RDA Resourceshttps://repository.oceanbestpractices.org/handle/11329/2722024-03-29T09:33:02Z2024-03-29T09:33:02ZPrinciples and best practices in data versioning for all data sets big and small. Version 1.1.Klump, JensWyborn, LesleyWu, MingfangDowns, RobertAsmi, AriRyder, GerryMartin, Juliahttps://repository.oceanbestpractices.org/handle/11329/21572023-03-16T21:00:34Z2020-01-01T00:00:00ZPrinciples and best practices in data versioning for all data sets big and small. Version 1.1.
Klump, Jens; Wyborn, Lesley; Wu, Mingfang; Downs, Robert; Asmi, Ari; Ryder, Gerry; Martin, Julia
The demand for better reproducibility of research results is growing. More and more data is becoming available online. In some cases, the datasets have become so large that downloading the data is no longer feasible. Data can also be offered through web services and accessed on demand. This means that parts of the data are accessed at a remote source when needed. In this scenario, it will become increasingly important for a researcher to be able to cite the exact extract of the data set that was used to underpin their research publication. However, while 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.
Versioning procedures and best practices are well established for scientific software. The related Wikipedia article gives an overview of software versioning practices. The codebase of large software projects does bear some semblance to large dynamic datasets. Are therefore versioning practices for code also suitable for data sets or do we need a separate suite of practices for data versioning? How can we apply our knowledge of versioning code to improve data versioning practices? This Working Group investigated to which extent these practices can be used to enhance the reproducibility of scientific results.
The Research Data Alliance (RDA) Data Versioning Working Group produced this white paper to document use cases and practices, and to make recommendations for the versioning of research data. To further adoption of the outcomes, the Working Group contributed selected use cases and recommended data versioning practices to other groups in RDA and W3C. The outcomes of the RDA Data Versioning Working Group add a central element to the systematic management of research data at any scale by providing recommendations for standard practices in the versioning of research data. These practice guidelines are illustrated by a collection of use cases.
2020-01-01T00:00:00ZFAIR Data Maturity Model Specification and Guidelines. [Proposed recommendation; version for public review]Herczog, EditRussell, KeithStall, ShelleyJones, Sarahhttps://repository.oceanbestpractices.org/handle/11329/21562023-03-16T20:31:47Z2020-01-01T00:00:00ZFAIR Data Maturity Model Specification and Guidelines. [Proposed recommendation; version for public review]
Herczog, Edit; Russell, Keith; Stall, Shelley; Jones, Sarah
The FAIR Data Maturity Model defines a set of indicators, their priorities and evaluation methods for the evaluation of the FAIR principles to be used as a common approach across assessment methodologies. This document specifies the indicators for the FAIR assessment designed for re-use in evaluation approaches and provides guidelines for their use. The guidelines are intended to assist evaluators to implement the indicators in the evaluation approach or tool they manage.
The exact way to evaluate data based on the core criteria is up to the owners of the evaluation approaches, taking into account the requirements of their community. The objective here is then to make sure that the indicators, the maturity levels and the prioritisation are understood in the same way. The maturity model is not meant as a “how to”, but instead as a way to normalise assessment. Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access, interoperate and re-use research data and metadata. The FAIR principles have to be considered as inspiring concepts but not strict rules. Unfortunately, they often lead to diverse interpretations and ambiguity.
To remedy the proliferation of FAIRness measurements based on different interpretations of the principles, the RDA Working Group “FAIR data maturity model” established in January 2019 aims to develop a common set of core assessment criteria for FAIRness, as an RDA Recommendation. In the course of 2019 and the first half of 2020, the WG established a set of indicators and maturity levels for those indicators.
As a result of the work, a first set of guidelines and a checklist related to the implementation of the indicators were produced, with the objective to further align the guidelines for evaluating FAIRness with the needs of the community.
2020-01-01T00:00:00ZPersistent Identification of Instruments.Stocker, MarkusDarroch, LouiseKrahl, RolfHabermann, TedDevaraju, AnusuriyaSchwardmann, UlrichD'Onofrio, ClaudioHäggström, Ingemarhttps://repository.oceanbestpractices.org/handle/11329/14012020-08-26T12:14:19Z2020-01-01T00:00:00ZPersistent Identification of Instruments.
Stocker, Markus; Darroch, Louise; Krahl, Rolf; Habermann, Ted; Devaraju, Anusuriya; Schwardmann, Ulrich; D'Onofrio, Claudio; Häggström, Ingemar
Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
2020-01-01T00:00:00ZThe TRUST Principles for digital repositories.Lin, DaweiCrabtree, JonathanDillo, IngridDowns, Robert R.Edmunds, RorieGiaretta, DavidDe Giusti, MarisaL’Hours, HervéHugo, WimJenkyns, ReynaKhodiyar, VarshaMartone, Maryann E.Mokrane, MustaphaNavale, VivekPetters, JonathanSierman, BarbaraSokolova, Dina V.Stockhause, MartinaWestbrook, Johnhttps://repository.oceanbestpractices.org/handle/11329/13362020-05-21T13:48:32Z2020-01-01T00:00:00ZThe TRUST Principles for digital repositories.
Lin, Dawei; Crabtree, Jonathan; Dillo, Ingrid; Downs, Robert R.; Edmunds, Rorie; Giaretta, David; De Giusti, Marisa; L’Hours, Hervé; Hugo, Wim; Jenkyns, Reyna; Khodiyar, Varsha; Martone, Maryann E.; Mokrane, Mustapha; Navale, Vivek; Petters, Jonathan; Sierman, Barbara; Sokolova, Dina V.; Stockhause, Martina; Westbrook, John
As information and communication technology has become pervasive in our society, we
are increasingly dependent on both digital data and repositories that provide access to
and enable the use of such resources. Repositories must earn the trust of the communities
they intend to serve and demonstrate that they are reliable and capable of appropriately
managing the data they hold.
Following a year-long public discussion and building on existing community consensus1, several stakeholders,
representing various segments of the digital repository community, have collaboratively developed
and endorsed a set of guiding principles to demonstrate digital repository trustworthiness. Transparency,
Responsibility, User focus, Sustainability and Technology: the TRUST Principles provide a common framework
to facilitate discussion and implementation of best practice in digital preservation by all stakeholders.
2020-01-01T00:00:00ZLegal Interoperability of Research Data: Principles and Implementation Guidelines. Version 1.0.https://repository.oceanbestpractices.org/handle/11329/2952021-08-24T17:31:11Z2016-01-01T00:00:00ZLegal Interoperability of Research Data: Principles and Implementation Guidelines. Version 1.0.
The ability of the research community to share, access, and reuse data, as well as to integrate data from diverse sources for research, education, and other purposes requires effective technical, syntactic, semantic, and legal interoperability rules and practices. These Principles focus on legal interoperability because there tends to be misunderstanding and lack of knowledge and guidance about legal issues concerning research data generally.
These Legal Interoperability Principles are offered as high-level guidance to all members of the research community—the funders, managers of data centers, librarians, archivists, publishers, policymakers, university administrators, individual researchers, and their legal counsel—who are engaged in activities that involve the access to and reuse of research data from diverse sources.3 The Principles are synergistic, so their greatest benefit is realized when they are considered together.
2016-01-01T00:00:00ZMetadata standards directory.https://repository.oceanbestpractices.org/handle/11329/2732021-08-24T17:36:38Z2016-01-01T00:00:00ZMetadata standards directory.
The Research Data Alliance Metadata Standards Directory Working Group set out to develop a directory that would enable researchers, and those who support them, to discover metadata standards that would be appropriate for documenting their research data, regardless of their academic discipline. It happened that a directory with similar aims had recently been developed independently by the UK Digital Curation Centre (DCC), so the group collaborated with the DCC on developing the directory further to achieve additional goals regarding coverage, ease of maintenance, and sustainability.
The group provided updates and additions to the entries in the DCC directory, and developed a second instance of the directory that could be maintained by the community. Additions and updates to the second instance were and are fed back to the DCC version. The second instance was designed in such a way as to simplify any future development e ort, and indeed such development is being taken forward by the Metadata Standards Catalog Working Group.
As well as developing the directory itself, the group also collected use cases that will inform the work of the Metadata Standards Catalog Working Group, the Data in Context Interest Group, and the Metadata Interest Group.
2016-01-01T00:00:00Z