Related papers: The Software Observatory: aggregating and analysin…
This paper extends the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines to provide criteria for assessing if software conforms to best practices in open source. By adding 'USE' (User-Centered, Sustainable, Equitable),…
Scientists strive to make their datasets available in open repositories, with the goal that they be findable, accessible, interoperable, and reusable (FAIR). Although it is hard for most investigators to remember all the guiding principles…
According to the FAIR (findability, accessibility, interoperability, and reusability) principles, scientific data should always be stored with machine-readable descriptive metadata. Existing solutions to store data with metadata, such as…
Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging.…
Modern workflows run on increasingly heterogeneous computing architectures and with this heterogeneity comes additional complexity. We aim to apply the FAIR principles for research reproducibility by developing software to collect metadata…
Software testing ensures that a system functions correctly, meets specified requirements, and maintains high quality. As artificial intelligence and machine learning (ML) technologies become integral to software systems, testing has evolved…
Open science movement has established reproducibility, transparency, and validation of research outputs as essential norms for conducting scientific research. It advocates for open access to research outputs, especially research data, to…
FAIR principles have the intent to act as a guideline for those wishing to enhance the reusability of their data holdings and put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to…
The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of modern and Open Science, particularly…
The broad sharing of research data is widely viewed as of critical importance for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data, and the…
The Hybrid Technology Hub and many other research centers work in cross-functional teams whose workflow is not necessarily linear and where in many cases technology advances are done through parallel work. The lack of proper tools and…
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…
The long-term sustainability of research software is a critical challenge, as it usually suffers from poor maintainability, lack of adaptability, and eventual obsolescence. This paper proposes a novel approach to addressing this issue by…
With the advent of open source software, a veritable treasure trove of previously proprietary software development data was made available. This opened the field of empirical software engineering research to anyone in academia. Data that is…
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The…
Biobanks are indispensable resources for medical research collecting biological material and associated data and making them available for research projects and medical studies. For that, the biobank data has to meet certain criteria which…
Life sciences research increasingly requires identifying, accessing, and effectively processing data from an ever-evolving array of information sources on the Linked Open Data (LOD) network. This dynamic landscape places a significant…
Software is now a vital scientific instrument, providing the tools for data collection and analysis across disciplines from bioinformatics and computational physics, to the humanities. The software used in research is often home-grown and…
Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in software systems, researchers have been developing techniques…
Scientific research needs a system that better values rigorous, reusable contributions. Although open knowledge and FAIR (findable, accessible, interoperable, and reusable) principles, along with coalitions and infrastructures, are…