Bohl, Michael · Esteban-Medina, Marina · Lenhof, Kerstin · et al.

5 files · 5.0 GB · csv, torch, zipdeclared

This Zenodo record contains all data necessary to reproduce the benchmark results described in the following publication: M. Bohl, M. Esteban-Medina, N. Beerenwinkel, and K. Lenhof, Domain-adaptation deep learning models do not outperform simple baseline models in single-cell anti-cancer drug sensitivity prediction, bioRxiv (2026). Processed bulk and single-cell RNA-Seq datasets with response labels are in processed.zip. scATD model weights are in checkpoint_fold1_epoch_30.pth Full hyperparameter tuning logs/results are in hyperparam_tuning_results.csv A revised version of the source code (without model weights) is in code.zip. If it gets updated in the future, check the latest version at https://github.com/cbg-ethz/SC-Bulk-Domain-Adaptation/

BROCHARD, Pierre

3 files · 141 MB · torchdeclared

YOLOv26 models specialized in text region (TextRegion) and text line (TextLine) segmentation for medieval manuscripts. Sources: The Manicule corpus: https://nakala.fr/collection/10.34847/nkl.e0ef83vx The Alcar-HOME database: https://zenodo.org/record/5600884 The e-NDP corpus: https://zenodo.org/record/7575693 The Himanis project: https://zenodo.org/record/5535306 OCR ground truth for Caroline Miniscule : https://github.com/rescribe/carolineminuscule-groundtruth Ground Truth for ONB-Cod. 3891 : https://zenodo.org/record/7467249 Cremma Medieval : https://zenodo.org/record/7506657 DISTINGUO : https://doi.org/10.34847/NKL.48AD8B8D and synthetic data. HuggingFace Mirror : https://huggingface.co/LaMOP/Yolo-Seg-TextRegion-TextLine-Manuscript

Costes, Jean C.

28 files · 8.0 GB · fitsdeclared

Stellar activity is the main barrier to detecting and/or confirming low-mass/long-period (and Earth-analogue) planets using radial-velocity (RV) measurements. Searching forreliable indicatorsthat bettertrace magnetic activity may be key for both distinguishing more clearly between stellar and planetary signals, and for probing the underlying physics occurring on the stellar surface. In this work we have compared observations taken for magnetically active and inactive stellar phases over multiple time-scales to study the spectral imprint due to varying stellar activity. This serves as a proof-of-concept demonstration of a technique (named Spectral Ratio Analysis, SRA) that can be used to isolate activity-driven changes directly in the stellar photospheric absorption lines where RVs are measured. Using 14 relatively quiet and well sampled G- and K-type starsthatshow stellar activity cycles, we identified hundreds of activity-sensitive spectral features. Reducing this variability information into two global metrics - amplitude and velocity shift - uncovers potential evidence of a decoupling of the photospheric and chromospheric responsesto stellar activity in earlier-type stars. Additionally, potential signatures of the variations in the magnitude of the suppression of the convective blueshift throughout the activity cycle were observed via SRA. Finally, we show that these SRA indicators better capture RV variability than classical activity proxies, such as the chromospheric log R HK index and other cross-correlation function-based parameters such as bisector span and full width at half-maximum, by up to a factor of two. The direct link between photospheric line behaviour and stellar-induced RV variability offers a promising path for improving astrophysical noise mitigation. In these files you will find for each star a .fits file in which the seasonal and nightly difference spectra and its associated SDATemplate are given per order. Their respective errors have been added as well. Finally, the wavelength for each order, as well as the seasonal and nightly bjd of each observation and its measured logRHK are availbale in these files. Additionally, a .txt file have been added for each star showing the atlas of all the SDA detected features. The wavelength of each feature, its amplitude, its width and its potentially associated lines obtained from VALD are given in these files.

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.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 15 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 59.1-78.7°, Dec -39.9--24.9°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

67 files · 46 GB · csv, fits, tardeclared

Part 14 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 62-archive subset (RA 45.0-70.2°, Dec -48.1--30.1°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

66 files · 46 GB · csv, fits, tardeclared

Part 13 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 61-archive subset (RA 51.5-81.5°, Dec -54.3--30.0°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

64 files · 46 GB · csv, fits, tardeclared

Part 12 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 59-archive subset (RA 34.0-213.7°, Dec -66.5-80.4°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Walker, Matthew Gregory

4 files · 8.9 GB · fits, gzipdeclared

Data files, see Appendix of for description of contents

Yang, Jiazhuo · Cai, Hongyan · Xu, Xinliang · et al.

3 files · 408 MB · tiff, torchdeclared

This record contains the 10 m farmland shelterbelt distribution products for Northeast China in 2020 and 2025, together with the pretrained deep learning model weights used for farmland shelterbelt inference. The dataset was generated from spring Sentinel-2 surface reflectance composites using B4, B8, and NDVI features and a ResNet-50/CA deep learning model. The two GeoTIFF files represent binary farmland shelterbelt maps, where 1 indicates farmland shelterbelt and 0 indicates non-shelterbelt. The pretrained model weights are provided as ForestNet50V1.pth to support reproducible inference and local model adaptation. The dataset covers major agricultural regions of Northeast China, including Heilongjiang, Jilin, Liaoning, and eastern Inner Mongolia. It is intended for regional- and landscape-scale analyses of farmland shelterbelt distribution, spatial continuity, fragmentation, stage-based change, and ecological engineering assessment.

Anonymous Authors

15 files · 2.2 GB · parquet, rar, torchdeclared

Economic losses caused by extreme climate are not confined to the locations where events occur, but can propagate across regions through physical and economic linkages. Yet existing climate-impact assessment methods remain poorly suited to tracing how shocks spread across space and reshape the geography of economic loss. Here we develop a mechanistically informed multi-scale spatiotemporal autoregressive graph neural network model to quantify spatially cascading climate impacts. The model couples scale-specific, physically structured spatiotemporal autoregressive processes through an adaptive gating mechanism, allowing heterogeneous cross-scale interactions to be learned from data. Model estimation is achieved through tailored graph convolutional neural networks that are mathematically equivalent to spatiotemporal autoregressive models, enabling scalability while preserving transparent parameter interpretation. Monte Carlo simulation experiments show that the model accurately recovers true parameters and distinguishes between scale-dependent processes. Applying the framework to extreme precipitation, we find that large-scale upwind-to-downwind cascades driven by atmospheric circulations dominate aggregated economic losses. A one-standard-deviation increase in log extreme precipitation is associated with a 0.19 percentage-point decline in economic growth rate at the large scale, with 62.3% of the loss arising from spatial cascades. These findings highlight the need for transboundary risk governance that incorporates spatial cascading into climate-extremes monitoring and early-warning. Description of the uploaded file Monte Carlo simulation code data_generator_factors.py: Data generation script for multi-scale Monte Carlo simulation experiments. sarnn_model.py:Implementation of the proposed MS-STARGNNs model architecture definition. train.py:Training pipeline script for the MS-STARGNNs model. Data and spatial weights matrices for empirical analysis global_panel_1deg_std.parquet:Standardized Large-scale (1°) datase; global_panel_2km_std.parquet:Standardized Small-scale (2 km) dataset. W_global_2km_knn8.pt:Small-scale spatial weights matrix based on 8-nearest neighbors (KNN8). W_Large-scale:A large-scale spatial weights matrix derived from moisture transport pathways (2005-2021) mapping_1deg_to_2km.parquet: Correspondence file mapping large-scale (1°) grid cells to small-scale (2km) grid cells. ipcc_region_mapping_coarse_8regions.parquet: Mapping file linking Large-scale grid cells to the 8 IPCC AR6 reference regions. ipcc_region_mapping_fine_8regions.parquet: Mapping file linking Small-scale grid cells to the 8 IPCC AR6 reference regions. Code model.py: Core architecture definitions for empirical analysis. Provides the base classes and computational layers engineered to handle real-world geospatial complexities. All subsequent training scripts import modules from this file. STARGNNs.py: Implementation of the single-scale baseline. Serves as a reference point for evaluating the efficacy of cross-scale feature fusion. MS-STARGNNs_fixed.py: Configuration script for the MS-STARGNNs model utilizing fixed autoregressive coefficients. MS-STARGNNs.py: Configuration script for the MS-STARGNNs model utilizing annually varying autoregressive coefficients. MS-STARGNNs_8 regions.py: Executes the MS-STARGNNs model with decoupled regional parameters, loading unique autoregressive weights and βvectors for each IPCC region. Implements null value handling for regions lacking observational data (e.g., Antarctica).

zhang, Wenjun

10 files · 459 MB · torchdeclared

This archive contains full training dataset, preprocessing scaler .pkl files and five groups of pre-trained neural network checkpoints (.pth) for reproducing all results in the manuscript. Corresponding source code repository on GitHub: https://github.com/wenjunzhang2020/Prediction-of-combined-effects-of-binary-mixed-systems .

Mutluel, Abdullah Mevlüt

12 files · 449 KB · pdf, torchdeclared

Code and data accompanying the manuscript "Disentangling Electro-osmotic Drag and Back-Diffusion Water Fluxes in Polymer Electrolyte Membranes: A Physics-Informed Neural Network for Net-Flux Inversion" submitted to the Journal of the Electrochemical Society. Contents: - gen_data_lit.py: synthetic benchmark generation. Solves the through-plane membrane water transport boundary-value problem for 24 operating conditions using the Springer drag coefficient and the Nguyen-White diffusion correlation. - pinn_lit.py: physics-informed neural network training. Recovers n_d(lambda) and D_w(lambda) from net-flux data with a hard conservation constraint and a single anchor point. - plots_lit.py: reproduces all manuscript figures (Figs. 1-3). - rev_lib.py: validation experiments, including trend-line baseline comparison, noise robustness, non-monotonic diffusion coefficient recovery, anchor ablation, multi-seed statistics, and held-out condition tests (Fig. 4 and Table 2). - ga_final.py: graphical abstract. - data_lit.json: generated benchmark data (24 operating conditions with internal water-content profiles and net fluxes). - model_lit.pt: trained PyTorch model weights. - manuscript.tex and figure PDFs. Requirements: Python 3 with PyTorch, NumPy, SciPy, and Matplotlib. Run gen_data_lit.py first, then pinn_lit.py, and then plots_lit.py to reproduce the results from scratch, or load model_lit.pt directly with the Model class defined in pinn_lit.py.

Ye, Renhao · Shen, Shiyin

69 files · 47 GB · csv, fits, tardeclared

Part 2 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 64-archive subset (RA 137.7-270.0°, Dec 52.9-79.9°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 3 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 180.0-270.0°, Dec 68.7-80.3°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

66 files · 46 GB · csv, fits, tardeclared

Part 4 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 61-archive subset (RA 84.4-285.5°, Dec -41.4-80.4°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 5 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 47.7-93.4°, Dec -67.7--16.7°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

66 files · 46 GB · csv, fits, tardeclared

Part 6 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 61-archive subset (RA 1.2-69.9°, Dec -66.6--54.3°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.

Ye, Renhao · Shen, Shiyin

65 files · 46 GB · csv, fits, tardeclared

Part 7 of 20. Synthetic Euclid VIS-band galaxy images generated from DESI Legacy Imaging Surveys r,z-band cutouts using an Image-to-Image Schrödinger Bridge (I2SB) model, over the Euclid Q1 footprint. The full processed dataset covers 2,981,033 FITS cutouts packed into 1,235 tar archives grouped by HEALPix sky pixel (nside=64, NESTED ordering), ~1 GB each. Because Zenodo limits each record to 100 files and 50 GB, the full set is split across 20 records. This record publishes a 60-archive subset (RA 32.7-60.0°, Dec -63.4--48.2°). Integrity and provenance: checksums.tsv and desi2euclid_index.csv record the SHA256 of all 1,235 archives from this processing run, not just the ones published as downloadable files here. These values let this exact version of the data be independently verified in the future -- including before any analysis built on it has been published -- by recomputing an archive's SHA256 and comparing it against the recorded value, to confirm the file has not been modified, corrupted, or substituted since it was originally produced. See README.md for the FITS layout (predicted VIS image, DESI g,r,z input -- of which only r,z were used to generate the prediction -- and prediction-uncertainty map), how to locate the archive for a given sky position, and how to run the integrity check.