European Research Data Landscape researcher survey FAIR data components.
Creators: name: Horton, Laurence
name: Davidson, Joy
Publisher: name: Zenodo
Year: 2022
Subjects: FAIR data
Research Data Management
European Researcher Data Landscape
European Open Science Cloud
FAIR
RDM

description: The aim of this study was to provide a detailed characterisation of the research data ecosystem in the European context, covering the EU Member States, Horizon 2020 Associated Countries (AC) and the UK. It will intends to help the European Commission develop targeted policies and support actions to make FAIR and open data practices mainstream. The results and recommendations stemming from the study will serve as important input in shaping the future development of EOSC. It focused on: Collecting data on data production and consumption by scientific disciplines and relevant sub-disciplines Collecting and analysing information on data deposition practices, depending on the data typology and volume Collecting data on the level of maturity with respect to FAIR data implementation Assessing the responsiveness and readiness of research data repositories in terms of the implementation of FAIR principles, including certifications A survey was been carried out as part of a study funded under Specific Contract under the Multiple Service Framework Contract N° 2018/RTD/A2/OP/PP- 07001-2018, LOT 2 - Exploring, Documenting and Analysing R&amp;I policy issues (EDAR). European Research Data Landscape (RTD/2020/SC/018). The study is being carried out by a consortium comprised of Visionary Analytics, EFIS, DCC, and DANS. The survey was closed on February 17th, 2022, at 11:00am. In order to ensure the quality of the data the following steps were taken by Visionary Analytics: Only the respondents who answered the first key question (“Do you use data made available by researchers outside your research team in your research activities?”) were kept in the analysis. This resulted in the elimination of 1,949 respondent answers leaving 15,078 respondents who answered the researcher’ survey. There were 1,566 respondents who spent less than 5 minutes filling out the questionnaire. Analysis of the responses showed that many of those respondents either abandoned the survey early or did not reuse/produce data, making the survey shorter for them. Responses under five minutes that both reuse and produce data and responded not only to core, but to additional questions as well were removed. Checked whether respondents rushed the survey by clicking on the same response every time (straightlining). 54 cases were identified. Subsequently 4 of these were deleted from the analysis due to the short time spent filling the survey and answers to other questions. Potential duplicates were checked for by comparing respondent primary/secondary country and FOS, age, role in research and whether the researcher produce and reuse data. This exercise showed a single case of duplication with some variation in response, thus, both instances were removed. The open questions were checked for nonsensical letter combinations. However, answers to open questions did not lead to the elimination of any respondents. After data cleaning the final dataset consists of 15,066 respondent answers, of which 11,077 are complete responses, including core and additional questions. Subsequent verification and cleaning checks on this file for the descriptives and crosstab visualisations undertaken by the DCC, and is documented in erdl_researchers_cleaning_MASTER.Rmd. Data was initially supplied by Visionary Analytics as an excel file. The column containing email address for follow-up contacts was removed. Columns were renamed to match their question number. Typos that lead to extra categories were corrected for consistency There were ten FAIR-related questions in the survey (Q5.1-5.10). The DCC has prepared this spreadsheet and the initial analysis of the raw data provided by Visionary Analytics. The cross tabs took responses to questions on FAIR behaviours against a set of demographic variables in the survey: Demographics: Country/Region Country Regional groupings: Northern Europe (Group 1: Sweden, Finland, Denmark, Lithuania, Latvia, Estonia) North Western Europe (Group 2: Ireland, Netherlands, Luxembourg, France, Belgium) South Western Europe (Group 3: Spain, Portugal, Malta, Italy) Central Europe (Group 4: Slovakia, Poland, Hungary, Germany, Czech Republic, Austria) South Eastern Europe (Group 5: Slovenia, Romania, Greece, Cyprus, Croatia, Bulgaria) Associated Countries with higher income level (Group 6: Turkey, Israel, Iceland, Norway, Switzerland, Faroe Islands, the UK) Associated Countries with lower income level (Group 7: Albania, Armenia, Bosnia &amp; Herzegovina, Georgia, North Macedonia, Moldova, Montenegro, Serbia, Ukraine). Relation to the European Union Associated Countries with higher GDP per capita (Turkey, Israel, Iceland, Norway, Switzerland, Faroe Islands, the UK) Associated Countries with lower GDP per capita (Albania, Armenia, Bosnia &amp; Herzegovina, Georgia, North Macedonia, Moldova, Montenegro, Serbia, Ukraine) EU13 countries (Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia and Slovenia) EU14 countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden) Other (Respondents were not asked to specify 'other' countries) Primary field of science (FOS) Do you produce/collect data? Yes No Career Stage: First Stage Researchers (R1) Recognised Researcher (R2) Established Researcher (R3) Leading Researcher (R4). Role Researcher Research Manager Principal Investigator Technical Staff Research Software Engineer Those who work individually. FAIR questions How important is it to you that other researchers are able to: Find your research data Access your research data Reuse your research data Connect their data to your data Please indicate how frequently you do the following during your research: Look for existing data that you can reuse in new research projects Develop a data management plan when starting your research Acquire a globally unique and persistent identifiers such as a DOIs for your data Acquire a globally unique and persistent identifier for your software and code Depositing your data with a repository Describe your datasets using controlled vocabularies (e.g. subject headings, thesauri, ontologies, and taxonomies) Use community standards for data or metadata, relevant to your discipline or sub-discipline Release datasets with a clear and accessible data usage licence Make use of a researcher identifier such as an ORCID to link you to your outputs How familiar are you with the FAIR principles (Findable, Accessible, Interoperable and Reusable) in relation to managing and sharing data? Which of the following policy factors would positively or negatively influence you to share your data and/or make it FAIR?: Compliance monitored Penalties introduced Rewards introduced Policy easy to understand Support provided To what extent do you see the following as obstacles to research data management (RDM) and sharing? Dealing with data protection and confidentiality requirements Legal restrictions (e.g. copyright, patent law, trademark) Financial costs of research data management (RDM) and sharing Skills needed to manage and share data Time and effort required to manage and share data Lack of recognition given to research data management (RDM) and sharing activities Risk of misinterpretation and/or falsification of data Economic competitiveness/undesired commercial use Lack of data repositories and data centres where research data could be stored This set consists of R markdown and HTML files for cleaning the ERDL researcher dataset, visualising basic descriptives of responses to the qualitative survey questions (counts and percentages), and cross-tab visualisations for questions in the survey related to FAIR data concepts. erdl_researchers_cleaning_MASTER.Rmd: R code for importing and replicating the data cleaning tasks undertaken by DCC on the data on which descriptives and crosstabs depend. erdl_researchers_cleaning_MASTER.html: Description of survey and data cleaning and citations. erdl_researchers_descriptives_MASTER.Rmd: R code for basic descriptive tabulations and visualisations of quantitative survey questions in the survey. Includes counts and percentages. erdl_researchers_descriptives_MASTER.html: Visualisations of basic descriptive tabulations and visualisations of quantitative survey questions in the survey. Includes counts and percentages. erdl_researchers_crosstabs_MASTER.Rmd: R code for cross-tabulations and visualisations of FAIR data questions and demographic variables in the survey. Includes counts and percentages. erdl_researchers_crosstabs_MASTER.html: Visualisations of cross-tabulations and visualisations of FAIR data questions and demographic variables in the survey. Includes counts and percentages.
description: Project funded under European Commission, DG RTD. Specific Contract LC-01651865 No RTD/2020/SC/018
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