An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action

Anzt, Hartwig; Bach, Felix; Druskat, Stephan; Löffler, Frank; Loewe, Axel; Renard, Bernhard Y.; Seemann, Gunnar; Struck, Alexander; Achhammer, Elke; Aggarwal, Piush; Appel, Franziska; Bäder, Michael; Brusch, Lutz; Busse, Christian E.; Chourdakis, Gerasimos; Dabrowski, Piotr Wojtek; Ebert, Peter; Flemisch, Bernd; Friedl, Sven; Fritzsch, Bernd; Funk, Maximilian D.; Gast, Volker; Goth, Florian; Grad, Jean-Noël; Schmitz, Hermann; Hohmann, Florian; Janosch, Stephan; Kutra, Dominik; Linxweiler, Jan; Muth, Thilo; Peters-Kottig, Wolfgang; Rack, Fabian; Raters, F. H. C.; Rave, Stephan; Reina, Guido; Reißig, Malte; Ropinski, Timo; Schaarschmidt, Joerg; Seibold, Heidi; Thiele, Jan Philipp; Uekerman, Benjamin; Unger, Stefan; Weeber, Rudolf

doi:10.12688/f1000research.23224.1

Cited by 24 publications

(27 citation statements)

References 62 publications

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“…Despite their significance, funding for the sustainable development and maintenance of core tools for research is scarce. 237 Resources for the continued improvement of software, such as the Chan Zuckerberg Initiative's “Essential Open Source Software for Science” program ( https://chanzuckerberg.com/rfa/essential-open-source-software-for-science/ ) or NumFOCUS ( https://numfocus.org ) may be poised to improve the reproducibility infrastructure more than proof-of-concept tools.…”

Section: Discussionmentioning

confidence: 99%

The role of metadata in reproducible computational research

et al. 2021

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Summary Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

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Section: Discussionmentioning

confidence: 99%

The role of metadata in reproducible computational research

et al. 2021

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“…One person can have multiple roles, the role can be shared by multiple people, and the role of a person can change. Maintainers are responsible for the infrastructure and maintenance of the software, they can give the rules how research software should be written, but are often not part of the active feature development -their problems are discussed within the research software engineers (RSE) movement 16 . Users want to use the software without writing code.…”

Section: Methodsmentioning

confidence: 99%

Documenting Research Software in Engineering Science

Hermann

Fehr

2022

Preprint

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The reuse of research software needs good documentation, however, the documentation in particular is often criticized. Especially in non-IT specific disciplines, the lack of documentation is attributed to the lack of training, the lack of time or missing rewards. This article addresses the hypothesis that scientists do document but do not know exactly what they need to document, why, and for whom. In order to evaluate the actual documentation practice of research software, we examined existing recommendations, and we evaluated their implementation in everyday practice using a concrete example from the engineering sciences and compared the findings with best practice examples. In order to get a broad overview of what documentation of research software entailed, we defined categories and used them to conduct the research. Our results show that the big picture of what documentation of research software means is missing. Recommendations do not consider the important role of developers whose documentation takes mainly place in their research articles. Moreover, we show that research software always has a history that influences the documentation.

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“…Research on CEP resulting in discovery has been traditionally regarded as being of higher academic merit than research on building the enabling CEP modeling methodology, which requires a similar amount of human resources. Thus, modeling has been significantly less well funded, which has opened a gap between the often highly ambitious scientific goals and the available technical capabilities [23].…”

Section: Historical Cep Software Developmentmentioning

confidence: 99%

TheopenCARPSimulation Environment for Cardiac Electrophysiology

Plank

Loewe

Neic

et al. 2021

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Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential.Research in cardiac electrophysiology increasingly relies on computational modeling and simulation. A survey of large parts of the community revealed a current lack of reproducibility, time investment (productivity) and functionality originating from the fact that no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could address these needs. openCARP and the Python-based carputils framework allow to develop and share simulation pipelines that automate in silico experiments, including all modeling and simulation steps. Documentation and training material facilitate access and decrease training time for new users. The openCARP user community is supported by tailored infrastructure, interactive support and is aiming for future expansion. This paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, simple tissue, organ and body level and finally outline future development potential.

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An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action

Cited by 24 publications

References 62 publications

The role of metadata in reproducible computational research

The role of metadata in reproducible computational research

Documenting Research Software in Engineering Science

TheopenCARPSimulation Environment for Cardiac Electrophysiology

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