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Data sources: ZENODO
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Knowledge transfer and science communication in researchers' engagement portfolios: Survey data from Spain, 2022

Authors: Azagra-Caro, Joaquín M.;

Knowledge transfer and science communication in researchers' engagement portfolios: Survey data from Spain, 2022

Abstract

This dataset contains anonymised survey data on knowledge transfer (KT) and science communication (SC) activities among researchers affiliated with Spanish universities and public research organisations. The data come primarily from the 2022 wave of a national survey of Spanish corresponding authors of Web of Science publications from 2013–2016, complemented with selected researcher-level variables collected in the 2018 wave and bibliometric indicators derived from publication and citation records. The dataset includes aggregated domain-level and channel-level variables capturing researchers’ engagement in KT and SC. These include binary indicators of engagement in KT and SC, breadth measures capturing the extent of engagement within each domain, and channel variables derived from theoretically informed groupings and exploratory factor analysis. KT channels include informal KT, response-oriented KT, commercial KT, training-related KT and co-creation KT. SC channels include media-based SC, professional SC, fiction-based SC and artistic SC. In some cases, particularly for SC, channel variables correspond to single-item measures where the survey design did not support broader factor-based aggregation. The data were used to examine whether KT and SC coexist within researchers’ engagement portfolios and whether their relationship varies across specific channels. The dataset also includes variables used as controls in robustness analyses, such as scientific field, multidisciplinarity, gender, age, organisational affiliation, scientific impact, affective and motivational orientations, resilience, social desirability measures and prior literary/artistic breadth. All data have been anonymised. Direct identifiers have been removed, and variables have been prepared to reduce disclosure risk while preserving the possibility of reproducing the main descriptive and regression analyses reported in the associated article. A related preprint will be made available through the CSIC institutional repository. The record will be updated with the preprint citation and DOI/link once available.

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