OSMI Working Group 2: Understanding the open science monitoring landscape · Szybisty, Tereza · Yahia Mohamed Elkheir, Lamis
hybrid · semantic + lexical · 504 datasets ranked · 3.29s
OSMI Working Group 2: Understanding the open science monitoring landscape · Szybisty, Tereza · Yahia Mohamed Elkheir, Lamis
819 rows × 35 cols · 159 KB · xlsx
27 categorical · 8 text
This dataset contains a community-curated inventory of Open Science monitoring initiatives compiled by Working Group 2 (WG2) of the Open Science Monitoring Initiative (OSMI). The dataset aims to provide a structured overview of initiatives that monitor and evaluate the implementation, uptake, and impact of Open Science across different geographical regions, disciplines, and organisational contexts. The Open Science monitoring initiative, following the OSMI definition, is defined as an initiative that: Publicly and regularly monitors and evaluates trends or impacts related to open science policies, mechanisms, interventions, and/or incentives; May use quantitative, qualitative, or mixed-methods approaches; Monitors the degree of openness of scientific processes and outputs such as publications, software, or research data, and ideally also covers other pillars of Open Science (e.g. citizen science, economic or societal impact), across multiple stages of the research cycle. The first version of this dataset was created collaboratively by members of OSMI Working Group 2, who identified, consolidated, and described Open Science monitoring initiatives based on their expertise and knowledge of the global landscape. Following this initial compilation, the dataset was opened for public consultation, inviting the wider Open Science community to review the inventory, suggest additional initiatives, and propose corrections or improvements. We are grateful to everyone who contributed their time and expertise during this consultation, helping to make the dataset more comprehensive and representative. The dataset is intended to be a living resource and will continue to evolve as the Open Science monitoring landscape develops. Future versions will incorporate newly identified initiatives, metadata updates, and community feedback. We welcome suggestions for additional monitoring initiatives or improvements to existing records. If you would like to contribute, please contact the OSMI Working Group 2 co-chairs. Contact information is available on the OSMI website. For more information about the methodology and the activities of Working Group 2, see: WG2. Understanding the open science monitoring landscape - Open science monitoring initiative - OSMI
Zamora, Esteban · Perez de Lara, David · Guidi, Luca · et al.
164 rows × 70 cols · 94 KB · csv, xlsx
70 numeric
Dataset corresponding to the figures of the article: Esteban Zamora-Amo, et al. "Ultra-Conformal memristors for on-skin electronics".
Mirzaie, Farzad
16 rows × 11 cols · 9.8 KB · xlsx
11 categorical
Data for all figures of paper https://doi.org/10.1039/D5RE00551E
Bonaventura, Leonardo · CIURLUINI, Cristiano · Giannetti, Fabio
27 rows × 8 cols · 6.5 MB · xlsx
8 categorical
This work was carried out by the Nuclear Engineering Research Group of Sapienza University of Rome, within the framework of the Euratom EASI-SMR project. It was focused on the development of a dataset referred to a SMR-like nuclear system, called E-LOOP-SMR. The dataset can be adopted to fully characterize the reactor thermal-hydraulic behavior during normal operations. It also includes a preliminary layout of the safety systems needed to cope with main selected accidental scenarios (DBCs). An update with the DEC-A systems is planned to be released ASAP.
Jungmyoung, Son · Sihoon, Lee · Jiyeon, Hong
3 files · 20 KB · docx, xlsx
This dataset provides the complete double-coding matrix, PRISMA 2020 checklist, and search strategy supporting the systematic review "Representation-to-AI Transformation in K-12 Generative AI Learning: A Theory-Building Systematic Review of Semantic Transformation Mechanisms." It includes: (1) study-level tier classification (Core/Supporting/Context) for two independent coders and consensus tier for all 18 included studies; (2) the full semantic transformation unit (STU) coding matrix (18 studies x 10 STUs = 180 cells) with pre-consensus and consensus scores; (3)evidence-weighting consensus scores; (4) inter-rater reliability statistics (Cohen's kappa); (5) the completed PRISMA 2020 checklist; and (6) the full database-specific Boolean search strategy.
Siccardi, Eugenia · Volanti, Virginia Amanda · Coppi, Andrea · et al.
999 rows × 26 cols · 500 KB · xlsx
23 categorical · 2 text · 1 numeric
Putri Lenggo Genni · Tavasya Alia Anjani · Muhammad Fasha Asshofa
3 files · 2.8 MB · csv, pdf, xlsx
Ionescu, Maria
90 rows × 9 cols · 35 KB · xlsx
5 categorical · 3 numeric · 1 text
Sulasi, Hilyah
610 rows × 40 cols · 93 KB · xlsx
30 numeric · 9 categorical · 1 datetime
Pratama, Cecilia Tyara · Noviana, Lina
11 rows × 6 cols · 7.3 KB · xlsx
6 categorical
Kosasih, Femerio · Yorick, Zeon · Sarjono, Haryadi
23 rows × 46 cols · 121 KB · xlsx
46 categorical
Cinar, Zelal
12 rows × 8 cols · 9.3 KB · xlsx
8 categorical
Zahra, Euis Kusumah Khalifatu · Azzahra, Ratu Anisa · Sarjono, Haryadi
500 rows × 33 cols · 59 KB · xlsx
28 numeric · 4 categorical · 1 datetime
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 .
Altenhoff, Adrian
6 files · 100 MB · hdf5
OMAmer - tree-driven and alignment-free protein assignment to subfamilies OMAmer is an alignment-free protein family assignment method designed to avoid overly specific subfamily predictions and to scale efficiently to phylogenomic databases containing thousands of genomes. It relies on an innovative approach that uses evolutionarily informed k-mers for alignment-free mapping to ancestral protein subfamilies. This dataset provides precomputed OMAmer databases derived from the Hierarchical Orthologous Groups in the OMA Browser . We aim to update these databases with every new OMA Browser release. Each OMAmer database is built using the latest version of the OMAmer package available at the time of the corresponding OMA Browser release. The dataset includes databases for different subsets of the species taxonomy. In most cases, we recommend using the LUCA.h5 database, which contains information from all species in the OMA database. The subset-specific databases are mainly useful when disk space is limited. The release May2026 is based on the OMA Browser release May 2026 which comprises 2983 species. We used OMAmer version 2.1.0 to build these databases.
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.
Goncalves dos Santos, Karen Cristine · Desgagné-Penix, Isabel · Merindol, Natacha
16 files · 8.0 MB · gzip
General description Compiled dataset of transcriptome assemblies, transcriptome annotation and expression quantification for 27 Amaryllidoideae species and 4 hybrid cultivars: Amaryllis belladonna , Clivia miniata , Crinum asiaticum , Crinum x powellii , Galanthus elwesii , Galanthus sp., Hippeastrum cv. Blossom Peacock, Hippeastrum cv. Jewel, Hippeastrum cv. Royal Velvet, Hippeastrum striatum , Hippeastrum vittatum , Leucojum aestivum , Lycoris aurea , Lycoris chinensis , Lycoris incarnata , Lycoris longituba , Lycoris radiata , Lycoris sprengeri , Narcissus aff pseudonarcissus , Narcissus papyraceus , Narcissus pseudonarcissus , Narcissus cv. Tête-à-Tête, Narcissus tazetta , Narcissus viridiflorus , Phycella aff cyrtanthoides , Rhodophiala pratensis , Scadoxus multiflorus , Traubia modesta , Zephyranthes candida , Zephyranthes carinata , Zephyranthes treatiae. Data includes transcriptome assemblies, Transdecoder predictions of peptide sequences (along with predicted coding seqeunces, GFF3 and BED files), expression quantification (count and TPM matrices for genes and trinity isoforms obtained with Kallisto and from Salmon), and annotation results (from EggNOG, Pfam, Uniprot Swissprot and Rfam), as well as signal peptide and transmemberane domain predictions (from SignalP and TmHMM). Annotations were compiled into a report for each species using Trinotate. For Lycoris aurea and Narcissus cv. Tête-à-Tête, there are two assemblies and corresponding files: Lycoris_aurea_PB and Lycoris_aurea_TH, and Narcissus_TaT_PB Narcissus_TaT_TH. PB assemblies were constructed solely with long-read sequencing data (PacBio, PB); while TH assemblies were constructed with short reads using Trinity, with long-read assembly being used for scaffolding step of Trinity (Trinity Hybrid, TH). Expression quantification was published on NCBI GEO (accessions GSE329951 , GSE329957 , GSE330014 and GSE331457 ). File descriptions All unitigs and predicted protein sequences are prefixed with an acronym to identify the species: Species Acronym NCBI TSA accession Amaryllis belladonna Ambel deposited on GSE331457 Clivia miniata Clmin DBNKRK000000000 Crinum asiaticum Crasi DBNIJL000000000 Crinum x powellii Crpow DBNKRS000000000 Galanthus elwesii Gaelw DBNKRO000000000 Galanthus sp. Gasp DBNKRR000000000 Hippeastrum cv. Blossom Peacock HispBP DBNMYE000000000 Hippeastrum cv. Jewel HispJW DBNMYC000000000 Hippeastrum cv. Royal Velvet HispRV DBNMYD000000000 Hippeastrum striatum Histr DBNIJG000000000 Hippeastrum vittatum Hivit DBNKRF000000000 Leucojum aestivum Leaes DBNIJJ000000000 Lycoris aurea (PB) Lyaur DBNFTY000000000 Lycoris aurea (TH) Lyaur DBNKRI000000000 Lycoris chinensis Lychi DBNKRE000000000 Lycoris incarnata Lyinc DBNKRD000000000 Lycoris longituba Lylon DBNKRG000000000 Lycoris radiata Lyrad deposited on GSE331457 Lycoris sprengeri Lyspr DBNKRL000000000 Narcissus aff pseudonarcissus Naafps DBNKRP000000000 Narcissus papyraceus Napap DBNKRN000000000 Narcissus pseudonarcissus Napse DBNIJK000000000 Narcissus Tête-à-Tête (PB) NaspPB DBNNFO000000000 Narcissus Tête-à-Tête (TH) NaspTH DBNUFN000000000 Narcissus tazetta Nataz DBNPME000000000 Narcissus viridiflorus Navir deposited on GSE331457 Phycella sp. Phsp deposited on GSE331457 Rhodophiala pratensis Rhpra deposited on GSE331457 Scadoxus multiflorus Scmul DBNIJH000000000 Traubia modesta Trmod deposited on GSE331457 Zephyranthes candida Zecan DBNKRH000000000 Zephyranthes carinata Zecar DBNIJI000000000 Zephyranthes treatiae Zetre deposited on GSE331457 Files Files are organized by type of analysis/data, meaning all expression quantification data obtained with Kallisto are compressed into a single file, all results from BlastP are in the same file, etc. Compressed file name Individual file type Content Blastp_Uniprot.tar.gz Tabular 33 files (1 per assembly) generated with BLASTP against Uniprot SwissProt release 2024_04. Files in blast output format 6 (standard columns). EggNOG_Emapper.tar.gz Tabular 33 tabular files (1 per assembly). Generated with eggnog.emapper (annotations file format described in eggnog.emapper's wiki ) Expression_Kallisto.tar.gz Tabular 128 files (4 per assembly, 2 for long-read assemblies): read counts and TPM values for both trinity "genes" and trinity "isoforms"). Row names are "gene"/ "isoform" IDs, column names are SRA run IDs. Expression_Salmon.tar.gz Tabular 128 files (4 per assembly, 2 for long-read assemblies): read counts and TPM values for both trinity "genes" and trinity "isoforms"). Row names are "gene"/ "isoform" IDs, column names are SRA run IDs. Final_assemblies.tar.gz Fasta 33 files (1 per assembly). Assemblies generated in this study, after contamination and expression filtering. HMMScan_PfamA.tar.gz Domain hits table 33 files (1 per assembly). Generated with hmmscan (option ‑‑domtblout, domain hits table, explained in hmmer's user guide [119] ) against the Pfam-A database. Infernal_Rfam.tar.gz Target hits table format 2 33 files (1 per assembly) generated with Infernal's cmscan (using the Trinotate wrapper) against the Rfam database. Table format 2 described in Infernal's user guide section 6 ). Metadata_studies.tar.gz Tabular (semi-colon separated columns) 31 tabular files (one per species/cultivar) indicating: Bioproject ID, SRA run ID, Sample name, Biosample ID, tissue, genotype (cultivar, when specified), treatment, batch, original publication citation, and DOI of original publication. Signalp6.tar.gz Tabular or GFF3 99 files (3 per assembly: prediction_results.txt, output.gff3 and region_output.gff3) generated with SignalP6. TmHMM2.tar.gz Tabular 33 files (1 per assembly) generated with TmHMM2 (short format, described in the guide tab of https://services.healthtech.dtu.dk/services/TMHMM-2.0/ Transcriptomes_unfiltered.tar.gz Fasta 33 files (1 per assembly). Unfiltered (prior to contamination and expression screening) assemblies generated in this study. Transdecoder_bed.tar.gz BED 33 BED files (1 per assembly) generated with TransDecoder.Predict (using BLASTp against Uniprot and hmmscan against Pfam) Transdecoder_cds.tar.gz Fasta 33 fasta files (1 per assembly) of predicted coding sequences generated with TransDecoder.Predict (using BLASTp against Uniprot and hmmscan against Pfam) Transdecoder_gff3.tar.gz GFF3 33 GFF3 files (1 per assembly) generated with TransDecoder.Predict (using BLASTp against Uniprot and hmmscan against Pfam). Transdecoder_proteomes.tar.gz Fasta 33 predicted proteome files (.pep, 1 per assembly) generated with TransDecoder.Predict (using BLASTp against Uniprot and hmmscan against Pfam).
R. Panicker, Vyshakh · J. Smug, Bogna · Klein-Sousa, Victor · et al.
1 files · 8.0 MB · gzip
Data deposit for: Panicker VR, Smug BJ, Klein-Sousa V, Enright MC, Taylor NMI, Drulis-Kawa Z, Mostowy RJ. "Structural modularity of receptor-binding proteins underlies host-range strategy diversification in Klebsiella pneumoniae phages." bioRxiv 2026. https://doi.org/10.64898/2026.05.12.724579 This archive contains all large binary data files required to reproduce the analyses and figures in the paper above. The associated code is available at: https://github.com/VyshakhRP/RBP-div-hostrange Contents: - 01_raw/02_assemblies/ - Raw lysate assemblies for unpublished Klebsiella pneumoniae phages - 01_raw/03_host_genomes/ - Klebsiella pneumoniae host genome sequences (.fasta) - 01_raw/04_phage_genomes/ - Phage genome sequences (.fasta, .gb) - 02_intermediate/09_af3_predictions/ - AlphaFold 3 structure predictions for all receptor-binding proteins (.cif) - 02_intermediate/14_rbps-ecods/ecod.develop288.domains.txt - ECOD domain database (v20230309, develop288) - 02_intermediate/14_rbps-ecods/ecod.develop288.fasta.txt - ECOD domain FASTA (v20230309, develop288) - 05_output/01_genomes/ - Processed phage genome files - 05_output/03_proteins/ - Protein FASTAs and receptor-binding protein structures Usage: Download the archive, extract it, and place each subdirectory into the corresponding location in the cloned GitHub repository. Full instructions are provided in the repository README under Data Availability. Note: The ECOD database files can alternatively be downloaded directly from http://prodata.swmed.edu/ecod/distributions/ (v20230309 / develop288).