cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25921/v5hh-4z88
title: NOAA/WDS Paleoclimatology - Sea of Japan orbital and millennial changes during the late Quaternary

description: Late Quaternary sediments of the Japan Sea are characterized by centimeter- to meter-scale alternations of dark and light layers which are synchronous basinwide. High-resolution analyses of the sediments from Ocean Drilling Program site 797 reveal that deposition of the meter-scale alternations reflect variations in paleoceanographic conditions which were closely associated with glacio-eustatic sea level changes through the modulation of the volume and character of the influx to the sea through the Tsushima Strait. The centimeter- to decimeter-scale alternations reflect millennial-scale variations which are possibly associated with Dansgaard-Oeschger (D-O) cycles, with each dark layer appearing to correspond to an interstadial. This variability is attributed to the development of a humid climate in central to eastern Asia and the consequent increase in discharge from the Huanghe and Changjiang Rivers during interstadials. This caused expansion of the East China Sea coastal water (ECSCW), which penetrated more strongly into the Japan Sea. The increased influence of the lower-salinity, nutrient-enriched ECSCW reduced deep water ventilation and enhanced the surface productivity, leading to the development of anoxic bottom waters and deposition of the dark layers. Thus the centimeter- to decimeter-scale alternations of the dark and light layers record wet and dry cycles in central to eastern Asia possibly associated with D-O cycles.
DOI: 10.5281/zenodo.17560422
title: Intrinsic alignment amplitude vs luminosity (SDSS-III BOSS LOWZ; Singh et al. 2015)

description: Dataset compiled by the echoIA Collaboration under the aia-lum schema.Contains intrinsic alignment amplitude measurements as a function of luminosity forSDSS-III BOSS LOWZ sample, derived from Singh et al. (2015), "Intrinsic alignments ofSDSS-III BOSS LOWZ sample galaxies", MNRAS, Vol 450, Issue 2, https://doi.org/10.1093/mnras/stv778.Values were digitized and reformatted into a uniform tabular dataset with metadata for reuse inintrinsic alignment analyses.The echoIA Collaboration does not claim ownership of the original measurements; please citethe source publication(s) when using these data.
DOI: 10.5281/zenodo.17558751
title: Intrinsic alignment amplitude vs luminosity (DES Y3 CMASS redMaGiC high-z; Samuroff et al. 2023)

description: Dataset compiled by the echoIA Collaboration under the `aia-lum` schema.Contains intrinsic alignment amplitude measurements as a function of luminosityfor DES Y3+CMASS redMaGiC high-redshift sample (RMH), derived from Samuroff et al. (2023), "The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space", MNRAS, Volume 524, Issue 2, https://doi.org/10.1093/mnras/stad2013. This dataset refers to the combination of CMASS data andthe redMaGiC high-redshift sample (RMH) from DES Y3.Values were digitized and reformatted into a uniform tabular dataset with metadata for reuse in intrinsic alignment analyses.The echoIA Collaboration does not claim ownership of the original measurements; please citethe source publication(s) when using these data.
DOI: 10.5281/zenodo.17558971
title: Intrinsic alignment amplitude vs luminosity (DES Y3 CMASS redMaGiC high-z; Samuroff et al. 2023)

description: Dataset compiled by the echoIA Collaboration under the `aia-lum` schema.Contains intrinsic alignment amplitude measurements as a function of luminosityfor DES Y3+CMASS redMaGiC high-redshift sample (RMH), derived from Samuroff et al. (2023), "The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space", MNRAS, Volume 524, Issue 2, https://doi.org/10.1093/mnras/stad2013. This dataset refers to the combination of CMASS data andthe redMaGiC high-redshift sample (RMH) from DES Y3.Values were digitized and reformatted into a uniform tabular dataset with metadata for reuse in intrinsic alignment analyses.The echoIA Collaboration does not claim ownership of the original measurements; please citethe source publication(s) when using these data.
DOI: 10.5281/zenodo.17558639
title: Intrinsic alignment amplitude vs luminosity (DES Y3 CMASS redMaGiC low-z; Samuroff et al. 2023)

description: Dataset compiled by the echoIA Collaboration under the `aia-lum` schema.Contains intrinsic alignment amplitude measurements as a function of luminosityfor DES Y3 CMASS redMaGiC low-redshift sample (RML), derived from Samuroff et al. (2023),"The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space", MNRAS, Volume 524, Issue 2,https://doi.org/10.1093/mnras/stad2013. This dataset refers to the combination of CMASS data andthe redMaGiC low-redshift sample (RML) from DES Y3.Values were digitized and reformatted into a uniform tabular dataset with metadata for reuse in intrinsic alignment analyses.The echoIA Collaboration does not claim ownership of the original measurements; please citethe source publication(s) when using these data.
DOI: 10.5281/zenodo.17558873
title: Intrinsic alignment amplitude vs luminosity (DES Y3 CMASS redMaGiC low-z; Samuroff et al. 2023)

description: Dataset compiled by the echoIA Collaboration under the `aia-lum` schema.Contains intrinsic alignment amplitude measurements as a function of luminosityfor DES Y3 CMASS redMaGiC low-redshift sample (RML), derived from Samuroff et al. (2023),"The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space", MNRAS, Volume 524, Issue 2,https://doi.org/10.1093/mnras/stad2013. This dataset refers to the combination of CMASS data andthe redMaGiC low-redshift sample (RML) from DES Y3.Values were digitized and reformatted into a uniform tabular dataset with metadata for reuse in intrinsic alignment analyses.The echoIA Collaboration does not claim ownership of the original measurements; please citethe source publication(s) when using these data.
DOI: 10.5281/zenodo.17555792
title: Intrinsic alignment amplitude vs luminosity (DES Y3 CMASS; Samuroff et al. 2023)

description: Dataset compiled by the echoIA Collaboration under the `aia-lum` schema.Contains intrinsic alignment amplitude measurements as a function of luminosityfor DES Y3+CMASS, derived from Samuroff et al. (2023), "The Dark Energy Survey Year 3 and eBOSS:constraining galaxy intrinsic alignments across luminosity and colour space", MNRAS, Volume 524, Issue 2,https://doi.org/10.1093/mnras/stad2013.Values were digitized and reformatted into a uniform tabular dataset with metadata for reuse inintrinsic alignment analyses.The echoIA Collaboration does not claim ownership of the original measurements; please citethe source publication(s) when using these data.
DOI: 10.5281/zenodo.17555793
title: Intrinsic alignment amplitude vs luminosity (DES Y3 CMASS; Samuroff et al. 2023)

description: Dataset compiled by the echoIA Collaboration under the `aia-lum` schema.Contains intrinsic alignment amplitude measurements as a function of luminosityfor DES Y3+CMASS, derived from Samuroff et al. (2023), "The Dark Energy Survey Year 3 and eBOSS:constraining galaxy intrinsic alignments across luminosity and colour space", MNRAS, Volume 524, Issue 2,https://doi.org/10.1093/mnras/stad2013.Values were digitized and reformatted into a uniform tabular dataset with metadata for reuse inintrinsic alignment analyses.The echoIA Collaboration does not claim ownership of the original measurements; please citethe source publication(s) when using these data.
DOI: 10.15468/dl.akz9rn
title: Occurrence Download

description: A dataset containing 3604 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((14.14918 50.84458,14.5206 50.84458,14.5206 50.99299,14.14918 50.99299,14.14918 50.84458))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2019-2022"
 ]
}

 The dataset includes 3604 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023295-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17560780
title: carrefour_categoria_YYYYMMDD

description: Datasets dels productes del supermercat online oferts per la cadena de supermercats Carrefour.

El nom de cada dataset indica la subsecció a la qual pertanyen i la data d'adquisició de les dades.
DOI: 10.15468/dl.sf2z5c
title: Occurrence Download

description: A dataset containing 734 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((14.11914 50.84013,14.50287 50.84013,14.50287 50.97992,14.11914 50.97992,14.11914 50.84013))",
 "HasGeospatialIssue is false",
 "Month is one of (January, June, April, May, March, February)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2022-2022"
 ]
}

 The dataset includes 734 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023296-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2vycv8
title: Occurrence Download

description: A dataset containing 1242 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((14.16667 50.8438,14.51774 50.8438,14.51774 50.99334,14.16667 50.99334,14.16667 50.8438))",
 "HasGeospatialIssue is false",
 "Month is one of (July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2022-2023"
 ]
}

 The dataset includes 1242 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023297-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.snaa3n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is not null",
 "BasisOfRecord is Human Observation",
 "DecimalLongitude is greater than or equal to -73.623861",
 "DecimalLongitude is less than or equal to -73.569454",
 "DecimalLongitude is greater than or equal to 45.485771",
 "DecimalLatitude is less than or equal to 45.51994",
 "HasCoordinate is true",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023298-251025141854904/datasets/export for details.
DOI: 10.34740/kaggle/dsv/13660599
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.m9zdgm
title: Occurrence Download

description: A dataset containing 4284 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((14.12982 50.86405,14.50694 50.86405,14.50694 51.00385,14.12982 51.00385,14.12982 50.86405))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2023-2025"
 ]
}

 The dataset includes 4284 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023299-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k84gr5
title: Occurrence Download

description: A dataset containing 119 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "GbifRegion is North America",
 "StateProvince is one of (Utah (UT), Utah (State), UTAH (ALT: 2000 FT), UTAH (ALT: 2000FT), UTAH (ALT: 700 FT), UTAH (ALT: 8500FT), UTAH (ALT: 8000FEET), UTAH (ALT: 4300FT), UTah, Utah, Utah State, Utah :, Utah ?, Utah, UT)",
 "TaxonKey is Lagopus leucura"
 ]
}

 The dataset includes 119 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023301-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5kd1qz9w
title: MassIVE MSV000099801 - Addition to the orginal paper_MSVdataset
title: carbonic anhydrase measurement via the total proteomics. The bacteria is grown at mid log phase and subjected to the proteomic analysis. Total proteins are extracted and subjected to mass spectrometer, the analysis is done via pd.
DOI: 10.15468/dl.hdvjnz
title: Occurrence Download

description: A dataset containing 119 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "GbifRegion is North America",
 "StateProvince is one of (Utah (UT), Utah (State), UTAH (ALT: 2000 FT), UTAH (ALT: 2000FT), UTAH (ALT: 700 FT), UTAH (ALT: 8500FT), UTAH (ALT: 8000FEET), UTAH (ALT: 4300FT), UTah, Utah, Utah State, Utah :, Utah ?, Utah, UT)",
 "TaxonKey is Lagopus leucura"
 ]
}

 The dataset includes 119 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023302-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/6934
title: OpenTextVault
DOI: 10.5281/zenodo.17560784
title: Entropy–Ecological Cycles Theory (EECT) – EEI 2.0 Workflow and Datasets

description:  
 




Title
Entropy–Ecological Cycles Theory (EECT) – EEI 2.0 Workflow and Datasets


Upload type
Dataset


Publication 
date2025-11-08


Authors
Ramin Gozarani Eghtesad (University of Tabriz, ORCID: 0009-0002-8667-8738)


Description
 







This repository contains the datasets, scripts, and model outputs supporting the paper:“Satellite-Derived Entropy Indicators for Early Warning of Ecosystem Collapse: A Machine Learning Approach Based on the Extended Entropy–Ecological Cycles Theory (EECT)” submitted to International Journal of Applied Earth Observation and Geoinformation (JAG).

The dataset includes processed MODIS, Sentinel-2, SMAP, and ERA5 variables, along with the entropy modelling workflow (EEI 2.0).

DOI will be used for reproducibility and citation purposes after acceptance.
DOI: 10.5281/zenodo.17560783
title: Entropy–Ecological Cycles Theory (EECT) – EEI 2.0 Workflow and Datasets

description:  
 




Title
Entropy–Ecological Cycles Theory (EECT) – EEI 2.0 Workflow and Datasets


Upload type
Dataset


Publication 
date2025-11-08


Authors
Ramin Gozarani Eghtesad (University of Tabriz, ORCID: 0009-0002-8667-8738)


Description
 







This repository contains the datasets, scripts, and model outputs supporting the paper:“Satellite-Derived Entropy Indicators for Early Warning of Ecosystem Collapse: A Machine Learning Approach Based on the Extended Entropy–Ecological Cycles Theory (EECT)” submitted to International Journal of Applied Earth Observation and Geoinformation (JAG).

The dataset includes processed MODIS, Sentinel-2, SMAP, and ERA5 variables, along with the entropy modelling workflow (EEI 2.0).

DOI will be used for reproducibility and citation purposes after acceptance.
DOI: 10.15468/dl.yjneza
title: Occurrence Download

description: A dataset containing 800 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material citation, Material sample, Specimen, Human Observation)",
 "Geometry POLYGON((-43.96404 -19.88301,-43.96415 -19.87954,-43.9644 -19.87922,-43.96468 -19.87855,-43.96467 -19.87795,-43.96417 -19.87639,-43.96491 -19.87546,-43.9662 -19.87476,-43.95874 -19.87028,-43.95748 -19.87074,-43.9572 -19.87009,-43.95572 -19.86952,-43.95904 -19.8608,-43.96158 -19.86009,-43.97022 -19.86304,-43.9687 -19.86714,-43.9691 -19.86831,-43.96997 -19.86907,-43.97179 -19.86944,-43.97379 -19.86417,-43.97605 -19.86464,-43.97609 -19.86625,-43.97596 -19.86786,-43.97616 -19.87026,-43.97638 -19.87074,-43.97678 -19.87317,-43.97687 -19.87526,-43.97702 -19.87491,-43.97711 -19.87746,-43.97717 -19.87969,-43.97726 -19.88125,-43.97778 -19.88266,-43.97778 -19.88345,-43.9775 -19.88496,-43.97691 -19.88528,-43.97684 -19.88556,-43.97698 -19.88672,-43.97746 -19.88903,-43.97781 -19.89127,-43.97728 -19.89236,-43.97425 -19.89071,-43.96404 -19.88301))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 800 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023304-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17560895
title: Mercury and its Behavior

description:  

This paper presents a theoretical explanation of thermal behavior within the framework of the Solitonic Universe Model (SEM). In SEM, temperature is not defined as particle motion or kinetic agitation, but as the manifestation of wave-order disruption in the finite solitonic domain of the Universe. Atoms are described as vortex-like structures whose nuclei bend the universal medium, forming stable electron channels. When the universal field becomes wavy, these channels oscillate vertically, producing what is classically perceived as heat.

However, certain heavy elements—most notably mercury—possess intrinsic nuclear asymmetry that generates lateral (forward–backward) oscillations instead of vertical ones. This internal topological irregularity causes thermal energy to spread slowly, diffusively, and without a clear direction. As a result, mercury behaves like a warm fluid even when it is physically cold. Its apparent warmth does not arise from external excitation or classical heat, but from the broken symmetry and topological dynamics of its own nucleus.
DOI: 10.15468/dl.htjep4
title: Occurrence Download

description: A dataset containing 1855 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pteronura brasiliensis"
 ]
}

 The dataset includes 1855 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023305-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.16507.37926
title: قائمة في أشغال البحث والمنشورات العلميّة والبيداغوجية والأنشطة الآكاديمية-د.ة. فاتن ريدان للترشح لمناظرة انتداب أساتذة مساعدين في تقنيات السمعي البصري والسينما -دورة 2019-جانفي 2022
DOI: 10.15468/dl.cs5dcg
title: Occurrence Download

description: A dataset containing 17326 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Ochotona princeps"
 ]
}

 The dataset includes 17326 records from 101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023306-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5f5urw
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "GbifRegion is North America",
 "StateProvince is one of (Utah (UT), Utah (State), UTAH (ALT: 2000 FT), UTAH (ALT: 2000FT), UTAH (ALT: 700 FT), UTAH (ALT: 8500FT), UTAH (ALT: 8000FEET), UTAH (ALT: 4300FT), UTah, Utah, Utah State, Utah :, Utah ?, Utah, UT)",
 "TaxonKey is Lagopus leucura"
 ]
}

 The dataset's 1 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023303-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17504046
title: TS-Cast: Deep Learning for Subsurface Ocean Reconstruction from Satellite Observations in the Northwestern Pacific

description: Since the 1990s, satellite observations have been providing reliable estimates of the ocean surface states, including absolute dynamic topography (ADT), sea surface temperature (SST), and sea surface salinity (SSS) at sufficient space and time scales to characterize ocean dynamics.  Together with the extensive hydrographic dataset from Argo and ship-based hydrographic profiles, these measurements provide a comprehensive view of oceanic conditions.  While ADT represents integrated information for subsurface water properties, it is challenging to relate SST, SSS, and ADT with subsurface water profiles due to their complex spatial and temporal variations. To address this issue, we introduce a novel deep neural network, the thermohaline profile estimating network termed TS-Cast. Sourcing from monthly climatological profiles, TS-Cast is designed to adjust these profiles to align with satellite-measured SST, SSS, and ADT data, by training with approximately 150,000 Argo and ship-based thermohaline profiles in the northwestern Pacific. TS-Cast’s excellent capability is demonstrated by comparisons with independent time series data from moorings that measured temperature and salinity or vertical acoustic travel time. The network produces water-property profiles with accuracy that is comparable to, or even surpasses that of data-assimilated numerical models. This work provides a powerful tool for ocean monitoring and introduces a validation framework. Critically, this framework reveals not only the model's high fidelity but also demonstrates that its predictive skill is fundamentally constrained by the physical information content and limitations of the input satellite data, thereby establishing a clear link between AI performance and observational physics.
DOI: 10.5281/zenodo.17504047
title: TS-Cast: Deep Learning for Subsurface Ocean Reconstruction from Satellite Observations in the Northwestern Pacific

description: Since the 1990s, satellite observations have been providing reliable estimates of the ocean surface states, including absolute dynamic topography (ADT), sea surface temperature (SST), and sea surface salinity (SSS) at sufficient space and time scales to characterize ocean dynamics.  Together with the extensive hydrographic dataset from Argo and ship-based hydrographic profiles, these measurements provide a comprehensive view of oceanic conditions.  While ADT represents integrated information for subsurface water properties, it is challenging to relate SST, SSS, and ADT with subsurface water profiles due to their complex spatial and temporal variations. To address this issue, we introduce a novel deep neural network, the thermohaline profile estimating network termed TS-Cast. Sourcing from monthly climatological profiles, TS-Cast is designed to adjust these profiles to align with satellite-measured SST, SSS, and ADT data, by training with approximately 150,000 Argo and ship-based thermohaline profiles in the northwestern Pacific. TS-Cast’s excellent capability is demonstrated by comparisons with independent time series data from moorings that measured temperature and salinity or vertical acoustic travel time. The network produces water-property profiles with accuracy that is comparable to, or even surpasses that of data-assimilated numerical models. This work provides a powerful tool for ocean monitoring and introduces a validation framework. Critically, this framework reveals not only the model's high fidelity but also demonstrates that its predictive skill is fundamentally constrained by the physical information content and limitations of the input satellite data, thereby establishing a clear link between AI performance and observational physics.
DOI: 10.15468/dl.kqrw47
title: Occurrence Download

description: A dataset containing 500164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (UKR.4_1, UKR.20_1)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 500164 records from 439 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023289-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.