Chile’s artisanal and industrial fisheries operate under differentiated institutional, spatial, and operational arrangements, yet the regional structure of their multispecies landing compositions has not been quantified using a common exploratory framework. This study analyses official Chilean landings by species, subsector, and administrative region for 2022–2024. For each region, a multispecies landing signature was constructed from proportional landings. k-Means clustering was then applied separately to artisanal, industrial, and total landings, with the number of clusters evaluated using inertia and average silhouette diagnostics. The analysis identified two broad industrial landing-composition types, seven artisanal landing-composition types, and two types in the aggregate dataset. These results should not be interpreted as evidence that the two subsectors are independently organised in space, since part of their differentiation is already embedded in Chile’s regulatory and administrative framework. Rather, the clusters describe how regional landing compositions are structured within subsectors that are already institutionally differentiated. The industrial subset is summarised by a limited number of regional landing profiles, whereas the artisanal subset requires a more differentiated partition to represent variation in relative species composition. The study provides an exploratory and reproducible framework for summarising multispecies regional landings. The resulting typologies are best understood as regional landing-composition types, not as biological stocks, causal governance effects, or formal management units. They can support comparative description, identify regions with similar relative species mixes, and generate hypotheses for future analyses incorporating biological, environmental, fleet, market, or institutional variables.

A Te Ao Māori (Māori) perspective teaches us that food cannot be separated from land, water, or people. This view is increasingly shared by scientists, policymakers, and industry leaders seeking to address the complexity of food system sustainability, but New Zealand (NZ) data systems have not caught up with this reality. Previous research in NZ that matches economic, environmental, nutrition and health data for food has been inconsistent or partial, leaving findings and positions less comparable. The one consistent resource has been the NZ Food Composition Database (NZFCD), the national standard repository for the nutritional content of food. The NZFCD is jointly owned by the New Zealand Institute for Bioeconomy Science and the Ministry of Health. It contains 2857 foods with up to 434 components, and its maintenance is funded by the NZ government. The NZ nutrition community place high trust in the NZFCD and its rigorous and transparent data, and it has diverse uses. Here we report on the planned expansion of the NZFCD into a multi-tiered resource spanning diverse food information, including food labelling, environmental impact, and economic indicators. This follows consultation with the sector on future-proofing the NZFCD, where branded foods data, along with data to support broader sustainability research, were raised as useful future directions. The current version of the NZFCD contains largely generic or aggregate food items of relevance to NZ food consumption and a set of core components for these foods. The data are highly reliable, drawing largely on analytical composition data produced by independent laboratories and using a nationally representative sample of each food. However, generating such data is expensive and time-consuming, meaning new foods are often added years after becoming widely consumed and existing records are only updated sporadically. A new tiered approach will extend the NZFCD and contain four classes of data: Class 1: NZ analytical data (the majority of the existing data in the NZFCD); Class 2: Data borrowed from international sources or calculated by recipe or extrapolation; Class 3: Branded food items, including nutrition information panel and claims data; Class 4: Specialised datasets for specific foods or components, e.g. phytochemicals.

In addition, sustainability indicators for food items and groups will be added, including greenhouse gas emissions, land use, and retail price. These data will be drawn from both NZ and international datasets(1,2), appropriately adjusted to provide representative NZ values. These data will be available via the interactive NZFCD website and available for download, with users able to specify which parts of the enlarged dataset they require. A unified source of broad sustainability data for NZ foods will standardise approaches to investigating the sustainability of diets and provide useful information for food formulation to meet nutritional and environmental targets.

Health and care (H&C) actuaries are well positioned to benefit from recent advances in data science as machine learning (ML) techniques have become increasingly transparent and accessible. The ML developments allow actuaries to detect complex nonlinear patterns and interactions that are difficult to capture using traditional generalised linear models (GLMs), without sacrificing the clarity and governance advantages that make GLMs central to actuarial practice. Using a large life insurance data set, we demonstrate and appraise three emerging hybrid approaches: interpretable boosted linear models, XGBoost-informed GLM and an interaction detection workflow. Our findings show that actuaries can improve modelling accuracy, measured by Poisson deviance, by integrating ML insights into traditional modelling techniques, achieving a practical balance of interpretability, expert judgement, and modern analytical innovation.

The fast-moving field of data science is increasingly permeating into the health and care actuarial sciences. Given this context, the Institute and Faculty of Actuaries set out to form a “techniques in data science in health and care” working party. This working party was tasked with creating a framework for those actuaries working within the health and care domain that would assist them in determining which techniques are appropriate for a project. The framework presented here was developed through a combination of literature review and synthesis of expert opinion from experienced practitioners from diverse backgrounds. The framework offers a structured, itemised approach, serving as a checklist to ensure that all relevant analytics and decisions are considered and documented. Each itemised topic is covered by a summary providing guidance and relevant references for further reading. The checklist follows the natural workflow of a data analytics project, guiding users through each step to prevent omissions and maintain rigour in both analysis, reporting and peer-review. The framework blends relevant analytics elements from actuarial science, data science and epidemiology. We hope the framework will enhance transparency, reproducibility, and comprehensiveness in the reporting and peer-review of health and care data analytics projects.

This paper charts the rapid rise of data science methodologies in manuscripts published in top journals for third sector scholarship, indicating their growing importance to research in the field. We draw on critical quantitative theory (QuantCrit) to challenge the assumed neutrality of data science insights that are especially prone to misrepresentation and unbalanced treatment of sub-groups (i.e., those marginalized and minoritized because of their race, gender, etc.). We summarize a set of challenges that result in biases within machine learning methods that are increasingly deployed in scientific inquiry. As a means of proactively addressing these concerns, we introduce the “Wells-Du Bois Protocol,” a tool that scholars can use to determine if their research achieves a baseline level of bias mitigation. Ultimately, this work aims to facilitate the diffusion of key insights from the field of QuantCrit by showing how new computational methodologies can be improved by coupling quantitative work with humanistic and reflexive approaches to inquiry. The protocol ultimately aims to help safeguard third sector scholarship from systematic biases that can be introduced through the adoption of machine learning methods.

Clinical data science, like the broader discipline of all data science, has quickly grown from obscurity only a few decades ago to one of the fastest growing specialties in biomedical research today. Yet, the education and training of the workforce has not kept pace with the growth of the field, the complexity of science, or the needs of the profession. The purpose of this paper is to provide a template for an intensive short course on fundamentals of clinical data science that meets the needs of working professionals in academic, industry, and government research settings. Care will be taken to introduce students to essential roles, responsibilities, and practice patterns within the field, the foundational components from which they come, and many of the soft skills needed for professional practice and advancement in the field today. The course is designed as an evidence-based, immersive learning experience taught over a 5-day period on a university campus, taught using principles of best educational practice and multiple modalities, to assure optimal interaction and engagement throughout the week. This template may be reproduced by any institution interested in and capable of offering such a program.

Numerous symposia and conferences have been held to discuss the promise of Artificial Intelligence (AI). Many center on its potential to transform fields like health and medicine, law, education, business, and more. Further, while many AI-focused events include those data scientists involved in developing foundational models, to our knowledge, there has been little attention on AI’s role for data science and the data scientist. In a new symposium series with its inaugural debut in December 2024 titled AI for Data Science, thought leaders convened to discuss both the promises and challenges of integrating AI into the workflows of data scientists. A keynote address by Michael Pencina from Duke University together with contributions from three panels covered a wide range of topics including rigor, reproducibility, the training of current and future data scientists, and the potential of AI’s integration in public health.