cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11191244
title: GunPoint

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset involves one female actor and one male actor making a motion with their hand. The two classes are: Gun-Draw and Point: For Gun-Draw the actors have their hands by their sides. They draw a replicate gun from a hip-mounted holster, point it at a target for approximately one second, then return the gun to the holster, and their hands to their sides. For Point the actors have their gun by their sides. They point with their index fingers to a target for approximately one second, and then return their hands to their sides. For both classes, we tracked the centroid of the actor's right hands in both X- and Y-axes, which appear to be highly correlated. The data in the archive is just the X-axis. Class 1 is "gun" and class 2 is "no gun (pointing)"

http://alumni.cs.ucr.edu/~ratana/RatanamC.pdf

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11194425
title: GunPointAgeSpan

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is a remake of the famous GunPoint dataset released in 2003. We strive to mimic in every aspect the recording of the original GunPoint. The actors include one male and one female. They are the same actors who created the original GunPoint.  We record two scenarios, Gun and Point (also known as Gun and NoGun). In each scenario, the actors aim at a eye-level target. The difference between Gun and Point is that for the Gun scenario, the actors hold a gun, and in the Point scenario, the actors point with just their fingers. A complete Gun action involves the actor moves hand from an initial rest position, points the gun at target, puts gun back to waist holster and then brings free hand to the initial rest position. Each complete action conforms to a five-second cycle. With 30fps, this translates into 150 frames per action. We extract the centroid of the hand from each frame and use its x-axis coordinate to form a time series.  We refer to the old GunPoint as GunPoint 2003 and the new GunPoint as Gunpoint 2018. We merged GunPoint 2003 and GunPoint 2018 to make three datasets.  Let us denote: - G: Gun - P: Point - M: Male - F: Female - 03: The year 2003 - 18: The year 2018  ## GunPointAgeSpan  The task is to classify Gun and Point. There are 4 flavors of each class. - Class 1: Gun (FG03, MG03, FG18, MG18) - Class 2: Point (FP03, MP03, FP18, MP18)   ## GunPointMaleVersusFemale  The task is to classify Male and Female. There are 4 flavors of each class. - Class 1: Female (FG03, FP03, FG18, FP18) - Class 2: Male (MG03, MP03, MG18, MP18)    ## GunPointOldVersusYoung  The task is to classify the older and younger version of the actors. There are 4 flavors of each class. - Class 1: Young (FG03, MG03, FP03, MP03) - Class 2: Old (FG18, MG18, FP18, MP18)    There is nothing to infer from the order of examples in the train and test set.  Data created by Ann Ratanamahatana and Eamonn Keogh. Data edited by Hoang Anh Dau.

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11194424
title: GunPointAgeSpan

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is a remake of the famous GunPoint dataset released in 2003. We strive to mimic in every aspect the recording of the original GunPoint. The actors include one male and one female. They are the same actors who created the original GunPoint.  We record two scenarios, Gun and Point (also known as Gun and NoGun). In each scenario, the actors aim at a eye-level target. The difference between Gun and Point is that for the Gun scenario, the actors hold a gun, and in the Point scenario, the actors point with just their fingers. A complete Gun action involves the actor moves hand from an initial rest position, points the gun at target, puts gun back to waist holster and then brings free hand to the initial rest position. Each complete action conforms to a five-second cycle. With 30fps, this translates into 150 frames per action. We extract the centroid of the hand from each frame and use its x-axis coordinate to form a time series.  We refer to the old GunPoint as GunPoint 2003 and the new GunPoint as Gunpoint 2018. We merged GunPoint 2003 and GunPoint 2018 to make three datasets.  Let us denote: - G: Gun - P: Point - M: Male - F: Female - 03: The year 2003 - 18: The year 2018  ## GunPointAgeSpan  The task is to classify Gun and Point. There are 4 flavors of each class. - Class 1: Gun (FG03, MG03, FG18, MG18) - Class 2: Point (FP03, MP03, FP18, MP18)   ## GunPointMaleVersusFemale  The task is to classify Male and Female. There are 4 flavors of each class. - Class 1: Female (FG03, FP03, FG18, FP18) - Class 2: Male (MG03, MP03, MG18, MP18)    ## GunPointOldVersusYoung  The task is to classify the older and younger version of the actors. There are 4 flavors of each class. - Class 1: Young (FG03, MG03, FP03, MP03) - Class 2: Old (FG18, MG18, FP18, MP18)    There is nothing to infer from the order of examples in the train and test set.  Data created by Ann Ratanamahatana and Eamonn Keogh. Data edited by Hoang Anh Dau.

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11194428
title: GunPointMaleVersusFemale

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is a remake of the famous GunPoint dataset released in 2003. We strive to mimic in every aspect the recording of the original GunPoint. The actors include one male and one female. They are the same actors who created the original GunPoint.  We record two scenarios, Gun and Point (also known as Gun and NoGun). In each scenario, the actors aim at a eye-level target. The difference between Gun and Point is that for the Gun scenario, the actors hold a gun, and in the Point scenario, the actors point with just their fingers. A complete Gun action involves the actor moves hand from an initial rest position, points the gun at target, puts gun back to waist holster and then brings free hand to the initial rest position. Each complete action conforms to a five-second cycle. With 30fps, this translates into 150 frames per action. We extract the centroid of the hand from each frame and use its x-axis coordinate to form a time series.  We refer to the old GunPoint as GunPoint 2003 and the new GunPoint as Gunpoint 2018. We merged GunPoint 2003 and GunPoint 2018 to make three datasets.  Let us denote: - G: Gun - P: Point - M: Male - F: Female - 03: The year 2003 - 18: The year 2018  ## GunPointAgeSpan  The task is to classify Gun and Point. There are 4 flavors of each class. - Class 1: Gun (FG03, MG03, FG18, MG18) - Class 2: Point (FP03, MP03, FP18, MP18)   ## GunPointMaleVersusFemale  The task is to classify Male and Female. There are 4 flavors of each class. - Class 1: Female (FG03, FP03, FG18, FP18) - Class 2: Male (MG03, MP03, MG18, MP18)    ## GunPointOldVersusYoung  The task is to classify the older and younger version of the actors. There are 4 flavors of each class. - Class 1: Young (FG03, MG03, FP03, MP03) - Class 2: Old (FG18, MG18, FP18, MP18)    There is nothing to infer from the order of examples in the train and test set.  Data created by Ann Ratanamahatana and Eamonn Keogh. Data edited by Hoang Anh Dau.

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11194429
title: GunPointMaleVersusFemale

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is a remake of the famous GunPoint dataset released in 2003. We strive to mimic in every aspect the recording of the original GunPoint. The actors include one male and one female. They are the same actors who created the original GunPoint.  We record two scenarios, Gun and Point (also known as Gun and NoGun). In each scenario, the actors aim at a eye-level target. The difference between Gun and Point is that for the Gun scenario, the actors hold a gun, and in the Point scenario, the actors point with just their fingers. A complete Gun action involves the actor moves hand from an initial rest position, points the gun at target, puts gun back to waist holster and then brings free hand to the initial rest position. Each complete action conforms to a five-second cycle. With 30fps, this translates into 150 frames per action. We extract the centroid of the hand from each frame and use its x-axis coordinate to form a time series.  We refer to the old GunPoint as GunPoint 2003 and the new GunPoint as Gunpoint 2018. We merged GunPoint 2003 and GunPoint 2018 to make three datasets.  Let us denote: - G: Gun - P: Point - M: Male - F: Female - 03: The year 2003 - 18: The year 2018  ## GunPointAgeSpan  The task is to classify Gun and Point. There are 4 flavors of each class. - Class 1: Gun (FG03, MG03, FG18, MG18) - Class 2: Point (FP03, MP03, FP18, MP18)   ## GunPointMaleVersusFemale  The task is to classify Male and Female. There are 4 flavors of each class. - Class 1: Female (FG03, FP03, FG18, FP18) - Class 2: Male (MG03, MP03, MG18, MP18)    ## GunPointOldVersusYoung  The task is to classify the older and younger version of the actors. There are 4 flavors of each class. - Class 1: Young (FG03, MG03, FP03, MP03) - Class 2: Old (FG18, MG18, FP18, MP18)    There is nothing to infer from the order of examples in the train and test set.  Data created by Ann Ratanamahatana and Eamonn Keogh. Data edited by Hoang Anh Dau.

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11194436
title: GunPointOldVersusYoung

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is a remake of the famous GunPoint dataset released in 2003. We strive to mimic in every aspect the recording of the original GunPoint. The actors include one male and one female. They are the same actors who created the original GunPoint.  We record two scenarios, Gun and Point (also known as Gun and NoGun). In each scenario, the actors aim at a eye-level target. The difference between Gun and Point is that for the Gun scenario, the actors hold a gun, and in the Point scenario, the actors point with just their fingers. A complete Gun action involves the actor moves hand from an initial rest position, points the gun at target, puts gun back to waist holster and then brings free hand to the initial rest position. Each complete action conforms to a five-second cycle. With 30fps, this translates into 150 frames per action. We extract the centroid of the hand from each frame and use its x-axis coordinate to form a time series.  We refer to the old GunPoint as GunPoint 2003 and the new GunPoint as Gunpoint 2018. We merged GunPoint 2003 and GunPoint 2018 to make three datasets.  Let us denote: - G: Gun - P: Point - M: Male - F: Female - 03: The year 2003 - 18: The year 2018  ## GunPointAgeSpan  The task is to classify Gun and Point. There are 4 flavors of each class. - Class 1: Gun (FG03, MG03, FG18, MG18) - Class 2: Point (FP03, MP03, FP18, MP18)   ## GunPointMaleVersusFemale  The task is to classify Male and Female. There are 4 flavors of each class. - Class 1: Female (FG03, FP03, FG18, FP18) - Class 2: Male (MG03, MP03, MG18, MP18)    ## GunPointOldVersusYoung  The task is to classify the older and younger version of the actors. There are 4 flavors of each class. - Class 1: Young (FG03, MG03, FP03, MP03) - Class 2: Old (FG18, MG18, FP18, MP18)    There is nothing to infer from the order of examples in the train and test set.  Data created by Ann Ratanamahatana and Eamonn Keogh. Data edited by Hoang Anh Dau.

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11194437
title: GunPointOldVersusYoung

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is a remake of the famous GunPoint dataset released in 2003. We strive to mimic in every aspect the recording of the original GunPoint. The actors include one male and one female. They are the same actors who created the original GunPoint.  We record two scenarios, Gun and Point (also known as Gun and NoGun). In each scenario, the actors aim at a eye-level target. The difference between Gun and Point is that for the Gun scenario, the actors hold a gun, and in the Point scenario, the actors point with just their fingers. A complete Gun action involves the actor moves hand from an initial rest position, points the gun at target, puts gun back to waist holster and then brings free hand to the initial rest position. Each complete action conforms to a five-second cycle. With 30fps, this translates into 150 frames per action. We extract the centroid of the hand from each frame and use its x-axis coordinate to form a time series.  We refer to the old GunPoint as GunPoint 2003 and the new GunPoint as Gunpoint 2018. We merged GunPoint 2003 and GunPoint 2018 to make three datasets.  Let us denote: - G: Gun - P: Point - M: Male - F: Female - 03: The year 2003 - 18: The year 2018  ## GunPointAgeSpan  The task is to classify Gun and Point. There are 4 flavors of each class. - Class 1: Gun (FG03, MG03, FG18, MG18) - Class 2: Point (FP03, MP03, FP18, MP18)   ## GunPointMaleVersusFemale  The task is to classify Male and Female. There are 4 flavors of each class. - Class 1: Female (FG03, FP03, FG18, FP18) - Class 2: Male (MG03, MP03, MG18, MP18)    ## GunPointOldVersusYoung  The task is to classify the older and younger version of the actors. There are 4 flavors of each class. - Class 1: Young (FG03, MG03, FP03, MP03) - Class 2: Old (FG18, MG18, FP18, MP18)    There is nothing to infer from the order of examples in the train and test set.  Data created by Ann Ratanamahatana and Eamonn Keogh. Data edited by Hoang Anh Dau.

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.15468/dl.rqaueb
title: Occurrence Download

description: A dataset containing 46883 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Celastrus orbiculatus"
 ]
}

 The dataset includes 46883 records from 415 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011834-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17913038
title: MyBAD: Malaysian Bird Activity Detection Dataset

description: MyBAD (Malaysian Bird Activity Detection) is a large-scale audio dataset for Edge AI bird detection. It includes 25,000 positive (bird-present) and 25,000 negative (bird-absent) 3-second WAV clips, sampled at 16 kHz. 

The dataset is designed for automated bird activity detection, biodiversity monitoring, and real-time acoustic sensing on low-power devices. It is fully machine-learning ready.

Positive samples were extracted from Xeno-canto recordings in Malaysia and neighboring countries (Thailand, Indonesia, Singapore, and Brunei) through December 2025. Samples were obtained by extracting the highest-energy 3-second segment from each Xeno-canto recording.

Negative samples were drawn primarily from the BirdCLEF “BAD” competition datasets (BirdVox, Freefield1010, and Warblr) and were further supplemented with non-bird environmental audio from the FSC22, ESC50, and DataSEC datasets.

Released under CC BY 4.0, MyBAD provides an open, reproducible resource for developing Edge AI models for tropical bird monitoring and acoustic biodiversity assessment.

Note: This dataset will be formally described in a forthcoming paper introducing the dataset and companion featherweight bird-activity detector.
DOI: 10.5281/zenodo.18290494
title: MyBAD: Malaysian Bird Activity Detection Dataset

description: MyBAD (Malaysian Bird Activity Detection) is a large-scale audio dataset for Edge AI bird detection. It includes 25,000 positive (bird-present) and 25,000 negative (bird-absent) 3-second WAV clips, sampled at 16 kHz. 

The dataset is designed for automated bird activity detection, biodiversity monitoring, and real-time acoustic sensing on low-power devices. It is fully machine-learning ready.

Positive samples were extracted from Xeno-canto recordings in Malaysia and neighboring countries (Thailand, Indonesia, Singapore, and Brunei) through December 2025. Samples were obtained by extracting the highest-energy 3-second segment from each Xeno-canto recording.

Negative samples were drawn primarily from the BirdCLEF “BAD” competition datasets (BirdVox, Freefield1010, and Warblr) and were further supplemented with non-bird environmental audio from the FSC22, ESC50, and DataSEC datasets.

Released under CC BY 4.0, MyBAD provides an open, reproducible resource for developing Edge AI models for tropical bird monitoring and acoustic biodiversity assessment.

Note: This dataset will be formally described in a forthcoming paper introducing the dataset and companion featherweight bird-activity detector.
DOI: 10.5281/zenodo.11198408
title: Yoga

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The dataset was obtained by capturing two actors transiting between yoga poses in front of a green screen. The problem is to discriminate between one actor (male) and another (female). Each image was converted to a one dimensional series by finding the outline and measuring the distance of the outline to the centre. The dataset was obtained by capturing two actors transiting between yoga poses in front of a green screen. The problem is to discriminate between one actor (male) and another (female). Each image was converted to a one dimensional series by finding the outline and measuring the distance of the outline to the centre.

Donator: L. Wei & E. Keogh
DOI: 10.5281/zenodo.11198405
title: WormsTwoClass

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Caenorhabditis elegans is a roundworm commonly used as a model organism in the study of genetics. The movement of these worms is known to be a useful indicator for understanding behavioural genetics. Brown {\em et al.} "A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion" describe a system for recording the motion of worms on an agar plate and measuring a range of human-defined features. It has been shown that the space of shapes Caenorhabditis elegans adopts on an agar plate can be represented by combinations of four base shapes, or eigenworms. Once the worm outline is extracted, each frame of worm motion can be captured by four scalars representing the amplitudes along each dimension when the shape is projected onto the four eigenworms.

The data relates to 258 traces of worms converted into four "eigenworm" series. The eigenworm data are lengths from 17984 to 100674 (sampled at 30 Hz, so from 10 minutes to 1 hour) and in four dimensions (eigwnworm 1 to 4). There are five classes:N2,goa-1,unc-1,unc-38 and un63. N2 is wildtype (i.e. normal) the other 4 are mutant strains. These datasets are the first dimension only (first eigenworm)

The problems Worms.arff and WormsTwoClass.arff are series of first eigenworm1 averaged down so that all series are lengths 900 (the single hour long series is discarded). This smoothing is likely to discard discriminatory information. The Yemini features obtains nearly 100\% accuracy, although we have not independently verified this. 

we address the problem of classifying individual worms as wild-type or mutant based on the time series of the first eigenworm, down-sampled to second-long intervals. We have 257 cases, which we split 70\%/30\% into a train and test set. Each series has 900 observations, and each worm is classified as either wild-type (the N2 reference strain - 109 cases) or one of four mutant types: goa-1 (44 cases); unc-1 (35 cases); unc-38 (45 cases) and unc-63 (25 cases). The data were extracted from the {\em C. elegans} behavioural database~\cite{wormWeb}. The formatted classification problems are available from the website associated with this paper~\cite{tscWeb}.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11198406
title: WormsTwoClass

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Caenorhabditis elegans is a roundworm commonly used as a model organism in the study of genetics. The movement of these worms is known to be a useful indicator for understanding behavioural genetics. Brown {\em et al.} "A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion" describe a system for recording the motion of worms on an agar plate and measuring a range of human-defined features. It has been shown that the space of shapes Caenorhabditis elegans adopts on an agar plate can be represented by combinations of four base shapes, or eigenworms. Once the worm outline is extracted, each frame of worm motion can be captured by four scalars representing the amplitudes along each dimension when the shape is projected onto the four eigenworms.

The data relates to 258 traces of worms converted into four "eigenworm" series. The eigenworm data are lengths from 17984 to 100674 (sampled at 30 Hz, so from 10 minutes to 1 hour) and in four dimensions (eigwnworm 1 to 4). There are five classes:N2,goa-1,unc-1,unc-38 and un63. N2 is wildtype (i.e. normal) the other 4 are mutant strains. These datasets are the first dimension only (first eigenworm)

The problems Worms.arff and WormsTwoClass.arff are series of first eigenworm1 averaged down so that all series are lengths 900 (the single hour long series is discarded). This smoothing is likely to discard discriminatory information. The Yemini features obtains nearly 100\% accuracy, although we have not independently verified this. 

we address the problem of classifying individual worms as wild-type or mutant based on the time series of the first eigenworm, down-sampled to second-long intervals. We have 257 cases, which we split 70\%/30\% into a train and test set. Each series has 900 observations, and each worm is classified as either wild-type (the N2 reference strain - 109 cases) or one of four mutant types: goa-1 (44 cases); unc-1 (35 cases); unc-38 (45 cases) and unc-63 (25 cases). The data were extracted from the {\em C. elegans} behavioural database~\cite{wormWeb}. The formatted classification problems are available from the website associated with this paper~\cite{tscWeb}.

Donator: A. Bagnall