The known challenge of underutilization of data and biological material from biorepositories as potential resources for medical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability-entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)-are likely to address only parts of the problem. In this article, we argue that biological material and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks. We propose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.

• Klíčová slova
• FAIR (Findable, Accessible, Interoperable, and Reusable) principles, incentives, open science, privacy protection, provenance information management, quality,
• MeSH
• banky biologického materiálu * organizace a řízení   normy   MeSH
• databáze faktografické normy   MeSH
• důvěrnost informací normy   MeSH
• lidé MeSH
• šíření informací metody   MeSH
• směrnice jako téma MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Food integrity is a general term for sound, nutritive, healthy, tasty, safe, authentic, traceable, as well as ethically, safely, environment-friendly, and sustainably produced foods. In order to verify these properties, analytical methods with a higher degree of accuracy, sensitivity, standardization and harmonization and a harmonized system for their application in analytical laboratories are required. In this view, metrology offers the opportunity to achieve these goals. In this perspective article the current global challenges in food analysis and the principles of metrology to fill these gaps are presented. Therefore, the pan-European project METROFOOD-RI within the framework of the European Strategy Forum on Research Infrastructures (ESFRI) was developed to establish a strategy to allow reliable and comparable analytical measurements in foods along the whole process line starting from primary producers until consumers and to make all data findable, accessible, interoperable, and re-usable according to the FAIR data principles. The initiative currently consists of 48 partners from 18 European Countries and concluded its "Early Phase" as research infrastructure by organizing its future structure and presenting a proof of concept by preparing, distributing and comprehensively analyzing three candidate Reference Materials (rice grain, rice flour, and oyster tissue) and establishing a system how to compile, process, and store the generated data and how to exchange, compare them and make them accessible in data bases.

• Klíčová slova
• Horizon 2020, METROFOOD-RI, food authenticity, food fraud, food safety, metrological traceability, reference materials, research infrastructures,
• Publikační typ
• časopisecké články MeSH

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

• Klíčová slova
• FAIR principles, INCF, INCF endorsement process, Neuroinformatics, Neuroscience, Standards and best practices, Standards organization,
• MeSH
• neurovědy * MeSH
• reprodukovatelnost výsledků MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: The EURO-NMD Registry collects data from all neuromuscular patients seen at EURO-NMD's expert centres. In-kind contributions from three patient organisations have ensured that the registry is patient-centred, meaningful, and impactful. The consenting process covers other uses, such as research, cohort finding and trial readiness. RESULTS: The registry has three-layered datasets, with European Commission-mandated data elements (EU-CDEs), a set of cross-neuromuscular data elements (NMD-CDEs) and a dataset of disease-specific data elements that function modularly (DS-DEs). The registry captures clinical, neuromuscular imaging, neuromuscular histopathology, biological and genetic data and patient-reported outcomes in a computer-interpretable format using selected ontologies and classifications. The EURO-NMD registry is connected to the EURO-NMD Registry Hub through an interoperability layer. The Hub provides an entry point to other neuromuscular registries that follow the FAIR data stewardship principles and enable GDPR-compliant information exchange. Four national or disease-specific patient registries are interoperable with the EURO-NMD Registry, allowing for federated analysis across these different resources. CONCLUSIONS: Collectively, the Registry Hub brings together data that are currently siloed and fragmented to improve healthcare and advance research for neuromuscular diseases.

• Klíčová slova
• FAIR data, Neuromuscular Diseases, Rare Diseases, Registry, Registry Hub,
• MeSH
• lidé MeSH
• neuromuskulární nemoci * genetika   MeSH
• registrace MeSH
• vzácné nemoci MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Improving reproducibility and replicability in preclinical research is a widely discussed and pertinent topic, especially regarding ethical responsibility in animal research. INFRAFRONTIER, the European Research Infrastructure for the generation, phenotyping, archiving, and distribution of model mammalian genomes, is addressing this issue by developing internal quality principles for its different service areas, that provides a quality framework for its operational activities. This article introduces the INFRAFRONTIER Quality Principles in Systemic Phenotyping of genetically altered mouse models. A total of 11 key principles are included, ranging from general requirements for compliance with guidelines on animal testing, to the need for well-trained personnel and more specific standards such as the exchange of reference lines. Recently established requirements such as the provision of FAIR (Findable, Accessible, Interoperable, Reusable) data are also addressed. For each quality principle, we have outlined the specific context, requirements, further recommendations, and key references.

• MeSH
• genom * MeSH
• modely nemocí na zvířatech MeSH
• myši MeSH
• reprodukovatelnost výsledků MeSH
• savci * MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR's vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

• Klíčová slova
• Common best practices, Data management, Data management plans, Data management training, Data stewardship, FAIR principles, Research data life cycle, community standards,
• MeSH
• data management * metody   MeSH
• lidé MeSH
• výzkum MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

• Klíčová slova
• Education, FAIR Principles, Open Access, Open Data, Open Science, Quality Management, Standardisation,
• MeSH
• biologické vědy * MeSH
• důvěra * MeSH
• kvalita života MeSH
• metadata MeSH
• mezinárodní spolupráce MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Low volatility of ionic liquids (ILs), being one of their most valuable properties, is also the principal factor making reliable measurements of vapor pressures and vaporization (or sublimation) enthalpies of ILs extremely difficult. Alternatively, vaporization enthalpies at the temperature of the triple point can be obtained from the enthalpies of sublimation and fusion. While the latter can be obtained calorimetrically with a fair accuracy, the former is in principle accessible through ab initio computations. This work assesses the performance of the first-principles calculations of sublimation properties of ILs. Namely, 3 compounds, coupling the 1-ethyl-3-methylimidazolium cation [emIm] with either tetrafluoroborate [BF4], hexafluorophosphate [PF6], or bis(trifluoromethylsulfonyl)imide [NTf2] anions were selected for a case study. A computational methodology, originally developed for molecular crystals, is adopted for crystals of ILs. It exploits periodic density functional theory (DFT) calculations of the unit-cell geometries and quasi-harmonic phonons and many-body expansion schemes for ab initio refinements of the lattice energies of crystalline ILs. The vapor phase is treated as the ideal gas whose properties are obtained combining the rigid rotor-harmonic oscillator model with corrections from the one-dimensional hindered rotors and molecular-dynamics simulations capturing the contributions from the interionic interaction modes. Although the given computational approach enables one to reach the chemical accuracy (4 kJ mol-1) of calculated sublimation enthalpies of simple molecular crystals, reaching the same level of accuracy for ionic liquids proves challenging as crystals of ionic liquids are bound appreciably stronger than common molecular crystals, the underlying cohesive energies of solid ionic liquids is up to 1 order of magnitude larger. Still, combination of the mentioned computational and experimental frameworks results in a novel promising scheme that is expected to generate reliable and accurate temperature-dependent data on sublimation (and vaporization) of ILs.

• Publikační typ
• časopisecké články MeSH

Electronic Health Record (EHR) systems currently in use are not designed for widely interoperable longitudinal health data. Therefore, EHR data cannot be properly shared, managed and analyzed. In this article, we propose two approaches to making EHR data more comprehensive and FAIR (Findable, Accessible, Interoperable, and Reusable) and thus more useful for diagnosis and clinical research. Firstly, the data modeling based on the LinkML framework makes the data interoperability more realistic in diverse environments with various experts involved. We show the first results of how diverse health data can be integrated based on an easy-to-understand data model and without loss of available clinical knowledge. Secondly, decentralizing EHRs contributes to the higher availability of comprehensive and consistent EHR data. We propose a technology stack for decentralized EHRs and the reasons behind this proposal. Moreover, the two proposed approaches empower patients because their EHR data can become more available, understandable, and usable for them, and they can share their data according to their needs and preferences. Finally, we explore how the users of the proposed solution could be involved in the process of its validation and adoption.

• Klíčová slova
• Distributed electronic health records, FAIR principles, HL7 FHIR, bio-data management, ontology,
• MeSH
• data management MeSH
• elektronické zdravotní záznamy * MeSH
• lidé MeSH
• sémantický web * MeSH
• software MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

• Klíčová slova
• FAIR research data, data integration tools, drug discovery, drug sensitivity assays,
• Publikační typ
• časopisecké články MeSH