cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15579881
title: Multimodal Astrophysical Object Classification Dataset (SDSS, NASA Exoplanet Archive, Pan-STARRS, Gaia)

description: This dataset contains a multimodal collection of astrophysical objects compiled from several major astronomical surveys and archives, including:





Sloan Digital Sky Survey (SDSS)




NASA Exoplanet Archive




Pan-STARRS (Panoramic Survey Telescope and Rapid Response System)




Gaia DR3



The dataset includes 6000 entries labeled across various object types such as GALAXY, STAR, QSO (quasi-stellar objects), EXOPLANET, and ASTEROID. Each object is described by photometric and positional features like right ascension, declination, magnitudes in multiple bands (u, g, r, i, z), redshift (if available), and survey-specific parameters.

The goal of this dataset is to support research and benchmarking in machine learning-based classification of astrophysical objects. The data is preprocessed and split into:





features.csv: feature matrix




labels.csv: corresponding class labels




merged_dataset.csv: full combined dataset



All scripts used to retrieve and merge the data (veri_cek.py, veri_birlestir.py) are included for reproducibility.

This dataset is suitable for training and evaluating supervised classification algorithms, as well as exploring feature distributions across diverse astronomical object classes.
DOI: 10.5281/zenodo.15579882
title: Multimodal Astrophysical Object Classification Dataset (SDSS, NASA Exoplanet Archive, Pan-STARRS, Gaia)

description: This dataset contains a multimodal collection of astrophysical objects compiled from several major astronomical surveys and archives, including:





Sloan Digital Sky Survey (SDSS)




NASA Exoplanet Archive




Pan-STARRS (Panoramic Survey Telescope and Rapid Response System)




Gaia DR3



The dataset includes 6000 entries labeled across various object types such as GALAXY, STAR, QSO (quasi-stellar objects), EXOPLANET, and ASTEROID. Each object is described by photometric and positional features like right ascension, declination, magnitudes in multiple bands (u, g, r, i, z), redshift (if available), and survey-specific parameters.

The goal of this dataset is to support research and benchmarking in machine learning-based classification of astrophysical objects. The data is preprocessed and split into:





features.csv: feature matrix




labels.csv: corresponding class labels




merged_dataset.csv: full combined dataset



All scripts used to retrieve and merge the data (veri_cek.py, veri_birlestir.py) are included for reproducibility.

This dataset is suitable for training and evaluating supervised classification algorithms, as well as exploring feature distributions across diverse astronomical object classes.
DOI: 10.5281/zenodo.15579790
title: Reproducibility and Accuracy of Nanopore-Based Methylome Profiling of Streptococcus dysgalactiae Strains from Cancer Patients - 3.

description:    9728_10237_ORA+HRA_all_reps_microbemod_output.zip



contain all raw MicrobeMod output for both strains including Methylation site calls, motif call summary and RM system calls for all replicates



   Methylation calls mapped to parsed genbank features & feature count matrix files.








9728_HRA_reproducibility_analysis_tables.zip 

9728_ORA_reproducibility_analysis_tables.zip

10237_HRA_5mC_6mA_reproducibility_analysis_tables.zip

10237_ORA_reproducibility_analysis_tables.zipPython code for annotation feature-based modular analysis




     3.     Python_code:

methylation_feature_hash_assert_FILTER_9728.py     methylation_feature_hash_NOFILTER_9728.py     methylation_feature_hash_motif_10237.py     hyb_vs_ont_per_replicate_comparison.py     hyb_vs_ont_combined_plot.py
DOI: 10.5281/zenodo.15579791
title: Reproducibility and Accuracy of Nanopore-Based Methylome Profiling of Streptococcus dysgalactiae Strains from Cancer Patients - 3.

description:    9728_10237_ORA+HRA_all_reps_microbemod_output.zip



contain all raw MicrobeMod output for both strains including Methylation site calls, motif call summary and RM system calls for all replicates



   Methylation calls mapped to parsed genbank features & feature count matrix files.








9728_HRA_reproducibility_analysis_tables.zip 

9728_ORA_reproducibility_analysis_tables.zip

10237_HRA_5mC_6mA_reproducibility_analysis_tables.zip

10237_ORA_reproducibility_analysis_tables.zipPython code for annotation feature-based modular analysis




     3.     Python_code:

methylation_feature_hash_assert_FILTER_9728.py     methylation_feature_hash_NOFILTER_9728.py     methylation_feature_hash_motif_10237.py     hyb_vs_ont_per_replicate_comparison.py     hyb_vs_ont_combined_plot.py
DOI: 10.34740/kaggle/dsv/12040585
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.18739/a2f18sh0t
title: Yukon-Kuskokwim Delta, Alaska unburned and burned aquatic data, 2022

description: The Yukon-Kuskokwim Delta (YKD) in southwestern Alaska in the discontinuous permafrost region is undergoing rapid change as a result of climate warming and disturbance. This area is made up upland peat plateau tundra interspersed with patchwork steams, fens, ponds, and lakes. In 2015, a tundra wildfire burned more than 541.54 square kilometers (km2) in the YKD. In 2022, a research team conducted terrestrial and aquatic studies to determine wildfire and permafrost thaw effects on carbon and nutrient cycling and vegetation change.
 This aquatic data set includes surface water collected from unburned and burned (2015 wildfire) small ponds, fens, and streams. The water samples were analyzed for dissolved organic carbon (DOC), total dissolved nitrogen (TDN), deuterium and oxygen-18 (O-18) isotopes, and chromophoric dissolved organic matter (CDOM). CDOM indices calculated include a250, A254, a350, a375, the log transformed spectral slope in the 275-295 nanometer (nm) region, slope ratio, and specific ultraviolet absorbance (SUVA). Field measurements at the location where each sample was collected include temperature, pressure, pH, conductivity, dissolved oxygen (DO), oxidation reduction potential (ORP), and dissolved carbon dioxide (CO2) and methane (CH4) fluxes.
 At two of the ponds (one unburned and one burned) where samples were collected, a dissolved CO2 probe (eosense eosGP) and temperature/light sensor was deployed 1 meter below the surface to collect half hourly carbon dioxide (CO2) concentrations, temperature, and photosynthetically active radiation (PAR).
DOI: 10.5281/zenodo.18441807
title: THEROLEOFDIGITALTECHNOLOGIESANDARTIFICIALINTELLIGENCEINMODERNHEALTHCARESYSTEMS

description: The rapid advancement of digital technologies and artificial intelligence (AI) has fundamentally transformed modern healthcare systems. These innovations have introduced new approaches to disease prevention, diagnosis, treatment, and patient management. This paper examines the role of digital health technologies and AI in contemporary medicine, highlighting their applications, benefits, and challenges. Particular attention is given to AI-driven diagnostic systems, telemedicine, electronic health records, and personalized medicine. The study also discusses ethical, legal, and infrastructural issues associated with the adoption of intelligent technologies in healthcare. The findings suggest that, when implemented responsibly, digital technologies and AI significantly enhance healthcare quality, efficiency, and accessibility.
DOI: 10.5281/zenodo.18441808
title: THEROLEOFDIGITALTECHNOLOGIESANDARTIFICIALINTELLIGENCEINMODERNHEALTHCARESYSTEMS

description: The rapid advancement of digital technologies and artificial intelligence (AI) has fundamentally transformed modern healthcare systems. These innovations have introduced new approaches to disease prevention, diagnosis, treatment, and patient management. This paper examines the role of digital health technologies and AI in contemporary medicine, highlighting their applications, benefits, and challenges. Particular attention is given to AI-driven diagnostic systems, telemedicine, electronic health records, and personalized medicine. The study also discusses ethical, legal, and infrastructural issues associated with the adoption of intelligent technologies in healthcare. The findings suggest that, when implemented responsibly, digital technologies and AI significantly enhance healthcare quality, efficiency, and accessibility.
DOI: 10.5281/zenodo.6579074
title: SuperDARN data in netCDF format (2009-Jan)

description: 2009-Jan SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.mb3gnn
title: Occurrence Download

description: A dataset containing 4273389 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Geometry POLYGON((-180 -39,180 -39,180 33,-180 33,-180 -39))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Decapoda"
 ]
}

 The dataset includes 4273389 records from 1521 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003378-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18440980
title: SuperDARN data in netCDF format (2009-Jan)

description: 2009-Jan SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.18440109
title: EIT dataset with MAX30009 and ADG706

description: This is a time difference dataset from EIT, consisting of 200,000 simulation data points built using EIDORS and 79 data points collected by ourselves. The acquisition devices used were MAX30009 and ADG706, with a constant current source excitation of 49.984 kHz 1.28 mA (RMS) and 16 electrodes.

The `base` folder contains our basic experiments, including simulation and test datasets of blocks, triangles, and squares, labels, training files, and test files; the `transfer` folder contains the training set, validation set, training scripts, test results on simulation and test datasets, and comparison results with other methods; the `pretrained_checkpoint.pth` file contains pre-trained weights that can be directly used for transfer learning or testing.
DOI: 10.5281/zenodo.18440110
title: EIT dataset with MAX30009 and ADG706

description: This is a time difference dataset from EIT, consisting of 200,000 simulation data points built using EIDORS and 79 data points collected by ourselves. The acquisition devices used were MAX30009 and ADG706, with a constant current source excitation of 49.984 kHz 1.28 mA (RMS) and 16 electrodes.

The `base` folder contains our basic experiments, including simulation and test datasets of blocks, triangles, and squares, labels, training files, and test files; the `transfer` folder contains the training set, validation set, training scripts, test results on simulation and test datasets, and comparison results with other methods; the `pretrained_checkpoint.pth` file contains pre-trained weights that can be directly used for transfer learning or testing.
DOI: 10.5281/zenodo.18436144
title: Spin interferometry in a beam of ultracold molecules

description: Data presented in our paper "Spin interferometry in a beam of ultracold molecules"
DOI: 10.5281/zenodo.18436145
title: Spin interferometry in a beam of ultracold molecules

description: Data presented in our paper "Spin interferometry in a beam of ultracold molecules"
DOI: 10.15468/kbecte
title: Check-list of the Vascular Flora of the Samara Region (Vasjukov, 2024): accepted names

description: A list of the tracheophyte names accepted in "Check-list of the Vascular Flora of the Samara Region" (Vasjukov, 2024). The dataset was compiled as a first step towards further processing of the modern checklist for Samara Oblast, Russia.Stable archival version (not edited, not curated).The dataset includes 2,037 accepted (i.e. numbered) species names.Several minor orthographic errors in either scientific names or their authors were corrected manually. Some names were not automatically cross-linked with the GBIF backbone.
DOI: 10.5281/zenodo.18441748
title: Daten der Notaufnahmesurveillance

description: Der Datensatz "Daten der Notaufnahmesurveillance" wird durch das Robert Koch-Institut und das AKTIN-Notaufnahmeregister bei Krankenhausaufnahmen bereitgestellt. Der Datensatz beinhaltet aggregierte Routinedaten aus deutschen Notaufnahmen zur syndromischen Überwachung von akuten Erkrankungen. Dazu zählen grippeähnliche Erkrankungen (ILI), Coronavirus-Erkrankungen (COVID-19), akute respiratorische Erkrankungen (ARE), gastrointestinale Infektionen (GI) und schwere akute respiratorische Infektionen (SARI). Dabei wird der relative Anteil dieser Erkrankungen an der Gesamtzahl der Notaufnahmevorstellungen sowie die berechneten Erwartungswerte und Prädiktionsintervalle ausgewiesen. Die Daten sind nach Notaufnahmetypen und Altersgruppen aggregiert. Damit bietet der Datensatz eine wertvolle Ressource für die Forschung im Bereich der Notfallmedizin und der Überwachung akuter Gesundheitsereignisse in Deutschland.
DOI: 10.5281/zenodo.18441841
title: ETHICSOFADAPTATIONINTRANSLATION:BETWEENFAITHFULNESSANDCULTURALRESPONSIBILITY

description: The paper explores the ethical boundaries of adaptation in literary translation and examines the role of the translator as an interpreter and cultural mediator. The tensions between loyalty to the author, the expectations of the target audience, and the translator's personal ethical responsibility are discussed. Relying on theoretical concepts such as skopos theory, Lawrence Venuti's idea of translator visibility, and postcolonial approaches, key dilemmas are analysed: linguistic adaptation, replacement of cultural references, and translation of sensitive political content. Suggested practical criteria for making ethically sound decisions include transparency, respect for the original text, and awareness of cultural implications. The conclusion is that ethical adaptation is always contextualised and demands reflective practice, particularly in an era of extensive use of automated translation systems.
DOI: 10.5281/zenodo.18441842
title: ETHICSOFADAPTATIONINTRANSLATION:BETWEENFAITHFULNESSANDCULTURALRESPONSIBILITY

description: The paper explores the ethical boundaries of adaptation in literary translation and examines the role of the translator as an interpreter and cultural mediator. The tensions between loyalty to the author, the expectations of the target audience, and the translator's personal ethical responsibility are discussed. Relying on theoretical concepts such as skopos theory, Lawrence Venuti's idea of translator visibility, and postcolonial approaches, key dilemmas are analysed: linguistic adaptation, replacement of cultural references, and translation of sensitive political content. Suggested practical criteria for making ethically sound decisions include transparency, respect for the original text, and awareness of cultural implications. The conclusion is that ethical adaptation is always contextualised and demands reflective practice, particularly in an era of extensive use of automated translation systems.
DOI: 10.5281/zenodo.18441856
title: DEVELOPING METHODS TO IDENTIFY AND REDUCE LOSSES DUE TO EVAPORATION DURING THE TRANSPORTATION AND STORAGE OF GTL SYNTHETIC LIQUID FUELS
DOI: 10.5281/zenodo.18441857
title: DEVELOPING METHODS TO IDENTIFY AND REDUCE LOSSES DUE TO EVAPORATION DURING THE TRANSPORTATION AND STORAGE OF GTL SYNTHETIC LIQUID FUELS
DOI: 10.5281/zenodo.18440512
title: Climate-adjusted and climate-structural-adjusted credit risk (Probability of Default) scenarios across sector-firm size categories in Developing Countries

description: This dataset contains projected climate-adjusted and climate–structural-adjusted credit risk measures (Probability of Default, PD) for firms in developing economies under alternative climate change pathways from the IPCC AR6 Shared Socioeconomic Pathways (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Projections are provided at 10-year intervals from 2025 to 2105.

Baseline and forward-looking PDs are derived from a structural, liability-based default model calibrated using firm-level balance-sheet data from the World Bank Enterprise Survey. Asset values and asset volatility for unlisted firms are reconstructed using a production-based framework with country- and firm-size-specific volatility estimates. Climate adjustment is introduced by scaling asset risk and volatility according to projected physical climate stress under each SSP. A second adjustment incorporates firm-level adaptive capacity, captured through indicators of financial access, formalization, and structural capability, yielding climate–structural-adjusted PDs.

The dataset covers the following regions, countries, sectors, and firm size categories:

East Asia and Pacific (EAP)Countries: Cambodia, China, Indonesia, Lao, Malaysia, PhilippinesSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Europe and Central Asia (ECA)Countries: Bosnia, Bulgaria, Georgia, Greece, Hungary, Kyrgyzstan, Moldova, Montenegro, Portugal, RomaniaSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Latin America and the Caribbean (LAC)Countries: Colombia, Paraguay, PeruSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Middle East and North Africa (MENA)Countries: Jordan, MoroccoSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Small; Medium; Large; Very Large

South Asia (SA)Countries: Bangladesh, Bhutan, India, Nepal, PakistanSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Sub-Saharan Africa (SSA)Countries: Côte d’Ivoire, Eswatini, Ghana, Lesotho, NigeriaSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Small; Medium; Large; Very Large

PD projections are available at both the firm level and as aggregated summaries (means and selected quantiles) across sector–firm size groups within each region. The dataset is designed to support research and policy analysis on climate-related credit risk, SME resilience, climate stress testing, and climate-finance design in developing economies.
DOI: 10.5281/zenodo.18440511
title: Climate-adjusted and climate-structural-adjusted credit risk (Probability of Default) scenarios across sector-firm size categories in Developing Countries

description: This dataset contains projected climate-adjusted and climate–structural-adjusted credit risk measures (Probability of Default, PD) for firms in developing economies under alternative climate change pathways from the IPCC AR6 Shared Socioeconomic Pathways (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Projections are provided at 10-year intervals from 2025 to 2105.

Baseline and forward-looking PDs are derived from a structural, liability-based default model calibrated using firm-level balance-sheet data from the World Bank Enterprise Survey. Asset values and asset volatility for unlisted firms are reconstructed using a production-based framework with country- and firm-size-specific volatility estimates. Climate adjustment is introduced by scaling asset risk and volatility according to projected physical climate stress under each SSP. A second adjustment incorporates firm-level adaptive capacity, captured through indicators of financial access, formalization, and structural capability, yielding climate–structural-adjusted PDs.

The dataset covers the following regions, countries, sectors, and firm size categories:

East Asia and Pacific (EAP)Countries: Cambodia, China, Indonesia, Lao, Malaysia, PhilippinesSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Europe and Central Asia (ECA)Countries: Bosnia, Bulgaria, Georgia, Greece, Hungary, Kyrgyzstan, Moldova, Montenegro, Portugal, RomaniaSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Latin America and the Caribbean (LAC)Countries: Colombia, Paraguay, PeruSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Middle East and North Africa (MENA)Countries: Jordan, MoroccoSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Small; Medium; Large; Very Large

South Asia (SA)Countries: Bangladesh, Bhutan, India, Nepal, PakistanSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Micro; Small; Medium; Large; Very Large

Sub-Saharan Africa (SSA)Countries: Côte d’Ivoire, Eswatini, Ghana, Lesotho, NigeriaSectors: Manufacturing; Services; Wholesale and retail tradeFirm sizes: Small; Medium; Large; Very Large

PD projections are available at both the firm level and as aggregated summaries (means and selected quantiles) across sector–firm size groups within each region. The dataset is designed to support research and policy analysis on climate-related credit risk, SME resilience, climate stress testing, and climate-finance design in developing economies.