cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24637649
title: Read me text file from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Text file containing summary of files and summary of column headings in data file
DOI: 10.6084/m9.figshare.24637649.v3
title: Read me text file from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Text file containing summary of files and summary of column headings in data file
DOI: 10.6084/m9.figshare.24637652
title: Data file from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Comma-delimited file containing adult mass, activity and metabolic rate data
DOI: 10.6084/m9.figshare.24637646.v2
title: Female counter-singing data from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Data from experiment comparing response of females to song or tone playback
DOI: 10.6084/m9.figshare.24637652.v3
title: Data file from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Comma-delimited file containing adult mass, activity and metabolic rate data
DOI: 10.6084/m9.figshare.24637649.v2
title: Female counter-singing analysis script from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: R Markdown file used to analyze female responses to song or tone playback
DOI: 10.6084/m9.figshare.24637652.v2
title: Male-female comparison data from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Data from experiment comparing female and male responses to song playback
DOI: 10.6084/m9.figshare.24637658
title: R code from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Text file containing the R script to run statistical analyses
DOI: 10.6084/m9.figshare.24637658.v3
title: R code from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Text file containing the R script to run statistical analyses
DOI: 10.6084/m9.figshare.24637658.v2
title: Male-female comparison analysis script from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: R Markdown file used to compare female and male responses to song playback
DOI: 10.6084/m9.figshare.24845299
title: Male-female comparison analysis script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: R Markdown file used to compare female and male responses to song playback
DOI: 10.6084/m9.figshare.24845299.v1
title: Male-female comparison analysis script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: R Markdown file used to compare female and male responses to song playback
DOI: 10.6084/m9.figshare.24845305
title: Song analysis script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Matlab script used to measure acoustic features of extracted song recordings
DOI: 10.6084/m9.figshare.24845305.v1
title: Song analysis script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Matlab script used to measure acoustic features of extracted song recordings
DOI: 10.6084/m9.figshare.24845308
title: Male-female comparison data from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Data from experiment comparing female and male responses to song playback
DOI: 10.6084/m9.figshare.24845308.v1
title: Male-female comparison data from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Data from experiment comparing female and male responses to song playback
DOI: 10.6084/m9.figshare.24845311
title: Auto extract script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Matlab script used to extract singing mouse songs from audio recording files
DOI: 10.6084/m9.figshare.24845311.v1
title: Auto extract script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Matlab script used to extract singing mouse songs from audio recording files
DOI: 10.6084/m9.figshare.24845314
title: Female counter-singing data from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Data from experiment comparing response of females to song or tone playback
DOI: 10.6084/m9.figshare.24845314.v1
title: Female counter-singing data from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: Data from experiment comparing response of females to song or tone playback
DOI: 10.6084/m9.figshare.24845317
title: Female counter-singing analysis script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: R Markdown file used to analyze female responses to song or tone playback
DOI: 10.6084/m9.figshare.24845317.v1
title: Female counter-singing analysis script from Females counter-sing, but response to male song differs by sex in Alston's singing mouse

description: R Markdown file used to analyze female responses to song or tone playback
DOI: 10.34740/kaggle/dsv/7215198
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.10394763
title: Artifacts of the ISSTA-2024 Submission——DroidTEC

description: This release includes the experimental results that are used for the ISSTA artifact analysis. All the results are described in our paper. All required artifacts are attached to this release.

The source code is made publicly available on the github: page: https://anonymous.4open.science/r/TypeStateMisuseDetectorUsers can easily access and execute our tool following the steps in the Setup part.

In addition, all of our experimental results are publicly available on this page. For simplicity, we provide the following instructions for our artifacts:



Experimental_Setup.txt : Experimental Setup, including 10,000 Android apps

RQ1_Rules.zip: TypeState API rules.

RQ2_typestate_API_usage.zip : The prevalence of typestate APIs in Android applications.

RQ4_Typestate_Misuses.zip : Identified typestate misuses in real-world applications.

RQ5_CrySL_Results.zip : Execution results of CrySL on 10,000 Android apps.

RQ5_AsyncChecker_Results.zip : Execution results of AsyncChecker on 10,000 Android apps.

RQ5_VALA_Results.zip : Execution results of VALA on 10,000 Android apps.

RQ5_CiD_Results.zip : Execution results of CiD on 10,000 Android apps.

RQ5_BenchMark_Apps.zip : Execution results of VALA on 10,000 Android apps.
DOI: 10.5281/zenodo.10394764
title: Artifacts of the ISSTA-2024 Submission——DroidTEC

description: This release includes the experimental results that are used for the ISSTA artifact analysis. All the results are described in our paper. All required artifacts are attached to this release.

The source code is made publicly available on the github: page: https://anonymous.4open.science/r/TypeStateMisuseDetectorUsers can easily access and execute our tool following the steps in the Setup part.

In addition, all of our experimental results are publicly available on this page. For simplicity, we provide the following instructions for our artifacts:



Experimental_Setup.txt : Experimental Setup, including 10,000 Android apps

RQ1_Rules.zip: TypeState API rules.

RQ2_typestate_API_usage.zip : The prevalence of typestate APIs in Android applications.

RQ4_Typestate_Misuses.zip : Identified typestate misuses in real-world applications.

RQ5_CrySL_Results.zip : Execution results of CrySL on 10,000 Android apps.

RQ5_AsyncChecker_Results.zip : Execution results of AsyncChecker on 10,000 Android apps.

RQ5_VALA_Results.zip : Execution results of VALA on 10,000 Android apps.

RQ5_CiD_Results.zip : Execution results of CiD on 10,000 Android apps.

RQ5_BenchMark_Apps.zip : Execution results of VALA on 10,000 Android apps.
DOI: 10.5281/zenodo.10394672
title: Synthetic basement depth, gravity anomalies, density and observation points training dataset to train deep learning model

description: Contains 200000 training data for our DNN model.
DOI: 10.5281/zenodo.10394671
title: Synthetic basement depth, gravity anomalies, density and observation points training dataset to train deep learning model

description: Contains 200000 training data for our DNN model.
DOI: 10.5281/zenodo.10394900
title: Genomic plasticity is a blueprint of diversity in Salmonella lineages

description: The flexible genome, organised in regions of genomic plasticity (RGP), serves as a potent facilitator of the dynamic evolution of bacterial genomes through gene acquisition and loss. Here we explored the genomic plasticity across Salmonella lineages, revealing a purposeful, non-random integration pattern of pathogenicity-related gene clusters into specific RGP. Noteworthy examples include the correlation between the type I-E CRISPR-Cas system, gold tolerance, and specific RGP. The scattered prevalence of RGP across Salmonella lineages profoundly shapes the pathogenicity makeup of Salmonella strains. The preferences of RGP seem guided by conserved flanking genes that likely share regulatory and functional coordination. For example, metal resistance genes are predominant in RGP positioned near stress resistance genes, indicating a regulatory network to efficiently counter stressors. Additionally, we observed that different plasmid incompatibility types and prophage genera carry distinct pathogenicity genes. Similar to RGP, their distribution across Salmonella lineages plays a critical role in defining pathogenicity. By uncovering these intricate connections among gene clusters, RGP, mobile genetic elements, and pathogenic attributes, our study offers novel insights into the evolutionary trajectory of Salmonella and holds promise for predicting future adaptations and developing targeted interventions against infections.
DOI: 10.5281/zenodo.10394901
title: Genomic plasticity is a blueprint of diversity in Salmonella lineages

description: The flexible genome, organised in regions of genomic plasticity (RGP), serves as a potent facilitator of the dynamic evolution of bacterial genomes through gene acquisition and loss. Here we explored the genomic plasticity across Salmonella lineages, revealing a purposeful, non-random integration pattern of pathogenicity-related gene clusters into specific RGP. Noteworthy examples include the correlation between the type I-E CRISPR-Cas system, gold tolerance, and specific RGP. The scattered prevalence of RGP across Salmonella lineages profoundly shapes the pathogenicity makeup of Salmonella strains. The preferences of RGP seem guided by conserved flanking genes that likely share regulatory and functional coordination. For example, metal resistance genes are predominant in RGP positioned near stress resistance genes, indicating a regulatory network to efficiently counter stressors. Additionally, we observed that different plasmid incompatibility types and prophage genera carry distinct pathogenicity genes. Similar to RGP, their distribution across Salmonella lineages plays a critical role in defining pathogenicity. By uncovering these intricate connections among gene clusters, RGP, mobile genetic elements, and pathogenic attributes, our study offers novel insights into the evolutionary trajectory of Salmonella and holds promise for predicting future adaptations and developing targeted interventions against infections.
DOI: 10.6084/m9.figshare.24784263
title: Dataset

description: This is the dataset for testing FastGAN and CycleGAN.
DOI: 10.6084/m9.figshare.24784263.v1
title: Dataset

description: This is the dataset for testing FastGAN and CycleGAN.
DOI: 10.6084/m9.figshare.24845394
title: MPDSR Tables

description: Table 1: List of Key organizations involved in the MPDSR committee for primary data collection.Table 2 List of interventions to prevent maternal and neonatal deathsTable 3 Comparison between key indicators from 2019 and 2021 (Source: RMNCAH Scorecard- DGHS)
DOI: 10.6084/m9.figshare.24845394.v1
title: MPDSR Tables

description: Table 1: List of Key organizations involved in the MPDSR committee for primary data collection.Table 2 List of interventions to prevent maternal and neonatal deathsTable 3 Comparison between key indicators from 2019 and 2021 (Source: RMNCAH Scorecard- DGHS)
DOI: 10.5281/zenodo.10381523
title: Determinants of Refugees destinations - data

description: This folder contains the data on access to social security and months of ban from the labor market used in the article: Di Iasio, V. and Wahba, J. (2022) The determinants of Refugees' Destinations: Where do Refugees Locate within the EU? QuantMig Project Deliverable D3.4. University of Southampton.

Included is a read_me.pdf file containing basic information on the variables included.
DOI: 10.5281/zenodo.10381524
title: Determinants of Refugees destinations - data

description: This folder contains the data on access to social security and months of ban from the labor market used in the article: Di Iasio, V. and Wahba, J. (2022) The determinants of Refugees' Destinations: Where do Refugees Locate within the EU? QuantMig Project Deliverable D3.4. University of Southampton.

Included is a read_me.pdf file containing basic information on the variables included.
DOI: 10.6084/m9.figshare.24845403
title: Paper 2 MPDSR Raw data file final.xlsx

description: Raw data MPDSR
DOI: 10.6084/m9.figshare.24845403.v1
title: Paper 2 MPDSR Raw data file final.xlsx

description: Raw data MPDSR
DOI: 10.5281/zenodo.10394959
title: Data from 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'

description: Datasets used in the manuscript 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'

Format: CPLEX .dat data files
DOI: 10.5281/zenodo.8091093
title: Data from 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'

description: Datasets used in the manuscript 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'

Format: CPLEX .dat data files
DOI: 10.5281/zenodo.8091094
title: Data from 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'

description: Datasets used in the manuscript 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations' - bi-criteria-analysis-instances : instances corresponding to the experiments of section 4.3 - dedicated-matheuristic-instances : instances corresponding to the experiments of section 4.4 Format: CPLEX .dat data files
DOI: 10.6084/m9.figshare.24832464
title: Peripheral blood and karyotype information of MDS patients

description: Peripheral blood and karyotype information of MDS patients
DOI: 10.6084/m9.figshare.24832464.v1
title: Peripheral blood and karyotype information of MDS patients

description: Peripheral blood and karyotype information of MDS patients
DOI: 10.5281/zenodo.10382243
title: Islamic gold coins data

description: Elemental, colorimetric and physical description of the Islamic gold goins
DOI: 10.5281/zenodo.10382244
title: Islamic gold coins data

description: Elemental, colorimetric and physical description of the Islamic gold goins
DOI: 10.5281/zenodo.8360288
title: Dataset of Effect of science outreach activities on chemophobic conceptions at the high school level

description: Dataset corresponding to the article "Effect of science outreach activities on chemophobic conceptions at the high school level"
DOI: 10.5281/zenodo.8360289
title: Dataset of Effect of science outreach activities on chemophobic conceptions at the high school level

description: Dataset corresponding to the article "Effect of science outreach activities on chemophobic conceptions at the high school level"
DOI: 10.5281/zenodo.10391276
title: LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 2 of 3

description: Summary

This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.

Description

The uploaded content includes reconstructed images for noise levels 3 and 4. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:



Noise1 and Noise2 https://zenodo.org/records/10356955

Noise3 and Noise4 (this one) https://zenodo.org/records/10391277

Noise5 and Reference https://zenodo.org/records/10391412


The scanning parameters for all noise levels are summarized in the table below:




 
Averaged Projs
Exposure Time/ms
Scan Time/min
Voltage/kV
Current/uA


Reference
6
333
59.3
140
180


Noise Level 1
1
333
9.9
140
180


Noise Level 2
1
333
9.9
140
90


Noise Level 3
1
333
9.9
140
45


Noise Level 4
1
333
9.9
140
23


Noise Level 5
1
333
9.9
140
12




Additional link

The code for supervised learning-based denoising is available code .

Acknowledgment

This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing).
DOI: 10.5281/zenodo.10391277
title: LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 2 of 3

description: Summary

This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.

Description

The uploaded content includes reconstructed images for noise levels 3 and 4. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:



Noise1 and Noise2 https://zenodo.org/records/10356955

Noise3 and Noise4 (this one) https://zenodo.org/records/10391277

Noise5 and Reference https://zenodo.org/records/10391412


The scanning parameters for all noise levels are summarized in the table below:




 
Averaged Projs
Exposure Time/ms
Scan Time/min
Voltage/kV
Current/uA


Reference
6
333
59.3
140
180


Noise Level 1
1
333
9.9
140
180


Noise Level 2
1
333
9.9
140
90


Noise Level 3
1
333
9.9
140
45


Noise Level 4
1
333
9.9
140
23


Noise Level 5
1
333
9.9
140
12




Additional link

The code for supervised learning-based denoising is available code .

Acknowledgment

This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing).
DOI: 10.34740/kaggle/dsv/7215641
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.10391411
title: LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 3 of 3

description: Summary

This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.

Description

The uploaded content includes reconstructed images for noise levels 5 and reference. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:



Noise1 and Noise2 https://zenodo.org/records/10356955

Noise3 and Noise4 https://zenodo.org/records/10391277

Noise5 and Reference (this one) https://zenodo.org/records/10391412


The scanning parameters for all noise levels are summarized in the table below:




 
Averaged Projs
Exposure Time/ms
Scan Time/min
Voltage/kV
Current/uA


Reference
6
333
59.3
140
180


Noise Level 1
1
333
9.9
140
180


Noise Level 2
1
333
9.9
140
90


Noise Level 3
1
333
9.9
140
45


Noise Level 4
1
333
9.9
140
23


Noise Level 5
1
333
9.9
140
12




Additional link

The code for supervised learning-based denoising is available code .

Acknowledgment

This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing).
DOI: 10.5281/zenodo.10391412
title: LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 3 of 3

description: Summary

This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.

Description

The uploaded content includes reconstructed images for noise levels 5 and reference. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:



Noise1 and Noise2 https://zenodo.org/records/10356955

Noise3 and Noise4 https://zenodo.org/records/10391277

Noise5 and Reference (this one) https://zenodo.org/records/10391412


The scanning parameters for all noise levels are summarized in the table below:




 
Averaged Projs
Exposure Time/ms
Scan Time/min
Voltage/kV
Current/uA


Reference
6
333
59.3
140
180


Noise Level 1
1
333
9.9
140
180


Noise Level 2
1
333
9.9
140
90


Noise Level 3
1
333
9.9
140
45


Noise Level 4
1
333
9.9
140
23


Noise Level 5
1
333
9.9
140
12




Additional link

The code for supervised learning-based denoising is available code .

Acknowledgment

This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing).
DOI: 10.20375/0000-0011-49c2-0
title: Documentation of Data Collection_German Talk Show Debates.pdf
DOI: 10.20375/0000-0011-49c3-f
title: Results.pdf
DOI: 10.20375/0000-0011-49c4-e
title: TV Talkshows1 mw3.ods
DOI: 10.48320/e480bda7-ef7c-4110-a4c0-0cc54cb344e0
title: The Russo-Ukrainian Conflict 2013-2023 in German Talk Show Debates
DOI: 10.34740/kaggle/dsv/7215642
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.24845469
title: Acetic acid stress - supplementary material

description: Supplementary tables to Chapter 2 thesis
DOI: 10.6084/m9.figshare.24845469.v1
title: Acetic acid stress - supplementary material

description: Supplementary tables to Chapter 2 thesis