cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7053442
title: Aerial Multi-Vehicle Detection Dataset

description: <strong>Aerial Multi-Vehicle Detection Dataset</strong>: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments. The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1] The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training. <strong>Subset</strong> <strong>Images</strong> <strong>Car</strong> <strong>Bus</strong> <strong>Truck</strong> Training 7239 200301 1601 6247 Validation 904 23397 193 727 Testing 905 24715 208 770 It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing). [1] Makrigiorgis, R., 2021. <em>Harpy Data Dataset</em>. [online] Kios.ucy.ac.cy. Available at: &lt;https://www.kios.ucy.ac.cy/harpydata/&gt; [Accessed 22 September 2022]. <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong> Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442
DOI: 10.5281/zenodo.7053441
title: Aerial Multi-Vehicle Detection Dataset

description: <strong>Aerial Multi-Vehicle Detection Dataset</strong>: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments. The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1] The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training. <strong>Subset</strong> <strong>Images</strong> <strong>Car</strong> <strong>Bus</strong> <strong>Truck</strong> Training 7239 200301 1601 6247 Validation 904 23397 193 727 Testing 905 24715 208 770 It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing). [1] Makrigiorgis, R., 2021. <em>Harpy Data Dataset</em>. [online] Kios.ucy.ac.cy. Available at: &lt;https://www.kios.ucy.ac.cy/harpydata/&gt; [Accessed 22 September 2022]. <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong> Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442
DOI: 10.6084/m9.figshare.16912366.v9
title: antspymm helper data

description: multi-channel and time series example data for antsx processing
DOI: 10.25318/9810052001-eng
title: Languages used at work by industry and occupation: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on language used most often at work by other language(s) used regularly at work, industry - North American Industry Classification System (NAICS) 2017, occupation - National Occupational Classification (NOC) 2021, labour force status and age for the population aged 15 years and over who worked since 2020, in private households of Canada, provinces and territories, census metropolitan areas and census agglomerations with parts.
DOI: 10.25318/9810052001-fra
title: Langues utilisées au travail selon l'industrie et la profession : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties

description: Données sur la langue utilisée le plus souvent au travail selon les autres langues utilisées régulièrement au travail, l’industrie - Système de classification des industries de l’Amérique du Nord (SCIAN) 2017, la profession - Classification nationale des professions (CNP) 2021, la situation d'activité et l’âge de la population âgée de 15 ans et plus qui travaillait depuis 2020, dans des ménages privés du Canada, des provinces et des territoires, des régions métropolitaines de recensement et des agglomérations de recensement y compris les parties.
DOI: 10.25318/9810044701-eng
title: Occupation unit group by highest level of education, major field of study, age and gender: Canada, provinces and territories

description: Data on occupation unit group (5-digit code) from the National Occupational Classification (NOC) 2021 version 1.0 by highest level of education, major field of study, age and gender.
DOI: 10.25318/9810044701-fra
title: Groupe de base des professions selon le plus haut niveau de scolarité, le principal domaine d’études, l’âge et le genre : Canada, provinces et territoires

description: Données sur le groupe de base des professions (code à 5 chiffres) de la Classification nationale des professions (CNP) 2021 version 1.0 selon le plus haut niveau de scolarité, le principal domaine d’études, l’âge et le genre.
DOI: 10.5517/ccdc.csd.cc2781l5
title: CCDC 2063316: Experimental Crystal Structure Determination

description: Related Article: Damien Bissessar, Julien Egly, Thierry Achard, Pascal Steffanut, Matteo Mauro, Stéphane Bellemin-Laponnaz|2022|Eur.J.Inorg.Chem.|2022|e202200101|doi:10.1002/ejic.202200101
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: 10.25318/9810044801-eng
title: Industry groups by class of worker including job permanency, labour force status, age and gender: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on industry groups (4-digit code) from the North American Industry Classification System (NAICS) Canada 2017 version 3.0 by class of worker including job permanency, labour force status, age and gender.
DOI: 10.5517/ccdc.csd.cc28xh5w
title: CCDC 2112748: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xh6x
title: CCDC 2112749: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xh7y
title: CCDC 2112750: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xhb1
title: CCDC 2112753: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xh8z
title: CCDC 2112751: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xh90
title: CCDC 2112752: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xhc2
title: CCDC 2112754: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xhd3
title: CCDC 2112755: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28xhf4
title: CCDC 2112756: Experimental Crystal Structure Determination

description: Related Article: Elena A. Mikhalyova, Matthias Zeller, Evgeny A. Goreshnik, Jerry P. Jasinski, Raymond J. Butcher, Yulia V. Nelyubina, Jordan L. Hunter, Alexander E. Sedykh, Svitlana V. Shishkina, Klaus Müller-Buschbaum, Igor L. Eremenko, Anthony W. Addison, Vitaly V. Pavlishchuk|2022|Eur.J.Inorg.Chem.|2022|e202100941|doi:10.1002/ejic.202100941
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.cc28d8cb
title: CCDC 2097161: Experimental Crystal Structure Determination

description: Related Article: Ling-Li Li, Fei Ding, Hui Zheng, Bing-Ying Pan|2022|Eur.J.Inorg.Chem.|2022|e202100994|doi:10.1002/ejic.202100994
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.cc28tz7b
title: CCDC 2110332: Experimental Crystal Structure Determination

description: Related Article: Soumalya Roy, Pooja Shukla, Prem Prakash Sahu, Yu-Chen Sun, Naushad Ahmed, Subash Chandra Sahoo, Xin-Yi Wang, Saurabh Kumar Singh, Sourav Das|2022|Eur.J.Inorg.Chem.|2022|e202100983|doi:10.1002/ejic.202100983
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.cc28tz8c
title: CCDC 2110333: Experimental Crystal Structure Determination

description: Related Article: Soumalya Roy, Pooja Shukla, Prem Prakash Sahu, Yu-Chen Sun, Naushad Ahmed, Subash Chandra Sahoo, Xin-Yi Wang, Saurabh Kumar Singh, Sourav Das|2022|Eur.J.Inorg.Chem.|2022|e202100983|doi:10.1002/ejic.202100983
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.cc28tz9d
title: CCDC 2110334: Experimental Crystal Structure Determination

description: Related Article: Soumalya Roy, Pooja Shukla, Prem Prakash Sahu, Yu-Chen Sun, Naushad Ahmed, Subash Chandra Sahoo, Xin-Yi Wang, Saurabh Kumar Singh, Sourav Das|2022|Eur.J.Inorg.Chem.|2022|e202100983|doi:10.1002/ejic.202100983
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.cc28tzbf
title: CCDC 2110335: Experimental Crystal Structure Determination

description: Related Article: Soumalya Roy, Pooja Shukla, Prem Prakash Sahu, Yu-Chen Sun, Naushad Ahmed, Subash Chandra Sahoo, Xin-Yi Wang, Saurabh Kumar Singh, Sourav Das|2022|Eur.J.Inorg.Chem.|2022|e202100983|doi:10.1002/ejic.202100983
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.cc29842t
title: CCDC 2122975: Experimental Crystal Structure Determination

description: Related Article: Ling-Li Li, Fei Ding, Hui Zheng, Bing-Ying Pan|2022|Eur.J.Inorg.Chem.|2022|e202100994|doi:10.1002/ejic.202100994
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: 10.25318/9810044801-fra
title: Groupes d’industries selon la catégorie de travailleur incluant la permanence de l'emploi, la situation d’activité, l’âge et le genre : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties

description: Données sur les groupes d’industries (code à 4 chiffres) du Système de classification des industries de l’Amérique du Nord (SCIAN) Canada 2017 version 3.0 selon la catégorie de travailleur incluant la permanence de l’emploi, la situation d’activité, l’âge et le genre.
DOI: 10.25318/9810044901-eng
title: Occupation unit group by labour force status, highest level of education, age and gender: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on occupation unit group (5-digit code) from the National Occupational Classification (NOC) 2021 version 1.0 by labour force status, highest level of education, age and gender.
DOI: 10.25318/9810044901-fra
title: Groupe de base des professions selon la situation d’activité, le plus haut niveau de scolarité, l’âge et le genre : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties

description: Données sur le groupe de base des professions (code à 5 chiffres) de la Classification nationale des professions (CNP) 2021 version 1.0 selon la situation d’activité, le plus haut niveau de scolarité, l’âge et le genre.
DOI: 10.48366/r280270
title: Datasets for NLP tasks on scholarly articles

description: This comparison surveys existing datasets for NLP tasks (mainly IE, NER, and summarization) on scholarly articles. It includes information such as annotation level, generation method, data sources, and the concepts and relations that were annotated in each dataset, along with inter-annotator agreement scores. It also includes the baselines and models that were used, as well as their results.
DOI: 10.34740/kaggle/dsv/4762915
title: Mahanagar Yatayat Dataset
DOI: 10.25318/9810045001-eng
title: Occupation (training, education, experience and responsibility category - TEER) by mobility status 5 years ago, place of residence 5 years ago and labour force status: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on the TEER (Training, Education, Experience and Responsibility) variant of the National Occupational Classification (NOC) 2021 version 1.0 by mobility status 5 years ago, place of residence 5 years ago, labour force status, age and gender.