cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7231855
title: Aerial Water Buoys Dataset

description: <strong>Aerial Water Buoys Dataset:</strong> Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technologies have made possible advanced UAV-based search and rescue operations with transformative impact on the outcome of critical life-saving missions. This dataset aims into helping the challenging task of multi-castaway tracking and following using a single UAV. Due to the difficulty and data protection of capturing footage of people in the sea, we have captured a dataset of buoys in order to conduct experiments on multi-castaway tracking and following. A paper on multi-castaway tracking and following technical details and experiments will be published soon using this dataset. The dataset consists of top-view images of buoys from various altitudes on the coasts of Larnaca and Protaras in Cyprus. Images were captured at different altitudes in order to challenge object detectors to be able to detect smaller objects in case a UAV needs to track multiple targets, which leads to flying at a higher altitude. There is only one class annotated on all images which is labeled as 'buoy'. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (buoys): Subset Images Buoys Training 10814 14811 Validation 1350 1865 Testing 1352 1827 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). <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong> Antreas Anastasiou, Rafael Makrigiorgis, & Panayiotis Kolios. (2022). Aerial Water Buoys Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7231855
DOI: 10.5281/zenodo.7288444
title: Aerial Water Buoys Dataset

description: <strong>Aerial Water Buoys Dataset:</strong> Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technologies have made possible advanced UAV-based search and rescue operations with transformative impact on the outcome of critical life-saving missions. This dataset aims into helping the challenging task of multi-castaway tracking and following using a single UAV. Due to the difficulty and data protection of capturing footage of people in the sea, we have captured a dataset of buoys in order to conduct experiments on multi-castaway tracking and following. A paper on multi-castaway tracking and following technical details and experiments will be published soon using this dataset. The dataset consists of top-view images of buoys from various altitudes on the coasts of Larnaca and Protaras in Cyprus. Images were captured at different altitudes in order to challenge object detectors to be able to detect smaller objects in case a UAV needs to track multiple targets, which leads to flying at a higher altitude. There is only one class annotated on all images which is labeled as 'buoy'. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (buoys): Subset Images Buoys Training 10814 14811 Validation 1350 1865 Testing 1352 1827 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). <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong> Antreas Anastasiou, Rafael Makrigiorgis, & Panayiotis Kolios. (2022). Aerial Water Buoys Dataset (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7288444
DOI: 10.5281/zenodo.7755621
title: Aerial Water Buoys Dataset

description: <strong>Aerial Water Buoys Dataset:</strong> Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technologies have made possible advanced UAV-based search and rescue operations with transformative impact on the outcome of critical life-saving missions. This dataset aims into helping the challenging task of multi-castaway tracking and following using a single UAV. Due to the difficulty and data protection of capturing footage of people in the sea, we have captured a dataset of buoys in order to conduct experiments on multi-castaway tracking and following. A paper on multi-castaway tracking and following technical details and experiments will be published soon using this dataset. The dataset consists of top-view images of buoys from various altitudes on the coasts of Larnaca and Protaras in Cyprus. Images were captured at different altitudes in order to challenge object detectors to be able to detect smaller objects in case a UAV needs to track multiple targets, which leads to flying at a higher altitude. There is only one class annotated on all images which is labeled as 'buoy'. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (buoys): Subset Images Buoys Training 10814 14811 Validation 1350 1865 Testing 1352 1827 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). <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong> Antreas Anastasiou, Rafael Makrigiorgis, & Panayiotis Kolios. (2022). Aerial Water Buoys Dataset (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7288444
DOI: 10.5281/zenodo.7231854
title: Aerial Water Buoys Dataset

description: <strong>Aerial Water Buoys Dataset:</strong> Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technologies have made possible advanced UAV-based search and rescue operations with transformative impact on the outcome of critical life-saving missions. This dataset aims into helping the challenging task of multi-castaway tracking and following using a single UAV. Due to the difficulty and data protection of capturing footage of people in the sea, we have captured a dataset of buoys in order to conduct experiments on multi-castaway tracking and following. A paper on multi-castaway tracking and following technical details and experiments will be published soon using this dataset. The dataset consists of top-view images of buoys from various altitudes on the coasts of Larnaca and Protaras in Cyprus. Images were captured at different altitudes in order to challenge object detectors to be able to detect smaller objects in case a UAV needs to track multiple targets, which leads to flying at a higher altitude. There is only one class annotated on all images which is labeled as 'buoy'. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (buoys): Subset Images Buoys Training 10814 14811 Validation 1350 1865 Testing 1352 1827 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). <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong> Antreas Anastasiou, Rafael Makrigiorgis, & Panayiotis Kolios. (2022). Aerial Water Buoys Dataset (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7288444
DOI: 10.23729/507b9134-bde5-4212-8bf1-8759e44920b0
title: Seasonal (summer, winter and migration seasons separately) GPS-remote sensing data for collared Wild Forest Reindeer in Suomenselkä population
title: GPS-pannoilla merkittyjen metsäpeurojen paikkatietoaineistot kesällä, keskitalvella ja vaellusten (syksy-kevät) aikaan Suomenselän populaatiossa

description: Seasonal (summer, winter and migration seasons separately) GPS-remote sensing data for collared Wild Forest Reindeer in Suomenselkä population Seasonal (winter, summer, migration) fix density data deduced from the large remote sensing data of GPS-collared Wild Forest Reindeer females for Suomenselkä population. Contact for data requests: kirjaamo@luke.fi Taxonomic coverage: Wild Forest Reindeer (Rangifer tarandus fennicus) Citation recommendation: Luonnonvarakeskus (Luke) ## Methods Wild forest reindeer females were marked using helicopter. Animals were anaesthetized with CO2 dart tranquilizer instrument and Vectronic-type GPS GSM- or IRIDIUM collars were fitted. GPS collars updated their location every fourth hour. Also 1 or 2- hour fix rate was used during summer season. Data includes about 600 000 fixes from 123 collared females. Data from collars is stored to WRAM data base (Umeå Center for Wireless Remote Animal Monitoring Dept. for Wildlife, Fish and Environmental Studies SLU). Raw data was modified to density raster separately to seasons (summer, winter and migration seasons). For density raster data, all marked individuals and fixes were pooled within season and population. Density raster (size 5x5 km) for winter and summer was created using ArgGis Desktop 10.6.1 Spatial Analyst tools and point density function with options: circle radius = 2000 meters. Density raster (size 5x5 km) for migration corridors was created using ArgGis Desktop 10.6.1 Data management tools; from point to lines-tool, Spatial Analyst tools; line density function with options: circle radius = 2000 meters. Data was stored to georeferred tif – format (FGDBR) with projection EUREF_FIN_TM35FIN. Data is highly skewed. Therefore, for visual inspection, we recommend that user should categorize data to at least four classes and use geometric class interval where smallest class was left open.
description: GPS-pannoilla merkittyjen metsäpeurojen paikkatietoaineistot kesällä, keskitalvella ja vaellusten (syksy-kevät) aikaan Suomenselän populaatiossa. Esitysmuoto 5x5 kilometrin ruudukkona. Suomenselän GPS-pannoilla merkittyjen metsäpeuravaadinten paikannustiheysaineisto eriteltynä kesälle, talvelle ja vaelluksille. Esitysmuoto 5x5 kilometrin ruudukkona. Tietopyynnöt: kirjaamo@luke.fi
DOI: 10.23729/eeb135cf-7604-424d-864d-1ea2177c458f
title: Kysely paliskunnille talven 2019-2020 lumioloista

description: Luonnonvarakeskuksen webropol-kysely paliskunnille talven 2019-2020 lumi- ja muista luonnonoloista. Aineiston keruu huhti-kesäkuussa 2020. Poronhoitoa kohdanneesta poikkeuksellisesta talvesta johtuen Paliskuntain yhdistys toimitti 21.2.2020 Maa- ja metsätalousministeriölle tiedoksiannon talven 2019–2020 vaikeista lumi- ja luonnonolosuhteista, joiden voidaan perustellusta syystä epäillä aiheuttavan porotaloutta kohdanneiden vahinkojen koskemista annetussa laissa (26.8.2011/987) ja asetuksessa (656/2016) tarkoitettuja vahinkoja. Maa- ja metsätalousministeriö katsoi Paliskuntain yhdistyksen selvityspyynnön sekä saamiensa tilanneraporttien ja kuulemiensa asiantuntija-arvioiden perusteella, että on olemassa riittävät syyt ryhtyä mahdollisen poronhoitoon kohdistuneen tuhon tai sen uhkan selvittämiseen nykyisen Porovahinkolain (987/2011) mukaisesti. Tämä selvitystyö on ohjattu lain mukaan Luonnonvarakeskukselle. Luonnonvarakeskus keräsi huhti-kesäkuussa webropol-kyselytutkimusaineiston paliskunnista. Kyselyaineisto on osin maa- ja metsätalousministeriölle tehtävän selvityksen pohjana. Kyseessä on viranomaisselvitys ja viranomaisaineisto. Kaikille poronhoitoalueen paliskunnille laadittiin ja lähetettiin Webropol-internetkysely, jossa pyydettiin paliskuntia arvioimaan sekä syksyn, talven ja kevään 2019–2020 ajalta että paliskunnan alueen tavanomaisen vuoden osalta sää- lumi- ja kaivuolosuhteita sekä porojen ravinnonsaantia ja kuntoa. Samalla paliskunnilta kysyttiin ruokinnan kestoon ja kustannuksiin sekä erilaisiin poronhoitotöiden määriin liittyviä arvioita talvella 2019–2020 verrattuna edellisen kolmen talven vastaaviin. Paliskunnilta tiedusteltiin myös talven 2019–2020 aikana havaittuja ja arvioituja kuolleiden porojen määriä sekä mahdollisten sairauksien esiintymistä poroissa. Tavoite oli nimenomaan kartoittaa poikkeuksellisten lumi-, kaivu- ja ravinnonsaantiolosuhteiden vaikutuksia porojen kuntoon ja terveyteen sekä havaittujen ja todennäköisten porokuolemien määriin. Samalla pyydettiin paliskunnan arviota tulevan teurastuskauden vasaprosentista ja teurasmääristä verrattuna aikaisemman kolmen vuoden keskimääräisiin lukuihin. Lopuksi paliskuntia pyydettiin arvioimaan syitä vaikeaan laiduntilanteeseen sekä kartoitettiin näkemyksiä paliskunnan valmiudesta reagoida poikkeuksellisiin laidunolosuhteisiin ja käynnistää omia toimenpiteitä niihin. Edelleen kartoitettiin paliskuntien näkemyksiä nykyiseen lakiin ja järjestelmään porotaloutta kohdanneiden poikkeuksellisten luonnonolojen aiheuttamien menetysten ja tuhojen ehkäisemiseksi ja korvaamiseksi valtiovallan taholta, sekä poikkeusolosuhteiden kuormittavuutta paliskunnissa. Kyselyn tulokset vedettiin yhteen ja esitettiin kysymyksittäin taulukoina, graafeina ja karttoina. Kyselyyn vastasi määräajassa 47 paliskuntaa 54 paliskunnasta.
DOI: 10.25345/c5dz03b6h
title: MassIVE MSV000091540 - different ipomea species nector metabolomics
title: different ipomea species nector metabolomics southern india, tamilnadu
DOI: 10.21227/ngxk-6952
title: Whole slide images

description: 69 whole slide images for early gastric cancer diagnosis, evaluating the proposed variational energy network (VENet).
DOI: 10.5517/ccdc.csd.cc2f2c1y
title: CCDC 2236589: Experimental Crystal Structure Determination

description: Related Article: Shenghai Guo, Ziyi Zhang, Zhaotong Wei, Yuanqing Zhu, Xuesen Fan|2023|J.Org.Chem.|88|3845|doi:10.1021/acs.joc.3c00117
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.cc2f2c2z
title: CCDC 2236590: Experimental Crystal Structure Determination

description: Related Article: Shenghai Guo, Ziyi Zhang, Zhaotong Wei, Yuanqing Zhu, Xuesen Fan|2023|J.Org.Chem.|88|3845|doi:10.1021/acs.joc.3c00117
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.cc2f2c30
title: CCDC 2236591: Experimental Crystal Structure Determination

description: Related Article: Shenghai Guo, Ziyi Zhang, Zhaotong Wei, Yuanqing Zhu, Xuesen Fan|2023|J.Org.Chem.|88|3845|doi:10.1021/acs.joc.3c00117
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.cc2f2c41
title: CCDC 2236592: Experimental Crystal Structure Determination

description: Related Article: Shenghai Guo, Ziyi Zhang, Zhaotong Wei, Yuanqing Zhu, Xuesen Fan|2023|J.Org.Chem.|88|3845|doi:10.1021/acs.joc.3c00117
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.cc2dpnkb
title: CCDC 2225352: Experimental Crystal Structure Determination

description: Related Article: Xueli Cui, Jing Qu, Jianfeng Yi, Weiqiang Sun, Jinhui Hu, Suqin Guo, Jing-Wei Jin, Wen-Hua Chen, Wing-Leung Wong, Jia-Qiang Wu|2023|Chem.Commun.|59|3747|doi:10.1039/D3CC00529A
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.cc2cnbhw
title: CCDC 2194288: Experimental Crystal Structure Determination

description: Related Article: Magdalena Siedzielnik, Monika Pawłowska, Mateusz Daśko, Hubert Kleinschmidt, Anna Dołęga|2023|RSC Advances|13|8830|doi:10.1039/D2RA07089H
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.cc2cnblz
title: CCDC 2194291: Experimental Crystal Structure Determination

description: Related Article: Magdalena Siedzielnik, Monika Pawłowska, Mateusz Daśko, Hubert Kleinschmidt, Anna Dołęga|2023|RSC Advances|13|8830|doi:10.1039/D2RA07089H
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.cc2d7yn8
title: CCDC 2212180: Experimental Crystal Structure Determination

description: Related Article: Magdalena Siedzielnik, Monika Pawłowska, Mateusz Daśko, Hubert Kleinschmidt, Anna Dołęga|2023|RSC Advances|13|8830|doi:10.1039/D2RA07089H
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.cc2f3dyx
title: CCDC 2237609: Experimental Crystal Structure Determination

description: Related Article: Magdalena Siedzielnik, Monika Pawłowska, Mateusz Daśko, Hubert Kleinschmidt, Anna Dołęga|2023|RSC Advances|13|8830|doi:10.1039/D2RA07089H
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.cc2df1yv
title: CCDC 2217087: Experimental Crystal Structure Determination

description: Related Article: Sha Li, Yahan Sun, Xiaofang Li, Oskar Smaga, Sebastian Koniarz, Miłosz Pawlicki, Piotr J. Chmielewski|2023|Chem.Commun.|59|3739|doi:10.1039/D2CC06648C
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.cc2df297
title: CCDC 2217098: Experimental Crystal Structure Determination

description: Related Article: Sha Li, Yahan Sun, Xiaofang Li, Oskar Smaga, Sebastian Koniarz, Miłosz Pawlicki, Piotr J. Chmielewski|2023|Chem.Commun.|59|3739|doi:10.1039/D2CC06648C
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.cc2df1zw
title: CCDC 2217088: Experimental Crystal Structure Determination

description: Related Article: Sha Li, Yahan Sun, Xiaofang Li, Oskar Smaga, Sebastian Koniarz, Miłosz Pawlicki, Piotr J. Chmielewski|2023|Chem.Commun.|59|3739|doi:10.1039/D2CC06648C
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.cc2df253
title: CCDC 2217094: Experimental Crystal Structure Determination

description: Related Article: Sha Li, Yahan Sun, Xiaofang Li, Oskar Smaga, Sebastian Koniarz, Miłosz Pawlicki, Piotr J. Chmielewski|2023|Chem.Commun.|59|3739|doi:10.1039/D2CC06648C
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.cc2dnrqk
title: CCDC 2224489: Experimental Crystal Structure Determination

description: Related Article: Dongyan Jiang, Chunya Du, Zhenyu Yan, Shuyuan Ge, Zijun Feng, Liang Wan, Ping Lu|2023|J.Mater.Chem.C|11|4203|doi:10.1039/D2TC05237G
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.cc2dns51
title: CCDC 2224503: Experimental Crystal Structure Determination

description: Related Article: Dongyan Jiang, Chunya Du, Zhenyu Yan, Shuyuan Ge, Zijun Feng, Liang Wan, Ping Lu|2023|J.Mater.Chem.C|11|4203|doi:10.1039/D2TC05237G
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.