cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7632523
title: Dataset for "Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools"

description: This dataset contains the data to replicate the findings in the publication "Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools". The dataset includes the bibliographies and intermediate analysis tables for the analyzed literature. Further, it contains the experiment materials providing context for the task discussed in Section V of the publication.


The artifact contains the following files:



	
icpc-from-survey.bib: The bibliographic entries of all publications selected from the corpus of the paper "Confounding parameters on program comprehension: a literature survey" by Janet Siegmund and Jana Schumann (https://doi.org/10.1007/s10664-014-9318-8)
	
icpc-manually-added.bib: The bibliographic entries of manually added publications.
	
icpc-phase-2-and-3.[csv|xlsx]: The intermediate analysis tables for phases 2 and 3 as described in the paper. The table also contains columns transferred from the original dataset of the Siegmund and Schumann study (marked as "transferred")
	
experiment-materials.zip: Includes the materials for the experiment used in Section V
	

		
introduction-english.[docx|rtf]: The experiment protocol we read to participants, including the introduction to the system architecture.
		
JumpODrom.package.zip: The code of the used system.
		
task-16-description.txt: The textual description of the task discussed in Section V.
		
task-16.patch: The change applied to the system code prior to the task, which causes the faulty behavior outlined in the task description.
DOI: 10.6084/m9.figshare.27126567.v1
title: jb_cu-LMe2-vycl_oxadd_ts_1.xyz

description: Transition state for oxidative addition of vinyl chloride to anionic Cu complex
DOI: 10.6084/m9.figshare.27126567
title: jb_cu-LMe2-vycl_oxadd_ts_1.xyz

description: Transition state for oxidative addition of vinyl chloride to anionic Cu complex
DOI: 10.15468/dl.bctyc3
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Assa wollumbin Mahony, Hines, Mahony, Moses, Catalano, Myers & Donnellan, 2021)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 15 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033694-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27119589
title: <b>code and dataset</b>

description: <b>The code and dataset of Understanding navigation</b><b>-</b><b>lost </b><b>scene</b><b>s based on knowledge graphs using a deep learning approach</b>
DOI: 10.6084/m9.figshare.27119589.v3
title: <b>code and dataset</b>

description: <b>The code and dataset of Understanding navigation</b><b>-</b><b>lost </b><b>scene</b><b>s based on knowledge graphs using a deep learning approach</b>
DOI: 10.6084/m9.figshare.27119589.v2
title: <b>Understanding navigation</b><b>-</b><b>lost </b><b>scene</b><b>s based on knowledge graphs using a deep learning approach</b>

description: <b>The code and dataset of Understanding navigation</b><b>-</b><b>lost </b><b>scene</b><b>s based on knowledge graphs using a deep learning approach</b>
DOI: 10.15468/dl.4a37bp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Atelopus gigas Coloma, Duellman, Almendáriz, Ron, Terán-Valdez & Guayasamin, 2010)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033695-240906103802322/datasets/export for details.
DOI: 10.5281/zenodo.11171708
title: Codecfake dataset - training set (part 1 of 3)

description: This dataset is the training set (part 1 of 3) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z06
https://zenodo.org/records/11171708


training set (part 2 of 3)
train_split.z07 - train_split.z14
https://zenodo.org/records/11171720


training set (part 3 of 3)
train_split.z15 - train_split.z19
https://zenodo.org/records/11171724


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/11169872


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/11169781


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.5281/zenodo.11171720
title: Codecfake dataset - training set (part 2 of 3)

description: This dataset is the training set (part 2 of 3) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z06
https://zenodo.org/records/11171708


training set (part 2 of 3)
train_split.z07 - train_split.z14
https://zenodo.org/records/11171720


training set (part 3 of 3)
train_split.z15 - train_split.z19
https://zenodo.org/records/11171724


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/11169872


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/11169781


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.15468/dl.zv99gq
title: Occurrence Download

description: A dataset containing 116169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-97.57817 25.78757, -97.41818 25.78757, -97.41818 25.89702, -97.57817 25.89702, -97.57817 25.78757))",
 "Year is greater than or equal to 2010",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 116169 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033693-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.axdjax
title: Occurrence Download

description: A dataset containing 21863 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pelobates cultripes (Cuvier, 1829), Pelobates cultripes (Tschudi, 1838))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 21863 records from 174 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033696-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4usj69
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Apometzgeria pubescens (Schrank) Kuwah.",
 "Year 1900-2021"
 ]
}

 The dataset includes 20 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033697-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11171724
title: Codecfake dataset - training set (part 3 of 3)

description: This dataset is the training set (part 3 of 3) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z06
https://zenodo.org/records/11171708


training set (part 2 of 3)
train_split.z07 - train_split.z14
https://zenodo.org/records/11171720


training set (part 3 of 3)
train_split.z15 - train_split.z19
https://zenodo.org/records/11171724


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/11169872


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/11169781


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.15468/dl.dfxrgu
title: Occurrence Download

description: A dataset containing 1782 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rana latastei Boulenger, 1879, Raja latastei Delfin, 1902)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1782 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033699-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6j9p9g
title: Occurrence Download

description: A dataset containing 64873 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-93.20099 16.71941, -93.02956 16.71941, -93.02956 16.80164, -93.20099 16.80164, -93.20099 16.71941))",
 "Year is greater than or equal to 2010",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 64873 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033698-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3q9x7s
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bolitoglossa awajun Cusi, Gagliardi-Urrutia, Brcko, Wake & von May, 2020)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033701-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.845w36
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Andinobates claudiae (Jungfer, Lötters & Jörgens, 2000))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033704-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ce83c
title: Occurrence Download

description: A dataset containing 23148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Heraclides cresphontes (Cramer, 1777)"
 ]
}

 The dataset includes 23148 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033703-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9pyp5z
title: Occurrence Download

description: A dataset containing 34628 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sphagnum squarrosum Crome",
 "Year 1801-2024"
 ]
}

 The dataset includes 34628 records from 347 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033705-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8py32d
title: Occurrence Download

description: A dataset containing 7190 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rana iberica Boulenger, 1879)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7190 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033706-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.etfdf8
title: Occurrence Download

description: A dataset containing 1027896 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ghana",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1027896 records from 841 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033700-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.thbfz9
title: Occurrence Download

description: A dataset containing 936620 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-98.73285 29.3187, -98.28366 29.3187, -98.28366 29.60004, -98.73285 29.60004, -98.73285 29.3187))",
 "Year is greater than or equal to 2010",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 936620 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033702-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11169872
title: Codecfake dataset - development set

description: This dataset is the development set of the Codecfake dataset, corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z06
https://zenodo.org/records/11171708


training set (part 2 of 3)
train_split.z07 - train_split.z14
https://zenodo.org/records/11171720


training set (part 3 of 3)
train_split.z15 - train_split.z19
https://zenodo.org/records/11171724


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/11169872


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/11169781


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.15468/dl.fneqwu
title: Occurrence Download

description: A dataset containing 2741 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material sample, Material citation, Specimen, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate precision invalid, Country derived from coordinates, Continent derived from coordinates, The taxon concept ID was not used in backbone matching, Modified date invalid, Collection match fuzzy, Institution match fuzzy, Institution match none)",
 "OccurrenceStatus is Present",
 "TaxonKey is Anthoceros punctatus L.",
 "Year 1970-2024"
 ]
}

 The dataset includes 2741 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033708-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.