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Research data related to the ​Department of Earth and Environmental Sciences – DISAT of the University of Milano - Bicocca

# Raw Raman Spectra of Fluid and Melt Inclusions in Mantle Peridotites from Sal Island (Cape Verde) This dataset contains approximately 553 unprocessed Raman spectra acquired from Type I and Type II fluid-rich melt inclusions hosted in mantle peridotite xenoliths from Sal Island, Cape Verde. The data constitute the primary Raman spectroscopic records supporting the published study by Maffeis et al. (2026), Carbon enrichment processes in the oceanic upper mantle preserved in peridotites from Sal Island (Cape Verde), Geoscience Frontiers, 17, 102179 (https://doi.org/10.1016/j.gsf.2025.102179), and provide source data for further publications currently in preparation. The investigated inclusions record interactions between mantle peridotites and infiltrating volatile-rich silicate–carbonate metasomatic melts. Type I inclusions, predominantly hosted in olivine, contain silicate glass, variable carbonate phases, and CO2-rich fluids with subordinate CO and N2. Type II inclusions, hosted in relic orthopyroxene within metasomatic reaction domains, contain silicate glass and secondary mineral phases associated with CO2-CO fluids, locally characterised by elevated CO concentrations and carbonaceous material. The database includes Raman spectra of fluid species, silicate and carbonate phases, and other solid components identified within or associated with the inclusions. Spectral measurements encompass different Raman-shift intervals according to the analytical targets and the spectral features observed during acquisition. Each spectrum is provided as an individual plain-text file containing two numerical columns: Raman shift (cm-1) and measured Raman intensity (counts). The data are supplied in their original, unprocessed form, without baseline subtraction, spectral smoothing, peak fitting, or intensity normalisation. File names retain the original analytical identifiers, including combinations of sample identification, host mineral, inclusion number, analytical spot, and/or analysed component. Although naming conventions are not entirely uniform, the identifiers preserve sufficient information to reconstruct the relationships between samples, inclusions, analysed phases, and individual Raman measurements.

This archive contains the code and data used to generate the results reported in Generalized and Scalable Deep Gaussian Process Emulation. It enables reproducibility of the experiments and supports further research on deep Gaussian process emulation. The code was developed and tested using development version 2.6.0-9000 of the R package dgpsi, available at https://github.com/mingdeyu/dgpsi-R.

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