Mercè Crosas

We present scalable-htr, a user-friendly open-source Python library for end-to-end segmentation and transcription of historical manuscripts. Built upon Kraken but extensible to other engines, the tool provides a framework for reproducible experimentation and efficient, large-scale inference on high-performance computing (HPC) environments. The tool facilitates systematic benchmarking across datasets and models by computing quantitative end-to-end metrics, and it generates interactive visualizations that allow users to qualitatively assess model adaptation to their target domains by inspecting segmentation and transcription results directly on the images. By combining systematic evaluation with scalable transcription, scalable-htr has been designed to assist researchers and cultural heritage professionals convert digitized manuscript collections into machine-readable data. The tool is demonstrated via a case study on medieval notarial manuscripts.

High‐quality research data are essential for reproducibility, trust, and innovation in the chemical sciences. While the FAIR principles have improved the findability and accessibility of data, intrinsic data quality, such as accuracy, completeness, and consistency, remains insufficiently addressed. This editorial calls for coordinated community action to strengthen chemical data infrastructures and to treat research data as a first‐class scientific output. Building on successful models and through collaboration under organizations including CODATA and IUPAC, the chemical sciences community can advance interoperable standards and robust quality assessment to ensure that open, validated data drive future discovery and innovation.

High‐quality research data are essential for reproducibility, trust, and innovation in the chemical sciences. While the FAIR principles have improved the findability and accessibility of data, intrinsic data quality, such as accuracy, completeness, and consistency, remains insufficiently addressed. This editorial calls for coordinated community action to strengthen chemical data infrastructures and to treat research data as a first‐class scientific output. Building on successful models and through collaboration under organizations including CODATA and IUPAC, the chemical sciences community can advance interoperable standards and robust quality assessment to ensure that open, validated data drive future discovery and innovation.

La reproducibilidad es un principio fundamental para la ciencia, ya que garantiza la validez y fiabilidad de los resultados. Sin esta capacidad de verificación, la investi-gación pierde su credibilidad y su impacto en la sociedad se ve comprometido (Ioannidis, 2005). En los últimos años, la comunidad científica internacional ha se-ñalado la necesidad de adoptar prácticas más rigurosas y transparentes para afron-tar la conocida “crisis de reproducibilidad” (Baker, 2016; Open Science Collaboration, 2015). Ante este contexto, la Red Española de Reproducibilidad (SPRN) surge como una respuesta estratégica para fortalecer la reproducibilidad en España. El mejor ca-mino ha sido alinearse con iniciativas internacionales, como las Reproducibility Net-works (RNs), que buscan mejorar las prácticas científicas mediante la colaboración interdisciplinar y el desarrollo de políticas conjuntas. El objetivo principal de la SPRN es promover la reproducibilidad y la ciencia abierta en el Sistema Español de Ciencia, Tecnología e Innovación (SECTI), contribuyendo al avance científico y reforzando la confianza de la sociedad en la investigación.

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema.org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying “just enough” Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article ^{1 1} https://w3id.org/ro/doi/10.5281/zenodo.5146227 is archived at https://doi.org/10.5281/zenodo.5146227 .

A presentation from the European Dataverse Workshop 2020.

A presentation from the European Dataverse Workshop 2020.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators – community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests – small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine “sees” when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.

Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators - community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests - small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine “sees” when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.