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7,195 results for “SET”
Spatial targeting of Screening + Eave tubes (SET), a house-based malaria control intervention, in Côte d'Ivoire: A geostatistical modelling study
<p>New malaria control tools and tailoring interventions to local contexts are needed to reduce the malaria burden and meet global goals. The housing modification, screening plus a targeted house-based insecticide delivery system called the In2Care® Eave Tubes, has been shown to reduce clinical malaria in a large cluster randomised controlled trial. However, the widescale suitability of this approach is unknown. We aimed to predict household suitability and define the most appropriate locations for ground-truthing where Screening + Eave Tubes (SET) could be implemented across Côte d'Ivoire. We classified DHS sampled households into suitable for SET based on the walls and roof materials. We fitted a Bayesian beta-binomial logistic model using the integrated nested Laplace approximation (INLA) to predict suitability of SET and to define priority locations for ground-truthing and to calculate the potential population coverage and costs. Based on currently available data on house type and malaria infection rate, 31% of the total population and 17.5% of the population in areas of high malaria transmission live in areas suitable for SET. The estimated cost of implementing SET in suitable high malaria transmission areas would be $46m ($13m –$108m). Ground-truthing and more studies should be conducted to evaluate the efficacy and feasibility of SET in these settings. The study provides an example of implementing strategies to reflect local socio-economic and epidemiological factors, and move beyond blanket, one-size-fits-all strategies.</p>
Data set of eradication project on Tsuken Island
<p>Host plant survey.csv: Result of host plant surveys</p> <p>SIT.csv: Date and the number of sterile insects released</p> <p>Sweet potato.csv: Result of host plant survey on sweet potato</p> <p>Trap.csv: Result of trap surveys</p>
Good/Bad data set
<p>The Good/Bad data set is used for the image-quality research, containing unsuccessfully and successfully synthetic samples. </p>
Single Cell Phenotypic Profiling to Identify a Set of Immune Cell Protein Biomarkers for Relapsed and Refractory Diffuse Large B Cell Lymphoma: A Single-Center Study
<p>Diffuse large B-cell lymphoma (DLBCL) is the most common invasive type of non-Hodgkin lymphoma. Cell-of-origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)-DLBCL remains unclear. In the current study, using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single-cell resolution from 18 diagnostic peripheral blood mononuclear cell (PBMC) specimens collected from patients with R/R DLBCL. As controls, five PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA-seq of eight R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C-MYC expression designation. Immune cells from each patient occupied unique regions in the 32-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C-MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single-cell resolution provide precise biomarkers which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones.</p> <p>Here we uploaded the dataset of CyTOF for external validation. For more detailed information, please contact Dr zheng (zenki_zheng@163.com)</p> <p> </p>
Benchmark set inputs for absolute binding free energy calculations of fragment optimisations
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Provided here are the various scripts, input files, and results necessary to reproduce the outcomes of the above mentioned publication. Please see the provided README.md files for further information on the contents of this dataset.</p>
Modelling and Enforcing Access Control Requirements for Smart Contracts - Data Set
<p>This data set contains all artifacts for the master thesis of Jan-Philipp Töberg at the Karlsruher Institute of Technology. This includes the Eclipse project for the metamodel and the generator, the extension of the Slither framework and the use case instances employed during the evaluation. Additionally, extensive instructions regarding the installation and usage are provided.</p>
Pultruded carbon fiber profiles - 3D x-ray tomography data-sets for two different pultruded profiles
<p>3D X-ray scan on Zeiss Xradia 520</p> <table> <tbody> <tr> <td> <table> <tbody> <tr> <td> </td> <td>A1</td> <td>A2</td> <td>A2S</td> <td>B1</td> <td>B1S</td> <td>B2</td> <td>B3</td> </tr> <tr> <td>Scanning Voxel size [μm]</td> <td>1.9767</td> <td>1.97</td> <td>1.9731</td> <td>1.9751</td> <td>1.9753</td> <td>1.9771</td> <td>1.9752</td> </tr> <tr> <td>FoV: (x; y; z) [mm]</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> </tr> <tr> <td>Accelerating Voltage [kV]</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> </tr> <tr> <td>Power [W]</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> <tr> <td>Filter</td> <td>Air</td> <td>LE1</td> <td>LE1</td> <td>LE1</td> <td>LE1</td> <td>Air</td> <td>LE1</td> </tr> <tr> <td>Optical magnification</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> </tr> <tr> <td>Detector to sample distance [mm]</td> <td>25.5</td> <td>25.52</td> <td>26.82</td> <td>25.4</td> <td>26.75</td> <td>25.5</td> <td>25.8</td> </tr> <tr> <td>Source to sample distance [mm] </td> <td>10.6</td> <td>10.61</td> <td>11.11</td> <td>10.5</td> <td>11.1</td> <td>10.6</td> <td>10.7</td> </tr> <tr> <td>Exposure time [s]</td> <td>15</td> <td>18</td> <td>20</td> <td>18</td> <td>18</td> <td>15</td> <td>18</td> </tr> <tr> <td>No. of projections</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> </tr> <tr> <td>Rotation</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> </tr> <tr> <td>Binning</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> <tr> <td>Total scanning time [h]</td> <td>26</td> <td>33</td> <td>33</td> <td>32</td> <td>32</td> <td>26</td> <td>29</td> </tr> <tr> <td>File-size [GB]</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> </tbody> </table> </td> </tr> </tbody> </table>
Impact of green clay authigenesis on element sequestration in marine settings
<p>Supplementary Information to article "<strong>Impact of green clay authigenesis on element sequestration in marine settings</strong>", published in Nature Communications (Baldermann et al., 2022).</p>
Data set for "Token-Level Multilingual Epidemic Dataset for Event Extraction"
<p>This is the data for the TPDL 2021 paper "<a href="https://zenodo.org/record/5780020">Token-Level Multilingual Epidemic Dataset for Event Extraction</a>". If you use this resource, please cite the paper:</p> <pre><code>@inproceedings{mutuvi2021dataset, title = "Token-level Multilingual Epidemic Dataset for Event Extraction", author = {Mutuvi, Stephen and Boros, Emanuela and Doucet, Antoine, and Lejeune, Gaël and Jatowt, Adam and Odeo, Moses}, booktitle = "Proceedings of the 25th International Conference on Theory and Practice of Digital Libraries, September 13–17, 2021, TPDL 2021", year = "2021", location = "Online" }</code></pre> <p> </p> <p>This work has been supported by the European Union Horizon 2020 research and innovation programme under grants 825153 (Embeddia) and 770299 (NewsEye).</p>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
HisVis: Scene Detection Pilot Training Set
<p>The <em>De Boer</em> photo collection, provided by the Noord-Hollands Archief, consists of approximately two million negatives and metadata for the period 1945-2004. This training set consists of a subset of 2,545 images from the NHA online repository, an early batch of digitized images, and digitized small negatives.</p> <p>We took the categorization scheme for Places-365 as a starting point. We combined this scheme with information from the catalog cards that De Boer used. In making these categories, we kept in mind whether there remained a historical and visual consistency in the categories and whether the category would be of use for users of the collection. To gauge the interest of the users, we conducted several interviews.</p> <p>Together with archivists and cultural historians, we studied the images in the training set and settled on a categorization scheme of 159 categories. During annotation, we noticed the difficulties in distinguishing between scenes that were characterized by a particular object and scenes defined by the location or the action performed in the image.</p>
Annotated timeseries from yeast cell lifespans - Training and Test sets - DetecDiv (id03)
<p>This dataset represents timeseries lables of cell divisions, to train & test a classifier to detect cell-cycle slowdown.<br> It has been generated by manual annotation from yeast cells lifespans using the DetecDiv software (see below).</p> <p>It is made of 1 file containing :</p> <ul> <li>Groundtruth Input data (Xdata): 250 timeseries of classes "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog" which are outputs from the <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> network</li> <li>Groundtruth outputdata (Ydata): 250 timeseries of classes "1. pre-slowdown", "2. post-slowdown", which have been annotated manually.</li> </ul> <p>The indexes from the Xdata correspond to that of the Ydata. Timeseries from 1->200 were used as training while 201->250 were used as validation.</p> <p>It is related to the trained network <a href="https://https://doi.org/10.5281/zenodo.5553829">doi.org/10.5281/zenodo.5553829</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a></p> <p><a href="http://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p> </p> <p><strong>Data type</strong>: Vector timeseries (.mat) (250xF with F the number of frames, between 700 and 1000).</p> <p><strong>File format</strong>: .mat</p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p> <p> </p>
High quality rainfall data set for Australia's northern Murray-Darling Basin
<p>Monthly precipitation data were obtained from the Bureau of Meteorology’s homogeneous climate record through its climate change site network. (http://www.bom.gov.au/climate/change/?ref=ftr#tabs=Tracker&tracker=site-networks). These data have undergone complex quality control to address inconsistencies and errors. There are 8 precipitation stations comprising Augathella, Cunnamulla, Normandy, Miles, Surat, Bellata, Bingara and Curlewis. Additionally, monthly mean maximum temperature (TMax) and mean minimum temperature (TMin) data were obtained for Charleville, Miles, Thargomindah, St. George. Moree, Inverell, Walgett and Gunnedah from the Bureau of Meteorology’s climate change site network. </p>
Annotated pixels from brightfield images from yeast cell in microfluidic traps - Training and Test sets - DetecDiv (id02)
<p>This dataset has been generated by manual segmentation from timelapse images of yeast cells dividing using the DetecDiv software (see below).</p> <p>It contains ~1950 raw images (folder "images") associated with their mask (folder "labels") with 3 classes of pixels according to their value on the mask image, "1. background", "2. mother", "3. other".</p> <p>A splitSets.mat indicates which images have been used for training or for testing.</p> <p>It is related to the trained network <a href="https://doi.org/10.5281/zenodo.5553851">doi.org/10.5281/zenodo.5553851</a> from the software DetecDiv: github.com/gcharvin/DetecDiv</p> <p><a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p>-------------------------------------------------------</p> <p><strong>Data type</strong>: 2D microscopy images (brightfield) (.tif) + mask image (.tif)</p> <p><strong>Microscopy data type</strong>: Brightfield images</p> <p><strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS</p> <p><strong>Cell type</strong>: Budding yeast wild type cell (BY4742)</p> <p><strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat</p> <p><strong>Image size</strong>: Brightfield: 60x60x1 (Pixel size: x,y: 325 nm) // Mask: 60x60x1 (Pixel size: x,y: 325 nm), 3 pixel values (one per class).</p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Diffuse-optical data set measured with a smartphone-based sensor on Potato Hill, Oregon, USA
<p>This data set contains both raw data and derived data obtained on Potato Hill, Oregon, on December 17th 2021 using a diffuse-optical, smartphone-based sensor. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the blue (405nm) or red (650nm) laser or spatial calibration data using a 9mm x 9mm calibration pattern. Spectral albedo measurements are contained in the subfilder ./Albedo, the raw images in ./Phone. The root directory contains the matlab code (Matlab R2021b) needed for analysis as well as the derived data.</p> <p>For analyzing the raw data set, use "CameraMatchPotatoHillFinal.m". It wraps around the function "CameraAnalysisFinal.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfGInf.m". It saves a derived data set (attenuation, absorption and scattering coefficients, albedos, absorption enhancement factor and snow density.</p> <p>The script "Albedo.m" analyzes the derived data set along with measured albedo and simulated albedo deposited in the file "snicar_120ppb.txt". The obtained albedo curves and black carbon mixing ratio are as shown in the below manuscript.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022)</p>
Data set of "Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils"
<p>The uploaded data set contains all the measurements collected during the research that led to the publication of " "Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils"</p>
Input features and benchmark data sets for protein complex prediction and E. coli proteome application by AF2Complex
<p>Benchmark data sets of AF2Complex, input features for application to E. coli proteome, and predicted structural models of E. coli Ccm I as described in</p> <p><strong>Predicting direct physical interactions in multimeric proteins with deep learning</strong></p> <p><em>Mu Gao, Davi Nakajima An, Jerry M. Parks, Jeffrey Skolnick</em></p> <ol> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/af2complex_bench.tar.gz">af2complex_bench.tar.gz</a>: Benchmark data sets CP17, Dimer1193 and Oligomer562, including input features for AF2Complex/AF-Multimer, both paired and unpaired MSAs, as well as sequences, experimental structures, and results presented in the AF2Complex work (~90GB de-compressed size)</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_Ccm_I.tar.gz">ecoli_Ccm_I.tar.gz</a>: Computational models of the<em> E. coli</em> Ccm I system</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_set.tar.gz">ecoli_sets.tar.gz</a>: Lists of benchmark sets of positive and negative PPIs from <em>E. coli</em></li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_af_fea.tar.gz">ecoli_af_fea.tar.gz: </a>Pre-generated input features of <em>E. coli</em> proteome for protein complex prediction and modeling by AF2Complex (4,429 proteins, ~800 GB de-compressed size). This data set can be used with AF2Complex to probe the interactions of any combinations among the 4,429 proteins of E. coli.</li> </ol> <p> </p>
Architecture-based Uncertainty Impact Analysis for Confidentiality (Reproduction Set)
<p>Reproduction set for master thesis of Niko Benkler.</p> <p>Zip file contains code, data set and installation guides. </p> <p>Please open README.md to get information about content structure.</p>
Data sets for 'Mechanical Compliance of Individual Fractures in a Heterogeneous Rock Mass from Production-type Full-waveform Sonic Data', submitted to JGR: Solid Earth
<p>Synthetic and field FWS data sets are supplied for validation of proposed methods for compliance estimation of individual fractures. Each zip contains ‘ReadMe.txt ‘, which illustrates the files and corresponding parameters. More details about the setup in the submitted paper ‘Mechanical Compliance of Individual Fractures in a Heterogeneous Rock Mass from Production-type Full-waveform Sonic Data’.</p>
Data set for the article 'Temporal scaling in C. elegans larval development'
<p>This directory contains all analyzed data and data analysis scripts to create all figures for the article<br> Filina et al., Temporal scaling in C. elegans larval development, PNAS 2022 119:e2123110119</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.