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Domain-specific metadata schemas are essential to improve the findability and reusability of research software and to follow the FAIR4RS principles. However, many domains, including energy research, lack established metadata schemas. To…
Leaderboards are crucial in the machine learning (ML) domain for benchmarking and tracking progress. However, creating leaderboards traditionally demands significant manual effort. In recent years, efforts have been made to automate…
Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from…
Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management…
A metadata schema, named Plasma-MDS, is introduced to support research data management in plasma science. Plasma-MDS is suitable to facilitate the publication of research data following the FAIR principles in domain-specific repositories…
Contextual metadata is the unsung hero of research data. When done right, standardized and structured vocabularies make your data findable, shareable, and reusable. When done wrong, they turn a well intended effort into data cleanup and…
In High Energy Physics (HEP), analysis metadata comes in many forms -- from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in…
Accuracy in spreadsheet modelling systems can be reduced due to difficulties with the inputs, the model itself, or the spreadsheet implementation of the model. When the "true" outputs from the system are unknowable, accuracy is evaluated…
Spreadsheet collaboration provides valuable opportunities for learning and expertise sharing between colleagues. Sharing expertise is essential for the retention of important technical skillsets within organisations, but previous studies…
Thanks to their flexibility and capability to perform different tasks and organize data in the best form and format, spreadsheets are widely used in different organizations and by different end users. Many business organizations rely on…
Purpose. The increasing emphasis on data quantity in research infrastructures has highlighted the need for equally robust mechanisms ensuring data quality, particularly in bibliographic and citation datasets. This paper addresses the…
The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics…
High-quality, "rich" metadata are essential for making research data findable, interoperable, and reusable. The Center for Expanded Data Annotation and Retrieval (CEDAR) has long addressed this need by providing tools to design…
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems that directly affect people's lives. Many of the ethical…
Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lacks the essential metadata required for researchers to find and search them effectively. The lack of metadata poses a significant…
This paper presents the results of an empirical evaluation of the quality of a structured methodology for the development of spreadsheet models, proposed in numerous previous papers by Rajalingham K, Knight B and Chadwick D et al. This…
Spreadsheet engineering methodologies are diverse and sometimes contradictory. It is difficult for spreadsheet developers to identify a spreadsheet engineering methodology that is appropriate for their class of spreadsheet, with its unique…
Scientific metadata often suffer from incompleteness, inconsistency, and formatting errors, which hinder effective discovery and reuse of the associated datasets. We present a method that combines GPT-4 with structured metadata templates…
A huge amount of data is everyday managed in large organizations in many critical business sectors with the support of spreadsheet applications. The process of elaborating spreadsheet data is often performed in a distributed, collaborative…
While automated experiments and high-throughput methods are becoming more mainstream in the age of data, empowering individual researchers to capture, collate, and contextualize their data faster and more reproducibly still remains a…