cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2pr21l
title: CCDC 2494819: Experimental Crystal Structure Determination

description: Related Article: Yuji Mikata, Saori Yasuda, Poonyawee Suwankeeree, Runo Ozaki, Hideo Konno, Takashi Matsuo, Sunao Shoji|2025|J.Inorg.Biochem.|276|113194|doi:10.1016/j.jinorgbio.2025.113194
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: 10.15468/dl.xbnzjt
title: Occurrence Download

description: A dataset containing 2547 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Trachinotus ovatus"
 ]
}

 The dataset includes 2547 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082204-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18189464
title: Supplementary Tables
DOI: 10.5281/zenodo.17054740
title: Supplementary Tables
DOI: 10.5281/zenodo.8043523
title: DYNAMIC TZ Cluster RCT Dataset

description: De-identified dataset from the DYNAMIC Tanzania cluster-randomized controlled trial evaluating the impact of the ePOCT+ package on antibiotic prescription (superiority analysis) and clinical failure (non-inferiority analysis) compared to usual care. Study conducted in the Mbeya and Morogoro regions of Tanzania between December 2021 to October 2022.**Update 08.01.2026: Upon review by the authors, we have realized that this dataset was not sufficiently de-identified. We have thus restricted access to v1 of this dataset and uploaded a new dataset: DYNAMIC TZ Cluster RCT Dataset Anonymized
DOI: 10.5281/zenodo.8043522
title: DYNAMIC TZ Cluster RCT Dataset Anonymized

description: Anonymized dataset from the DYNAMIC Tanzania cluster-randomized controlled trial evaluating the impact of the ePOCT+ package on antibiotic prescription (superiority analysis) and clinical failure (non-inferiority analysis) compared to usual care. Study conducted in the Mbeya and Morogoro regions of Tanzania between December 2021 to October 2022.
DOI: 10.3886/e242722
title: DQS NHANES Select Oral Health Prevalence Estimates

description: These data represent prevalence estimates of select oral health topics from the National Health and Nutrition Examination Survey (NHANES).
Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.
Read less
description: (Downloaded 1/7/2026)
DOI: 10.3886/e242722v1
title: DQS NHANES Select Oral Health Prevalence Estimates

description: These data represent prevalence estimates of select oral health topics from the National Health and Nutrition Examination Survey (NHANES).
Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.
Read less
description: (Downloaded 1/7/2026)
DOI: 10.15468/dl.rab5pc
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.884513 24.508298, 71.80734 24.502721, 71.732088 24.486129, 71.66062599999999 24.458934, 71.594729 24.421812, 71.536029 24.375686, 71.485972 24.321701, 71.44579 24.261194, 71.416461 24.195663, 71.39869299999999 24.126729, 71.39290699999999 24.056092, 71.399224 23.985492, 71.41747100000001 23.916668, 71.447181 23.851309, 71.48760799999999 23.791018, 71.53774799999999 23.737273, 71.59636500000001 23.691387, 71.66201700000001 23.654482, 71.733099 23.627458, 71.807872 23.610976, 71.884513 23.605437, 71.96115399999999 23.610976, 72.035927 23.627458, 72.10700900000001 23.654482, 72.17266100000001 23.691387, 72.231278 23.737273, 72.281418 23.791018, 72.321845 23.851309, 72.351555 23.916668, 72.36980200000001 23.985492, 72.376119 24.056092, 72.370333 24.126729, 72.352565 24.195663, 72.32323599999999 24.261194, 72.28305400000001 24.321701, 72.232997 24.375686, 72.174297 24.421812, 72.1084 24.458934, 72.03693800000001 24.486129, 71.961686 24.502721, 71.884513 24.508298))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082205-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14438361
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.31032004
title: Dataset of United States Incident Management Situation Reports from 2007 to 2025

description: This structured dataset comprises four text (csv) files that represent four categories of wildfire information extracted from the original PDFs of the US Incident Management Situation Reports between 2007 and 2025. There is an Excel file (2007-2025-IMSR-1.09.xlsx) that contains corresponding information to the text files. The dataset was generated by IMSR-Tool version 1.09, which is available at https://doi.org/10.5281/zenodo.15039313If you are going to use the 2007-2025 IMSR data, please cite our paper: "Nguyen, D., Belval, E.J., Wei, Y. et al. Dataset of United States Incident Management Situation Reports from 2007 to 2021. Sci Data 11, 23 (2024). https://doi.org/10.1038/s41597-023-02876-8"<br>
DOI: 10.6084/m9.figshare.31032004.v1
title: Dataset of United States Incident Management Situation Reports from 2007 to 2025

description: This structured dataset comprises four text (csv) files that represent four categories of wildfire information extracted from the original PDFs of the US Incident Management Situation Reports between 2007 and 2025. There is an Excel file (2007-2025-IMSR-1.09.xlsx) that contains corresponding information to the text files. The dataset was generated by IMSR-Tool version 1.09, which is available at https://doi.org/10.5281/zenodo.15039313If you are going to use the 2007-2025 IMSR data, please cite our paper: "Nguyen, D., Belval, E.J., Wei, Y. et al. Dataset of United States Incident Management Situation Reports from 2007 to 2021. Sci Data 11, 23 (2024). https://doi.org/10.1038/s41597-023-02876-8"<br>
DOI: 10.5281/zenodo.18189492
title: ATMOSPHERIC AIR AND DISEASE

description:  

This article discusses the sanitary protection of atmospheric air, the most important factor of the external environment, and ensuring its quality indicators comply with environmental standards, one of the most pressing global problems of today. 91% of the world's population lives in areas with polluted air. According to 2018 data, 7 million people die annually as a result of excessive air pollution. When analyzing the overall prevalence of diseases of the population, it was found that respiratory diseases increased by 29.5%, musculoskeletal diseases by 2.64%, congenital malformations by 9.7%, injuries by 50.1%, digestive system diseases by 23.2%, circulatory system diseases by 11.8%, and skin and subcutaneous tissue diseases decreased by 7.9%.
DOI: 10.5281/zenodo.18189491
title: ATMOSPHERIC AIR AND DISEASE

description:  

This article discusses the sanitary protection of atmospheric air, the most important factor of the external environment, and ensuring its quality indicators comply with environmental standards, one of the most pressing global problems of today. 91% of the world's population lives in areas with polluted air. According to 2018 data, 7 million people die annually as a result of excessive air pollution. When analyzing the overall prevalence of diseases of the population, it was found that respiratory diseases increased by 29.5%, musculoskeletal diseases by 2.64%, congenital malformations by 9.7%, injuries by 50.1%, digestive system diseases by 23.2%, circulatory system diseases by 11.8%, and skin and subcutaneous tissue diseases decreased by 7.9%.
DOI: 10.5281/zenodo.17953079
title: Atmosphere mixed layer height measurements in Paris, France from 2023-08-20 to 2023-08-22 [L2]

description: Curated data files and derived quantities from Automatic Lidar Ceilometer (ALC) measurements.
description: TECHNICAL INFO

The mixed-layer height was derived from the attenuated backscatter profile with the STRATfinder algorithm, which uses vertical gradient- and variance-based layer detection with adaptive thresholds and temporal consistency constraints. Data quality control was carried out using the QC STRATfinder algorithm that applies empirically defined physical plausibility criteria to flag and reject unreliable mixed-layer height detections. No gap-filling has been applied.

ACKNOWLEDGEMENTS

Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding.

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project.

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). The curation of the data has received support from the UrbanAIR project funded by the European Union (grant agreement No 101188131).

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

LICENSE URI

Creative Commons Attribution 4.0 International License
DOI: 10.5281/zenodo.17953078
title: Atmosphere mixed layer height measurements in Paris, France from 2023-08-20 to 2023-08-22 [L2]

description: Curated data files and derived quantities from Automatic Lidar Ceilometer (ALC) measurements.
description: TECHNICAL INFO

The mixed-layer height was derived from the attenuated backscatter profile with the STRATfinder algorithm, which uses vertical gradient- and variance-based layer detection with adaptive thresholds and temporal consistency constraints. Data quality control was carried out using the QC STRATfinder algorithm that applies empirically defined physical plausibility criteria to flag and reject unreliable mixed-layer height detections. No gap-filling has been applied.

ACKNOWLEDGEMENTS

Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding.

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project.

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). The curation of the data has received support from the UrbanAIR project funded by the European Union (grant agreement No 101188131).

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

LICENSE URI

Creative Commons Attribution 4.0 International License
DOI: 10.25824/redu/h5tk0v
title: Quais são os limites éticos da utilização da inteligência artificial e machine learning nas áreas de marketing no contexto contemporâneo brasileiro?

description: Estas são as respostas de um questionário elaborado e executado em 2024 pela estudante Débora Dias Panicachi, aluna do curso de Mestrado em Administração da Faculdade de Ciências Aplicadas da Unicamp, orientada pelo Professor Doutor Eric David Cohen. Este levantamento de dados tem como objetivo analisar o comportamento dos consumidores diante das novas tecnologias e como se aplicam ao setor de marketing para dispositivos móveis os princípios éticos e a privacidade de informações. Esta pesquisa foi desenvolvida durante a participação dos autores na Meta 7 - IA para Marketing, no H.IAAC - Hub de Inteligência Artificial e Arquiteturas Cognitivas, com apoio do Ministério
DOI: 10.15468/dl.fhgwxz
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.50822 45.70103,-73.74419 45.68941,-73.89999 45.54234,-73.89092 45.51372,-73.76537 45.50418,-73.66402 45.55081,-73.60654 45.63655,-73.50822 45.70103))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ulmus thomasii"
 ]
}

 The dataset includes 35 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082206-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e242723
title: DQS NHANES Select Mean Dietary Intake Estimates

description: These data represent mean intake, on a given day, estimates of nutrients from foods and beverages from the National Health and Nutrition Examination Survey (NHANES).
Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.
Read less
description: (Downloaded 1/7/2026)
DOI: 10.3886/e242723v1
title: DQS NHANES Select Mean Dietary Intake Estimates

description: These data represent mean intake, on a given day, estimates of nutrients from foods and beverages from the National Health and Nutrition Examination Survey (NHANES).
Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.
Read less
description: (Downloaded 1/7/2026)
DOI: 10.15468/dl.38m4n4
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.884513 24.508298, 71.80734 24.502721, 71.732088 24.486129, 71.66062599999999 24.458934, 71.594729 24.421812, 71.536029 24.375686, 71.485972 24.321701, 71.44579 24.261194, 71.416461 24.195663, 71.39869299999999 24.126729, 71.39290699999999 24.056092, 71.399224 23.985492, 71.41747100000001 23.916668, 71.447181 23.851309, 71.48760799999999 23.791018, 71.53774799999999 23.737273, 71.59636500000001 23.691387, 71.66201700000001 23.654482, 71.733099 23.627458, 71.807872 23.610976, 71.884513 23.605437, 71.96115399999999 23.610976, 72.035927 23.627458, 72.10700900000001 23.654482, 72.17266100000001 23.691387, 72.231278 23.737273, 72.281418 23.791018, 72.321845 23.851309, 72.351555 23.916668, 72.36980200000001 23.985492, 72.376119 24.056092, 72.370333 24.126729, 72.352565 24.195663, 72.32323599999999 24.261194, 72.28305400000001 24.321701, 72.232997 24.375686, 72.174297 24.421812, 72.1084 24.458934, 72.03693800000001 24.486129, 71.961686 24.502721, 71.884513 24.508298))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082207-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13026/7kvm-qe34
title: PSG-IPA: A PolySomnoGraphic Inter-scorer Performance Assessment database

description: This dataset, named PolySomnoGraphic Inter-scorer Performance Assessment (PSG-
IPA) database, comprises 20 polysomnography (PSG) recordings and related
multi-expert scoring annotations. It was utilized in a study to evaluate
inter-scorer variability, specifically assessing reliability and scoring times
associated with various PSG review tasks. The study also aimed to compare the
impact of computer-assisted (semi-automatic) scoring versus unassisted visual
analysis on these factors.

The dataset includes recordings of common raw PSG signal activity, such as
electroencephalography (EEG), electrooculography (EOG), electromyography (EMG)
of both the chin and lower limbs, electrocardiography (ECG), and various
respiratory derivations. Each recording is accompanied by both manual and
computer-assisted scorings, performed by 12 independent expert sleep
technologists. This comprehensive resource offers significant value for
further research into PSG inter-scorer variability, as well as for the
development and performance assessment of automated sleep stage and event
detection algorithms.
DOI: 10.13026/esx0-nw71
title: PSG-IPA: A PolySomnoGraphic Inter-scorer Performance Assessment database

description: This dataset, named PolySomnoGraphic Inter-scorer Performance Assessment (PSG-
IPA) database, comprises 20 polysomnography (PSG) recordings and related
multi-expert scoring annotations. It was utilized in a study to evaluate
inter-scorer variability, specifically assessing reliability and scoring times
associated with various PSG review tasks. The study also aimed to compare the
impact of computer-assisted (semi-automatic) scoring versus unassisted visual
analysis on these factors.

The dataset includes recordings of common raw PSG signal activity, such as
electroencephalography (EEG), electrooculography (EOG), electromyography (EMG)
of both the chin and lower limbs, electrocardiography (ECG), and various
respiratory derivations. Each recording is accompanied by both manual and
computer-assisted scorings, performed by 12 independent expert sleep
technologists. This comprehensive resource offers significant value for
further research into PSG inter-scorer variability, as well as for the
development and performance assessment of automated sleep stage and event
detection algorithms.
DOI: 10.18434/mds2-3040
title: Per- and polyfluoroalkyl substances (PFAS) Interferents List
DOI: 10.5281/zenodo.18189521
title: LEKSEMALARNING SEMANTIK ASOSDAGI AYRIM MUNOSABATI XUSUSIDA

description: Ushbu maqolada leksik-semantik munosabatlar, xususan, giponimiya va partonimiya xususida fikr yuritiladi. Shuningdek, o‘zbek tilidagi leksik birliklarning giponimik, partonimik munosabatlari appelyativ birliklar misolida tahlil qilingan. Leksemalarning semantik strukturasidagi semema va semalarning leksik-semantik munosabat hosil qilishdagi o‘rni, vazifasi yuzasidan fikr-mulohazalar yoritilgan.
DOI: 10.5281/zenodo.18189522
title: LEKSEMALARNING SEMANTIK ASOSDAGI AYRIM MUNOSABATI XUSUSIDA

description: Ushbu maqolada leksik-semantik munosabatlar, xususan, giponimiya va partonimiya xususida fikr yuritiladi. Shuningdek, o‘zbek tilidagi leksik birliklarning giponimik, partonimik munosabatlari appelyativ birliklar misolida tahlil qilingan. Leksemalarning semantik strukturasidagi semema va semalarning leksik-semantik munosabat hosil qilishdagi o‘rni, vazifasi yuzasidan fikr-mulohazalar yoritilgan.
DOI: 10.5281/zenodo.18189463
title: The Standard model DeHolted with v5.0 with astonishing results preliminära

description: Comparison of DeHoLT v5.0 to Standard Model ParametersThe Standard Model (SM) is the most successful theory of particle physics, describing all known non-gravitational interactions with extraordinary precision. However, it is widely regarded as incomplete because it contains 19 free parameters (in its minimal form) that must be measured experimentally and entered by hand. There is no deeper explanation for their values within the SM itself.DeHoLT v5.0 takes a radically different approach: it claims to derive the equivalent of the SM parameters (and more) as emergent, frozen values from the single entropy evolution equation in a 3D time manifold, with zero free floating-point parameters. All constants arise naturally from topological integers, Planck-scale normalization, and saturation dynamics
DOI: 10.15468/dl.f5anc4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.79381600000001 24.437368, 71.71668699999999 24.431791, 71.641476 24.415199, 71.570054 24.388004, 71.504194 24.350883, 71.445526 24.304757, 71.39549700000001 24.250771, 71.35533599999999 24.190264, 71.32602199999999 24.124733, 71.30826399999999 24.055798, 71.30247900000001 23.98516, 71.30879299999999 23.91456, 71.327029 23.845735, 71.35672099999999 23.780375, 71.397126 23.720084, 71.447239 23.666337, 71.50582300000001 23.62045, 71.57144 23.583544, 71.642483 23.556521, 71.71721599999999 23.540038, 71.79381600000001 23.534499, 71.87041600000001 23.540038, 71.945149 23.556521, 72.016192 23.583544, 72.08180900000001 23.62045, 72.140393 23.666337, 72.190506 23.720084, 72.23091100000001 23.780375, 72.260603 23.845735, 72.278839 23.91456, 72.28515299999999 23.98516, 72.27936800000001 24.055798, 72.26161 24.124733, 72.23229600000001 24.190264, 72.19213499999999 24.250771, 72.142106 24.304757, 72.083438 24.350883, 72.017578 24.388004, 71.946156 24.415199, 71.87094500000001 24.431791, 71.79381600000001 24.437368))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082209-251120083545085/datasets/export for details.
DOI: 10.3886/e242724
title: DQS NHANES Select Infectious Diseases Prevalence Estimates

description: These data represent prevalence estimates of select infectious diseases from the National Health and Nutrition Examination Survey (NHANES). This version of the NHANES dataset is specific to visualization within the NCHS DQS.
Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.
Read less
description: (Downloaded 1/7/2026)