cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gu4m6w
title: Occurrence Download

description: A dataset containing 88 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 Ficus pygmaea Welw. ex Hiern",
 "Continent is Africa"
 ]
}

 The dataset includes 88 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044835-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9a2mg6
title: Occurrence Download

description: A dataset containing 988 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 Pterocarpus angolensis DC.",
 "Continent is Africa"
 ]
}

 The dataset includes 988 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044836-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qm593k
title: Occurrence Download

description: A dataset containing 590991 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "TaxonKey is one of (Acer platanoides L., Adelges tsugae Annand, 1924, Agrilus planipennis Fairmaire, 1888, Alliaria petiolata (M.Bieb.) Cavara & Grande, Channa argus (Cantor, 1842), Crocodylus niloticus Laurenti, 1768, Dreissena polymorpha (Pallas, 1771), Faxonius rusticus (Girard, 1852), Frangula alnus Mill., Heracleum mantegazzianum Sommier & Levier, Heracleum sphondylium L., Hydrocharis morsus-ranae L., Impatiens glandulifera Royle, Kali tragus (L.) Scop., Lissachatina fulica (Bowdich, 1822), Lycorma delicatula (White, 1845), Myriophyllum spicatum L., Phragmites australis subsp. australis, Potamogeton crispus L., Pterois volitans (Linnaeus, 1758), Pueraria lobata (Willd.) Ohwi, Python bivittatus Kuhl, 1820, Reynoutria japonica Houtt., Reynoutria sachalinensis (F.Schmidt) Nakai, Reynoutria ×bohemica Chrtek & Chrtková, Rhamnus cathartica L., Rhinella marina (Linnaeus, 1758), Stratiotes aloides L., Trapa natans L., Vincetoxicum nigrum (L.) Moench, Vincetoxicum rossicum (Kleopow) Barbar.)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 590991 records from 575 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044814-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qdf7du
title: Occurrence Download

description: A dataset containing 220389 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Coregonus clupeaformis (Mitchill, 1818), Coregonus lavaretus (Linnaeus, 1758), Coregonus hoyi (Milner, 1874), Coregonus artedi Lesueur, 1818, Coregonus albula (Linnaeus, 1758), Coregonus fontanae Schulz & Freyhof, 2003)"
 ]
}

 The dataset includes 220389 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044829-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13157704
title: Binary black-hole simulation SXS:BBH:1463

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157705
title: Binary black-hole simulation SXS:BBH:1463

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157722
title: Binary black-hole simulation SXS:BBH:1435

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157723
title: Binary black-hole simulation SXS:BBH:1435

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157740
title: Binary black-hole simulation SXS:BBH:1469

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157741
title: Binary black-hole simulation SXS:BBH:1469

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.8015984
title: Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies

description: We introduce a new bimodal dataset recorded during affect elicitation by means of audio-visual stimuli for human emotion recognition based on facial and corporal expressions. Our dataset was collected using three devices: an RGB camera, Kinect 1, and Kinect 2. The Kinect 1 and Kinect 2 sensors provide 121 and 1347 face key points, respectively, offering a more comprehensive analysis of facial expressions. Additionally, for the 2D RGB sequences, we utilized the feature points provided by the open-source OpenFace, which includes 2D 68 facial landmarks. From these landmarks, we selected 26 facial points that were most relevant for our emotion recognition task.
To gather the data, we conducted experiments involving 17 participants. We captured both facial and skeleton keypoints, allowing for a comprehensive understanding of the participants' emotional expressions. By combining the RGB and RGB-D data from the various devices, our dataset provides a rich and diverse set of information for human emotion recognition research.

This new dataset not only expands the available resources for studying human emotions but also offers a more detailed analysis with the increased number of facial keypoints provided by the Kinect sensors. Researchers can leverage this dataset to develop and evaluate more accurate and robust models for human emotion recognition, ultimately advancing our understanding of how emotions are expressed through facial and corporal cues.
Please cite as:
K. Amara, O. Kerdjidj and N. Ramzan, "Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies," in IEEE Access, doi: 10.1109/ACCESS.2023.3285398.



 


Please state your name, contact details (e-mail), institution, and position, as well as the reason for requesting access to our database.


For additional info contact:


kahina.amara88@gmail.com or kamara@cdta.dz


Naeem.Ramzan@uws.ac.uk


okerdjidj@ud.ac.ae
DOI: 10.5281/zenodo.8015985
title: Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies

description: We introduce a new bimodal dataset recorded during affect elicitation by means of audio-visual stimuli for human emotion recognition based on facial and corporal expressions. Our dataset was collected using three devices: an RGB camera, Kinect 1, and Kinect 2. The Kinect 1 and Kinect 2 sensors provide 121 and 1347 face key points, respectively, offering a more comprehensive analysis of facial expressions. Additionally, for the 2D RGB sequences, we utilized the feature points provided by the open-source OpenFace, which includes 2D 68 facial landmarks. From these landmarks, we selected 26 facial points that were most relevant for our emotion recognition task.
To gather the data, we conducted experiments involving 17 participants. We captured both facial and skeleton keypoints, allowing for a comprehensive understanding of the participants' emotional expressions. By combining the RGB and RGB-D data from the various devices, our dataset provides a rich and diverse set of information for human emotion recognition research.

This new dataset not only expands the available resources for studying human emotions but also offers a more detailed analysis with the increased number of facial keypoints provided by the Kinect sensors. Researchers can leverage this dataset to develop and evaluate more accurate and robust models for human emotion recognition, ultimately advancing our understanding of how emotions are expressed through facial and corporal cues.
Please cite as:
K. Amara, O. Kerdjidj and N. Ramzan, "Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies," in IEEE Access, doi: 10.1109/ACCESS.2023.3285398.



 


Please state your name, contact details (e-mail), institution, and position, as well as the reason for requesting access to our database.


For additional info contact:


kahina.amara88@gmail.com or kamara@cdta.dz


Naeem.Ramzan@uws.ac.uk


okerdjidj@ud.ac.ae
DOI: 10.5281/zenodo.13157782
title: Binary black-hole simulation SXS:BBH:1428

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157783
title: Binary black-hole simulation SXS:BBH:1428

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.4fvwj4
title: Occurrence Download

description: A dataset containing 821 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 Diplorhynchus condylocarpon (Müll.Arg.) Pichon",
 "Continent is Africa"
 ]
}

 The dataset includes 821 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044837-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13157798
title: Binary black-hole simulation SXS:BBH:1471

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157799
title: Binary black-hole simulation SXS:BBH:1471

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157818
title: Binary black-hole simulation SXS:BBH:1495

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157819
title: Binary black-hole simulation SXS:BBH:1495

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157840
title: Binary black-hole simulation SXS:BBH:1450

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157841
title: Binary black-hole simulation SXS:BBH:1450

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157862
title: Binary black-hole simulation SXS:BBH:1447

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157863
title: Binary black-hole simulation SXS:BBH:1447

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157872
title: Binary black-hole simulation SXS:BBH:1506

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157873
title: Binary black-hole simulation SXS:BBH:1506

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157884
title: Binary black-hole simulation SXS:BBH:1488

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157885
title: Binary black-hole simulation SXS:BBH:1488

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157886
title: Binary black-hole simulation SXS:BBH:1482

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157887
title: Binary black-hole simulation SXS:BBH:1482

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157890
title: Binary black-hole simulation SXS:BBH:1419

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157891
title: Binary black-hole simulation SXS:BBH:1419

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157892
title: Binary black-hole simulation SXS:BBH:1486

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157893
title: Binary black-hole simulation SXS:BBH:1486

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157895
title: Binary black-hole simulation SXS:BBH:1508

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157896
title: Binary black-hole simulation SXS:BBH:1508

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.10443185
title: Open Grid Emissions Dataset 2019-2022 (v0.3.0)

description: The Open Grid Emissions Initiative seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets. The initiative includes this repository of open-source grid emissions data processing tools that use peer-reviewed, well-documented, and validated methodologies to create the accompanying public dataset of hourly, monthly, and annual U.S. electric grid generation, GHG, and air pollution data.

Data Structure



data/outputs contains intermediate outputs from the data pipeline (any files created by our code that are not final results)

data/results contains all final output files that will be published


Release Notes

The 2019-2022 data archived here was created by v0.3.0 of the Open Grid Emissions Initiative, archived on Zenodo here.

For a summary of what's new in the 2019-2022 v.0.3.0 data, see the v0.3.0 release notes.

Using the archived files

The outputs and results files have been saved as separate zip folders for each year.
DOI: 10.5281/zenodo.10909484
title: Open Grid Emissions Dataset 2005-2022 (v0.5.0)

description: The Open Grid Emissions Initiative seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets. The initiative includes this repository of open-source grid emissions data processing tools that use peer-reviewed, well-documented, and validated methodologies to create the accompanying public dataset of hourly, monthly, and annual U.S. electric grid generation, GHG, and air pollution data.

Data Structure



data/outputs contains intermediate outputs from the data pipeline (any files created by our code that are not final results)

data/results contains all final output files that will be published


Release Notes

The 2005-2022 data archived here was created by v0.5.0 of the Open Grid Emissions Initiative, archived on Zenodo here.

For a summary of what's new in the 2005-2022 v.0.5.0 data, see the v0.5.0 release notes.

Using the archived files

The outputs and results files have been saved as separate zip folders for each year.
DOI: 10.5281/zenodo.7026723
title: Open Grid Emissions Dataset

description: The Open Grid Emissions Initiative seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets. The initiative includes this repository of open-source grid emissions data processing tools that use peer-reviewed, well-documented, and validated methodologies to create the accompanying public dataset of hourly, monthly, and annual U.S. electric grid generation, GHG, and air pollution data. Data Structure <code>data/downloads</code> contains all files that are downloaded by functions in <code>load_data</code> <code>data/manual</code> contains all manually-created files, including the egrid static tables <code>data/outputs</code> contains intermediate outputs from the data pipeline... any files created by our code that are not final results <code>data/results</code> contains all final output files that will be published <strong>Using the archived files</strong> The outputs and results files have been saved as separate zip folders for each year. We did not archive the manual or downloads folder since the manual files are archived as part of the v0.1.0 code release, and downloading these files using v0.1.0 of the code should return versioned results. The exception to this is the pudl.sqlite database, which is downloaded from the datasette version of pudl and thus changes over time. If you would like to replicate these results, run the pipeline to download all of the data inputs using version 0.1.0 of the code. Once those files are downloaded, replace the downloaded `data/downloads/pudl/pudl_data/sqlite/pudl.sqlite` file with the archived version.
DOI: 10.5281/zenodo.7600513
title: Open Grid Emissions Dataset 2019-2021 (v0.2.1)

description: The Open Grid Emissions Initiative seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets. The initiative includes this repository of open-source grid emissions data processing tools that use peer-reviewed, well-documented, and validated methodologies to create the accompanying public dataset of hourly, monthly, and annual U.S. electric grid generation, GHG, and air pollution data. Data Structure <code>data/downloads</code> contains all files that are downloaded by functions in <code>load_data</code> <code>data/manual</code> contains all manually-created files, including the egrid static tables <code>data/outputs</code> contains intermediate outputs from the data pipeline... any files created by our code that are not final results <code>data/results</code> contains all final output files that will be published <strong>Release Notes</strong> The 2019-2021 data archived here was created by v0.2.1 of the Open Grid Emissions Initiative, archived on Zenodo here. For a summary of what's new in the 2019-2020 v.0.2.1 data, see the v0.2.1 release notes. <strong>Using the archived files</strong> The outputs and results files have been saved as separate zip folders for each year. We did not archive the manual or downloads folder since the manual files are archived as part of the v0.2.1 code release, and downloading these files using v0.2.1 of the code should return versioned results.
DOI: 10.5281/zenodo.13157899
title: Binary black-hole simulation SXS:BBH:1502

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13157900
title: Binary black-hole simulation SXS:BBH:1502

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.7026722
title: Open Grid Emissions Dataset 2005-2022 (v0.5.0)

description: The Open Grid Emissions Initiative seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets. The initiative includes this repository of open-source grid emissions data processing tools that use peer-reviewed, well-documented, and validated methodologies to create the accompanying public dataset of hourly, monthly, and annual U.S. electric grid generation, GHG, and air pollution data.

Data Structure



data/outputs contains intermediate outputs from the data pipeline (any files created by our code that are not final results)

data/results contains all final output files that will be published


Release Notes

The 2005-2022 data archived here was created by v0.5.0 of the Open Grid Emissions Initiative, archived on Zenodo here.

For a summary of what's new in the 2005-2022 v.0.5.0 data, see the v0.5.0 release notes.

Using the archived files

The outputs and results files have been saved as separate zip folders for each year.
DOI: 10.5281/zenodo.7063072
title: Open Grid Emissions Dataset 2019-2020 v0.1.1

description: The Open Grid Emissions Initiative seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets. The initiative includes this repository of open-source grid emissions data processing tools that use peer-reviewed, well-documented, and validated methodologies to create the accompanying public dataset of hourly, monthly, and annual U.S. electric grid generation, GHG, and air pollution data. Data Structure <code>data/downloads</code> contains all files that are downloaded by functions in <code>load_data</code> <code>data/manual</code> contains all manually-created files, including the egrid static tables <code>data/outputs</code> contains intermediate outputs from the data pipeline... any files created by our code that are not final results <code>data/results</code> contains all final output files that will be published <strong>Release Notes</strong> The 2019-2020 v0.1.1 data archived here was created by v0.1.1 of the Open Grid Emissions Initiative, archived on Zenodo here. For a summary of what's new in the 2019-2020 v.0.1.1 data, see the v0.1.1 release notes. <strong>Using the archived files</strong> The outputs and results files have been saved as separate zip folders for each year. We did not archive the manual or downloads folder since the manual files are archived as part of the v0.1.1 code release, and downloading these files using v0.1.0 of the code should return versioned results. The exception to this is the pudl.sqlite database, which is downloaded from the datasette version of pudl and thus changes over time. If you would like to replicate these results, run the pipeline to download all of the data inputs using version 0.1.1 of the code. Once those files are downloaded, replace the downloaded `data/downloads/pudl/pudl_data/sqlite/pudl.sqlite` file with the archived version.
DOI: 10.5281/zenodo.7495900
title: Open Grid Emissions Dataset 2019-2021 (v0.2.0)

description: The Open Grid Emissions Initiative seeks to fill a critical need for high-quality, publicly-accessible, hourly grid emissions data that can be used for GHG accounting, policymaking, academic research, and energy attribute certificate markets. The initiative includes this repository of open-source grid emissions data processing tools that use peer-reviewed, well-documented, and validated methodologies to create the accompanying public dataset of hourly, monthly, and annual U.S. electric grid generation, GHG, and air pollution data. Data Structure <code>data/downloads</code> contains all files that are downloaded by functions in <code>load_data</code> <code>data/manual</code> contains all manually-created files, including the egrid static tables <code>data/outputs</code> contains intermediate outputs from the data pipeline... any files created by our code that are not final results <code>data/results</code> contains all final output files that will be published <strong>Release Notes</strong> The 2019-2021 data archived here was created by v0.2.0 of the Open Grid Emissions Initiative, archived on Zenodo here. For a summary of what's new in the 2019-2020 v.0.2.0 data, see the v0.2.0 release notes. <strong>Using the archived files</strong> The outputs and results files have been saved as separate zip folders for each year. We did not archive the manual or downloads folder since the manual files are archived as part of the v0.2.0 code release, and downloading these files using v0.2.0 of the code should return versioned results.