The European Social Survey (ESS) is a cross-national survey based on scientific standards that has been conducted every two years since 2001. The ESS collects data on attitudes and behaviour patterns of the population in more than 30 European countries. The survey data are provided by the ESS-ERIC (ERIC = European Research Infrastructure Consortium). In cooperation with national institutions, data from interviews on attitudes, beliefs and behavioural patterns are collected throughout Europe.
What is new?
ESS data curation, dissemination and storage have been moved to the cloud. The FAIRness of ESS data holdings has increased through a powerful search tool, automated data processing, increased compliance with standards, controlled vocabularies and persistent identifiers as well as more interoperable metadata.
Brief description of the tool
The new ESS data and metadata service consists of multiple data and metadata repositories to support an efficient, collaborative, version-controlled data archive workflow. The service makes use of the Colectica software solutions for metadata management and the Azure cloud for data storage and management. The repositories are accessed via ESS API for management and dissemination of data and metadata.
The Colectica software solutions and cloud storage for data enables powerful data search, analysis, visualization and download services by adhering to the DDI Lifecycle Metadata Standard. The new ESS service has made ESS data more findable – for humans and machines. DOIs at study and dataset levels allow easier data reuse
The system interconnects existing and new infrastructures. The NSD-developed APIs, which connect the repositories, make them effective tools for the storage and dissemination of ESS data and metadata. Furthermore, the APIs allow data curators to process, document and publish data and metadata in a secure and stable environment. The APIs have been designed to enable replacement of single elements without breaking the system.
Easier access to data/metadata for ESS users (e.g. researchers and students)
Easier data retrieval and better options for reuse of data through implementation of FAIR data principles (automated data processing, powerful search tool, more use of persistent identifiers and more interoperable metadata)
Reduced processing costs / time
Less dependence on resource providers (licensed statistical packages, on-premises servers).