Report from URSSI workshop on research software incubators
Nic Weber • March 28, 2019
At the first URSSI community workshop, a small group of participants started to discuss a model for incubating research software projects. Incubation in this context might include a structured program that helps developers plan a new community-based software project, or improve existing projects that need mentorship, strategy, or other resources in order to sustainably grow.
To further explore this topic we brought together 16 experienced funders, community managers, developers, researchers, and software users. We discussed differences between research software incubators and technology start-up accelerators - identifying important questions about the period of time for incubation, the goals for participants and funders, and potential benefits to a research community. We then heard presentations from 6 different existing programs that have an interest in software development, project management, and incubation. Using these examples we engaged in a design activity to make some initial decisions about an incubator program that URSSI could organize in the future.
Report from URSSI workshop on software credit, citation, and metrics
Karthik Ram • March 24, 2019
Summary:
One of the biggest obstacles to making research software sustainable is ensuring appropriate credit and recognition for researchers who develop and maintain such software. We convened 16 experts over two days to identify core issues around software credit and propose concrete steps that a software institute might take to solve them. We identified six core issues directly related to credit (career paths, individual impact, disincentives in the academic credit model, quality versus impact, recognition of software value, lack of funding) and two broader challenges (lack of funding for maintenance and lack of awareness of best practices). Using a strategy development exercise we brainstormed these topics in small groups and came up with lists of short and long term actions for an institute to tackle.
Software Incubator Workshop: A Synthesis
Sebastian P. Benthall • February 25, 2019
Thanks for comments from: Dan Katz, Suresh Marru, Abby Cabunoc Mayes, Micaela S. Parker, Danielle Robinson, and Nic Weber.
Research software is software created by scholarly researchers. Often researchers produce source code that is ‘open’, in alignment with scientific norms. But this code and the research it supports is not always “sustainable”, because it does not have a viable community of developers and users working with it in an ongoing way. For open source software in general, there are several detailed, existing taxonomies of successful open source community models, including the Apache Project Maturity Model and Mozilla’s Open Source Archetypes. Are there specific patterns of open community challenges and growth for research software that an incubator could address?
The Molecular Sciences Software Institute: The Time is Ripe for Change
T. Daniel Crawford and Daniel G. A. Smith • February 6, 2019
We are witnessing the early stages of a revolution in the computational molecular sciences. Numerous community codes in quantum chemistry, biomolecular simulation, and computational materials science are beginning to adopt modern, collaborative software engineering practices and tools, to the benefit of the broader field.
Over their long history, the computational molecular sciences have emerged as an essential partner with experiment in elucidating the structures and mechanisms that control chemical processes, and, in fact, often precede experiment in the knowledge-based design of new systems. The central tools of the field are its software products: hundreds of community-built scientific programs that have evolved over decades. In aggregate, these programs are comprised of tens of millions of lines of code written by thousands of developers and are used by hundreds of thousands of molecular scientists worldwide. This software is the computational incarnation of the many impressive molecular dynamics and quantum chemical models that have achieved such a level of robustness and accuracy that they are often considered “computational experiments” – in many cases with greater reliability than even laboratory measurements.
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