cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1yvnk0
title: CCDC 1813083: Experimental Crystal Structure Determination

description: Related Article: Ni-Hong Lu, Zi-Wei Zhang, Rui-Wei Guo, Li-Xia Yang, Ya-Xian Song, Jin-Shan Ye, Yan-Kun Shi|2018|RSC Advances|8|15036|doi:10.1039/C8RA01895B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1yvnr6
title: CCDC 1813089: Experimental Crystal Structure Determination

description: Related Article: Feize Liu, Xueling Hou, Li Fei Nie, Khurshed Bozorov, Michael Decker, Guozheng Huang|2018|SynOpen|2|207|doi:10.1055/s-0037-1610157
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1yvnxc
title: CCDC 1813094: Experimental Crystal Structure Determination

description: Related Article: Sh.Yu.Khazhiev, M.A.Khusainov, R.A.Khalikov, T.V.Tyumkina, E.S.Meshcheryakova, L.M.Khalilov, V.V.Kuznetsov|2018|Zh.Org.Khim.|54|1076|doi:10.1134/S1070428018070175
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.26093/cds/vizier.36010004
title: IRC+10216 carbon chains mapped with ALMA

description: VizieR online Data Catalogue associated with article published in journal Astronomy and Astrophysics with title ' Growth of carbon chains in IRC+10216 mapped with ALMA.' (bibcode: 2017A&A...601A...4A)
DOI: 10.5281/zenodo.17425611
title: Calibration and validation datasets

description: Calibration and validation datasets required for the code in the one repository available at https://github.com/sbi-via-tre-anonymous/
DOI: 10.5281/zenodo.17425612
title: Calibration and validation datasets

description: Calibration and validation datasets required for the code in the one repository available at https://github.com/sbi-via-tre-anonymous/
DOI: 10.15468/dl.4sa78r
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-77.22241 0.69935,-74.35067 -0.47062,-72.86161 -2.4383,-71.61188 -2.17239,-70.09623 -2.67761,-70.70781 -3.63486,-70.04305 -4.24643,-69.59102 -3.04987,-69.48466 -1.21515,-69.9101 -0.23131,-69.51125 0.59299,-69.56443 1.20456,-69.9101 1.63001,-77.67445 1.17797,-77.22241 0.69935))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bradypus tridactylus"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055506-251009101135966/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.29868518.v42
title: Logic and being pdf v1-2-3-4-5-6 NEW

description: Logic and being
DOI: 10.6084/m9.figshare.29868518.v43
title: Logic and being pdf v1-2-3-4-5-6 NEW

description: Logic and being
DOI: 10.15468/dl.9rcesj
title: Occurrence Download

description: A dataset containing 2305 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is CC0 1.0",
 "TaxonKey is Crotalus scutulatus"
 ]
}

 The dataset includes 2305 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055513-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17437383
title: Spatial transcriptomics uncovers the organization of mouse spinal cord and neuronal subtypes regulating neuropathic pain

description: The spinal cord integrates diverse somatosensory inputs and coordinates motor outputs through anatomically and functionally segregated circuits. To decode the cellular and circuit organization of such diverse somatosensory and motor functions, we employed spatially resolved single-cell transcriptomic profile of the adult mouse spinal cord. Our results revealed the distinct laminar distributions of spinal cord neurons and non-neuronal cells and distinct regional and sex-specific enrichment of cell subtypes, alluding to the subregion and sex-specificity of several spinal cord functions. We uncovered unique cell-cell communication through ligand-receptor interactions, including neuropeptide and neurotransmitters. Finally, we identified distinct neuronal cell types underlying neuropathic pain, a major healthcare challenge with insufficient molecular understanding. Collectively, this comprehensive molecular and cellular map will facilitate the decoding of the molecular, cellular, and circuit mechanisms underlying somatosensory and motor functions in the spinal cord in health and disease.
DOI: 10.15468/dl.ugjy7u
title: Occurrence Download

description: A dataset containing 280301 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Geometry POLYGON((-76.92897 40.71438,-85.52159 34.94445,-84.49619 33.41741,-75.90356 39.18734,-76.92897 40.71438))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caudata"
 ]
}

 The dataset includes 280301 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055501-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aetzkr
title: Occurrence Download

description: A dataset containing 12954 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is SDSM Vertebrate Paleontology Collection"
 ]
}

 The dataset includes 12954 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055514-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.37xr4f
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.26081 -0.49393,-74.88618 -0.02051,-75.14918 0.54057,-75.23101 0.72759,-76.39993 0.56979,-75.75702 0.02624,-75.4531 -0.10818,-75.09074 -0.14909,-74.88618 -0.27183,-74.40692 -0.61082,-74.26081 -0.49393))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Inia geoffrensis"
 ]
}

 The dataset includes 40 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055515-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dfy6a2
title: Occurrence Download

description: A dataset containing 657 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trachyderes succinctus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 657 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055516-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p2yvrh
title: Occurrence Download

description: A dataset containing 152628 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Potamopyrgus antipodarum",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.542322 25.637500, 53.987701 25.637500, 53.987701 73.184014, -26.542322 73.184014, -26.542322 25.637500))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 152628 records from 343 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055484-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yx5nk4
title: Occurrence Download

description: A dataset containing 767 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gracilaria vermiculophylla (Ohmi) Papenfuss)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Literature Occurrence)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 767 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002655-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7wx6fp
title: Occurrence Download

description: A dataset containing 1640 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Carduus platypus Lange)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 1640 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002656-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xwzr4q
title: Occurrence Download

description: A dataset containing 32190 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lysichiton americanus Hultén & H.St.John)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Literature Occurrence)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 32190 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002683-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2dnavu
title: Occurrence Download

description: A dataset containing 26603 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Carduus pycnocephalus L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 26603 records from 551 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002684-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14699848
title: Deleted

description: This dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

Detailed data for each city, including industrial land area per capita indicators, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling

URB_ECOREGION: Assigned urban ecoregion




INDcap_YEAR represents the annual industrial land per capita indicator (in m²/person), symbolizing sustainable industrial land development. Currently available from 2017 to 2020, with plans to update through 2023 as WorldPOP data becomes available.
DOI: 10.5281/zenodo.14712581
title: Deleted

description: his dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

A detailed summary of city-specific information, including the annual total industrial land area, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling

URB_ECOREGION: Assigned urban ecoregion

CLUSTER: Assigned cluster number in industrial land modeling

IND_YEAR: Total industrial land area for each year (in m²),
DOI: 10.5281/zenodo.13859823
title: Deleted

description: [This dataset was revised] This dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

Detailed data for each city, including industrial land area per capita indicators, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling




INDcap_YEAR represents the annual industrial land per capita indicator (in m²/person), symbolizing sustainable industrial land development. Currently available from 2017 to 2020, with plans to update through 2023 as WorldPOP data becomes available.
DOI: 10.5281/zenodo.14699847
title: Deleted

description: This dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

Detailed data for each city, including industrial land area per capita indicators, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling

URB_ECOREGION: Assigned urban ecoregion




INDcap_YEAR represents the annual industrial land per capita indicator (in m²/person), symbolizing sustainable industrial land development. Currently available from 2017 to 2020, with plans to update through 2023 as WorldPOP data becomes available.
DOI: 10.5281/zenodo.14712580
title: Deleted

description: his dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

A detailed summary of city-specific information, including the annual total industrial land area, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling

URB_ECOREGION: Assigned urban ecoregion

CLUSTER: Assigned cluster number in industrial land modeling

IND_YEAR: Total industrial land area for each year (in m²),
DOI: 10.5281/zenodo.13358508
title: Dataset_deleted

description: This dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

Detailed data for each city, including industrial land area per capita indicators, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling




INDcap_YEAR represents the annual industrial land per capita indicator (in m²/person), symbolizing sustainable industrial land development. Currently available from 2017 to 2020, with plans to update through 2023 as WorldPOP data becomes available.
DOI: 10.5281/zenodo.13859822
title: Deleted

description: [This dataset was revised] This dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

Detailed data for each city, including industrial land area per capita indicators, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling




INDcap_YEAR represents the annual industrial land per capita indicator (in m²/person), symbolizing sustainable industrial land development. Currently available from 2017 to 2020, with plans to update through 2023 as WorldPOP data becomes available.
DOI: 10.5281/zenodo.13358509
title: Dataset_deleted

description: This dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023. 

The dataset includes:



10 m resolution industrial land maps for each year (GeoTIFF, Zip)

Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)


File Naming Convention



GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif



XXX: Country code

YYY: City ID

YEAR: Year of data

Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.




Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:



Class 1: Industrial land in built-up areas

Class 2: Non-industrial land in built-up areas

Class 0: Non-built-up areas


City Information

Detailed data for each city, including industrial land area per capita indicators, is provided in 1093_city_indices.xlsx. This file includes:



ID_HDC_G0: Unique city ID (Urban Centre)

CTR_MN_NM: Main country name

CTR_MN_ISO: ISO-3 country codes

UC_NM_MN: Main city (Urban Centre) name

UC_NM_LST: Full list of assigned city (Urban Centre) names

CLUSTER: Assigned cluster number in industrial land modeling




INDcap_YEAR represents the annual industrial land per capita indicator (in m²/person), symbolizing sustainable industrial land development. Currently available from 2017 to 2020, with plans to update through 2023 as WorldPOP data becomes available.