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.

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 .

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 .

Elder, Will

1 files · 1.4 GB · hdf5declared

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