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7,195 results for “SET”
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 2)
<p>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.</p> <p>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:</p> <ol> <li>ribfrac-train-images-2.zip: 120 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-train-labels-2.zip: 120 annotations in NII format (nii.gz).</li> <li>ribfrac-train-info-2.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-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. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><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). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><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></p> <p> </p> <p>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.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 1)
<p>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.</p> <p>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:</p> <ol> <li>ribfrac-train-images-1.zip: 300 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-train-labels-1.zip: 300 annotations in NII format (nii.gz).</li> <li>ribfrac-train-info-1.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-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. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><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). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><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></p> <p> </p> <p>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.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
San Solomon Springs Baseflow Period Hydrochemistry Data Set
<p>San Solomon Springs Dec 19' - April 20'</p>
Experimental Data Set for the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy"
<p>This are the feature values used in the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy".</p> <p>The dataset regroups feature values for every "cheap" features available in the R package <em>flacco </em>and are computed using 5 sampling strategies and in dimension <span class="math-tex">\($d=5$\)</span>:</p> <ol> <li>Random: the classical Mersenne-Twister algorithm;</li> <li>Randu: a random number generator that is notoriously bad;</li> <li>LHS: a centered Latin Hypercube Design;</li> <li>iLHS: an improved Latin Hypercube Design;</li> <li>Sobol: points extracted from a Sobol' low-discrepancy sequence.</li> </ol> <p>The csv file <em>features_summury_dim_5_ppsn.csv </em>regroups 100 values for every features whereas <em>features_summury_dim_5_ppsn_median.csv </em>regroups for every feature the median of the 100 values.</p> <p>In the folder <em>PPSN_feature_plots</em> are the histograms of feature values on the 24 COCO functions for 3 sampling strategies: Random, LHS and Sobol.</p> <p>The Python file <em>sampling_ppsn.py</em> is the code used to generate the sample points from which the feature values are computed.</p> <p>The file <em>stats50_knn_dt.csv</em> provide the raw data of median and IQR (inter quartile interval) for the heatmaps and boxplots available in the paper.</p> <p>Finally, the files <em>results_classif_knn100.csv</em> (resp. dt) provide the accuracy of 100 classifications for every settings.</p> <p> </p>
Data set for Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation
<p>The dataset contains over-the-air measurements on a 64 active antenna array (Anokiwave AWMF-0129) operating at 28 GHz carrier frequency and transmitting a 200 MHz OFDM waveform with FFT size of 4096, 3168 active subcarriers, subcarrier spacing of 60 kHz and 5 times oversampling w.r.t the critical sampling rate. The dataset contains the I/Q samples of the TX waveform as well as the corresponding over-the-air received signals when the electrical beam is steered toward different beamforming directions. This dataset allows to observe and study the so-called beam-dependent load modulation, which is the phenomenon that causes the nonlinear characteristics of the antenna array to change with the beamforming direction. A Matlab script for data visualization is also provided.</p> <p>For further details please refer to the following papers:</p> <p>A. Brihuega <em>et al</em>., "Piecewise Digital Predistortion for mmWave Active Antenna Arrays: Algorithms and Measurements," in <em>IEEE Transactions on Microwave Theory and Techniques</em>, doi: 10.1109/TMTT.2020.2994311.</p> <p>A. Brihuega <em>et al</em>., “Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation,” in <em>IEEE Microwave and Wireless Components Letters, </em>doi: 10.1109/LMWC.2020.3004003</p>
Data set for "Dynamic perceptual feature selectivity in primary somatosensory cortex upon reversal learning"
<p>This repository contains the data used to generate the figures and well as the main codes that were used for analyses.</p>
Sensor data set, electromechanical cylinder at ZeMA testbed (2kHz)
<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p> </p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in HDF5 file as a 3D-matrix</li> <li>one row represents one second of the return stroke of one working cycle (6292 rows: 6292 cycles)</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p> </p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p> </p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p> </p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>
Sensor data set of 3 electromechanical cylinder at ZeMA testbed (2kHz)
<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p> </p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in three HDF5 file as a 3D-matrix, one file is for one axis</li> <li>one row represents one second of the return stroke of one working cycle<br> axis 3: 6292 cycles<br> axis 5: 6083 cycles<br> axis 7: 5732 cycles</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p> </p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p> </p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p> </p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>
Paracoherent Answer Set Computation
<p>Instances used for the experiments of the paper "Paracoherent Answer Set Computation".</p>
Data from: Hybrid zone barriers comparative data set
<p>Many recent studies have addressed the mechanisms operating during the early stages of speciation, but surprisingly few studies have tested theoretical predictions on the evolution of strong reproductive isolation (RI). To help address this gap, we first undertook a quantitative review of the hybrid zone literature for flowering plants in relation to reproductive barriers. Then, using Populus as an exemplary model group, we analysed genome-wide variation for phylogenetic tree topologies in both early- and late-stage speciation taxa to determine how these patterns may be related to the genomic architecture of RI. Our plant literature survey revealed variation in barrier complexity and an association between barrier number and introgressive gene flow. Focusing on Populus, our genome-wide analysis of tree topologies in speciating poplar taxa points to unusually complex genomic architectures of RI, consistent with earlier genome-wide association studies. These architectures appear to facilitate the 'escape' of introgressed genome segments from polygenic barriers even with strong RI, thus affecting their relationships with recombination rates. Placed within the context of the broader literature, our data illustrate how phylogenomic approaches hold great promise for addressing the evolution and temporary breakdown of RI during late stages of speciation. This article is part of the theme issue 'Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers'.</p>
Data sets v.1.1 for Perspective: SARS-CoV-2 may regulate cellular responses through depletion of specific host miRNAs
<p>The list of potential (bioinformatic predictions) interactions of human miRNA with 7 coronavirus genomes that include 3 pathogenic and 4 non-pathogenic coronaviruses. (<strong>Data Set 1</strong>). <em>The HCoVs' RNA genomes of pathogenic strains were SARS-CoV-2 (NC_045512.2), SARS-CoV (NC_004718.3), MERS-CoV (NC_019843.3). </em>The non-pathogenic strains were HCoV-OC43 (KU131570.1), HCoV-229E (NC_002645.1), HCoV-HKU1 (KF686346.1), and HCoV-NL63 (NC_005831.2). These coronaviruses were tested against the set of 896 confident mature human miRNA sequences that were obtained from the miRBbase v2.21 using the RNA22 v2 microRNA target discovery tool web-server. In order to reduce the false discovery rate of the MTS predictions, the most strict parameters were applied to the default computation workflow using a specificity of 92% versus a sensitivity of 22%.</p> <p><strong>Data set 2.</strong> The potential targets of miRNA that could be bound to either the pathogenic, the non-pathogenic or both groups of HCoVs. Predicted base on miRDIP database (with only top 1% of the most probable targets considered),</p> <p><strong>Data set 3. </strong> Pre-miRNA sequences in the <em>SARS-CoV-2</em> RNA sequence that could potentially enter the human RNAi pathway, base on miRNAFold webserver.</p>
Detrended SSH data set used for Rossby Wave Analysis, extraction at 39N from ORCA12.L46-MJM189 DRAKKAR simulation
<p>This data set corresponds to the detrended Sea Surface Heigh (SSH) simulated by the NEMO ocean circulation model, under the ORCA12.L46-MJM189 configuration, developped in the frame of the DRAKKAR project. This particular data set is an interpolation from the native numerical grid, covering the latitude 39N in the North Altantic ocean, for the period 1970 to 2015. The data are concatenated in a single file with 5-days average of SSH. This subset was used in Watelet et al. (2020) submitted paper, dealing with Rossby waves analysis.</p>
Dataset for the OOPSLA'20 paper "Regex Matching with Counting-Set Automata"
<p>This is the dataset used for the experimental evaluation in the OOPSLA'20 paper "Regex Matching with Counting-Set Automata".</p>
Calorie Harmony Data Set
<p>Sample de-identified dataset from the Calorie Harmony study.</p>
Data sets used in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes"
<p><span>This data set reports water and energy fluxes, soil water content and sap fluxes measured at two adjacent pinon-juniper woodlands in central New Mexico from January 2009 to December 2016. This data set was used for the analysis in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes" submitted to JGR-Biogeosciences. </span></p>
Hathi: An MCDM-based Approach for the Capacity Planning of Cloud-hosted DBMS — Data Sets
<p>These data sets provide supplemental material for the paper "Hathi: An MCDM-based Approach for<br> the Capacity Planning of Cloud-hosted DBMS" submitted to the 13th IEEE/ACM International<br> Conference on Utility and Cloud Computing.</p>
Atmospheric aerosol, gases and meteorological parameters measured during the LAPSE-RATE campaign - Finnish Meteorological Institute data sets
<p>This publication summarizes the measurements and data sets generated by Finnish Meteorological Institute (FMI) during the LAPSE-RATE that took place in the San Luis Valley of Colorado during the summer of 2018. These data sets offer observations of atmospheric aerosols and gases at the surface and in vertical column acquired by FMI rotary-wing Unmanned Aerial System and FMI ground module. </p>
Test set - 4023 PubMed abstracts (for manuscript: Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait )
<p>A .zip archive containing the set of abstracts used in the test set (4023 abstracts from PubMed) in .txt format.</p> <p>This archive contains supplementary files for the manuscript Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait.</p>
GPS 50 Hz data set for the case study. Part 2 (Version sbf)
<p>GPS 50 Hz data set for the case study entitled "Comparison of TEC calculations based on Trimble, Javad, Leica and Septentrio GNSS receiver data". Part 2 (Septentrio dataset).</p>
Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"
<p>Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.