cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3893497
title: RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 2)

description: RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification. Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 2 of RibFrac dataset, including 120 CTs and the corresponding annotations. Files include: ribfrac-train-images-2.zip: 120 chest-abdomen CTs in NII format (nii.gz). ribfrac-train-labels-2.zip: 120 annotations in NII format (nii.gz). ribfrac-train-info-2.csv: labels in the annotation NIIs. public_id: anonymous patient ID to match images and annotations. label_id: discrete label value in the NII annotations. label_code: 0, 1, 2, 3, 4, -1 0: it is background 1: it is a displaced rib fracture 2: it is a non-displaced rib fracture 3: it is a buckle rib fracture 4: it is a segmental rib fracture -1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as: <em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (DOI)</em> or using bibtex <em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em> The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DOI: 10.5517/ccdc.csd.cc11kwfx
title: CCDC 970499: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc244v16
title: CCDC 1970857: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc244w06
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DOI: 10.5517/ccdc.csd.cc244w17
title: CCDC 1970888: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc244w28
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DOI: 10.5517/ccdc.csd.cc244w7f
title: CCDC 1970894: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc244w8g
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DOI: 10.5517/ccdc.csd.cc244w9h
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DOI: 10.5517/ccdc.csd.cc244wbj
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DOI: 10.5517/ccdc.csd.cc244wck
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DOI: 10.5517/ccdc.csd.cc244wfm
title: CCDC 1970900: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc244wqx
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DOI: 10.5517/ccdc.csd.cc24djdh
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DOI: 10.5517/ccdc.csd.cc24djgk
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DOI: 10.5517/ccdc.csd.cc24ymqg
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DOI: 10.5517/ccdc.csd.cc24ymvl
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DOI: 10.5517/ccdc.csd.cc25ch02
title: CCDC 2007033: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc25hzqd
title: CCDC 2011364: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc25hzrf
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DOI: 10.5517/ccdc.csd.cc25hzth
title: CCDC 2011367: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc25hzwk
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DOI: 10.25416/ntr.13317767
title: The impact of teaching experimental research on-lineResearch-informed teaching and COVID [Article].pdf

description: As part of the BSc (Hons) Diagnostic Radiography programme students learn and undertake research relevant to their development as first post radiographers (dose optimisation and image quality) within the Research-Informed Teaching experience (RiTe). Due to the COVID-19 pandemic, the delivery of RiTe to our year 2 students was moved to an online format using Microsoft Teams and Blackboard Collaborate and focused on a key area of current practice - COVID-19 and chest X-ray imaging. Within RiTe students are placed into collaborative enquiry-based learning (CEBL) groups to share tasks, but to also support and learn from one another. <br><br>An online survey was used to explore the year 2 student cohort task value and self-efficacy of this online version of RiTe.
DOI: 10.25416/ntr.13317767.v1
title: The impact of teaching experimental research on-lineResearch-informed teaching and COVID [Article].pdf

description: As part of the BSc (Hons) Diagnostic Radiography programme students learn and undertake research relevant to their development as first post radiographers (dose optimisation and image quality) within the Research-Informed Teaching experience (RiTe). Due to the COVID-19 pandemic, the delivery of RiTe to our year 2 students was moved to an online format using Microsoft Teams and Blackboard Collaborate and focused on a key area of current practice - COVID-19 and chest X-ray imaging. Within RiTe students are placed into collaborative enquiry-based learning (CEBL) groups to share tasks, but to also support and learn from one another. <br><br>An online survey was used to explore the year 2 student cohort task value and self-efficacy of this online version of RiTe.
DOI: 10.21227/fma1-1v29
title: LDoS attacks in SDN

description: A dataset of LDoS attacks against bottleneck links in software-defined networks. LDoS attacks are based on a flawed implementation of the TCP congestion control mechanism and can seriously affect legitimate traffic on bottlenecked or shared links in software-defined networks.
DOI: 10.21227/rm3r-1j80
title: LDoS attacks in SDN

description: A dataset of LDoS attacks against bottleneck links in software-defined networks. LDoS attacks are based on a flawed implementation of the TCP congestion control mechanism and can seriously affect legitimate traffic on bottlenecked or shared links in software-defined networks.
DOI: 10.5281/zenodo.3893508
title: RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 1)

description: RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification. Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 1 of RibFrac dataset, including 300 CTs and the corresponding annotations. Files include: ribfrac-train-images-1.zip: 300 chest-abdomen CTs in NII format (nii.gz). ribfrac-train-labels-1.zip: 300 annotations in NII format (nii.gz). ribfrac-train-info-1.csv: labels in the annotation NIIs. public_id: anonymous patient ID to match images and annotations. label_id: discrete label value in the NII annotations. label_code: 0, 1, 2, 3, 4, -1 0: it is background 1: it is a displaced rib fracture 2: it is a non-displaced rib fracture 3: it is a buckle rib fracture 4: it is a segmental rib fracture -1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as: <em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (DOI)</em> or using bibtex <em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em> The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DOI: 10.5281/zenodo.3893496
title: RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Tuning/Validation Set)

description: RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification. This is the Tuning Set (a.k.a. Validation Set in machine learning terminology) of RibFrac dataset, including 80 CTs and the corresponding annotations. Files include: ribfrac-val-images.zip: 80 chest-abdomen CTs in NII format (nii.gz). ribfrac-val-labels.zip: 80 annotations in NII format (nii.gz). ribfrac-val-info.csv: labels in the annotation NIIs. public_id: anonymous patient ID to match images and annotations. label_id: discrete label value in the NII annotations. label_code: 0, 1, 2, 3, 4, -1 0: it is background 1: it is a displaced rib fracture 2: it is a non-displaced rib fracture 3: it is a buckle rib fracture 4: it is a segmental rib fracture -1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as: <em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (DOI)</em> or using bibtex <em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em> The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.