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

McHenry, Kenton; Reinking, Caleb; Haines, Robert

doi:10.1109/se4science.2019.00009

Cited by 19 publications

(11 citation statements)

References 4 publications

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“…• having an organizational unit to handle the emerging challenges of research software development and sustainability (Katz, DS et al, 2019, Geiger et al, 2018;…”

Section: Assumptionsmentioning

confidence: 99%

“…Promotion pathways and sustainability for data scientists and research software engineers (RSEs) has been a topic of discussion for years (reviewed in Cohen et al, 2020), yet there are disappointingly few examples of universities who have made real strides in this area until very recently (Katz DS et al, 2019b). Re-visit the descriptions of current payroll titles and/or create new titles for data scientists that recognize and elevate the knowledge they have to contribute to campus.…”

Section: Stakeholders -Especially Donors -Need Storiesmentioning

confidence: 99%

“…Policies and metrics of success in higher education need to change so that open science, software and data citations become recognized as equal partners to publications on CVs, and team members are recognized for their contributions, not their position on the author list (Katz DS et al 2019b). Your champions can help by steering the metrics on hiring/tenure/promotion committees, but changes will happen faster if these ideas are echoed from above.…”

Section: Stakeholders -Especially Donors -Need Storiesmentioning

confidence: 99%

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Ten Simple Rules for Starting (and Sustaining) an Academic Data Science Initiative

Parker¹,

Burgess²,

Bourne³

2020

Preprint

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The past decade has seen an explosion of data science centers, institutes, and programs appearing across the U.S. as universities increasingly recognize the importance and promise of data science to university research and education. It has been, and continues to be, an exciting time. But there are systemic challenges faced by these initiatives in the context of the higher education system. Some, but not all, of these challenges center around funding. Campuses fortunate enough to receive initial funding, often as a result of philanthropy or private sector investment, have some measure of sustainability, especially if these funds are in the form of an endowment. However, at most smaller colleges and universities, or those without a lucrative alumnus or local industry investor, just getting started with very limited funding can be daunting. And yet, every school is facing the reality that to truly prepare their student body for the expectations of 21st century employers, they must find a way to incorporate core critical thinking and data-intensive skills into nearly every discipline. This call to action challenges traditional disciplinary silos and begs for new models of higher education. As data science initiatives attempt to breakdown old barriers, they each face a unique set of challenges due to their campus’ political, financial, and structural environments. Our consideration of these challenges and those we have faced directly ourselves lead us to recognize a set of global commonalities. We capture them here in the familiar Ten Simple Rules (TSR) format for simplicity, recognizing much more could be said.

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“…• having an organizational unit to handle the emerging challenges of research software development and sustainability (Katz, DS et al, 2019, Geiger et al, 2018;…”

Section: Assumptionsmentioning

confidence: 99%

Section: Stakeholders -Especially Donors -Need Storiesmentioning

confidence: 99%

Section: Stakeholders -Especially Donors -Need Storiesmentioning

confidence: 99%

See 1 more Smart Citation

Ten Simple Rules for Starting (and Sustaining) an Academic Data Science Initiative

Parker¹,

Burgess²,

Bourne³

2020

Preprint

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“…At the same time, the developments in ImageJ2 and Fiji represented an opportunity to improve BoneJ's engineering standard and user experience, as well as opening avenues for new algorithm development and implementation. ImageJ2's new Ops framework and dependency management with Maven meant that libraries and plugins that had been copy-pasted into BoneJ_.jar, such as Analyze Skeleton 10 , could be removed and used from a single location rather than duplicated, eliminating the overhead involved in maintaining copies of active projects. Maven combined with the ImageJ updater enabled updating resources such as the linear algebra library JAMA (last updated in 2012) to JOML (under active development) and to use Eclipse Collections' fast IntHashSet implementation in place of the standard Java HashSet<Integer>.…”

Section: Introductionmentioning

confidence: 99%

BoneJ2 - refactoring established research software

2021

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Research software is often developed with expedience as a core development objective because experimental results, but not the software, are specified and resourced as a project output. While such code can help find answers to specific research questions, it may lack longevity and flexibility to make it reusable. We reimplemented BoneJ, our software for skeletal biology image analysis, to address design limitations that put it at risk of becoming unusable. We improved the quality of BoneJ code by following contemporary best programming practices. These include separation of concerns, dependency management, thorough testing, continuous integration and deployment, source code management, code reviews, issue and task ticketing, and user and developer documentation. The resulting BoneJ2 represents a generational shift in development technology and integrates with the ImageJ2 plugin ecosystem.

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“…This was especially so because as university faculty MD no longer had the long stretches of time needed to concentrate on software engineering, nor were software outputs or community support part of his employment performance criteria. In the UK, an awareness of Research Software Engineers (RSE) as valuable team members was growing 11 . Here, we describe the steps taken to modernise the BoneJ project, and use BoneJ to provide a perspective on the role software engineering takes in the contemporary research ecosystem.…”

Section: Introductionmentioning

confidence: 99%