Share your research data

Electronic supplementary material (ESM)

Supplementary Figure with histopathology images (400x) demonstrating anti-PD1 and anti-PDL1 staining for case vs control patients with lichenoid drug eruptions.

erived data and Python code for a study comparing 23 years (2000–2022) of gravimetric root-zone soil moisture from an irrigated plot at Namangan, Uzbekistan, and measurements on the surrounding rain-fed foothills (adirs) with ERA5-Land reanalysis. The deposit contains the station profiles aggregated to the ERA5-Land layers, annual climate and soil moisture series, station–ERA5-Land and adir–ERA5-Land pairs across three bulk densities, the rain-fed adir measurements, site coordinates, ERA5-Land and ERA5 daily extracts for seven grid points, a quality-control log, model outputs and the scripts that reproduce the comparison and the leave-one-year-out residual model. Raw station tables of the Namangan Regional Hydrometeorology Department are not included and are available from the Department on request.

The data contains a SWOT and TOWS analysis, as well as a proposed model serving as a framework for a smart digital marketing strategy.

Background Telemedicine (TM) has become indispensable to rheumatology care. Accumulating evidence suggests its high acceptance, satisfaction, and feasibility. There is a paucity of data on TM in systemic lupus erythematosus (SLE). This study aimed to evaluate the accuracy of SLE virtual outcome measures by evaluating whether physician assessments of SLE disease activity (DA) obtained during TM visits are comparable with those from face-to-face (F2F) visits. Methods This was a longitudinal study of SLE patients with variable DA levels from four academic lupus centers. Each participant was evaluated by the same physician at two visits, using a TM interview followed by F2F evaluation. SLE DA measures and physician/participant feedback were completed. Agreement between TM and F2F measures was analyzed using paired t-test, intraclass correlation coefficient (ICC), and Bland-Altman method. Results 163 participants completed 308 visits. At V1, TM slightly underestimated DA compared to F2F, with a mean score difference (SD) of –0.306 (1.671) for SLEDAI, -0.141 (2.869) for BILAG, -2.184 (16.140) for LFA-REAL, and -0.975 (8.209) for PhGA. Differences at V2 tended to be smaller than at V1. ICC showed high agreement at V1 (r=0.86-1) and V2 (r=0.92–1). Bland-Altman plots showed a high level of agreement. Conclusions TM assessments showed a high level of agreement between TM and F2F DA assessments at both visits. Smaller mean score differences and greater agreement at V2 than at V1 may be due to improvement with TM practice over time. The study supports increased use of TM in lupus clinical care and research.

Research hypothesis: A non-neural learner built from screened Neumann potentials on a graph of training records (HARBOUR) can give exact case-based explanations, detect inputs from unseen classes as well as strong combined baselines, and learn from few labels when labels are chosen by a rule derived from a discrete trace Poincaré inequality, while staying close in accuracy to tree ensembles and tabular neural networks. What the data are: Code, raw experiment results and analysis scripts for the article “HARBOUR: A non-neural harmonic learner with exact case-based explanations and calibrated rejection for tabular data”. No new primary data are included. The eleven benchmark datasets (NSL-KDD, UNSW-NB15, Heart disease, Diabetes 130-US, Dry Bean, Covertype, Dermatology, Soybean, ann-thyroid, Satimage, Pendigits) are public; the included script downloads them and checks their SHA-256 checksums. How the data were gathered: Experiments ran on Google Colab (NVIDIA T4 GPU). HARBOUR was compared with logistic regression, k-NN, random forest, XGBoost, MLP, ResNet and FT-Transformer, each with the same budget of eight tuned configurations. Each line of results/r2_<experiment>.jsonl stores one run and the metrics of every method (macro-F1, balanced accuracy, calibration error, unseen-class AUROC, selective risk, running time). E1E2/E1E2v2: main benchmark of six datasets with shift and unknown-class splits. E2C: five confirmation datasets, run after the decision criteria were fixed. E3: label efficiency. E4: explanations. E5: ablations. E8: running time. Notable findings: On the confirmation datasets, HARBOUR’s rejection rule reached a mean AUROC of 0.879 for held-out classes, the highest of 21 scores and level with XGBoost plus nearest-neighbour distance (0.876). Its attributions were exact, faithful under refitting and about 287 times faster than KernelSHAP. Theory-guided label selection improved macro-F1 (p < 1e-9). HARBOUR was about 0.025 macro-F1 below random forest, XGBoost and an MLP. Its limits are dependence on the input distance and over-rejection under covariate shift. How to use: The README maps each file to the article. The analysis folder rebuilds every table and figure from the results folder in a few minutes without re-running models. Class HARBOUR2 in the code is the model of the article; class HARBOUR has the same classifier with an earlier rejection rule, used for comparison. notebook/HARBOUR_reproduce.ipynb repeats all experiments. Licences: code MIT, results CC BY 4.0.

Input data for: Reviving Grid-Stranded Micro-Hydro as Hybrid-Inertia Assets for Weak Rural Feeders: A Grid-Forming Storage Retrofit Assessed Across Grid Archetypes

This dataset examines audience perceptions of women's gendered trauma, emotional suffering, displacement and survival narratives in Indian Partition cinema. Data were collected using a structured questionnaire comprising four screening questions, nine demographic and film-exposure questions, and 20 measurement items across four constructs: Gendered Trauma (GT), Emotional Suffering (ES), Displacement (DP) and Survival Narratives (SV). Responses were recorded using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The supporting documents include the questionnaire, codebook and semi-structured interview guide. The dataset supports further research on gender representation, historical memory, cinematic narratives and audience interpretations of women's experiences during the Partition of India.

This dataset holds the data behind the article "Residual isocyanate in a Jatropha polyurethane/PES matrix anchors chitosan and adds to single-pass copper capacity" (Zeng, Zainal Abidin, Omar, Harun; Universiti Putra Malaysia). Asymmetric membranes were cast by nonsolvent-induced phase separation from N,N-dimethylformamide dopes in which a Jatropha-derived polyurethane (JPU) precursor carrying residual isocyanate (NCO) was blended 50/50 with polyethersulfone (PES), with chitosan and ZIF-8 as modifiers. Four modifier-bearing formulations (JPU/PES/C, JPU/PES/Z, JPU/PES/C/Z and PES/C/Z), NCO-capped precursor controls and a randomised 2 x 2 x 2 matrix x chitosan x ZIF-8 factorial were characterised and tested for single-pass Cu capture at 50-150 L m-2 h-1. The deposit is one workbook, 03_Supporting_Data.xlsx: - T_ sheets: Tables 1-4 and Tables S1-S54 of the article and Supporting Information as reported, with full captions. - A_ sheets: run-level records of the factorial and its replication, single-pass breakthrough and digests, breakthrough fits, eight-cycle EDTA regeneration, 30-day hydraulic duty, sequential chitosan extraction, NCO register, modifier inventory, stack and pressure-tap series, XPS, tensile and AFM. - B_ sheets: coupon- and point-level records of the protonatable-nitrogen titration, Cu isotherms with the membrane-free vessel blank, N 1s/O 1s matrix controls, zinc release, active-versus-capped paired blocks, gel fraction, porosity estimators, extraction controls and stack pressure under the Cu challenge. - C_ sheets: 15N qNMR and 13C/15N HETCOR records with all NMR spectra and acceptance series, the 13C coagulation tracer, XRD and ATR-FTIR spectra, EDX composition, EDS spectra and maps, FESEM morphometry and organic-fouling records. START_HERE lists the sheet groups; TABLE_INDEX maps every table to its sheet and record sheets; SHEET_INDEX describes each sheet; PROVENANCE gives the source of each data family. The reference data of the unmodified JPU/PES (50/50 DMF) matrix are those of Zeng et al., J. Membr. Sci. (2026) 126309 (https://doi.org/10.1016/j.memsci.2026.126309), deposited at https://doi.org/10.17632/wn47krc8p6.5.

This dataset provides the reproducibility material accompanying the manuscript “Consistency-Projected Sliding DFT: Finite-Precision Analysis and Structured Multi-Constraint Correction.” It contains three frozen reproducibility packages. The SFDB Numerical Reproducibility Package v2.2.1 contains MATLAB code, locked campaign configuration, compact results, and validation material for the 70,500-trial fixed-point campaign comprising 141 arithmetic settings, five signal classes, ten methods, and ten common seeds. The principal recursive-method comparison reports a 98.5993% paired win rate and a geometric MSE ratio of 6.9791363 for SFDB-p4-neareven-FQ against Classical, mSDFT, oSDFT-Res, and oSDFT-Mod. The SFDB Conservative Design and Holdout Package v1.0.0 contains the operation-graph-based conservative rank/precision certificate, the frozen 648-candidate design campaign, and a separate predeclared holdout of 36 designs evaluated over 468 bounded-input trials. All 468 holdout trials respect their computed error bounds and are overflow-free. The largest observed per-frame RMS error is 5.4811848e-4 and the largest observed-to-bound ratio is 0.6303922. The certificate provides sufficient, not necessary, design conditions. The SFDB Practical FPGA Reproducibility Package v1.0.0 contains MATLAB, SystemVerilog, ModelSim, Quartus, reporting material, raw synthesis/STA evidence, and matched-format numerical results for the N=32 implementation study targeting the Intel Cyclone 10 GX 10CX105YF672E5G. It includes the seven-method common-60-MHz comparison, three fitter seeds, multicorner timing evidence, and 350 matched-format numerical trials. No physical FPGA board was used, and external I/O paths were not constrained with board-level input/output delays. Each archive includes package documentation, citation metadata, version information, SHA-256 manifests, source material, and approved result files. The packages are intended to reproduce and audit the numerical, conservative-design, holdout, and synthesis-level results reported in the associated paper.