cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29133784.v1
title: Global, regional, and national burden of chronic kidney disease among adolescents and emerging adults from 1990 to 2021

description: There are limited studies on the epidemiology of chronic kidney disease (CKD) burden among adolescents and emerging adults. We aimed to assess the global, regional, and national trends in CKD burden among adolescents and emerging adults. The Global Burden of Disease 2021 study was utilized to evaluate the incidence, prevalence, mortality, disability-adjusted life years (DALYs) and average annual percentage changes (AAPC) in CKD among populations aged 15 to 29 years from 1990 to 2021. From 1990 to 2021, age-standardized incidence (AAPC: 0.85%, 95% uncertainty interval [95% UI]: 0.81%-0.88%), prevalence (AAPC: 0.22%, 95% UI: 0.19%–0.25%), and mortality (AAPC: 0.18%, 95% UI: 0.04%–0.32%) rates of CKD have risen globally among adolescents and emerging adults. In 2021, Southeast Asia had the highest age-standardized prevalence (5370.39 [95% UI: 4060.97–6929.79] per 100,000 population), while Central sub-Saharan Africa had the highest mortality rate (5.05 [95% UI: 3.49–7] per 100,000 population). In 2021, glomerulonephritis and ‘other and unspecified causes’ accounted for 94% of new cases, 83% of prevalent cases, and 92% of mortality cases. Frontier analyses suggest that regions at varying stages of development still hold substantial potential for further improvements in addressing CKD. Globally, the burden of CKD among adolescents and emerging adults continues to rise, with Southeast Asia and sub-Saharan Africa bearing a disproportionate burden. Nevertheless, there remain substantial opportunities across all levels of the development spectrum to alleviate the CKD burden through enhanced health interventions and resource allocation. CKD presents a significant public health problem that affects people of all ages. In the age group of 15–29 years, referred to as adolescents and emerging adults, the epidemiological study of CKD in this demographic has been limited. This was the first study to comprehensively analyze the trends in the burden of CKD among adolescents and emerging adults at global, regional, and national levels. The burden of CKD among adolescents and emerging adults requires attention. The results of this study provide a basis for formulating subsequent disease prevention and control policies.
DOI: 10.15468/dl.smmg8k
title: Occurrence Download

description: A dataset containing 227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ethiopia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Melia azedarach L."
 ]
}

 The dataset includes 227 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015151-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15493513
title: FIShBOT Archive 2025-05-23

description: This dataset is a static snapshot of the FIShBOT data product before new data are aggregated. This version of fishbot is archived for reproducibility and is not updated. The data are aggregated from the FIShBOT system, which collects and processes oceanographic from fishery dependent and independent sources.
DOI: 10.5281/zenodo.15493514
title: FIShBOT Archive 2025-05-23

description: This dataset is a static snapshot of the FIShBOT data product before new data are aggregated. This version of fishbot is archived for reproducibility and is not updated. The data are aggregated from the FIShBOT system, which collects and processes oceanographic from fishery dependent and independent sources.
DOI: 10.6084/m9.figshare.29133791
title: PRISMA-P checklist for <b>Effect of Multimodal Interventions on Pain, Stress and Neurodevelopmental Outcomes in Neonates with Malignant and Benign Tumour:</b><b> </b><b>A Systematic Review Protocol</b>. docx

description: <b>Effect of Multimodal Interventions on Pain, Stress and Neurodevelopmental Outcomes in Neonates with Malignant and Benign Tumour:</b><b> </b><b>A Systematic Review Protocol</b>
DOI: 10.6084/m9.figshare.29133791.v1
title: PRISMA-P checklist for <b>Effect of Multimodal Interventions on Pain, Stress and Neurodevelopmental Outcomes in Neonates with Malignant and Benign Tumour:</b><b> </b><b>A Systematic Review Protocol</b>. docx

description: <b>Effect of Multimodal Interventions on Pain, Stress and Neurodevelopmental Outcomes in Neonates with Malignant and Benign Tumour:</b><b> </b><b>A Systematic Review Protocol</b>
DOI: 10.15468/dl.y7k9mk
title: Occurrence Download

description: A dataset containing 20206924 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Material citation, Specimen, Fossil, Living Specimen, Occurrence evidence)",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 20206924 records from 1544 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015153-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10477050
title: ChinaHighCO: Daily Seamless 10 km Ground-Level CO Dataset for China (2013–2018)

description: ChinaHighCO is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level CO dataset for China from 2013 to 2018. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m-3, and a mean absolute error (MAE) of 0.16 mg m-3 on a daily basis.

If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023



Note that the ChinaHighCO dataset was improved to a 1 km resolution after 2019:

        all (including daily) data for the years after 2019 are accessible at: https://doi.org/10.5281/zenodo.10477022

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8), and measured at the standard condition (i.e., 273 K and 1013 hPa). The concentrations can be converted to the room condition (i.e., 298 K and 1013 hPa) by dividing by a factor of 1.09375 (MEE, 2018).

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.4641530
title: ChinaHighCO: Daily Seamless 10 km Ground-Level CO Dataset for China (2013–2018)

description: ChinaHighCO is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level CO dataset for China from 2013 to 2018. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m-3, and a mean absolute error (MAE) of 0.16 mg m-3 on a daily basis.

If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023



Note that the ChinaHighCO dataset was improved to a 1 km resolution after 2019:

        all (including daily) data for the years after 2019 are accessible at: https://doi.org/10.5281/zenodo.10477022

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8), and measured at the standard condition (i.e., 273 K and 1013 hPa). The concentrations can be converted to the room condition (i.e., 298 K and 1013 hPa) by dividing by a factor of 1.09375 (MEE, 2018).

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.4641531
title: ChinaHighCO: Big Data Full-coverage Ground-level CO L3 Yearly 0.1 Deg Product

description: ChinaHighCO is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the Big Data Level 3 (L3) yearly 0.1 degree (≈ 10 km) gridded <strong>full-coverage</strong> ground-level CO products in Eastern China (ECHAP_CO_Y10K) from 2013 to 2020, which are averaged from Level 2 daily products. This dataset has high accuracy with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80 and a root-mean-square error (RMSE) of 0.29 µg m<sup>-3</sup> on a daily basis. If you use the ChinaHighCO dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2021). <strong>Note that this dataset is continuously updated, and if you need a longer period or higher temporal-resolution (e.g., daily, monthly) data, please contact me.</strong>
description: Email: weijing_rs@163.com; weijing.rs@gmail.com.
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.5765577
title: ChinaHighCO: Big Data Seamless 10 km Ground-level CO dataset for China

description: ChinaHighCO is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level CO dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80 and a root-mean-square error (RMSE) of 0.29 mg m<sup>-3</sup> on a daily basis. <strong>This old version was closed now since a new version has been published.</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775", "Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.7574144
title: ChinaHighCO: Big Data Seamless 10 km Ground-level CO dataset for China

description: ChinaHighCO is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. 

This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level CO dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m-3, and a mean absolute error (MAE) of 0.16 mg m-3 on a daily basis.

If you use the ChinaHighCO dataset for related scientific research, please cite the corresponding reference (Wei et al., ACP, 2023):





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023



More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing@umd.edu).
DOI: 10.15468/dl.deg87g
title: Occurrence Download

description: A dataset containing 134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "GadmGid is one of (USA.10_1, USA.11_1, USA.41_1, USA.1_1, USA.34_1, USA.25_1, USA.43_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Muscadinia rotundifolia (Michx.) Small",
 "Year 1980-2025"
 ]
}

 The dataset includes 134 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015154-250515123054153/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.29133797
title: Land Cover and Land Use Data in the Indo-Malaysian Region from 1982 to 2015.

description: This dataset was produced by He et al (2022).
DOI: 10.15468/dl.hc58vn
title: Occurrence Download

description: A dataset containing 223001 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "Geometry POLYGON((-76.65064 5.90607,-75.78157 5.92745,-75.77852 6.64378,-76.61551 6.63003,-76.65064 5.90607))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 223001 records from 273 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015152-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18659842
title: Data from Phenocam (PHE) measurements at Bristol – University of Bristol, Faculty of Engineering (BRENGI) from 2025-07-24 to 2025-12-31 [RAW]

description: Original phenocam images separated into near-infrared (NIR) and visible (VIS).
description: TECHNICAL INFO

Files were submitted and recorded as part of the global Phenocam Network with the site name "rothberurb". Access to the "rothberurb" phenocam on the global phenocam network through: https://phenocam.nau.edu/webcam/sites/bristoluni/

Files are organised in sub-directories per year and month of the year.

LICENSE

Creative Commons Attribution 4.0 International. 

Please read the Phenocam Fair Use Policy.

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005).

Data were managed and attributed by the PhenoCam Network, which has been supported by the National Science Foundation, the Long-Term Agroecosystem Research (LTAR) network which is supported by the United States Department of Agriculture (USDA), the U.S. Department of Energy, the U.S. Geological Survey, the Northeastern States Research Cooperative, and the USA National Phenology Network. 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project

Global Phenocam Network
DOI: 10.15468/tny6j8
title: 2017 Onwards Rare Invertebrates in the Cairngorms Small Scabious Mining Bee Survey Data

description: This dataset contains small scabious mining bee records gathered as part of the Rare Invertebrates in the Cairngorms Project. The Rare Invertebrates in the Cairngorms (RIC) project will work to improve the conservation fortunes of some of the Cairngorm National Parkís (CNPA) rarest species by recruiting local volunteers to help survey for them, as well as offering advice to landowners on how best to manage habitats for their needs. RIC is a partnership project involving RSPB Scotland, the Cairngorms National Park Authority (CNPA), Buglife Scotland, Butterfly Conservation Scotland and NatureScot.
DOI: 10.15468/w3rhj6
title: 2017 Onwards Rare Invertebrates in the Cairngorms Kentish Glory Survey Data

description: This dataset contains kentish glory records gathered as part of the Rare Invertebrates in the Cairngorms Project. The Rare Invertebrates in the Cairngorms (RIC) project will work to improve the conservation fortunes of some of the Cairngorm National Parkís (CNPA) rarest species by recruiting local volunteers to help survey for them, as well as offering advice to landowners on how best to manage habitats for their needs. RIC is a partnership project involving RSPB Scotland, the Cairngorms National Park Authority (CNPA), Buglife Scotland, Butterfly Conservation Scotland and NatureScot.
DOI: 10.15468/dl.wjg5hh
title: Occurrence Download

description: A dataset containing 119 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hennediella heimii",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 119 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023783-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57907/mirri/neufl1/ezijnq
title: MIRRI0052902.json

description: Strain json file
DOI: 10.57907/mirri/nytekh
title: MIRRI0053764

description: Organism type: Filamentous Fungi ; Taxon name: Sphaeridium candidum ; Collection: CBS 883.73 ; Country: Netherlands
DOI: 10.57907/mirri/pl8dpx/zn2y2q
title: MIRRI0053796.json

description: Strain json file
DOI: 10.57907/mirri/q2isfi/qxj51j
title: MIRRI0052871.json

description: Strain json file
DOI: 10.57907/mirri/qdpap7
title: MIRRI0053172

description: Organism type: Filamentous Fungi ; Taxon name: Hypoxylon fragiforme ; Collection: CBS 140.73 ; Country: Germany
DOI: 10.57907/mirri/sayu6z/6nclhd
title: MIRRI0053794.json

description: Strain json file
DOI: 10.57907/mirri/sbliih
title: MIRRI0053747

description: Organism type: Filamentous Fungi ; Taxon name: Gonytrichum macrocladum ; Collection: CBS 861.73 ; Country: Chile
DOI: 10.57907/mirri/sseqsq/o2lymq
title: MIRRI0053773.json

description: Strain json file
DOI: 10.57907/mirri/ut9xmb
title: MIRRI0053185

description: Organism type: Filamentous Fungi ; Taxon name: Aphanomyces euteiches ; Collection: CBS 156.73 ; Country: Norway
DOI: 10.57907/mirri/vkpcon/o9whhm
title: MIRRI0053769.json

description: Strain json file
DOI: 10.57907/mirri/wkk2a9
title: MIRRI0053138

description: Organism type: Filamentous Fungi ; Taxon name: Talaromyces minioluteus ; Collection: CBS 996.72 ; Country: Netherlands
DOI: 10.57907/mirri/wkk2a9/bhdber
title: MIRRI0053138.json

description: Strain json file
DOI: 10.57907/mirri/yglgbo
title: MIRRI0053127

description: Organism type: Filamentous Fungi ; Taxon name: Aspergillus egyptiacus ; Collection: CBS 990.72 ; Country: Egypt
DOI: 10.57907/mirri/zmvind/zehmaz
title: MIRRI0053141.json

description: Strain json file
DOI: 10.3535/764-9k8-g6e
title: Erythroxylum macrophyllum var. macrocnemum (Mart.) Plowman

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3tp-lc4-zn4
title: Erythroxylum daphnites Mart.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/lfd-csb-dsv
title: Erythroxylum cincinnatum Mart.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.57907/mirri/2s6ja2
title: MIRRI0053219

description: Organism type: Filamentous Fungi ; Taxon name: Stilbella aciculosa ; Collection: CBS 201.73 ; Country: Turkey
DOI: 10.57907/mirri/4olykg
title: MIRRI0053864

description: Organism type: Filamentous Fungi ; Taxon name: Byssochlamys spectabilis ; Collection: CBS 102.74 ; Country: France
DOI: 10.57907/mirri/5n2is0/haehtj
title: MIRRI0053274.json

description: Strain json file
DOI: 10.57907/mirri/5qvkbs/saceff
title: MIRRI0053766.json

description: Strain json file
DOI: 10.57907/mirri/6mcm3f
title: MIRRI0053199

description: Organism type: Filamentous Fungi ; Taxon name: Morchella elata ; Collection: CBS 171.73B ; Country: Netherlands
DOI: 10.57907/mirri/7dvtqw/58lk1v
title: MIRRI0053235.json

description: Strain json file
DOI: 10.57907/mirri/9wwe2m/ftfo4q
title: MIRRI0052896.json

description: Strain json file
DOI: 10.57907/mirri/anqcsz
title: MIRRI0052120

description: Organism type: Filamentous Fungi ; Taxon name: Piedraia hortae var. hortae ; Collection: CBS 375.71 ; Country: Brazil
DOI: 10.57907/mirri/b3qqun/9dkfgn
title: MIRRI0052890.json

description: Strain json file
DOI: 10.57907/mirri/basynq/k9dxhx
title: MIRRI0053799.json

description: Strain json file
DOI: 10.57907/mirri/bis6fx/vqpuub
title: MIRRI0053879.json

description: Strain json file