cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11186385
title: DistalPhalanxOutlineAgeGroup

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 series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

http://www.ncbi.nlm.nih.gov/pubmed/10940607

Donator: L. Davis, A. Bagnall
DOI: 10.5281/zenodo.11186386
title: DistalPhalanxOutlineAgeGroup

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 series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

http://www.ncbi.nlm.nih.gov/pubmed/10940607

Donator: L. Davis, A. Bagnall
DOI: 10.5281/zenodo.11186597
title: DistalPhalanxOutlineCorrect

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 series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

http://www.ncbi.nlm.nih.gov/pubmed/10940607

Donator: L. Davis, A. Bagnall
DOI: 10.5281/zenodo.11186596
title: DistalPhalanxOutlineCorrect

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 series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

http://www.ncbi.nlm.nih.gov/pubmed/10940607

Donator: L. Davis, A. Bagnall
DOI: 10.5281/zenodo.11186609
title: DistalPhalanxTW

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 series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

http://www.ncbi.nlm.nih.gov/pubmed/10940607

Donator: L. Davis, A. Bagnall
DOI: 10.5281/zenodo.11186610
title: DistalPhalanxTW

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 series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

http://www.ncbi.nlm.nih.gov/pubmed/10940607

Donator: L. Davis, A. Bagnall
DOI: 10.5281/zenodo.11186659
title: Earthquakes

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 earthquake classification problem involves predicting whether a major event is about to occur based on the most recent readings in the surrounding area. The data is taken from Northern California Earthquake Data Center and each data is an averaged reading for one hour, with the first reading taken on Dec 1st 1967, the last in 2003. We transform this single time series into a classification problem by first defining a major event as any reading of over 5 on the Rictor scale. Major events are often followed by aftershocks. The physics of these are well understood and their detection is not the objective of this exercise. Hence we consider a positive case to be one where a major event is not preceded by another major event for at least 512 hours. To construct a negative case, we consider instances where there is a reading below 4 (to avoid blurring of the boundaries between major and non major events) that is preceded by at least 20 readings in the previous 512 hours that are non-zero (to avoid trivial negative cases). None of the cases overlap in time (i.e. we perform a segmentation rather than use a sliding window). Of the 86,066 hourly readings, we produce 368 negative cases and 93 positive.

Donator: A. Bagnall
DOI: 10.5281/zenodo.11186658
title: Earthquakes

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 earthquake classification problem involves predicting whether a major event is about to occur based on the most recent readings in the surrounding area. The data is taken from Northern California Earthquake Data Center and each data is an averaged reading for one hour, with the first reading taken on Dec 1st 1967, the last in 2003. We transform this single time series into a classification problem by first defining a major event as any reading of over 5 on the Rictor scale. Major events are often followed by aftershocks. The physics of these are well understood and their detection is not the objective of this exercise. Hence we consider a positive case to be one where a major event is not preceded by another major event for at least 512 hours. To construct a negative case, we consider instances where there is a reading below 4 (to avoid blurring of the boundaries between major and non major events) that is preceded by at least 20 readings in the previous 512 hours that are non-zero (to avoid trivial negative cases). None of the cases overlap in time (i.e. we perform a segmentation rather than use a sliding window). Of the 86,066 hourly readings, we produce 368 negative cases and 93 positive.

Donator: A. Bagnall
DOI: 10.48544/54e4db17-24f6-45b6-b7bf-7637748bf06e
title: Canastell Zero Waste
DOI: 10.5281/zenodo.11186674
title: ECG200

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 was formatted by R. Olszewski as part of his thesis "Generalized feature extraction for structural pattern recognition in time-series data" at Carnegie Mellon University, 2001. Each series traces the electrical activity recorded during one heartbeat. The two classes are a normal heartbeat and a Myocardial Infarction. 

http://dl.acm.org/citation.cfm?id=935627

Donator: R. Olszewski
DOI: 10.5281/zenodo.11186675
title: ECG200

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 was formatted by R. Olszewski as part of his thesis "Generalized feature extraction for structural pattern recognition in time-series data" at Carnegie Mellon University, 2001. Each series traces the electrical activity recorded during one heartbeat. The two classes are a normal heartbeat and a Myocardial Infarction. 

http://dl.acm.org/citation.cfm?id=935627

Donator: R. Olszewski
DOI: 10.5281/zenodo.11186691
title: ECG5000

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 original dataset for "ECG5000" is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record "chf07". It was originally published in "Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23)". The data was pre-processed in two steps: (1) extract each heartbeat, (2) make each heartbeat equal length using interpolation. This dataset was originally used in paper "A general framework for never-ending learning from time series streams", DAMI 29(6). After that, 5,000 heartbeats were randomly selected. The patient has severe congestive heart failure and the class values were obtained by automated annotation.

http://dl.acm.org/citation.cfm?id=2833467

Donator: Y. Chen, E. Keogh
DOI: 10.5281/zenodo.11186692
title: ECG5000

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 original dataset for "ECG5000" is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record "chf07". It was originally published in "Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23)". The data was pre-processed in two steps: (1) extract each heartbeat, (2) make each heartbeat equal length using interpolation. This dataset was originally used in paper "A general framework for never-ending learning from time series streams", DAMI 29(6). After that, 5,000 heartbeats were randomly selected. The patient has severe congestive heart failure and the class values were obtained by automated annotation.

http://dl.acm.org/citation.cfm?id=2833467

Donator: Y. Chen, E. Keogh
DOI: 10.5281/zenodo.10422423
title: Data set of strain, differential phase, RS value, strain and PGV.

description: The data stored here is the observations of strain, differential phase, RS value, strain and PGV in the paper of ‘Potential of Earthquake Strong Motion Observation Utilizing a Linear Estimation Method for Phase Cycle Skipping in Distributed Acoustic Sensing’, by Katakami et al.
DOI: 10.5281/zenodo.10422424
title: Data set of strain, differential phase, RS value, strain and PGV.

description: The data stored here is the observations of strain, differential phase, RS value, strain and PGV in the paper of ‘Potential of Earthquake Strong Motion Observation Utilizing a Linear Estimation Method for Phase Cycle Skipping in Distributed Acoustic Sensing’, by Katakami et al.
DOI: 10.5281/zenodo.11186701
title: ECGFiveDays

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/.

Data is from a 67 year old male. The two classes are simply ECG date: 12/11/1990 ECG date: 17/11/1990

Donator: Y. Chen, E. Keogh
DOI: 10.5281/zenodo.11186702
title: ECGFiveDays

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/.

Data is from a 67 year old male. The two classes are simply ECG date: 12/11/1990 ECG date: 17/11/1990

Donator: Y. Chen, E. Keogh
DOI: 10.5281/zenodo.11190879
title: ElectricDevices

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/.

These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint. The data contains readings from 251 households, sampled in two-minute intervals over a month. Each series is length 720 (24 hours of readings taken every 2 minutes). 

Donator: A. Bagnall, J. Lines
DOI: 10.5281/zenodo.11190880
title: ElectricDevices

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/.

These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint. The data contains readings from 251 households, sampled in two-minute intervals over a month. Each series is length 720 (24 hours of readings taken every 2 minutes). 

Donator: A. Bagnall, J. Lines