cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.36915
title: Research Data for Article “Modeling the Impact of Tributary Debris Flow on a Confluence: A Case Study in Tianmo Gully, Southeast Tibet Plateau, China”
title: Research Data for Article “Modeling the Impact of Tributary Debris Flow on a Confluence: A Case Study in Tianmo Gully, Southeast Tibet Plateau, China”

description: This database contains the raw data for the original research paper “Modeling the Impact of Tributary Debris Flow on a Confluence: A Case Study of Tianmo Gully, Southeastern Tibetan Plateau, China.” The data are fully described in the manuscript submitted to CATENA: Weipeng Hou, Chendi Zhang, Peng Cui, Carlo Gualtieri (2026). Modeling the Impact of Tribitray Debris Flow on a Confluence: A Case Study of Tianmo Gully, Southeastern Tibetan Plateau, China. (submitted).This database mainly includes sediment transport rates over the fan and the riverbed with the discharge ratio between the tributary and mainstream ranging from 0.17 to 0.83.
description: This database contains the raw data for the original research paper “Modeling the Impact of Tributary Debris Flow on a Confluence: A Case Study of Tianmo Gully, Southeastern Tibetan Plateau, China.” The data are fully described in the manuscript submitted to CATENA: Weipeng Hou, Chendi Zhang, Peng Cui, Carlo Gualtieri (2026). Modeling the Impact of Tribitray Debris Flow on a Confluence: A Case Study of Tianmo Gully, Southeastern Tibetan Plateau, China. (submitted).This database mainly includes sediment transport rates over the fan and the riverbed with the discharge ratio between the tributary and mainstream ranging from 0.17 to 0.83.
DOI: 10.5517/ccdc.csd.cc2p9607
title: CCDC 2481488: Experimental Crystal Structure Determination

description: Related Article: Krystyna A. Deresz, Artem Mikhailov, Joanna Jankowska, Lorenzo Donà, Bartolomeo Civalleri, Adam Krówczyński, Radosław Kamiński, Dominik Schaniel, Katarzyna N. Jarzembska|2025|ACS Omega|10|51940|doi:10.1021/acsomega.5c08267
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.cc2p9618
title: CCDC 2481489: Experimental Crystal Structure Determination

description: Related Article: Krystyna A. Deresz, Artem Mikhailov, Joanna Jankowska, Lorenzo Donà, Bartolomeo Civalleri, Adam Krówczyński, Radosław Kamiński, Dominik Schaniel, Katarzyna N. Jarzembska|2025|ACS Omega|10|51940|doi:10.1021/acsomega.5c08267
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.cc2p9n1q
title: CCDC 2481923: Experimental Crystal Structure Determination

description: Related Article: J. T. Mague, M. S. Balakrishna|2025|CSD Communication|||
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.cc2p9pg5
title: CCDC 2481967: Experimental Crystal Structure Determination

description: Related Article: Zhi Yang|2025|CSD Communication|||
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.cc2p9ph6
title: CCDC 2481968: Experimental Crystal Structure Determination

description: Related Article: Zhi Yang|2025|CSD Communication|||
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.cc2p9qlb
title: CCDC 2482002: Experimental Crystal Structure Determination

description: Related Article: Xiaolan Tong, Junyu Zhang, Yao Chen, Hao Wang, Dongming Li, Zhipeng Yu, Li Wang, Jianbo Xiong, Feng Luo|2025|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04613
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.cc2p9vgb
title: CCDC 2482122: Experimental Crystal Structure Determination

description: Related Article: Kaito Nakamura, Kodai Shimizu, Ryusei Oketani, Haruka Tokuyama, Takashi Harimoto, Rajendra Prasad Paitandi, Yusuke Ishigaki, Akihito Konishi, Shu Seki, Makoto Yasuda, Ichiro Hisaki|2025|Mater. Chem. Front.|9|3589|doi:10.1039/d5qm00649j
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.cc2l3dgl
title: CCDC 2386549: Experimental Crystal Structure Determination

description: Related Article: Yangyang Cao, Zhengzhan Zhang, Zhenzhen Xu, Hua Geng, Tianna Zhang, Mingyang Wu, Ziyan Chang, Hongbing Fu|2025|J.Mater.Chem.C|14|1395|doi:10.1039/d5tc03473f
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.cc2pdcs8
title: CCDC 2484550: Experimental Crystal Structure Determination

description: Related Article: Jun-Chao Qi, Xiao-Gang Chen, Zhen-Yu Wang, Yuan-Yuan Tang, Xian-Jiang Song, Yan Qin, Hui-Peng Lv, Ren-Gen Xiong, Wei-Qiang Liao|2025|Chemical Science|17|1792|doi:10.1039/d5sc07784b
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.cc2pg3s2
title: CCDC 2486224: Experimental Crystal Structure Determination

description: Related Article: Jia-Hui Zhang, Yu-Xia Li, Kai-Ge Gao, Xin-Li Shi, Jie-Sheng Hu, Hui Ai, Zi-Shuo Yao, Jun Tao|2025|Dalton Trans.|55|135|doi:10.1039/d5dt02338f
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.cc2q32rp
title: CCDC 2505412: Experimental Crystal Structure Determination

description: Related Article: J.K. Salazar, J.A. Kaduk|2026|Acta Crystallogr.,Sect.E:Cryst.Commun.|82|33|doi:10.1107/S2056989025010515
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: 10.22008/fk2/arq2l1
title: PROMICE-2022 Ice Mask QGIS Bundle

description: PROMICE-2022 Ice Mask QGIS Bundle This dataset contains the PROMICE-2022 Ice Mask packaged as a ready-to-use QGIS project. The zipped bundle includes the QGIS project file and all associated vector/raster layers and styles, so you can open the project directly and view the pre-styled layers in a GIS environment. Contents QGIS project file (.qgz) with preconfigured layouts Raster and vector layers of the PROMICE-2022 ice mask WMS layer for the topographic map of Greenland WMS layer for SPOT 6/7 imagery WMS layers for Sentinel-2 imagery (2022) Layer style files README with usage notes Brief description The PROMICE-2022 Ice Mask provides an updated delineation of ice/non-ice areas of the Greenland Ice Sheet from August 2022. It is intended for visualization, mapping, and as an input layer for further geospatial analysis in QGIS or other GIS software. For detailed methodology, processing steps, validation, and full technical documentation, please consult the primary repository (link below). Citation Please cite this dataset as: Luetzenburg, Gregor; Korsgaard, Niels J.; Deichmann, Anna K.; Socher, Tobias; Gleie, Karin; Scharffenberger, Thomas; Fahrner, Dominik; Nielsen, Eva B.; How, Penelope; Bjørk, Anders A.; Kjeldsen, Kristian K.; Ahlstrøm, Andreas P.; Fausto, Robert S., 2025, PROMICE-2022 Ice Mask, GEUS Dataverse. DOI: https://doi.org/10.22008/FK2/O8CLRE How to use Download and unzip the package. Open the included .qgz QGIS project in QGIS (recommended QGIS 3.## or newer). Related Datasets Google Earth Engine The PROMICE-2022 Ice Mask (file 06) is available as an asset in the Google Earth Engine Feature Collection Access it here: projects/promice-data-ee/assets/ice_masks/PROMICE_2022_ICE_MASK PROMICE-2022 Ice Mask (Master Dataset) This is the dataset containing the updated PROMICE-2022 ice mask. The QGIS bundle provided here includes this dataset but users seeking the raw data files, metadata, and versioning should refer to the master record. Access it here: https://doi.org/10.22008/FK2/O8CLRE PROMICE-2022 Ice Mask Sentinel-2 RGB Mosaic (August 2022) This dataset provides the Greenland-wide Sentinel-2 RGB mosaic used as the primary visual reference during ice mask delineation. In the QGIS project, the Sentinel-2 images from 2022 are included as a WMS layer, but users who need the underlying mosaic files should reference the dedicated dataset. Access it here: https://doi.org/10.22008/FK2/OUKHBW
DOI: 10.15468/dl.57gsjz
title: Occurrence Download

description: A dataset containing 22250 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Neltuma glandulosa"
 ]
}

 The dataset includes 22250 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013405-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ddzn4y
title: Occurrence Download

description: A dataset containing 285 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is RUS.11_1",
 "TaxonKey is Rana arvalis"
 ]
}

 The dataset includes 285 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013408-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.36880
title: Shanghai Meteorological Station Monitoring Data (20270711-20270713)
title: 上海气象站点监测数据（20210711-20210713）

description: The meteorological observation data used in this study were collected from 35 Shanghai meteorological stations during the midday period (12:00–14:00) from July 11 to 13, 2021. These data include air temperature, relative humidity, wind speed, and the calculated wet-bulb temperature (Tw) derived from these variables, which constitute the foundational dataset for this research.
description: The meteorological observation data used in this study were collected from 35 Shanghai meteorological stations during the midday period (12:00–14:00) from July 11 to 13, 2021. These data include air temperature, relative humidity, wind speed, and the calculated wet-bulb temperature (Tw) derived from these variables, which constitute the foundational dataset for this research.
DOI: 10.15468/dl.99umyy
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ukraine",
 "GadmGid is UKR.8_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nyctereutes",
 "Year 2015-2026"
 ]
}

 The dataset includes 21 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013413-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.36901
title: Key molecules and regulatory processes of lysine succinylation
title: 赖氨酸琥珀酰化的关键分子与调控过程

description: Key molecules and regulatory processes of lysine succinylation. Note: The abbreviations and full names of key molecules involved in this figure are as follows: 1. Metabolic intermediates and enzyme complexes: AKG (α-ketoglutarate); α-KGDHC (α-ketoglutarate dehydrogenase complex); Succinyl-CoA (succinyl coenzyme A); SUCL (succinate-CoA ligase) 2. Enzymes related to succinylation/desuccinylation: CoBb (prokaryotic desuccinylase, Sir2-like bacterial lysine deacetylase); SIRT5 (silent information regulator 5); SIRT7 (silent information regulator 7); HAT1 (histone acetyltransferase 1); HDAC1/2/3 (histone deacetylase 1/2/3); KAT2A (lysine acetyltransferase 2A) 3. Key target proteins and metabolic enzymes: CPS1 (carbamoyl-phosphate synthase 1); CPT1A (carnitine palmitoyltransferase 1A); ENO1 (enolase 1); Histone H3 (histone H3); OXCT1 (3-oxoacid CoA-transferase 1); PGAM1 (phosphoglycerate mutase 1).
description: Key molecules and regulatory processes of lysine succinylation. Note: The abbreviations and full names of key molecules involved in this figure are as follows: 1. Metabolic intermediates and enzyme complexes: AKG (α-ketoglutarate); α-KGDHC (α-ketoglutarate dehydrogenase complex); Succinyl-CoA (succinyl coenzyme A); SUCL (succinate-CoA ligase) 2. Enzymes related to succinylation/desuccinylation: CoBb (prokaryotic desuccinylase, Sir2-like bacterial lysine deacetylase); SIRT5 (silent information regulator 5); SIRT7 (silent information regulator 7); HAT1 (histone acetyltransferase 1); HDAC1/2/3 (histone deacetylase 1/2/3); KAT2A (lysine acetyltransferase 2A) 3. Key target proteins and metabolic enzymes: CPS1 (carbamoyl-phosphate synthase 1); CPT1A (carnitine palmitoyltransferase 1A); ENO1 (enolase 1); Histone H3 (histone H3); OXCT1 (3-oxoacid CoA-transferase 1); PGAM1 (phosphoglycerate mutase 1).
DOI: 10.57760/sciencedb.08736
title: scCompass
title: scCompass

description: We construct a large-scale single-cell pre-training corpus. This corpus consists of more than one hundred million single-cell transcriptomes consisting of 13 different species, humans, mice, monkeys, nematodes, zebrafish, fruit flies, rats, pigs, cows, dogs, horses, chickens, and sheep. Multispecies single-cell data provides a rich resource for understanding cellular heterogeneity across different organisms. However, assembling and preprocessing such data can be challenging due to differences in biological processes and technical variability between species. In this study, we describe the assembly and preprocessing of multispecies single-cell training data from three common model organisms: human, mouse, and monkey. Among the species, the cells of humans and mice have the highest ratio, and each of them consists of 50 million cells. This data is curated from publicly available datasets in the NCBI, CellXgene, EBI, and DDBJ databases. To prepare the multispecies single-cell data for downstream analyses, we performed several preprocessing steps. For quality control, we exclude low-quality and damaged cells, with less than 7 genes for proteins or miRNAs. Then we conduct normalization and log1p transformation to reduce the skewness.
description: We construct a large-scale single-cell pre-training corpus. This corpus consists of more than one hundred million single-cell transcriptomes consisting of 13 different species, humans, mice, monkeys, nematodes, zebrafish, fruit flies, rats, pigs, cows, dogs, horses, chickens, and sheep. Multispecies single-cell data provides a rich resource for understanding cellular heterogeneity across different organisms. However, assembling and preprocessing such data can be challenging due to differences in biological processes and technical variability between species. In this study, we describe the assembly and preprocessing of multispecies single-cell training data from three common model organisms: human, mouse, and monkey. Among the species, the cells of humans and mice have the highest ratio, and each of them consists of 50 million cells. This data is curated from publicly available datasets in the NCBI, CellXgene, EBI, and DDBJ databases. To prepare the multispecies single-cell data for downstream analyses, we performed several preprocessing steps. For quality control, we exclude low-quality and damaged cells, with less than 7 genes for proteins or miRNAs. Then we conduct normalization and log1p transformation to reduce the skewness.
DOI: 10.6084/m9.figshare.30256258
title: <b>Costs of pharmaceutical innovation in non-commercial research and development: analysis of investments by the Drugs for Neglected Diseases initiative, 2003-2023</b>

description: Understanding the true cost of pharmaceutical innovation is essential for shaping policies that promote innovation and drive equitable access to treatment. This study analyses 21 therapeutic innovations developed by the Drugs for Neglected Diseases initiative (DNDi), a non-profit R&D organization focused on neglected diseases and accelerating therapeutic innovation for neglected tropical diseases. Historical costs ranged from 2 to 110 million USD (or up to 114 million USD including in-kind support). When modelling standard attrition, the estimated median investment rose to 184 million USD for novel drugs, with a high-end projection of 365 million USD, and 9 million USD for repurposed drugs, with a higher estimate of 41 million USD . These figures include the cost of failures but exclude capitalization and inflation adjustments, reflecting DNDi’s non-profit model. The analysis identifies 13 cost drivers and 9 cost-efficiency factors unique to neglected disease R&D, offering insights into the variability of costs and the benefits of the collaborative, open science approach. Compared with industry benchmarks, DNDi’s costs align more closely with orphan drugs and reflect the lower attrition in anti-infective indications, while remaining significantly lower than industry headlines. The study, contributing valuable evidence to the broader field of pharmaceutical costing, including non-commercial ventures, underscores the need to distinguish between scientific failure and business-driven attrition when evaluating innovation costs, and to further explore policies to support collaborative innovation for global health.
DOI: 10.6084/m9.figshare.30256258.v1
title: <b>Costs of pharmaceutical innovation in non-commercial research and development: analysis of investments by the Drugs for Neglected Diseases initiative, 2003-2023</b>

description: Understanding the true cost of pharmaceutical innovation is essential for shaping policies that promote innovation and drive equitable access to treatment. This study analyses 21 therapeutic innovations developed by the Drugs for Neglected Diseases initiative (DNDi), a non-profit R&D organization focused on neglected diseases and accelerating therapeutic innovation for neglected tropical diseases. Historical costs ranged from 2 to 110 million USD (or up to 114 million USD including in-kind support). When modelling standard attrition, the estimated median investment rose to 184 million USD for novel drugs, with a high-end projection of 365 million USD, and 9 million USD for repurposed drugs, with a higher estimate of 41 million USD . These figures include the cost of failures but exclude capitalization and inflation adjustments, reflecting DNDi’s non-profit model. The analysis identifies 13 cost drivers and 9 cost-efficiency factors unique to neglected disease R&D, offering insights into the variability of costs and the benefits of the collaborative, open science approach. Compared with industry benchmarks, DNDi’s costs align more closely with orphan drugs and reflect the lower attrition in anti-infective indications, while remaining significantly lower than industry headlines. The study, contributing valuable evidence to the broader field of pharmaceutical costing, including non-commercial ventures, underscores the need to distinguish between scientific failure and business-driven attrition when evaluating innovation costs, and to further explore policies to support collaborative innovation for global health.
DOI: 10.15468/dl.c6fghw
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2.11798 47.13784,-1.60384 47.13784,-1.60384 47.35779,-2.11798 47.35779,-2.11798 47.13784))",
 "TaxonKey is Botaurus stellaris"
 ]
}

 The dataset includes 15 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013414-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nyxanm
title: Occurrence Download

description: A dataset containing 30611 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Reynoutria sachalinensis"
 ]
}

 The dataset includes 30611 records from 507 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013406-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kp5r56
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cardiolucina eucosmia (Dall, 1901)"
 ]
}

 The dataset includes 83 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0105040-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.78w63j
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Marseniopsis conica (E.A.Smith, 1902)"
 ]
}

 The dataset includes 142 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0091429-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.