cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ew925j
title: Occurrence Download

description: A dataset containing 312 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leptodactylus vastus"
 ]
}

 The dataset includes 312 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009612-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/97vrvc5bf2
title: FreshWaterFishBD-28: A Dataset of 28 Freshwater Fish Species in Bangladesh

description: 1.Freshwater fishes are an essential part of Bangladesh’s aquaculture and play a crucial role in nutrition, commerce, and local livelihoods. However, accurate identification of species is often challenging due to the diversity of fishes, their similar appearances, and variations in environmental conditions. This Freshwater Fish Dataset is carefully curated to assist researchers, aquaculture specialists, and machine learning practitioners in identifying and classifying freshwater fish species. This dataset contains high-resolution images of 28 fish species, collected from four different marketplaces in Gazipur under diverse environmental conditions.
2.The dataset includes the following species with the corresponding number of images:Aair (1804), Black Roi (306), Boal (1710), Chapila (428), Common Carp (517), DeshiPuti (412), Foli (562), Grass Carp (410), Ilish (1031), KalBaush (917), Katla (2196), Koi (842), Magur (574), Mirror Carp (415), Mrigel (2213), Nile Telapiya (409), Pabda (1764), Pangas (1394), Puti (1560), Rui (3240), Shing (60), Shol (1424), Shorputi (44), Silver Carp (400), Taki (2223), Tarabaim (1262), Telapiya (2058), and Tengra (1431).
3.Modern computer vision and deep learning approaches offer tremendous potential for automated fish species recognition and monitoring, which can enhance fisheries management, conservation efforts, and aquaculture operations.
4.With a total of 31,606 images, captured under various lighting, angles, and market settings, the dataset provides real-world variability for robust AI model development. Images can be further augmented using techniques like flipping, rotation, zooming, and brightness adjustment to expand the dataset and improve model performance. Species annotations were verified with local fisheries experts to ensure high-quality and reliable labeling.
DOI: 10.17632/97vrvc5bf2.1
title: FreshWaterFishBD-28: A Dataset of 28 Freshwater Fish Species in Bangladesh

description: 1.Freshwater fishes are an essential part of Bangladesh’s aquaculture and play a crucial role in nutrition, commerce, and local livelihoods. However, accurate identification of species is often challenging due to the diversity of fishes, their similar appearances, and variations in environmental conditions. This Freshwater Fish Dataset is carefully curated to assist researchers, aquaculture specialists, and machine learning practitioners in identifying and classifying freshwater fish species. This dataset contains high-resolution images of 28 fish species, collected from four different marketplaces in Gazipur under diverse environmental conditions.
2.The dataset includes the following species with the corresponding number of images:Aair (1804), Black Roi (306), Boal (1710), Chapila (428), Common Carp (517), DeshiPuti (412), Foli (562), Grass Carp (410), Ilish (1031), KalBaush (917), Katla (2196), Koi (842), Magur (574), Mirror Carp (415), Mrigel (2213), Nile Telapiya (409), Pabda (1764), Pangas (1394), Puti (1560), Rui (3240), Shing (60), Shol (1424), Shorputi (44), Silver Carp (400), Taki (2223), Tarabaim (1262), Telapiya (2058), and Tengra (1431).
3.Modern computer vision and deep learning approaches offer tremendous potential for automated fish species recognition and monitoring, which can enhance fisheries management, conservation efforts, and aquaculture operations.
4.With a total of 31,606 images, captured under various lighting, angles, and market settings, the dataset provides real-world variability for robust AI model development. Images can be further augmented using techniques like flipping, rotation, zooming, and brightness adjustment to expand the dataset and improve model performance. Species annotations were verified with local fisheries experts to ensure high-quality and reliable labeling.
DOI: 10.5281/zenodo.18248090
title: Survey Data for Smart Manufacturing and Production Flexibility

description: This dataset contains anonymized questionnaire responses collected from industrial organizations to examine the role of smart manufacturing in supporting production flexibility in light of market and demand fluctuations. The dataset includes the SPSS data file used for statistical analysis and the original questionnaire instrument. No personal identifiers are included.
DOI: 10.17632/p6z9ryzgg7
title: PM10 Multiple Sclerosis

description: These data are part of the article "Exposure to particulate matter (PM10) and hospitalizations for multiple sclerosis". Some words are in the native language due to the nomenclature used in the script and data legends during the analysis.
DOI: 10.5281/zenodo.18248089
title: Survey Data for Smart Manufacturing and Production Flexibility

description: This dataset contains anonymized questionnaire responses collected from industrial organizations to examine the role of smart manufacturing in supporting production flexibility in light of market and demand fluctuations. The dataset includes the SPSS data file used for statistical analysis and the original questionnaire instrument. No personal identifiers are included.
DOI: 10.17632/p6z9ryzgg7.1
title: PM10 Multiple Sclerosis

description: These data are part of the article "Exposure to particulate matter (PM10) and hospitalizations for multiple sclerosis". Some words are in the native language due to the nomenclature used in the script and data legends during the analysis.
DOI: 10.15468/dl.5aps5r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.37.28_1","matchCase":false},{"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/0009613-260108223611665/datasets/export for details.
DOI: 10.17632/5hty8mcyrj
title: Mix design effect

description: This database focuses on the effects of mix design parameters on the mechanical properties and damage evolution of recycled aggregate concrete. It contains systematically generated data from mesoscale numerical modelling, including variations in key mix design variables such as recycled aggregate replacement ratio, aggregate grading, mortar content, and interfacial transition zone properties. The database provides corresponding outputs in terms of stress–strain behaviour, elastic modulus, peak strength, damage evolution, and crack development patterns.

The dataset is intended to support parametric analysis, comparative studies, and model validation related to the mesoscale mechanical response of recycled aggregate concrete under different mix design scenarios.
DOI: 10.17632/5hty8mcyrj.1
title: Mix design effect

description: This database focuses on the effects of mix design parameters on the mechanical properties and damage evolution of recycled aggregate concrete. It contains systematically generated data from mesoscale numerical modelling, including variations in key mix design variables such as recycled aggregate replacement ratio, aggregate grading, mortar content, and interfacial transition zone properties. The database provides corresponding outputs in terms of stress–strain behaviour, elastic modulus, peak strength, damage evolution, and crack development patterns.

The dataset is intended to support parametric analysis, comparative studies, and model validation related to the mesoscale mechanical response of recycled aggregate concrete under different mix design scenarios.
DOI: 10.17632/6n67ymrwy8
title: Multi Class SQLI

description: This dataset comprises over 38,000 records representing multiple classes of SQL Injection (SQLi) attacks, specifically designed for research and development of Intrusion Detection Systems (IDS). The dataset includes more than five distinct attack types, with each record capturing features that describe the behavior of malicious web requests.
DOI: 10.17632/6n67ymrwy8.1
title: Multi Class SQLI

description: This dataset comprises over 38,000 records representing multiple classes of SQL Injection (SQLi) attacks, specifically designed for research and development of Intrusion Detection Systems (IDS). The dataset includes more than five distinct attack types, with each record capturing features that describe the behavior of malicious web requests.
DOI: 10.17632/dzrpgj48k3
title: AI-enabled prediction and transformation analysis of phases in multi-component nitride thin films and coatings

description: A dataset of phase structures of multi-component nitride thin film coatings from multiple systems derived from over 100 different literature sources.
DOI: 10.17632/dzrpgj48k3.1
title: AI-enabled prediction and transformation analysis of phases in multi-component nitride thin films and coatings

description: A dataset of phase structures of multi-component nitride thin film coatings from multiple systems derived from over 100 different literature sources.
DOI: 10.17632/jx3mc68mc5
title: From Ease-of-Use to Emotional Bonding: A Three-Wave Longitudinal Model of How AI Coach Anthropomorphism Drives System Use

description: The dataset for the paper “From Ease-of-Use to Emotional Bonding: A Three-Wave Longitudinal Model of How AI Coach Anthropomorphism Drives System Use.”
DOI: 10.17632/jx3mc68mc5.1
title: From Ease-of-Use to Emotional Bonding: A Three-Wave Longitudinal Model of How AI Coach Anthropomorphism Drives System Use

description: The dataset for the paper “From Ease-of-Use to Emotional Bonding: A Three-Wave Longitudinal Model of How AI Coach Anthropomorphism Drives System Use.”
DOI: 10.17632/y5sg54fspk
title: Experimental data supporting the radiolabelling of polystyrene microplastics with technetium-99m

description: This workbook is intended to provide transparent data associated with yield and in-vitro stability work with the Ps-NH2-Tc99m microplastic-radiotracer compound. The data included quality control yield measurements, DLS hydrodynamic size and zeta potential measurements, and time-dependent stability in various environmentally and biologically relevant media. This work was conducted at Ege University, Institute of Nuclear Sciences.
DOI: 10.17632/y5sg54fspk.1
title: Experimental data supporting the radiolabelling of polystyrene microplastics with technetium-99m

description: This workbook is intended to provide transparent data associated with yield and in-vitro stability work with the Ps-NH2-Tc99m microplastic-radiotracer compound. The data included quality control yield measurements, DLS hydrodynamic size and zeta potential measurements, and time-dependent stability in various environmentally and biologically relevant media. This work was conducted at Ege University, Institute of Nuclear Sciences.
DOI: 10.5281/zenodo.18248343
title: FIShBOT Archive 2026-01-14

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.18248342
title: FIShBOT Archive 2026-01-14

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: 10.15468/dl.b5eczh
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.29.12_1","matchCase":false},{"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 36 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009614-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/txhmtp
title: Ventes de montres - archives historiques

description: Ce corpus comprend 186 observations correspondant à des ventes de montres observées sur le marché des enchères. Chaque observation est décrite par un ensemble de variables permettant de caractériser à la fois le contexte de la vente, les caractéristiques matérielles et esthétiques de la montre, ainsi que certaines informations relatives aux acteurs impliqués dans la transaction.

L’année correspond à l’année civile au cours de laquelle la vente a eu lieu, tandis que le lieu indique l’emplacement géographique de la vente. La variable vente renseigne le nom de la vente ou de la vacation dans laquelle le lot a été proposé, et le numéro de lot identifie la position du lot au sein de cette vente. La description fournit un résumé textuel des caractéristiques de la montre tel qu’il apparaît dans le catalogue de vente.

Le prix correspond au prix d’adjudication exprimé en francs courants de l’année de la vente. Le nom du vendeur est indiqué lorsque l’information est disponible, de même que celui de l’adjudicataire, auquel peuvent être associées des informations d’adresse. Deux variables binaires permettent ensuite de qualifier le profil de l’acheteur : la variable marchand prend la valeur 1 lorsque l’acheteur est identifié comme marchand professionnel et 0 sinon, tandis que la variable collectionneur prend la valeur 1 lorsque l’acheteur est identifié comme collectionneur privé.

Le corpus intègre également des variables décrivant la qualité éditoriale du catalogue de vente. La variable image_catalogue indique si le catalogue contient des illustrations, et la variable catalogue_decoré précise si le catalogue est orné ou décoré. La variable vente_uniquement_montres permet d’identifier les ventes exclusivement consacrées aux montres, par opposition aux ventes généralistes.

Afin de permettre des comparaisons intertemporelles, le prix d’adjudication a été ajusté et exprimé en francs constants de 1923 à l’aide de la variable prix_francs_1923. Les caractéristiques matérielles de la montre sont décrites à l’aide de plusieurs variables binaires indiquant la présence d’or, d’argent ou de cuivre. La variable matériaux renseigne quant à elle le matériau principal de la montre.

Les dimensions décoratives et esthétiques sont également prises en compte. La variable émail indique la présence d’émail, tandis que les variables sujet et décor permettent d’identifier l’existence d’ornementations figuratives ou décoratives. Enfin, le siècle de fabrication de la montre est renseigné par une variable catégorielle, complétée par des indicateurs binaires précisant si la montre a été fabriquée au XVIe, XVIIe, XVIIIe ou XIXe siècle.

L’ensemble de ces variables permet une analyse à la fois quantitative et qualitative des mécanismes de formation des prix sur le marché des montres de collection, en croisant informations historiques, matérielles et institutionnelles.
DOI: 10.15468/dl.ucc4b6
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.37.29_1","matchCase":false},{"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 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009615-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kqdpvc
title: Occurrence Download

description: A dataset listing the 11 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is COL.15_2",
 "TaxonKey is Isopoda"
 ]
}

 The dataset's 11 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009600-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/23804
title: Chandra X-ray Observatory ObsId 23804

description: The Unique Power of Chandra Observations of Gravitationally Lensed Quasars
DOI: 10.25574/23878
title: Chandra X-ray Observatory ObsId 23878

description: Ultra-Deep Chandra Monitoring of the JWST-NEP Time Domain Field
DOI: 10.25574/23921
title: Chandra X-ray Observatory ObsId 23921

description: Flows of hot Baryons in the Milky Way center: How are fountains & chimneys linked to the Galactic-scale outflow?
DOI: 10.25574/23923
title: Chandra X-ray Observatory ObsId 23923

description: Flows of hot Baryons in the Milky Way center: How are fountains & chimneys linked to the Galactic-scale outflow?
DOI: 10.15468/dl.rtznd9
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cajanus volubilis"
 ]
}

 The dataset includes 22 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009616-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ms9egk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.29.13_1","matchCase":false},{"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/0009617-260108223611665/datasets/export for details.
DOI: 10.5517/ccdc.csd.cc219z3g
title: CCDC 1886415: Experimental Crystal Structure Determination

description: Related Article: Qizhao Li, Chengjie Li, Glib Baryshnikov, Yubin Ding, Chengxi Zhao, Tingting Gu, Feng Sha, Xu Liang, Weihua Zhu, Xinyan Wu, Hans Ågren, Jonathan L. Sessler, Yongshu Xie|2020|Nat.Commun.|11|5289|doi:10.1038/s41467-020-19118-9
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.cc219z4h
title: CCDC 1886416: Experimental Crystal Structure Determination

description: Related Article: Qizhao Li, Chengjie Li, Glib Baryshnikov, Yubin Ding, Chengxi Zhao, Tingting Gu, Feng Sha, Xu Liang, Weihua Zhu, Xinyan Wu, Hans Ågren, Jonathan L. Sessler, Yongshu Xie|2020|Nat.Commun.|11|5289|doi:10.1038/s41467-020-19118-9
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.cc219z5j
title: CCDC 1886417: Experimental Crystal Structure Determination

description: Related Article: Qizhao Li, Chengjie Li, Glib Baryshnikov, Yubin Ding, Chengxi Zhao, Tingting Gu, Feng Sha, Xu Liang, Weihua Zhu, Xinyan Wu, Hans Ågren, Jonathan L. Sessler, Yongshu Xie|2020|Nat.Commun.|11|5289|doi:10.1038/s41467-020-19118-9
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.cc22b09r
title: CCDC 1916243: Experimental Crystal Structure Determination

description: Related Article: D. S. Milokhov, O. V. Khilya, I. S. Konovalova, V. V. Dyakonenko, S. V. Shishkina, Y. M. Volovenko|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.cc23m7cc
title: CCDC 1954902: Experimental Crystal Structure Determination

description: Related Article: Irina Konovalova|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.cc24byq5
title: CCDC 1976737: Experimental Crystal Structure Determination

description: Related Article: Sudeshna Chatterjee, Pallabi Mukherjee, Sukriti Mukherjee, Debasis Das|2025|Fenxi Huaxue||100688|doi:10.1016/j.cjac.2025.100688
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.