scientific culture, and a system where competition is replaced by collaboration, resources (including time and people) are shared and acknowledged more efficiently, and the research becomes inherently more rigorous, verified, and reproducible.

Amy X. Zhang, Michael Muller, and Dakuo Wang. 2020. How do Data Science Workers Collaborate? Roles, Workflows, and Tools. Proc. ACM Hum.-Comput. Interact. 4, CSCW1, Article 22 (May 2020), 23 pages. https://doi.org/10.1145/3392826

Abstract: Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.

L. Ramakrishnan and D. Gunter, "Ten Principles for Creating Usable Software for Science," 2017 IEEE 13th International Conference on e-Science (e-Science), Auckland, New Zealand, 2017, pp. 210-218, doi: 10.1109/eScience.2017.34.https://ieeexplore.ieee.org/abstract/document/8109139

Abstract: The volume and variety of scientific data being generated at experimental facilities requires the seamless interaction of the scientist's knowledge with the large-scale machines and software that is required to process the data. In the last few years, scientific software tools are being developed to address these increasingly complex workflow and data management needs. However, current approaches for designing systems and tools focus on the hardware and software of the machine and do not consider the user. Our experience shows us that user experience research needs to be tightly integrated with the software development life cycle for building sustainable software for science. It has become not just necessary, but critical, to consider the user interaction in the design of the entire system for data-intensive sciences that have complex human interaction with the data, software and systems. The dynamic nature of science projects and the complex roles of personnel in the projects makes it difficult to apply classical user research methodologies from industry. In this paper, we make three specific contributions towards improving the usability and sustainability of scientific software. First, we examine the software life cycle in science environments and identify the differences with commercial software development. Next, we outline ten principles we have developed to guide user engagement and software development and illustrate it with examples from our projects over the last several years. Finally, we provide guidelines to other eScience projects on applying the ten principles in the software development life cycle.

Joppa LN, McInerny G, Harper R, Salido L, Takeda K, O'Hara K, Gavaghan D, Emmott S. Computational science. Troubling trends in scientific software use. Science. 2013 May 17;340(6134):814-5. doi: 10.1126/science.1231535. PMID: 23687031. https://pubmed.ncbi.nlm.nih.gov/23687031/ e https://www.researchgate.net/publication/236921394_Troubling_Trends_in_Scientific_Software_Use

Abstract: Software pervades every domain of science (1–3), perhaps nowhere more decisively than in modeling. In key scientific areas of great societal importance, models and the software that implement them define both how science is done and what science is done (4, 5). Across all science, this dependence has led to concerns around the need for open access to software (6, 7), centered on the reproducibility of research (1, 8–10). From fields such as high-performance computing, we learn key insights and best practices for how to develop, standardize, and implement software (11). Open and systematic approaches to the development of software are essential for all sciences. But for many scientists this is not sufficient. We describe problems with the adoption and use of scientific software.

James Howison and James D. Herbsleb. 2011. Scientific software production: incentives and collaboration. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (CSCW '11). Association for Computing Machinery, New York, NY, USA, 513–522. https://doi.org/10.1145/1958824.1958904

Abstract: Software plays an increasingly critical role in science, including data analysis, simulations, and managing workflows. Unlike other technologies supporting science, software can be copied and distributed at essentially no cost, potentially opening the door to unprecedented levels of sharing and collaborative innovation. Yet we do not have a clear picture of how software development for science fits into the day-to-day practice of science, or how well the methods and incentives of its production facilitate realization of this potential. We report the results of a multiple-case study of software development in three fields: high energy physics, structural biology, and microbiology. In each case, we identify a typical publication, and use qualitative methods to explore the production of the software used in the science represented by the publication. We identify several different production systems, characterized primarily by differences in incentive structures. We identify ways in which incentives are matched and mismatched with the needs of the science fields, especially with respect to collaboration.

Macaulay, Catriona & Sloan, David & Jiang, Xinyi & Forbes, Paula & Loynton, Scott & Swedlow, Jason & Gregor, Peter. (2009). Usability and User-Centered Design in Scientific Software Development. IEEE Software. 26. 96-102. 10.1109/MS.2009.27. https://www.researchgate.net/publication/232627737_Usability_and_User-Centered_Design_in_Scientific_Software_Development

Abstract: Usability is a growing issue for developers of scientific software. Scientists seeking software to support scientific discovery and funding bodies seeking better return on investment increase the pressure to produce scientific software that has an impact beyond a limited set of users (that is, scientists in a single lab). However, developing software for even a limited set of users is challenging, and commercial design techniques are rarely available for scientific software development in academic settings. Lessons learned from a case study in which developers integrated usability and user-centered design (UCD) methods into an image-data-management software suite for cell biologists might be useful to others working in similar contexts.

J.M. Carroll, Five reasons for scenario-based design, Interacting with Computers, Volume 13, Issue 1, 2000, Pages 43-60, https://doi.org/10.1016/S0953-5438(00)00023-0

Abstract: Scenarios of human–computer interaction help us to understand and to create computer systems and applications as artifacts of human activity—as things to learn from, as tools to use in one's work, as media for interacting with other people. Scenario-based design of information technology addresses five technical challenges: scenarios evoke reflection in the content of design work, helping developers coordinate design action and reflection. Scenarios are at once concrete and flexible, helping developers manage the fluidity of design situations. Scenarios afford multiple views of an interaction, diverse kinds and amounts of detailing, helping developers manage the many consequences entailed by any given design move. Scenarios can also be abstracted and categorized, helping designers to recognize, capture and reuse generalizations and to address the challenge that technical knowledge often lags the needs of technical design. Finally, scenarios promote work-oriented communication among stakeholders, helping to make design activities more accessible to the great variety of expertise that can contribute to design, and addressing the challenge that external constraints designers and clients face often distract attention from the needs and concerns of the people who will use the technology.