cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.mt3zfr
title: Occurrence Download

description: A dataset containing 5183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Eleutherodactylus planirostris (Cope, 1862))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5183 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137624-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4x75xx
title: Occurrence Download

description: A dataset containing 694 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Apogon lineatus Temminck & Schlegel, 1842"
 ]
}

 The dataset includes 694 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0121552-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ws3dbz
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hesperia leonardus montana (Skinner, 1911)"
 ]
}

 The dataset includes 103 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0135100-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9jgedx
title: Occurrence Download

description: A dataset containing 104 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Hong Kong, Chinese Taipei)",
 "TaxonKey is Neptis miah Moore, 1857"
 ]
}

 The dataset includes 104 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0139583-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cyhx9u
title: Occurrence Download

description: A dataset containing 243 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hyperioides longipes Chevreux, 1900"
 ]
}

 The dataset includes 243 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0121554-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tapnp8
title: Occurrence Download

description: A dataset listing the 498 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-86.92269 21.01129,-86.92269 20.96039,-86.95881 20.8701,-87.00642 20.81099,-87.04418 20.77159,-87.09015 20.7404,-87.10328 20.66652,-87.05567 20.66324,-87.02448 20.69607,-86.99492 20.7404,-86.95224 20.78801,-86.88657 20.87338,-86.8406 20.97681,-86.92269 21.01129))",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 498 records were derived from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0250712-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r9kd6z
title: Occurrence Download

description: A dataset containing 791 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dasycladus vermicularis (Scop.) Krasser"
 ]
}

 The dataset includes 791 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0139599-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uvbwb7
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hyssura bacescui (George & Negoescu-Vladescu, 1982)"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0121576-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2xeq6v
title: Occurrence Download

description: A dataset containing 395 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tarenna mollissima (Hook. & Arn.) B.L.Rob."
 ]
}

 The dataset includes 395 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0250735-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ff6qk9
title: Occurrence Download

description: A dataset containing 347 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Halimeda macrophysa Askenasy"
 ]
}

 The dataset includes 347 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0139650-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ask85z
title: Occurrence Download

description: A dataset containing 111842 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zea mays L.",
 "Year 1000-2022"
 ]
}

 The dataset includes 111842 records from 618 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137621-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15486277
title: Spatially continuous monthly precipitation stable isotope estimates across the Australian continent at 0.25° resolution from 1962–2023

description: Data repository for Falster et al. (2026) High resolution monthly precipitation isotope estimates across Australia from machine learning

This repository contains the random forest-modelled precipitation δ<sup>2</sup>H, δ<sup>18</sup>O, and deuterium excess (dxs) datasets described in the paper  High resolution monthly precipitation isotope estimates across Australia from machine learning [https://doi.org/10.5194/hess-30-289-2026].

The repository contains 27 netcdf files, which together comprise the outputs described in the paper. In all cases, please see the paper for important details on the data and how they were produced. 

A comprehensive analysis of spatio-temporal variability in Australian precipitation stable isotopes (including climatic drivers) is currently underway, based on this dataset. We expect to submit this for publication in the first half of 2026. Please contact the creator of this dataset (Georgy Falster; georgina.falster@adelaide.edu.au) if you are interested in hearing more about this work.

The following files are available for each of δ<sup>2</sup>H<sub>P</sub>, δ<sup>18</sup>O<sub>P</sub>, and dxs<sub>P</sub>: 



Netcdf files containing only the median value from the individual estimates calculated with the 50 random seeds (i.e., only latitude, longitude, and time dimensions)



Monthly estimates



e.g., `aus_prec.d2H_v1_196201-202312_monthly_median.nc`



Monthly anomalies (relative to the long-term monthly means)



e.g., `aus_prec.d2H.anoms_v1_196201-202312_monthly_median.nc`



Amount-weighted annual mean estimates



e.g., `aus_prec.d2H_v1_1962-2023_ann_median.nc`



Anomalies in the amount-weighted annual mean



e.g., `aus_prec.d2H.anoms_v1_1962-2023_ann_median.nc`



Long-term annual mean (with only latitude and longitude dimensions)



e.g., aus_prec.d2H_v1_1962-2023_long-term-annual-mean_median.nc






In all cases, the data are provided at 0.25° resolution across the entire Australian continent, with the following two options:



Longer models trained over 1962–2023, using the reduced set of predictor variables (without weather objects)

Shorter models trained over 1980–2019, using the full set of predictor variables (including weather objects)


Please contact the lead author if you would like the following outputs (assuming fewer if any people will be interested in these):



Netcdf files including the individual estimates calculated with the 50 random seeds (i.e., latitude, longitude, time, and ensemble dimensions)



Monthly estimates



e.g., `aus_prec.d2H_v1_196201-202312_monthly_ensemble.nc`



Amount-weighted annual mean estimates



e.g., `aus_prec.d2H_v1_1962-2023_ann_ensemble.nc`






How to cite this repository

If using this data, please cite the original publication, available from https://hess.copernicus.org/articles/30/289/2026/
DOI: 10.5281/zenodo.15486278
title: Spatially continuous monthly precipitation stable isotope estimates across the Australian continent at 0.25° resolution from 1962–2023

description: Data repository for Falster et al. (2026) High resolution monthly precipitation isotope estimates across Australia from machine learning

This repository contains the random forest-modelled precipitation δ<sup>2</sup>H, δ<sup>18</sup>O, and deuterium excess (dxs) datasets described in the paper  High resolution monthly precipitation isotope estimates across Australia from machine learning [https://doi.org/10.5194/hess-30-289-2026].

The repository contains 27 netcdf files, which together comprise the outputs described in the paper. In all cases, please see the paper for important details on the data and how they were produced. 

A comprehensive analysis of spatio-temporal variability in Australian precipitation stable isotopes (including climatic drivers) is currently underway, based on this dataset. We expect to submit this for publication in the first half of 2026. Please contact the creator of this dataset (Georgy Falster; georgina.falster@adelaide.edu.au) if you are interested in hearing more about this work.

The following files are available for each of δ<sup>2</sup>H<sub>P</sub>, δ<sup>18</sup>O<sub>P</sub>, and dxs<sub>P</sub>: 



Netcdf files containing only the median value from the individual estimates calculated with the 50 random seeds (i.e., only latitude, longitude, and time dimensions)



Monthly estimates



e.g., `aus_prec.d2H_v1_196201-202312_monthly_median.nc`



Monthly anomalies (relative to the long-term monthly means)



e.g., `aus_prec.d2H.anoms_v1_196201-202312_monthly_median.nc`



Amount-weighted annual mean estimates



e.g., `aus_prec.d2H_v1_1962-2023_ann_median.nc`



Anomalies in the amount-weighted annual mean



e.g., `aus_prec.d2H.anoms_v1_1962-2023_ann_median.nc`



Long-term annual mean (with only latitude and longitude dimensions)



e.g., aus_prec.d2H_v1_1962-2023_long-term-annual-mean_median.nc






In all cases, the data are provided at 0.25° resolution across the entire Australian continent, with the following two options:



Longer models trained over 1962–2023, using the reduced set of predictor variables (without weather objects)

Shorter models trained over 1980–2019, using the full set of predictor variables (including weather objects)


Please contact the lead author if you would like the following outputs (assuming fewer if any people will be interested in these):



Netcdf files including the individual estimates calculated with the 50 random seeds (i.e., latitude, longitude, time, and ensemble dimensions)



Monthly estimates



e.g., `aus_prec.d2H_v1_196201-202312_monthly_ensemble.nc`



Amount-weighted annual mean estimates



e.g., `aus_prec.d2H_v1_1962-2023_ann_ensemble.nc`






How to cite this repository

If using this data, please cite the original publication, available from https://hess.copernicus.org/articles/30/289/2026/
DOI: 10.15468/dl.3pnqgr
title: Occurrence Download

description: A dataset containing 376 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Anabasis salsa"
 ]
}

 The dataset includes 376 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002191-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18537575
title: Jadidchilik harakati va uning  ta'lim-tarbiyaviy qarashlarining  shakllanish bosqichlari

description: Mazkur maqolada XIX asr oxiri  XX asr boshlarida Turkiston ijtimoiy-madaniy hayotida muhim burilish yasagan jadidchilik harakati hamda uning ta’lim-tarbiyaviy qarashlarining shakllanish jarayoni tarixiy-pedagogik yondashuv asosida tahlil qilinadi. Jadidchilik harakatining paydo bo‘lishiga olib kelgan ijtimoiy-siyosiy va ma’naviy omillar, an’anaviy ta’lim tizimining inqirozi, yangi usul maktablarining vujudga kelishi va jadid pedagogikasining konseptual asoslari bosqichma-bosqich yoritiladi. Tadqiqot davomida tarixiy-genetik, tarixiy-taqqoshlash, kontent-tahlil, aksiologik va pedagogik-germenevtik metodlardan foydalanildi. Natijalar jadidchilik harakati nafaqat madaniy yoki siyosiy hodisa, balki milliy ta’lim tizimini yangilashga qaratilgan mukammal pedagogik konsepsiya ekanligini ko‘rsatadi.
DOI: 10.5281/zenodo.18537576
title: Jadidchilik harakati va uning  ta'lim-tarbiyaviy qarashlarining  shakllanish bosqichlari

description: Mazkur maqolada XIX asr oxiri  XX asr boshlarida Turkiston ijtimoiy-madaniy hayotida muhim burilish yasagan jadidchilik harakati hamda uning ta’lim-tarbiyaviy qarashlarining shakllanish jarayoni tarixiy-pedagogik yondashuv asosida tahlil qilinadi. Jadidchilik harakatining paydo bo‘lishiga olib kelgan ijtimoiy-siyosiy va ma’naviy omillar, an’anaviy ta’lim tizimining inqirozi, yangi usul maktablarining vujudga kelishi va jadid pedagogikasining konseptual asoslari bosqichma-bosqich yoritiladi. Tadqiqot davomida tarixiy-genetik, tarixiy-taqqoshlash, kontent-tahlil, aksiologik va pedagogik-germenevtik metodlardan foydalanildi. Natijalar jadidchilik harakati nafaqat madaniy yoki siyosiy hodisa, balki milliy ta’lim tizimini yangilashga qaratilgan mukammal pedagogik konsepsiya ekanligini ko‘rsatadi.
DOI: 10.18150/rgmemb
title: Tailored copper-based ionic liquids as multifunctional admixtures for cementitious materials

description: The dataset contains source data obtained within the framework of the NCN project 2022/45/B/ST8/02288 entitled “Design of cement composites using sustainable ionic compounds: Evaluation of structural and functional properties,” related to the research results obtained for novel copper-based ionic liquids ([bmim]Cl-CuCl2).The presented data concern the characterization of the synthesized ionic liquid as well as the cement composites produced using this compound.A series of [bmim]Cl–CuCl2 ionic liquids (ILs) with different molar ratios, expressed as the molar fraction of CuCl2 (χCuCl2 = 0.15, 0.25, and 0.33), were incorporated into the cement matrix. The obtained cement composites contained 0, 0.125, 0.25, and 0.50 wt.% of each admixture. Two series of cement composites were produced: the first using ordinary Portland cement (CEM I) and the second using CEM II, which contains fly ash. For all prepared composites, the following analyses were performed: setting time of cement paste (according to PN-EN 196-3), workability of fresh cement mortar (according to PN-EN 12350-5), heat of hydration (according to PN-EN 196-9), and mechanical strength parameters (according to PN-EN 196-1).
DOI: 10.6084/m9.figshare.27079861
title: <b>Upland methane sinks under climate change: global patterns, drivers and trends</b>

description: Well-aerated upland soils serve as a crucial biological sink for atmospheric methane (CH<sub>4</sub>), playing a key role in mitigating climate change. However, current understanding of how this CH<sub>4</sub> sink responds to global climate change remains limited. To address this, we integrated 1,092 observational data points to construct a dataset covering multiple global change factors and used meta-analysis to quantify the response mechanisms of the upland CH<sub>4</sub> sink. Results show that warming, reduced precipitation, and elevated carbon dioxide concentrations significantly strengthened the CH<sub>4</sub> sink, while increased precipitation and nitrogen addition weakened it. Interactive effects were also observed: low-level nitrogen deposition acted antagonistically with increased precipitation, but synergistically with warming. We subsequently optimized a CH<sub>4</sub> oxidation model to explore the global distribution patterns and future trends under different climate scenarios. The current global upland soil CH<sub>4</sub> sink is estimated at approximately 37 Tg year<sup>-1</sup> and generally shows an increasing temporal trend. Spatially, the sink exhibits heterogeneity: a greater extent of desert areas in the Northern Hemisphere leads to a lower CH<sub>4</sub> sink per unit area compared to the Southern Hemisphere. Future spatiotemporal trends of the soil CH<sub>4</sub> sink will depend on the climate pathway. Under the Shared Socioeconomic Pathway (SSP) 1-2.6 scenario, the CH<sub>4</sub> sink declines over time, whereas under SSP5-8.5, it follows a unimodal trajectory. Variations in the soil CH<sub>4</sub> sink also differ across regions. These changes are primarily associated with atmospheric CH<sub>4</sub> concentrations under different climate pathways, as well as alterations in soil temperature and moisture resulting from various climate change drivers. These findings underscore the importance of the upland CH<sub>4</sub> sink in the global CH<sub>4</sub> cycle and significantly advance our understanding of its response mechanisms to climate change.
DOI: 10.6084/m9.figshare.27079861.v1
title: <b>Upland methane sinks under climate change: global patterns, drivers and trends</b>

description: Well-aerated upland soils serve as a crucial biological sink for atmospheric methane (CH<sub>4</sub>), playing a key role in mitigating climate change. However, current understanding of how this CH<sub>4</sub> sink responds to global climate change remains limited. To address this, we integrated 1,092 observational data points to construct a dataset covering multiple global change factors and used meta-analysis to quantify the response mechanisms of the upland CH<sub>4</sub> sink. Results show that warming, reduced precipitation, and elevated carbon dioxide concentrations significantly strengthened the CH<sub>4</sub> sink, while increased precipitation and nitrogen addition weakened it. Interactive effects were also observed: low-level nitrogen deposition acted antagonistically with increased precipitation, but synergistically with warming. We subsequently optimized a CH<sub>4</sub> oxidation model to explore the global distribution patterns and future trends under different climate scenarios. The current global upland soil CH<sub>4</sub> sink is estimated at approximately 37 Tg year<sup>-1</sup> and generally shows an increasing temporal trend. Spatially, the sink exhibits heterogeneity: a greater extent of desert areas in the Northern Hemisphere leads to a lower CH<sub>4</sub> sink per unit area compared to the Southern Hemisphere. Future spatiotemporal trends of the soil CH<sub>4</sub> sink will depend on the climate pathway. Under the Shared Socioeconomic Pathway (SSP) 1-2.6 scenario, the CH<sub>4</sub> sink declines over time, whereas under SSP5-8.5, it follows a unimodal trajectory. Variations in the soil CH<sub>4</sub> sink also differ across regions. These changes are primarily associated with atmospheric CH<sub>4</sub> concentrations under different climate pathways, as well as alterations in soil temperature and moisture resulting from various climate change drivers. These findings underscore the importance of the upland CH<sub>4</sub> sink in the global CH<sub>4</sub> cycle and significantly advance our understanding of its response mechanisms to climate change.
DOI: 10.15468/dl.vfruup
title: Occurrence Download

description: A dataset containing 1553285 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 41.89415215079384
 AND decimallatitude &lt;= 46.479780028101516
 AND decimallongitude &gt;= -6.294398091796208
 AND decimallongitude &lt;= 0.0
 AND GBIF_Within('POLYGON ((0.000000 41.894152, 0.000000 46.479780, -6.294398 46.479780, -6.294398 41.894152, 0.000000 41.894152))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 1553285 records were derived from 331 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002184-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18505688
title: CoDaKG: Content-Based Dataset Knowledge Graphs for Dataset Search

description: Description

This repository contains the dataset resources for the paper "CoDaKG: Content-Based Dataset Knowledge Graphs for Dataset Search". It provides two Content-based Dataset Knowledge Graph (CoDaKG) instances constructed for the NTCIR and ACORDAR test collections, along with the underlying pairwise similarity data derived from file content analysis.

 

File Inventory

1. Knowledge Graph Dumps (RDF/Turtle) 

These files contain the full CoDaKG instances in .ttl format. 





ntcir_full_rdflib.ttl: The CoDaKG instance constructed for the NTCIR test collection.




acordar_full_rdflib.ttl: The CoDaKG instance constructed for the ACORDAR test collection.



2. Content Similarity Data (CSV)

The following CSV files contain the raw pairwise similarity scores calculated between dataset distributions. These files only include pairs with a Jaccard similarity threshold > 0.5.

Files:





NTCIR_dataOverlap_similarity.csv




NTCIR_schemaOverlap_similarity.csv




ACORDAR_dataOverlap_similarity.csv




ACORDAR_schemaOverlap_similarity.csv



Column Structure:





file_id1 / file_id2: The identifier for the dcat:Distribution (file).




dataset_id1 / dataset_id2: The identifier for the corresponding dcat:Dataset.




sim: The calculated Jaccard similarity score (float).



Similarity Types:





Schema Overlap: Similarity based on structural elements (e.g., column headers, JSON paths).




Data Overlap: Similarity based on data content units (e.g., table rows, sentence overlaps).



Usage Notes:





ID Correspondence: The file_id and dataset_id in the CSV files correspond to the local names of the URIs for dcat:Distribution and dcat:Dataset entities in the Turtle (.ttl) files.




Customizing Thresholds: To revise the Knowledge Graph using different similarity thresholds:





Remove all existing triples with the predicates base:schemaOverlap and base:dataOverlap from the .ttl file.




Filter the CSV files to select pairs meeting your desired threshold.




Generate and append new triples linking the corresponding entities based on their IDs.