Identifying the challenges of code/theory translation: report from the Code/Theory 2017 workshop

Jay, Caroline; Haines, Robert; Vigo, Markel; Matentzoglu, Nicolas; Stevens, Robert A.; Boyle, Jonathan; Davies, Annette; Vescovo, Chiara Del; Gruel, N.; Blanc, Anja Le; Mawdsley, David; Mellor, D.; Mikroyannidi, Eleni; Rollins, R. P.; Rowley, Andrew; Vega, J. Lozano de la

doi:10.3897/rio.3.e13236

Cited by 4 publications

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“…In the Results section, we present the themes that emerged from the analysis, and map these to potential research questions. In the Discussion section, we compare our findings to those of other work in this area, in particular the earlier Code/Theory workshop that took place in the UK 3 . We conclude by summarising the case for theory-software translation research, and proposing future activities that will lead towards establishing it as a new and fruitful domain.…”

Section: Introductionmentioning

confidence: 79%

“…Supporting evidence for the identified themes comes from the Code/Theory workshop organised by authors CJ and RH in the UK, which examined the challenges faced by practising research software engineers and data scientists 3 . The primary themes that emerged at the UK event related to designing software, sustaining software, and communication issues between domain scientists and software engineers, all of which map to our themes of research software design , infrastructure , and culture .…”

Section: Discussionmentioning

confidence: 99%

“…During the workshop, it was agreed verbally that these would be analysed and written up in a report, which all participants would be invited to author. This model of collective data ownership and knowledge production had previously been used successfully in the Code/Theory Workshop 3 .…”

Section: Methodsmentioning

confidence: 99%

“…Compounding the difficulties of formally translating between theory and software are the many cultural and organisational factors that add further challenges to the process of building research software and using its results to advance science. These include the bespoke and highly dynamic nature of research software, the funding model, the academic hierarchy and career structure (in particular the difference in status accorded to domain and software specialists), the difficulties communicating in large scientific teams, and the pressures exerted by the current publication model 3 .…”

Section: Introductionmentioning

confidence: 99%

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The challenges of theory-software translation

et al. 2020

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Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.

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Section: Introductionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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“…RSEs and data scientists do not work in a vacuum; rather they are key to common research activities, such as hypothesis generation, study design, data analysis, and interpretation of results [17]. There are a number of efforts and communities addressing these issues [18]; however, there is still effort needed to make these aspects well understood and accepted by the scientific community at large.…”

Section: Pushing To Change the Scientific Culturementioning

confidence: 99%

Research Software Development & Management in Universities: Case Studies from Manchester's RSDS Group, Illinois' NCSA, and Notre Dame's CRC

McHenry

Reinking

Haines

2019

2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science)

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Modern research in the sciences, engineering, humanities, and other fields depends on software, and specifically, research software. Much of this research software is developed in universities, by faculty, postdocs, students, and staff. In this paper, we focus on the role of university staff. We examine three different, independently-developed models under which these staff are organized and perform their work, and comparatively analyze these models and their consequences on the staff and on the software, considering how the different models support software engineering practices and processes. This information can be used by software engineering researchers to understand the practices of such organizations and by universities who want to set up similar organizations and to better produce and maintain research software.

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What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure

Griego,

Agate,

Iosif

et al. 2024

Stat

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Clinical and academic research continues to become more complex as our knowledge and technology advance. A substantial and growing number of specialists in biostatistics, data science and library sciences are needed to support these research systems and promote high‐calibre research. However, that support is often marginalized as optional rather than a fundamental component of research infrastructure. By building research infrastructure, an institution harnesses access to tools and support/service centres that host skilled experts who approach research with best practices in mind and domain‐specific knowledge at hand. We outline the potential roles of data scientists and statisticians in research infrastructure and recommend guidelines for advocating for the institutional resources needed to support these roles in a sustainable and efficient manner for the long‐term success of the institution. We provide these guidelines in terms of resource efficiency, monetary efficiency and long‐term sustainability. We hope this work contributes to—and provides shared language for—a conversation on a broader framework beyond metrics that can be used to advocate for needed resources.

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Identifying the challenges of code/theory translation: report from the Code/Theory 2017 workshop

Cited by 4 publications

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The challenges of theory-software translation

The challenges of theory-software translation

Research Software Development & Management in Universities: Case Studies from Manchester's RSDS Group, Illinois' NCSA, and Notre Dame's CRC

What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure

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