cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.12176904
title: ADD 2023 Challenge Track 2 Evaluation Dataset

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12151403
title: ADD 2023 Challenge Track 1.2 Training/Development Dataset

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12151404
title: ADD 2023 Challenge Track 1.2 Training/Development Dataset

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12175883
title: ADD 2023 Challenge Track 1.2 Evaluation Dataset (Round 1)

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12175884
title: ADD 2023 Challenge Track 1.2 Evaluation Dataset (Round 1)

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12179631
title: ADD 2023 Challenge Track 3 Training/Development Dataset

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12179632
title: ADD 2023 Challenge Track 3 Training/Development Dataset

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12145772
title: ADD 2023 Challenge Track 1.1 Evaluation Dataset

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.5281/zenodo.12145773
title: ADD 2023 Challenge Track 1.1 Evaluation Dataset

description: Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.


The ADD 2023 dataset is publicly available.


This data set is licensed with a CC BY-NC-ND 4.0 license.


If you use this dataset, please cite the following paper:



Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130
DOI: 10.15468/dl.ws3gg2
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Melaleuca incana subsp. incana)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 38 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037047-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ebx88b
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Dasypus pastasae (Thomas, 1901) Thomas, 1901"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037048-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3n6zq7
title: Occurrence Download

description: A dataset containing 195186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Gesneriaceae"
 ]
}

 The dataset includes 195186 records from 793 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037009-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nbk25c
title: Occurrence Download

description: A dataset containing 247178 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulex europaeus L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 247178 records from 1539 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037042-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5255/ukda-sn-9309-1
title: Survey of Personal Incomes, 2021-2022: Public Use Tape
title: Survey of Personal Incomes, 1985-
title: SPI

description: The &lt;em&gt;Survey of Personal Incomes&lt;/em&gt; (SPI) is based on information held by HM Revenue and Customs tax offices on individuals who could be liable to UK income tax. It is carried out annually by HMRC and covers income assessable to tax for each tax year. Not all of them are taxpayers because the operation of personal reliefs and allowances may remove them from liability. Where income exceeds the threshold for operation of Pay-As-You-Earn (PAYE), the survey provides the most comprehensive and accurate official source of data on personal incomes. &lt;br&gt; &lt;br&gt; The SPI is compiled to provide a quantified evidence base from which to cost proposed changes to tax rates, personal allowances and other tax reliefs for Treasury Ministers. It is used to inform policy decisions within HMRC and the Treasury, as well as for tax modelling and forecasting purposes. In addition, it is used to provide summary information for the National Accounts that are prepared by the Office for National Statistics. Finally, it is used to provide information to Members of Parliament, other Government Departments, companies, organisation and individuals.&lt;br&gt; &lt;br&gt; The UK Data Archive currently holds the Public Use Tape (PUT) data for 1985-86 and 1995-96 onwards (data for 2008-09 is currently unavailable). For further details of sampling and coverage criteria, see documentation. Further information about the SPI, including income tax and personal incomes statistics, is available on the GOV.UK &lt;a title="Statistics about personal incomes" href="https://www.gov.uk/government/collections/personal-incomes-statistics"&gt;Statistics about personal incomes&lt;/a&gt; webpage.&lt;br&gt;
&lt;br&gt;
 
&lt;p&gt;&lt;br&gt;&lt;/p&gt;
&lt;p&gt;&lt;span&gt;&lt;br&gt;
&lt;/span&gt;&lt;br&gt;
&lt;/p&gt;
&lt;p&gt;&lt;span&gt;&lt;br&gt;
&lt;/span&gt;&lt;/p&gt;
DOI: 10.15468/dl.ar7sy9
title: Occurrence Download

description: A dataset containing 6728 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pyrrhura amazonum",
 "DecimalLongitude is not null",
 "DecimalLatitude is not null"
 ]
}

 The dataset includes 6728 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063467-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5255/ukda-sn-9360-1
title: Farm Business Survey, 2023-2024: Special Licence Access
title: Farm Business Survey, 1982- : Special Licence Access
title: FBS

description: &lt;p&gt;The &lt;span style="font-style: italic;"&gt;Farm Business Survey&lt;/span&gt; (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers. &lt;/p&gt;&lt;p&gt;The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample. &lt;/p&gt;&lt;p&gt;The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK &lt;a href="https://www.gov.uk/guidance/farm-business-survey-technical-notes-and-guidance#fbs-documents"&gt;FBS documents&lt;/a&gt; web page.&lt;/p&gt;&lt;p&gt;
The &lt;i&gt;Farm Business Survey&lt;/i&gt; is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.&lt;br&gt;
&lt;br&gt;
&lt;/p&gt;
DOI: 10.5255/ukda-sn-9422-1
title: Survey of Personal Incomes, 2022-2023: Public Use Tape
title: Survey of Personal Incomes, 1985-
title: SPI

description: The &lt;em&gt;Survey of Personal Incomes&lt;/em&gt; (SPI) is based on information held by HM Revenue and Customs tax offices on individuals who could be liable to UK income tax. It is carried out annually by HMRC and covers income assessable to tax for each tax year. Not all of them are taxpayers because the operation of personal reliefs and allowances may remove them from liability. Where income exceeds the threshold for operation of Pay-As-You-Earn (PAYE), the survey provides the most comprehensive and accurate official source of data on personal incomes. &lt;br&gt; &lt;br&gt; The SPI is compiled to provide a quantified evidence base from which to cost proposed changes to tax rates, personal allowances and other tax reliefs for Treasury Ministers. It is used to inform policy decisions within HMRC and the Treasury, as well as for tax modelling and forecasting purposes. In addition, it is used to provide summary information for the National Accounts that are prepared by the Office for National Statistics. Finally, it is used to provide information to Members of Parliament, other Government Departments, companies, organisation and individuals.&lt;br&gt; &lt;br&gt; The UK Data Archive currently holds the Public Use Tape (PUT) data for 1985-86 and 1995-96 onwards (data for 2008-09 is currently unavailable). For further details of sampling and coverage criteria, see documentation. Further information about the SPI, including income tax and personal incomes statistics, is available on the GOV.UK &lt;a title="Statistics about personal incomes" href="https://www.gov.uk/government/collections/personal-incomes-statistics"&gt;Statistics about personal incomes&lt;/a&gt; webpage.&lt;br&gt;
&lt;br&gt;
 
&lt;p&gt;&lt;br&gt;&lt;/p&gt;
DOI: 10.5281/zenodo.18026528
title: Risk Models for TRIQUETRA pilot sites

description: Risk models regarding TRIQUETRA pilot sites, produced by AUTH, DLR and SUR researchers, including:



PRD Index

Coastline Dynamics

EVI vegetation anomaly timeseries

Prediction of water fluxes

Snow cover extent timeseries

SAT - Seismic Analysis Toolbox

SST - Slope Stability Toolbox.