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This dataset contains the anonymized responses of a cross-sectional questionnaire survey describing how nurses promote sleep hygiene in adult psychiatric inpatient care, and comparing the practices of day-shift and night-shift nurses. The study tested two hypotheses: (1) that sleep hygiene promotion is only partially implemented by nurses, and (2) that practices differ between day- and night-shift teams. Data were collected between 20 January and 13 April 2025 in three French public hospitals located in two regions, using a 14-item self-administered questionnaire (pre-tested with five nurses). Registered nurses working either day or night shifts in full-time adult psychiatric inpatient units were eligible; nurses working interchangeably on both shifts were excluded. Participants answered on paper or online (Google Forms). The dataset is provided as a single table (128 rows, one per participant; 40 variables) in CSV and XLSX formats, accompanied by a codebook describing every variable. Categorical variables (sex, years of experience, work shift, unit type, centre, training, self-rated knowledge, frequency of advice given, interest in a support tool) are coded with readable labels. Multiple-answer questions (methods used to identify needs, tools/approaches, recommendations given, and perceived barriers) are coded as binary variables (1 = selected, 0 = not selected). To protect participant confidentiality, submission timestamps were removed, hospital names were recoded as Centre A/B/C, the free-text unit field was grouped into categories, and all free-text answers (including "other" specifications) were removed. What the data show: the transmission of sleep hygiene recommendations was generally low (most nurses had advised only one to five patients over the previous four weeks, and 13% none), and lack of theoretical knowledge was the most frequently reported barrier. Several clinically relevant recommendations (smoking, alcohol, heavy meals) were seldom given. Night-shift nurses reported better knowledge, transmitted recommendations more often, gathered patients' medical history more frequently, and more often advised stress management and CBT-I, whereas day-shift nurses more often reported a lack of time. How to interpret and use the data: the dataset reproduces the descriptive statistics and the day/night comparisons reported in the associated article. Frequencies and percentages can be computed on the categorical and binary variables; between-group (day vs night) comparisons can be reproduced using the chi-square test (expected counts ≥ 5) or Fisher's exact test otherwise, with p ≤ 0.05 considered significant. Note that the published Table 1 further split the "Other" unit category into Rehabilitation (n = 7) and Other (n = 8) from manual review of free-text answers, which is not reproducible from the coded data alone. Refer to the codebook for the full definition and coding of each variable.

This dataset contains the data, code, and documentation for replicating the results in “Chronologically Consistent Large Language Models,” forthcoming in the Journal of Financial Economics. The package reproduces all tables and figures from the included derived series and provides the code for the underlying asset-pricing and language-model pipelines. Dow Jones Newswires and CRSP are proprietary and therefore are not redistributed; synthetic stand-ins with identical schemas are provided so that the data-processing and asset-pricing pipeline can be run end-to-end without access to the proprietary data. The package also documents the public pretraining corpora and released ChronoBERT and ChronoGPT model checkpoints. See README.md for data provenance, computational requirements, and detailed reproduction instructions.

RSBdSL38 — an expert-validated Bangla Sign Language (BdSL) image dataset of 10,874 images across all 38 canonical BdSL hand signs (covering the 51 letters of the Bangla alphabet), collected from 36 real signers and volunteers at three special-needs schools across Bangladesh and validated in full by a sign language expert. If you use this dataset please cite this work: @misc{ahmed2026deployablebanglasignlanguage, title={Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model}, author={Saad Ahmed and Md Khalid Syfullaha}, year={2026}, eprint={2608.06252}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2608.06252}, }

Dataset of 1715 included studies in "Global epidemiology of snakebite envenoming: a scoping review" by Uppal et al.

This record contains the R code used for the age-stratified dietary-risk-function calculations and the aggregation of dietary-risk contributions for the Health Nutritional Index (HENI) analysis. The code requires prepared input objects derived from the CHNS data and parameter sources described in the manuscript. Aggregate coefficients and scenario-level results are reported in the Supplementary Information.

cheCkOVER: Assessment-support workflow for biogeographic metrics from species occurrence data

Three-dimensional quantification of Ryugu matrix properties and constraints on aqueous alteration heterogeneity.

This dataset supports the manuscript "Inference Relevance and Audience Access in the Subtitling of Netflix Nature Documentaries from English to Indonesian" (Baharuddin et al., submitted to Discover Global Society, Springer Nature). It contains all primary data generated during an empirical study of English-to-Indonesian subtitle quality assessment using the Audience-Centred Subtitling Adequacy (ACSA) framework.

This dataset contains survey data collected from tertiary-level students in Bangladesh to examine psychosocial risk factors and depression, anxiety, and stress measured using the Depression Anxiety Stress Scales-21 (DASS-21). The dataset includes demographic characteristics, psychosocial risk factors, and DASS-21-derived depression, anxiety, and stress measures. The dataset is provided in de-identified form to support research, replication, and secondary analysis.

Hurricane events strongly affect the pecan crop by uprooting and lodging trees. Additionally, current fallen-tree monitoring relies on manual field surveys, which are invasive, time-consuming, and costly, constraining timely decision-making. Therefore, in this study, we deployed a deep learning (DL) framework based on the You Only Look Once (YOLO26) model to detect fallen pecan trees using unmanned aerial vehicle (UAV) RGB images. Flights were conducted over four pecan fields, ten days after Hurricane Helene crossed the state of Georgia, USA. As a result, 546 images were acquired and individually analyzed to detect fallen trees. Initially, ground-truth data were generated through assisted image processing, resulting in 2,408 annotations labeled “Fallen”. For our analysis, three fields were considered for the model development (training and validation). Subsequently, to ensure the model accuracy and reliability, a fourth independent field was used as the test dataset. Our results showed that the fallen tree detection models achieved a precision of 80.98–88.48%, a recall of 61.25–72.08%, and a mAP@50 of 70.93–77.50%. Among the evaluated variants, YOLO26m demonstrated the best performance on the independent test dataset, achieving an R2 of 0.74 and a mean absolute error (MAE) of 0.61 trees per image. Furthermore, we designed a user-friendly platform as a proof of concept to evaluate the model’s operability. This study, therefore, presents a novel UAV-based object detection framework for detecting fallen pecan trees, empowering stakeholders with a precise, accurate, non-invasive, safe, and rapid solution. These findings also support precision agriculture practices and promote the integration of advanced technologies into tree-crop management systems.