DANS introduces domain-specific Data Stations

2 June 2021

This year, DANS is launching domain-specific Data Stations, which are places where researchers from various scientific disciplines can store, share and publish their data online, both during their research and beyond.

Types and sizes of datasets differ by scientific domain, and so do the practical requirements when working with them. A social scientist, for example, will be interested in data about human behaviour, while a life-sciences researcher will want to know more about types of organisms or pathologies within datasets; each discipline uses its own metadata to describe the research with associated discipline-specific terms and thesauri.

Launching domain-specific Data Stations, DANS ensures that users can go beyond depositing and downloading data. DANS director Henk Wals explains: “At a Data Station, both individual researchers and research teams can create, manage and share their own data collections in an environment dedicated to their discipline and connected to their research infrastructures. This creates a growing domain-specific collection which can be searched based on specific metadata and words, a portal for a well-defined discipline, which in turn forms part of a larger research infrastructure, such as ODISSEI for the social sciences.”

DANS will assign a Data Station Manager to each Data Station, who will serve as the point of contact for the relevant research community. The Data Stations are built on Harvard University’s open source Dataverse technology. This gives the great advantage of benefiting from developments pioneered by a large and growing international community. While DANS meets the specific needs of different scientific communities with the Data Stations, there is also focus on another development: the trend towards more thematic, cross-disciplinary research. DANS therefore ensures that the data sets in the various Data Stations are fully interoperable.

Secure, reliable and certified

Is long-term preservation possible at Data Stations? Henk Wals: “Yes, it certainly is. Long-term preservation is one of DANS’s basic functions. It ensures that research is verifiable, reproducible and reusable. Data Stations forward their data to the DANS Data Vault, a secure, reliable and certified repository for long-term data preservation. The Vault also contains all datasets that have previously been entrusted to DANS. In addition, it is offered as a service to organizations looking for reliable long-term preservation of their data archives.

Services at a glance

The Data Stations are being introduced carefully, starting with the Data Station for Archaeology. The main focus of DANS will remain on social sciences, humanities and life sciences. These are the domains for which no specific repositories exist and from which tens of thousands of data sets have already been deposited at DANS. For data from physical and technical sciences, 4TU.ResearchData will remain the designated Dutch repository as far as DANS is concerned. This Data Station provides access to the modest collection that DANS already manages in this field.

Datasets archived at DANS via EASY, the online repository, are successively included in the various Data Stations. In this way, these data are also secure stored, more focused on the domain of research. In addition to the Data Stations, DANS will continue to offer DataverseNL, the platform service which allows universities, colleges and research institutions to establish their own repositories. Datasets stored in DataverseNL can also be found through the Data Stations.

Looking at the future

Looking at the future, Henk Wals concludes:  “The Data Stations and the Vault will form part of the basic infrastructure for research data which DANS is developing in collaboration with SURF. DANS and SURF want their combined services to be a complete package, offering its users a seamless experience. Local and thematic Digital Competence Centres will then deploy this service package to help researchers process and deposit their data according to FAIR principles. In this way, a clear and efficient national infrastructure for research data could ultimately be created.”

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