cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4302013
title: Supplementary Information for "Biocatalytic synthesis of 2-Seleno pyrimidine nucleosides via transglycosylation"

description: This is the external Supplementary Information for our publication "Biocatalytic synthesis of 2-Seleno pyrimidine nucleosides via transglycosylation". The .pdf file contains the Supplementary Information: author contributions and supplementary items. The .zip file contains the raw data and metadata for all items (supplementary and main text) and the calculations. The Python code used for spectral unmixing is available on this platform (10.5281/zenodo.3243376) and has been described in our previous publications (10.3390/mps2030060, 10.5281/zenodo.3333469, 10.1002/cbic.202000204, 10.5281/zenodo.3723806 ). Further applications of this method can be found in our recent article in <em>Adv. Synth. Catal.</em> (10.1002/adsc.201901230) and its supporting material (10.5281/zenodo.3568858).
DOI: 10.6084/m9.figshare.13317632
title: ICR

description: This file contains the codes of ICR, ILR, and RRU. The processed data for each function were also provided.
DOI: 10.6084/m9.figshare.13317632.v1
title: ICR

description: This file contains the codes of ICR, ILR, and RRU. The processed data for each function were also provided.
DOI: 10.5281/zenodo.4302006
title: Supplementary material for "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws"

description: This archive contains supplementary material for the paper "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws", containing the source codes for the numerial results presented in the paper. An arXiv pre-print version of the paper is available here. The following codes are provided: <code>codes/simulation1D_small.jl</code>: small amplitudes (linear) 1D simulation <code>codes/simulation1D_large.jl</code>: large amplitudes (nonlinear) 1D simulation <code>codes/simulation1D_analytical_gradient</code>: large amplitudes 1D simulation, but using an analytical nonlinear Hamiltonian gradient expression <code>codes/simulation2D.jl</code>: large amplitudes (nonlinear) 2D simulation <code>codes/convergence1D.jl</code>: convergence analysis of the 1D linear case <code>codes/convergence2D.m</code>: convergence analysis of the 2D linear case A GitHub with the codes and a few instructions on usage is available here. <strong>Acknowledgements</strong> This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled "Interconnected of Infinite-Dimensional systems for Heterogeneous Media", nº ANR-16-CE92-0028. Further information is available here.
DOI: 10.5281/zenodo.4302005
title: Supplementary material for "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws"

description: This archive contains supplementary material for the paper "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws", containing the source codes for the numerial results presented in the paper. An arXiv pre-print version of the paper is available here. The following codes are provided: <code>codes/simulation1D_small.jl</code>: small amplitudes (linear) 1D simulation <code>codes/simulation1D_large.jl</code>: large amplitudes (nonlinear) 1D simulation <code>codes/simulation1D_analytical_gradient</code>: large amplitudes 1D simulation, but using an analytical nonlinear Hamiltonian gradient expression <code>codes/simulation2D.jl</code>: large amplitudes (nonlinear) 2D simulation <code>codes/convergence1D.jl</code>: convergence analysis of the 1D linear case <code>codes/convergence2D.m</code>: convergence analysis of the 2D linear case A GitHub with the codes and a few instructions on usage is available here. <strong>Acknowledgements</strong> This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled "Interconnected of Infinite-Dimensional systems for Heterogeneous Media", nº ANR-16-CE92-0028. Further information is available here.
DOI: 10.6084/m9.figshare.13065986.v8
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: 10.6084/m9.figshare.13317641
title: A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring

description: The raw data for this paper and performance evaluation of the EE, LOF, OCSVM and the voting-based outlier detection method evaluation results have been provided. The raw data are divided into five parts: the Australian credit dataset (Australian.csv), the German credit dataset (GermanData.csv), the Japanese credit dataset (JapanData.csv), the Taiwan credit dataset (TaiwanData.csv), and the Creator credit dataset (Creator.csv).
DOI: 10.6084/m9.figshare.13317641.v1
title: A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring

description: The raw data for this paper and performance evaluation of the EE, LOF, OCSVM and the voting-based outlier detection method evaluation results have been provided. The raw data are divided into five parts: the Australian credit dataset (Australian.csv), the German credit dataset (GermanData.csv), the Japanese credit dataset (JapanData.csv), the Taiwan credit dataset (TaiwanData.csv), and the Creator credit dataset (Creator.csv).
DOI: 10.5525/gla.researchdata.1084
title: The SUMOylation pathway suppresses arbovirus replication in Aedes aegypti cells
DOI: 10.6084/m9.figshare.13317569
title: Supplementary Material for: CT-Derived Pulmonary Artery Diameters to Preselect for Echocardiography in COPD Patients Eligible for Bronchoscopic Treatments

description: <b><i>Background:</i></b> Currently, patients with COPD who are evaluated for bronchoscopic treatments are routinely screened for pulmonary hypertension (PH) and systolic left ventricle dysfunction by echocardiography. <b><i>Objectives:</i></b> We evaluated the prevalence of PH and systolic left ventricle dysfunction in this patient group and investigated if the previously proposed CT-derived pulmonary artery to aorta (PA:A) ratio &gt;1 and PA diameter measurements can be used as alternative screening tools for PH. <b><i>Methods:</i></b> Two hundred fifty-five patients were included in this retrospective analysis (FEV<sub>1</sub> 25%pred, RV 237%pred). All patients received transthoracic echocardiography and chest CT scans on which diameters of the aorta and pulmonary artery were measured at the bifurcation and proximal to the bifurcation. <b><i>Results:</i></b> Following echocardiography, 3 patients (1.2%) had PH and 1 (0.4%) had systolic left ventricle dysfunction. Using a PA:A ratio &gt;1, only 10.3% of the patients with a right ventricular systolic pressure (RVSP) ≥35 mm Hg were detected and none of the patients with an RVSP &gt;50 mm Hg were detected. Patients with an RVSP ≥35 mm Hg had significantly higher PA diameters (29.5 vs. 27.5 mm; <i>p</i> = 0.02) but no significantly different PA:A ratios. All patients with an RVSP &gt;50 mm Hg had PA diameters &gt;30 mm. <b><i>Conclusions:</i></b> The prevalence of PH and systolic left ventricle dysfunction is low in this preselected cohort of patients with severe COPD. In this population, a PA:A ratio &gt;1 is not a useful cardiac screening tool for PH. A PA diameter &gt;30 mm could substitute for routinely performed echocardiography in the screening for PH in this patient group.
DOI: 10.25646/7706
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5285/e5a119c7-15be-4ad8-bf70-6cc8d112af87
title: Satellite-derived velocity maps for Larsen C Ice Shelf, Antarctica, between November 2017-April 2019

description: Velocity maps were derived, for regions of Larsen C ice shelf, from satellite imagery spanning the period November 2017 to April 2019. This period was selected to monitor any change in the velocity field of Larsen C, in the months following the calving of iceberg A68 from the front of the ice shelf. The archive contains two sets of maps. The first are derived from Sentinel-1 satellite data, and span the complete ice shelf for the full 18-month epoch. The second are derived from TerraSAR-X data, and show high-resolution velocity trends between 2017 and 2018, covering the frontal region of Larsen C ice shelf. The maps were produced by Professor Adrian Luckman, Swansea University. The data is part of the NERC RACE project, NE/R012334/1.
description: Data was acquired by TerraSAR-X and Sentinel-1 satellites. The TerraSAR-X data acquisitions were ordered specifically for the RACE68 Project. Standard 'feature tracking' algorithms were applied to derive ice surface velocity. Key parameters in the feature tracking were: TerraSAR-X: window size 500 x 500 (pixels ~2m resolution), sample spacing of 20 pixels. Sentinel-1: window size 416 x 128 (pixels ~10m resolution), sample spacing of 10 pixels.
description: Feature tracking was applied using Gamma Software with a December 2017 license.
description: Velocity retrieval error is generally found to be one tenth of a pixel or better over the repeat period between images, depending on how surface conditions (melt, snowfall) have changed over that period. For these datasets, therefore, the error will be approximately: TerraSAR-X: 0.2m (2m resolution x 0.1) over 11 days, or ~0.02 m/day. Sentinel-1: 1m (10m resolution x 0.1) over 6 days, or ~0.2 m/day.
DOI: 10.6084/m9.figshare.13317674
title: Data&codes_IJGIS

description: This file contains the codes of ILR, ICR, and RRU, developed with MATLAB platform. The data for each function were also attached.
DOI: 10.6084/m9.figshare.13317623.v1
title: Database for Journal of Thermal Biology Paper entitled: "High developmental temperature leads to low reproduction despite adult temperature". Authors: Marta A. Santos, Ana Carromeu-Santos, Ana S. Quina, Mauro Santos, Margarida Matos & Pedro Simões. 2020. doi: 10.1016/j.jtherbio.2020.102794

description: This is the raw data from the plasticity assay published in the Journal of Thermal Biology Paper entitled: "High developmental temperature leads to low reproduction despite adult temperature". Authors: Marta A. Santos, Ana Carromeu-Santos, Ana S. Quina, Mauro Santos, Margarida Matos & Pedro Simões. 2020 (doi: 10.1016/j.jtherbio.2020.102794)
DOI: 10.5281/zenodo.4302036
title: Hiding_behind_machines

description: The transfer of tasks with sometimes far-reaching moral implications to au- tonomous systems raises a number of ethical questions. In addition to fundamental questions about the moral agency of these systems, behavioral issues arise. This ar- ticle focuses on the responsibility of agents who decide on our behalf. We investigate the empirically accessible question of whether the production of moral outcomes by an agent is systematically judged differently when the agent is artificial and not human. The results of a laboratory experiment suggest that decision-makers can actually rid themselves of guilt more easily by delegating to machines than by del- egating to other people. Our results imply that the availability of artificial agents could provide stronger incentives for decision makers to delegate morally sensitive decisions.
DOI: 10.5880/sfb806.55
title: Supplementary data of Hensel et al. 2020, Frontiers in Earth Science

description: This data corresponds to the article and shall be quoted as such using the provided DOI: Hensel, E. A., Vogelsang, R., Noack, T., Bubenzer, O.: Stratigraphy and Chronology of Sodicho Rockshelter – A new sedimentological record of past environmental changes and human settlement phases in Southwestern Ethiopia, DOI: .... 2020
DOI: 10.6084/m9.figshare.13317698
title: data entry.csv

description: survival and predictors of mortality among ICU admission <br>
DOI: 10.6084/m9.figshare.13317698.v1
title: data entry.csv

description: survival and predictors of mortality among ICU admission <br>
DOI: 10.5281/zenodo.4302055
title: Social contact data for Belgium (2010-2011)

description: Social contact data for Belgium in 2010-2011
description: {"references": ["Willem L, Van Kerckhove K, Chao DL, Hens N, Beutels P (2012) A Nice Day for an Infection? Weather Conditions and Social Contact Patterns Relevant to Influenza Transmission. PLoS ONE 7(11): e48695. doi:10.1371/journal.pone.0048695"]}
DOI: 10.5281/zenodo.4059825
title: Social contact data for Belgium (2010-2011)

description: Social contact data for Belgium in 2010-2011
description: {"references": ["Willem L, Van Kerckhove K, Chao DL, Hens N, Beutels P (2012) A Nice Day for an Infection? Weather Conditions and Social Contact Patterns Relevant to Influenza Transmission. PLoS ONE 7(11): e48695. doi:10.1371/journal.pone.0048695"]}
DOI: 10.5281/zenodo.4291954
title: Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data

description: The five 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 the following ratios (amount in µg): Sample HEK E.coli MS method<br> Sample1 2.5 0.00 DDA<br> Sample2 2.5 0.05 DDA<br> Sample3 2.5 0.15 DDA<br> Sample4 2.5 0.40 DDA<br> Sample5 2.5 0.80 DDA Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search.<br> Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.
DOI: 10.25625/9npeqa/jppmvm
title: Potential structural complexity_Map_Confidence interval.tif

description: :unav
DOI: 10.25625/9npeqa/vp5kuu
title: Potential structural complexity_Map.tif

description: :unav
DOI: 10.25625/9npeqa
title: Ehbrecht et al_Global patterns and climatic controls of forest structural complexity_Map of potential structural complexity across biomes

description: Ehbrecht et al. "Global patterns and climatic controls of forest structural complexity". Maps of modelled potential structural complexity and confidence intervals for the (sub-)tropical moist broadleaf forest, (sub-)tropical tree savanna and woodland, temperate broadleaf and mixed forest, temperate conifer forest, and boreal forest biome. Extent of model predictions is confined to ecoregions that were classified as forest or woodland according to Olson et al. (2001).
DOI: 10.5281/zenodo.3993380
title: RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Test 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 Test Set of RibFrac dataset, including 160 CTs and the corresponding annotations. Files include: ribfrac-test-images.zip: 160 chest-abdomen CTs in NII format (nii.gz) Note that only the images are released; the corresponding ground truth labels are not available. Check the MICCAI 2020 RibFrac Challenge website to evaluate your algorithm. 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.3993379
title: RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Test 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 Test Set of RibFrac dataset, including 160 CTs and the corresponding annotations. Files include: ribfrac-test-images.zip: 160 chest-abdomen CTs in NII format (nii.gz) Note that only the images are released; the corresponding ground truth labels are not available. Check the MICCAI 2020 RibFrac Challenge website to evaluate your algorithm. 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.3893498
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.