Quantifying differences between flow fields is a key challenge in fluid mechanics, particularly when evaluating the effectiveness of flow control or other problem parameters. Traditional vector metrics, such as the Euclidean distance, provide straightforward pointwise comparisons but can fail to distinguish distributional changes in flow fields. To address this limitation, we employ optimal transport (OT) theory, which is a mathematical framework built on probability and measure theory. By aligning Euclidean distances between flow fields in a latent space learned by an autoencoder with the corresponding OT geodesics, we seek to learn low-dimensional representations of flow fields that are interpretable from the perspective of unbalanced OT. As a demonstration, we utilise this OT-based analysis on separated flows past a NACA 0012 airfoil with periodic heat flux actuation near the leading edge. The cases considered are at a chord-based Reynolds number of 23 000 and a free-stream Mach number of 0.3 for two angles of attack (AoA) of $6^\circ$ and $9^\circ$. For each angle of attack, we identify a two-dimensional embedding that succinctly captures the different effective regimes of flow responses and control performance, characterised by the degree of suppression of the separation bubble and secondary effects from laminarisation and trailing-edge separation. The interpretation of the latent representation was found to be consistent across the two AoA, suggesting that the OT-based latent encoding was capable of extracting physical relationships that are common across the different suites of cases. This study demonstrates the potential utility of optimal transport in the analysis and interpretation of complex flow fields.

This introductory article to Democratic Theory's special issue on the marginalized democracies of the world begins by presenting the lexical method for understanding democracy. It is argued that the lexical method is better than the normative and analytical methods at finding democracies in the world. The argument then turns to demonstrating, mainly through computational research conducted within the Google Books catalog, that an empirically demonstrable imbalance exists between the democracies mentioned in the literature. The remainder of the argument is given to explaining the value of working to correct this imbalance, which comes in at least three guises: (1) studying marginalized democracies can increase our options for alternative democratic actions and democratic innovations; (2) it leads to a conservation and public outreach project, which is epitomized in an “encyclopedia of the democracies”; and (3) it advocates for a decolonization of democracies’ definitions and practices and decentering academic democratic theory.

Cross-border philanthropy occurs across multiple dimensions simultaneously. Seemingly domestic actors become players in international spheres, shattering the idea of a domestic/international dichotomy with clear lines delineating these spaces. This line blurring obscures monetary flows and highlights questions regarding nonprofit accountability in a transnational context. We present a study tracking money from US INGOs to Israeli NGOs, demonstrating the advantages and challenges to a big data approach and highlighting the importance of local partners.

Use of big data in the nonprofit sector is on the rise as a part of a trend toward “data-driven” management. While big data has its critics, few have addressed fundamental ontological and epistemological issues big data presents for the nonprofit sector. In this article, we address some of these issues including most prominently the notion that big data are value neutral and divorced from context. Drawing on data feminism, an intersectional feminist framework focusing on critically interrogating our experience with data and data-driven technologies, we examine the power differentials inherent in the construction of big data and challenge the claims, priorities, and inequities it produces specifically for nonprofit work. We conclude the article with a call for nonprofit scholars and practitioners to employ a data feminist framework to harness the power of big (and small) data for justice, equity, and co-liberation through nonprofit work.

In 2010 Milja Kurki explained that although scholars recognize that democracy is described in a variety of ways, they do not typically engage with its many and diverse descriptions. My aim in this agenda-setting research note is to tackle this quandary by first providing a minimum empirical account of democracy’s descriptions (i.e., a catalogue of 2,234 adjectives that have been used to describe democracy) and secondly by suggesting what democracy studies may gain by compiling this information. I argue that the catalogue of descriptors be applied in four ways: (1) drilling down into the meaning of each description, (2) making taxonomies, (3) rethinking the phenomenology of democracy, and (4) visualizing democracy’s big data. Each of the four applications and their significance is explained in turn. This research note ends by looking back on the catalogue and its four applications.

Over the last decade, the field of political science has been exposed to two concomitant developments: a surge of Big Data (BD) and a growing demand for transparency. To date, however, we do not know the extent to which these two developments are compatible with one another. The purpose of this article is to assess, empirically, the extent to which BD political science (broadly defined) adheres to established norms of transparency in the discipline. To address this question, we develop an original dataset of 1555 articles drawn from the Web of Science database covering the period 2008–2019. In doing so, we also provide an assessment of the current level of transparency in empirical political science and quantitative political science in general. We find that articles using Big Data are significantly less likely than other, more traditional works of political science, to share replication files. Our study also illustrates some of the promises and challenges associated with extracting data from Web of Science and similar databases.

The digital transformation of Chinese companies offers a new frontier for organizational research. Widespread use of workplace platforms creates rich archives of unobtrusive data, providing continuous, real-time insights into organizational life that traditional surveys cannot capture. The central challenge for scholars is turning this data abundance into meaningful theory. This special issue highlights three studies that meet this challenge by using innovative methods to convert granular data into valuable knowledge. The papers employ digital-context experiments, real-time behavioral tracking, and machine-learning-assisted theory building to study phenomena from interpersonal dynamics to crisis productivity. Looking ahead, we explore the potential of unstructured multimodal data and new AI tools to make complex analysis more accessible. We conclude with a research agenda calling for methodological rigor, interdisciplinary collaboration, and a firm balance between technological innovation and theoretical depth.