cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11169662
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-16

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-16. 

            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.11170031
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-16

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-16. 

            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.11170434
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-16

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-16. 

            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.14929960
title: Archaeological field survey data from the West Area of Samos Archaeological Project (WASAP), 2021–2024

description: This data publication represents the release of all field data acquired in the lifetime of the West Area of Samos Archaeological Project.

This deposit includes table data acquired in the field via the KoBo Collect application, field photos taken on tablet devices running KoBo Collect, GIS shapefiles, and georeferenced drone orthophotos.
DOI: 10.6084/m9.figshare.28497362
title: Smart hotel and SDG

description: This study aims to predict the factors of behavioral intentions of guests’ perceptions of contactless technology.
DOI: 10.48597/t5nv-35cv
title: Measurement of Aerosol_optical_depth at Ishigakijima

description: Measurement of Aerosol_optical_depth at Ishigakijima
DOI: 10.48597/325b-a47n
title: Measurement of Aerosol_optical_depth at Ishigakijima

description: Measurement of Aerosol_optical_depth at Ishigakijima
DOI: 10.48597/466q-3kbm
title: Measurement of Aerosol_optical_depth at Ishigakijima

description: Measurement of Aerosol_optical_depth at Ishigakijima
DOI: 10.48597/hndx-fypa
title: Measurement of Aerosol_optical_depth at Ishigakijima

description: Measurement of Aerosol_optical_depth at Ishigakijima
DOI: 10.48597/n42x-m6p6
title: Measurement of Aerosol_optical_depth at Ishigakijima

description: Measurement of Aerosol_optical_depth at Ishigakijima
DOI: 10.5281/zenodo.14904759
title: Remote sensing-enabled machine learning for modeling turbidity in regulating lakes and reservoirs across inter-basin
DOI: 10.5281/zenodo.14904760
title: Remote sensing-enabled machine learning for modeling turbidity in regulating lakes and reservoirs across inter-basin
DOI: 10.5281/zenodo.14930330
title: Remote sensing-enabled machine learning for modeling turbidity in regulating lakes and reservoirs across inter-basin
DOI: 10.48597/3f9r-yg2y
title: Measurement of Aerosol_optical_depth at Ishigakijima

description: Measurement of Aerosol_optical_depth at Ishigakijima
DOI: 10.48364/isimip.749905.1
title: Hourly Coastal water levels with Counterfactual (HCC)

description: The Hourly Coastal water levels with Counterfactual (HCC) dataset provides coastal water levels to be used as input data for ISIMIP3a (www.isimip.org, Frieler et al. 2023).<br>The dataset describes relative and geocentric historical coastal water levels, utilizing data from several specialized sources. It incorporates reconstructed water level data from the Hybrid Reconstructions (HR) dataset by Dangendorf et al. (2019), which describes the long-term evolution of historical water levels. Vertical land motion information is provided by Oelsmann et al. (2023), adding crucial details about changes in land elevation. The dataset also integrates a reanalysis of hourly variations in water levels caused by wind, air pressure, and astronomical tides, as detailed in the Coastal Dataset for the Evaluation of Climate Impact (CoDEC) by Muis et al. (2020).<br>A counterfactual scenario is derived by removing the quadratic trend observed in sea levels from 1900 to 2015. This results in a hypothetical condition where the sea level has been stationary since 1900.<br>When referring to the HCC dataset, the paper Treu et al. (2023) https://doi.org/10.5194/essd-2023-112 should be cited.<br>Version 1.1 resolves a two issues with the dataset (see https://data.isimip.org/issues/55/ for the details.
DOI: 10.15468/dl.6vse9x
title: Occurrence Download

description: A dataset listing the 62 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Exogone naidina Örsted, 1845, Eusyllis blomstrandi Malmgren, 1867, Eurydice pulchra Leach, 1816, Eupolymnia nebulosa (Montagu, 1819), Leodice harassii (Audouin & Milne Edwards, 1833), Eulalia bilineata (Johnston, 1840), Macroclymene santanderensis (Rioja, 1917), Euclymene palermitana (Grube, 1840), Euclymene oerstedii (Claparède, 1863), Euclymene lombricoides (Quatrefages, 1866), Eteone longa (Fabricius, 1780), Hypereteone foliosa (Quatrefages, 1865), Erinaceusyllis erinaceus (Claparède, 1863), Ericthonius punctatus (Spence Bate, 1857), Elasmopus rapax A.Costa, 1853, Echinocyamus pusillus (O.F.Müller, 1776), Ebalia tumefacta (Montagu, 1808), Dodecaceria concharum Örsted, 1843, Diogenes pugilator (Roux, 1828), Dialychone acustica Claparède, 1868, Cyathura carinata (Krøyer, 1847), Ctenodrilus serratus (Schmidt, 1857), Crassicorophium crassicorne (Bruzelius, 1859), Varicorbula gibba (Olivi, 1792), Leiochone leiopygos (Grube, 1860), Cirriformia tentaculata (Montagu, 1808), Cirratulus cirratus (O.F.Müller, 1776), Parthenina terebellum (R.A.Philippi, 1844), Cheirocratus sundevallii (Rathke, 1843), Kirkegaardia serrata (Eliason, 1962), Caulleriella alata (Southern, 1914), Caprella acanthifera Leach, 1814, Calyptraea chinensis (Linnaeus, 1758), Callista chione (Linnaeus, 1758), Bradabyssa villosa (Rathke, 1843), Bodotria scorpioides (Montagu, 1804), Bodotria arenosa Goodsir, 1843, Bittium reticulatum (da Costa, 1778), Berthella plumula (Montagu, 1803), Pista maculata (Dalyell, 1853), Ascidia conchilega Müller, 1776, Arabella iricolor (Montagu, 1804), Apseudopsis latreillii (Milne Edwards, 1828), Apseudes holthuisi Bacescu, 1961, Apohyale prevostii (H.Milne Edwards, 1830), Apherusa jurinei (H.Milne Edwards, 1830), Aphelochaeta filiformis (Keferstein, 1862), Aonides oxycephala (Sars, 1862), Anthura gracilis (Montagu, 1808), Antalis vulgaris (da Costa, 1778), Amphipholis squamata (Delle Chiaje, 1828), Apolochus neapolitanus (Della Valle, 1893), Ampharete acutifrons (Grube, 1860), Ampelisca spinipes Boeck, 1861, Ampelisca diadema (A.Costa, 1853), Ampelisca brevicornis (A.Costa, 1853), Amaeana trilobata (Sars, 1863), Alkmaria romijni Horst, 1919, Acanthocardia echinata (Linnaeus, 1758), Abra alba (W.Wood, 1802))"
 ]
}

 The dataset's 62 records were derived from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000298-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57833/cidacs/cappm0
title: Replication Data for: Pagamento do Novo Bolsa Família - A partir de 2023

description: Dataset contendo informações sobre os pagamentos do Novo Programa Bolsa Família (2023). O Bolsa Família é um programa de transferência de renda do Brasil. O Governo Federal relançou o programa com mais proteção às famílias, com um modelo de benefício que considera o tamanho e as características familiares. O Programa Bolsa Família busca integrar políticas públicas, fortalecendo o acesso das famílias a direitos básicos como saúde, educação e assistência social.
DOI: 10.57833/cidacs/r8d9wc
title: Replication Data for: Saques do Bolsa Família

description: Dataset contendo informações sobre os saques do Programa Bolsa Família. O Bolsa Família é um programa de transferência de renda do Brasil. O Governo Federal relançou o programa com mais proteção às famílias, com um modelo de benefício que considera o tamanho e as características familiares. O Programa Bolsa Família busca integrar políticas públicas, fortalecendo o acesso das famílias a direitos básicos como saúde, educação e assistência social.
DOI: 10.15468/dl.f9mv9m
title: Occurrence Download

description: A dataset containing 1270080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "Year is 2011",
 "Month is November"
 ]
}

 The dataset includes 1270080 records from 329 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000294-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14825843
title: Fossil-based P1. PO/TBA  production (PO:250/TBA:603 kt)

description: The repository contains process data for Propylene Oxide (PO)/Tert-butyl Alcohol (TBA) production modelled in Aspen Plus v12. Data is presented in the form of an Excel file, which includes details of the process, a process flow diagram, an Aspen simulation diagram, Aspen inputs and assumptions, simulation equipment details, and mass and energy balance information.



Process name: P1. PO/TBA

Process type: Fossil-based

Chemical type: Intermediate

Feedstock: n-Butane

Products: PO, TBA
DOI: 10.5281/zenodo.14825844
title: Fossil-based P1. PO/TBA  production (PO:250/TBA:603 kt)

description: The repository contains process data for Propylene Oxide (PO)/Tert-butyl Alcohol (TBA) production modelled in Aspen Plus v12. Data is presented in the form of an Excel file, which includes details of the process, a process flow diagram, an Aspen simulation diagram, Aspen inputs and assumptions, simulation equipment details, and mass and energy balance information.



Process name: P1. PO/TBA

Process type: Fossil-based

Chemical type: Intermediate

Feedstock: n-Butane

Products: PO, TBA
DOI: 10.15468/dl.warafq
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coffea toshii A.P.Davis & Rakotonas."
 ]
}

 The dataset includes 14 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000305-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ynujj
title: Occurrence Download

description: A dataset containing 3324164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "Geometry POLYGON((-78.93184 -0.31726,-77.78458 -0.31726,-77.78458 0.51414,-78.93184 0.51414,-78.93184 -0.31726))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3324164 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000292-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vkm6j2
title: Occurrence Download

description: A dataset containing 2897 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Necora puber (Linnaeus, 1767), Tritia reticulata (Linnaeus, 1758), Tritia varicosa (W.Turton, 1825), Tritia incrassata (Strøm, 1768), Mysta picta (Quatrefages, 1866), Monopseudocuma gilsoni (Bacescu, 1950), Microjassa cumbrensis (Stebbing & Robertson, 1891), Microdeutopus gryllotalpa A.Costa, 1853, Metaphoxus simplex (Spence Bate, 1857), Melita palmata (Montagu, 1804), Melinna palmata Grube, 1870, Megaluropus agilis Hoek, 1889, Medicorophium runcicorne (Della Valle, 1893), Marphysa sanguinea (Montagu, 1813), Paucibranchia bellii (Audouin & Milne Edwards, 1833), Maldane sarsi Malmgren, 1865, Malacoceros fuliginosus (Claparède, 1868), Maera grossimana (Montagu, 1808), Macropodia rostrata (Linnaeus, 1761), Lysianassa pilicornis Heller, 1866, Lutraria lutraria (Linnaeus, 1758), Lumbrineris latreilli Audouin & Milne Edwards, 1833, Scoletoma fragilis (O.F.Müller, 1776), Lumbrineris coccinea (Renier, 1804), Idunella picta (Norman, 1889), Liocarcinus pusillus (Leach, 1816), Leptosynapta inhaerens (O.F.Müller, 1776), Lepton squamosum (Montagu, 1803), Leptomysis lingvura (G.O.Sars, 1866), Chondrochelia savignyi (Kroyer, 1842), Leptocheirus pectinatus (Norman, 1869), Lepidochitona cinerea (Linnaeus, 1767), Lekanesphaera monodi (Arcangeli, 1934), Lekanesphaera levii (Argano & Ponticelli, 1981), Lasaea adansoni (Gmelin, 1791), Lanice conchilega (Pallas, 1766), Laevicardium crassum (Gmelin, 1791), Psamathe fusca Johnston, 1836, Jassa marmorata Holmes, 1905, Jasmineira elegans Saint-Joseph, 1894, Iphinoe trispinosa (Goodsir, 1843), Iphinoe tenella Sars, 1878, Iphinoe serrata Norman, 1867, Peringia ulvae (Pennant, 1777), Apohyale perieri (Lucas, 1846), Hyala vitrea (Montagu, 1803), Heterotanais oerstedii (Krøyer, 1842), Heteroclymene robusta Arwidsson, 1906, Hermodice carunculata (Pallas, 1766), Hemilepton nitidum (W.Turton, 1822), Hediste diversicolor (O.F.Müller, 1776), Harpinia crenulata (Boeck, 1871), Harmothoe reticulata (Claparède, 1870), Gouldia minima (Montagu, 1803), Goniada maculata Örsted, 1843, Glycera unicornis Lamarck, 1818, Glycera tridactyla Schmarda, 1861, Glycera capitata Örsted, 1842, Glycera alba (O.F.Müller, 1776), Gattyana cirrhosa (Pallas, 1766), Gari fervensis (Gmelin, 1791), Exogone verugera (Claparède, 1868))"
 ]
}

 The dataset includes 2897 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000306-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14825921
title: Fossil-based M6. MTBE production (400kt/y)

description: The repository contains process data for Methyl Tert Butyl Ether (MTBE) production modelled in Aspen Plus v12. Data is presented in the form of an Excel file, which includes details of the process, a process flow diagram, an Aspen simulation diagram, Aspen inputs and assumptions, simulation equipment details, and mass and energy balance information.



Process name: M6. MTBE

Process type: Fossil-based

Chemical type: Intermediate

Feedstock: Tert-butyl Alcohol (TBA)

Products: MTBE, Isobutene (i.e, Isobutylene).
DOI: 10.5281/zenodo.14825922
title: Fossil-based M6. MTBE production (400kt/y)

description: The repository contains process data for Methyl Tert Butyl Ether (MTBE) production modelled in Aspen Plus v12. Data is presented in the form of an Excel file, which includes details of the process, a process flow diagram, an Aspen simulation diagram, Aspen inputs and assumptions, simulation equipment details, and mass and energy balance information.



Process name: M6. MTBE

Process type: Fossil-based

Chemical type: Intermediate

Feedstock: Tert-butyl Alcohol (TBA)

Products: MTBE, Isobutene (i.e, Isobutylene).
DOI: 10.15468/dl.t8jy38
title: Occurrence Download

description: A dataset containing 284 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myriactis wightii DC."
 ]
}

 The dataset includes 284 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000311-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.