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7,195 results for “SET”
Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting
<p>This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. </p> <p>It is composed of: </p> <p>- Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. </p> <p>- Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. </p> <p>- Embeddings and Document Representations used for experiments on both datasets. </p> <p>Scripts to run experiments can be found at: <a href="https://github.com/afcarvallo/active_learning_document_screening">https://github.com/afcarvallo/active_learning_document_screening</a></p> <p> </p> <p><strong>Paper abstract:</strong></p> <p>Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.</p>
Data Sets for Measuring and Modeling the Performance Configurations of Distributed DBMS
<p>These data sets contain the performance measurements and additional metadata as accompanying material for the research paper <strong>Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS</strong><em> </em>that is published in the <em>Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) 2020.</em></p> <p>The attached readme describes the data set structure.</p>
Dale_Wonsho demographic data set
<p>These are the data sets used for analyzing demographic data for the manuscript entitled " <strong>Births and deaths in Sidama in southern Ethiopia: Findings from the 2018 Dale-Wonsho Health and Demographic Surveillance System (HDSS)". This data set is used for the first demographic study done in the newly established HDSS site by Hawassa University.</strong></p>
LoDoPaB-CT Challenge Set
<p>Observation data for the <a href="https://lodopab.grand-challenge.org/">LoDoPaB-CT challenge</a>, which asks to reconstruct CT images of the human lung from (simulated) low photon count measurements.</p> <p>The setting is identical to the one of the <a href="https://zenodo.org/record/3384092">LoDoPaB-CT dataset</a> (documented in this <a href="https://www.nature.com/articles/s41597-021-00893-z">Data Descriptor article</a>), which is supposed to be employed for training learned methods. This challenge set contains observations for a separate set of patients.</p> <p>Python utilities for accessing this challenge set and creating the submission file are available at <a href="https://github.com/jleuschn/lodopab_challenge">github.com/jleuschn/lodopab_challenge</a>. The LoDoPaB-CT dataset for training can be accessed using the <a href="https://zenodo.org/record/3970517">DIVal</a> python library (<a href="https://github.com/jleuschn/dival">github.com/jleuschn/dival</a>).</p> <p>Like for the LoDoPaB-CT dataset, reconstructions from the <a href="http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX">LIDC/IDRI dataset</a> are used as a basis for this challenge set.</p> <p> </p> <p>The ZIP file contains multiple <a href="https://www.hdfgroup.org/HDF5/">HDF5</a> files. Each HDF5 file contains one HDF5 dataset named <code>"data"</code>, that provides a number of samples (128 except for the last file). For example, the <code>n</code>-th observation sample is stored in the file <code>"observation_challenge_%03d.hdf5"</code> where <code>"%03d"</code> is <code>floor(n / 128)</code>, at row <code>(n mod 128)</code> of <code>"data"</code>.</p> <p>For this challenge set no patient IDs are provided (in contrast to the fully public parts of the LoDoPaB-CT dataset), since the reconstruction algorithm should not rely on this information.</p> <p><em>Acknowledgements</em></p> <p>Johannes Leuschner, Maximilian Schmidt and Daniel Otero Baguer acknowledge the support by the Deutsche<br> Forschungsgemeinschaft (DFG) within the framework of GRK 2224/1 “π3: Parameter Identification – Analysis,<br> Algorithms, Applications”. We thank Simon Arridge, Ozan Öktem, Carola-Bibiane Schönlieb and Christian<br> Etmann for the fruitful discussion about the procedure, and Felix Lucka and Jonas Adler for their ideas and<br> helpful feedback on the simulation setup. The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.</p>
Statistics of turbulent tracer dispersion from UV camera observations of SO 2, Data set
<p>LES and radiation transport data sets used to produce the figures in the manuscript:</p> <p>Kylling, A., Ardeshiri, H., Cassiani, M., Dinger, A. S., Park, S.-Y., Pisso, I., Schmidbauer, N., Stebel, K., and Stohl, A.: Can statistics of turbulent tracer dispersion be inferred from camera observations of SO2 in the ultraviolet?, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-286, in review, 2019</p> <p>See README file for further description of files.</p>
Dispa-SET Output files for the JRC report "Power System Flexibility in a variable climate"
<p>Here you can find the model results of the <a href="https://doi.org/10.2760/75312">report</a>:</p> <pre><code>De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I., Power system flexibility in a variable climate, EUR 30184 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online), JRC120338. </code></pre> <p>This dataset contains both the raw GDX files generated by the GAMS (<www.gams.com>) optimiser for the <a href="http://www.dispaset.eu">Dispa-SET model</a>. Details on the output format and the names of the variables can be found in the Dispa-SET documentation. A markdown notebook in R (and the rendered PDF) contains an example on how to read the GDX files in R.</p> <p>We also include in this dataset a data frame saved in the <a href="https://parquet.apache.org/">Apache Parquet format</a> that can be read both <a href="http://arrow.apache.org/blog/2019/08/08/r-package-on-cran/">in R</a> and <a href="https://arrow.apache.org/docs/python/parquet.html">Python</a>.</p> <p>A description of the methodology and the data sources with the references can be found into the report.</p> <p><strong>Linked resources</strong></p> <ul> <li>Input files:<strong> </strong>https://zenodo.org/record/3775569#.XqqY3JpS-fc</li> <li>Source code for the figures: https://github.com/energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability</li> </ul> <p><strong>Update</strong></p> <p>[29/06/2020] Updated new version of the Parquet file with the right data in the column `climate_year`</p>
Data set for "Anatomically and functionally distinct thalamocortical inputs to primary and secondary mouse whisker somatosensory cortices"
<p>Data set for: El-Boustani S, Sermet BS, Foustoukos G, Oram TB, Yizhar O, Petersen CCH (2020) Anatomically and functionally distinct thalamocortical inputs to primary and secondary mouse whisker somatosensory cortices. Nature Communications 11: 3342. doi: 10.1038/s41467-020-17087-7</p> <p>There are 4 files in this upload:</p> <p>1. The file named "2020_El-Boustani_NCOMMS.pdf" is the Open Access pdf file of the manuscript published in Nature Communications.</p> <p>2. The file named "2020_El-Boustani_NCOMMS_SupMovie1.avi" is Supplementary Movie 1 in .avi format, accompanying the Nature Communications publication.</p> <p>2. The file named "2020_El-Boustani_NCOMMS_SupMovie2.avi" is Supplementary Movie 2 in .avi format, accompanying the Nature Communications publication.</p> <p>4. The file named "El-Boustani_data_code.zip" (~36 GB) is a zipped version of a folder "El-Boustani_data_code" (~45 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. In the main folder, data for all experiments are stored within folders starting by the prefix “SB”. The code for generating population data and plotting figures from the paper are in the folder “Matlab_code”. In this folder, several Matlab scripts are named after the panels or figures they will plot such as “Plot_Fig3d_Axon_GCaMP6s_traces_example.m”. After opening each file, executing the script will automatically plot the panels and name them accordingly. In some files, the type of data to plot should be specified at the very beginning of the script: “VPM” for VPM data, “POMf” for POm-FO data and “Layer1” for POm-HO data in layer 1. For figure 2, the code is located in a dedicated folder where a Matlab file “Plot_Fig2d_g_Populatin_Plot_POm_FO_HO.m” is used to generate the figures. Finally, other Matlab files are included that are used to create population .mat files or for additional analysis related to the manuscript.</p>
M4DB Sample Data Set
<p>A sample data set for the MicroMagnetic-Metadata DataBase (M4DB).</p>
IEEE 14 bus system data set
<p>IEEE 14 bus system data set</p> <p>High quality data set for data-driven power system assessment.</p>
IEEE 39 bus system data set
<p>IEEE 39 bus system data set.</p> <p>High quality data set for data-driven power system assessment.</p>
WSCC 9 bus system data set
<p>WSCC 9 bus system data set</p> <p>High quality data set for data-driven power system assessment.</p>
IEEE 68 bus system data set
<p>IEEE 68 bus system data set</p> <p>High quality data set for data-driven power system assessment.</p>
Data set for "Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task"
<p>Data set for: Vavladeli A, Daigle T, Zeng H, Crochet S, Petersen CCH (2020) Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task. FUNCTION 1: zqaa008. doi: 10.1093/function/zqaa008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2020_Vavladeli_FUNCTION.pdf" is the Open Access pdf file of the manuscript published in FUNCTION.</p> <p>2. The file named "Vavladeli_data_code.zip" (~2 GB) is a zipped version of a folder named "Vavladeli_data_code" (~2 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and two '.mat' data files. In order to run the analysis of the data set, you need to execute the '.m' file with the corresponding figure name.</p>
Data from: Association between night-shift work, sleep quality, and health-related quality of life : a cross-sectional study among manufacturing workers in a middle-income setting
<p>Objectives: Night-shift work may adversely affect health. This study aimed to determine the impact of night-shift work on health-related quality of life (HRQoL), and assess whether sleep quality was a mediating factor.</p> <p>Design: Cross-sectional study.</p> <p>Setting: 11 manufacturing factories in Malaysia.</p> <p>Participants: 177 night-shift workers aged 40 to 65 years old were compared with 317 non-night-shift work.</p> <p>Primary and secondary outcomes: Participants completed a self-administered questionnaire on socio-demography and lifestyle factors, short Form-12v2 Health Survey (SF-12), and the Pittsburgh Sleep Quality Index (PSQI). Baron and Kenny's method, Sobel test and multiple mediation model with bootstrapping were used to determine whether PSQI score or its components mediated the association between night-shift work and HRQoL.</p> <p>Results: Night-shift work was associated with sleep impairment and HRQoL. Night-shift workers had significantly lower mean scores in all the eight SF-12 domains (p<0.001). Compared to non-night shift workers, night-shift workers were significantly more likely to report poorer sleep quality, longer sleep latency, shorter sleep duration, sleep disturbances, and daytime dysfunction (p<0.001). Mediation analyses showed that PSQI global score mediated the association between night-shift work and HRQoL. "Subjective sleep quality" (indirect effect=-0.24, standard error [SE]=0.14, bias corrected 95%Confidence Interval [BC 95%CI]: -0.58 to -0.01) and "sleep disturbances" (indirect effect=-0.79, SE=0.22, BC 95%CI: -1.30 to -0.42) were mediators for the association between night-shift work and physical wellbeing, whereas "sleep latency" (indirect effect=-0.51, SE=0.21, BC 95%CI: -1.02 to -0.16) and "daytime dysfunction" (indirect effect=-1.11, SE=0.32, BC 95%CI: -1.86 to -0.58) were mediators with respect to mental wellbeing.</p> <p>Conclusion: Sleep quality partially explains the association between night-shift work and poorer HRQoL. Organisations should treat the sleep quality of night-shift workers as a top priority area for action in order to improve their employees' overall wellbeing.</p>
Data set of labeled scenes in a barn in front of automatic milking system
<p>This data set includes supplementary files to our article \emph{"Deep learning image recognition of cow behavior near an automatic milking robot with an open data set"} (Leonardo Santiago Benitez Pereira, Olli Koskela, Ilpo Pölönen, Ilmo Aronen and Iivari Kunttu, in review, 2020). We acquired continuous video data over two-month period of cows in front of Automatic Milking Station (AMS). Each frame of the video is labeled as a single image belonging to one of classes described below.</p> <p>This data set includes 253 files of video data having in total \num{1526473} labeled frames. The data set used in the article consisted of 280 videos, but for privacy reasons, 27 video files including persons were removed from this data set, but their Java Script Object Notation (JSON) label files are left for, e.g., temporal analyses.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Test Set)
<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>This is the Test Set of RibFrac dataset, including 160 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-test-images.zip: 160 chest-abdomen CTs in NII format (nii.gz) </li> </ol> <p>Note that only the images are released; the corresponding ground truth labels are not available. Check the <a href="https://ribfrac.grand-challenge.org/">MICCAI 2020 RibFrac Challenge website</a> to evaluate your algorithm.</p> <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>
Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors
<p>The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets.</p> <p>Data were collected at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona. <br> This site hosts a large field scanner with fifteen sensors, many of which are capable of capturing mm-scale images and point clouds at daily to weekly intervals.</p> <p>These data are intended to be re-used, and are accessible as a combination of files and databases linked by spatial, temporal, and genomic information. In addition to providing open access data, the entire computational pipeline is open source, and we enable users to access high-performance computing environments.</p> <p>The study has evaluated a sorghum diversity panel, biparental cross populations, and elite lines and hybrids from structured sorghum breeding populations. <br> In addition, a durum wheat diversity panel was grown and evaluated over three winter seasons.<br> The initial release includes derived data from from two seasons in which the sorghum diversity panel was evaluated.<br> Future releases will include data from additional seasons and locations.</p> <p>The TERRA-REF reference dataset can be used to characterize phenotype-to-genotype associations, on a genomic scale, that will enable knowledge-driven breeding and the development of higher-yielding cultivars of sorghum and wheat. <br> The data is also being used to develop new algorithms for machine learning, image analysis, genomics, and optical sensor engineering.</p>
Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny
<p class="BodyAA">The pace of the sequencing and computational assembly of novel reference genomes is accelerating. Though DNA sequencing technologies and assembly software tools continue to improve, biological features of genomes such as repetitive sequence as well as molecular artifacts that often accompany sequencing library preparation can lead to fragmented or chimeric assemblies. If left uncorrected, defects like these trammel progress on understanding genome structure and function, or worse, positively mislead this research. Fortunately, integration of additional, independent streams of information, such as a marker-dense genetic map and conserved orthologous gene order from related taxa, can be used to scaffold together unlinked, disordered fragments and to restructure a reference genome where it is incorrectly joined. We present a tool set for automating these processes, one that additionally tracks any changes to the assembly and to the genetic map, and which allows the user to scrutinize these changes with the help of web-based, graphical visualizations. Chromonomer takes a user-defined reference genome, a map of genetic markers, and, optionally, conserved synteny information to construct an improved reference genome of chromosome models: a "chromonome". We demonstrate Chromonomer's performance on genome assemblies and genetic maps that have disparate characteristics and levels of quality.</p>
Data sets to all figures in our article Appl. Phys. Lett. 115, 193507 (2019)
<p>Data sets to all figures in our article in Applied Physics Letters:<br> <br> Quartz tuning fork—A potential low temperature thermometer in high magnetic fields<br> Appl. Phys. Lett. <strong>115</strong>, 193507 (2019)<br> doi: 10.1063/1.5124736</p>
Data set for 'Lunge filter feeding biomechanics constrain rorqual foraging ecology across scale'...
<p>Fundamental scaling relationships influence the physiology of vital rates, which in turn shape the ecology and evolution of organisms. For diving mammals, benefits conferred by large body size include reduced transport costs and enhanced breath-holding capacity, thereby increasing overall foraging efficiency. Rorqual whales feed by engulfing a large mass of prey-laden water at high speed and filter it through baleen plates. However, as engulfment capacity increases with body length across species (Engulfment Volume ∝ Body Length <sup>3.57</sup>), the surface area of the baleen filter does not increase proportionally (Baleen Area ∝ Body Length<sup>1.82</sup>), and thus the filtration time of larger rorquals predictably increases because the baleen surface area must filter a disproportionally large amount of water. We predicted that filtration time should scale with body length to the power of 1.75 (Filter Time ∝ Body Length<sup>1.75</sup><i>)</i>. We tested this hypothesis on four rorqual species using multi-sensor tags with corresponding unoccupied aerial systems (UAS) -based body length estimates. We found that filter time scales with body length to the power of 1.79 (95% CI: 1.61 - 1.97). This result highlights a scale-dependent trade-off between engulfment capacity and baleen area that creates a biomechanical constraint to foraging through increased filtration time. Consequently, larger whales must target high density prey patches commensurate to the gulp size to meet their increased energetic demands. If these optimal patches are absent, larger rorquals may experience reduced foraging efficiency compared to smaller whales if they do not match engulfment capacity to the size of targeted prey aggregations.</p>
ScienceDex guides
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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.