cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11190929
title: EOGHorizontalSignal

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 data are electrooculography signal (EOG). EOG is measurements of the electrical potential between electrodes placed at points close to the eyes. The EOG recording device is BlueGain, a commercial biomedical amplifier. Four measurement electrodes are attached to the left, right, upper and lower sides of the left eye. A two-channel (horizontal and vertical) EOG signal is obtained by taking the differences of the signals between the electrodes on the left and right sides and those between the upper and lower sides, respectively. The sampling rate was 1.0KHz.  We use the "Isolated data" described in [1]. This dataset includes 6 participants eye-writing 12 types of Japanese Katakana strokes.  There are 12 classes. Class labels correspond to stroke ID. See Fig 7 in [1] for the mapping of stroke ID - stroke type.   The horizontal eye movement (left-right)  There is nothing to infer from the order of examples in the train and test set.  Data created by Fang Fuming and Takahiro Shinozaki (see [1]. Data edited by Hoang Anh Dau.  

[1] Fang, Fuming, and Takahiro Shinozaki. "Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems." PloS one 13.2 (2018): e0192684.

Donator: E. Keogh, H. A. Dau
DOI: 10.5281/zenodo.11190930
title: EOGHorizontalSignal

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 data are electrooculography signal (EOG). EOG is measurements of the electrical potential between electrodes placed at points close to the eyes. The EOG recording device is BlueGain, a commercial biomedical amplifier. Four measurement electrodes are attached to the left, right, upper and lower sides of the left eye. A two-channel (horizontal and vertical) EOG signal is obtained by taking the differences of the signals between the electrodes on the left and right sides and those between the upper and lower sides, respectively. The sampling rate was 1.0KHz.  We use the "Isolated data" described in [1]. This dataset includes 6 participants eye-writing 12 types of Japanese Katakana strokes.  There are 12 classes. Class labels correspond to stroke ID. See Fig 7 in [1] for the mapping of stroke ID - stroke type.   The horizontal eye movement (left-right)  There is nothing to infer from the order of examples in the train and test set.  Data created by Fang Fuming and Takahiro Shinozaki (see [1]. Data edited by Hoang Anh Dau.  

[1] Fang, Fuming, and Takahiro Shinozaki. "Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems." PloS one 13.2 (2018): e0192684.

Donator: E. Keogh, H. A. Dau
DOI: 10.5281/zenodo.11190950
title: EOGVerticalSignal

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 data are electrooculography signal (EOG). EOG is measurements of the electrical potential between electrodes placed at points close to the eyes. The EOG recording device is BlueGain, a commercial biomedical amplifier. Four measurement electrodes are attached to the left, right, upper and lower sides of the left eye. A two-channel (horizontal and vertical) EOG signal is obtained by taking the differences of the signals between the electrodes on the left and right sides and those between the upper and lower sides, respectively. The sampling rate was 1.0KHz.  We use the "Isolated data" described in [1]. This dataset includes 6 participants eye-writing 12 types of Japanese Katakana strokes.  There are 12 classes. Class labels correspond to stroke ID. See Fig 7 in [1] for the mapping of stroke ID - stroke type.   The vertical eye movement (up-down)  There is nothing to infer from the order of examples in the train and test set.  Data created by Fang Fuming and Takahiro Shinozaki (see [1]. Data edited by Hoang Anh Dau.  

[1] Fang, Fuming, and Takahiro Shinozaki. "Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems." PloS one 13.2 (2018): e0192684.

Donator: E. Keogh, H. A. Dau
DOI: 10.5281/zenodo.11190951
title: EOGVerticalSignal

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 data are electrooculography signal (EOG). EOG is measurements of the electrical potential between electrodes placed at points close to the eyes. The EOG recording device is BlueGain, a commercial biomedical amplifier. Four measurement electrodes are attached to the left, right, upper and lower sides of the left eye. A two-channel (horizontal and vertical) EOG signal is obtained by taking the differences of the signals between the electrodes on the left and right sides and those between the upper and lower sides, respectively. The sampling rate was 1.0KHz.  We use the "Isolated data" described in [1]. This dataset includes 6 participants eye-writing 12 types of Japanese Katakana strokes.  There are 12 classes. Class labels correspond to stroke ID. See Fig 7 in [1] for the mapping of stroke ID - stroke type.   The vertical eye movement (up-down)  There is nothing to infer from the order of examples in the train and test set.  Data created by Fang Fuming and Takahiro Shinozaki (see [1]. Data edited by Hoang Anh Dau.  

[1] Fang, Fuming, and Takahiro Shinozaki. "Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems." PloS one 13.2 (2018): e0192684.

Donator: E. Keogh, H. A. Dau
DOI: 10.5281/zenodo.11190985
title: EthanolLevel

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 part of the project with Scotch Whisky Research Institute into detecting forged spirits in a non-intrusive manner. One way of detecting forgery without sampling the wine is through inspecting ethanol level by spectrograph.  The dataset covers 20 different bottle types and four levels of alcohol: 35%, 38%, 40% and 45%. Each series is a spectrograph of 1751 observations.  This dataset is an example of when it is wrong to merge and resample, because the train/test split are constructed so that the same bottle type is never in both train and test sets.  There are 4 classes.  - Class 1: E35 - Class 2: E38 - Class 3: E40 - Class 4: E45  For more information about this dataset, see [1,2].  

[1] Lines, Jason, Sarah Taylor, and Anthony Bagnall. "Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification." Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016.

[2] J. Large, E. K. Kemsley, N.Wellner, I. Goodall, and A. Bagnall, Detecting forged alcohol non-invasively through vibrational spectroscopy and machine learning," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2018.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11191011
title: FaceAll

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/.

FaceAll was generated at UCR by Xiaopeng Xi. The faces belong to 14 grad students. Each facial outline is mapped onto a one dimensional series.

Donator: X. Xi, E. Keogh
DOI: 10.5281/zenodo.11190984
title: EthanolLevel

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 part of the project with Scotch Whisky Research Institute into detecting forged spirits in a non-intrusive manner. One way of detecting forgery without sampling the wine is through inspecting ethanol level by spectrograph.  The dataset covers 20 different bottle types and four levels of alcohol: 35%, 38%, 40% and 45%. Each series is a spectrograph of 1751 observations.  This dataset is an example of when it is wrong to merge and resample, because the train/test split are constructed so that the same bottle type is never in both train and test sets.  There are 4 classes.  - Class 1: E35 - Class 2: E38 - Class 3: E40 - Class 4: E45  For more information about this dataset, see [1,2].  

[1] Lines, Jason, Sarah Taylor, and Anthony Bagnall. "Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification." Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016.

[2] J. Large, E. K. Kemsley, N.Wellner, I. Goodall, and A. Bagnall, Detecting forged alcohol non-invasively through vibrational spectroscopy and machine learning," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2018.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11191010
title: FaceAll

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/.

FaceAll was generated at UCR by Xiaopeng Xi. The faces belong to 14 grad students. Each facial outline is mapped onto a one dimensional series.

Donator: X. Xi, E. Keogh
DOI: 10.5281/zenodo.11191042
title: FaceFour

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/.

A Four class face outline problem, we are not sure of the exact provenance

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11191041
title: FaceFour

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/.

A Four class face outline problem, we are not sure of the exact provenance

Donator: A. Ratanamahatana, E. Keogh
DOI: 10.5281/zenodo.11191065
title: FacesUCR

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/.

FacesAll is rotationally aligned version of FaceAll with a different train/test split

Donator: X. Xi, E. Keogh
DOI: 10.5281/zenodo.11191064
title: FacesUCR

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/.

FacesAll is rotationally aligned version of FaceAll with a different train/test split

Donator: X. Xi, E. Keogh
DOI: 10.5281/zenodo.11191097
title: FiftyWords

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/.

FiftyWords is a data set of word outlines taken from the George Washington library by T. Rath and used in the paper "Word image matching using dynamic time warping", CVPR 2003. Each case is a word. A series is formed by taking the height profile of the word.

Donator: T. Rath, R. Manmatha
DOI: 10.5281/zenodo.11191096
title: FiftyWords

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/.

FiftyWords is a data set of word outlines taken from the George Washington library by T. Rath and used in the paper "Word image matching using dynamic time warping", CVPR 2003. Each case is a word. A series is formed by taking the height profile of the word.

Donator: T. Rath, R. Manmatha
DOI: 10.5281/zenodo.11191140
title: Fish

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/.

A set of Fish outlines originally used with contour matching in the paper "Contour Matching for Fish Species Recognition and Migration Monitoring" by D. Lee. Each class is a different species. Outlines were derived at UCR.

http://link.springer.com/chapter/10.1007%2F978-3-540-78534-7_8

Donator: D. Lee
DOI: 10.5281/zenodo.11191141
title: Fish

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/.

A set of Fish outlines originally used with contour matching in the paper "Contour Matching for Fish Species Recognition and Migration Monitoring" by D. Lee. Each class is a different species. Outlines were derived at UCR.

http://link.springer.com/chapter/10.1007%2F978-3-540-78534-7_8

Donator: D. Lee
DOI: 10.5281/zenodo.11191163
title: FordA

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 data was originally used in a competition in the IEEE World Congress on Computational Intelligence, 2008. The classification problem is to diagnose whether a certain symptom exists or does not exist in an automotive subsystem. Each case consists of 500 measurements of engine noise and a classification. There are two separate problems:

For FordA the train and test data set were collected in typical operating conditions, with minimal noise contamination.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11191164
title: FordA

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 data was originally used in a competition in the IEEE World Congress on Computational Intelligence, 2008. The classification problem is to diagnose whether a certain symptom exists or does not exist in an automotive subsystem. Each case consists of 500 measurements of engine noise and a classification. There are two separate problems:

For FordA the train and test data set were collected in typical operating conditions, with minimal noise contamination.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11191172
title: FordB

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 data was originally used in a competition in the IEEE World Congress on Computational Intelligence, 2008. The classification problem is to diagnose whether a certain symptom exists or does not exist in an automotive subsystem. Each case consists of 500 measurements of engine noise and a classification. There are two separate problems:

For FordB the training data were collected in typical operating conditions, but the test data samples were collected under noisy conditions.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11191171
title: FordB

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 data was originally used in a competition in the IEEE World Congress on Computational Intelligence, 2008. The classification problem is to diagnose whether a certain symptom exists or does not exist in an automotive subsystem. Each case consists of 500 measurements of engine noise and a classification. There are two separate problems:

For FordB the training data were collected in typical operating conditions, but the test data samples were collected under noisy conditions.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11191183
title: FreezerRegularTrain

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 collection of this data was part of the project titled Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology (REFIT). The REFIT dataset includes data from 20 households from Loughborough area over the period 2013-2014 (see [1]).  We use data of freezers in House 1 to make two datasets. These two datasets share a same test set and only differ in the number of training instances. There is nothing to infer from the order of examples in the train and test set  Data created by David Murray et al. (see [1]). There are two classes, one representing the power demand of the fridge freezer in the kitchen, the other representing the power demand of the (less frequently used) freezer in the garage. They are hard to tell apart globally but they differ locally. Data edited by Shaghayegh Gharghabi, Hoang Anh Dau and Eamonn Keogh.  

[1] Murray, David et al., "A data management platform for personalised real-time energy feedback", Proceedings of the 8th International Conference on Engery Efficiency in Domestic Appliances and Lighting, 2015.  

Donator: REFIT project
DOI: 10.5281/zenodo.11191184
title: FreezerRegularTrain

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 collection of this data was part of the project titled Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology (REFIT). The REFIT dataset includes data from 20 households from Loughborough area over the period 2013-2014 (see [1]).  We use data of freezers in House 1 to make two datasets. These two datasets share a same test set and only differ in the number of training instances. There is nothing to infer from the order of examples in the train and test set  Data created by David Murray et al. (see [1]). There are two classes, one representing the power demand of the fridge freezer in the kitchen, the other representing the power demand of the (less frequently used) freezer in the garage. They are hard to tell apart globally but they differ locally. Data edited by Shaghayegh Gharghabi, Hoang Anh Dau and Eamonn Keogh.  

[1] Murray, David et al., "A data management platform for personalised real-time energy feedback", Proceedings of the 8th International Conference on Engery Efficiency in Domestic Appliances and Lighting, 2015.  

Donator: REFIT project
DOI: 10.5281/zenodo.11191210
title: FreezerSmallTrain

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 collection of this data was part of the project titled Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology (REFIT). The REFIT dataset includes data from 20 households from Loughborough area over the period 2013-2014 (see [1]).  We use data of freezers in House 1 to make two datasets. These two datasets share a same test set and only differ in the number of training instances.  There are two classes, one representing the power demand of the fridge freezer in the kitchen, the other representing the power demand of the (less frequently used) freezer in the garage. They are hard to tell apart globally but they differ locally.

[1] Murray, David et al., "A data management platform for personalised real-time energy feedback", Proceedings of the 8th International Conference on Engery Efficiency in Domestic Appliances and Lighting, 2015.

Donator: REFIT project
DOI: 10.5281/zenodo.11191211
title: FreezerSmallTrain

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 collection of this data was part of the project titled Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology (REFIT). The REFIT dataset includes data from 20 households from Loughborough area over the period 2013-2014 (see [1]).  We use data of freezers in House 1 to make two datasets. These two datasets share a same test set and only differ in the number of training instances.  There are two classes, one representing the power demand of the fridge freezer in the kitchen, the other representing the power demand of the (less frequently used) freezer in the garage. They are hard to tell apart globally but they differ locally.

[1] Murray, David et al., "A data management platform for personalised real-time energy feedback", Proceedings of the 8th International Conference on Engery Efficiency in Domestic Appliances and Lighting, 2015.

Donator: REFIT project
DOI: 10.5281/zenodo.17823191
title: edna_japan: v1.0.0

description: Code scripts for Osada et al. "Large-scale environmental DNA survey reveals niche axes of a regional coastal fish community"
DOI: 10.5281/zenodo.17823192
title: edna_japan: v1.0.0

description: Code scripts for Osada et al. "Large-scale environmental DNA survey reveals niche axes of a regional coastal fish community"
DOI: 10.5281/zenodo.11191243
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