cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5kjjnh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-74.181 1.507182, -74.251287 1.501614, -74.31984199999999 1.485048, -74.38497700000001 1.45789, -74.445087 1.420811, -74.49869200000001 1.374723, -74.544471 1.320763, -74.581298 1.260259, -74.608266 1.194701, -74.624713 1.125704, -74.630233 1.054967, -74.62469299999999 0.984232, -74.608228 0.915239, -74.581245 0.849689, -74.544409 0.789194, -74.498627 0.735243, -74.445025 0.6891659999999999, -74.384925 0.652095, -74.319804 0.624945, -74.251267 0.608383, -74.181 0.602816, -74.110733 0.608383, -74.042196 0.624945, -73.977075 0.652095, -73.91697499999999 0.6891659999999999, -73.863373 0.735243, -73.81759099999999 0.789194, -73.780755 0.849689, -73.753772 0.915239, -73.737307 0.984232, -73.731767 1.054967, -73.73728699999999 1.125704, -73.75373399999999 1.194701, -73.78070200000001 1.260259, -73.81752899999999 1.320763, -73.863308 1.374723, -73.91691299999999 1.420811, -73.977023 1.45789, -74.042158 1.485048, -74.110713 1.501614, -74.181 1.507182))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002309-260120142942310/datasets/export for details.
DOI: 10.15468/dl.at7wft
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((99.455 1.534182, 99.38471199999999 1.528614, 99.31615600000001 1.512047, 99.25102 1.48489, 99.19091 1.44781, 99.137305 1.401723, 99.091525 1.347763, 99.054698 1.287258, 99.02773000000001 1.221701, 99.01128300000001 1.152704, 99.005763 1.081967, 99.011304 1.011231, 99.02776900000001 0.942239, 99.05475199999999 0.876688, 99.091589 0.8161929999999999, 99.137371 0.762243, 99.190973 0.716166, 99.251074 0.679095, 99.31619499999999 0.651945, 99.384733 0.635383, 99.455 0.629816, 99.525267 0.635383, 99.593805 0.651945, 99.65892599999999 0.679095, 99.719027 0.716166, 99.77262899999999 0.762243, 99.818411 0.8161929999999999, 99.855248 0.876688, 99.882231 0.942239, 99.898696 1.011231, 99.90423699999999 1.081967, 99.898717 1.152704, 99.88227000000001 1.221701, 99.85530199999999 1.287258, 99.81847500000001 1.347763, 99.772695 1.401723, 99.71908999999999 1.44781, 99.65898 1.48489, 99.593844 1.512047, 99.525288 1.528614, 99.455 1.534182))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002310-260120142942310/datasets/export for details.
DOI: 10.5281/zenodo.17522874
title: HRP4K

description: HRP4K: A high-resolution perspective-view road image dataset for pothole detection

This work is licensed under CC BY 4.0.
DOI: 10.5281/zenodo.17522873
title: HRP4K

description: HRP4K: A high-resolution perspective-view road image dataset for pothole detection

This work is licensed under CC BY 4.0.
DOI: 10.5281/zenodo.18323521
title: Dataset for "From Halos to Galaxies. VI. Improved Halo Mass Estimation for SDSS Groups and Measurement of the Halo Mass Function"

description: Zhao et al. (2025) developed a simulation-trained machine-learning (ML) framework to improve dark-matter halo mass estimates for SDSS galaxy groups, addressing systematic biases of abundance-matching halo masses (notably differences between groups with star-forming vs. passive centrals). They apply the calibrated ML model to the widely used Yang et al. SDSS group catalog and recommend using a model trained with Gaussian noise matched to observational measurement errors when predicting halo masses for SDSS groups.

This Zenodo record provides a minimal crosswalk table (three columns) that lets you attach the Zhao+2025 noise-matched ML halo mass to galaxies identified in NYU-VAGC / Yang groups:





NYU_VAGC_ID — NYU-VAGC object ID (row number in the NYU-VAGC files).




Group_ID — Yang et al. SDSS group identifier (model-magnitude based group catalog); galaxies in the same group share the same Group_ID.




ML_halo_mass_2 — ML halo mass estimate from Zhao et al. (2025), using the recommended noise-matched training strategy; units: $M_\odot/h$.
DOI: 10.5281/zenodo.18323522
title: Dataset for "From Halos to Galaxies. VI. Improved Halo Mass Estimation for SDSS Groups and Measurement of the Halo Mass Function"

description: Zhao et al. (2025) developed a simulation-trained machine-learning (ML) framework to improve dark-matter halo mass estimates for SDSS galaxy groups, addressing systematic biases of abundance-matching halo masses (notably differences between groups with star-forming vs. passive centrals). They apply the calibrated ML model to the widely used Yang et al. SDSS group catalog and recommend using a model trained with Gaussian noise matched to observational measurement errors when predicting halo masses for SDSS groups.

This Zenodo record provides a minimal crosswalk table (three columns) that lets you attach the Zhao+2025 noise-matched ML halo mass to galaxies identified in NYU-VAGC / Yang groups:





NYU_VAGC_ID — NYU-VAGC object ID (row number in the NYU-VAGC files).




Group_ID — Yang et al. SDSS group identifier (model-magnitude based group catalog); galaxies in the same group share the same Group_ID.




ML_halo_mass_2 — ML halo mass estimate from Zhao et al. (2025), using the recommended noise-matched training strategy; units: $M_\odot/h$.
DOI: 10.15468/dl.ybcrd9
title: Occurrence Download

description: A dataset containing 591 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Roystonea borinquena"
 ]
}

 The dataset includes 591 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002312-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j7qwcu
title: Occurrence Download

description: A dataset containing 648 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-78.687 1.911181, -78.757302 1.905612, -78.82587100000001 1.889045, -78.891019 1.861886, -78.95114 1.824805, -79.00475299999999 1.778716, -79.050538 1.724754, -79.087369 1.664249, -79.114339 1.59869, -79.130785 1.529692, -79.136302 1.458955, -79.130757 1.38822, -79.11428600000001 1.319229, -79.08729700000001 1.253679, -79.050453 1.193186, -79.00466299999999 1.139238, -78.951054 1.093162, -78.890946 1.056094, -78.825818 1.028945, -78.757274 1.012384, -78.687 1.006817, -78.616726 1.012384, -78.548182 1.028945, -78.483054 1.056094, -78.422946 1.093162, -78.369337 1.139238, -78.323547 1.193186, -78.286703 1.253679, -78.259714 1.319229, -78.24324300000001 1.38822, -78.23769799999999 1.458955, -78.24321500000001 1.529692, -78.25966099999999 1.59869, -78.286631 1.664249, -78.32346200000001 1.724754, -78.369247 1.778716, -78.42286 1.824805, -78.482981 1.861886, -78.548129 1.889045, -78.616698 1.905612, -78.687 1.911181))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 648 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002313-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31111805.v1
title: Diamond potential of southern Indian kimberlites using bulk-rock trace elements and REE mineral chemistry, along with insights from mantle metasomatism

description: First row transition elements (Sc to Zn) in kimberlites, compositions of olivine, accompanied with REE mineral chemistry of garnets and clinopyroxenes from published data are used to study the diamond potential of kimberlites from the Southern Indian Diamond Province. The Wajrakarur kimberlites show lower Ti, Y, Ga, Mn, Fe, Sc, Zn, V and Cu, and higher Ni and Cr compared to the non-prospective Narayanpet kimberlites. The compositions of mantle minerals i.e. olivine, garnet and clinopyroxene have been studied as indicators to strong mantle metasomatism in the Eastern Dharwar Craton. Olivines with low Mg# and high Ti associated with Ti-rich metasomatism and poor diamond potential belong to Narayanpet kimberlites and <i>p</i>-5 kimberlite from the Wajrakarur Kimberlite Field (WKF). Subcalcic harzburgite garnets (G10D) from the diamond stability field showing sinusoidal REE<sub>N</sub> patterns belong to <i>p</i>-9 and <i>p</i>-10 kimberlites from the WKF. These are indicative of depleted nature and are potentially diamondiferous. Whereas olivines, garnets and clinopyroxenes from kimberlites displaying modified or normal REE<sub>N</sub> patterns are indicative of strong mantle metasomatism (i.e. enrichment in HFSE such as Ti, Y, Ga, Yb, and Lu). These have been previously associated with diamond destruction events. Subcalcic harzburgite garnets (G10D) from the diamond stability field are indicators of clinopyroxene-absent, depleted cratonic peridotites. These react with carbonate-rich metasomatic fluids under specific redox conditions to form diamonds during the mineralization event. Carbonate-rich metasomatic fluids with geochemical signatures e.g. high LREE/HREE, Nb/La, Ca/Al, moderate to low Zr/Hf and low Ti/Eu ratios are responsible for the diamond formation event. Lithospheric diamond formation is confined to depths greater than 135 km within the depleted mantle, characterized by high-Mg olivines (Mg# &gt; 90) and harzburgitic garnets from the diamond stability field. However, diamonds remain susceptible to dissolution through intense carbonate and Fe-Ti metasomatism at depths exceeding 150 km.
DOI: 10.6084/m9.figshare.31111805
title: Diamond potential of southern Indian kimberlites using bulk-rock trace elements and REE mineral chemistry, along with insights from mantle metasomatism

description: First row transition elements (Sc to Zn) in kimberlites, compositions of olivine, accompanied with REE mineral chemistry of garnets and clinopyroxenes from published data are used to study the diamond potential of kimberlites from the Southern Indian Diamond Province. The Wajrakarur kimberlites show lower Ti, Y, Ga, Mn, Fe, Sc, Zn, V and Cu, and higher Ni and Cr compared to the non-prospective Narayanpet kimberlites. The compositions of mantle minerals i.e. olivine, garnet and clinopyroxene have been studied as indicators to strong mantle metasomatism in the Eastern Dharwar Craton. Olivines with low Mg# and high Ti associated with Ti-rich metasomatism and poor diamond potential belong to Narayanpet kimberlites and <i>p</i>-5 kimberlite from the Wajrakarur Kimberlite Field (WKF). Subcalcic harzburgite garnets (G10D) from the diamond stability field showing sinusoidal REE<sub>N</sub> patterns belong to <i>p</i>-9 and <i>p</i>-10 kimberlites from the WKF. These are indicative of depleted nature and are potentially diamondiferous. Whereas olivines, garnets and clinopyroxenes from kimberlites displaying modified or normal REE<sub>N</sub> patterns are indicative of strong mantle metasomatism (i.e. enrichment in HFSE such as Ti, Y, Ga, Yb, and Lu). These have been previously associated with diamond destruction events. Subcalcic harzburgite garnets (G10D) from the diamond stability field are indicators of clinopyroxene-absent, depleted cratonic peridotites. These react with carbonate-rich metasomatic fluids under specific redox conditions to form diamonds during the mineralization event. Carbonate-rich metasomatic fluids with geochemical signatures e.g. high LREE/HREE, Nb/La, Ca/Al, moderate to low Zr/Hf and low Ti/Eu ratios are responsible for the diamond formation event. Lithospheric diamond formation is confined to depths greater than 135 km within the depleted mantle, characterized by high-Mg olivines (Mg# &gt; 90) and harzburgitic garnets from the diamond stability field. However, diamonds remain susceptible to dissolution through intense carbonate and Fe-Ti metasomatism at depths exceeding 150 km.
DOI: 10.15468/dl.34q33w
title: Occurrence Download

description: A dataset listing the 387 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((7.09285 50.73033,7.10129 50.73033,7.10129 50.73498,7.09285 50.73498,7.09285 50.73033))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset's 387 records were derived from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002295-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16308682
title: Mapping 12 million lakes globally at 10 m resolution

description: This repository contains the relevant code and data for the paper Mapping 12 million lakes globally at 10 m resolution

Specifically, the U-Net.zip file includes the associated codes for segmenting global lakes by using the U-Net model, and the labels used in this process. The GLAKESplus.zip file includes the GLAKES lake polygon product (.gdb & .shp). Please refer to the README file in both the U-Net.zip and the GLAKES.zip file for more detailed information.
DOI: 10.5281/zenodo.16308681
title: Mapping 12 million lakes globally at 10 m resolution

description: This repository contains the relevant code and data for the paper Mapping 12 million lakes globally at 10 m resolution

Specifically, the U-Net.zip file includes the associated codes for segmenting global lakes by using the U-Net model, and the labels used in this process. The GLAKESplus.zip file includes the GLAKES lake polygon product (.gdb & .shp). Please refer to the README file in both the U-Net.zip and the GLAKES.zip file for more detailed information.
DOI: 10.15468/dl.csa2kd
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is RUS.45_1",
 "Geometry POLYGON((23.32120 61.82082,47.91851 61.82082,47.91851 71.65553,23.32120 71.65553,23.32120 61.82082))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Impatiens glandulifera"
 ]
}

 The dataset includes 18 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002317-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14569655
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.ubfp87
title: Occurrence Download

description: A dataset containing 308 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Comocladia glabra"
 ]
}

 The dataset includes 308 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002316-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3929/ethz-b-000666313
title: Data of: a global time series of traffic volumes on extra-urban roads

description: Traffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5 % and 95 % prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R2 = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications.
DOI: 10.6084/m9.figshare.30195726
title: Exogenous jasmonic acid alleviates Cu-induced damages in guinea grass by enhancing photosynthesis, antioxidant enzyme activity and modifying associated metabolites

description: This study investigated the regulatory mechanism of exogenous Jasmonic acid (JA) in detoxifying Cu stress in guinea grass (<i>Panicum maximum</i>). Seedlings were treated with Cu (300 µM), JA (10 µM), and their combinations <i>via</i> Hoagland solution in controlled growth chambers for 30 days. The results indicated that Cu stress significantly reduced superoxide dismutase (−51.2%) and peroxidase (−38.0%), chlorophyll content, net photosynthetic rate (<i>Pn</i>), and led to decreases in leaf length, width, plant height, and biomass (−49.7%). Conversely, exogenous JA effectively mitigated the adverse effects of Cu stress by reducing membrane damage, and increasing chlorophyll, <i>Pn</i> (+107%), and antioxidant enzymes (<i>p</i><b> </b>&lt;<b> </b>0.05), and biomass (+84.7%), modifying associated metabolites. A total of 63 metabolites with differential accumulations were identified when exposed to JA, Cu, or their combination, mainly including amino acids, organic acids, and carbohydrates. Excessive Cu significantly reduced the levels of capric acid, salicylic acid, and glucosaminic acid, while increasing malic acid and serine content, which are primarily involved in regulating the citrate cycle and alanine-aspartate and glutamate metabolism. Overall, these findings demonstrates that guinea grass alleviates Cu toxicity by enhancing photosynthetic efficiency, antioxidant enzyme activity, and modifying associated metabolites and pathways under JA, thereby exhibiting potential for phytoremediation of Cu stress. This study explored the effects of exogenous jasmonic acid (JA) application on guinea grass (<i>Panicum maximum</i>) seedlings subjected to Cu stress, highlighting its phytoremediation potential. The findings demonstrated that JA significantly mitigated the Cu-induced detrimental effects by reducing membrane damage, enhancing chlorophyll content, improving net photosynthetic rate and antioxidant enzymes, modifying metabolic profiles. A total of 63 metabolites with differential accumulations were identified in response to Cu, JA alone, or their combination. Notably, metabolites detected in Cu-stressed plants following JA treatment were closely related to the pentose phosphate pathway, tricarboxylic acid cycle, and glyoxylate and dicarboxylate metabolism.
DOI: 10.6084/m9.figshare.30195726.v1
title: Exogenous jasmonic acid alleviates Cu-induced damages in guinea grass by enhancing photosynthesis, antioxidant enzyme activity and modifying associated metabolites

description: This study investigated the regulatory mechanism of exogenous Jasmonic acid (JA) in detoxifying Cu stress in guinea grass (<i>Panicum maximum</i>). Seedlings were treated with Cu (300 µM), JA (10 µM), and their combinations <i>via</i> Hoagland solution in controlled growth chambers for 30 days. The results indicated that Cu stress significantly reduced superoxide dismutase (−51.2%) and peroxidase (−38.0%), chlorophyll content, net photosynthetic rate (<i>Pn</i>), and led to decreases in leaf length, width, plant height, and biomass (−49.7%). Conversely, exogenous JA effectively mitigated the adverse effects of Cu stress by reducing membrane damage, and increasing chlorophyll, <i>Pn</i> (+107%), and antioxidant enzymes (<i>p</i><b> </b>&lt;<b> </b>0.05), and biomass (+84.7%), modifying associated metabolites. A total of 63 metabolites with differential accumulations were identified when exposed to JA, Cu, or their combination, mainly including amino acids, organic acids, and carbohydrates. Excessive Cu significantly reduced the levels of capric acid, salicylic acid, and glucosaminic acid, while increasing malic acid and serine content, which are primarily involved in regulating the citrate cycle and alanine-aspartate and glutamate metabolism. Overall, these findings demonstrates that guinea grass alleviates Cu toxicity by enhancing photosynthetic efficiency, antioxidant enzyme activity, and modifying associated metabolites and pathways under JA, thereby exhibiting potential for phytoremediation of Cu stress. This study explored the effects of exogenous jasmonic acid (JA) application on guinea grass (<i>Panicum maximum</i>) seedlings subjected to Cu stress, highlighting its phytoremediation potential. The findings demonstrated that JA significantly mitigated the Cu-induced detrimental effects by reducing membrane damage, enhancing chlorophyll content, improving net photosynthetic rate and antioxidant enzymes, modifying metabolic profiles. A total of 63 metabolites with differential accumulations were identified in response to Cu, JA alone, or their combination. Notably, metabolites detected in Cu-stressed plants following JA treatment were closely related to the pentose phosphate pathway, tricarboxylic acid cycle, and glyoxylate and dicarboxylate metabolism.