LIFE TODAY is data driven – and the basis of data collection. Industries and institutions gather more digital data for their potential to generate value and action than ever before. These range from health data (Stevens et al. 2018; Vezuridis & Timmons 2021) to environmental data (Deitz 2023; Vardy 2020) to “alternative” data which are cleaned, repackaged and sold for use by industries in need of predictive models, like finance (Hansen & Borch 2022; see also Plantin 2019).

This special issue provides a window onto the meeting of critical data studies and sociology in Scandinavia to explore the situated, localised modes of “being and beco-ming” with data (Lupton 2018:9) in a particular moment in time, a time when in the Northern European context, public administrators, institutions, software developers and scientists are busy imagining and trialling arrays of data-driven solutions to au-tomate public services, intellectual work and infrastructure operations. Data, in these contexts, often figure as an element of already ongoing, much older practices of clas-sifying, categorising, quantifying and producing actionability on the social. However, data bring to these the novel element of the imagined possibility for feeding these practices into machine learning, automation algorithms and computational models to fulfil ideas of efficiency, increased productivity or predictive potentials. Data become, in this context, part of a landscape of instrumenting actions and socio-technical struc-tures that order social life at a distance and according to formal rules (Savolainen & Ruckenstein 2024).

This document provides guidance on organizing and documenting datasets that contain synthetic data to simplify publication in a research data repository. Unlike datasets collected from the "real world", synthetic data often require additional details to facilitate reproduction and reuse. This document summarizes the essential information that you should provide when sharing synthetic data in a research data repository to ensure that the data can be easily understood and efficiently reused by others.  In many cases, synthetic data must be handled differently if it is based on personal data, and a section specifically addressing synthetic personal data is included. 

Publication of AI-generated synthetic structural data in data repositories is beginning to reveal the specific documentation elements that need to accompany synthetic datasets so as to ensure reproducibility and enable data reuse. This document identifies actions that research repositories can take to encourage users to provide AI-generated synthetic datasets with appropriate structure and documentation. The recommendations are specifically for AI generated data, not (for example) data produced using pre-configured models or missing data created by statistical inference. Additionally, this document discusses metadata/README elements for synthetic structured datasets (tabular and multi-modal) and not textual data from LLMs or images for computer vision. 

The document is the result of a workshop held on 23rd January 2025, with participants from the Swedish National Data Service, Linköping University and Manchester University. It also draws on survey responses about current practice from 17 data repositories and a review of existing metadata and README requirements. 

Synthetic data is increasingly used as a substitute for real data due to ethical, legal, and logistical reasons. However, the rise of synthetic data also raises critical questions about its entanglement with the politics of classification and the reproduction of social norms and categories. This paper aims to problematize the use of synthetic data by examining how its production is intertwined with the maintenance of certain worldviews and classifications. We argue that synthetic data, like real data, is embedded with societal biases and power structures, leading to the reproduction of existing social inequalities. Through empirical examples, we demonstrate how synthetic data tends to highlight majority elements as the “normal” and minimize minority elements, and that the slight changes to the data structures that create synthetic data will also inevitably result in what we term “intersectional hallucinations.” These hallucinations are inherent to synthetic data and cannot be entirely eliminated without compromising the purpose of creating synthetic datasets. We contend that decisions about synthetic data involve determining which intersections are essential and which can be disregarded, a practice which will imbue these decisions with norms and values. Our study underscores the need for critical engagement with the mathematical and statistical choices in synthetic data production and advocates for careful consideration of the ontological and political implications of these choices during curatorial style production of synthetic structured data.

Post COVID-19 Condition (PCC) is a complex condition presenting significant challenges for patients. Individuals suffering from severe PCC are often assessed in rehabilitation medicine departments or specialized post-COVID centres, where their condition is evaluated using the International Classification of Functioning, Disability and Health (ICF). The ICF framework primarily focuses on functional impairments, disabilities, and restrictions in participation, with an emphasis on the concept of “functioning.” However, a critical question remains: how does this notion of functioning relate to the well-being of these individuals? This paper explores this issue by examining three fictionalized but typical case studies of PCC patients in relation to two distinct theoretical approaches. First, we engage with theories about well-being from the philosophy of well-being emphasizing the individual’s perspective. Second, we explore relational approaches in bioethics and their theoretical underpinnings, which emphasize how people are situated, considering context and relations rather than purely individual conditions. The paper highlights the potential tensions between these approaches while arguing that a more comprehensive understanding of well-being can emerge by integrating insights from both traditions. Through the examination of PCC patient cases, we propose that well-being can be better understood when approached from multiple angles, enriching the understanding of patient outcomes in rehabilitation medicine. 

Traditional GAN (Generative Adversarial Network) architectures often reproduce biases present in their training data, leading to synthetic data that may unfairly impact certain subgroups. Past efforts to improve fairness in GANs usually target single demographic categories, like sex or race, but overlook intersectionality. Our approach addresses this gap by integrating an intersectionality framework with explainability techniques to identify and select problematic sensitive features. These insights are then used to develop intersectional fairness constraints integrated into the GAN training process. We aim to enhance fairness and maintain diverse subgroup representation by addressing intersections of multiple demographic attributes. Specifically, we adjusted the loss functions of two state-of-the-art GAN models for tabular data, including an intersectional demographic parity constraint. Our evaluations indicate that this approach significantly improves fairness in synthetically generated datasets. We compared the outcomes using Adult, and Diabetes datasets when considering the intersection of two sensitive features versus focusing on a single sensitive attribute, demonstrating the effectiveness of our method in capturing more complex biases.