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This dataset contains 180 plot-level averaged visible–near-infrared to short-wave infrared (VNIR–SWIR) reflectance spectra from Russet Burbank potato grown under nitrogen fertilization treatments near Carberry, Manitoba, Canada, during the 2024 growing season. A randomized complete block design comprised 15 nitrogen treatments and four replicates, giving 60 experimental plots. Each plot is represented by field-canopy, laboratory fresh-leaflet and laboratory dried-powder spectra, linked to petiole nitrate-N (PNN) measurements and treatment metadata. Spectra were measured using an ASD FieldSpec Pro spectroradiometer and exported over 350–2500 nm on an interpolated 1-nm wavelength grid. This spacing does not represent the instrument’s native spectral resolution. The lowest-level deposited spectral products are plot-level averaged reflectance spectra before dataset-level cleaning; individual acquisitions are not included. The repository provides averaged spectral exports, long- and wide-format tables, cleaned reflectance spectra, preprocessed spectra, quality-control summaries, method documentation and reconstruction scripts. Cleaned spectra exclude the inclusive wavelength intervals 350–400, 1290–1500, 1790–2050 and 2350–2500 nm. Preprocessing follows Savitzky–Golay smoothing, standard normal variate correction and a Savitzky–Golay first derivative. Both Savitzky–Golay operations use an 11-point window and polynomial order 2; complete numerical settings are documented in the repository. PNN was determined by AGVISE Laboratories using 2% acetic-acid extraction and cadmium-reduction flow-injection colorimetry. Results were reported in ppm on a dry-matter basis and converted to percent by dividing by 10,000. The dataset supports crop-spectroscopy and laboratory-to-field calibration-transfer investigations within the represented experimental conditions. Transfer to other cultivars, sites, seasons, growth stages or instruments requires independent validation. The dataset is associated with the research article “VNIR–SWIR hyperspectral spectroscopy for nitrogen assessment in potato crops: Deep learning and regression models across field and laboratory conditions,” published in Smart Agricultural Technology.

This dataset provides the derived data, diagnostic outputs, figures, and reproducibility materials supporting the study “Geographical heterogeneity in interpretive and screening robustness of PM₂.₅ mortality decomposition across 204 populations.” The study decomposes the change in ambient PM₂.₅-attributable mortality between 2000 and 2023 across 204 countries and territories into population growth, population ageing, and the composite attributable mortality rate. Contributions are evaluated over all six replacement orders. The package reports symmetric order-robust contributions and complementary diagnostics of interpretive sensitivity, including sign changes, changes in the largest absolute contributor, and order-induced uncertainty widths. This version adds the analyses developed during manuscript revision: alternative UN M49 geographic classification; population-threshold sensitivity analyses; 10-group and 5-group age-resolution checks; task-specific screening comparisons based on mean attributable burden, final attributable burden, raw attributable deaths, and the direction of change; and exploratory spatial-dependence diagnostics. The spatial extension uses frozen Natural Earth 1:50m geographic inputs, k-nearest-neighbour spatial weights (k = 4, 8, and 12), Moran’s I statistics, unrestricted and within-region permutation tests, and Holm adjustment across the full family of 16 tests. The deposited materials consist of: (1) a machine-checkable reproducibility package containing scripts, frozen derived inputs, geographic resources, expected outputs, checksums, and verification routines; (2) a supplementary data workbook containing the complete tabular results; and (3) supplementary information documenting the methods, diagnostic definitions, sensitivity analyses, and supporting figures. The package reproduces the analyses from supplied derived data without requiring access to the restricted source archives. Optional source-level reconstruction requires the five licensed GBD 2023 archive files listed in the README. Those source archives are not redistributed. All reported results are based on point estimates and should not be interpreted as GBD uncertainty intervals, causal effects, or direct measures of intervention need.

This dataset was collected during a pre-post interventional study evaluating the acute effects of short-duration, interval isometric handgrip exercise (IHE) on working memory (WM) performance, processing speed, and cognitive response bias. The Hypothesis of the study was Acute interval IHE transiently enhances working memory capacity, processing speed, and perceptual discrimination without increasing impulsive errors in healthy young adults. Participants (N=98) were Healthy, right-handed young adults aged 18–25 years (55 males,43 females). The Intervention used was four cycles of alternating 18-second maximal hand squeezes and 30-second rests per hand (Right hand followed by Left hand) using therapy exercise balls. The assessment of working memory was done by computerized visual n-back task (0-back, 1-back, 2-back, and 3-back levels) administered using Inquisit 6 software before and immediately after IHE. The analyzed findings derived from this dataset are published in the following article: https://doi.org/10.1016/j.jbmt.2026.07.011

Harmonised partner-level dataset covering 22 publicly funded RDI actions and cycles in Greece (2014–2020), comprising 2,287 projects, 5,411 beneficiary records and 54,993 performance indicator records across 26 indicators. Data include financial information, beneficiary characteristics, RIS3 sectors, NUTS 2 regions, implementation dates, and target and achieved indicator values. Compiled from official administrative sources, the dataset was validated, harmonised, translated into English and pseudonymised. Missing values were not imputed, and provenance flags document the origin of reported and harmonised values.

This dataset contains 1,018 questionnaire responses collected in Bangladesh for a research study on early detection of dengue infection using machine learning. Responses were recorded through a bilingual English–Bengali Google Forms questionnaire. The researcher has confirmed this as the final collection. The survey covers age, gender, district and residence type; reported dengue history and environmental or household exposure; fever duration and maximum recorded temperature; headache, retro-orbital, muscle and joint pain rated on 0–10 scales; vomiting frequency; eight symptoms reported during illness days 1–3; and the recorded final dengue test result. There are 630 recorded positive and 388 recorded negative outcomes. The recorded test outcomes have not been independently verified against laboratory or clinical records. Test modality and illness day of testing are not specified in the supplied data. Dengue_Raw_Research_Data_v1.csv contains 1,018 rows and 23 research columns, preserving the original research values, missing entries, anomalies, outcome labels and row order. Three administrative fields—submission timestamp, participation-consent response and data-entry role—are excluded from both public CSVs. Short English headings are mapped to the original bilingual questions in Data_Dictionary.csv. Dengue_Cleaned_Research_Data_v1.csv contains the same responses and all 23 research fields, including every symptom and pain-scale variable. Cleaning standardizes headings, category labels, district spelling and surrounding whitespace. The cleaned file has zero missing cells: 12 source temperature blanks and two invalid entries were filled with 103°F, the median of 1,004 valid recorded temperatures. These 14 estimates are individually traceable in Cleaning_Log.csv; original values remain in raw. All recorded valid measurements, pain ratings, outcomes and row order are preserved. Two recorded values of 107°F are retained and flagged. No rows were removed or generated. No scaling or model-specific feature selection was applied. Data_Dictionary.csv, Cleaning_Log.csv and README.md document definitions, transformations, imputation positions, uncertain responses, quality checks and hashes. README.md includes a tested reconstruction example. For machine-learning evaluation, restore the 14 imputed positions to missing before splitting and fit imputation within training folds. Assess predictor availability at the intended prediction time, since some questions refer to the whole illness rather than its first three days.

This dataset contains the spatially gridded Relative Drought Risk Index (RDRI) and three risk components for the Indian mainland at a 0.25° spatial resolution. The dataset was generated for the study “Improved Human Development Index may Mitigate Drought Risk under the Changing Climate and Socio-Economic Conditions." The Relative Drought Risk Index (RDRI) is derived by integrating three risk components: hazard, exposure, and vulnerability. Hazard is quantified as drought frequency, derived from the Standardized Precipitation Evapotranspiration Index (SPEI). Exposure is represented by population density. Societal vulnerability is characterized using the time-varying Human Development Index (HDI), expressed as (1 - HDI). The three components are normalized using inverse marginal distributions and subsequently integrated using a geometric mean approach to obtain the RDRI for each grid cell, scaled from 0 to 100. The dataset is provided for three analysis periods: 1991–2015, 1991–2003, and 2004–2015. For each period, the dataset contains four variables: normalized drought hazard, normalized population exposure, normalized vulnerability, and RDRI. The component variables represent values aggregated over the corresponding analysis period, and the resulting RDRI represents the corresponding integrated drought-risk value for each grid cell. The datasets are intended to support analysis of the spatial distribution and temporal evolution of drought risk and its underlying components. Thus, the dataset consists of 24 files, corresponding to four variables, three analysis periods and two data formats. Each variable is provided in two distinct formats. The first format contains latitude, longitude, and the corresponding value for each grid cell, which can be used for further spatial analysis and processing. The second format provides the same information as a gridded matrix, in which the rows represent latitude and the columns represent longitude, facilitating direct spatial representation and map generation.

This dataset supports the review “Evidence and validation for applying game theory to international river management”. It contains the published Harirud payoff matrix and allocation-scenario percentages from Mirzaei-Nodoushan et al. (2022; DOI: 10.1038/s41598-022-07971-1), together with Python code and deterministic outputs for unilateral-deviation, equilibrium and bounded-perturbation checks. Published numerical values are unchanged and their source is attributed. These are model outputs, not new field observations or a replication of the original hydrological model. ChatGPT assisted development of the code and documentation; no AI model is used at runtime. Individual file descriptions and reproduction instructions accompany the archive.

Playback stimuli, data, and R code for reproducing the statistical analyses reported in: Lopes GM, Santieff IZ, Pizo MA, Diniz P (2026). Partners coordinate territorial defense regardless of distance between paired intruders in a duetting bird. Avian Research, 100442. https://doi.org/10.1016/j.avrs.2026.100442

Everything needed to check or rebuild the results of the GraFold manuscript: the pipeline that builds each dataset from public sources, trains both stages, runs the baselines and produces the tables and figures; the second implementation used for the replication on an 8 GB GPU and for the later experiments; the pre-registration documents written before those experiments; the stored results, audits and logs; the trained checkpoints; and the derived datasets. Baseline code and weights from other groups are not redistributed; the scripts fetch them from their authors. Start with README.md.

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