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Generative AI tools are entering classrooms faster than evidence about how teach- ers learn to use them well, and a central design worry is overreliance: that fluent, sometimes wrong outputs will be accepted uncritically. We report a two-site, pre– post professional development study with school teachers in Karnataka, India (122 pre- and 119 post-intervention responses). Both sites received hands-on practice on authentic teaching tasks; one additionally received structured training in prompt construction and output verification. Adoption intention was already at ceiling be- fore training and did not move. What moved was interaction readiness: perceived ease of use (pooled d = 0.42) and AI self-efficacy (d = 0.34). Where verification was trained, teachers’ reported propensity to scrutinise AI outputs rose most of all (d = 0.52) with no accompanying decline in adoption. We measured disposi- tion rather than detection performance, so this is an initial result: it indicates that guarding against overreliance need not be traded against adoption.

Replication Package for "Charitable Capital Allocation: Evidence from Private Foundations"

Replication package for "Elections Have Consequences: The Impact of Political Agency on Climate Policy and Asset Prices" (William Cassidy), forthcoming in the Journal of Financial Economics. This package contains the code and author-generated data needed to reproduce the tables and figures in the paper. The paper studies how political agency shapes the origination and pricing of climate transition risk, combining a structural model of elections, climate policy, and asset prices with an empirical analysis of high-frequency asset-price reactions to presidential climate-policy announcements. Contents: - Full analysis code (Python, R, Stata, MATLAB, SAS) for the data pipeline, the option-implied expected-return (SVIX) construction, the regressions, and all figures/tables. - A README following the Social Science Data Editors template, documenting software requirements, execution order, and a mapping from each manuscript table/figure to the program that produces it. - Author-derived intermediate data that may be redistributed: the public-domain presidential-remark transcripts and the fitted 260-topic LDA weights used to construct the climate/energy text measures. Proprietary source data are NOT included and cannot be redistributed under their license terms: NYSE TAQ Millisecond, OptionMetrics IvyDB US, and CRSP (all via WRDS subscription), and Gallup polling series. The README gives full acquisition instructions, variable definitions, and the exact datasets used so that a researcher with the corresponding subscriptions can regenerate every intermediate file. The high-frequency market panel is rebuilt from WRDS TAQ; the option-implied expected returns follow Martin (2017) and Martin–Wagner (2019). Software: Python 3.10 (pandas 1.5.3, numpy 1.24.2), R 4.3.x (fixest, data.table), and MATLAB and SAS (for the option-implied SVIX construction). The only stochastic step (the LDA topic model) uses a fixed seed and is deterministic. Approximate end-to-end runtime is 1–3 days. License: This dataset is released under CC BY 4.0. The author-generated code in the package is additionally released under the MIT license (see LICENSE.txt); neither license extends to the proprietary source data (WRDS, Gallup), which are not included.

This repository contains images and datasets generated during a comparative study evaluating two semen extenders — Tris-egg yolk-based and skim UHT milk-based — for the preservation of chilled ram semen. The folders include raw and processed experimental data, as well as photographs and videos obtained during semen quality assessment throughout refrigerated storage. The datasets cover information regarding sperm kinetics and the integrity of the plasma membrane and acrosome, evaluated under the different dilution treatments and storage periods. The image files provide visual records of the analyses performed and aid in the interpretation of the experimental results. All files included in this repository are associated with the study described in the corresponding manuscript and are made available to support transparency, reproducibility, and the verification of the reported results.

Data used in papers

This dataset contains processed optical-flow descriptors, machine-learning inputs, statistical outputs, and reproducibility materials supporting a comparative evaluation of RAFT, FastFlowNet, and Farnebäck for image-based river surface-velocity estimation using the open River Dart dataset. For each optical-flow method, separate calibration and independent temporal-validation descriptor tables are provided. The common descriptors used for Random Forest regression include mean optical-flow magnitude, standard deviation of magnitude, 75th and 99th percentiles of magnitude, and mean horizontal and vertical flow components. The repository also includes temporal cross-validation definitions, model-selection outputs, out-of-fold and independent validation predictions, performance metrics, bootstrap confidence intervals, pairwise statistical comparisons, permutation feature importance, feature-ablation results, velocity-range analyses, computational-demand summaries, and code to reproduce the downstream analysis and manuscript figures. The calibration set contains 4,241 observations and the independent validation set 5,910 observations. Validation data were not used for hyperparameter selection. Raw River Dart videos are not redistributed. They remain available from the original Newcastle University dataset by Perks, DOI: 10.25405/data.ncl.19762027. This repository begins at the processed descriptor stage; the complete video-level optical-flow extraction implementation is outside its reproducibility boundary.

This dataset contains 425 valid responses collected from business students in Vietnam. It includes four demographic and contextual variables and 27 measurement items covering six constructs: academic orientation (ACA), social extension (SOE), responsible management (RMA), perceived social responsibility values (PSR), attitudes toward corporate social responsibility (ASR), and CSR-oriented career intention (SRCI). Data were collected between March and September 2025 using both online and offline survey distribution channels. Eligible respondents were students enrolled in business-related university programs in Vietnam who had completed at least two consecutive semesters. The dataset can support research on university social responsibility, responsible management education, social responsibility values, CSR attitudes, and career-related orientations among business students.

This dataset contains a real Adult Income dataset and an author-generated synthetic income dataset prepared for comparative statistical and machine learning analysis. The real dataset is based on the Adult dataset from the UCI Machine Learning Repository, while the synthetic dataset was developed as a Bangladesh-oriented tabular income dataset using demographic, educational, employment, working-hour, experience, and income-related variables. The datasets are provided to support the comparison of real and synthetic tabular income data in terms of their structure, distributions, statistical relationships, and usefulness for machine learning experiments. The accompanying analysis includes data inspection, preprocessing, descriptive statistics, correlation analysis, statistical testing, classification experiments, class-imbalance handling, and model evaluation. The machine learning analysis includes Logistic Regression, Decision Tree, Random Forest, and XGBoost, with additional experiments using class weighting and Synthetic Minority Over-sampling Technique (SMOTE) where applicable. Evaluation measures include accuracy, precision, recall, F1-score, ROC-AUC, and Balanced Error Rate (BER). The synthetic dataset is intended as a controlled data resource for methodological comparison and experimentation. It should not be interpreted as nationally representative or as observed income data from the Bangladeshi population. In particular, the relationships within the synthetic data may reflect the assumptions and construction process used to generate the data rather than naturally occurring socioeconomic relationships. The repository is intended to support transparency, reproducibility, and future reuse of the datasets and associated analysis materials. The real and synthetic datasets should be considered separately because their variables, target representations, and data-generation processes differ.

Traditional pneumatic underactuated grippers mostly rely on external air compressors for air supply, which suffer from bulky size, poor portability, and low integration, thus limiting their application on mobile and lightweight robotic platforms. To explore potential solutions to these issues, this paper proposes a Peristaltic pump-driven compliant Underactuated force-controlled robotic Grasping (PUG) system. Specifically, first a force-controlled underactuated gripper driven by a miniature peristaltic pump is designed and fabricated, without requiring an external air compressor as the power source. Second, three connection configurations between the double-acting cylinder and the peristaltic pump are designed, and their actuation characteristics are comparatively evaluated through experiments, from which a preferable pneumatic scheme is selected. Finally, a control strategy combining a single-stage pressure-rise model with feedforward–proportional-integral (PI) control is proposed, providing a feasible solution for force control of the underactuated gripper without requiring fingertip-mounted force sensors. Experimental results show that the system achieves a force control nonlinearity error of 4.3%, an average force tracking error of 0.15 N, and an average grasping range perception error of 2.69 mm. Adaptive grasping experiments on regular, irregular, fragile, and miniature objects preliminarily validate the system's versatility and non-destructive grasping capability. Compared with conventional air-compressor-driven schemes, the proposed system offers certain advantages in terms of mass and volume, and may serve as a technical reference for the development of lightweight and highly integrated grippers.

This dataset provides the foundational data used to analyze the spatial distribution and driving mechanisms of nighttime street vitality in the high-altitude historic town of Songpan, Sichuan, China. The repository contains two primary files: Cleaned_Combined_Illuminance_Data.csv: Micro-scale nighttime street illuminance data collected via mobile measurement, joined with commercial interface attributes, timestamps, and seasonal tags. Invalid coordinates and null physical anomalies have been cleaned and removed. Built_Environment_Indicators.csv: Multi-dimensional physical, spatial, and functional metrics (e.g., Store Area Ratio, Isochronal Circle Area, POI densities) mapped for 25 independent street segments. These datasets support the multivariate linear regression modeling and correlation analyses detailed in the associated manuscript. For full column definitions, variable mappings, and unit descriptions, please refer to the included README.txt file.