UNEKE - Survey on Storing Practice and Storing Requirements for Research Data

UNEKE is a joint project of the University of Duisburg-Essen and the RWTH Aachen University funded by the Federal Ministry of Education and Research. Under the leadership of the University Library Duisburg-Essen, the project aims to develop a strategic approach for a future-oriented research data management for institutions. UNEKE conducted a survey to determine both current storage practices and storage needs at universities. Throughout Germany, 13 universities took part in the survey. Especially in the accompanying scientific study, UNEKE aimed at the question of what keeps researchers busy and encourages them to publish and share data in the future. In order to identify the hindering and supporting factors in the practice of open data in higher education, a large-scale study with an online questionnaire was designed and carried out. The aim of the quantitative method was to collect descriptive knowledge about the status quo of researchers on the practices and requirements of research data management and to test and validate the presented research model. Therefore, the data set was divided into two parts: In the first part (part one), the participants were asked descriptive questions about their storage behaviour; in the second part (part two), scientifically valid constructs were used to learn more about the users´ attitudes towards data exchange. In this context, several predictors of data exchange behaviour are measured, including perceived (dis)benefits and risk factors. Part one of the survey was available in two languages, German and English. The second part consisted of different instruments, either taken from already validated instruments or slightly modified. All instruments used were originally published in English. In order to avoid undesired effects from the translation, the English form of the instruments was retained as in the original and the second part of the questionnaire was asked for in English. The study focuses on university members from Germany. The data set contains data from members of ten large universities (over 20,000 members) and three medium-sized universities (over 10,000 members). Due to internal requirements, the survey did not start at all participating universities at the same time, so that the survey will run between March 2018 and January 2019.<br>Part 1: Descriptive questionnaire

Topics: 1. background research project: duration of a typical research project (in months); generated data volume of a typical research project (gigabyte); percentage of the data volume in the initial phase, the intermediate phase and the final phase of the research project; storage location of the research data (e.g. at an external data centre, centrally on a server of the university, locally on the official computer, etc.); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); percentage of total data with which the respondent works regularly (in the first, second and last third of the research project); percentage of total data used and processed by several persons; number of persons working together on a typical research project; percentage of total data volume which, in the sense of good scientific practice, is 10 years or more 25 years should be stored (in the first, second and last third of the research project); type of archiving of own frog data for at least 25 years; expertise in the institute/department with systems or tools that support research data management.

2. General: data sources (e.g. observations and experiments, surveys and interviews, meta-studies, etc.); type of data formats; server administration, file server, backup and/or archive available at the faculty (data storage, content data maintenance); hours for server administration; importance of data protection; anonymisation of data in the course of the research process; guidelines or recommendations for handling research data; guidelines to fulfil; access to own data; protocol on data access; importance of regulation of data access; external persons or institutions who need to gain access to data; publication of raw data after first use; metadata collected; schema or standard for collecting metadata.

Demography: faculty; position; department; self-assessment knowledge of research data management; problems related to research data (e.g. outdated data formats, etc.); desired services of the university in handling research data

Additionally coded were: Interview number; university or university of applied sciences; language; interview mode; time of the start of the interview; time spent on the individual questionnaire pages; total time spent; time of sending the invitation email; time of the last change to the data set; survey completed; respondent only viewed questionnaire without answering mandatory fields; page that the respondent last edited; last page that was edited in the questionnaire; proportion of missing answers as a percentage and weighted according to relevance; penalty points for quick completion; serial number (if used).

Part 2: Constructs

1. Perceived value in sharing primary research data; intention to share data between researchers; data sharing behaviour; attitude towards data sharing.

2. Perceived pains: sharing cost (time, effort, resources, difficult); career cost (e.g. losing publication opportunities, data sharing may cause own research ideas to be stolen by other researchers, etc.).

3. Perceived gains: switching benefits; network possibilities; career benefit.

4. Risk factors: fear of having to compete with colleagues; fear of misuse; fear of losing one’s unique value; fear of losing control.

5. Control variables: technology affinity; normative influence; perceived availability of data repository.