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1,214 results for “Detection data”
Data for: Detecting Long-Term Balancing Selection Using Allele Frequency Correlation
<p>Genome-wide and top 1% scores for 1KG project data output from BetaScan reported in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/28981714/">Detecting Long-Term Balancing Selection Using Allele Frequency Correlation.</a></p> <p>Siewert KM, Voight BF. Mol Biol Evol. 2017 Nov 1;34(11):2996-3005. doi: 10.1093/molbev/msx209.</p> <p>PMID: 28981714</p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
Data for: BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data
<p>Genome-wide scan using BetaScan2 in 1KG populations report in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/32011695/">BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.</a></p> <p>Siewert KM, Voight BF.Genome Biol Evol. 2020 Feb 1;12(2):3873-3877. doi: 10.1093/gbe/evaa013.</p> <p>PMID: 32011695 </p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"
<p>Processed data used for the manuscript "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations".</p> <p>Includes input data for kriging algorithm as "SSF1deg_shipkrige_Terra.nc" and output data files as "Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc" for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or "clim" for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, "Obs" is the original data, "Est" is the mean counterfactual field obtained via kriging, "lowEst" and "highEst" are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, "krSims" stores the results of the 5,000 simulated kriged fields, "Semivariance" is the binned empirical variogram values, "pVal" is the raw field significance (not adjusted for multiple testing), "nOut" is the number of individually significant grid boxes, "tran" is the transform applied (none for cer, logit for Acld), "iniPhi" and "iniSigma2" are the initial values for the fitted variogram, "Phi" and "Sigma2" are the fitted values using weighted least squares, and "parSel" is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>
Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data]
<p>Dataset of all TROPOMI detected methane plumes in 2021, including estimates for the source location, emission quantification and source type. Corresponds to Figure 6 of Schuit et al. 2023 [Automated detection and monitoring of methane super-emitters using satellite data, https://doi.org/10.5194/acp-23-9071-2023]. Additional details and context are provided in Section 3 of the paper.</p> <p> </p> <p><em>Contents and data formats</em></p> <p><strong>date</strong>, date of the TROPOMI observation. format: YYYYMMDD</p> <p><strong>time_UTC</strong>, time of the TROPOMI observation in UTC. format: HH:MM:SS</p> <p><strong>lat</strong>, latitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>lon</strong>, longitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>source_rate_t/h</strong>, estimated emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>uncertainty_t/h</strong>, the uncertainty of the emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>estimated_source_type</strong>, the locally dominant anthropogenic source sector based on bottom-up inventories, the methodology is described in Section 2.5.3 of the paper. format: str</p> <p> </p> <p>Full citation of the paper:</p> <p>Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Automated detection and monitoring of methane super-emitters using satellite data, Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, 2023.</p>
SST_front_data: ocean thermal fronts detected by the Cayula and Cornillon SIED algorithm
<p>This dataset includes the post-processed data and a demo MATLAB script used for the paper titled "Global trends of fronts and chlorophyll in a warming ocean"</p> <p><strong>SST_FRONT_data.zip</strong> contains maps of sea surface temperature (SST) fronts detected by the Cayula and Cornillon single image edge detection algorithm over global ocean warming hotspot regions and covering the period 2003-2020. The original data was obtained from NASA OB.DAAC MODIS sea surface temperature (SST) product (MODIS Aqua Level 3 SST MID-IR 8 Day 4km Nighttime V2019.0: https://podaac.jpl.nasa.gov/dataset/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0?ids=&values=&search=MODIS%20Aqua&provider=POCLOUD). </p> <p>SST_FRONT_data.zip also contains <strong>Fdens_Ffreq_Fstre_example.mlx</strong>, which<strong> </strong>is a MATLAB live script showing how to compute metrics of fronts based on frontal maps: frontal frequency (Ffreq), frontal density (Fdens), and frontal strength (Fstre). </p> <p><strong>Fdens_Ffreq_Fstre_example.pdf</strong> is intended for quick viewing of the script above.</p> <p> </p>
Software Plagiarism Detection on Intermediate Representation Data Set
<p>This data set contains the necessary files used for the bachelor's thesis Software Plagiarism Detection on Intermediate Representation.</p> <p>This includes the data sets for the tests, the implemented code and scripts for the evaluation as well as referenced work.</p> <p> </p>
Data for the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter"
<p>This contains the data used for the paper by Ahmed et. al in the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter". Each of the four CSV files corresponds to one of the quarters discussed in the paper, and each has a metadata file containing information about the query that produced them. The CSV files contain the 'raw' log messages, and an eventID that identifies which pattern the log entry matched; the eventID is used to group together log messages of the same type. These logfiles were collected on the CloudLab facility (https://cloudlab.us/) from Jan 1 - Dec 30, 2019.</p> <p>The violated_unviolated_sessions_*.txt files each contain 20 randomly-selected sessions: half of the sessions were labeled by the invariant miner as being 'normal', and the other half 'anomalous'. CloudLab developers and system administrators were asked to label these sessions manually (and were not given the invariant miner's labels). The corresponding *_manual_labels.txt contain the labels that the administrators assigned, and in some cases additional correspondence with the administrators and information about which manual labels matched the invariant miner and which did not.</p>
Data From: Powerful detection of polygenic selection and environmental adaptation in US beef cattle
<p>GEMMA output containing summary statistics for generation proxy selection mapping (GPSM) and environmental GWAS (envGWAS) selection analyses from <br> Rowan et al. "Powerful detection of polygenic selection and environmental adaptation in US beef cattle" 2021<br> https://doi.org/10.1101/2020.03.11.988121 </p> <p>File names identify the analysis run, for example<br> "Gelbvieh_envgwas_desert_summary_stats.txt.gz"<br> Is the Gelbvieh dataset analyzed using the Desert ecoregion as the dependent variable <br> in a univariate envGWAS model. </p> <p>Files are formated according to GEMMA output.</p>
Data and code for "A Signal-Detection Approach to Modeling Forgiveness Decisions"
<p>Contains the data and code that supplements the journal article, "A Signal-Detection Approach to Modeling Forgiveness Decisions".</p> <p>Article doi: 10.1016/j.evolhumbehav.2016.06.004</p>
OCDetect - A Real-World Dataset to Detect Handwashing in Daily-Life using Wrist Motion Data from Wearables
<p>Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with ~3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.</p>
Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila - Processed Data
<p>Processed data and code for plotting figures for the paper:</p><p>"Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila", by Kit D. Longden, Edward M. Rogers, Aljoscha Nern, Heather Dionne, Michael B. Reiser.</p><p>Data (compressed results folder) and plotting code (compressed src folder) are MATLAB files (see READ_ME for version information and toolboxes). The Source Data excel file also contains the data plotted in the paper figures.</p>
LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
<p><strong>LSD4WSD V2.0</strong></p><p><strong>L</strong>earning <strong>S</strong>AR <strong>D</strong>ataset for <strong>W</strong>et <strong>S</strong>now <strong>D</strong>etection - Full Analysis Version. </p><p>The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample.</p><p>Modification and improvements of the version 2.0.0 :</p><ul><li><i>Number of massif:</i> add 7 new massif to cover the all Sentinel-1 images (cf `info.pdf`).</li><li><i>Acquisition:</i> add images of the descending pass in addition to those originally used in the ascending pass.</li><li><i>Sample: </i>reduction in the size of the samples considered to 15 by 15 to facilitate evaluation at the central pixel.</li><li><i>Sample: </i>increased density of extracted windows, with a distance of approximately 500 meters between the centers of the windows.</li><li><i>Sample:</i> removal of the pre-processing involving the use of logarithms.</li><li><i>Sample:</i> removal of the pre-processing involving the normalisation.</li><li><i>Labels:</i> new structure for the labels part: dictionary with keys: `topography`, `metadata` and `physics`.</li><li><i>Labels:</i> `physics`: addition of direct information from the CROCUS model for 3 simulations: Liquid Water Content, snow height and minimum snowpack temperature.</li><li><i>Labels:</i> `topography`: information on the slope, altitude and average orientation of the sample.</li><li><i>Labels:</i> `metadata` : information on the date of the sample, the mountain massif and the run (ascending or descending).</li><li><i>Dataset</i>: removal of the train/test split*</li></ul><p>*We leave it up to the user to use the Group Kfold method to validate the models using the alpine massif information.</p><p>Finally, it consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the <a href="https://www.umr-cnrm.fr/spip.php?article265&lang=en">Crocus</a> physical model:</p><ul><li>topography:<ul><li>elevation (meters) (average),</li><li>orientation (degrees) (average),</li><li>slope (degrees) (average),</li></ul></li><li>metadata:<ul><li>name of the alpine massif,</li><li>date of acquisition,</li><li>type of acquisition (ascending/descending),</li></ul></li><li>physics<ul><li>Liquid Water Content (km/m2),</li><li>snow height (m),</li><li>minimum snowpack temperature (Celsius degree).</li></ul></li></ul><p>The 9 channels are in the following order:</p><ul><li>Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH in linear,</li><li>Topographical features: altitude, orientation, slope</li><li>Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref**</li></ul><p>** The reference image selected is that of August 9th 2020, as a reference image without snow (cf. <a href="https://ieeexplore.ieee.org/document/842004">Nagler&al</a>)</p><p>An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.</p><p>The data is stored in .hdf5 format with gzip compression. We provide a python script to read and request the data. The script is dataset_load.py. It is based on the h5py, numpy and pandas libraries. It allows to select a part or the whole dataset using requests on the metadata. The script is documented and can be used as described in the README.md file</p><p>The processing chain is available at the following <a href="https://github.com/Matthieu-Gallet/LSD4WSD-dataset"><strong>Github</strong></a> address.</p><p>The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.</p><p>The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations.</p><p><strong>Erratum :</strong></p><p>In the dataloader file, the name of the "aquisition" column must be added twice, see the correction below.:</p><blockquote><p>dtst_ld = Dataset_loader(path_dataset,shuffle=False,descrp=["date","massif","aquisition","aquisition","elevation","slope","orientation","tmin","hsnow","tel",],) </p></blockquote><p>If you have any comments, questions or suggestions, please contact the authors: </p><ul><li>matthieu.gallet@univ-smb.fr</li><li>fatima.karbou@meteo.fr</li><li>abdourrahmane.atto@univ-smb.fr</li><li>emmanuel.trouve@univ-smb.fr</li></ul>
Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics
<p>Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.</p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
Data associated to the paper "Phase diagram detection via Gaussian fitting of number probability distribution"
<p>We investigate the number probability density function that characterizes subportions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with more sophisticated traditional and machine learning techniques. We argue that the studied quantity should be considered among the most informative bipartite properties, being moreover readily accessible in atomic gases experiments.<br><br>The dataset contains the entanglement spectra of several configurations of the extended Bose-Hubbard model ground state for different systems' sizes. </p>
Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection
<h2><strong>Dragon_Pi</strong></h2> <div> <div>For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:</div> <div>Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> - specifically Section 3.2: Dataset Overview.</div> <div> </div> </div> <p>Dragon_Pi is an intrusion detection dataset for IoT devices. In the field of IoT security there are few datasets, and those which do exist tend to focus solely on network traffic. The Dragon_Pi dataset seeks to provide not only more data for the field of IoT security, but also, data of a somewhat under-published type: linear time series power consumption data.</p> <p>Dragon_Pi is a fully labelled Intrusion Detection dataset for IoT devices. It is composed of both normal and under-attack power consumption data obtained from two separate testbeds - one using a DragonBoard 410c and the other a Raspberry Pi Model 3 - Hence the moniker <em>Dragon_Pi</em>. </p> <p>These testbeds were set up with predefined normal behavour as described in the attached publications. The normal linear time series power consumption was sampled from the testbed under these normal conditions. Both testbeds were then attacked using some common attacks on IoT - the linear time series power consumption captured under these condtions as well. </p> <p>Specifically, the testbeds were subjected to the Port Scan (using Nmap), SSH Brute Force (using Hydra) and SYNFlood Denial of Service (using Hping3) attacks. These attacks were repeated to gain insight to what their signatures looked like and also how varying the tool settings effected the resultant signature. A fourth type of scenario was also conducted on the testbeds - the "Capture the Flag" scenarios. In these files multiple attack types were used with a more specific target - to exfiltrate a hidden file from the testbeds.</p> <p>Each file has three hierarchical levels of annotation for <strong>each sample</strong> within:</p> <ol> <li>A simple "Normal or Anomaly" label for the specific sample</li> <li>A specifc attack type label e.g. "SSH Bruteforce", for the specific sample</li> <li>A specific tool setting for that attack e.g. "Hydra_T16", for the specific sample</li> </ol> <p>Users can decide for themselves what level of annotation they require for their specific task. </p> <p>Each file in the Dragon_Pi dataset is accompanied by its own legend file. This file explains the contents of the specific .csv file and the specific indexes of the events within.</p> <p>The Dragon_Pi dataset consists of approximately 67 files, as shown in Table 1. Compressed, the datset totals approximately 13GB. Completely decompressed the dataset is approximately 80GB ( 30GB Pi data, 50 GB Dragon data). </p> <div> </div> <div> <table> <tbody> <tr> <td>Label Type</td> <td>Specific Label </td> <td>Number of Files DragonBoard 410c</td> <td>Number of Files Raspberry Pi</td> </tr> <tr> <td>Normal </td> <td>Normal </td> <td>3 </td> <td>2</td> </tr> <tr> <td>Port Scan Attack </td> <td>Nmap_T5</td> <td>2</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T4</td> <td>1</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T3</td> <td>1</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T2</td> <td>1</td> <td>1</td> </tr> <tr> <td>SSH Brute Force</td> <td>Hydra_T32</td> <td>4</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T16</td> <td>16</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T3</td> <td>8</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T1</td> <td>5</td> <td>2</td> </tr> <tr> <td>SYNFlood DOS</td> <td>SYNFlood DOS</td> <td>1</td> <td>1</td> </tr> <tr> <td>Capture the Flag</td> <td>Misc Attacks</td> <td>3</td> <td>5</td> </tr> </tbody> </table> </div> <div>Table 1. Enumeration of the in the Dragon_Pi dataset.</div> <div> </div> <div> </div> <div>For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:</div> <div>Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> - specifically Section 3.2: Dataset Overview.</div> <div> </div> <div> </div> <div><strong>Publication of this dataset:</strong></div> <div> </div> <div>This dataset was published in Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a>. Consult and cite this article for a more in depth dataset description, as well as an in depth review of first AI Intrusion Detection model trained on this dataset. </div> <div> </div> <div>See article Lightbody <em>et al.</em>, Future Internet, 2023, <a href="https://doi.org/10.3390/fi15050187">https://doi.org/10.3390/fi15050187</a> for a detailed investigation on the attack signatures discovered while creating this dataset. This work was an inital investigation of the dataset and can serve as a part 1 to the Dragon_Pi paper.</div> <div> </div> <div> </div> <div><strong>How to cite this dataset in your work: </strong></div> <div> </div> <div>Please cite these two DOIs when publishing using this dataset:</div> <div> <ol> <li>Dragon_Pi release publication: <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> (most important)</li> <li>Zenodo Dataset DOI: https://doi.org/10.5281/zenodo.10784947</li> </ol> </div> <div> <div> </div> </div> <p> </p>
Testing strengths, limitations and biases of current Pulsar Timing Arrays detection analyses on realistic data
<p>In this project, we carried out an extensive investigation of the performance of current Pulsar Timing Arrays (PTA) analyses on simulated PTA datasets where we modeled the gravitational waves (GW) signal as the incoherent superposition of sinusoidal signals from a cosmic population of super-massive black hole binaries (SMBHBs). Here we publish the dataset referred to in the paper as the <em><strong>SMBHB_set</strong></em>: 100 realisations of PTA datasets with 25 pulsars and a GWB signal of nominal amplitude 2.4e-15. For each realisation, listed from 1001 to 1100, this repository contains the pulsars .par and .tim files, the mcmc chain (sampling over 66 parameters: 60 for pulsars intrinsic noise parameters and log amplitude and slope of a common red noise process), and the SMBHB details for that specific realisation of the GWB. The columns of the <em>MBHB_list.dat</em> files contain (from left to right): log10 MBH chirp mass (in solar masses in the source frame), mass ratio, source redshift, log10 of GW fundamental mode (n=2) in the observer frame, phi and theta angles in the sky, source inclination angle, source polarisation angle, source initial orbital phase, source initial direction of periastron and source initial eccentricity.</p> <p>If you make use of any of this data, please cite:<br><br></p> <div>@article{ refId0,</div> <div> author = {{Valtolina, Serena} and {Shaifullah, Golam} and {Samajdar, Anuradha} and {Sesana, Alberto}},</div> <div> title = {Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data},</div> <div> DOI= "10.1051/0004-6361/202348084",</div> <div> url= "https://doi.org/10.1051/0004-6361/202348084",</div> <div> journal = {A&A},</div> <div> year = 2024,</div> <div> volume = 683,</div> <div> pages = "A201",</div> <div>}</div>
HGT breakpoints and events detected by LocalHGT in 2098 real data
<p> </p> <table> <tbody> <tr> <td>filter_hgt_results.zip</td> <td>the HGT breakpoints of all samples, in this folder, each CSV file represents a sample. </td> </tr> <tr> <td>identified_event.csv</td> <td>the HGT events of all samples, the results of all samples are in this file.</td> </tr> <tr> <td>TableS1</td> <td>phenotypes of all samples</td> </tr> <tr> <td>use.zip</td> <td>Microbial abundances of all samples, in this folder, all samples from the same cohort are in a single file.</td> </tr> </tbody> </table> <p> </p>
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</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.