ORCID Profile
0000-0002-8876-7606
Current Organisation
The University of Edinburgh
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Publisher: IEEE
Date: 10-2018
Publisher: Pensoft Publishers
Date: 12-10-2022
DOI: 10.3897/RIO.8.E95724
Abstract: In academic research virtually every field has increased its use of digital and computational technology, leading to new scientific discoveries, and this trend is likely to continue. Reliable and efficient scholarly research requires researchers to be able to validate and extend previously generated research results. In the digital era, this implies that digital objectsKahn and Wilensky 2006 used in research should be Findable, Accessible, Interoperable and Reusable (FAIR). These objects include (but are not limited to) data, software, models (for ex le, machine learning), representations of physical objects, virtual research environments, workflows, etc. Leaving any of these digital objects out of the FAIR process may result in a loss of academic rigor and may have severe consequences in the long term for the field, such as a reproducibility crisis. In this extended abstract, we focus on research software as a FAIR digital object (FDO). The FDO framework De Smedt et al. 2020 describes FDOs as being actionable units of knowledge, which can be aggregated, analyzed, and processed by different types of algorithms. Such algorithms must be implemented by software in one form or another. The framework also describes large software stacks supporting FDOs enabling responsible data science and increasing reproducibility. This implies that software is a key ingredient of the FDO framework, and should adhere to the FAIR principles. Software plays multiple roles: it is a DO itself, it is responsible for creating new FDOs (e.g., data) and it helps to make them available to the public (e.g., via repositories and registries). However there is a need to specify in more detail how non-data DOs, in particular software, fit in this framework. Different classes of digital objects have different intrinsic properties and ways to relate to other DOs. This means that while they, in principle, are subject to the high-level FAIR principles, there are also differences depending on their type and properties, requiring an adaptation so FAIR implementations are more aligned to the digital object itself. This holds true in particular to software. Software has intrinsic properties (executability, composite nature, development practices, continuous evolution and versioning, and packaging and distribution) and specific needs that must be considered by the FDO framework. For ex le, open source software is typically developed in the open on social coding platforms, where releases are distributed through package management systems, unlike data that is typically published in archival repositories. These social coding platforms do not provide long term archiving, permanent identifiers, or metadata, and package management systems, while somewhat better, similarly do not make a commitment to long term archiving, do not use identifiers that fit the scholarly publication system well, and provide metadata that may be missing key elements. The FAIR for research software (FAIR4RS, Chue Hong et al. 2021) working group has dedicated significant effort in building a community consensus around developing FAIR principles that are customized for research software, providing methods for researchers to understand and address these gaps. In this presentation we will highlight the importance of software for the FAIR landscape and why different (but related) FAIR principles are needed for software (vs those originally developed for data). Our goal here is to contribute to building an FDO landscape together, where we consider all different types of digital objects that are essential in today's research, and we are enthusiastic about contributing our expertise on research software in helping shape this landscape.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: Center for Open Science
Date: 30-01-2020
Abstract: The Community of Open Scholarship Grassroots Networks (COSGN), includes 120 grassroots networks, representing virtually every region of the world and every research discipline. These networks communicate and coordinate on topics of common interest. We propose, using an NSF 19-501 Full-Scale implementation grant, to formalize governance and coordination of the networks to maximize impact and establish standard practices for sustainability. In the project period, we will increase the capacity of COSGN to advance the research and community goals of the participating networks in idually and collectively, and establish governance, succession planning, shared resources, andcommunication pathways to ensure an active, community-sustained network of networks. By the end of the project period, we will have established a self-sustaining network of networks that leverages disciplinary and regional ersity, actively collaborates across networks for grassroots organizing, and shares resources for maximum impact on culture change for open scholarship.
Publisher: Elsevier BV
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 14-10-2022
DOI: 10.1038/S41597-022-01710-X
Abstract: Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value. The FAIR for Research Software (FAIR4RS) Working Group has adapted the FAIR Guiding Principles to create the FAIR Principles for Research Software (FAIR4RS Principles). The contents and context of the FAIR4RS Principles are summarised here to provide the basis for discussion of their adoption. Ex les of implementation by organisations are provided to share information on how to maximise the value of research outputs, and to encourage others to lify the importance and impact of this work.
Publisher: IOS Press
Date: 12-06-2020
DOI: 10.3233/DS-190026
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Ubiquity Press, Ltd.
Date: 2022
DOI: 10.5334/JORS.384
Publisher: PeerJ
Date: 19-09-2016
DOI: 10.7717/PEERJ-CS.86
Abstract: Software is a critical part of modern research and yet there is little support across the scholarly ecosystem for its acknowledgement and citation. Inspired by the activities of the FORCE11 working group focused on data citation, this document summarizes the recommendations of the FORCE11 Software Citation Working Group and its activities between June 2015 and April 2016. Based on a review of existing community practices, the goal of the working group was to produce a consolidated set of citation principles that may encourage broad adoption of a consistent policy for software citation across disciplines and venues. Our work is presented here as a set of software citation principles, a discussion of the motivations for developing the principles, reviews of existing community practice, and a discussion of the requirements these principles would place upon different stakeholders. Working ex les and possible technical solutions for how these principles can be implemented will be discussed in a separate paper.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Neil Chue Hong.