Towards FAIR principles for research software

Lamprecht, Anna‐Lena; García, Leyla; Kuzak, Mateusz; Martínez, Carlos A.; Arcila, Ricardo; Pico, Eva Martin del; Angel, Victoria Domínguez Del; Sandt, Stephanie van de; Ison, Jon C.; Martinez, Paula Andrea; McQuilton, Peter; Valencia, Alfonso; Harrow, Jennifer; Psomopoulos, Fotis; Gelpi, Josep L. L.; Hong, Neil Chue; Goble, Carole; Capella-Gutiérrez, Salvador

doi:10.3233/ds-190026

Cited by 234 publications

(233 citation statements)

References 42 publications

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“…Their focus is on re-building the research software for repeatability and on sharing contracts, not FAIR and open publishing. Lamprecht et al [29] propose that research software and its associated metadata should be included in a searchable software registry. Research software observatories could serve this purpose.…”

Section: Observatories For Fair and Open Research Softwarementioning

confidence: 99%

From FAIR research data toward FAIR and open research software

Hasselbring

Carr

Hettrick

et al. 2020

It - Information Technology

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The Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research data must be FAIR (Findable, Accessible, Interoperable, and Reusable) in order to advance the findability, reproducibility and reuse of research results. Besides the research data, all the processing steps on these data – as basis of scientific publications – have to be available, too.For good scientific practice, the resulting research software should be both open and adhere to the FAIR principles to allow full repeatability, reproducibility, and reuse. As compared to research data, research software should be both archived for reproducibility and actively maintained for reusability.The FAIR data principles do not require openness, but research software should be open source software. Established open source software licenses provide sufficient licensing options, such that it should be the rare exception to keep research software closed.We review and analyze the current state in this area in order to give recommendations for making research software FAIR and open.

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Section: Observatories For Fair and Open Research Softwarementioning

confidence: 99%

From FAIR research data toward FAIR and open research software

Hasselbring

Carr

Hettrick

et al. 2020

It - Information Technology

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“…When it is shut down, they are permanently deleted processing is critical. There is currently growing support for metadata definitions in FAIR standards [66]. Integration of metadata tracking system into DolphinNext in FAIR standards will increase data portability and enable a simpler interface for users to integrate and compare across diverse datasets.…”

Section: Current Limitations and Future Plansmentioning

confidence: 99%

DolphinNext: a distributed data processing platform for high throughput genomics

et al. 2020

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Background: The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. Results: To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as highperformance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with Rmarkdown and shiny support for interactive data visualization and analysis. Conclusion: DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.

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“…Materials such as hands-on exercises may include related software and data, which should also be provided in interoperable formats, following the FAIR principles [7,45]. An overview of the main advantages and disadvantages of the most common training material formats is provided in Table 1.…”

Section: Rule 6: Use An Interoperable Format For Your Training Materialsmentioning

confidence: 99%

Ten simple rules for making training materials FAIR

et al. 2020

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Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it's sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They're often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all.

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Towards FAIR principles for research software

Abstract: This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).

Cited by 234 publications

References 42 publications

From FAIR research data toward FAIR and open research software

From FAIR research data toward FAIR and open research software

DolphinNext: a distributed data processing platform for high throughput genomics

Ten simple rules for making training materials FAIR

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