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This dataset provides a comprehensive, multi-variable panel combining agricultural market commodity prices, meteorological factors, and macroeconomic indicators across major administrative regions and markets in Mali. The primary objective of this compiled dataset is to support predictive modeling, econometric forecasting, machine learning applications, and vulnerability assessments concerning cereal price volatility and agricultural market integration in the West African Sahel. The dataset compiles information from multiple authoritative open sources: - Agricultural Commodity Prices: Cereal and staple crop price dynamics across local retail and wholesale markets, originating from the World Food Programme Vulnerability Analysis and Mapping (WFP VAM) database. - Climate and Weather Variables: Historical meteorological indicators (such as precipitation, 2-meter surface temperatures, and related agro-climatic metrics) extracted and aggregated via the Open-Meteo historical weather platform. - Macroeconomic Indicators: Global crude oil price benchmarks and imported inflation indices reflecting broader economic fluctuations impacting domestic food supply chains. The provided tabular file (df_final.csv) contains temporally aligned, geographically indexed, and pre-cleaned records suitable for immediate statistical analysis, time-series forecasting, and econometric modeling without additional feature merging requirements.

The integrated scRNA datasets of dendritic cells

This dataset accompanies the article "CFD-based optimization of a gas–liquid feed-injection nozzle: the relevance of high-fidelity inlet modeling" (submitted to Chemical Engineering Science). It contains the inlet-pressure measurements and high-speed recordings from a 45° inclined gas–liquid pipe unit at GLR 1–5 %, the corresponding simulation outputs, and complete ESI OpenFOAM v2312 cases (interIsoFoam) for the pipe-only, decoupled nozzle-only and pipe–nozzle assembly configurations, the nozzle-only case being fed by transient pipe outlet fields mapped as a time-varying inlet condition. It also includes the two Dakota/COBYLA nozzle optimization campaigns, differing only in the inlet condition (mapped vs. uniform), with their inputs, driver, evaluation histories, best designs, cross-evaluation and sensitivity studies, as well as the Python scripts that reproduce the article's figures. README files describe the structure and how to run each part.

Reproducibility package for the paper "Where 5G Throughput Prediction Actually Fails: A Transition-Aware, Leakage-Audited Evaluation on Real Drive-Test Traces" (submitted to Future Internet, MDPI). It contains the analysis code and all derived artifacts (processed 1 Hz features, out-of-fold predictions, metric tables, and publication figures) needed to reproduce every number, table, and figure in the paper. The study audits how machine-learning models for short-horizon 5G downlink-throughput prediction are evaluated: the optimism of random-split versus leave-sessions-out evaluation, the dominance of autoregressive self-lag features, where error concentrates under absolute versus scale-invariant metrics, and whether lightweight transition-context features help (a null result). All headline numbers carry per-session bootstrap confidence intervals. This record does NOT redistribute the raw data; the pipeline regenerates the processed features from the public Raca et al. (2020) 5G production dataset (https://github.com/uccmisl/5Gdataset). Requirements: Python 3.12, CPU-only (numpy, pandas, scikit-learn, scipy, matplotlib, pyarrow); all randomness is seeded. See README for run order. Code is MIT-licensed.

This dataset contains the data and R code used in the manuscript "Genus-level ubiquity masks amplicon sequence variant structure in Aspergillus, Penicillium and Fusarium from Vitellaria paradoxa soils across three Nigerian savanna zones". Rhizosphere and paired non-rhizosphere soils were collected beneath Vitellaria paradoxa (shea) trees at six sites, two in each of the Guinea (Zaria), Sudan (Kano) and Derived (Saki) savanna zones of Nigeria. The resulting 12 composite samples were analysed by ITS2 metabarcoding (primers ITS3/ITS4; Illumina MiSeq, 2 × 300 bp). Reads were denoised with DADA2, yielding 929,746 reads and 2,613 amplicon sequence variants (ASVs). Taxonomy was assigned with QIIME 1.9.1/UCLUST against the UNITE release of 20 November 2016. Soil physicochemical properties were measured in triplicate at the Soil Laboratory, Forestry Research Institute of Nigeria, Ibadan: pH, organic carbon and matter, particle size, total N, exchangeable K, Na, Ca and Mg, available P, and Cu, Zn, Fe and Mn. The dataset includes: (1) the ASV count table (12 samples × 2,613 ASVs); (2) ASV sequences in FASTA format; (3) the taxonomic assignment of each ASV; (4) sample metadata giving savanna zone, soil compartment and site pairing; (5) soil properties as means and standard deviations of three analytical replicates; (6) R scripts that reproduce all analyses and figures, together with their output tables and figures. The analyses examine occupancy, zone and compartment restriction, and within-genus ASV composition of the three most abundant named genera, Aspergillus, Penicillium and Fusarium. Exact permutation tests respect the paired, site-level design. Although all three genera occurred in every sample, 50–61% of their ASVs were detected in only one sample. Savanna zone explained 25–35% of within-genus compositional variation. Raw sequence reads are available in the NCBI Sequence Read Archive under BioProject PRJNA1531940. A README file describes every file, column and unit, and explains how to rerun the analyses (R ≥ 4.3 with the vegan package; run scripts/run_all.R).

This dataset includes ten soil properties, five crop growth traits, and crop yield from 108 sampling points along 12 transects across three gullies in the Black Soil Region of Northeast China. Soil variables include bulk density, water content, clay content, saturated hydraulic conductivity, aggregate stability, penetration resistance, organic matter, and total nitrogen, phosphorus, and potassium. Crop traits include root length density, aboveground and belowground biomass, plant height, and hundred-grain weight. The dataset supports comparative analyses of spatial variations in soil conditions and crop performance along distance gradients from gully edges.

This dataset contains source and processed experimental data supporting a study of Persicaria odorata-mediated silver nanoparticles (PO-AgNPs) and their functionalisation onto cotton and polyester fabrics. The deposited material includes UV–Vis spectroscopy, transmission electron microscopy (TEM) particle-size measurements, dynamic light scattering (DLS) and zeta-potential data, field-emission scanning electron microscopy (FESEM) images and energy-dispersive X-ray spectroscopy (EDX) records, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric and differential thermal analysis (TGA/DTA), disc diffusion measurements, AATCC TM100 and ASTM E2149 antibacterial data, air permeability, water-vapour transmission rate (WVTR), tensile testing, and CCK-8 cytocompatibility data. Sample abbreviations are C-C (untreated cotton), C-AgNP (PO-AgNP-functionalised cotton), P-C (untreated polyester), and P-AgNP (PO-AgNP-functionalised polyester). The dataset includes consolidated supporting data together with relevant source files, instrument exports, reports, and microscopy images.

This repository provides supplementary methods, results, and a reproducibility package for a study of job-specific experience, time into shift, and injury severity in U.S. mining during 2000–2025. The analysis integrates publicly available Mine Safety and Health Administration (MSHA) Open Government Accident Injuries records with historical Part 50 files and annual employment data from MinesProdYearly and ContractorProdYearly. Historical records were linked by DOCUMENT_NO, with agreement on mine identifier and injury date required before importing demographic information. The source snapshot was retrieved on 2 October 2026 and preserved with file-level checksums. The initial dataset contained 271,042 records for 2000–2025. Sequential eligibility restrictions, linkage, complete-case selection, and exclusion of selected disease and inflammation codes yielded a primary sample of 197,159 injury reports from 12,121 mines. Outcomes comprised 62,244 minor injuries (degree 06), 131,716 injuries involving lost or restricted work (degrees 03–05), and 3,199 fatal or permanently disabling injuries (degrees 01–02), including 1,067 deaths and 2,132 permanent disabilities. The main exposures are job-specific experience and time into shift. Time into shift was calculated from reported injury and shift-start times, allowing a midnight crossing, with the primary analysis restricted to 0–16 hours. Covariates include age, recorded sex, total mining experience, calendar year, contractor status, commodity, operational subunit, annual mine employment, and shift-start period. The package contains retained source archives and data definitions, scripts for reconstructing the linked analytical sample, selection and linkage audits, missingness summaries, model coefficients and covariance matrices, numerical tables, figures, and supplementary documentation. Statistical outputs cover sequential multinomial regression, restricted cubic splines, mine-clustered standard errors, nonlinearity and interaction tests, and standardized severity probabilities. Sensitivity analyses address alternative time windows and severity comparisons, zero-time records, accident mechanism, operational strata, contractor clustering, exclusion of 2025, and experience mismatch. These data describe severity conditional on a reported injury. They do not provide exposure-specific denominators for injury incidence or establish causal effects of experience or shift timing. Actual shift duration, individual work histories, and repeated-worker identifiers were unavailable. To reproduce the analysis, extract the package, follow README.txt, install the dependencies listed in requirements_revision.txt, and run scripts/reproduce_revision.py. The retained source snapshot should be used because live MSHA files may subsequently be updated.

# Raw 3D Point Cloud Dataset of Weld Beads This dataset contains raw 3D point cloud scans (`.ply` format) of various weld bead profiles on cylindrical aluminum tubes, captured using a REVOPOINT 3D Acusense RGB-D camera. For methodology, preprocessing, and model implementations, please refer to the published paper: > Chunhui Chung, Chia-Yuan Wu, "Weld bead recognition and robotic grinding path planning via deep learning point cloud segmentation," *Measurement*, Vol. 284, 122250, 2026. > DOI: https://doi.org/10.1016/j.measurement.2026.122250 --- ### File Naming Convention Files are named according to the workpiece sample and scan index: `<Sample>_<Index>.ply` (e.g., `A_1.ply`, `A_2.ply`, `B_1.ply`). * **Samples A – E:** In-distribution weld geometries (used for model training in the paper). * **Samples F – J:** Unseen weld geometries (used for generalization testing in the paper). --- ### Data Format & Notes * **Format:** Unordered point cloud in Stanford Triangle Format (`.ply`). * **Coordinates:** Point coordinates `(x, y, z)` are defined in the camera coordinate frame. * **Condition:** Unprocessed raw sensor data containing optical noise, minor surface reflections, and occlusion gaps from single-view scanning. --- ### License This dataset is distributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

This dataset contains selected vertical-component seismic waveform records from 77 stations near Shenzhen Bao'an International Airport during an aircraft approach on 6 November 2025. The files are provided in SAC format and represent a subset of the recordings analyzed in the accompanying study.