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Register a new userTowards FAIR Principles for Open Hardware
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Added by
Nadica Miljkovi\\'c
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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The lack of scientific openness is identified as one of the key challenges of computational reproducibility. In addition to Open Data, Free and Open-source Software (FOSS) and Open Hardware (OH) can address this challenge by introducing open policies, standards, and recommendations. However, while both FOSS and OH are free to use, study, modify, and redistribute, there are significant differences in sharing and reusing these artifacts. FOSS is increasingly supported with software repositories, but support for OH is lacking, potentially due to the complexity of its digital format and licensing. This paper proposes leveraging FAIR principles to make OH findable, accessible, interoperable, and reusable. We define what FAIR means for OH, how it differs from FOSS, and present examples of unique demands. Also, we evaluate dissemination platforms currently used for OH and provide recommendations.
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Recent advances in the area of legal information systems have led to a variety of applications that promise support in processing and accessing legal documents. Unfortunately, these applications have various limitations, e.g., regarding scope or extensibility. Furthermore, we do not observe a trend towards open access in digital libraries in the legal domain as we observe in other domains, e.g., economics of computer science. To improve open access in the legal domain, we present our approach for an open source platform to transparently process and access Legal Open Data. This enables the sustainable development of legal applications by offering a single technology stack. Moreover, the approach facilitates the development and deployment of new technologies. As proof of concept, we implemented six technologies and generated metadata for more than 250,000 German laws and court decisions. Thus, we can provide users of our platform not only access to legal documents, but also the contained information.
There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.
The main contributors of scientific knowledge, researchers, generally aim to disseminate their findings far and wide. And yet, publishing companies have largely kept these findings behind a paywall. With digital publication technology markedly reducing cost, this enduring wall seems disproportionate and unjustified; moreover, it has sparked a topical exchange concerning how to modernize academic publishing. This discussion, however, seems to focus on how to compensate major publishers for providing open access through a pay to publish model, in turn transferring financial burdens from libraries to authors and their funders. Large publishing companies, including Elsevier, Springer Nature, Wiley, PLoS, and Frontiers, continue to earn exorbitant revenues each year, hundreds of millions of dollars of which now come from processing charges for open-access articles. A less expensive and equally accessible alternative exists: widespread self-archiving of peer-reviewed articles. All we need is awareness of this alternative and the will to employ it
The Institutional Repositories (IR) have been consolidated into the institutions in scientific and academic areas, as shown by the directories existing open access repositories and the deposits daily of articles made by different ways, such as by self-archiving of registered users and the cataloging by librarians. IR systems are based on various conceptual models, so in this paper a bibliographic survey Model-Driven Development (MDD) in systems and applications for RI in order to expose the benefits of applying MDD in IR. The MDD is a paradigm for building software that assigns a central role models and active under which derive models ranging from the most abstract to the concrete, this is done through successive transformations. This paradigm provides a framework that allows interested parties to share their views and directly manipulate representations of the entities of this domain. Therefore, the benefits are grouped by actors that are present, namely, developers, business owners and domain experts. In conclusion, these benefits help make more formal software implementations, resulting in a consolidation of such systems, where the main beneficiaries are the end users through the services are offered
Recent reproducibility case studies have raised concerns showing that much of the deposited research has not been reproducible. One of their conclusions was that the way data repositories store research data and code cannot fully facilitate reproducibility due to the absence of a runtime environment needed for the code execution. New specialized reproducibility tools provide cloud-based computational environments for code encapsulation, thus enabling research portability and reproducibility. However, they do not often enable research discoverability, standardized data citation, or long-term archival like data repositories do. This paper addresses the shortcomings of data repositories and reproducibility tools and how they could be overcome to improve the current lack of computational reproducibility in published and archived research outputs.
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