Kędziora, Mateusz

49 files · 1.8 MB · npy

Dataset for figures in the Article: Chiral polariton transport enabled by optical spin Hall effect in perovskite waveguides

Braun, Romane · bartolo, denis · Morin, Alexandre · et al.

1 files · 20 MB · zip

This dataset accompanies the publication 'Phase coherence and disorder-induced wave propagation in micromotor arrays'. It contains the data used to produce the figures reported in the article.

Liubchenko, Oleksii · Jacyna, Iwanna · Albert, Thies Johannes · et al.

5 files · 7.0 MB · zip

This repository contains the underlying research dataset for the publication "Ultrafast Pulsed Laser Annealing of Pd100-xSix Thin Films" (Acta Physica Polonica A, Vol. 148, No. 3, 2025). The data comprises raw and processed high-energy synchrotron X-ray diffraction (XRD) measurements collected at beamline P07 (High-Energy Material Science, Experimental Hutch 2) of the PETRA III storage ring at DESY (Hamburg, Germany) under Proposal ID 20230294 (Beamtime ID 11017285). The experiment utilized a Dectris Pilatus CdTe 2M 2D detector to record spatial area mapping profiles crossing directly through laser-irradiated spots on Pd100-xSix thin films of various chemical compositions (pure Pd reference, Pd97Si03, Pd95Si05, and Pd90Si10). The dataset is systematically organized into four separate components: 1. Calibration data: PyFAI .poni geometry files, master mask arrays, and CeO2 standard reference states. 2. Raw data: Unprocessed 2D Pilatus detector image sequences and experimental runtime logs mapping the full spatial profiles. 3. Integrated 1D patterns: Azimuthally integrated Intensity vs. 2-Theta, including raw integration matrices, global integration parameters (JSON format), and files where the substrate background was subtracted. 4. Data analysis workflows: Working directories for Profex/BGMN Rietveld phase refinement files, structure definitions, and the specific amorphous background pattern (bkg.xy, corresponding to an unannealed Pd83Si17 sample) used to isolate thin-film metallic glass fractions. While the raw sequences and integrated patterns contain the complete sequence of spatial steps recorded across the sample area (covering the area far from the laser center, passing through the boundary, and moving completely through the irradiated spot zone), only a selected subset of scan coordinates was utilized for the final figures in the journal publication. All files are provided in their entirety to ensure complete open-science transparency. Full details regarding structural tracking parameters, coordinate file tracking nomenclature, and technical hardware setups are outlined in the accompanying plain-text README.txt file. This research was funded in whole or in part by the National Science Centre (NCN), Poland, under the OPUS 22 funding scheme (Grant No. 2021/43/B/ST5/02480). All repository files are released under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license.

Yu, Hongzheng

3 files · 1006 KB · zip

Data and code for "The Argument of Perigee as a First-Order Control on Tidal Heating Asymmetry in Coupled Eccentricity-Obliquity Regimes"

Bittnar, Petr · Padevět, Pavel

13 files · 1.7 MB · zip

Measured data from experimental tests on concrete specimens with passive confinement during uniaxial compression testing -- various ages, various concrete mixes, and various levels of passive confinement. The data is stored in 13 datasets.

Hubbard, Alfred · Solares, Edwin · Hemming-Schroeder, Elizabeth

88 rows · 18 KB · fasta

These are the files needed to run the Broad Institute's malaria amplicon pipeline for the PvGAP Plasmodium vivax panel, described in detail here . They consist of FASTA files containing the forward and reverse primers and another FASTA file containing reference sequences for each target, derived from the PvP01 reference genome.

Yuan, Guangyuan

72 rows × 13 cols · 6.0 KB · csv, fasta

11 numeric · 2 categorical

This dataset supports the findings of the manuscript "Root anatomical traits modulate the assembly and nitrogen transformation potential of root-associated microbiomes in a temperate steppe" (NPH-MS-2026-55667). It contains root traits data, bacterial 16S rRNA gene absolute abundances, functional genes relative abundances, DNA extraction metadata, and phylogenetic marker sequences for 37 plant species from a temperate steppe ecosystem. The dataset includes the following files: 1. root traits.csv - Root traits including average diameter (AD), specific root length (SRL), specific root area (SRA), root tissue density (RTD), root nitrogen content (RNC), root carbon content (RCC), carbon‑nitrogen ratio (RCN), cortex layer number (CLN), cortex thickness (CT), and the ratio of cortex thickness to root diameter (CTRD). The first column lists plant species names. 2. Absolute abundance of 16S rRNA gene.csv - Quantitative PCR (qPCR) derived absolute abundances of bacterial 16S rRNA gene copies (copies/ng DNA) across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 3. DNA extraction sample weight.csv - Fresh weight (grams) of root material used for DNA extraction for each sample, linked by SampleID to the abundance data. 4. DNA extraction concentration.csv - Qubit‑measured DNA concentrations (ng/μL) and the sample volume (μL) used for quality control, together with sample metadata. 5. 37species.fasta - DNA sequences of two chloroplast markers (matK and rbcL) for the 37 plant species included in the study. The sequences are in FASTA format with headers formatted as ">Species". These were used for host phylogeny construction and Pagel's λ analyses. 6. Quantitative PCR results of functional gene.csv - Quantitative PCR (qPCR) derived relative abundances of bacterial 16S rRNA gene and functional genes across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 7. README.md - A detailed description of each file, column headers, abbreviations, units, and any missing value codings (NA). All data are provided to ensure transparency and reproducibility of the analyses. For methodological details, please refer to the Materials and Methods section of the associated publication. These data are under embargo until the associated research article is published. After that date, they will be freely available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. During the embargo period, the metadata (title, authors, abstract) and the DOI remain publicly visible, but the data files are not accessible. For access requests before the embargo expires, please contact the corresponding author.

Budzinski, Lisa · Beenken, Anne Elisabeth · Sempert, Toni · et al.

9 rows × 1 cols · 743 B · csv, docx, zip

1 categorical

We have investigated an IgG4-RD (IgG4-RD) cohort by our multi-parameter microbiota flow cytometry approach to characterise the microbiota on single-cell level for attributes of the disease. The microbiota is isolated from stool samples and stained according to the published protocol for (a) host immunoglobulins IgA1, IgA2, IgM, IgG and (b) agglutinin binding to mannose, galactose or N-Acetyl-glucosamine surface sugar moieties. For all samples we also determined the microbiome composition by 16S rRNA (V3-V4) sequencing on the illumina MiSeq platform. We provide the raw .fcs and FASTQ files of 40 IgG4-RD patients. For comparison we additionally analysed 36 healthy donors. All .fcs files were generated on BD Influx®. The metadata is collected in the provided meta.csv. The staining parameters are summarized in provided panel.csv.

Saha, Sudipto · Raghava, Gajendra

1 files · 186 KB · zip

VICMpred: SVM-Based Prediction of Functional Proteins of Gram-Negative Bacteria Using Amino Acid Patterns and Composition VICMpred is a computational web server developed for predicting the major functional classes of Gram-negative bacterial proteins from amino acid sequences. The tool classifies Gram-negative bacterial proteins into four broad functional categories: virulence factors, information molecules, cellular process proteins, and metabolism-related proteins. VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, and class-specific tetrapeptide patterns. Web Server: https://webs.iiitd.edu.in/raghava/vicmpred/ Citation Saha, S., and Raghava, G. P. S. VICMpred: An SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics, Proteomics & Bioinformatics, 4(1), 42-47, 2006. https://doi.org/10.1016/S1672-0229(06)60015-6 About the Research Functional annotation of proteins is one of the major challenges in the post-genomic era. Due to the rapid growth of protein sequence databases, experimental functional characterization of every newly discovered protein is not practical. Traditional methods such as BLAST, FASTA, and PSI-BLAST depend on sequence similarity. However, proteins with similar functions may show poor sequence similarity, making direct function prediction difficult. VICMpred was developed as a direct function prediction method for Gram-negative bacterial proteins. Instead of only predicting subcellular localization, it predicts broad biological functions directly from protein sequence features. Data Compilation: The final dataset contained 670 non-redundant Gram-negative bacterial proteins. These included 255 cellular process proteins, 60 information molecules, 285 metabolism proteins, and 70 virulence factors. Methodology: VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, PSI-BLAST similarity search, class-specific tetrapeptide patterns, and hybrid combinations of these features.

Bilén, Frida · Nahavandizadeh, Nasim · Larsson, Moa · et al.

6 files · 13 MB · zip

NMR datas (raw and processed)

Li, Zhiyao · Wang, Ningbo · Zhong, Jiahao

6 files · 48 MB · docx, zip

GIFT-BDS is a regional ionospheric total electron content (TEC) and TEC-gradient dataset over China derived from BeiDou geostationary Earth orbit (GEO) observations and a dense ground-based GNSS receiver network. The dataset is designed to provide high-resolution observations of ionospheric TEC variability and horizontal TEC-gradient structures over China and adjacent regions. The versioned release covers the period from 19 July 2024 to 31 December 2025, corresponding to DOY 201 of 2024 to DOY 365 of 2025. The geographical coverage is 15°N-50°N and 95°E-135°E. The dataset is provided in daily NetCDF files and contains two product levels. Level-1 products provide observation-level GEO-derived slant TEC (STEC) and rate of TEC index (ROTI) records for individual receiver-GEO satellite lines of sight, with a temporal resolution of 30 s. Level-2 products provide gridded regional TEC and TEC-gradient variables, including VTEC, VTEC t , ROTI, GIX, GIX std , GIX x , GIX y , GIX t,x , and GIX t,y , with a temporal resolution of 15 min. IPP-based variables are provided on a 1° × 1° grid, while inter-IPP-gradient variables are provided on a 0.25° × 0.25° grid. The main processing steps include observation screening, cycle-slip and data-gap detection, continuous-arc segmentation, carrier-to-code leveling, satellite and receiver DCB correction, IPP calculation, inter-IPP pair selection, gradient estimation, and gridding. Quality control is applied before release. Missing values may occur because of station outages, data gaps, quality-control exclusions, or insufficient valid samples within a grid cell. Users should check the NetCDF variable attributes, including units and fill values, before analysis. The dataset is suitable for regional ionospheric studies, TEC-gradient monitoring, space-weather-related analyses, and investigations of ionospheric effects on GNSS positioning applications.

Guastamacchia, Carlo · Piersanti, Roberto · Giardini, Francesco · et al.

3 files · 31 KB · zip

This dataset contains the results of the research presented in: https://doi.org/10.48550/arXiv.2604.00881 In particular, it includes the output of electrophysiology, passive mechanics, and electromechanics simulations performed on a biventricular geometry of a murine heart. The same simulations were run on multiple fiber fields obtained by applying a smoothing procedure to the experimental fiber field, using different regularization radii. The dataset contains the following data: Fiber fields corresponding to regularization radii of 0.0 mm, 0.1 mm, 0.25 mm, 0.5 mm, 1.0 mm, and 1.5 mm. Volume-pressure curves from passive inflation tests. Activation time maps obtained by solving the eikonal problem. Displacement fields from the full electromechanical model. The simulations were run on a mesh with an average edge length of 0.15 mm.

Ahmad, Mushtaq

2 files · 16 MB · zip

This archive is the public reproducibility package for the manuscript "HYPER-Z4c: A Paper-I Reproducible Constraint-Energy-Ledger Framework for Hyperboloidal Z4c Evolution and Direct Scri Waveform Extraction," prepared for submission to the Journal of Scientific Computing. The archive contains the manuscript source, Online Resource 1 source, publication figures, processed CSV/JSON validation records, waveform-level comparison summaries, metric-worldtube readiness audit records, figure-generation scripts, convergence-fit reproduction scripts, environment files, checksum manifests, and a one-command reproduction workflow. The package supports the methods-paper evidence for a stage-synchronous constraint-energy ledger for hyperboloidal Z4c evolution, scri-compatible SAT closure, full-discrete finite-part regularization, evolved-cut BMS waveform extraction, official SXS/SpECTRE Ext-CCE waveform-level comparison, and a separate CceR0207.h5 metric-worldtube input-readiness audit. The archive does not claim a completed same-spacetime HYPER-Z4c-to-SPECTRE-CCE binary-black-hole validation. The manuscript treats the official Ext-CCE waveform-level comparison and the metric-worldtube readiness audit as separate evidence categories. A matched same-spacetime worldtube-to-CCE comparison remains future work. A minimal reproduction workflow verifies the checksum manifest, runs archive smoke tests, recomputes headline convergence-fit quantities, and regenerates the main figures from the archived records.

De Alwis Watuthanthrige, Nethmi

6 files · 5.6 MB · zip

Computational Network Science

1 files · 415 KB · gzip

The drug-target-interaction dataset is a combinatorial complex representing drug-target interactions and similarity relationships. The dataset is based on the work by Perlman et al., which combines multiple drug and gene similarity measures to predict drug-target interactions. Find the dataset details in AHORN .

Beck, Edgar

1 files · 100 MB · zip

This dataset includes models and results of the SINFONY software v3.0.0, which is now complete: https://doi.org/10.5281/zenodo.8006567

Anonymous

1 files · 8.0 MB · gzip

This dataset contains 2134 unique malicious Python packages collected from PyPI, used for the evaluation of H2GLM (Hierarchical Heterogeneous Graph Learning framework enhanced by LLMs for Malicious package detection). Malicious samples are derived from a large-scale benchmark for malicious Python package detection, supplemented by packages collected from public security advisories and threat intelligence feeds. Deduplication was enforced via MD5 and fuzzy hashing. The archive malicious_2134.tar.gz contains the original .tar.gz distribution files as collected from PyPI. Intended for academic security research only.

The Paleobiology Database Community

4 files · 100 MB · zip

Comprehensive denormalized occurrence table built with the API of the Paleobiology Database ( http://paleobiodb.org/ ). Archived for the Chronosphere project by Ádám T. Kocsis. Downloaded on 2026-06-21 with the API call: https://paleobiodb.org/data1.2/occs/list.csv?datainfo&rowcount&all_records&show=class,classext,genus,subgenus,coll,coords,loc,paleoloc,strat,stratext,lith,env,ref,crmod,timebins,timecompare,refattr,entname,attr,ident,img,plant,abund,ecospace,taphonomy,etbasis,pres,prot,lithext,geo,methods,resgroup,refattr,ent Contents: pbdb_occs.rds : occurrence table in binary R data file. pbdb_parquet.zip : occurrence table, zipped parquet file pbdb_csv.zip : occurrence table, zipped csv file metadata.txt : The metadata of the download.

Sasai, Kazuto · Yukio-Pegio, Gunji

7 files · 8.0 MB · gzip

# Data archive - Internal-state criticality in Bayesian-inverse-Bayesian inference **Paper.** *Internal-state criticality in Bayesian-inverse-Bayesian inference*, K. Sasai and Y.-P. Gunji (Physical Review Research, submitted). **Source repository.** <https://github.com/kazsasai/bayesian-inverse-bayesian-rps> **DOI.** `10.5281/zenodo.20533918`. --- ## Contents This deposit contains the raw simulation outputs underlying every data figure of the paper. Three archives are provided: two cover the main simulation data (*full reproducibility* vs *quick figure rebuild*), and one small archive holds the reinforcement-learning baseline-control data: | Archive | Size (compressed) | Contains | Use case | |---|---|---|---| | `paperA_data_full.tar.gz` | ~2.7 GB | Full simulation output tree (~16 GB uncompressed; 2713 files): all per-run JSONs and NPZs from `simulation/{reward_huge,nhand,reward_huge_v2,analyze_sharpness_plateau,reward}/data/` and `simulation_tie_mode_ablation/data/` | Independent re-analysis from raw outputs | | `paperA_data_figure_only.tar.gz` | ~1.1 GB | The 163 specific JSON/NPZ files actually read by `build_all.py` (~1.7 GB uncompressed) | Rebuild figures only | | `paperA_data_baseline_control.tar.gz` | ~21 MB | Pooled run-length arrays (`pnas_rl_comparison/data/baseline_dwells.npz`) for the RL-baseline control - WSLS, tabular Q-learning, and regret matching vs BIB; 40 seeds, T=2e5 - backing Fig. 4 (`fig_control_ab`) | Rebuild the RL-baseline control figure | The two main archives preserve the relative-path layout so that extracting either at `<repo>/data/` lets `build_all.py` find the data without further configuration. The baseline-control archive instead carries the `pnas_rl_comparison/data/...` path and extracts at the **repository root**. See **Reproducing the figures** below. Supporting files: * `MANIFEST_canonical.txt` - the in-repo data manifest (`BIB_Levy_v2/latex/figures/scripts/zenodo_data_manifest.txt`), listing each data tree, the figure(s) it feeds, and the generating script. * `figure_only_file_list.txt` - exhaustive 163-line list of relative paths inside `paperA_data_figure_only.tar.gz`, captured by auditing every `open()` call from a clean `build_all.py` run (and re-running with caches cleared so that no precomputed intermediate hid raw-data references). * `checksums.sha256` - SHA-256 of all three archives. ## Reproducing the figures Both tarballs preserve the same layout, so the workflow is identical: ```bash # 1. Clone the source repo git clone https://github.com/kazsasai/bayesian-inverse-bayesian-rps.git cd bayesian-inverse-bayesian-rps # 2. Get the data: pick ONE archive # (full = raw-output independent re-analysis; # figure-only = just enough to rebuild figures) mkdir -p data tar xzf /path/to/paperA_data_figure_only.tar.gz -C data # OR _full # 3. Install dependencies pip install numpy matplotlib powerlaw # 4. Rebuild figures python BIB_Levy_v2/latex/figures/scripts/build_all.py # (or run individual scripts: build_Fig3_universality.py, etc.) ``` Alternatively, point `PAPERA_DATA` at an extraction directory anywhere on disk: ```bash tar xzf paperA_data_figure_only.tar.gz -C /scratch/papera_data export PAPERA_DATA=/scratch/papera_data python BIB_Levy_v2/latex/figures/scripts/build_all.py ``` `figdata.py` in the source repo searches `$PAPERA_DATA`, then `<repo>/data/`, then the in-repo `simulation/` tree, in that order. ### RL-baseline control figure (Fig. 4) `paperA_data_baseline_control.tar.gz` carries the `pnas_rl_comparison/data/...` path, so extract it at the **repository root** (not `<repo>/data/`): ```bash tar xzf /path/to/paperA_data_baseline_control.tar.gz -C bayesian-inverse-bayesian-rps python pnas_si/figures/build_fig_control.py # -> fig_control_ab.{pdf,png} ``` The figure's BIB curves are read from the main data (the `reward_huge_*` `durations_bib-*` JSONs in the full / figure-only archive, via `$PAPERA_DATA`); the baseline curves come from the archive above. To regenerate the baseline data from scratch instead (deterministic, ~minutes): ```bash python pnas_rl_comparison/run_baseline_control.py # -> baseline_dwells.npz python pnas_rl_comparison/analyze_baseline_control.py ``` ## What `paperA_data_figure_only.tar.gz` excludes * The 17 G of per-run / per-step JSONs in the data trees that no current figure reads. * Intermediate caches (`fig*_ccdf_cache.json`) - these are regenerated by `build_Fig4_robustness.py` and `build_FigS2_nh_ccdf.py` on first run. * The small bundled inputs already shipped with the GitHub repo at `BIB_Levy_v2/latex/figures/scripts/data/` (`scheme_summary.csv`, `bo_tournament_results.json`, `sigma_*_rs_bib-bib.json`, `data_ivb_{equil,biased}.npz`). The build scripts read these straight from the repo. ## Verifying integrity ```bash shasum -a 256 -c checksums.sha256 ``` ## Citation If you use these data, please cite both the paper (forthcoming) and this Zenodo record. The repository's `README` is updated with the final citation on publication. ## License Data are released under CC-BY-4.0 (deposit metadata sets this on Zenodo). Source code in the GitHub repository is under its own LICENSE file.