IASSIST Fellows 2023: Bridging Perspectives, Building Connections

IASSIST is an international organization of professionals working in and with information technology and data services to support research and teaching in the social sciences and beyond.

IASSIST 2023 Conference Presentations | Zenodo

UK Data Service shares global expertise at international conference to encourage research integrity — UK Data Service

Experts from the UK Data Service have recently hosted a series of talks at IASSIST 2023 – a prestigious international social science...

Queering data queries: Facilitating the discovery of MORGAI data

Researchers and research organizations have repeatedly pointed to the existence of a data gap concerning populations with marginalized orientations, relationships, genders, asexualities and intersex conditions (MORGAI) (see for example National Academies of Sciences, Engineering, and Medicine, 2020. https://doi.org/10.17226/25877). Yet, while data is needed to conduct research benefitting these populations, from an ethical perspective we need to protect these populations from repeated, duplicate collection of often highly sensitive data. Therefore, in addition to closing the data gap, we argue that efforts must also be made to close the “discovery gap” for this data. We will set out by presenting strategies currently employed by social science data repositories to make MORGAI data in their holdings visible and findable (if any), including curated collections and bibliographies, or the use of controlled vocabularies. We then present first findings from an analysis of web queries and search queries in the catalog of the GESIS data holdings for MORGAI data and put these into context of current research about data discovery strategies of researchers. We conclude by outlining planned measures to facilitate the discovery of MORGAI data in the GESIS collections. Once these have been implemented, we hope to be able to measure potential effects on data discovery by monitoring the download statistics for these data over the course of a 12-month period.

Out of One, Many: Using Language Models to Simulate Human Samples | Political Analysis | Cambridge Core

Out of One, Many: Using Language Models to Simulate Human Samples - Volume 31 Issue 3