Dataset: Conceptions about Deep Time in European First-Year University Students

This dataset provides cross-national data on first-year university students’ conceptions about deep time and evolutionary timescales collected within the framework of the European research project EuroScitizen. The data originate from a large-scale standardized survey conducted across 26 European countries among undergraduate students at the beginning of their university studies (see original publication: Kuschmierz et al., 2021). The present dataset specifically focuses on items addressing conceptions of deep time, including students’ understanding of temporal scales relevant to evolutionary processes and Earth history. The data set includes N = 7215 participants from 23 countries. The questionnaire was administered in multiple national languages following a Translation–Review–Adjudication–Pre-test–Documentation (TRAPD) procedure to ensure conceptual and linguistic equivalence across participating countries. Data were collected using standardized online and paper-based survey procedures coordinated among national research teams. Participation was voluntary and anonymous, and all procedures complied with national ethical regulations and the General Data Protection Regulation (GDPR). Data collection was based on the validated “Evolution Education Questionnaire” (EEQ; Beniermann et al., 2021), a comprehensive instrument designed to assess evolution-related knowledge, acceptance, and associated explanatory variables in a cross-cultural context. The presented dataset is based on the instrument “KAEVO 2.0” part C (Kuschmierz et al., 2020). In addition to the deep time items, the dataset contains information about the country of the participants as well as the sum scores of their knowledge about evolution (based on KAEVO 2.0 part A) and their attitudes towards evolution (based on ATEVO; Beniermann, 2019). These sum scores have been calculated based on the raw data published as supplementary material in Kuschmierz et al. (2021). These additional variables enable comparative analyses. For more information about material and methods, please see the original publication (Kuschmierz et al., 2021). For information about the used instrument KAEVO 2.0 part C, see the original publication of the instrument (Kuschmierz et al., 2020) as well as the method report about the EEQ (Beniermann et al., 2021). This publication is based upon work from COST Action EuroScitizen, supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.eu