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Supplementary Table network pharmacology

This dataset contains encrypted mobile network traffic collected from ten widely used Android social media applications: Facebook, Instagram, LinkedIn, Reddit, Snapchat, Telegram, TikTok, Twitter, WhatsApp, and YouTube. Traffic was captured over five independent collection days under controlled experimental conditions and processed using NFStream to generate bidirectional flow-level records. The dataset comprises 25,116 labeled network flows extracted from 50 packet capture (PCAP/PCAPNG) files, with each application represented by five independent capture sessions. The published data contain the complete set of NFStream-extracted flow features, including statistical flow characteristics, packet-size statistics, timing information, transport-layer attributes, and encrypted-session metadata. This repository provides the raw NFStream feature dataset used in our study. Feature selection, leakage-control preprocessing, temporal train/test partitioning, and multi-flow aggregation were performed during the experimental pipeline and are described in the accompanying manuscript. The dataset was developed to support reproducible research in encrypted traffic analysis, mobile application fingerprinting, digital forensics, network security, and explainable artificial intelligence. It accompanies the manuscript "Mobile Social Media Traffic Fingerprinting Using Encrypted Network Flows: A Benchmark Dataset and Comparative Study."

supplementary material, data, and codes associated with the paper: Ultrasound-Enhanced Hydrogen Peroxide Generation of Microwave Plasma Discharges in Conductive Aqueous Solutions

This dataset supports the hypothesis that thermal preconditioning, followed by warm aqueous surfactant treatment and agitation, weakens the cathode coating-to-aluminium interface and enables recovery of chemistry-specific LiFePO4 (LFP) and LiCoO2 (LCO) powders with limited aluminium carryover. The repository contains “Coupon_level.xlsx” with 162 coupon-level records; 36 raw 90° peel-test workbooks; and 27 optical images with 27 binary masks. Each chemistry was examined using a 3 × 3 × 3 factorial design: 250, 300 and 350 °C; 0.5, 1.5 and 3.0% (v/v) commercial product; and 30, 60 and 90 min washing. Three feedstock lots were represented at each condition. Principal responses are delamination efficiency, recovered-powder yield, mass closure and exposed-foil area. Peel files provide time, displacement and force data for pristine electrodes and specimens treated at 200, 240, 280, 320 and 350 °C. Optical files cover pristine, 240 °C and 280 °C specimens from 0 to 90 min. Maximum delamination was 98.11% for LFP and 96.19% for LCO at 300 °C, 3.0% (v/v) and 90 min. At 280 °C, normalized peel strength fell from 270 to 49 N m−1 for LFP and from 295 to 65 N m−1 for LCO, demonstrating weakening before washing. The 280 °C specimen reached approximately 95% detached area within 28 min. The coupon data may be used for factorial modelling; peel traces for force-displacement analysis; and masks for detached-area kinetics.

This dataset contains the genomic data supporting the analyses presented in the manuscript. It includes the list of Staphylococcus aureus genomes used in the study, raw pangenome presence/absence matrices generated for the ST30 and ST105 reference datasets, comparative Roary pangenome matrices used for lineage-specific analyses (ST105 × ST5, ST30 × ST105, and ST30 × ST36), and the volcano plots generated from ORF-based pangenome association analyses.

Supplementary Data and Methods

To address the issue of performance degradation due to calendar aging caused by long-term storage of mining power batteries before shipment and installation underground, this paper presents a comprehensive dataset of measured electrical and physical performance data from the early stages of calendar aging for 228 Ah commercial prismatic lithium iron phosphate cells under multi-temperature, multi-SOC coupled conditions. The entire aging test period spanned from 24 September 2024 to 16 May 2025. The test setup included three storage temperatures (25 °C, 45 °C, and 60 °C) and four idle SOC levels (30%, 50%, 80%, and 100%), resulting in six cross-comparison test conditions. For each cell, nine cycles of constant-temperature idling and 10 rounds of comprehensive, synchronized electrochemical characterization were conducted at fixed 7-day intervals. Specifically, under the 60 °C/50% SOC condition, cell B4 failed after 35 days of storage due to an irreversible sudden increase in internal resistance; the entire chronological data of the cell’s premature failure was fully recorded. The dataset comprehensively includes capacity decay data at each aging milestone, 30-second HPPC pulse test data compliant with the FreedomCar standard, segmented static OCV-SOC calibration curves, and electrochemical impedance spectra across the full frequency range of 0.001–1000 Hz. It also includes measured data on two types of physical deformation: multi-point thickness expansion and changes in cell weight. All raw data were batch-extracted and standardized using MATLAB software. Zview software was used in conjunction with a PEEC equivalent circuit-which has clear physical significance-to fit and solve the impedance spectra. This yielded a series of quantified derived characteristic parameters, including SEI film impedance, charge transfer resistance, and Warburg diffusion resistance. Existing publicly available aging datasets primarily focus on small-capacity consumer battery cells. This dataset fills the gap in early-stage, experimentally measured data for temperature-SOC-coupled calendar aging of high-capacity lithium-ion batteries used in mining applications.

The HugSelect Dataset and Replication Package accompanies the study “HugSelect: An Explainable Multi-Criteria Decision-Support Framework for Foundation Model Selection.” HugSelect treats foundation model selection as an explicit and auditable software-component selection problem by combining repository metadata, functional capabilities, community-perceived quality attributes, and multi-criteria decision-making (MCDM). The package contains the data and software artifacts used to construct and evaluate a curated knowledge base of 71,274 Hugging Face models. The processing pipelines integrate model metadata, model-card and README content, and community discussions into structured information describing model families, modalities, tasks, functional capabilities, operational characteristics, and ISO/IEC 25010-inspired perceived quality attributes. The repository is organized into six components: Raw Information contains the collected model metadata, descriptions, and supporting community-derived information. Processing Pipelines contains intermediate and final artifacts generated during metadata preparation, functional-feature extraction and clustering, model-family identification, sentiment analysis, and quality-attribute mapping. Pipeline Validation provides manually inspected and reference datasets used to evaluate the automated extraction pipelines for functional features, sentiment analysis, and quality-attribute mapping. Case Study contains 44 literature-derived foundation-model selection scenarios, recommendation outputs from HugSelect and LLM-based baselines, model- and family-level evaluation results, and ablation-analysis artifacts. User Study contains empirical data from the exploratory practitioner evaluation of HugSelect, including Technology Acceptance Model (TAM)-based assessments of usefulness, usability, and transparency. HugSelectCode contains the source code implementing the HugSelect framework, including data processing, knowledge-base preparation, requirement processing, candidate matching, WSM/SAW-based multi-criteria ranking, and generation of explainable recommendations. The package supports reproduction of the empirical evaluation reported in the accompanying paper and can be reused for research on foundation model selection, AI engineering, recommender systems, repository mining, software analytics, information retrieval, MCDM, and explainable decision support. By publishing the data, validation artifacts, evaluation results, and source code together, this repository provides an inspectable, reproducible, extensible, and reusable research artifact for evidence-driven foundation model selection.

Table S1. Major elements compositions (wt.%) of chromite in the Xiugugabu chromitites Table S2. Trace elements compositions (ppm) of chromite in the Xiugugabu chromitites Table S3. Major elements compositions (wt.%) of olivine, orthopyroxene, clinopyroxene, and amphibole in the Xiugugabu chromitites Table S4. Trace elements compositions (ppm) of clinopyroxene in the Xiugugabu chromitites Table S5. Whole-rock PGE concentrations (ppb) of the Xiugugabu chromitites Table S6. Re-Os abundances (ppb) and Os isotope compositions of chromite separates from the Xiugugabu chromitites Table S7. Materials used for pMELTS modeling of fractional crystallization

Extracted University of Pennsylvania Smell Identification Test (UPSIT) data ( from DOI 10.5281/zenodo.13323912) along with the files necessary to reproduce GSEA analysis, and full report of logistic regression to supplemen "A Proof-of-Concept Application of Gene Set Enrichment Analysis Principles to Olfactory Phenotyping Data".