cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3893507
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.3893495
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.
DOI: 10.6084/m9.figshare.13317836
title: Supplementary Material for: Impact of Body Mass Index Change on the Prognosis of Chronic Obstructive Pulmonary Disease

description: <b><i>Background:</i></b> Low body mass index (BMI) is an important prognostic factor in chronic obstructive pulmonary disease (COPD). However, the prognostic value of longitudinal BMI change in COPD has not been well studied. <b><i>Objective:</i></b> We aimed to evaluate the association between longitudinal change of BMI and prognosis of COPD in Korean COPD cohort. <b><i>Methods:</i></b> This study was conducted in a prospective Korean Obstructive Lung Disease (KOLD) cohort where COPD patients were recruited on an outpatient basis at 17 hospitals in South Korea. Annual BMI was measured over a period of 3 years or more. All patients were categorized into underweight (UW), normal weight (NW), and overweight (OW) groups by BMI. Clinical characteristics and outcomes including exacerbation and mortality were compared based on initial BMI grade and longitudinal change of BMI. <b><i>Results:</i></b> This analysis included 537 COPD patients (mean age = 67.4 ± 7.9 years, male = 97.0%, mean BMI = 23.0 ± 3.1) of KOLD cohort. The proportions of UW, NW, and OW groups were 6.9% (<i>n</i> = 37), 68.9% (<i>n</i> = 370), and 24.2% (<i>n</i> = 130) respectively. The UW group showed lower forced expiratory volume in 1 s (FEV<sub>1</sub>) (<i>p</i> &lt; 0.001), shorter 6-minute walk distance (<i>p</i> &lt; 0.001), higher modified Medical Research Council score (<i>p</i> = 0.002), higher St. George Respiratory Questionnaire score (<i>p</i> &lt; 0.001), higher emphysema index (<i>p</i> &lt; 0.001) and air-trapping index (<i>p</i> &lt; 0.001), and more frequent (<i>p</i> &lt; 0.001) and severe exacerbations (<i>p</i> = 0.003). Multivariable analyses demonstrated that decrease of BMI (hazard ratio [HR] = 0.786, <i>p</i> = 0.038) and the descent of BMI group (HR = 3.167, <i>p</i> = 0.016) at 3-year follow-up along with age, initial BMI, post-bronchodilator FEV<sub>1</sub>, and severe exacerbations were significantly associated with mortality. <b><i>Conclusions:</i></b> This study demonstrated that BMI decrease during follow-up was independently associated with exacerbation and higher mortality of COPD, suggesting BMI reduction in COPD should be carefully managed.
DOI: 10.6084/m9.figshare.13317836.v1
title: Supplementary Material for: Impact of Body Mass Index Change on the Prognosis of Chronic Obstructive Pulmonary Disease

description: <b><i>Background:</i></b> Low body mass index (BMI) is an important prognostic factor in chronic obstructive pulmonary disease (COPD). However, the prognostic value of longitudinal BMI change in COPD has not been well studied. <b><i>Objective:</i></b> We aimed to evaluate the association between longitudinal change of BMI and prognosis of COPD in Korean COPD cohort. <b><i>Methods:</i></b> This study was conducted in a prospective Korean Obstructive Lung Disease (KOLD) cohort where COPD patients were recruited on an outpatient basis at 17 hospitals in South Korea. Annual BMI was measured over a period of 3 years or more. All patients were categorized into underweight (UW), normal weight (NW), and overweight (OW) groups by BMI. Clinical characteristics and outcomes including exacerbation and mortality were compared based on initial BMI grade and longitudinal change of BMI. <b><i>Results:</i></b> This analysis included 537 COPD patients (mean age = 67.4 ± 7.9 years, male = 97.0%, mean BMI = 23.0 ± 3.1) of KOLD cohort. The proportions of UW, NW, and OW groups were 6.9% (<i>n</i> = 37), 68.9% (<i>n</i> = 370), and 24.2% (<i>n</i> = 130) respectively. The UW group showed lower forced expiratory volume in 1 s (FEV<sub>1</sub>) (<i>p</i> &lt; 0.001), shorter 6-minute walk distance (<i>p</i> &lt; 0.001), higher modified Medical Research Council score (<i>p</i> = 0.002), higher St. George Respiratory Questionnaire score (<i>p</i> &lt; 0.001), higher emphysema index (<i>p</i> &lt; 0.001) and air-trapping index (<i>p</i> &lt; 0.001), and more frequent (<i>p</i> &lt; 0.001) and severe exacerbations (<i>p</i> = 0.003). Multivariable analyses demonstrated that decrease of BMI (hazard ratio [HR] = 0.786, <i>p</i> = 0.038) and the descent of BMI group (HR = 3.167, <i>p</i> = 0.016) at 3-year follow-up along with age, initial BMI, post-bronchodilator FEV<sub>1</sub>, and severe exacerbations were significantly associated with mortality. <b><i>Conclusions:</i></b> This study demonstrated that BMI decrease during follow-up was independently associated with exacerbation and higher mortality of COPD, suggesting BMI reduction in COPD should be carefully managed.
DOI: 10.5281/zenodo.4301690
title: Training dataset: DIA data analysis of a HEK/Ecoli Spike-in dataset using OpenSwathWorkflow

description: The eight raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/). HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured: Sample HEK E.coli MS method<br> Sample1 2.5 0.15 DIA<br> Sample2 2.5 0.15 DIA<br> Sample3 2.5 0.15 DIA<br> Sample4 2.5 0.15 DIA<br> Sample5 2.5 0.80 DIA<br> Sample6 2.5 0.80 DIA<br> Sample7 2.5 0.80 DIA<br> Sample8 2.5 0.80 DIA Additionally, iRT peptides were added and 1µg of each samples was measured using data independent acquisition with a Q-Exactive Plus mass spectrometer. Briefly, a scan range from 400-1000 m/Z was first covered by an MS1 scan followed by 25 consecutive MS2 scans (each 24 m/z broad). In the next cycle another MS1 scan was acquired followd by 26 MS2 scans (also 24m/z broad) in which the window centers were shifted by 50% compared to the previous cycle of MS2 scans. The resulting raw files contain overlapping MS2 scans. Besides the eight raw files, we uploaded a spectral library, a transition list for the iRT peptides as well as an sample annotation file.<br> Additionally, we uploaded the Galaxy PyProphet score training result files: PyProphet score report and PyProphet score.
DOI: 10.25345/c5mz14
title: MassIVE MSV000086542 - rawR - Direct access to raw mass spectrometry data in R
title: raw files analyzed in rawR - Direct access to raw mass spectrometry data in R https://www.biorxiv.org/content/10.1101/2020.10.30.362533v1.full
DOI: 10.5281/zenodo.4302039
title: Bathymetry of Large, Straight and Immobile bed-forms found in the Great South Channel (2015 - 2017)

description: Digital Bathymetry produced from the raw data set collected with a Reson Seabat 7125 Multibeam echo-sounder. <br> The grid cell size is 1 m×1m. The area is a representative sample of the Great South Channel seafloor describing Large, Straight, and Immobile bedforms (LSI).<br> The survey data were collected during the 2015 and 2017 - NOAA annual federal Sea Scallop Survey on board R/V Hugh Sharp, operated by the University of Delaware, and funded by the Improve a Stock Assessment Program of the NOAA Northeast National Marine Fisheries Service (NEMFS)<br> The data processing was supported by NOAA GRANT NA15NOS400002000.
description: This dataset has been used to develop a "GIS Rule-Based Model" published in MDPI geosciences-250279 - https://www.mdpi.com/2076-3263/8/1/28/htm - A reproducible workflow is available at https://github.com/epifanio/geosciences-250279
DOI: 10.5281/zenodo.4302038
title: Bathymetry of Large, Straight and Immobile bed-forms found in the Great South Channel (2015 - 2017)

description: Digital Bathymetry produced from the raw data set collected with a Reson Seabat 7125 Multibeam echo-sounder. <br> The grid cell size is 1 m×1m. The area is a representative sample of the Great South Channel seafloor describing Large, Straight, and Immobile bedforms (LSI).<br> The survey data were collected during the 2015 and 2017 - NOAA annual federal Sea Scallop Survey on board R/V Hugh Sharp, operated by the University of Delaware, and funded by the Improve a Stock Assessment Program of the NOAA Northeast National Marine Fisheries Service (NEMFS)<br> The data processing was supported by NOAA GRANT NA15NOS400002000.
description: This dataset has been used to develop a "GIS Rule-Based Model" published in MDPI geosciences-250279 - https://www.mdpi.com/2076-3263/8/1/28/htm - A reproducible workflow is available at https://github.com/epifanio/geosciences-250279
DOI: 10.21227/z6h8-3g44
title: new

description: under construction feb or dontt knw - PVt page Many softwares are used , Software name along with source((university website and github) and the reuslts obtained from them are mentioned inThe particular scripts fom these are also attached ,Fully modified not submitted
DOI: 10.5281/zenodo.4302084
title: Training dataset: Statistical analysis of a HEK/Ecoli Spike-in DIA dataset using MSstats

description: The uploaded files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/). HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured and analysed using OpenSwathWorkflow in Galaxy. Results were exported using PyProphet and can be used for the statistical analysis and detection of the two different Spike-in Ratios. The Spike-in ratios were the following: Sample HEK E.coli <br> Spike_in_1 2.5 0.15<br> Spike_in_2 2.5 0.80 Besides the two PyProphet export files, we uploaded a sample annotation file as well as a comparison matrix file.<br> Additionally, we uploaded the Galaxy MSstats training result files: MSstats_ComparisonResult_export_tabular and MSstats_ComparisonResult_msstats_input.
DOI: 10.21227/dgtf-1415
title: wind turbine 2

description: under construction later in Jan/Feb 2021 pls visit laThis dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on on Energy Conversion".In the submitted dataset " Wii ",the comparison on the existing withIn this dataset and the Researchppers Part 1, 2, 3 submitted to Journal, author has extened this to
DOI: 10.21227/bnxm-7q87
title: Biomedical KEH B

description: under construction later in Jan/Feb 2021 This dataset is in support of my research paper submitted to "IEEE Transactions on Circuits and Systems I: Regular Papers".It is known that in IOT wearable medical monitoting systems such as Heart rate monitor,Blood pressure sensor,Blood glucose system,Pulse oximeter sensor, Temperature sensor,Accelerometers,Wireless communication, etc., theAuthor extends this to artificial leg(project which was abandoned by the author due to finances) or artifical handIn this paper and dataset, mass-spring system
DOI: 10.5281/zenodo.3776011
title: Daily Net Ecosystem Exchange data for the Rur Catchment Area 2010-2018

description: Daily raster between 2010 and 2018 of Net Ecosystem Exchange (NEE) for the Rur catchment in western Germany at 250 m spatial resolution as upscaling results for the article <em>Upscaling Net Ecosystem Exchange over Heterogeneous Landscapes with Machine Learning</em> in JGR Biogeosciences. COSMO REA6 data and daily grids of potential evapotranspiration and soil moisture were obtained from the German Weather Service (DWD). ftp://opendata.dwd.de/ Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., et al. (2015). Towards a high-resolution regional re-analysis for the European CORDEX domain. <em>Quarterly Journal of the Royal Meteorological Society, 141</em>, 1–15. https://doi.org/10.1002/qj.2486 DWD Climate Data Center (CDC): Daily grids of potential evapotranspiration over grass, version 0.x, 2020. DWD Climate Data Center (CDC): Daily grids of soil moisture under grass and sandy loam, version 0.x,2020.
DOI: 10.5281/zenodo.3776010
title: Daily Net Ecosystem Exchange data for the Rur Catchment Area 2010-2018

description: Daily raster between 2010 and 2018 of Net Ecosystem Exchange (NEE) for the Rur catchment in western Germany at 250 m spatial resolution as upscaling results for the article <em>Upscaling Net Ecosystem Exchange over Heterogeneous Landscapes with Machine Learning</em> in JGR Biogeosciences. COSMO REA6 data and daily grids of potential evapotranspiration and soil moisture were obtained from the German Weather Service (DWD). ftp://opendata.dwd.de/ Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., et al. (2015). Towards a high-resolution regional re-analysis for the European CORDEX domain. <em>Quarterly Journal of the Royal Meteorological Society, 141</em>, 1–15. https://doi.org/10.1002/qj.2486 DWD Climate Data Center (CDC): Daily grids of potential evapotranspiration over grass, version 0.x, 2020. DWD Climate Data Center (CDC): Daily grids of soil moisture under grass and sandy loam, version 0.x,2020.
DOI: 10.6084/m9.figshare.13065986.v11
title: Gastric cancer lymph node data set

description: This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".<br>
DOI: 10.21227/2fv8-b498
title: wind turbine 2

description: under construction pls visit later in Jan/Feb 2021 This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on on Energy Conversion".In the submitted dataset " Wii ",the comparison on the existing withIn this dataset and the Researchppers Part 1, 2, 3 submitted to Journal, author has extened this to
DOI: 10.21227/nanj-3s33
title: Biomedical KEH B

description: under construction pls visit later in Jan/Feb 2021 This dataset is in support of my research paper submitted to "IEEE Transactions on Circuits and Systems I: Regular Papers".It is known that in IOT wearable medical monitoting systems such as Heart rate monitor,Blood pressure sensor,Blood glucose system,Pulse oximeter sensor, Temperature sensor,Accelerometers,Wireless communication, etc., theAuthor extends this to artificial leg(project which was abandoned by the author due to finances) or artifical handIn this paper and dataset, mass-spring system
DOI: 10.6084/m9.figshare.13317854
title: Supplementary Material for: Vertebral Fractures Associated with Spinal Sagittal Imbalance and Quality of Life in Acromegaly: A Radiographic Study with EOS 2D/3D Technology

description: <b><i>Introduction:</i></b> Acromegaly is commonly complicated by arthropathy and skeletal fragility with high risk of vertebral fractures (VFs). <b><i>Objective:</i></b> This study aimed to assess whether VFs may be associated with sagittal spine deformities, arthropathy, impaired quality of life (QoL), pain, and disability. <b><i>Methods:</i></b> Thirty-eight patients with acromegaly (median age: 55 years, 20 males) and 38 matched control subjects were evaluated by a low-dose sagittal and coronal planes, X-ray imaging system (EOS®-2D/3D) for morphometric VFs, radiological signs of spine arthropathy, and spine deformities (Cobb thoracic index ≥40°, pelvic incidence minus lumbar lordosis ≥10°, pelvic tilt &gt;20°, and sagittal vertical axis ≥4 cm) determining sagittal spine imbalance. Acromegalic patients were also evaluated by questionnaires for QoL (Acromegaly QoL Questionnaire [AcroQoL] and Short Form-36 [SF-36]) and pain and disability (Western Ontario and McMaster University [WOMAC]). <b><i>Results:</i></b> Acromegalic patients showed higher prevalence of thoracic hyperkyphosis (i.e., Cobb thoracic index ≥40°; <i>p</i> = 0.04) and pelvic tilt &gt;20° (<i>p</i> = 0.02) than control subjects. VFs were found in 34.2% of acromegalic patients (<i>p</i> = 0.003 vs. control subjects), in relationship with higher prevalence of hyperkyphosis (<i>p</i> = 0.03), pelvic tilt &gt;20° (<i>p</i> = 0.04), sagittal vertical axis ≥4 cm (<i>p</i> = 0.03), and moderate/severe subchondral degeneration (<i>p</i> = 0.01). Moreover, patients with VFs had lower AcroQoL general health (<i>p</i> = 0.007) and SF-36 general health (<i>p</i> = 0.002) scores and higher WOMAC pain (<i>p</i> = 0.003) and global (<i>p</i> = 0.009) scores than patients who did not fracture. <b><i>Conclusions:</i></b> In acromegaly, VFs may be associated with spine deformities and sagittal imbalance, spine arthropathy, impaired QoL, and disability.