cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17555316
title: Slovak word2vec embeddings trained on OpenSubtitles

description: This dataset contains the subs2vec embeddings for Slovak, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:





Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)




Window size: varying context windows (e.g., 2, 5, 10, …)




Each file corresponds to a unique configuration (dimension × window size). 



Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

 

If you use this dataset, please cite:





Manuscript: https://doi.org/10.5281/zenodo.17243812 




Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17560035
title: Enhancing Solar Power Forecasting Accuracy Using HMPCS and Machine Learning Techniques: An Applied Study

description: This dataset contains simulated input data, parameter settings, source code, and experimental results supporting the article“Enhancing Solar Power Forecasting Accuracy Using HMPCS and Machine Learning Techniques: An Applied Study” (F1000Research, Article ID: 172121).

The data were synthetically generated through computational modeling to evaluate and validate the proposed Hybrid Metaheuristic and Machine Learning (HMPCS–ML) framework for solar power forecasting.No real-world or confidential data were used in this study.

Specifically, the dataset includes:• Simulated meteorological and irradiance variables (GHI, temperature, humidity, wind speed, etc.) used for training and testing.• Configuration files and parameter values used in the hybrid optimization algorithm.• Source code implementing the HMPCS–ML model for forecasting and optimization.• Numerical results, including MAE, RMSE, and R² metrics, as well as convergence statistics.• Supporting data used to generate all figures and tables presented in the manuscript.

All files are provided in open formats (CSV, TXT, XLSX, PY) to ensure full reproducibility and transparency.The dataset is distributed under the Creative Commons Attribution (CC BY 4.0) license, allowing reuse and adaptation with proper citation.

Keywords: Solar Power, Forecasting, Machine Learning, HMPCS, Metaheuristics, Optimization, Renewable Energy, Open Data.
DOI: 10.15468/dl.3tqymx
title: Occurrence Download

description: A dataset containing 847 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Material citation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "TaxonKey is Cerdocyon thous"
 ]
}

 The dataset includes 847 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023207-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17394622
title: Greek word2vec embeddings trained on OpenSubtitles Part 2

description: This dataset contains the subs2vec embeddings for Greek, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17394621
title: Greek word2vec embeddings trained on OpenSubtitles Part 2

description: This dataset contains the subs2vec embeddings for Greek, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17390625
title: Greek word2vec embeddings trained on OpenSubtitles Part 1

description: This dataset contains the subs2vec embeddings for Greek, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17390624
title: Greek word2vec embeddings trained on OpenSubtitles Part 1

description: This dataset contains the subs2vec embeddings for Greek, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17517193
title: Información de películas americanas de 2024

description: Dataset generado a través de los datos contenidos en Wikipedia acerca de todas las películas americanas estrenadas en 2024.

Este conjunto de datos ha sido elaborado en el contexto de la práctica 1 de la asignatura  Tipologia y ciclo de vida de los datos de la UOC y ha sido elaborado por Marc Garcia Millet y Enrique Sebastián Delgado de las Heras.
DOI: 10.5281/zenodo.17517194
title: Información de películas americanas de 2024

description: Dataset generado a través de los datos contenidos en Wikipedia acerca de todas las películas americanas estrenadas en 2024.

Este conjunto de datos ha sido elaborado en el contexto de la práctica 1 de la asignatura  Tipologia y ciclo de vida de los datos de la UOC y ha sido elaborado por Marc Garcia Millet y Enrique Sebastián Delgado de las Heras.
DOI: 10.5281/zenodo.17559874
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2019-07-14

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2019-07-14. 

            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: 

                No GPS.
            
                    
                

                
            

            
 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.17559967
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2019-07-14

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2019-07-14. 

            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: 

                No GPS.
            
                    
                

                
            

            
 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: 10.6084/m9.figshare.30572978
title: Juan Pablo Aranibar Osorio-Gestion de Proyectos Publicos, G2G y Cooperacion Internacional

description: Este documento técnico presenta la experiencia profesional de Juan Pablo Araníbar Osorio en gestión pública, proyectos Government-to-Government (G2G), cooperación internacional y modernización de la infraestructura en Perú. Se describen responsabilidades en contratos G2G, validación de adendas, gestión documental para fiscalización pública, aplicación de normativa del sector público peruano y fortalecimiento institucional en proyectos del Programa Subsectorial de Irrigaciones (PSI – MIDAGRI). El enfoque combina análisis jurídico y gestión de riesgos contractuales para asegurar cumplimiento presupuestal y sostenibilidad del proyecto.<br>Keywords principales: Juan Pablo Aranibar Osorio; Gestión Pública; Proyectos G2G; PSI MIDAGRI; Cooperación Internacional; Infraestructura Pública; Modernización del Estado
DOI: 10.6084/m9.figshare.30572978.v1
title: Juan Pablo Aranibar Osorio-Gestion de Proyectos Publicos, G2G y Cooperacion Internacional

description: Este documento técnico presenta la experiencia profesional de Juan Pablo Araníbar Osorio en gestión pública, proyectos Government-to-Government (G2G), cooperación internacional y modernización de la infraestructura en Perú. Se describen responsabilidades en contratos G2G, validación de adendas, gestión documental para fiscalización pública, aplicación de normativa del sector público peruano y fortalecimiento institucional en proyectos del Programa Subsectorial de Irrigaciones (PSI – MIDAGRI). El enfoque combina análisis jurídico y gestión de riesgos contractuales para asegurar cumplimiento presupuestal y sostenibilidad del proyecto.<br>Keywords principales: Juan Pablo Aranibar Osorio; Gestión Pública; Proyectos G2G; PSI MIDAGRI; Cooperación Internacional; Infraestructura Pública; Modernización del Estado
DOI: 10.5281/zenodo.17316611
title: Replication Package for "Economic Crisis and Disillusionment from Socialism: Evidence From a Quasi-Natural Experiment"

description: This package contains the data, programs, and instructions to replicate the manuscript "Economic Crisis and Disillusionment from Socialism: Evidence From a Quasi-Natural Experiment" by Ran Abramitzky, Netanel Ben-Porath, Victor Lavy & Michal Palgi, forthcoming at JEEA.
DOI: 10.5281/zenodo.17316610
title: Replication Package for "Economic Crisis and Disillusionment from Socialism: Evidence From a Quasi-Natural Experiment"

description: This package contains the data, programs, and instructions to replicate the manuscript "Economic Crisis and Disillusionment from Socialism: Evidence From a Quasi-Natural Experiment" by Ran Abramitzky, Netanel Ben-Porath, Victor Lavy & Michal Palgi, forthcoming at JEEA.
DOI: 10.6084/m9.figshare.29241983.v3
title: A-A

description: Dragonflies
DOI: 10.15468/dl.ecs68z
title: Occurrence Download

description: A dataset containing 657 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "StateProvince is one of (Antioquia)",
 "GadmGid is one of (COL.2.91_2, COL.2.82_2, COL.2.107_2)",
 "TaxonKey is one of (Mammalia [GBIF Backbone Taxonomy])"
 ]
 }
 ]
}

 The dataset includes 657 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023208-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.38mg9d
title: Occurrence Download

description: A dataset containing 657 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "StateProvince is one of (Antioquia)",
 "GadmGid is one of (COL.2.91_2, COL.2.82_2, COL.2.107_2)",
 "TaxonKey is one of (Mammalia [GBIF Backbone Taxonomy])"
 ]
 }
 ]
}

 The dataset includes 657 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023210-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n79tyf
title: Occurrence Download

description: A dataset containing 2971 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Sri Lanka",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations)",
 "OccurrenceStatus is Present",
 "TaxonKey is Galloperdix bicalcarata",
 "Year 2010-2025"
 ]
}

 The dataset includes 2971 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023211-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n43hw5
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is A review of the Malagasy Pachypanchax (Teleostei: Cyprinodontiformes, Aplocheilidae), with descriptions of four new species."
 ]
}

 The dataset includes 39 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023212-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y95cq2
title: Occurrence Download

description: A dataset containing 480 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Podocarpus pilgeri"
 ]
}

 The dataset includes 480 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023213-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2zqgqv
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Antarctica",
 "OccurrenceStatus is Present",
 "TaxonKey is Sterna paradisaea"
 ]
}

 The dataset includes 99 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023215-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17560069
title: TCDS 31 ATLAS PREDICCION

description: Descripción — TCDS: Predicción post-perihelio para 3I/ATLAS

10.5281/zenodo.17560070

Este documento presenta la primera predicción astrofísica falsable derivada de la Teoría de la Cromodinámica Sincrónica (TCDS), aplicando su formalismo Σ–χ al objeto interestelar 3I/ATLAS durante la fase posterior a su perihelio. La hipótesis central sostiene que el campo de coherencia Σ, mediado por el sincronón σ, produce efectos observables de recaptura coherencial y modulación espectral no gravitacional en cuerpos de baja cohesión sometidos a radiación solar intensa.

 

El estudio propone tres indicadores experimentales verificables en el intervalo de 0–100 días post-perihelio:

 

1. Asimetría fotométrica del 10–20 % en la curva de luz respecto al modelo de sublimación clásico.

 

 

2. Corrimiento espectral no Doppler de 2–3 Å en las líneas CN 388 nm y C₂ 516 nm, atribuible al gradiente de coherencia φ.

 

 

3. Deriva orbital residual de 0.02–0.05 arcsec/día asociada a un acoplamiento efectivo κΣ ≈ 10⁻⁴–10⁻³.

 

 

 

Cada canal se acompaña de criterios de éxito y falsación expresos, métricas Σ-metrics (R(t), LI, RMSE_SL, κΣ) y un protocolo reproducible para observaciones fotométricas, espectroscópicas y dinámicas.

 

El objetivo es proporcionar una prueba empírica directa del campo Σ en un entorno interestelar, estableciendo un precedente de validación para la TCDS y un marco metodológico replicable para futuras campañas.

 

Licencia: CC BY 4.0

Autor: Genaro Carrasco Ozuna (Proyecto TCDS / MSL, México)

DOI actual :10.5281/zenodo.17560070

DOI base: 10.5281/zenodo.17505875