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Altenhoff, Adrian
6 files · 100 MB · hdf5
hybrid · semantic + lexical · 21 datasets ranked · 1.04s
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.
Yang, Qingliu
6 files · 8.0 MB · bzip2
Dataset Description This dataset contains 3D lightning location results, DALMA and FALMA waveform for two Energetic Compact Stroke (ECS) events. location results are included: HF3D_1732785151.dat - 3D lightning locations for the ECS leader A flash. HF3D_1734785454.dat - 3D lightning locations for the ECS leader B flash. The timestamp 1734785454 and 1732785151 corresponds to the occurrence time of the lightning flash in Japan Standard Time. File format and parameters The first row contains the lightning occurrence time. Column descriptions: Time (ms) - time relative to the lightning source. X, Y, Z (m) - 3D spatial coordinates relative to ground level. The origin (0, 0, 0) corresponds to latitude 36.76°N and longitude 136.76°E. FALMA and DALMA waveform ECSLeaderA_DALMA_waveform.bz2 is DALMA waveform of Leader A. ECSLeaderA_FALMA_waveform.bz2 is FALMA waveform of Leader A. ECSLeaderB_DALMA_waveform.bz2 is DALMA waveform of Leader B. ECSLeaderB_FALMA_waveform.bz2 is FALMA waveform of Leader B. This dataset allows analysis of the spatial and temporal development of these two ECS flashes.
Zhang, Yunwei
6 files · 40 GB · hdf5, zipdeclared
Files related to INVNET, a deep learning model for surface wave dispersion spectrum inversion in geophysics.
Tiskus, Edvinas · Tiškuvienė, Rūta · Bučas, Martynas · et al.
37 files · 1.6 GB · hdf5, tiff, torchdeclared
This record contains the labeled data, trained models, and analysis code supporting the article "Comparing a Vision Foundation Model (DINOv3) and a Task-Specific U-Net for Mapping Emergent Aquatic Vegetation from Fused UAV Multispectral and LiDAR Data" (Remote Sensing in Ecology and Conservation). Contents: - masks/ : georeferenced ground-truth segmentation masks (five classes: aquatic vegetation, water, sand, other objects, background), aligned to the fused UAV orthomosaics and spanning 13 sites across nine Lithuanian waterbodies surveyed between May and August 2024. - models/ : the two final trained segmentation models, a Keras/HDF5 U-Net and a PyTorch DINOv3 model. - code/ : Python scripts for training, evaluation, the label-efficiency experiment, and full-scene prediction. The fused 9-band orthomosaics (five-band multispectral, RGB, and a LiDAR canopy height model; approximately 62 GB) are archived separately because of their size and are available from the corresponding author on request. The DINOv3 SAT-493M pretrained backbone is distributed by Meta under its own license and is not redistributed here; obtain it from the official DINOv3 release.
Vines, Jose I.
1 files · 3.0 GB · hdf5declared
Pre-computed, resolution-broadened (R=1500) stellar atmosphere spectra cache for the astroARIADNE SED fitting package. Contains 7 model grids (Phoenix v2, BT-Settl, BT-NextGen, BT-Cond, Castelli & Kurucz 2004, Kurucz 1993, Coelho 2014) resampled to a common logarithmic wavelength grid (0.125-4.629 µm). This cache eliminates the need to download the full ~770 GB model libraries for SED plotting.
Lopez Dubon, Sergio · Sgarabotto, Alessandro · Lanzoni, Stefano
11 files · 38 MB · hdf5, npydeclared
This record contains the trained autoencoder, extracted encoder, and processed world/real-river latent-space reference cloud associated with the manuscript *A data-driven approach to discern the curvature spectral complexity of compound meander bends*. The full autoencoder is provided to support reconstruction-based validation and reproducibility of the learned representation. The extracted encoder is provided for inference and future software tools. It maps preprocessed 64 × 64 single-channel curvature-spectrum images to the two-dimensional latent space used to analyse meander shape complexity and skewness. The file `world_latent_cloud.npy` contains the two-dimensional latent coordinates of the world/real-river meander dataset used as the reference background cloud in the manuscript latent-space figures. This file is a processed latent-coordinate dataset only; it does not contain raw satellite imagery, raw centreline geometries, or training images. The release includes model weights, architecture files, model summaries, export metadata, the world/real-river latent cloud, example inference scripts, a validation script, environment files, and a minimal example input. The models should only be applied to curvature-spectrum images generated consistently with the preprocessing workflow described in the associated manuscript. Main files included in this release are: - trained_autoencoder.h5: full trained autoencoder. - encoder_only.h5: extracted encoder in HDF5/Keras format. - encoder_only.keras: extracted encoder in native Keras format. - model_architecture.json: full autoencoder architecture. - encoder_architecture.json: encoder architecture. - model_summary.tx and encoder_summary.txt: layer summaries. - world_latent_cloud.npy: world/real-river reference latent-space cloud. - world_latent_cloud_metadata.json: metadata for the world/real-river latent-space cloud. - model_card.md: intended use, inputs, outputs, limitations, and citation guidance.
Meekes, Lisa · Tabaro, Francesco · Bexkens, Michiel · et al.
41 files · 8.2 GB · csv, fasta, pdfdeclared
This record contains the Python software for PEPTiGEN, a tool for generating tryptic peptides from prokaryotic gene sequences and their variants, and the associated antimicrobial resistance (AMR) peptide database. The database is provided as an SQL file and a CSV file containing all genes and predicted peptides. The README file contains explanation of the PEPTiGEN tool. The SQL database schema files contains both the database schema of the SQL database used in the PEPTiGEN analysis as the database schema of the AMR peptide datbase.
Patel, Maulik
2 files · 174 KB · hdf5declared
1. May24_CIRBE-GOES_flux.h5 contains the necessary flux data to recreate the flux plots. 2. Oct24_CIRBE-GOES_flux.h5 contains the necessary flux data to recreate the flux plots.
Hartig, Johannes
60 files · 11 GB · csv, hdf5, pdfdeclared
Keypoint-MoSeq model checkpoint and data from VNPD paper.
Kim, Kyurhi
10 files · 9.6 GB · bzip2, zipdeclared
This repository contains cis-xQTL mapping results, colocalization analysis results, and transcriptome-wide association study (xTWAS) weights and association test statistics for six transcriptomic modalities generated from bulk RNA-seq data of dorsolateral prefrontal cortex (DLPFC) tissue from the ROS/MAP cohorts (n = 1,035). The RNA trait tables (BED format) used for cis-xQTL mapping and xTWAS model training were generated using the Pantry pipeline but are not included in this repository. Colocalization and xTWAS analyses were performed using the publicly available Alzheimer's disease (AD) dementia GWAS summary statistics from Bellenguez et al. ( Nature Genetics , 2022).
Allendorf, Daniel · Bläsius, Thomas · Leonhardt, Alexander · et al.
2 files · 12 GB · bzip2, zipdeclared
About this Repository This repository contains the software, datasets, and experimental data to reproduce the experiments in the above mentioned article. Please refer to the README file for more details and instructions. Article Abstract We consider a maximum entropy edge weight model that allows for negative weights. Given a graph Gand possible weights W typically consisting of positive and negative values, the model selects edge weights w ∈ W^m uniformly at random from all weights that do not introduce a negative cycle. We propose an MCMC process and show that it converges to the required distribution. We then engineer an implementation of the process using a dynamic version of Johnson's algorithm in connection with a bidirectional Dijkstra search as well as an innovative resampling method. We empirically study the performance characteristics of these novel sampling algorithms as well as the output produced by the model. Dataset Most of the input data (graph data) is generated dynamically via random graph models. In addition to the result data from the experiments, unew.data.tar.bz2 also contains trimmed US road networks used for the ROAD dataset in the paper. Code The code is developed at https://codeberg.org/lukasgeis/unew --- you may want to check there for updates.
Plunkett, Cailin · Vitale, Salvatore · Zevin, Michael · et al.
4 files · 8.6 GB · hdf5declared
Posterior samples and posterior predictive distributions for the population analyses in Plunkett et al. 2026 .
OpenFF, YDS
18 files · 334 MB · bzip2, csv, pngdeclared
Generated by yammbs-dataset-submission: https://github.com/openforcefield/yammbs-dataset-submission
Green, Alexander
3 files · 5.4 GB · gzip, hdf5declared
This includes the source code, background plasma conditions, and simulation results that produced the simulation data reported in Green et al., "Evaluating the Impact of Multiscale E-Region Turbulence on HF/VHF Scintillation."
Wedig, Bryce · Daylan, Tansu · Huang, Alan · et al.
2 files · 6.5 GB · hdf5declared
Data Challenge Overview The Roman Space Telescope is expected to observe O(10^5) galaxy-galaxy strong gravitational lenses, providing high angular resolution images of galaxy-galaxy strong gravitational lenses that can be used to probe the nature of dark matter at sub-galactic scales ( Daylan and Birrer 2023 , Wedig et al. 2025 ). The Roman Data Challenge for Dark Matter Substructure with Galaxy-Galaxy Strong Gravitational Lenses provides realistic simulated Roman images of strong lenses with various dark matter substructure populations and challenges the community to test out substructure detection and characterization pipelines. Dataset Description The goal of this rung is to distinguish between mass distributions with Cold Dark Matter subhalos and no subhalos. In this rung, you will train a binary classifier to determine whether subhalos are present. This is the unlabeled dataset. It does not include the boolean substructure flag and a few other related parameters that were included in the labeled dataset. Rung 1 submissions will be scored for this dataset. Changelog v2.0: Fixes a bug where SNRs were calculated from 601 second exposures but images were simulated with exposure time of 610 seconds. The difference in SNR is approximately 1%. New major version because the systems are different from v1.0 v1.0: Initial version
Gupte, Nihar · Miller, M. Coleman · Udall, Rhiannon · et al.
36 files · 36 GB · hdf5, torchdeclared
Data release for the DINGO O4a eccentricity paper. It contains the per-event parameter-estimation products, population selection function, and hierarchical-inference posteriors needed to reproduce every figure, table, and number in the paper, plus the trained DINGO neural networks used for the analyses. Event data : eccentric, quasicircular, and precessing per-event posterior samples (posteriors_eccentric.h5, posteriors_quasicircular.h5, posteriors_precessing.h5); slimmed log-uniform-eccentricity-prior posteriors used as the hierarchical-likelihood input (posteriors_log_uniform_eccentric.h5); per-event posteriors reweighted by the population-informed posterior (posteriors_population_reweighted.h5); per-event summary statistics with pre-computed Bayes factors (summary_statistics.h5); e_gw conversions (egw_conversions.h5); and the eccentricity-mean-anomaly prior hull (e_zeta_prior_hull.h5). Selection function : the injection p_draw dataframe with detection probabilities including the analysis-window factor (injection_p_draw.h5), a fixed-injection eccentricity sweep (fixed_injection_ecc_sweep.h5), and matched-filter survival-function data (survival_function.h5). Hierarchical inference : the selection-corrected velocity-dispersion posterior marginalized over the GWTC-4 mass/spin/redshift hyperposterior (sigma_posterior.h5), the capture-eccentricity lookup table (capture_ecc_table.h5), the external GWTC-4 hyperposterior fit (gwtc4_hyperposterior.h5), and the GC/NSC branching-fraction posterior (branching_fraction_posterior.h5). Glitch analyses : glitch-marginalized posteriors for GW190701, GW231114_043211, and GW231223_032836. Networks : trained DINGO networks (SEOBNRv5EHM, SEOBNRv5HM, SEOBNRv5PHM) with their training settings; see MODEL_MANIFEST.md. Zenodo stores files flat; the companion code maps each file into the foldered layout the notebooks expect. Code to download the data and reproduce all figures: github.com/nihargupte-ph/o4a-eccentricity , archived at doi:10.5281/zenodo.21221948 .
Fathi, Pernille Undrum · Occhiodori, Irene · Devaney, Patrick · et al.
23 files · 37 MB · hdf5declared
Elder, Will
1 files · 846 MB · hdf5declared
Vybornyi, Ivan · Zhdanov, Artem · Bock, Matthias · et al.
6 files · 72 MB · hdf5declared