cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8852084.v3
title: Replication Package - PROMISE'19

description: Replication Package of the paper "From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects"<br><br>ABSTRACT:<br>Bugs appear in almost any software development. Solving all or at least a large part of them requires a great deal of time, effort, and budget. Software projects typically use issue tracking systems as a way to report and monitor bug-fixing tasks. In recent years, several researchers have been conducting bug tracking analysis to better understand the problem and thus provide means to reduce costs and improve the efficiency of the bug-fixing task. In this paper, we introduce a new dataset composed of more than 70,000 bug-fix reports from 10 years of bug-fixing activity of 55 projects from the Apache Software Foundation, distributed in 9 categories. We have mined this information from Jira issue track system concerning two different perspectives of reports with closed/resolved status: static (the latest version of reports) and dynamic (the changes that have occurred in reports over time). We also extract information from the commits (if they exist) that fix such bugs from their respective version-control system (Git).We also provide a change analysis that occurs in the reports as a way of illustrating and characterizing the proposed dataset. Once the data extraction process is an error-prone nontrivial task, we believe such initiatives like this could be useful to support researchers in further more detailed investigations.<br><br>You can find the full paper at: https://doi.org/10.1145/3345629.3345639<br>If you use this dataset for your research, please reference the following paper:<pre><br>@inproceedings{Vieira:2019:RBC:3345629.3345639, author = {Vieira, Renan and da Silva, Ant\^{o}nio and Rocha, Lincoln and Gomes, Jo\~{a}o Paulo}, title = {From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects}, booktitle = {Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering}, series = {PROMISE'19}, year = {2019}, isbn = {978-1-4503-7233-6}, location = {Recife, Brazil}, pages = {80--89}, numpages = {10}, url = {http://doi.acm.org/10.1145/3345629.3345639}, doi = {10.1145/3345629.3345639}, acmid = {3345639}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Bug-Fix Dataset, Mining Software Repositories, Software Traceability}, } <br> </pre>
DOI: 10.5522/04/8851790
title: SynthCity Dataset - Trajectory

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender. See our project website http://www.synthcity.xyz or paper https://arxiv.org/abs/1907.04758 for more information.
DOI: 10.6084/m9.figshare.8851955.v3
title: Multicenter dataset of multishell diffusion MRI in healthy traveling adults with identical setting

description: <strong>Introduction</strong>A multishell diffusion MRI dataset collected from three traveling subjects with identical acquisition setting in ten imaging centers. Both of the scanner type and imaging protocol for anatomical and diffusion imaging were well controlled.This dataset is expected to replenish the individual reproducible study via multicenter collaborations by providing an open resource for advanced and novel microstructure and tractography quantifications.<br><br><strong>Primary acquisition parameters</strong>• T1-weighted imagesSequence: MP2RAGEResolution = 1x1x1.2 mm<sup>3</sup><br>• Diffusion-weighted imagesSequence: SMS SE EPIResolution = 1.5x1.5x1.5 mm<sup>3</sup>b-value = 1000,2000,3000 mm<sup>2</sup>/sdirections = 30,30,30non-diffusion images = 6<strong><br></strong><strong>Data use agreement </strong><br>To use this dataset, we would like to follow the license CC BY-NC-SA (Attribution-NonCommercial-ShareAlike) https://creativecommons.org/licenses/by-nc-sa/4.0<br><strong>Contact</strong><br>For more information on the data collection and pre-processing procedure, you can contact Qiqi Tong (tongqq@zju.edu.cn)For other information or further cooperation with us, you can contact Dr. Hongjian He (hhezju@zju.edu.cn)<br>
DOI: 10.5522/04/8851790.v2
title: SynthCity Dataset - Trajectory

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender. See our project website http://www.synthcity.xyz or paper https://arxiv.org/abs/1907.04758 for more information.
DOI: 10.6084/m9.figshare.8851787
title: Data from: Long-term warming results in species-specific shifts in seed mass in alpine communities

description: Seed mass (mg) in control and experimental warming treatments in the LG and HG meadow sites. CK: control treatment; OTC: expermental warming; LG: "low" grazing intensity history; HG: "high" grazing intensity history.<br>
DOI: 10.6084/m9.figshare.8851955.v4
title: Multicenter dataset of multishell diffusion MRI in healthy traveling adults with identical setting

description: <strong>Introduction</strong>A multishell diffusion MRI dataset collected from three traveling subjects with identical acquisition setting in ten imaging centers. Both of the scanner type and imaging protocol for anatomical and diffusion imaging were well controlled.This dataset is expected to replenish the individual reproducible study via multicenter collaborations by providing an open resource for advanced and novel microstructure and tractography quantifications.<br><br><strong>Primary acquisition parameters</strong>• T1-weighted imagesSequence: MP2RAGEResolution = 1x1x1.2 mm<sup>3</sup><br>• Diffusion-weighted imagesSequence: SMS SE EPIResolution = 1.5x1.5x1.5 mm<sup>3</sup>b-value = 1000,2000,3000 mm<sup>2</sup>/sdirections = 30,30,30non-diffusion images = 6<strong><br></strong><strong>Data use agreement </strong><br>To use this dataset, we would like to follow the license CC BY-NC-SA (Attribution-NonCommercial-ShareAlike) https://creativecommons.org/licenses/by-nc-sa/4.0<br><strong>Contact</strong><br>For more information on the data collection and pre-processing procedure, you can contact Qiqi Tong (tongqq@zju.edu.cn)For other information or further cooperation with us, you can contact Dr. Hongjian He (hhezju@zju.edu.cn)<br>
DOI: 10.5522/04/8851790.v1
title: SynthCity Dataset - Trajectory

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.
DOI: 10.6084/m9.figshare.8851784
title: Long-term behavioral repeatability in wild adult and captive juvenile turtles: implications for personality development

description: Data and code for "Long-term behavioral repeatability in wild adult and captive juvenile turtles: implications for personality development" by Bradley Carlson and Sasha Tetzlaff.
DOI: 10.5522/04/8851781
title: SynthCity Dataset - Area 3 (Test)

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender. See our project website http://www.synthcity.xyz or paper https://arxiv.org/abs/1907.04758 for more information.
DOI: 10.6084/m9.figshare.8851784.v1
title: Long-term behavioral repeatability in wild adult and captive juvenile turtles: implications for personality development

description: Data and code for "Long-term behavioral repeatability in wild adult and captive juvenile turtles: implications for personality development" by Bradley Carlson and Sasha Tetzlaff.
DOI: 10.6084/m9.figshare.8851787.v1
title: peerj-31956-peerj-31956-rawdata20190710.xlsx

description: Seed mass (mg) in control and experimental warming treatments in the LG and HG meadow sites. CK: control treatment; OTC: expermental warming; LG: "low" grazing intensity history; HG: "high" grazing intensity history.<br>
DOI: 10.5522/04/8851781.v2
title: SynthCity Dataset - Area 3 (Test)

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.
DOI: 10.6084/m9.figshare.8851787.v3
title: Data from: Long-term warming results in species-specific shifts in seed mass in alpine communities

description: Seed mass (mg) in control and experimental warming treatments in the LG and HG meadow sites. CK: control treatment; OTC: expermental warming; LG: "low" grazing intensity history; HG: "high" grazing intensity history.<br>
DOI: 10.5522/04/8851658
title: SynthCity Dataset - Complete

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender. See our project website http://www.synthcity.xyz or paper https://arxiv.org/abs/1907.04758 for more information.
DOI: 10.5522/04/8851781.v1
title: SynthCity Dataset - Area 3 (Test)

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.
DOI: 10.6084/m9.figshare.8851787.v2
title: Long-term warming results in species-specific shifts in seed mass in alpine communities

description: Seed mass (mg) in control and experimental warming treatments in the LG and HG meadow sites. CK: control treatment; OTC: expermental warming; LG: "low" grazing intensity history; HG: "high" grazing intensity history.<br>
DOI: 10.5522/04/8851616
title: SynthCity Dataset - Area 1

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender. See our project website http://www.synthcity.xyz or paper https://arxiv.org/abs/1907.04758 for more information.
DOI: 10.5522/04/8851658.v2
title: SynthCity Dataset - Complete

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender. See our project website http://www.synthcity.xyz or paper https://arxiv.org/abs/1907.04758 for more information.
DOI: 10.6084/m9.figshare.8851484
title: Persistence of antibodies to pneumococcal conjugate vaccine compared to polysaccharide vaccine in patients with Crohn’s disease – one year follow up

description: <b>Background:</b> Patients suffering from Crohn’s disease (CD) are at increased risk of infectious diseases, such as pneumococcal infection. The risk increases with immunotherapy. Pneumococcal infection can be prevented by vaccination. <b>Methods:</b> We conducted a randomized trial of the 23-valent pneumococcal polysaccharide vaccine (PPV23) and the 13-valent pneumococcal conjugated vaccine (PCV13) in groups of CD patients treated with immunosuppressive (IS) drugs in the form of thiopurines (PPV23 <i>n</i> = 28, PCV13 <i>n</i> = 28) alone or in combination with TNF-α antagonists (PPV23 <i>n</i> = 13, PCV13 <i>n</i> = 13) and CD patients not treated with any of these drugs (untreated) (PPV23 <i>n</i> = 30, PCV13 <i>n</i> = 24). In this article, we report the immunogenicity of PPC23 and PCV13 one year after vaccination. <b>Results:</b> No overall differences in vaccine-induced serotype-specific immunoglobulin G (IgG) antibodies or functional antibodies (opsonophagocytic activity (OPA)) were found between the two vaccines. PCV13 induced a higher concentration of IgG antibodies for serotype 9V than PPV23 in untreated patients. In contrast, PPV23 induced higher OPA for serotypes 6B and 19F than PCV13 in IS treated patients. Untreated patients showed generally higher IgG and OPA antibody levels than patients treated with IS and TNF-α antagonists. <b>Conclusions:</b> In conclusion, we found no general differences in the persistence of induced antibodies when comparing PPV23 with PCV13 regardless of treatment and also within treatment groups (IS, IS + TNF-α and untreated). This was demonstrated for both serotype-specific IgG antibodies and as functional antibodies (OPA). Patients treated with thiopurines in combination with TNF-α inhibitors have an impaired immune response against both PPV23 and PCV13, as compared to untreated patients. This study has been registered in the European Clinical Trials Database (EudraCT, record no 2012-002867-86) and ClinicalTrials.gov (record no. NCT01947010). <b>Trial registration:</b>ClinicalTrials.gov identifier: NCT01947010.
DOI: 10.5522/04/8851658.v1
title: SynthCity Dataset - Complete

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.
DOI: 10.6084/m9.figshare.8851484.v2
title: Persistence of antibodies to pneumococcal conjugate vaccine compared to polysaccharide vaccine in patients with Crohn’s disease – one year follow up

description: <b>Background:</b> Patients suffering from Crohn’s disease (CD) are at increased risk of infectious diseases, such as pneumococcal infection. The risk increases with immunotherapy. Pneumococcal infection can be prevented by vaccination. <b>Methods:</b> We conducted a randomized trial of the 23-valent pneumococcal polysaccharide vaccine (PPV23) and the 13-valent pneumococcal conjugated vaccine (PCV13) in groups of CD patients treated with immunosuppressive (IS) drugs in the form of thiopurines (PPV23 <i>n</i> = 28, PCV13 <i>n</i> = 28) alone or in combination with TNF-α antagonists (PPV23 <i>n</i> = 13, PCV13 <i>n</i> = 13) and CD patients not treated with any of these drugs (untreated) (PPV23 <i>n</i> = 30, PCV13 <i>n</i> = 24). In this article, we report the immunogenicity of PPC23 and PCV13 one year after vaccination. <b>Results:</b> No overall differences in vaccine-induced serotype-specific immunoglobulin G (IgG) antibodies or functional antibodies (opsonophagocytic activity (OPA)) were found between the two vaccines. PCV13 induced a higher concentration of IgG antibodies for serotype 9V than PPV23 in untreated patients. In contrast, PPV23 induced higher OPA for serotypes 6B and 19F than PCV13 in IS treated patients. Untreated patients showed generally higher IgG and OPA antibody levels than patients treated with IS and TNF-α antagonists. <b>Conclusions:</b> In conclusion, we found no general differences in the persistence of induced antibodies when comparing PPV23 with PCV13 regardless of treatment and also within treatment groups (IS, IS + TNF-α and untreated). This was demonstrated for both serotype-specific IgG antibodies and as functional antibodies (OPA). Patients treated with thiopurines in combination with TNF-α inhibitors have an impaired immune response against both PPV23 and PCV13, as compared to untreated patients. This study has been registered in the European Clinical Trials Database (EudraCT, record no 2012-002867-86) and ClinicalTrials.gov (record no. NCT01947010). <b>Trial registration:</b>ClinicalTrials.gov identifier: NCT01947010.
DOI: 10.5522/04/8851616.v1
title: SynthCity Dataset - Area 1

description: With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist, which if proved effective can be exploited. We therefore argue that research in this domain would be hugely useful. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.