Data

Behavioral and educational researchers increasingly rely on rich datasets that capture how students respond to technology-enhanced instruction, yet few open resources document the full pipeline from experimental design to data curation in authentic classroom settings. This data descriptor presents a clustered quasi-experimental dataset on the impact of an instructional architecture that combines virtual reality (VR) simulations with artificial intelligence (AI)-driven formative feedback to enhance undergraduate students’ communication and problem-solving performance. The study was conducted at a large private university in Mexico during the 2024–2025 academic year and involved six intact classes (three intervention, three comparison; n = 180). Exposure to AI and VR was operationalized as a session-level “dose” (minutes of use, number of feedback events, number of scenarios, perceived presence), while performance was assessed with analytic rubrics (six criteria for communication and seven for problem solving) scored independently by two raters, with interrater reliability estimated via ICC (2, k). Additional Likert-type scales measured presence, perceived usefulness of feedback and self-efficacy. The curated dataset includes raw and cleaned tabular files, a detailed codebook, scoring guides and replication scripts for multilevel models and ancillary analyses. By releasing this dataset, we seek to enable reanalysis, methodological replication and cross-study comparisons in technology-enhanced education, and to provide an authentic resource for teaching statistics, econometrics and research methods in the behavioral sciences.

Forests provide a wide range of ecosystem services, and their importance in supporting human well-being is widely recognized. As goods and benefits from forests are exhaustible, it is therefore essential to gather sound data for their monitoring and management. Remote sensing has gained increasing importance in collecting data on forests, driven by the growing demand for regularly updated environmental data. However, remote sensing modeling of vegetation requires reference data to be collected in the field. This article presents a dataset on tree crown cover—both total and by species—of 528 georeferenced forest plots located in the Eastern Alps, Italy, an area affected by extensive wind and snow damage and subsequent widespread damage caused by bark beetles. The characteristic species of the forest types in the dataset are widely distributed over the Eurasian continent, making the dataset potentially useful to many users and researchers studying forest biodiversity or remote sensing applications to monitor forest cover changes. Data were collected within a still ongoing project aimed at detecting crown cover changes in small forest patches.

An essential first step to implement a control strategy against herbivorous insects is the monitoring of their populations. The efficacy of pheromone-based traps in capturing herbivorous insects can be enhanced by adding adjuvants and using slow-release dispensers to ensure long-lasting attractiveness. Here, we present datasets from a two-year field monitoring campaign of the invasive brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), using clear sticky traps baited with its aggregation pheromone and a synergist, tested towards different dispensers and adjuvants. Bycatch data for native stink bugs (all Hemiptera: Pentatomidae) and leaf-footed bugs (Hemiptera: Coreidae) are also presented. The R code provided was used to organize data and generate weekly captures or weekly density of both H. halys and non-target species. The information provided in this article may contribute to the optimization of pest control strategies in agriculture.