cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2kw86y
title: CCDC 2379690: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Takuma Mio, Kenta Rakumitsu, Masaya Shimabukuro, Mamiko Kobayashi, Tomoya Fukui, Nobutsugu Hamamoto, Toshiharu Ishizaki, Yang Kim, Yoshihiro Koide|2025|J.Mater.Chem.C|13|3408|doi:10.1039/D4TC03640A
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.cc2kw891
title: CCDC 2379693: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Takuma Mio, Kenta Rakumitsu, Masaya Shimabukuro, Mamiko Kobayashi, Tomoya Fukui, Nobutsugu Hamamoto, Toshiharu Ishizaki, Yang Kim, Yoshihiro Koide|2025|J.Mater.Chem.C|13|3408|doi:10.1039/D4TC03640A
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.cc2l2my8
title: CSD 2385820: Experimental Crystal Structure Determination

description: Related Article: Huanhuan Zhao, Chenxu Li, Juanjuan Lu, Zhihua Yang, Jian Han, Shilie Pan|2025|J.Mater.Chem.C|13|3251|doi:10.1039/D4TC04947K
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2l2n1d
title: CSD 2385823: Experimental Crystal Structure Determination

description: Related Article: Huanhuan Zhao, Chenxu Li, Juanjuan Lu, Zhihua Yang, Jian Han, Shilie Pan|2025|J.Mater.Chem.C|13|3251|doi:10.1039/D4TC04947K
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kw87z
title: CCDC 2379691: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Takuma Mio, Kenta Rakumitsu, Masaya Shimabukuro, Mamiko Kobayashi, Tomoya Fukui, Nobutsugu Hamamoto, Toshiharu Ishizaki, Yang Kim, Yoshihiro Koide|2025|J.Mater.Chem.C|13|3408|doi:10.1039/D4TC03640A
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.cc2l2n2f
title: CCDC 2385824: Experimental Crystal Structure Determination

description: Related Article: Huanhuan Zhao, Chenxu Li, Juanjuan Lu, Zhihua Yang, Jian Han, Shilie Pan|2025|J.Mater.Chem.C|13|3251|doi:10.1039/D4TC04947K
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.cc2kw8b2
title: CCDC 2379694: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Takuma Mio, Kenta Rakumitsu, Masaya Shimabukuro, Mamiko Kobayashi, Tomoya Fukui, Nobutsugu Hamamoto, Toshiharu Ishizaki, Yang Kim, Yoshihiro Koide|2025|J.Mater.Chem.C|13|3408|doi:10.1039/D4TC03640A
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: 10.5066/p14gcxuu
title: Climate Change Vulnerability Index Release 4.0: Excel Workbook

description: The Climate Change Vulnerability Index (CCVI) uses a scoring system that integrates a species’ exposure to projected climate change within an assessment area, including sea level rise, and three sets of factors associated with climate change sensitivity, each supported by published studies: 1) species-specific sensitivity and adaptive capacity factors, 2) threat multipliers such as barriers to dispersal and anthropogenic threats, and 3) documented and modeled responses to climate change. Assessing species with the CCVI facilitates grouping unrelated taxa by their relative risk to climate change as well as identifying patterns of climate stressors that affect multiple taxa.
DOI: 10.5281/zenodo.13945145
title: Supplemental Materials for Timescales of Drainage Network Evolution Revealed: Spatiotemporal Variability in Drainage Density in the Post-Glacial Central Lowlands, USA

description: This dataset is associated with Meghani & Anders (in review), TIMESCALES OF DRAINAGE NETWORK EVOLUTION REVEALED: SPATIOTEMPORAL VARIABILITY IN DRAINAGE DENSITY IN THE POST-GLACIAL CENTRAL LOWLANDS, USA. 

This upload includes the following files:



drainage_density.lpk

drainage_density.m

drainage_density.csv

drainage_density_code.m

drainage_density_code.txt


Files 1-3 are the same dataset in multiple file types. 

Files 4 and 5 are code used to analyze this data and produce the figures in the paper listed above.
description: Landscape dissection by rivers is a common qualitative measure of surface maturity. Quantitative studies of fluvial development over time indicate that drainage development increases non-linearly and is influenced by lithology, however, these studies typically take place over short timescales (10s of years), cover small areas, and focus on steep landscapes. In this work we use the Central Lowlands physiographic province (CL) as a natural laboratory in which we investigate rates and controls on drainage development in a post-glacial lowland landscape. Portions of the CL have been glaciated repeatedly in the Quaternary, and its topography is dominated by a patchwork of glacial landforms that have been developing drainage for 10 to more than 500 thousand years. We modify the National Hydrography Dataset to estimate pre-agriculture drainage density in these different age regions and reveal rates of drainage development in the CL. We find that drainage density in the CL increases non-linearly, increasing rapidly following glaciation before slowly approaching a maximum value. Much of the development is accomplished by 50 ka, well within a typical interglacial. The apparent maximum value, ~1.5 km/km2, is comparable to the median drainage density measured in regions in the CL that have not experienced Quaternary glaciation. Our study shows that this value is likely influenced by soil sand content and regional precipitation levels. We note that while drainage density increases to an apparent maximum within an interglacial, the fluvial network is unlikely to adjust to post-glacial base level conditions within that same length of time. Our results are most consistent with a model of drainage expansion driven by the connection of closed depressions, or ‘non-contributing area’ (NCA), the portion of a watershed that does not drain to a river. We find that NCA decreases in tandem with increasing drainage density, which implies that NCA could be a measure of landscape integration that is at least as sensitive as drainage density.
DOI: 10.5281/zenodo.13945146
title: Supplemental Materials for Timescales of Drainage Network Evolution Revealed: Spatiotemporal Variability in Drainage Density in the Post-Glacial Central Lowlands, USA

description: This dataset is associated with Meghani & Anders (in review), TIMESCALES OF DRAINAGE NETWORK EVOLUTION REVEALED: SPATIOTEMPORAL VARIABILITY IN DRAINAGE DENSITY IN THE POST-GLACIAL CENTRAL LOWLANDS, USA. 

This upload includes the following files:



drainage_density.lpk

drainage_density.m

drainage_density.csv

drainage_density_code.m

drainage_density_code.txt


Files 1-3 are the same dataset in multiple file types. 

Files 4 and 5 are code used to analyze this data and produce the figures in the paper listed above.
description: Landscape dissection by rivers is a common qualitative measure of surface maturity. Quantitative studies of fluvial development over time indicate that drainage development increases non-linearly and is influenced by lithology, however, these studies typically take place over short timescales (10s of years), cover small areas, and focus on steep landscapes. In this work we use the Central Lowlands physiographic province (CL) as a natural laboratory in which we investigate rates and controls on drainage development in a post-glacial lowland landscape. Portions of the CL have been glaciated repeatedly in the Quaternary, and its topography is dominated by a patchwork of glacial landforms that have been developing drainage for 10 to more than 500 thousand years. We modify the National Hydrography Dataset to estimate pre-agriculture drainage density in these different age regions and reveal rates of drainage development in the CL. We find that drainage density in the CL increases non-linearly, increasing rapidly following glaciation before slowly approaching a maximum value. Much of the development is accomplished by 50 ka, well within a typical interglacial. The apparent maximum value, ~1.5 km/km2, is comparable to the median drainage density measured in regions in the CL that have not experienced Quaternary glaciation. Our study shows that this value is likely influenced by soil sand content and regional precipitation levels. We note that while drainage density increases to an apparent maximum within an interglacial, the fluvial network is unlikely to adjust to post-glacial base level conditions within that same length of time. Our results are most consistent with a model of drainage expansion driven by the connection of closed depressions, or ‘non-contributing area’ (NCA), the portion of a watershed that does not drain to a river. We find that NCA decreases in tandem with increasing drainage density, which implies that NCA could be a measure of landscape integration that is at least as sensitive as drainage density.
DOI: 10.15468/dl.4qn3r4
title: Occurrence Download

description: A dataset containing 157007 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brotogeris chiriri (Vieillot, 1818)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 157007 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000728-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nd7wjy
title: Occurrence Download

description: A dataset containing 338 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Pithecellobium dulce (Roxb.) Benth."
 ]
}

 The dataset includes 338 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000741-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d9as4y
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gadus finnmarchicus (Koefoed, 1956)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000742-250213122211068/datasets/export for details.
DOI: 10.25378/janelia.26513119.v1
title: Manual nuclei segmentations in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of H89 treated HeLa cell (jrc_hela-h89-1)

description: <b>Annotation description</b>: Dense segmentations of nuclei in jrc_hela-h89-1 using Amira-Avizo (Thermo Fisher Scientific) and "Using Amira to manually segment organelles in vEM for machine learning V.3" protocol.<b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<b>Classes annotated</b>: Nuclei<b>Source (EM) dataset ID</b>: jrc_hela-h89-1<b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (X, Y, Z)<b>Source (EM) data dimensions (µm)</b>: 80 x 6 x 101.82 (X, Y, Z)<b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.13469964<b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_hela-h89-1/jrc_hela-h89-1.zarr/recon-1/labels/inference/segmentations/nuc/<b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_hela-h89-1
DOI: 10.25378/janelia.26513119
title: Manual nuclei segmentations in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of H89 treated HeLa cell (jrc_hela-h89-1)

description: <b>Annotation description</b>: Dense segmentations of nuclei in jrc_hela-h89-1 using Amira-Avizo (Thermo Fisher Scientific) and "Using Amira to manually segment organelles in vEM for machine learning V.3" protocol.<b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<b>Classes annotated</b>: Nuclei<b>Source (EM) dataset ID</b>: jrc_hela-h89-1<b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (X, Y, Z)<b>Source (EM) data dimensions (µm)</b>: 80 x 6 x 101.82 (X, Y, Z)<b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.13469964<b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_hela-h89-1/jrc_hela-h89-1.zarr/recon-1/labels/inference/segmentations/nuc/<b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_hela-h89-1
DOI: 10.5281/zenodo.10762137
title: 2SeasonWeedDet8: a two-season, 8-class dataset for cross-season weed detection generalization evaluation

description: The 2SeasonWeedDet8 dataset comprises two eight-class sub-datasets acquired in two consecutive seasons of 2021 and 2022. It was specifically curated for assessing the cross-season generalization assessment of weed detection models.





Images in both years were captured for naturally germinated weeds using smartphone or digital color cameras in cotton fields across Mississippi. 







Images were manually labeled by qualified personnel who draw bounding boxes for individual weed plants using the VGG Image Annotator (version 2.10). 







Initial annotations were examined by PI or trained personnel for weed identification for quality control before inclusions in the final dataset.







Weed classes of the dataset include: Waterhemp, Carpetweed, Morninglory, Goosegrass, Spotted Spurge, Palmer Amaranth, Purslane, and Ragweed.



 



Each weed image (in .jpg format) has one corresponding annotation file of the same file name, in both JSON and XML formats, placed in the same folder. 



For the JSON file, the annotated bounding box is defined in COCO format, i.e., [x_min, y_min, width, height].

For the XML file, the annotated bounding box is represented in Pascal VOC format, i.e., [x_min, y_min, x_max, y_max]. 



 

For the two sub-datasets (corresponding to the compressed files, "Year2021" and "Year2022")





Weed Data of Year 2021: derived from the CottonWeedDet12 dataset, the sub-dataset contains 4734 images with 7664 bounding boxes. It is broken down into two compressed files "Year2021_Part1" (with 2290 images) and "Year2021_Part2" (with 2444 images) for the convenience of data uploading and downloading. After downloading and unzipping the two files, you may merge them together for the complete data of Year 2021.  







Weed Data of Year 2022: this sub-dataset consists of 1930 images with 3184 bounding boxes



The combined two-season dataset has 6664 images with 10848 bounding boxes. More detailed documentation of the dataset curation and model benchmarking for weed detection are described in the accompanying journal paper: Deng, B., Lu, Y., & Xu, J. (2024). Weed Database Development: An Updated Survey of Public Weed Datasets and Cross-Season Weed Detection Adaptation. Ecological Informatics, 102546. https://doi.org/10.1016/j.ecoinf.2024.102546. 

 

If you use the dataset in published research, please consider citing the dataset or associated journal article above. Hopefully, you find this dataset useful.
DOI: 10.5281/zenodo.10762138
title: 2SeasonWeedDet8: a two-season, 8-class dataset for cross-season weed detection generalization evaluation

description: The 2SeasonWeedDet8 dataset comprises two eight-class sub-datasets acquired in two consecutive seasons of 2021 and 2022. It was specifically curated for assessing the cross-season generalization assessment of weed detection models.





Images in both years were captured for naturally germinated weeds using smartphone or digital color cameras in cotton fields across Mississippi. 







Images were manually labeled by qualified personnel who draw bounding boxes for individual weed plants using the VGG Image Annotator (version 2.10). 







Initial annotations were examined by PI or trained personnel for weed identification for quality control before inclusions in the final dataset.







Weed classes of the dataset include: Waterhemp, Carpetweed, Morninglory, Goosegrass, Spotted Spurge, Palmer Amaranth, Purslane, and Ragweed.



 



Each weed image (in .jpg format) has one corresponding annotation file of the same file name, in both JSON and XML formats, placed in the same folder. 



For the JSON file, the annotated bounding box is defined in COCO format, i.e., [x_min, y_min, width, height].

For the XML file, the annotated bounding box is represented in Pascal VOC format, i.e., [x_min, y_min, x_max, y_max]. 



 

For the two sub-datasets (corresponding to the compressed files, "Year2021" and "Year2022")





Weed Data of Year 2021: derived from the CottonWeedDet12 dataset, the sub-dataset contains 4734 images with 7664 bounding boxes. It is broken down into two compressed files "Year2021_Part1" (with 2290 images) and "Year2021_Part2" (with 2444 images) for the convenience of data uploading and downloading. After downloading and unzipping the two files, you may merge them together for the complete data of Year 2021.  







Weed Data of Year 2022: this sub-dataset consists of 1930 images with 3184 bounding boxes



The combined two-season dataset has 6664 images with 10848 bounding boxes. More detailed documentation of the dataset curation and model benchmarking for weed detection are described in the accompanying journal paper: Deng, B., Lu, Y., & Xu, J. (2024). Weed Database Development: An Updated Survey of Public Weed Datasets and Cross-Season Weed Detection Adaptation. Ecological Informatics, 102546. https://doi.org/10.1016/j.ecoinf.2024.102546. 

 

If you use the dataset in published research, please consider citing the dataset or associated journal article above. Hopefully, you find this dataset useful.
DOI: 10.15468/dl.zac25k
title: Occurrence Download

description: A dataset containing 96551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brotogeris tirica (Gmelin, 1788)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 96551 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000743-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ymtbkh
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plectropomus leopardus (Lacepède, 1802)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 44 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000744-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6wccfs
title: Occurrence Download

description: A dataset containing 12980 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.18_1",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 12980 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000745-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yy9cgx
title: Occurrence Download

description: A dataset containing 12562 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bubalus bubalis (Linnaeus, 1758)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 12562 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000746-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25378/janelia.26512453
title: Manual nuclei segmentations in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of Killer T-Cell attacking cancer cell (jrc_ctl-id8-1)

description: <b>Annotation description</b>: Dense segmentations of nuclei in jrc_ctl-id8-1 using Amira-Avizo (Thermo Fisher Scientific) and "Using Amira to manually segment organelles in vEM for machine learning V.3" protocol.<b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<b>Classes annotated</b>: Nuclei<b>Source (EM) dataset ID</b>: jrc_ctl-id8-1<b>Source (EM) voxel size (nm)</b>: 4.00 x 4.00 x 3.48 (X, Y, Z)<b>Source (EM) data dimensions (µm)</b>: 74 x 13 x 42 (X, Y, Z)<b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.13114454<b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_ctl-id8-1/jrc_ctl-id8-1.zarr/recon-1/labels/inference/segmentations/nuc/<b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_ctl-id8-1