cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.w57xt2
title: Occurrence Download

description: A dataset containing 133 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sauromatum venosum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 133 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001119-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30229711
title: A Generative Video Dataset to Represent Everyday Human Activity

description: Human activity datasets underpin much of ubiquitous computing and HCI research, but traditional methods of capturing video or sensor data are costly, time-consuming, and raise privacy concerns. Advances in generative AI offer a new path: instead of recording people, we can synthesise convincing audiovisual depictions of everyday activities that are ethically shareable and scalable.We introduce the Generative Human Activity Dataset, comprising:1,000 AI-generated short videos of diverse human activities4,000 paired audio clips of ambient sounds and effectsStructured metadata and text prompts for each clipPre-computed semantic embeddings for efficient searchAlongside the dataset, we provide a set of open-source tools:A semantic search engine supporting natural language queriesA lightweight web API and embeddable widget for integration into surveys and applicationsProgrammatic interfaces for research and prototypingThis dataset and toolkit make it possible to:Use synthetic clips as stimuli for experimentsSupport design prototyping and scenario explorationProvide training data for models without expensive annotation and minimal privacy risksBy combining generative media with accessible tools, our contribution offers an ethically shareable, scalable alternative to traditional human activity datasets, positioning synthetic datasets as practical research resources in their own right.
DOI: 10.6084/m9.figshare.30229711.v4
title: A Generative Video Dataset to Represent Everyday Human Activity

description: Human activity datasets underpin much of ubiquitous computing and HCI research, but traditional methods of capturing video or sensor data are costly, time-consuming, and raise privacy concerns. Advances in generative AI offer a new path: instead of recording people, we can synthesise convincing audiovisual depictions of everyday activities that are ethically shareable and scalable.We introduce the Generative Human Activity Dataset, comprising:1,000 AI-generated short videos of diverse human activities4,000 paired audio clips of ambient sounds and effectsStructured metadata and text prompts for each clipPre-computed semantic embeddings for efficient searchAlongside the dataset, we provide a set of open-source tools:A semantic search engine supporting natural language queriesA lightweight web API and embeddable widget for integration into surveys and applicationsProgrammatic interfaces for research and prototypingThis dataset and toolkit make it possible to:Use synthetic clips as stimuli for experimentsSupport design prototyping and scenario explorationProvide training data for models without expensive annotation and minimal privacy risksBy combining generative media with accessible tools, our contribution offers an ethically shareable, scalable alternative to traditional human activity datasets, positioning synthetic datasets as practical research resources in their own right.
DOI: 10.6084/m9.figshare.30229711.v3
title: A Generative Video Dataset to Represent Everyday Human Activity

description: Human activity datasets underpin much of ubiquitous computing and HCI research, but traditional methods of capturing video or sensor data are costly, time-consuming, and raise privacy concerns. Advances in generative AI offer a new path: instead of recording people, we can synthesise convincing audiovisual depictions of everyday activities that are ethically shareable and scalable.We introduce the Generative Human Activity Dataset, comprising:1,000 AI-generated short videos of diverse human activities4,000 paired audio clips of ambient sounds and effectsStructured metadata and text prompts for each clipPre-computed semantic embeddings for efficient searchAlongside the dataset, we provide a set of open-source tools:A semantic search engine supporting natural language queriesA lightweight web API and embeddable widget for integration into surveys and applicationsProgrammatic interfaces for research and prototypingThis dataset and toolkit make it possible to:Use synthetic clips as stimuli for experimentsSupport design prototyping and scenario explorationProvide training data for models without expensive annotation and minimal privacy risksBy combining generative media with accessible tools, our contribution offers an ethically shareable, scalable alternative to traditional human activity datasets, positioning synthetic datasets as practical research resources in their own right.
DOI: 10.6084/m9.figshare.28943957
title: Iran Traffic Sign Dataset (ITSD)

description: The Iran Traffic Sign Dataset (ITSD) is a comprehensive and valuable collection of traffic sign images, gathered under real driving conditions across various Iranian cities throughout all seasons of 2023 and 2024 (1402-1403 in the Persian calendar). This dataset has been designed and compiled to support artificial intelligence research in the field of traffic sign detection and recognition.The dataset consists of 14,111 high-resolution images containing 19,718 labeled traffic sign instances across 118 distinct classes, with a minimum of 10 samples per class. Annotation was performed using the MakeSense tool, with the results provided in two standard formats: Pascal VOC (.xml) and YOLO (.txt). The images, saved in .jpg format, have frame rates of 30fps or 60fps and resolutions of 1920x1080 or 1920x936. The recording was conducted using various mobile phones (including Galaxy S23 Ultra, iPhone 12, Galaxy J9, and Redmi 12) at speeds of up to 110 kilometers per hour.The dataset structure is designed to include six pre-defined folds to facilitate cross-validation. To ease its use by researchers, a Python script in the form of a Jupyter Notebook named Created Dataset is provided. This script takes the dataset path, the class configuration file (data_labels.yaml), an output path, and the name of the fold designated for testing as inputs. It then automatically splits the dataset into train, validation, and test subsets and saves them in the specified location. The six data folds, the dataset creation script, and the label mapping file are all packaged within a compressed folder named ITSD-dataset. Furthermore, the trained model weights for YOLOv9, generated for each test fold during the cross-validation process, are published within the same repository and are ready for use in evaluation or further training.There is an additional folder named Main, which contains the trained YOLOv12 weights for all six folds, along with a Python script named main.py. This script serves as the baseline detection system. After execution, it reads images from the inputs folder and saves the detection results in the outputs folder. Also a Readme file containing the version of the libraries used.When using the Iran Traffic Sign Dataset (ITSD) in your research, please support its development and respect the creators' rights by providing appropriate citation and acknowledgment.<b>Dataset specifications</b><b>Name</b>Iran Traffic Sign Dataset<b>Number of images</b>14111<b>Number of classes</b>118<b>Total number of objects</b>19718<b>Minimum number of samples per</b><b> </b><b>class</b>10<b>Annotation tool</b>Make-Sence<b>Annotation format</b>Voc pascal(.xml) وYolo (.txt)<b>Image size</b>1080*1920 , 936*1920<b>Image format</b>.jpg<b>Filming rate</b>30fps,60fps<b>Place of collection</b>All city s Iran<b>Collection season</b>All seasons<b>Accumulated time during the day</b>Full time<b>Number of folds</b>6 fold<b>Maximum video recording speed</b>110 km/h<b>Filming tool</b>Mobile(s23ultera,iphon12,GalaxyJ9,Redme12)<br>
DOI: 10.6084/m9.figshare.28943957.v1
title: Iran Traffic Sign Dataset (ITSD)

description: Iran Traffic Sign Dataset (ITSD) is a comprehensive and high-quality dataset of traffic sign images collected under real driving conditions across various cities in Iran during 2023 and 2024 (Persian years 1402 and 1403). The dataset was gathered across all seasons and under diverse lighting conditions to ensure robustness and generalizability for machine learning applications in traffic sign detection and recognition.The dataset contains 14,111 high-resolution images, annotated with 19,718 traffic signs across 118 distinct classes, with a minimum of 10 samples per class. Annotations were performed using the MakeSense labeling tool and are available in both Pascal VOC (.xml) and YOLO (.txt) formats. Images were captured at 30fps and 60fps, with resolutions of 1080×1920 and 936×1920, and saved in .jpg format. Recording was conducted using various mobile devices (e.g., Galaxy S23 Ultra, iPhone 12, Galaxy J9, Redmi 12) at driving speeds up to 110 km/h.To support reliable training and evaluation, the dataset is organized into 6 predefined folds for cross-validation purposes. A Jupyter Notebook script has been provided to facilitate data preparation. This script takes as input the dataset path, a YAML configuration file defining class names and indices, the destination path, and the name of the fold to be used as the test set. It then automatically creates the corresponding train, validation, and test splits and saves them accordingly.Additionally, pretrained YOLOv9 weights, trained and evaluated on each of the 6 folds where the designated fold is used as the test set, are published in the same repository and are readily available for further research and benchmarking.When using the Iran Traffic Sign Dataset (ITSD) in your research, please cite and acknowledge the dataset appropriately to support its continued development and to give credit to the authors.This dataset was collected as part of a Master's thesis in Computer Engineering, specializing in Artificial Intelligence and Robotics, at Razi University.<b>Dataset specifications</b><b>Name</b>Iran Traffic Sign Dataset<b>Number of images</b>14111<b>Number of classes</b>118<b>Total number of objects</b>19718<b>Minimum number of samples per</b><b> </b><b>class</b>10<b>Annotation tool</b>Make-Sence<b>Annotation format</b>Voc pascal(.xml) وYolo (.txt)<b>Image size</b>1080*1920 , 936*1920<b>Image format</b>.jpg<b>Filming rate</b>30fps,60fps<b>Place of collection</b>All city s Iran<b>Collection season</b>All seasons<b>Accumulated time during the day</b>Full time<b>Number of folds</b>6 fold<b>Maximum video recording speed</b>110 km/h<b>Filming tool</b>Mobile(s23ultera,iphon12,GalaxyJ9,Redme12)
DOI: 10.6084/m9.figshare.28943957.v2
title: Iran Traffic Sign Dataset (ITSD)

description: The Iran Traffic Sign Dataset (ITSD) is a comprehensive and valuable collection of traffic sign images, gathered under real driving conditions across various Iranian cities throughout all seasons of 2023 and 2024 (1402-1403 in the Persian calendar). This dataset has been designed and compiled to support artificial intelligence research in the field of traffic sign detection and recognition.The dataset consists of 14,111 high-resolution images containing 19,718 labeled traffic sign instances across 118 distinct classes, with a minimum of 10 samples per class. Annotation was performed using the MakeSense tool, with the results provided in two standard formats: Pascal VOC (.xml) and YOLO (.txt). The images, saved in .jpg format, have frame rates of 30fps or 60fps and resolutions of 1920x1080 or 1920x936. The recording was conducted using various mobile phones (including Galaxy S23 Ultra, iPhone 12, Galaxy J9, and Redmi 12) at speeds of up to 110 kilometers per hour.The dataset structure is designed to include six pre-defined folds to facilitate cross-validation. To ease its use by researchers, a Python script in the form of a Jupyter Notebook named Created Dataset is provided. This script takes the dataset path, the class configuration file (data_labels.yaml), an output path, and the name of the fold designated for testing as inputs. It then automatically splits the dataset into train, validation, and test subsets and saves them in the specified location. The six data folds, the dataset creation script, and the label mapping file are all packaged within a compressed folder named ITSD-dataset. Furthermore, the trained model weights for YOLOv9, generated for each test fold during the cross-validation process, are published within the same repository and are ready for use in evaluation or further training.There is an additional folder named Main, which contains the trained YOLOv12 weights for all six folds, along with a Python script named main.py. This script serves as the baseline detection system. After execution, it reads images from the inputs folder and saves the detection results in the outputs folder. Also a Readme file containing the version of the libraries used.When using the Iran Traffic Sign Dataset (ITSD) in your research, please support its development and respect the creators' rights by providing appropriate citation and acknowledgment.<b>Dataset specifications</b><b>Name</b>Iran Traffic Sign Dataset<b>Number of images</b>14111<b>Number of classes</b>118<b>Total number of objects</b>19718<b>Minimum number of samples per</b><b> </b><b>class</b>10<b>Annotation tool</b>Make-Sence<b>Annotation format</b>Voc pascal(.xml) وYolo (.txt)<b>Image size</b>1080*1920 , 936*1920<b>Image format</b>.jpg<b>Filming rate</b>30fps,60fps<b>Place of collection</b>All city s Iran<b>Collection season</b>All seasons<b>Accumulated time during the day</b>Full time<b>Number of folds</b>6 fold<b>Maximum video recording speed</b>110 km/h<b>Filming tool</b>Mobile(s23ultera,iphon12,GalaxyJ9,Redme12)<br>
DOI: 10.15468/dl.pgcaae
title: Occurrence Download

description: A dataset containing 145 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Ochagavia carnea"
 ]
}

 The dataset includes 145 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001120-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ff7s4m
title: Occurrence Download

description: A dataset containing 45 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 24.552553756517728
 AND decimallatitude &lt;= 28.773895969281842
 AND decimallongitude &gt;= 10.580091890460364
 AND decimallongitude &lt;= 16.23900266428802
 AND GBIF_Within('POLYGON ((16.239003 24.552554, 16.239003 28.773896, 10.580092 28.773896, 10.580092 24.552554, 16.239003 24.552554))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 45 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001109-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bx3d9d
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scindapsus coriaceus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 70 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001121-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mang49
title: Occurrence Download

description: A dataset containing 20473 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Opuntia basilaris"
 ]
}

 The dataset includes 20473 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001122-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18525233
title: CORPUS-BASED EMPIRICAL ANALYSIS OF COLLOQUIAL UNITS IN CONTEMPORARY ENGLISH

description: The article presents a corpus-based empirical analysis of colloquial units in contemporary English, treating colloquial vocabulary as a dynamic, multifunctional, and internally heterogeneous subsystem of the lexical system. Colloquial units are examined not merely as markers of informal speech, but as linguistically significant elements reflecting ongoing transformations in communicative practices, discourse conventions, and stylistic norms. Drawing on data from large-scale, register-diverse English language corpora, the study investigates the frequency, dispersion, contextual variability, and pragmatic functions of selected colloquial units across spoken and written registers. Special attention is paid to processes of stylistic diffusion, pragmatic refunctionalization, and partial desemanticization.
DOI: 10.5281/zenodo.18525234
title: CORPUS-BASED EMPIRICAL ANALYSIS OF COLLOQUIAL UNITS IN CONTEMPORARY ENGLISH

description: The article presents a corpus-based empirical analysis of colloquial units in contemporary English, treating colloquial vocabulary as a dynamic, multifunctional, and internally heterogeneous subsystem of the lexical system. Colloquial units are examined not merely as markers of informal speech, but as linguistically significant elements reflecting ongoing transformations in communicative practices, discourse conventions, and stylistic norms. Drawing on data from large-scale, register-diverse English language corpora, the study investigates the frequency, dispersion, contextual variability, and pragmatic functions of selected colloquial units across spoken and written registers. Special attention is paid to processes of stylistic diffusion, pragmatic refunctionalization, and partial desemanticization.
DOI: 10.15468/dl.etdkac
title: Occurrence Download

description: A dataset containing 579 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spathicarpa hastifolia",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 579 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001124-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11477690
title: MEDA: Mars Weather and Atmosphere Sensor Data
DOI: 10.15468/dl.gc8gm3
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudopalicus declivis Castro, 2000"
 ]
}

 The dataset includes 22 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0131360-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rjz52u
title: Occurrence Download

description: A dataset containing 451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-6410.0",
 "Month is one of (March, April, May, June, July)",
 "TaxonKey is Selasphorus sasin (R.Lesson, 1829)",
 "Year 1859-1941"
 ]
}

 The dataset includes 451 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0130856-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.468zcv
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Portulaca gilliesii Hook."
 ]
}

 The dataset includes 47 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0117777-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dkea88
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudoproto fallax Mayer, 1903"
 ]
}

 The dataset includes 67 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0131375-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rjdkht
title: Occurrence Download

description: A dataset containing 2099 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pontophilus norvegicus (M.Sars, 1861)"
 ]
}

 The dataset includes 2099 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0130857-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u5uzfb
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Richardia stellaris (Cham. & Schltdl.) Steud."
 ]
}

 The dataset includes 67 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0117905-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hr6s6s
title: Occurrence Download

description: A dataset containing 1005 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magnolia sieboldii K.Koch",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1005 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0264991-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b4hde8
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pontopolites typicus Scott T., 1894"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0130859-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.