cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17546166
title: Archivo de los Premios Goya: nominados y resultados

description: CSV con candidaturas de los Premios Goya por edición y categoría.Unidad de observación: una candidatura.Una fila equivale a una persona u obra nominada en una categoría de una edición.

Columnasid_fila: identificador estable por edición, categoría, obra, persona y resultadoedicion: slug de la ediciónanio_gala: año de la galacategoria: nombre de la categoríaes_ganador: 1 ganador; 0 resto de nominadostitulo_obra: título de la película u obrapersona: nombre del profesional cuando aplicaurl_detalle: enlace a la página para detalle de la obraurl_imagen: URL mostrada en la tarjetafuente: URL de la página desde la que se extrajo la información

FormatoCSV UTF-8. Una fila por candidatura. En candidaturas con varias personas se generan varias filas.

FuenteSitio oficial de la Academia de Cine: https://www.premiosgoya.com/

LicenciaDatos: CC BY 4.0 — https://creativecommons.org/licenses/by/4.0/legalcodeAtribución sugerida:“Nominaciones y ganadores de los Premios Goya, compilación propia a partir de premiosgoya.com; dataset publicado bajo CC BY 4.0.”Código: MIT (ver LICENSE en la raíz del repositorio)

Notas éticas y técnicasSe compilan hechos públicos; no se incluyen textos extensos ni se redistribuyen imágenes del sitio.Se mantienen enlaces a la fuente para trazabilidad.El scraping se realizó con User-Agent identificable, pausas y reintentos, respetando un uso responsable.
DOI: 10.5281/zenodo.17546167
title: Archivo de los Premios Goya: nominados y resultados

description: CSV con candidaturas de los Premios Goya por edición y categoría.Unidad de observación: una candidatura.Una fila equivale a una persona u obra nominada en una categoría de una edición.

Columnasid_fila: identificador estable por edición, categoría, obra, persona y resultadoedicion: slug de la ediciónanio_gala: año de la galacategoria: nombre de la categoríaes_ganador: 1 ganador; 0 resto de nominadostitulo_obra: título de la película u obrapersona: nombre del profesional cuando aplicaurl_detalle: enlace a la página para detalle de la obraurl_imagen: URL mostrada en la tarjetafuente: URL de la página desde la que se extrajo la información

FormatoCSV UTF-8. Una fila por candidatura. En candidaturas con varias personas se generan varias filas.

FuenteSitio oficial de la Academia de Cine: https://www.premiosgoya.com/

LicenciaDatos: CC BY 4.0 — https://creativecommons.org/licenses/by/4.0/legalcodeAtribución sugerida:“Nominaciones y ganadores de los Premios Goya, compilación propia a partir de premiosgoya.com; dataset publicado bajo CC BY 4.0.”Código: MIT (ver LICENSE en la raíz del repositorio)

Notas éticas y técnicasSe compilan hechos públicos; no se incluyen textos extensos ni se redistribuyen imágenes del sitio.Se mantienen enlaces a la fuente para trazabilidad.El scraping se realizó con User-Agent identificable, pausas y reintentos, respetando un uso responsable.
DOI: 10.5281/zenodo.17065168
title: Gastruloid phenotype live cell imaging dataset

description: The images were taken every 6 hours for 2 days (48 hours), resulting in 9 frames per gastruloid. 1,058 total gastruloids were grown, evenly split between euploid (array1) and aneuploid (array2) conditions, resulting in ~9,500 images. To increase contrast and reduce background lighting effects, we applied a gaussian blur to the raw images and removed the backgrounds before normalization. 

 

Curation:

Time-series transmitted light and fluorescence imaging of the gastruloids were performed using a custom microscopy system enclosed within an incubation chamber maintaining constant temperature, CO2, and humidity levels as previously described. Notably, the system consisted of an inverted MVX10 MacroView fluorescence microscope (Evident Scientific, Tokyo, Japan) with a 2x wide-field objective and was illuminated by a Lumen 200 mercury lamphouse (Prior Scientific Inc., Rockland, MA) through a 8907 ET - ECFP/EYFP/DsRed filter set (Chroma Technology Corp, Bellow Falls, VT). Images were acquired every 6 h over 48 h at 4x magnification (1.7 µm/pixel).

The python file used to do background correctioin and normalization of the images in this dataset is also includuded here.

The primary codebase associated with this project can be found here.
DOI: 10.5281/zenodo.17546242
title: Gastruloid phenotype live cell imaging dataset

description: The images were taken every 6 hours for 2 days (48 hours), resulting in 9 frames per gastruloid. 1,058 total gastruloids were grown, evenly split between euploid (array1) and aneuploid (array2) conditions, resulting in ~9,500 images. To increase contrast and reduce background lighting effects, we applied a gaussian blur to the raw images and removed the backgrounds before normalization. 

 

Curation:

Time-series transmitted light and fluorescence imaging of the gastruloids were performed using a custom microscopy system enclosed within an incubation chamber maintaining constant temperature, CO2, and humidity levels as previously described. Notably, the system consisted of an inverted MVX10 MacroView fluorescence microscope (Evident Scientific, Tokyo, Japan) with a 2x wide-field objective and was illuminated by a Lumen 200 mercury lamphouse (Prior Scientific Inc., Rockland, MA) through a 8907 ET - ECFP/EYFP/DsRed filter set (Chroma Technology Corp, Bellow Falls, VT). Images were acquired every 6 h over 48 h at 4x magnification (1.7 µm/pixel).

The python file used to do background correctioin and normalization of the images in this dataset is also includuded here.

The primary codebase associated with this project can be found here.
DOI: 10.34740/kaggle/dsv/13640254
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.30559532
title: Microscopy - Dean and Albert CAM 2024

description: Data from: Dean WF, Albert RM, Nawara TJ, Ubil M, Beggs RR, Mattheyses AL. Dsg2 ectodomain organization increases throughout desmosome assembly. Cell Adh Migr. 2024 Dec;18(1):1-13. doi: 10.1080/19336918.2024.2333366. PMID: 38566311; PMCID: PMC10993919.https://pubmed.ncbi.nlm.nih.gov/38566311/
DOI: 10.6084/m9.figshare.30559532.v1
title: Microscopy - Dean and Albert CAM 2024

description: Data from: Dean WF, Albert RM, Nawara TJ, Ubil M, Beggs RR, Mattheyses AL. Dsg2 ectodomain organization increases throughout desmosome assembly. Cell Adh Migr. 2024 Dec;18(1):1-13. doi: 10.1080/19336918.2024.2333366. PMID: 38566311; PMCID: PMC10993919.https://pubmed.ncbi.nlm.nih.gov/38566311/
DOI: 10.5281/zenodo.17529117
title: Census of Decentralized Autonomous Organizations Across Platforms (2025)

description: This dataset provides a comprehensive, multi-platform overview of Decentralized Autonomous Organizations (DAOs) as of October 2025, covering DAO deployments, proposals, and voting activity across 6 major platforms: Aragon, DAOHaus, DAOstack, Realms, Snapshot and Tally. It is designed to support research in blockchain governance, decentralized finance, and community-driven decision-making.

The dataset is structured into three CSV files:





deployments.csv – Contains metadata for each DAO deployment, including a unique identifier (deployment_id), platform name (platform), DAO name (name), number of proposals (proposals_count), number of distinct voters (unique_voters), total votes cast (votes_count), and estimated voting power (estimated_vp).




proposals.csv – Contains detailed information on proposals submitted to each DAO, including a unique proposal ID (proposal_id), associated DAO deployment (deployment_id), creation date (date), number of votes received (votes_count), total voting power used (total_vp), and the platform (platform).




votes.csv – Contains individual vote records, including a unique vote ID (vote_id), associated proposal (proposal_id), corresponding DAO deployment (deployment_id), voter address (voter), timestamp of the vote (date), voting power used (weight), and platform (platform).



Data collection for this release was completed in October 2025. The full data acquisition and processing pipeline typically requires approximately two to three weeks, involving the retrieval, validation, and integration of governance data from each platform’s public APIs and subgraphs.

This dataset is suitable for empirical research on DAO governance dynamics, voting behavior, and multi-platform analysis. Researchers can use this dataset to analyze trends in proposal creation, voter participation, and governance outcomes in decentralized communities.

The code for collecting and formatting the data is in the github repository linked. The instructions of use, requirements, the data loading and some example analyses are in the MSR submission folder of the github repository.

 

Acknowledgements

We would like to acknowledge the use of tools and support provided by DAO Analyzer dataset, GraphiQL, Tally API Reference, and AI-Powered RPC Infrastructure for dApps & Chains | dRPC for data extraction. These resources were indispensable for the successful completion of this project.
DOI: 10.5281/zenodo.17544911
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-06-02

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2018-06-02. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.5281/zenodo.17545578
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-06-02

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2018-06-02. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.5281/zenodo.17440768
title: Analysis of Adversarial Examples in Trained Neural Network Classifiers

description: This dataset accompanies the article “Analysis of Adversarial Examples in Trained Neural Network Classifiers”  (2025).

Deep learning models have achieved remarkable success across various domains, yet they remain vulnerable to adversarial examples — small, carefully crafted perturbations of input data that lead to incorrect predictions. This project explores the behavior of neural network architectures under adversarial attacks and provides all resources necessary for reproducing and extending the experiments presented in the paper.

The repository contains:





Trained neural network models – including fully connected, convolutional, and residual networks (ResNet)




Adversarial examples generated using four attack types constrained by different Lp norms




Computed distances and manifold projections between clean and adversarial samples across layers




Nearest-neighbor analyses and dimensionality-reduction data (e.g., PCA results)



These materials support the paper’s investigation into where and how misclassifications occur within network architectures and provide quantitative and qualitative insight into model robustness.

The data were generated using PyTorch, and all scripts are available athttps://github.com/JPolasko/Adversarial-Attack-visualization.

Contents





/networks/ – pretrained networks for different datasets


/data/ –  different datasets 



/adversarials/ – generated adversarial inputs




/distances/ – computed layer-wise distances and similarity metrics




/images/ – PCA, t-SNE, and projection data for visualization
DOI: 10.5281/zenodo.17544845
title: Analysis of Adversarial Examples in Trained Neural Network Classifiers

description: This dataset accompanies the article “Analysis of Adversarial Examples in Trained Neural Network Classifiers”  (2025).

Deep learning models have achieved remarkable success across various domains, yet they remain vulnerable to adversarial examples — small, carefully crafted perturbations of input data that lead to incorrect predictions. This project explores the behavior of neural network architectures under adversarial attacks and provides all resources necessary for reproducing and extending the experiments presented in the paper.

The repository contains:





Trained neural network models – including fully connected, convolutional, and residual networks (ResNet)




Adversarial examples generated using four attack types constrained by different Lp norms




Computed distances and manifold projections between clean and adversarial samples across layers




Nearest-neighbor analyses and dimensionality-reduction data (e.g., PCA results)



These materials support the paper’s investigation into where and how misclassifications occur within network architectures and provide quantitative and qualitative insight into model robustness.

The data were generated using PyTorch, and all scripts are available athttps://github.com/JPolasko/Adversarial-Attack-visualization.

Contents





/networks/ – pretrained networks for different datasets


/data/ –  different datasets 



/adversarials/ – generated adversarial inputs




/distances/ – computed layer-wise distances and similarity metrics




/images/ – PCA, t-SNE, and projection data for visualization
DOI: 10.7910/dvn/o3ybot
title: Statistically downscaled and bias corrected CMIP6 models for Continental Chile under scenario SSP5-8.5

description: This database contains statistically downscaled (SD) and bias-corrected (BC) daily time series of precipitation, maximum and minimum temperatures from 29 CMIP6 GCMs and 1 RCM forced with a CMIP5 GCM. The dataset spans Continental Chile from 1950 to 2099 at a 0.05° x 0.05° horizontal resolution. The emission scenarios are SSP5-8.5 and RCP8.5 for the GCMs and RCMs, respectively. 
The reference observational product is CR2MET (v2.5; Boisier, 2023).
description: IMPORTANT: The database can also be downloaded via FTP. Send an email to the authors for access.

If you use this database, please cite Vásquez et al. 2024 Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ad9d5b
DOI: 10.5281/zenodo.15006988
title: Collostructional Analysis: A Bayesian Approach
DOI: 10.5281/zenodo.17546279
title: Collostructional Analysis: A Bayesian Approach
DOI: 10.25584/3000993
title: Multiscale ACI Satellite Database

description: The SATELLITE_EAGLES_PNNL NetCDF dataset contains a suite of satellite- and reanalysis-derived atmospheric and surface parameters on a regular latitude–longitude grid. The dataset includes core geophysical fields such as land fraction, aerosol optical depth at multiple wavelengths (465, 550, 667, and 865 nm), sea surface temperature, estimated inversion strength, and various thermodynamic and dynamic quantities (e.g., relative humidity, vertical velocity, boundary-layer height, and surface fluxes) from both MERRA and ERA reanalysis products, provided as daily-mean and instantaneous values. A major component of the dataset consists of MODIS-retrieved cloud microphysical properties, including cloud droplet number concentration, cloud effective radius, optical thickness, and liquid water path, provided for three compositing regimes (“All,” “Q06,” and “G18”). Corresponding cloud-top parameters—temperature, height, and pressure—along with total and domain-mean cloud fraction fields are also included. The file further integrates additional satellite data from AMSR-E (for cloud water, rain water, and surface precipitation retrievals) and CERES (for top-of-atmosphere radiative fluxes, cloud fractions, and albedo). This dataset is designed to evaluate aerosol–cloud interactions in warm clouds, emphasizing the use of MODIS for deriving cloud droplet number concentration and liquid water path statistics. The complementary satellite and reanalysis fields are co-located and time-matched to the same instantaneous MODIS observations, enabling consistent comparisons between cloud properties, aerosol loading, and large-scale meteorological conditions. The dataset is recently featured in Christensen et al. (2025), Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3850, 2025.
DOI: 10.5281/zenodo.17546290
title: Bulk RNA-Seq from CRISPR-Cas9 Screening Identifies Resistance Mechanisms to KRAS Inhibition in Pancreatic Cancer

description: KRAS inhibitors (KRASi) targeting various KRAS mutations have entered clinical trials for pancreatic cancer. Despite promising preliminary clinical responses, most patients relapse due to intrinsic or acquired resistance. Thus, combination treatments are essential to extend the efficacy of KRAS-targeted therapies. To further determine genetic mechanisms of KRASi resistance, we performed KRASi-anchored CRISPR-Cas9 loss-of-function screens in KRASG12D, KRASG12C, KRASG12R, and KRASQ61H mutant PDAC cell lines, using six KRASi, to identify genes that modulate sensitivity to KRAS inhibition. Several hits from the screens, including EGFR, CK2, p110α and p110γ, and YAP were validated by combining targeted inhibitors with KRASi. KRASQ61H-mutant PDAC cell lines were intrinsically less dependent on KRAS for survival than other KRAS mutational subtypes. Further, EGFR inhibitor erlotinib synergized with the RAS(ON) multi-selective inhibitor RMC-7977 in KRASQ61H-mutant PDAC cell lines and in cell lines with highly active EGFR by mitigating ERK rebound activity. KRASi-resistant cell lines featured sustained ERK/MAPK dependence despite decreased ERK activity. Together, these findings enhance the understanding of intrinsic and acquired resistance to KRASi and identify therapeutic vulnerabilities that can potentially be exploited for KRASi combination therapies in pancreatic cancer patients.