cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xb2pju
title: Occurrence Download

description: A dataset containing 5030 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Colección de Laurales, Liliales, Lycopodiales, Magnoliales y Malpighiales del Museo Botánico CORD - IMBIV",
 "Country is Argentina"
 ]
}

 The dataset includes 5030 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0158253-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cr8mo3
title: Occurrence Download

description: A dataset containing 895 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chilomeniscus stramineus Cope, 1860"
 ]
}

 The dataset includes 895 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004827-150831095807960/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eevxym
title: Occurrence Download

description: A dataset containing 9027 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.61314 6.23148,-75.59187 6.23148,-75.59177 6.24932,-75.61295 6.24972,-75.61314 6.23148))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 9027 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0413641-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wfyjrc
title: Occurrence Download

description: A dataset containing 301 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Colección de Mamíferos - Universidad del Quindío"
 ]
}

 The dataset includes 301 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042200-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fclcoi
title: Occurrence Download

description: A dataset containing 3134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dermanura tolteca (Saussure, 1860)",
 "Country is Mexico"
 ]
}

 The dataset includes 3134 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004827-151016162008034/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gltaqz
title: Occurrence Download

description: A dataset containing 5621 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Computarización de la colección de plantas acuáticas mexicanas del Herbario Nacional (MEXU)"
 ]
}

 The dataset includes 5621 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003467-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mnd8bf
title: Occurrence Download

description: A dataset containing 95 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyperus pubens Kük."
 ]
}

 The dataset includes 95 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0158278-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w2whxp
title: Occurrence Download

description: A dataset containing 32237297 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 32237297 records from 3015 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0212202-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13285494
title: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

description: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction.

This Zenodo repository includes a .tar.gz archive containing all essential components to reproduce the experiments described in the study. This archive is designed to work seamlessly with the coding pipeline available at: https://github.com/manbaritone/SynProtX.

License:

Creative Commons Zero v1.0 Universal (CC0)This work is released under CC0, dedicating it to the public domain. You are free to use, modify, and distribute it without restriction.

Archive Contents:

This compressed file includes:



Datasets- Tissue Datasets: ALMANAC-Breast, ALMANAC-Lung, ALMANAC-Ovary, ALMANAC-Skin- Study Datasets: FRIEDMAN, ONEIL

Supporting Files- Raw and preprocessed data- Feature dictionaries- Hyperparameter configurations- Trained model weights


Folder Structure:



SynProtX/├── data/                   # Raw and preprocessed data│   ├── export/             # Processed protein/gene expression & drug combinations│   ├── nps/                # Numpy arrays for all datasets│   ├── nps_intersected/    # Dataset-specific numpy arrays│   └── raw/                # Original data from DrugComb, CCLE, COSMIC, ChEMBL V31, ProCan-DepMapSanger├── feature_dicts/          # Feature dictionaries for drug combinations├── hyperparams/            # Hyperparameter configs for SynProtX-GATFP│   ├── classification/     # For classification tasks│   └── regression/         # For regression tasks├── state_dict/             # Trained model weights│   ├── classification/     # PyTorch checkpoints for classification│   └── regression/         # PyTorch checkpoints for regression└── README_Zenodo.md        # This file


For more information, please visit:

GitHub: https://github.com/manbaritone/SynProtX
DOI: 10.5281/zenodo.13285495
title: SynProtX: Enhanced Deep Learning Model for Predicting Anti-Cancer Drug Synergy Using Large-Scale Protein Expression

description: This is a tarball file (.tar.gz) that includes the following:



Datasets for SynProtX

Downstream models


SynProtX is a deep learning model that leverages large-scale protein expression, molecular graphs, and fingerprints to enhance the prediction of synergistic effects in anti-cancer drug combinations.

For more information, please visit:

GitHub: https://github.com/manbaritone/SynProtX
DOI: 10.5281/zenodo.14040426
title: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

description: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

This is a tarball file (.tar.gz) that includes the following:



Datasets and Features for SynProtX

Hyperparameters of SynProtX

Downstream models of SynProtX


SynProtX is a deep learning model leveraging large-scale proteomics, molecular graphs, and fingerprints to enhance the prediction of synergistic effects in anti-cancer drug combinations.

For more information, please visit:

GitHub: https://github.com/manbaritone/SynProtX
DOI: 10.5281/zenodo.14656345
title: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

description: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

This is a tarball file (.tar.gz) that includes the following:



Datasets and Features for SynProtX

Hyperparameters of SynProtX

Downstream models of SynProtX


SynProtX is a deep learning model leveraging large-scale proteomics, molecular graphs, and fingerprints to enhance the prediction of synergistic effects in anti-cancer drug combinations.

For more information, please visit:

GitHub: https://github.com/manbaritone/SynProtX
DOI: 10.5281/zenodo.15603481
title: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

description: SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations

SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction.

This Zenodo repository includes a .tar.gz archive containing all essential components to reproduce the experiments described in the study. This archive is designed to work seamlessly with the coding pipeline available at: https://github.com/manbaritone/SynProtX.

License:

Creative Commons Zero v1.0 Universal (CC0)This work is released under CC0, dedicating it to the public domain. You are free to use, modify, and distribute it without restriction.

Archive Contents:

This compressed file includes:



Datasets- Tissue Datasets: ALMANAC-Breast, ALMANAC-Lung, ALMANAC-Ovary, ALMANAC-Skin- Study Datasets: FRIEDMAN, ONEIL

Supporting Files- Raw and preprocessed data- Feature dictionaries- Hyperparameter configurations- Trained model weights


Folder Structure:



SynProtX/├── data/                   # Raw and preprocessed data│   ├── export/             # Processed protein/gene expression & drug combinations│   ├── nps/                # Numpy arrays for all datasets│   ├── nps_intersected/    # Dataset-specific numpy arrays│   └── raw/                # Original data from DrugComb, CCLE, COSMIC, ChEMBL V31, ProCan-DepMapSanger├── feature_dicts/          # Feature dictionaries for drug combinations├── hyperparams/            # Hyperparameter configs for SynProtX-GATFP│   ├── classification/     # For classification tasks│   └── regression/         # For regression tasks├── state_dict/             # Trained model weights│   ├── classification/     # PyTorch checkpoints for classification│   └── regression/         # PyTorch checkpoints for regression└── README_Zenodo.md        # This file


For more information, please visit:

GitHub: https://github.com/manbaritone/SynProtX
DOI: 10.15468/dl.kvjgtx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Enteromius bifrenatus (Fowler, 1935)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.529 -24.0715,20.509 -24.0455,20.509 -24.0715,20.529 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023264-250525065834625/datasets/export for details.
DOI: 10.15468/dl.t4umrx
title: Occurrence Download

description: A dataset containing 1074 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is one of (China, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Agastache rugosa",
 "Year 1902-2025"
 ]
}

 The dataset includes 1074 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022001-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/9icihx
title: PAN-00179342 - composite crossbow brooch variant A2

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00179342&lt;/p&gt;
description: Modified: 2025-09-04T17:41:55+02:00
DOI: 10.18750/massbalance_point_2025_r2025
title: Swiss Glacier Point Mass Balance Observations (release 2025) 

description: Direct observations of point glacier mass balance have been carried out at one up to several dozens of sites on more than 50 individual glaciers in the Swiss Alps over the past century. The measurements represent the basis for seasonal mass balance monitoring. Data are aggregated to three time periods - annual (referring to a time interval of about 1 year, e.g. Oct.-Sept.), winter (referring to an entire winter season, e.g. Oct.-May), or intermediate (referring to an arbitrary period of a day up to several months). Metadata and estimated uncertainties are specified for different variables according to a system of quality indicators.
DOI: 10.15468/dl.762p69
title: Occurrence Download

description: A dataset containing 5699 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Alnus japonica, Alnus lanata, Alnus mandshurica, Alnus nepalensis, Alnus trabeculosa)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5699 records from 168 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022002-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21334/npolar.2021.21565514
title: Annual coastlines for Svalbard
DOI: 10.5281/zenodo.17775727
title: Dataset for "Machine Learning for Polymer Chemical Resistance to Organic Solvents"

description: This dataset accompanies the paper "Machine Learning for Polymer Chemical Resistance to Organic Solvents" (arXiv:2509.05344).

## Included Files

- **chemical_resistance_dataset.csv** (~20 MB): Main experimental dataset containing 2231 polymer-solvent combinations with chemical resistance labels and molecular descriptors.- **polymer_mpk_dataset.csv** (~285 MB): Virtual polymer dataset (40971 records).- **pe_solvent_dataset.csv** (~65 MB): Polyethylene virtual solvent dataset (9828 records).

## Data Sources

### polymer_mpk_dataset.csvThis dataset is based on PolyOmics:- Hugging Face: https://huggingface.co/datasets/yhayashi1986/PolyOmics- Related paper: arXiv:2511.11626

### pe_solvent_dataset.csvThe solvent SMILES data is derived from HSPiP (Hansen Solubility Parameters in Practice):- https://www.pirika.com/wp/chemistry-at-pirika-com/hsp/how2buy- Note: The HSP values themselves are not used in this study.

## Code Repository

The analysis code is available on GitHub:https://github.com/s-kunieda-1015/analysis-polymer-chemical-resistance

## Related Publication

arXiv: https://arxiv.org/abs/2509.05344

## License

This dataset is provided under CC BY 4.0 license.
DOI: 10.5281/zenodo.17786890
title: Dataset for "Machine Learning for Polymer Chemical Resistance to Organic Solvents"

description: This dataset accompanies the paper "Machine Learning for Polymer Chemical Resistance to Organic Solvents" (arXiv:2509.05344).

## Included Files

- **chemical_resistance_dataset.csv** (~20 MB): Main experimental dataset containing 2231 polymer-solvent combinations with chemical resistance labels and molecular descriptors.- **polymer_mpk_dataset.csv** (~285 MB): Virtual polymer dataset (40971 records).- **pe_solvent_dataset.csv** (~65 MB): Polyethylene virtual solvent dataset (9828 records).

## Data Sources

### polymer_mpk_dataset.csvThis dataset is based on PolyOmics:- Hugging Face: https://huggingface.co/datasets/yhayashi1986/PolyOmics- Related paper: arXiv:2511.11626

### pe_solvent_dataset.csvThe solvent SMILES data is derived from HSPiP (Hansen Solubility Parameters in Practice):- https://www.pirika.com/wp/chemistry-at-pirika-com/hsp/how2buy- Note: The HSP values themselves are not used in this study.

## Code Repository

The analysis code is available on GitHub:https://github.com/s-kunieda-1015/analysis-polymer-chemical-resistance

## Related Publication

arXiv: https://arxiv.org/abs/2509.05344

## License

This dataset is provided under CC BY 4.0 license.
DOI: 10.17026/ar/u5ujcs
title: PAN-00119037 - coin/coin-related, Antoninus Pius (138-161), voor Marcus Aurelius, denarius

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00119037&lt;/p&gt;
description: Modified: 2025-09-29T17:43:33+02:00
DOI: 10.6084/m9.figshare.28904672
title: <b>Ingeniería Genética de Plantas para la Tolerancia a la Sequía: Aplicaciones en Restauración Árida Terrestre y Trasformación Marciana Biología, Biotecnología, Ciencias Medioambientales biología bibliotecología</b>

description: Este conjunto de datos acompaña al artículo "Ingeniería Genética de Plantas para la Tolerancia a la Sequía: Aplicaciones en Restauración Árida Terrestre y Terraformación Marciana" e incluye el manuscrito completo en PDF que describe objetivos, materiales y métodos, resultados y discusión; los datos crudos de los ensayos de tolerancia a la sequía en condiciones de laboratorio y en simulación de regolito marciano (CSV/XLSX), con variables de crecimiento, índice de supervivencia y parámetros fisiológicos; los scripts de análisis en R y Python empleados para la evaluación estadística (ANOVA, pruebas de significancia y modelado de respuesta); los protocolos de laboratorio en PDF/Markdown para la edición génica mediante CRISPR/Cas9 y los ensayos de bioseguridad; las figuras y diagramas en alta resolución (SVG/PNG) de los resultados clave y esquemas experimentales; y las declaraciones de disponibilidad de datos, financiación y conflictos de interés. Este recurso facilita la reutilización, verificación y extensión de los experimentos descritos, promoviendo la colaboración en biotecnología vegetal y estudios de terraformación.Este conjunto de datos acompaña al artículo "Ingeniería genética de plantas para la tolerancia a la sequía: aplicaciones en la restauración de zonas áridas terrestres y la terraformación marciana" y comprende el manuscrito completo en PDF que detalla los objetivos, materiales y métodos, los resultados y la discusión; datos brutos de ensayos de tolerancia a la sequía en condiciones de laboratorio y simulaciones de regolito marciano (CSV/XLSX), incluidas mediciones de crecimiento, tasas de supervivencia y rasgos fisiológicos; scripts de análisis en R y Python para evaluación estadística (ANOVA, pruebas de significación y modelado de respuestas); protocolos de laboratorio en PDF/Markdown para la edición de genes CRISPR/Cas9 y evaluaciones de bioseguridad; figuras y diagramas de alta resolución (SVG/PNG) de los principales resultados y esquemas experimentales; y declaraciones sobre disponibilidad de datos, financiación y conflicto de intereses. Este recurso permite la reutilización, verificación y extensión de los experimentos descritos, fomentando la colaboración en biotecnología vegetal e investigación en terraformación.
DOI: 10.6084/m9.figshare.28904672.v2
title: <b>Ingeniería Genética de Plantas para la Tolerancia a la Sequía: Aplicaciones en Restauración Árida Terrestre y Trasformación Marciana Biología, Biotecnología, Ciencias Medioambientales biología bibliotecología</b>

description: Este conjunto de datos acompaña al artículo "Ingeniería Genética de Plantas para la Tolerancia a la Sequía: Aplicaciones en Restauración Árida Terrestre y Terraformación Marciana" e incluye el manuscrito completo en PDF que describe objetivos, materiales y métodos, resultados y discusión; los datos crudos de los ensayos de tolerancia a la sequía en condiciones de laboratorio y en simulación de regolito marciano (CSV/XLSX), con variables de crecimiento, índice de supervivencia y parámetros fisiológicos; los scripts de análisis en R y Python empleados para la evaluación estadística (ANOVA, pruebas de significancia y modelado de respuesta); los protocolos de laboratorio en PDF/Markdown para la edición génica mediante CRISPR/Cas9 y los ensayos de bioseguridad; las figuras y diagramas en alta resolución (SVG/PNG) de los resultados clave y esquemas experimentales; y las declaraciones de disponibilidad de datos, financiación y conflictos de interés. Este recurso facilita la reutilización, verificación y extensión de los experimentos descritos, promoviendo la colaboración en biotecnología vegetal y estudios de terraformación.Este conjunto de datos acompaña al artículo "Ingeniería genética de plantas para la tolerancia a la sequía: aplicaciones en la restauración de zonas áridas terrestres y la terraformación marciana" y comprende el manuscrito completo en PDF que detalla los objetivos, materiales y métodos, los resultados y la discusión; datos brutos de ensayos de tolerancia a la sequía en condiciones de laboratorio y simulaciones de regolito marciano (CSV/XLSX), incluidas mediciones de crecimiento, tasas de supervivencia y rasgos fisiológicos; scripts de análisis en R y Python para evaluación estadística (ANOVA, pruebas de significación y modelado de respuestas); protocolos de laboratorio en PDF/Markdown para la edición de genes CRISPR/Cas9 y evaluaciones de bioseguridad; figuras y diagramas de alta resolución (SVG/PNG) de los principales resultados y esquemas experimentales; y declaraciones sobre disponibilidad de datos, financiación y conflicto de intereses. Este recurso permite la reutilización, verificación y extensión de los experimentos descritos, fomentando la colaboración en biotecnología vegetal e investigación en terraformación.
DOI: 10.6084/m9.figshare.28904672.v1
title: Ingeniería Genética de Plantas para la Tolerancia a la Sequía: Aplicaciones en Restauración Árida Terrestre y Terraformación MarcianaBiología, Biotecnología, Ciencias Medioambientalesbiologiabibbiotecnologia

description: Este conjunto de datos acompaña al artículo “Ingeniería Genética de Plantas para la Tolerancia a la Sequía: Aplicaciones en Restauración Árida Terrestre y Terraformación Marciana” e incluye el manuscrito completo en PDF que describe objetivos, materiales y métodos, resultados y discusión; los datos crudos de los ensayos de tolerancia a la sequía en condiciones de laboratorio y en simulación de regolito marciano (CSV/XLSX), con variables de crecimiento, índice de supervivencia y parámetros fisiológicos; los scripts de análisis en R y Python empleados para la evaluación estadística (ANOVA, pruebas de significancia y modelado de respuesta); los protocolos de laboratorio en PDF/Markdown para la edición génica mediante CRISPR/Cas9 y los ensayos de bioseguridad; las figuras y diagramas en alta resolución (SVG/PNG) de los resultados clave y esquemas experimentales; y las declaraciones de disponibilidad de datos, financiación y conflictos de interés. Este recurso facilita la reutilización, verificación y extensión de los experimentos descritos, promoviendo la colaboración en biotecnología vegetal y estudios de terraformación.This dataset accompanies the article “Plant Genetic Engineering for Drought Tolerance: Applications in Terrestrial Arid Restoration and Martian Terraforming” and comprises the full manuscript in PDF detailing objectives, materials and methods, results, and discussion; raw data from drought-tolerance assays under laboratory conditions and Martian regolith simulations (CSV/XLSX), including growth measurements, survival rates, and physiological traits; analysis scripts in R and Python for statistical evaluation (ANOVA, significance tests, and response modeling); laboratory protocols in PDF/Markdown for CRISPR/Cas9 gene editing and biosafety assessments; high-resolution figures and diagrams (SVG/PNG) of key results and experimental schematics; and statements on data availability, funding, and conflict of interest. This resource enables reuse, verification, and extension of the described experiments, fostering collaboration in plant biotechnology and terraforming research.
DOI: 10.5281/zenodo.17789394
title: OPEN DATA_Strategi Tesla
DOI: 10.5281/zenodo.17789395
title: OPEN DATA_Strategi Tesla