cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20673255.v1
title: Replication Package of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: Description <strong>This is a Dockerized version of the repository https://github.com/software-rebels/RAR_Recommender . The package is developed to ease the replication of our study for researchers and practitioners.</strong> <br> This package contains the necessary information to replicate the study "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1]. It adds some functionality needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also has the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contain the following items: Table 1 contains the detail of the dataset and some related statistics for each studied project. Table 2 shows the risk measures used in our defect prediction model. We use Commit Guru Tool to extract the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how the defect probability of different periods is similar to the adjacent periods. Replication Steps: Read Requirements.md and ensure the necessary software/hardware requirements. Read Install.md and install the necessary packages to run the containers. Follow the steps in ReadMe.md to reproduce the results of our study. References: [1] E. Mirsaeedi and P. C. Rigby, 'Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution', στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.
DOI: 10.5281/zenodo.7026755
title: Identification and Validation of a Gene-based Signature Reveals SLC25A10 as a Novel Prognostic Indicator for Patients with Ovarian Cancer

description: The data and code used in the manuscript entitled "Identification and Validation of a Gene-based Signature Reveals SLC25A10 as a Novel Prognostic Indicator for Patients with Ovarian Cancer".
DOI: 10.5281/zenodo.7026754
title: Identification and Validation of a Gene-based Signature Reveals SLC25A10 as a Novel Prognostic Indicator for Patients with Ovarian Cancer

description: The data and code used in the manuscript entitled "Identification and Validation of a Gene-based Signature Reveals SLC25A10 as a Novel Prognostic Indicator for Patients with Ovarian Cancer".
DOI: 10.6084/m9.figshare.20674269
title: Learning-curve-based Simulation Training for Bronchoscopic Intubation

description: This study aims to determine whether a new learning-curve-based simulation training modality could contribute to a noninferiority regarding bronchoscopic guided intubation time in patients compared with the reference fixed-training time simulation training modality, albeit with less training time.
DOI: 10.6084/m9.figshare.20674269.v1
title: Learning-curve-based Simulation Training for Bronchoscopic Intubation

description: This study aims to determine whether a new learning-curve-based simulation training modality could contribute to a noninferiority regarding bronchoscopic guided intubation time in patients compared with the reference fixed-training time simulation training modality, albeit with less training time.
DOI: 10.34740/kaggle/dsv/4126739
title: WallStreetBets 2022
DOI: 10.6084/m9.figshare.20675085
title: Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children_minimal dataset

description: Minimal dataset for the original research manuscript Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children
DOI: 10.6084/m9.figshare.20675085.v1
title: Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children_minimal dataset

description: Minimal dataset for the original research manuscript Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children
DOI: 10.6084/m9.figshare.20675151
title: gene data_IPV.xlsx

description: Genetic raw data from the " <strong>Association of High Intra-patient Variability in Tacrolimus Exposure with Calcineurin Inhibitor Nephrotoxicity in Kidney Transplantation "</strong> <br> <br>
DOI: 10.5281/zenodo.6523073
title: Replication Package for "Timing Side Channel Mitigation via Automated Program Repair"

description: <strong>Pendulum: Timing Side Channel Mitigation via Automated Program Repair</strong> This is the replication artifact for the paper "Timing Side Channel Mitigation via Automated Program Repair". <strong>1. Requirements</strong> Java 8 Gradle 7.4.1 <strong>2. Setup</strong> <pre><code class="language-bash">cd tool ./setup.sh</code></pre> <strong>3. Replication</strong> For timing vulnerability repair & mitigation, check <code>evaluation/</code>. For repair validation, check <code>tool/validation/</code>.
DOI: 10.5281/zenodo.6523072
title: Replication Package for "Timing Side Channel Mitigation via Automated Program Repair"

description: <strong>Pendulum: Timing Side Channel Mitigation via Automated Program Repair</strong> This is the replication artifact for the paper "Timing Side Channel Mitigation via Automated Program Repair". It includes: a <strong>Docker</strong> file for easy setup and execution of Pendulum, the <strong>setup</strong> instructions, the instructions for a <strong>quick trial</strong> of the setup, the <strong>workflow</strong> description incl. how to <strong>apply</strong> Pendulum to other programs, and detailed instructions about how to <strong>replicate our experiments</strong>. Please read our README for more information and explanations.
DOI: 10.5281/zenodo.7018793
title: Replication Package for "Timing Side Channel Mitigation via Automated Program Repair"

description: <strong>Pendulum: Timing Side Channel Mitigation via Automated Program Repair</strong> This is the replication artifact for the paper "Timing Side Channel Mitigation via Automated Program Repair". It includes: a <strong>Docker</strong> file for easy setup and execution of Pendulum, the <strong>setup</strong> instructions, the instructions for a <strong>quick trial</strong> of the setup, the <strong>workflow</strong> description incl. how to <strong>apply</strong> Pendulum to other programs, and detailed instructions about how to <strong>replicate our experiments</strong>. Please read our README for more information and explanations.
DOI: 10.15151/esrf-es-883956931
title: Austrian Crystallographic Diffraction Consortium

description: This proposed Austrian Crystallographic.... TO BE FILLED IN
DOI: 10.5281/zenodo.7027052
title: Datasets

description: 本研究的数据集可在此获得
DOI: 10.5281/zenodo.7027051
title: Datasets

description: 本研究的数据集可在此获得
DOI: 10.5281/zenodo.6872771
title: Data_Changes in Tree Diversity, Structure and Functional Trait Identity Drive Biomass Increase along Elevational Gradients in Subtropical Forests of Southern China

description: In this article "Changes in Tree Diversity, Structure and Functional Trait Identity Drive Biomass Increase along Elevational Gradients in Subtropical Forests of Southern China", these files contain community inventory data collected at our three study sites.
DOI: 10.25574/17531
title: Chandra X-ray Observatory ObsId 17531

description: Chandra Observations of Comet C/2013 A1 (Siding Spring)'s Encounter With Mars
DOI: 10.25574/17532
title: Chandra X-ray Observatory ObsId 17532

description: Chandra Observations of Comet C/2013 A1 (Siding Spring)'s Encounter With Mars
DOI: 10.25574/17533
title: Chandra X-ray Observatory ObsId 17533

description: Chandra Observations of Comet C/2013 A1 (Siding Spring)'s Encounter With Mars
DOI: 10.25574/17566
title: Chandra X-ray Observatory ObsId 17566

description: A High-resolution Spectrum of an Unprecedented Tidal Disruption Event
DOI: 10.25574/17567
title: Chandra X-ray Observatory ObsId 17567

description: A High-resolution Spectrum of an Unprecedented Tidal Disruption Event
DOI: 10.5281/zenodo.7027105
title: Data_Changes in Tree Diversity, Structure and Functional Trait Identity Drive Biomass Increase along Elevational Gradients in Subtropical Forests of Southern China

description: In this article "Changes in Tree Diversity, Structure and Functional Trait Identity Drive Biomass Increase along Elevational Gradients in Subtropical Forests of Southern China", these files contain community inventory data collected at our three study sites.
DOI: 10.5281/zenodo.6872770
title: Data_Changes in Tree Diversity, Structure and Functional Trait Identity Drive Biomass Increase along Elevational Gradients in Subtropical Forests of Southern China

description: In this article "Changes in Tree Diversity, Structure and Functional Trait Identity Drive Biomass Increase along Elevational Gradients in Subtropical Forests of Southern China", these files contain community inventory data collected at our three study sites.
DOI: 10.25574/17649
title: Chandra X-ray Observatory ObsId 17649

description: Seizing a rare opportunity to catch a disk wind in a neutron star X-ray binary
DOI: 10.25574/17661
title: Chandra X-ray Observatory ObsId 17661

description: The Structure of the Accretion Flow in SAX J1808.4-3658 at Low Luminosities.
DOI: 10.25574/17696
title: Chandra X-ray Observatory ObsId 17696

description: Detecing Super-Eddington Driven Winds in V404 Cyg
DOI: 10.25574/17697
title: Chandra X-ray Observatory ObsId 17697

description: Detecing Super-Eddington Driven Winds in V404 Cyg
DOI: 10.25574/17715
title: Chandra X-ray Observatory ObsId 17715

description: Can Planets Affect Coronal Abundances?
DOI: 10.6084/m9.figshare.20676174
title: Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children_Rejected breaths dataset

description: Rejected breaths-dataset for the original research manuscript Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children
DOI: 10.6084/m9.figshare.20676174.v1
title: Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children_Rejected breaths dataset

description: Rejected breaths-dataset for the original research manuscript Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children
DOI: 10.6084/m9.figshare.20654589.v1
title: Spartina alterniflora code and dataset

description: Datasets and codes for identification of Invasive Species Spartina alterniflora
DOI: 10.6084/m9.figshare.20654589.v2
title: Spartina alterniflora code and dataset

description: Datasets and codes for identification of Invasive Species Spartina alterniflora
DOI: 10.6084/m9.figshare.20654589
title: Spartina alterniflora code and dataset

description: Datasets and codes for identification of Invasive Species Spartina alterniflora
DOI: 10.6084/m9.figshare.20654589.v4
title: Spartina alterniflora code and dataset

description: Datasets and codes for identification of Invasive Species Spartina alterniflora
DOI: 10.6084/m9.figshare.20673255.v2
title: Replication Package of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: Description <strong>This is a Dockerized version of the repository https://github.com/software-rebels/RAR_Recommender . The package is developed to ease the replication of our study for researchers and practitioners.</strong> <br> This package contains the necessary information to replicate the study "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1]. It adds some functionality needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also has the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contain the following items: Table 1 contains the detail of the dataset and some related statistics for each studied project. Table 2 shows the risk measures used in our defect prediction model. We use Commit Guru Tool to extract the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how the defect probability of different periods is similar to the adjacent periods. Replication Steps: Read Requirements.md and ensure the necessary software/hardware requirements. Read Install.md and install the necessary packages to run the containers. Follow the steps in ReadMe.md to reproduce the results of our study. References: [1] E. Mirsaeedi and P. C. Rigby, 'Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution', στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.
DOI: 10.5281/zenodo.6403760
title: Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. <strong>References:</strong> [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.
DOI: 10.5281/zenodo.6407313
title: Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. <strong>References:</strong> [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.
DOI: 10.5281/zenodo.6727155
title: Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. <strong>References:</strong> [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.
DOI: 10.5281/zenodo.6403759
title: Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: <strong>Note: Please find the dockerized version of this replication package in the following link:</strong> https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. <strong>References:</strong> [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.
DOI: 10.5281/zenodo.6760330
title: Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: <strong>Note: Please find the dockerized version of this replication package in the following link:</strong> https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. <strong>References:</strong> [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.
DOI: 10.5281/zenodo.7026834
title: Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: <strong>Note: Please find the dockerized version of this replication package in the following link:</strong> https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. <strong>References:</strong> [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.
DOI: 10.5281/zenodo.7026839
title: Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"

description: <strong>Note: Please find the dockerized version of this replication package in the following link:</strong> https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. <strong>References:</strong> [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.