Data Requests and Scenarios for Data Design of Unobserved Events in Corona-related Confusion Using TEEDA

Due to the global violence of the novel coronavirus, various industries have been affected and the breakdown between systems has been apparent. To understand and overcome the phenomenon related to this unprecedented crisis caused by the coronavirus infectious disease (COVID-19), the importance of data exchange and sharing across fields has gained social attention. In this study, we use the interactive platform called treasuring every encounter of data affairs (TEEDA) to externalize data requests from data users, which is a tool to exchange not only the information on data that can be provided but also the call for data, what data users want and for what purpose. Further, we analyze the characteristics of missing data in the corona-related confusion stemming from both the data requests and the providable data obtained in the workshop. We also create three scenarios for the data design of unobserved events focusing on variables.

💡 Research Summary

The paper investigates how to improve data exchange in the context of the COVID‑19 pandemic, focusing on the “corona‑related confusion” that emerged as societies struggled to interpret and act upon rapidly changing information. To capture both the data that can be supplied and the data that users request, the authors employ an interactive web‑based platform called TEEDA (Treasuring Every Encounter of Data Affairs). TEEDA requires data requesters to enter three description items – a data name, a set of variables, and an optional purpose of use – while data providers describe their assets through a “data jacket” (DJ) that records the data name, outline, variables, types, formats, and sharing conditions without exposing the raw data itself. The platform visualises all entered items as a network graph, automatically linking requests and supplies that share common variables, thereby making the otherwise opaque “call for data” process transparent.

The empirical component consists of a single workshop held on 15 June 2020. Fourteen participants (students and professionals, all aged 20 or older) received a brief 15‑minute tutorial on TEEDA and then spent 4–5 minutes entering data requests and provable data. In total, 61 items were collected: 33 distinct data requests and 28 provable data entries. The requests predominantly concerned behavioural, psychological and lifestyle variables (e.g., “behavioral history of COVID‑19‑infected individuals”, “anxiety coping mechanisms by age, gender and prefecture”). By contrast, the provable data were largely statistical aggregates (case counts by country or city), time‑series graphs, and image datasets, most of which were already publicly released by governments or international organisations.

A key finding is the dramatic shift in sharing conditions. In established data‑exchange ecosystems, only about 35 % of datasets are marked as “generally shareable”. In the workshop, this proportion rose to roughly 90 %, indicating that crisis contexts can motivate data owners to relax restrictions. Analysis of data types and formats shows that the distribution of time‑series, numerical, textual and tabular data remained stable before and after the pandemic, but the share of “graph” type data increased markedly. Graphs and images (classified under the “other” format) were the most common visual artefacts, reflecting a heightened demand for intuitive visualisations that can be understood by non‑specialists.

Based on these observations, the authors propose three design scenarios for “unobserved events” – phenomena that are either not directly measured or insufficiently documented:

1. Variable‑Extension Scenario – Augment existing variable sets with psychosocial and economic dimensions (e.g., anxiety scores, income changes, remote‑work prevalence) to build richer, multidimensional models of pandemic impact.

2. Real‑Time Matching Scenario – Integrate TEEDA’s matching engine with streaming data sources (social‑media trends, mobile location feeds) so that a newly posted request can be instantly paired with a suitable provider, enabling rapid response to emerging information gaps.

3. Privacy‑Preserving Scenario – Apply anonymisation and aggregation to sensitive variables, offering “conditional sharing” options that grant access only under defined research purposes, thereby balancing openness with confidentiality.

The study acknowledges several limitations. The participant pool is small and skewed toward academia, which may not capture the full spectrum of data needs from industry or public‑sector agencies. The optional “purpose of use” field reduces the granularity of purpose‑driven matching, potentially lowering the relevance of suggested matches. Moreover, the paper does not disclose the detailed algorithmic logic behind the variable‑based matching, limiting reproducibility.

Despite these constraints, the work demonstrates that a structured, visual platform like TEEDA can surface hidden data demands, clarify sharing conditions, and guide the design of new datasets for events that are otherwise unobserved. By making the “call for data” explicit and linking it to concrete, variable‑based supplies, TEEDA offers a practical pathway to improve data governance and collaborative analytics during fast‑moving crises such as the COVID‑19 pandemic.