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[Purpose/Significance] The FAIR principles, as a guideline for research data management, has been widely recognized and actively applied in the field of scientific research since it was proposed. However, at present, the FAIR principles are mainly implemented by scientific data management institutions, academic groups of different disciplines, and international cooperation projects. As enablers of open science, libraries are obligated to leverage their extensive expertise and long-standing digital resource management strengths to advance the implementation of FAIR principles in scientific data management. [Method/Process]According to The Times Higher Education World University Rankings 2020, the top 100 university library websites were surveyed to find how the FAIR principle service has been implemented by 29 university libraries. The service content is divided into three categories: publicity and introduction of FAIR principles, education and training of FAIR principles, and support and promotion of the impl

[目的/意义]FAIR原则作为研究数据管理指南自提出后很快得到科学研究领域的广泛认可和积极应用，图书馆作为开放科学的推动者，在推进FAIR原则实施过程中扮演着重要角色。[方法/过程]通过网络调查和文献调研，对国内外知名高校图书馆推进FAIR原则实施的情况进行调查、归纳和分析，总结图书馆参与FAIR原则实施的有效途径，加快科学数据重用和科学数据共享的步伐。[结果/结论]调查结果反映了国内外高校图书馆FAIR推广力度不足，缺少技术开发和实践，对外合作有待加强，在调研结果和借鉴国内外先进经验的基础上，中国图书馆应该加强宣传推广，推动FAIR原则的广泛认可；开发FAIR技术与服务，助推FAIR原则的实施；推动多方合作，塑造FAIR生态系统。

Metadata, data about other digital objects, play an important role in FAIR with a direct relation to all FAIR principles. In this paper we present and discuss the FAIR Data Point (FDP), a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles. We present the core components and features of the FDP, its approach to metadata provision, the criteria to evaluate whether an application adheres to the FDP specifications and the service to register, index and allow users to search for metadata content of available FDPs.

The web tool Adamant has been developed to systematically collect research metadata as early as the conception of the experiment. Adamant enables a continuous, consistent, and transparent research data management (RDM) process, which is a key element of good scientific practice ensuring the path to Findable, Accessible, Interoperable, Reusable (FAIR) research data. It simplifies the creation of on-demand metadata schemas and the collection of metadata according to established or new standards. The approach is based on JavaScript Object Notation (JSON) schema, where any valid schema can be presented as an interactive web-form. Furthermore, Adamant eases the integration of numerous available RDM methods and software tools into the everyday research activities of especially small independent laboratories. A programming interface allows programmatic integration with other software tools such as electronic lab books or repositories. The user interface (UI) of Adamant is designed to be as user friendly as possible. Each UI element is self-explanatory and intuitive to use, which makes it accessible for users that have little to no experience with JSON format and programming in general. Several examples of research data management workflows that can be implemented using Adamant are introduced. Adamant (client-only version) is available from: https://plasma-mds.github.io/adamant.

It is now a decade since The International Commission on the Taxonomy of Fungi (ICTF) produced an overview of requirements and best practices for describing a new fungal species. In the meantime the International Code of Nomenclature for algae, fungi, and plants (ICNafp) has changed from its former name (the International Code of Botanical Nomenclature ) and introduced new formal requirements for valid publication of species scientific names, including the separation of provisions specific to Fungi and organisms treated as fungi in a new Chapter F. Equally transformative have been changes in the data collection, data dissemination, and analytical tools available to mycologists. This paper provides an updated and expanded discussion of current publication requirements along with best practices for the description of new fungal species and publication of new names and for improving accessibility of their associated metadata that have developed over the last 10 years. Additionally, we provide: (1) model papers for different fungal groups and circumstances; (2) a checklist to simplify meeting ( i ) the requirements of the ICNafp to ensure the effective, valid and legitimate publication of names of new taxa, and ( ii ) minimally accepted standards for description; and, (3) templates for preparing standardized species descriptions.

This article provides the motivation and overview of the Collective Knowledge Framework (CK or cKnowledge). The CK concept is to decompose research projects into reusable components that encapsulate research artifacts and provide unified application programming interfaces (APIs), command-line interfaces (CLIs), meta descriptions and common automation actions for related artifacts. The CK framework is used to organize and manage research projects as a database of such components. Inspired by the USB ‘plug and play’ approach for hardware, CK also helps to assemble portable workflows that can automatically plug in compatible components from different users and vendors (models, datasets, frameworks, compilers, tools). Such workflows can build and run algorithms on different platforms and environments in a unified way using the customizable CK program pipeline with software detection plugins and the automatic installation of missing packages. This article presents a number of industrial projects in which the modular CK approach was successfully validated in order to automate benchmarking, auto-tuning and co-design of efficient software and hardware for machine learning and artificial intelligence in terms of speed, accuracy, energy, size and various costs. The CK framework also helped to automate the artifact evaluation process at several computer science conferences as well as to make it easier to reproduce, compare and reuse research techniques from published papers, deploy them in production, and automatically adapt them to continuously changing datasets, models and systems. The long-term goal is to accelerate innovation by connecting researchers and practitioners to share and reuse all their knowledge, best practices, artifacts, workflows and experimental results in a common, portable and reproducible format at cKnowledge.io. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico’.

The acronym FAIR stands for the attributes of ,,Findability\", Accessibility\", ,,Interoperability\" and ,Reusability\" and was introduced in a 2016 paper [1]. It is now widely recognized that these principles play a crucial role in enhancing the reproducibility and transparency of models and datasets, which are continuously being developed by interdisciplinary research teams. In this artificial intelligence driven era, compliance to the FAIR principles ensures that models and training data remain accessible, interoperable and reusable for further research. To encourage the adoption of these principles, we propose a set of semi-quantifiable measures for assessing the level of FAIRness. This is accomplished by using a set of FAIRness metrics for each principle and calculating a combined FAIRness score (1-5) for every model and dataset employed. Further on, a minimum threshold for each individual score is introduced in order to ensure proper adoption and usability. The importance of utilizing these principles within the medical community is particularly critical due to the complexity, multidimensionality and sparsity of medical datasets which are prone to misinterpretation. Thus, by following these guidelines, one can reduce the risk of incorrect predictions and enhance the overall patient experience.

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either to reuse solutions from existing implementations, or when they spot a gap, accept the to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.

Biological collections house millions of specimens with digital images increasingly available through open‐access platforms. However, most imaging protocols were developed for human interpretation without considering automated analysis requirements. As computer vision applications revolutionize taxonomic identification and trait extraction, a critical gap exists between current digitization practices and computational analysis needs. This review provides the first comprehensive practical framework for optimizing biological specimen imaging for computer vision applications. Through interdisciplinary collaboration between taxonomists, collection managers, ecologists and computer scientists, we synthesized evidence‐based recommendations addressing fundamental computer vision concepts and practical imaging considerations. We provide immediately actionable implementation guidance while identifying critical areas requiring community standards development. Our framework encompasses 10 interconnected considerations for optimizing image capture for computer vision‐powered taxonomic identification and trait extraction. We translate these into practical implementation checklists, equipment selection guidelines and a roadmap for community standards development, including filename conventions, pixel density requirements and cross‐institutional protocols. By bridging biological and computational disciplines, this approach unlocks automated analysis potential for millions of existing specimens and guides future digitization efforts towards unprecedented analytical capabilities.

Traits have become a crucial part of ecological and evolutionary sciences, helping researchers understand the function of an organism's morphology, physiology, growth and life history, with effects on fitness, behaviour, interactions with the environment and ecosystem processes. However, measuring, compiling and analysing trait data comes with data‐scientific challenges. We offer 10 (mostly) simple rules, with some detailed extensions, as a guide in making critical decisions that consider the entire life cycle of trait data. This article is particularly motivated by its last rule, that is, to propagate good practice. It has the intention of bringing awareness of how data on the traits of organisms can be collected and managed for reuse by the research community. Trait observations are relevant to a broad interdisciplinary community of field biologists, synthesis ecologists, evolutionary biologists, computer scientists and database managers. We hope these basic guidelines can be useful as a starter for active communication in disseminating such integrative knowledge and in how to make trait data future‐proof. We invite the scientific community to participate in this effort at http://opentraits.org/best‐practices.html.