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FAIR Principles are a set of high-level guidelines for sharing digital resources. The growing global adoption of the FAIR Principles by policymakers, funders, and organizations compels data professionals, projects, and repositories to demonstrate the level of FAIR-compliance (referred to as FAIRness) of their digital data, metadata, and infrastructures. Because the FAIR Principles offer general objectives rather than specific implementation instructions, discrepancies exist due to different interpretations, domain-specific requirements, and intended applications. These discrepancies hinder direct comparisons and integration of assessment outcomes. To address this issue, we propose a novel framework, including a consolidated FAIR vocabulary. This framework establishes quality measures upfront in FAIRness assessment workflows to surpass the intricacies arising from the aforementioned dependencies. The established quality measures encapsulate the distinctive core concepts inherent in individual FAIR principles and can serve as common, fundamental pillars of holistic FAIRness assessment workflows. Building upon this fundamental set of the quality measures, we introduce a FAIRness quality maturity matrix (FAIR-QMM) as a structured, tiered, and progressive approach for evaluating and reporting the degree of FAIR-compliance. The FAIR-QMM can be used as a FAIRness assessment tool independently and/or as a translator between other FAIRness assessment tools or models.

Datasets carry cultural and political context at all parts of the data life cycle. Historically, Earth science data repositories have taken their guidance and policies as a combination of mandates from their funding agencies and the needs of their user communities, typically universities, agencies, and researchers. Consequently, repository practices have rarely taken into consideration the needs of other communities such as the Indigenous Peoples on whose lands data are often acquired. In recent years, a number of global efforts have worked to improve the conduct of research as well as data policy and practices by the repositories that hold and disseminate it. One of these established the CARE Principles for Indigenous Data Governance (Carroll et al. 2020), representing ‘Collective Benefit’, ‘Authority to Control’, ‘Responsibility’, and ‘Ethics”’ hosted by the Global Indigenous Data Alliance (GIDA 2023a). In order to align to the CARE Principles, repositories may need to update their policies, architecture, service offerings, and their collaboration models. The question is how? Operationalizing principles into active repositories is generally a fraught process. This paper captures perspectives and recommendations from many of the repositories that are members of the Earth Science Information Partners (ESIPFed, n.d.) in conjunction with members of the Collaboratory for Indigenous Data Governance (Collaboratory for Indigenous Data Governance n.d.) and GIDA, defines and prioritizes the set of activities Earth and Environmental repositories can take to better adhere to CARE Principles in the hopes that this will help implementation in repositories globally.

Minimally, a research data repository exists to make a collection of data assets available to potential users. If a dataset cannot be discovered and found, it cannot be reused (Garnett et al. 2017). Harvestable metadata catalogues are a key strategy for achieving greater global findability of data assets, as they create a surveyable access point to discover data products within large data collections. Such catalogues can be especially effective if they are tailored for interoperability with feature-rich infrastructures (e.g. meta-catalogues, see Kapiszewski & Karcher 2020; CRFCB 2014) that are highly visible and widely used, and also themselves integrated within the larger ecosystem of research infrastructures. This study offers insight into a set of World Data System (WDS) research data repositories ongoing and successful implementations of harvestable metadata services, which apply established and emerging research data standards and practices to fit global, local and domain-specific interoperability contexts. Establishing a harvestable metadata service involves making choices in a space where standards and technologies are continuously evolving. The repositories in this study leverage the resources they have, within the policy and funding constraints of their institution, to serve the changing needs of heterogeneous user groups. This document encapsulates and completes the work that was carried out by the WDS International Technology Office (ITO) Harvestable Metadata Services Working Group (HMetS-WG).

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the 'quality' of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.

Information about data quality helps potential data users to determine whether and how data can be used and enables the analysis and interpretation of such data. Providing data quality information improves opportunities for data reuse by increasing the trustworthiness of the data. Recognizing the need for improving the quality of citizen science data, we describe quality assessment and quality control (QA/QC) issues for these data and offer perspectives on aspects of improving or ensuring citizen science data quality and for conducting research on related issues.

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, we analysed 39 use cases and current practices of data versioning across 33 organisations. We noticed that the term ‘version’ was used in a very general sense, extending beyond the more common understanding of ‘version’ to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, we developed six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation. These six principles provide a high-level framework for guiding the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.

This paper initially conducts a literature review and content analysis of the open research data policies in China. Next, a series of exemplars describe data practices to promote and enable the use of open research data, including open data practices in research programs, data repositories, data journals, and citizen science. Moreover, the top four driving forces are identified and analyzed along with their responsible guiding work. In addition, the “landscape of open research data ecology in China” is derived from the literature review and from observations of actual cases, where the interaction and mutual development of data policies, data programs, and data practices are recognized. Finally, future trends of research data practices within China and internationally are discussed. We hope the analysis provides perspective on current open data practices in China along with insight into the need for additional research on scientific data sharing and management.

Investments in research that produce scientific and scholarly data can be leveraged by enabling the resulting research data products and services to be used by broader communities and for new purposes, extending reuse beyond the initial users and purposes for which the data were originally collected. Submitting research data to a data repository offers opportunities for the data to be used in the future, providing ways for new benefits to be realized from data reuse. Improvements to data repositories that facilitate new uses of data increase the potential for data reuse and for gains in the value of open data products and services that are associated with such reuse. Assessing and certifying the capabilities and services offered by data repositories provides opportunities for improving the repositories and for realizing the value to be attained from new uses of data. The evolution of data repository certification instruments is described and discussed in terms of the implications for the curation and continuing use of research data.

Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use. Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks. The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship. The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities.

Despite growing recognition of the importance of public data to the modern economy and to scientific progress, long-term investment in the repositories that manage and disseminate scientific data in easily accessible-ways remains elusive. Repositories are asked to demonstrate that there is a net value of their data and services to justify continued funding or attract new funding sources. Here, representatives from a number of environmental and Earth science repositories evaluate approaches for assessing the costs and benefits of publishing scientific data in their repositories, identifying various metrics that repositories typically use to report on the impact and value of their data products and services, plus additional metrics that would be useful but are not typically measured. We rated each metric by (a) the difficulty of implementation by our specific repositories and (b) its importance for value determination. As managers of environmental data repositories, we find that some of the most easily obtainable data-use metrics (such as data downloads and page views) may be less indicative of value than metrics that relate to discoverability and broader use. Other intangible but equally important metrics (e.g., laws or regulations impacted, lives saved, new proposals generated), will require considerable additional research to describe and develop, plus resources to implement at scale. As value can only be determined from the point of view of a stakeholder, it is likely that multiple sets of metrics will be needed, tailored to specific stakeholder needs. Moreover, economically based analyses or the use of specialists in the field are expensive and can happen only as resources permit.

Science software has contributed to research practices, but the sustainability of scientific software presents challenges for the future use of research resources. Identifying improvements for science software sustainability practices can contribute to the re-use of science software. A focus group study was conducted to identify ways to improve science software sustainability practices of the Earth science community. A facilitated, roundtable discussion activity at the 2014 Federation of Earth Science Information Partners (ESIP) Summer Meeting elicited recommendations on community activities to improve practices for the sustainability of scientific software. These suggestions fell into three broad themes – (1) improving collaboration and community engagement through publications and presentations (2) developing workshops, training, and documenting best practices and (3) creating incentives and motivation with awards, citation and a reviewed software repository. In addition to the recommendations coming out of the roundtable activity, this paper highlights how community-led groups such as ESIP are key to move a sustainable software effort in its various forms from concept to reality.

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.