cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc13mbsv
title: CCDC 1031487: Experimental Crystal Structure Determination

description: Related Article: Mali H.Rosnes, Martin Opitz, Matthias Frontzek, Wiebke Lohstroh, Jan Peter Embs, Peter A.Georgiev, Pascal D.C.Dietzel|2015|J.Mater.Chem.A|3|4827|doi:10.1039/C4TA05794E
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.cc13mbtw
title: CCDC 1031488: Experimental Crystal Structure Determination

description: Related Article: Mali H.Rosnes, Martin Opitz, Matthias Frontzek, Wiebke Lohstroh, Jan Peter Embs, Peter A.Georgiev, Pascal D.C.Dietzel|2015|J.Mater.Chem.A|3|4827|doi:10.1039/C4TA05794E
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.cc13mbvx
title: CCDC 1031489: Experimental Crystal Structure Determination

description: Related Article: Mali H.Rosnes, Martin Opitz, Matthias Frontzek, Wiebke Lohstroh, Jan Peter Embs, Peter A.Georgiev, Pascal D.C.Dietzel|2015|J.Mater.Chem.A|3|4827|doi:10.1039/C4TA05794E
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.cc13mbwy
title: CCDC 1031490: Experimental Crystal Structure Determination

description: Related Article: Mali H.Rosnes, Martin Opitz, Matthias Frontzek, Wiebke Lohstroh, Jan Peter Embs, Peter A.Georgiev, Pascal D.C.Dietzel|2015|J.Mater.Chem.A|3|4827|doi:10.1039/C4TA05794E
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/cc13m8ff
title: CCDC 1031414: Experimental Crystal Structure Determination

description: Related Article: Yi Zheng, Shuai-Hua Wang, Shao-Fan Wu, Fa-Kun Zheng, A-Qing Wu|2015|Inorg.Chem.Commun.|53|20|doi:10.1016/j.inoche.2015.01.006
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.cc13mbxz
title: CCDC 1031491: Experimental Crystal Structure Determination

description: Related Article: Mali H.Rosnes, Martin Opitz, Matthias Frontzek, Wiebke Lohstroh, Jan Peter Embs, Peter A.Georgiev, Pascal D.C.Dietzel|2015|J.Mater.Chem.A|3|4827|doi:10.1039/C4TA05794E
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/cc13m8gg
title: CCDC 1031415: Experimental Crystal Structure Determination

description: Related Article: E. Mallah, K. Sweidan, W. Abu Dayyih, M. Steimann, M. Sunjuk|2014|Acta Crystallogr.,Sect.E:Struct.Rep.Online|70|o1227|doi:10.1107/S1600536814023800
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/cc13mby0
title: CCDC 1031492: Experimental Crystal Structure Determination

description: Related Article: Javad Farzanfar, Khaled Ghasemi, Ali Reza Rezvani, Hojat Samareh Delarami, Ali Ebrahimi, Hona Hosseinpoor, Amir Eskandari, Hadi Amiri Rudbari, Giuseppe Bruno|2015|J.Inorg.Biochem.|147|54|doi:10.1016/j.jinorgbio.2015.02.007
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.cc13mc14
title: CCDC 1031495: Experimental Crystal Structure Determination

description: Related Article: Toru Okawara, Masaaki Abe, Shiho Ashigara, Yoshio Hisaeda|2015|J.Porphyrins Phthalocyanines|19|233|doi:10.1142/S1088424614501120
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/cc13m8hh
title: CCDC 1031416: Experimental Crystal Structure Determination

description: Related Article: Miłosz Pawlicki, Mateusz Garbicz, Ludmiła Szterenberg, Lechosław Latos-Grażyński|2015|Angew.Chem.,Int.Ed.|54|1906|doi:10.1002/anie.201410595
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.cc13mc25
title: CCDC 1031496: Experimental Crystal Structure Determination

description: Related Article: Toru Okawara, Masaaki Abe, Shiho Ashigara, Yoshio Hisaeda|2015|J.Porphyrins Phthalocyanines|19|233|doi:10.1142/S1088424614501120
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.rgdf5j
title: Occurrence Download

description: A dataset containing 61005 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Tristerix verticillatus, Searsia dentata, Tephrosia grandiflora, Taraxacum praegracilens, Laurus novocanariensis, Thelymitra apiculata, Euchiton traversii, Asanthus solidaginifolius, Rhabdosciadium oligocarpum, Ventenata dubia, Prostanthera chlorantha, Aspalathus fasciculata, Senecio cymbalariifolius, Dactylorhiza dubreuilhii, Justicia conferta, Landerolaria stuartii, Baldellia ranunculoides, Weberbauera orophila, Lachnagrostis rudis, Lysiphyllum carronii, Pimelea ciliolaris, Senecio humidanus, Landerolaria newbeyi, Leptosiphon filipes, Millotia major, Lomatium caruifolium, Roepera pygmaea, Eragrostis turgida, Melilotus italicus, Jurinea stoechadifolia, Schoenus lucidus, Achnatherum virescens, Chordifex dimorphus, Chordifex isomorphus, Roepera hybrida, Xylanthemum gilletii, Polygala carueliana, Anarthrophyllum cumingii, Schoenus schonlandii, Jurinea carduiformis, Thryptomene cuspidata, Roemeria nigrotincta, Cytisopsis ahmedii, Asphodelus lusitanicus, Lapeirousia tenuis, Otatea acuminata, Achyranthemum mucronatum, Striga curviflora, Alstroemeria hookeri, Tamarix macrocarpa, Carpacoce scabra, Jacobaea calvescens, Carex negeri, Jacobaea persoonii, Coronilla atlantica, Nahuatlea hypoleuca, Penaea mundii, Elyhordeum macounii, Pentapogon, Penaea lanceolata, Eriastrum hooveri, Carex austrotenella, Dodonaea heteromorpha, Adenochloa ecklonii, Ammannia fitzgeraldii, Pentapogon, Dimorphotheca walliana, Carex brownii, Empodium flexile, Parakeelya stagnensis, Hemionitis yavapensis, Acacia dodonaeifolia, Senna planitiicola, Spatalla confusa, Acacia cochlearis, Hemionitis hispanica, Hemionitis robusta, Hemionitis brownii, Senna acclinis, Dovyalis caffra, Senna manicula, Acacia multisiliqua, Hibbertia vaginata, Draba minima, Hibbertia huegelii, Sairocarpus watsonii, Hemionitis lozanoi, Olearia paucidentata, Senna glaucifolia, Machaerina laxa, Draba kuemmerlei, Adeia discoidea, Limonium virgatum, Psoralea prodiens, Limonium lilacinum, Limonium album, Limonium insulare, Limonium albidum, Gypsophila coelesyriaca)",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 61005 records from 790 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051301-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11159986
title: Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05

description: This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11160509
title: Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05

description: This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11130212
title: Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-12

description: This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-12. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.15468/dl.ud28fc
title: Occurrence Download

description: A dataset containing 124084 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Occurrence evidence, Living Specimen, Human Observation, Observation)",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "TypeStatus is one of (Holotype, Isotype, Lectotype, Isolectotype, Neotype, Epitype)"
 ]
}

 The dataset includes 124084 records from 303 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002355-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1049973.10fu
title: SH1049973.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049983.10fu
title: SH1049983.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049984.10fu
title: SH1049984.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049985.10fu
title: SH1049985.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.