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Pie-chart showing the number of repositories that are recommended by 1 to 17 organisations. The highest number of recommendations received is 12.
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Researchers are increasingly required to make research data publicly available in data repositories. Although several organisations propose criteria to recommend and evaluate the quality of data repositories, there is no consensus of what constitutes a good data repository. In this paper, we investigate, first, which data repositories are recommend...
Context in source publication
Context 1
... total, we found 242 repositories that were recommended by publishers, funding agencies, and/or com- munity organisations, and the distribution of recommendations is depicted in Figure 2. A majority (88) were only recommended by a single organisation; ArrayExpress was mentioned most frequently, being rec- ommended by 12 out of 17 organisations. ...
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ARTICLE INFO ABSTRACT Data mining is the process of discovering the knowledge by analysing and extracting the data from various highly repositories and the result bases on the useful and functional information's for the user. By the passage of time data mining is growing very vastly and became the famous technology by analysing and extraction of kn...
Citations
... Comparisons of outcomes between repositories with and without certification are limited. Husen et al. (2017) studied the commonalities and divergences between recommended and certified repositories. Among their findings were that "less than 6% of recommended repositories obtained some form of certification" (p. ...
Academic libraries rarely discuss cases of digital repositories that do not meet the standards expected of trusted digital repositories. Implications from inconsistent adherence to technical and professional criteria often surface during migration projects. In 2020, Stony Brook University Libraries began migrating assets to a mono-repository environment. Persistent historical factors presented challenges to repository trustworthiness. This case study discusses a survey project to evaluate legacy repository statuses in the contexts of infrastructure, documentation, and staff capacity. It considers a paradigm of organizational accountability in digital asset stewardship and offers insights for reconciling inherited legacies with aspirations to be a trusted repository.
... indicate that there are thousands of uncertified repositories. Although the reasons for this disparity fall outside the scope of this paper, some researchers speculate that it is because most organizations do not require certification [20]. Other researchers have found that certification does not help practitioners address their operational problems, and they see little return in their investment in audit and certification for themselves or their institutions, all of which calls into question the usability and usefulness of TDR standards [18]. ...
In 1996, an international group of representatives from national archives and libraries, universities , industry, publishing offices, and other government and private sector organizations first articulated the need for certified Trustworthy Digital Repositories (TDRs). Henceforth, multiple standards for TDRs have developed worldwide and their reviewers provide third party audit of digital repositories. Even though hundreds of repositories are currently certified, we do not know if audit and certification of TDRs actually matters. For example, we do not know if digital repositories are actually better at preserving digital information after certification than they were before. Additionally, we do not know if TDRs preserve digital information better than their counterparts, although TDR standards definitely promulgate this assumption. One way of assessing whether audit and certification of TDRs matters is to study its impact on TDRs' stakeholders (e.g., funders, data producers, data consumers). As an initial critical step forward, this study examines what certification-related information repositories actually include on their websites since repository websites provide a means of disseminating information. Using findings from a content analysis of 91 TDR-certified repository websites, this research examines: 1) written statements about TDR status, 2) the presence of TDR seals and their location, 3) whether the seals hyperlink to additional certification information, 4) the extent to which the certification process is explained, and 5) whether audit reports are shared. Nearly three-fourths of the repository websites provide TDR status statements and put seals in one or more places; nearly 60% post audit reports and link seals to additional certification information; and over one-third explain the certification process. Directions for future research and practical application of the results are discussed.
... However, as reported by Assante et al. (2016), generalist data repositories are unable to deal with unusual datasets and their different usage, and they lack a "designated community". At the same time, researchers tend to prefer -or are often recommended -to use subject-based repositories (Husen et al., 2017), which provide specific tools for the types of data they create and allow their data to connect with other practitioners in their field. ...
Founded in 2008 as an initiative of the libraries of three of the four technical universities in the Netherlands, the 4TU.Centre for Research Data (4TU.Research Data) has provided a fully operational, cross-institutional, long-term archive since 2010, storing data from all subjects in applied sciences and engineering. Presently, over 90% of the data in the archive is geoscientific data coded in netCDF (Network Common Data Form) – a data format and data model that, although generic, is mostly used in climate, ocean and atmospheric sciences. In this practice paper, we explore the question of how 4TU.Research Data can stay relevant and forward-looking in a rapidly evolving research data management landscape. In particular, we describe the motivation behind this question and how we propose to address it.
... These suggestions align with (and reinforce) the criteria already put forth by certification bodies (e.g., CoreTrustSeal from the ICSU World Data System), 8 journals (e.g. PLOS ONE Editors, n.d.), and the principles of the "Enabling FAIR Data" project (Wilkinson et al., 2016;Husen et al., 2017). Here we emphasize an overarching goal of coordinated interoperability among repositories to improve the user experience for both, the data submitter or curator as well as the data re-user or synthesis researcher. ...
Environmental research data repositories provide much needed services for data preservation and data dissemination to diverse communities with domain specific or programmatic data needs and standards. Due to independent development these repositories serve their communities well, but were developed with different technologies, data models and using different ontologies. Hence, the effectiveness and efficiency of these services can be vastly improved if repositories work together adhering to a shared community platform that focuses on the implementation of agreed upon standards and best practices for curation and dissemination of data. Such a community platform drives forward the convergence of technologies and practices that will advance cross-domain interoperability. It will also facilitate contributions from investigators through standardized and streamlined workflows and provide increased visibility for the role of data managers and the curation services provided by data repositories, beyond preservation infrastructure. Ten specific suggestions for such standardizations are outlined without any suggestions for priority or technical implementation. Although the recommendations are for repositories to implement, they have been chosen specifically with the data provider/data curator and synthesis scientist in mind.
... Common practice and tools for repository recommendation Various stakeholders such as funders, publishers or journals are providing lists of recommended repositories, e.g. listed in FAIRsharing 4 (Husen et al., 2017). Predominantly publishers like the Public Library of Science 5 (PLOS) or Springer Nature 6 provide curated lists of recommended repositories for data deposits linked to journal publications on their websites. ...
Finding a suitable repository to deposit research data is a difficult task for researchers since the landscape consists of thousands of repositories and automated tool support is limited. Machine-actionable DMPs can improve the situation since they contain relevant context information in a structured and machine-friendly way and therefore enable automated support in repository recommendation.
This work describes the current practice of repository selection and the available support today. We outline the opportunities and challenges of using machine-actionable DMPs to improve repository recommendation. By linking the use case of repository recommendation to the ten principles for machine-actionable DMPs, we show how this vision can be realized. A filterable and searchable repository registry that provides rich metadata for each indexed repository record is a key element in the architecture described. At the example of repository registries we show that by mapping machine-actionable DMP content and data policy elements to their filter criteria and querying their APIs a ranked list of repositories can be suggested.
A guide to principles and methods for the management, archiving, sharing, and citing of linguistic research data, especially digital data.
“Doing language science” depends on collecting, transcribing, annotating, analyzing, storing, and sharing linguistic research data. This volume offers a guide to linguistic data management, engaging with current trends toward the transformation of linguistics into a more data-driven and reproducible scientific endeavor. It offers both principles and methods, presenting the conceptual foundations of linguistic data management and a series of case studies, each of which demonstrates a concrete application of abstract principles in a current practice.
In part 1, contributors bring together knowledge from information science, archiving, and data stewardship relevant to linguistic data management. Topics covered include implementation principles, archiving data, finding and using datasets, and the valuation of time and effort involved in data management. Part 2 presents snapshots of practices across various subfields, with each chapter presenting a unique data management project with generalizable guidance for researchers. The Open Handbook of Linguistic Data Management is an essential addition to the toolkit of every linguist, guiding researchers toward making their data FAIR: Findable, Accessible, Interoperable, and Reusable.
