Melnik, Elena

13 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

7 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

4 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

40 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

19 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

37 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Wolf, Gerhard · Kolbe, Georg

2 files · 14 MB · pdf, tiff

Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuch anzeigen] Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).

Wang, Yafei

15 files · 129 KB · tiff

A gridded time-series dataset reconstructing population distribution across the Yellow River Basin, China, from 1000 to 2000 AD, with a temporal resolution of one century and a spatial resolution of 10 km.

Wang, Wei · Liu, Cheng · Han, Lianhuan · et al.

10 files · 211 KB · tiff

Cyclic voltammograms (CVs) obtained at the 35-nm radius gold nanoelectrode in 0.5 M H2SO4 solution deoxygenated under argon at different scan rates (from 0.3 to 1 V/s). The potential range is 0-1.75 V vs RHE. The data in the text files is given in the following format: Potential vs. RHE (V) | Current (A) Scan rate is indicated in the text file name. The figure Voltammograms.tif shows all CVs in this dataset.

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

González-González, Pablo

24 files · 776 KB · npz

Vectorial representations for common image datasets: CIFAR10, CIFAR100, CIFAR100coarse MNIST, FashionMNIST SVHN Extracted using the scripts in this repo, where more details of the extraction can be consulted. The embeddings are provided in different modalities: features: the next-to-last representation of the network logits: the pre-softmax values predictions: the post-softmax values (posterior probabilities) targets: the true labels Each file is stored in format npz (dictionary-like) and contains the numpy representations for the keys "train", "val", and "test" partitions for each dataset. The "train" partition was used to train the corresponding neural model, "val" was used for training monitoring, and "test" was not used during training. The name of the file uses the format <dataset>_<neural-model>_<modality>.npz; for example: "svhn_resnet18_logits.npz". See reference list for publications related to each dataset.

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 .

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.

Manghi, Paolo

2 files · 8.0 MB · gzip, tsv

Using gene-level and species-level shotgun metagenomics, we provide the first characterization of the rural, Bolivian microbiome; we identified microbial genes which strongly correlate (rho>0.45) with arsenic in urine, and that overall contribute to substantiate that the gut microbiome helps tolerate arsenic via a evict-out-of-house mechanism. Mediation analysis, followed by phylogenetic investigation of metagenomic-assembled genomes, further strengthens this observation. This study elucidates the role of the microbiome in helping to tolerate arsenic-rich environments, and paves the way for probiotic interventions that may mitigate the effects of this toxic metal. This repository contains 2,478 HQ MAGs from the MicroToxBol cohort in fasta format and a descriptive table comprising taxonomic annotation, quality-checks, and coverage estimation.