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.

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.

Stockner, Thomas · Al Makhlouf, Mounaf

1 files · 8.0 MB · gzip

This data record contains all input data and scripts to rerun the simulations and it includes the output structures. The dataset contains the raw and processed data used to create figures 5I, 5J of the assocated manuscript.

Bertola, Anouk · Wenner, Nicolas · LEMOS ROCHA, Leonardo Filipe · et al.

16 files · 8.0 MB · fastq, gzip, xlsx

Sequencing data and raw data for Bertola et al .

Jiménez-Blanco, Albert · López-Villellas, Lorién · Moure, Juan Carlos · et al.

1 files · 8.0 MB · gzip

Datasets used in the "Theseus: Fast and Optimal Affine-Gap Sequence-to-Graph Alignment" paper. The data is compressed into the theseus_datasets.tar.gz file. This file includes the datasets for the two experiments on the paper: MSA datasets: File Size Source hiv_100.fasta 100 sequences, each of approximately 10Kbp https://github.com/niemasd/ViralMSA/blob/master/example/example_hiv.fas mtb_benchmark_50kbp_shortened.fna 342 sequences, each of approximately 50Kbp Derived from a set of 342 RefSeq-complete whole-genome assemblies of Mycobacterium tuberculosis genomes. Sequences start at the dnaA gene and are 50Kbp long. mtb_benchmark_250kbp_trmB.fna 342 sequences, each of approximately 250Kbp Derived from a set of 342 RefSeq-complete whole-genome assemblies of Mycobacterium tuberculosis genomes. Sequences start at the dnaA gene and are truncated at the trmB gene. mtb_benchmark_500kbp_thiE.fna 342 sequences, each of approximately 500Kbp Derived from a set of 342 RefSeq-complete whole-genome assemblies of Mycobacterium tuberculosis genomes. Sequences start at the dnaA gene and are truncated at the thiE gene. mtb_benchmark_1Mbp_gltA2.fna 342 sequences, each of approximately 1Mbp Derived from a set of 342 RefSeq-complete whole-genome assemblies of Mycobacterium tuberculosis genomes. Sequences start at the dnaA gene and are truncated at the gltA2 gene. covid_19_complete.fasta 2732 sequences, each of approximately &sim;30 Kbp in length 2732 GenBank-complete SARS-CoV-2 genome assemblies. monkeypox_100_seq.fasta 100 sequences, each of approximately &sim;200 Kbp in length 100 RefSeq-complete whole genome assemblies of Monkey pox's virus. Sequence-to-graph /pangenome read mapping datasets: File Size Source SRR062634_1.filt_REDUCED.fasta 250K sequences of length 100bp Human Pangenome Reference Consortium 211109_M024_V350038332_L01_HUMuarfR092940-606_1_REDUCED.fasta 250K sequences of length 150bp NIST Genome in a Bottle (GIAB) project D1_S1_L001_R1_001_REDUCED.fasta 250K sequences of length 250bp NIST Genome in a Bottle (GIAB) project This dataset is derived from original data produced by third parties, as detailed above. All rights to the original data remain with the original authors or copyright holders. Users are responsible for ensuring compliance with the licensing terms of the original data sources. Sequence-to-cyclic-graph datasets: These datasets have been synthetically generated to test the ability of Theseus to align against cyclic reference graphs, comparing it to the graph unfolding strategy used by vg map. The synthetic cyclic graphs with a controlled structure. For that, we create backbone graphs with N in {10, 100, 1000} nodes, with node sequences of average length 50 bp, and connect each node to its successor to ensure graph connectivity. Then, we add a bounded number of random edges per node. These random edges can connect to nearby forward nodes or to previously created nodes, introducing cycles into the graph. For each graph size, we generate queries of length 100, 150, and 250 bp. We have nine pairs of reference graph and queries. This happens because we have three graph sizes, with N in {10, 100, 1000} nodes, and 3 query sizes, of 100, 150, and 250 base pairs. Code snapshot: This dataset also contains a snapshot of the code use to conduct the experiments in the manuscript, corresponding to Theseus v0.1 on Github.

Bhat, Shreeharsha G · Mahajan, Daanish · Jain, Chirag

7 files · 8.0 MB · gzip, zip

Panngenome graph files (GFA format) used for benchmarking panbubble and hairpin detection in the paper Billi: Provably Accurate and Scalable Bubble Detection in Pangenome Graphs . Includes minigraph pangenome graphs and pangene gene graphs. Note: The HPRC Minigraph-Cactus pangenome graphs (hprc-v1.1-mc-chm13 and hprc-v2.0-mc-chm13) and chromosome-level graphs (chrX and chrY) are not included due to their large size. The chromosome-level graphs (chrX, chrY) are available as .vg files and must be converted to GFA format using vg. Direct download links: hprc-v1.1-mc-chm13.gfa.gz: https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/freeze/freeze1/minigraph-cactus/hprc-v1.1-mc-chm13/hprc-v1.1-mc-chm13.gfa.gz hprc-v2.0-mc-chm13.gfa.gz: https://human-pangenomics.s3.amazonaws.com/pangenomes/scratch/2025_02_28_minigraph_cactus/hprc-v2.0-mc-chm13/hprc-v2.0-mc-chm13.gfa.gz chrX.vg: https://human-pangenomics.s3.amazonaws.com/pangenomes/scratch/2025_02_28_minigraph_cactus/hprc-v2.0-mc-chm13/hprc-v2.0-mc-chm13.chroms/chrX.vg chrY.vg: https://human-pangenomics.s3.amazonaws.com/pangenomes/scratch/2025_02_28_minigraph_cactus/hprc-v2.0-mc-chm13/hprc-v2.0-mc-chm13.chroms/chrY.vg Vg command to convert .vg to .gfa format: vg view input.vg > output.gfa

Koide, Kenji

2 files · 8.0 MB · gzip

A dataset for LiDAR-IMU-GNSS localization. This is a synthetic dataset generated with the Prius on Sonoma Raceway scene in Gazebo. All sequences are recoreded in the ROS2 bag format. Files rosbag2_2026_07_02-13_42_01 : Sequence 1 (209.6s) rosbag2_2026_07_02-13_51_33 : Sequence 2 (179.6s) sonoma.ply : Map data generated using GLIM running on Sequence 1 Topics /prius/cmd_vel | Type: geometry_msgs/msg/Twist /prius/gnss/fix | Type: sensor_msgs/msg/NavSatFix /prius/gnss/pose | Type: geometry_msgs/msg/PoseStamped /prius/imu | Type: sensor_msgs/msg/Imu /prius/odom | Type: nav_msgs/msg/Odometry /prius/points | Type: sensor_msgs/msg/PointCloud2 /tf | Type: tf2_msgs/msg/TFMessage /tf_static | Type: tf2_msgs/msg/TFMessage sonoma.ply

Shixian, Dong · Chengxin, Jiang · Caroline M., Eakin · et al.

4 files · 8.0 MB · gzip

This repository contains the ambient noise cross-correlation functions (CCFs) and dispersion curves (group and phase velocity) for the paper 'Characterizing Crustal Structure for Natural Hydrogen Exploration in the Southeastern Gawler Craton Using Adaptive Ambient Noise Tomography'.

Kovaliov, Michael

1 files · 100 MB · parquet

Chen, Jianyong

6 files · 1.5 GB · gzipdeclared

A first sequence of the Ae. speltoides B chromosome has been available since 2020 (RUBAN et al. 2020b), but this assembly represents ~16% of the B only and thus cannot be used to uncover B-encoded genes. Now, high-molecular-weight DNA from +B leaf tissue of the clonally propagated +B plant was used to generate a chromosome-scale genome assembly. A total of 154 Gb of PacBio HiFi reads and 36.4 Gb of Nanopore reads (>25 kb) were generated for primary assembly using hifiasm (Cheng et al. 2026). The resulting 5.47 Gb assembly achieved 93.7% BUSCO completeness (contig N50 = 17.1 Mb). Approximately 104 Gb of Hi-C sequencing data derived from leaf tissue of the same +B plant were employed to scaffold the primary contigs. The assembly yielded eight large scaffolds (398-835 Mb), each displaying a characteristic Rabl configuration. Alignment of these scaffolds to the reference genome of Ae. speltoides accession AEG-9674-1 without B chromosome (Avni et al. 2022) revealed that seven of the eight scaffolds showed strong synteny with the standard A chromosomes 1S-7S. To determine whether the remaining large scaffold corresponded to the B chromosome, we generated ~60 Gb of whole-genome sequencing (WGS) data from 0B AR-derived lateral root tissue of the same plant, as well as approximately 36 Gb of WGS data from +B leaf tissue. Comparative read-mapping analyses showed that the eighth scaffold exhibited normal sequencing coverage in +B leaf-derived data but substantially reduced coverage in 0B AR-derived data. Thus, the 398 Mb scaffold represents the B chromosome, accounting for 69% of its size as estimated by flow cytometry. Additionally, 4.81 Gb of contigs were assigned to the seven pairs of A chromosomes, representing 91% of their estimated size (1C=5.27 Gb). Consequently, we produced a high-quality chromosome-scale assembly of Ae. speltoides carrying B chromosomes. To identify genes associated with the B chromosome elimination process, RNA-seq was performed across developmental stages and different tissues in which B chromosome behavior differs. Using all +B RNA-seq datasets, we annotated the Ae. speltoides genome assembly containing the B chromosome. This annotation identified 59,981 transcripts and 47792 protein-coding genes on the seven A chromosomes and 5,940 transcripts and 4,196 protein-coding genes on the B chromosome.

Madhusudan, Gujral

6 files · 29 MB · parquetdeclared

Large language models (LLMs) are trained on massive, publicly available text datasets comprising trillions of tokens, enabling them to excel at general language tasks like next-token prediction. However, LLMs often struggle with domain-specific prompts, exhibiting reduced accuracy or generating inaccurate information (hallucinations). This is because they lack sufficient subject matter expertise. Two primary approaches exist to address this limitation for augmenting LLMs knowledge: Retrieval-Augmented Generation (RAG) and fine-tuning. This presentation focuses on fine-tuning smaller LLMs with domain-specific instruct datasets using the LoRA (Low-Rank Adaptation) technique on Gaudi hardware. We will leverage publicly available LLMs and datasets from the Hugging Face Hub for this demonstration. Though it is possible to fine tune LLMs with plain text data - sourced from documents, articles, and other materials.

Lopez-Gomez, Ignacio

2 files · 45 GB · gzipdeclared

Trained GenFocal model checkpoints. This record contains checkpoints for GenFocal: Checkpoints for the Diffusion-based Super-Resolution model (~8.15 GiB) CHeckpoints for the Flow Matching Debiasing model (~36.45 GiB)

Bakare, Akeem

1 files · 34 KB · gzipdeclared

Contig-ARG-Linkage Reproducible Snakemake pipeline that links antibiotic resistance genes to bacterial hosts in metagenomic data. For each sample, it assembles reads (MEGAHIT), detects ARGs (BLASTn vs MEGARes), classifies contigs (Kraken2), and joins by contig ID. Pinned envs, checksummed databases, container builds, and CI-tested on synthetic data.

Stein, Samuel

5 files · 139 MB · gzipdeclared

Raw experimental dataset accompanying the paper "Calibration-Conditioned FiLM Decoders for Low-Latency Decoding of Quantum Error Correction Evaluated on IBM Repetition-Code Experiments." This dataset contains repetition-code experiments executed on three IBM Quantum processors -- ibm_kingston, ibm_pittsburgh, and ibm_fez. It comprises 352 hardware snapshots spanning code distances d = 3, 5, 7, 9, 11, syndrome-round counts r = 1 to 11, and both the X and Z logical bases. Each snapshot runs several repetition-code chains in parallel and carries the device calibration data captured at execution time, totalling several million measurement shots. All data is provided raw, exactly as returned by the hardware -- no machine-learning processing or sparsification is applied -- and is anonymized (no IBM Runtime job identifiers are released). CONTENTS - ibm_kingston.tar.gz, ibm_pittsburgh.tar.gz, ibm_fez.tar.gz per-device archives, each unpacking to <device>/d<D>_r<R>/job_<n>/ - index.csv one row per snapshot: path, backend, d, rounds, basis, logical_states, n_chains, shots - README.md full description of the layout and field definitions Each job_<n>/ directory contains: - info.json experiment parameters (device, d, rounds, basis, states, shots, n_chains) - calibration.json the device calibration snapshot at execution time (T1, T2, gate and readout error rates, coupling map) - circuit_state0.qasm, circuit_state1.qasm transpiled circuits as executed - bitstrings.json raw per-shot measurement records for every parallel chain USAGE Because each snapshot stores its own calibration data, the per-device and per-(d, r, basis) structure used in the paper is recoverable by filtering index.csv. The same calibration is consumed by both the FiLM decoder (via its calibration-graph encoder) and the modified MWPM baseline (via its detector-graph edge weights). Github to be updated for corresponding experiments on approval. Please contact Samuel Stein (samuel.stein@pnnl.gov) if you need any information or source docs before hand.

Hackl, Jürgen

6 files · 132 MB · parquet, tiffdeclared

EuroFlood is an open, cloud-native index over the JRC/Copernicus CEMS-EFAS Satellite-Derived Flood Depth Maps for Europe (Betterle & Salamon, 2025; CC-BY-4.0) - ~3,280 satellite-derived observed flood-depth maps across Europe, 2015-2024. The bundle is a sparse Cloud-Optimized GeoTIFF encoding, per pixel, the set of flood events that inundated it, plus a combo_id -sorted GeoParquet dictionary and a small events table. Query by region and time via HTTP range reads (GDAL /vsicurl + DuckDB) to retrieve matching events, then fetch only the source depth rasters needed. Built with the open-source EuroFlood Python package ( pip install euroflood ).

Bojarski, Krzysztof Kamil

1 files · 66 MB · gzipdeclared

This repository contains structural models and molecular dynamics input files for complexes of cathepsins B, S, and K with three glycosaminoglycan-like ligands: naphthalene-1,3,6-trisulfonate (NTS), amide-linked bis(naphthalene disulfonate) (BNS), and suramin. Initial protein and ligand structures used for molecular docking are provided in AutoDock3 format. For each cathepsin-ligand complex, the three most populated docking clusters are included, with three representative binding poses per cluster. The repository further contains system topologies and initial coordinates for molecular dynamics simulations in AMBER format ( .parm7 and .rst7 ), ligand library files required for tleap, molecular dynamics input files, and scripts used for post-processing and analysis of the MD trajectories.

Chander, Aishwarya

2 files · 8.7 GB · gzip, zipdeclared

Analysis code and processed data for the manuscript: "Multiple myeloma and therapy reshape the bone marrow niche to durably constrain immune reconstitution and vaccine responsiveness." This repository accompanies a longitudinal multi-omic study of immune dysfunction in multiple myeloma (MM). Matched bone marrow and peripheral blood from MM patients were profiled across diagnosis, induction, autologous stem cell transplant (ASCT), and recovery, together with matched healthy donors and vaccine response subcohorts. The analyses show that the tumor imposes a compartment specific immune program; marrow-restricted metabolic, inflammatory, and cytotoxic-effector changes not mirrored in blood; and, that adaptive immune reconstitution remains impaired up to two years post-ASCT. Half of patients failed to mount IgG responses to a high dose nonadjuvanted influenza vaccine, a defect overcome by the LNP adjuvanted COVID mRNA vaccine. Contents: Single cell RNA-seq (BMMC and PBMC), flow cytometry, Olink proteomics, and MSD cytokine analysis pipelines, plus the notebooks generating all manuscript figures. Processed/derived data needed to reproduce the figures are included; raw sequencing data are deposited in GEO.

Amilon, Jesper

1 files · 38 KB · gzipdeclared

Version 1.0 of VerNFR, a Frama-C plugin for verifying non-functional requirements of C code and interface contracts. Release to accompany submission to ISoLA2026 See the Readme for further details and instructions.

Walsh, Calum · Srinivas, Meghana · Stinear, Timothy · et al.

100 files · 2.7 GB · gzip, tsvdeclared

GROND (Genome-derived Ribosomal OperoN Database) A quality-checked and publicly-available database of 16S-ITS-23S RRNA operon sequences and their constituent 16S and 23S genes. Based on GTDB release R232.