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9,300 results for “detection”
Detecting Changes in the Caenorhabditis elegans Intestinal Environment Using an Engineered Bacterial Biosensor
<p>Data for the figures in the manuscript <br> <a href="https://pubs.acs.org/doi/10.1021/acssynbio.9b00166">https://pubs.acs.org/doi/10.1021/acssynbio.9b00166</a></p> <p>Abstract:<br> <em>Caenorhabditis elegans</em> has become a key model organism within biology. In particular, the transparent gut, rapid growing time, and ability to create a defined gut microbiota make it an ideal candidate organism for understanding and engineering the host microbiota. Here we present the development of an experimental model that can be used to characterize whole-cell bacterial biosensors <em>in vivo</em>. A dual-plasmid sensor system responding to isopropyl β-d-1-thiogalactopyranoside was developed and fully characterized <em>in vitro</em>. Subsequently, we show that the sensor was capable of detecting and reporting on changes in the intestinal environment of <em>C. elegans</em> after introducing an exogenous inducer into the environment. The protocols presented here may be used to aid the rational design of engineered bacterial circuits, primarily for diagnostic applications. In addition, the model system may serve to reduce the use of current animal models and aid in the exploration of complex questions within general nematode and host–microbe biology.</p>
Swedish Test Data for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p>This data collection contains the Swedish test data for <a href="https://competitions.codalab.org/competitions/20948">SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection:</a></p> <p>- a Swedish text corpus pair (`corpus1/`, `corpus2/`)<br> - 31 lemmas which have been annotated for their lexical semantic change between the two corpora (`targets.txt`)<br> - the annotated binary change scores of the targets for subtask 1, and their annotated graded change scores for subtask 2 (`truth/`)</p> <p>We sample from the KubHist2 corpus, digitized by the National Library of Sweden, and available through the Språkbanken corpus infrastructure Korp (<a href="https://www.researchgate.net/profile/Markus_Forsberg/publication/266352576_Korp_-_the_corpus_infrastructure_of_Sprakbanken/links/55bf1ee008aed621de121ba3/Korp-the-corpus-infrastructure-of-Sprakbanken.pdf">Borin et al., 2012</a>). The full corpus is available through a CC BY (attribution) license. Each word for which the lemmatizer in the Korp pipelien has found a lemma is replaced with the lemma. In cases where the lemmatizer cannot find a lemma, we leave the word as is (i.e., unlemmatized, no lower-casing). KubHist contains very frequent OCR errors, especially for the older data.More detail about the properties and quality of the Kubhist corpus can be found in (<a href="https://www.diva-portal.org/smash/get/diva2:1358014/FULLTEXT01.pdf#page=28">Adesam et al., 2019</a>).</p> <p>Lars Borin, Markus Forsberg, and Johan Roxendal. "Korp-the corpus infrastructure of Språkbanken." <em>LREC</em>. 2012.</p> <p>Adesam, Yvonne, Dana Dannélls, and Nina Tahmasebi. "Exploring the Quality of the Digital Historical Newspaper Archive KubHist." <em>DHN</em>. 2019.</p> <p>__Corpus 1__</p> <p>- based on: <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">Kubhist2</a><br> - language: Swedish<br> - time covered: 1790-1830<br> - size: ~71 million tokens<br> - format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled<br> - encoding: UTF-8<br> - note: contains frequent OCR errors</p> <p>__Corpus 2__</p> <p>- based on: <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">Kubhist2</a><br> - language: Swedish<br> - time covered: 1895-1903<br> - size: ~111 million tokens<br> - format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled<br> - encoding: UTF-8<br> - note: contains OCR errors</p> <p>Besides the official lemma version of the corpora for SemEval-2020 Task 1 we also provide the raw token version (`corpus1/token/`, `corpus2/token/`). It contains the raw sentences in the same order as in the lemma version. Find more information on the data and SemEval-2020 Task 1 in the paper referenced below.</p> <p> </p> <p>Reference:</p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi.<a href="https://competitions.codalab.org/competitions/20948">SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>. To appear in SemEval@COLING2020.</p>
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
<p>Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:</p> <ul> <li>51 converted mass spectral files in mzML format</li> <li>An .RData-file containing the extracted ion chromtograms (EICs)</li> <li>The randomly selected subset and the original output table of MZmine in .csv-format</li> <li>Example .xlsx files for the classification</li> <li>2 central classification tables</li> <li>Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs</li> </ul> <p>For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).</p>
Remote detection and recording of atomic-scale spin dynamics
<p>This folder contains all data and all processing files for the paper titled "Remote detection and recording of atomic-scale spin dynamics". View full paper here: https://www.nature.com/articles/s42005-020-0361-z</p>
Detecting East Asian Prejudice on Social Media
<p>This repository contains:</p> <ul> <li>A deep learning model which distinguishes between Hostililty against East Asia, Criticism of East Asia, Discussion of East Asian prejudice and Neutral content. The F1 score is 0.83.</li> <li>A detailed annotation codebook used for marking up the tweets.</li> <li>A labelled dataset with 20,000 entries.</li> <li>A dataset with all 40,000 annotations, which can be used to investigate annotation processes for abusive content moderation.</li> <li>A list of thematic hashtag replacements.</li> <li>Three sets of annotations for the 1,000 most used hashtags in the original database of COVID-19 related tweets. Hashtags were annotated for COVID-19 relevance, East Asian relevance and stance.</li> </ul> <p>The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. This data repository is for a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic.</p> <ul> </ul> <p>This work is a collaboration between The Alan Turing Institute and the Oxford Internet Institute. It was funded by the Criminal JusticeTheme of the Alan Turing Institute under Wave 1 of The UKRI Strategic Priorities Fund, EPSRC Grant EP/T001569/1</p>
COHERENT Collaboration data release from the first detection of coherent elastic neutrino-nucleus scattering on argon
<p>Release of COHERENT collaboration data from the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on argon. This data release corresponds with the results of "Analysis A" published in arXiv:2003.10630[nucl-ex]. The data release enables further studies of CEvNS.</p> <p>Use of the data release is presented in the accompanying pdf document within this submission. Example code is included within the release as part of this submission. The materials here are also available at http://coherent.ornl.gov/data/, which preserves the directory structure used within the accompanying document. Note the use of the example code in this release expects the directory structure written within the accompanying pdf document.</p>
Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms
<p>This repository contains a dataset for the research of domain generation algorithms (DGAs) and machine learning. More precisely, it targets dictionary-based DGAs.</p> <p><em>Constantinos Patsakis, Fran Casino: "Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms", Journal of Information Security and Applications, 2021.</em></p> <p>Features ordered as in the shared dataset:</p> <ul> <li>Family: DGA that the domain belongs to</li> <li>SLD: SLD of the Domain</li> <li>L-LEN: The length of Domain</li> <li>L-DIG: The number of digits in Domain</li> <li>L-CON-MAX: The maximum number of consecutive consonants Domain</li> <li>R-CON-VOW: Number of consonants divided by L-LEN </li> <li>L-SYM: The number of special characters</li> <li>R-SYM-LEN: L-SYM divided by L-LEN</li> <li>R-Dom-3G: Ratio of benign grams in Dom-3G</li> <li>R-Dom-4G: Ratio of benign grams in Dom-4G</li> <li>R-Dom-5G: Ratio of benign grams in Dom-5G</li> <li>L-W2: Number of words with more than 2 characters in Domain</li> <li>L-W3: Number of words with more than 3 characters in Domain</li> <li>R-WS-LEN: Dom-WS divided by L-LEN</li> <li>R-WDS-LEN: Dom-WDS divided by L-LEN</li> <li>R-W2-LEN: Dom-W2 divided by L-LEN</li> <li>R-W3-LEN: Dom-W3 divided by L-LEN</li> <li>M2-Dom-Ws: 2-Chain Markov English grams applied to Dom-WS</li> <li>M2-Dom-WDS: 2-Chain Markov English grams applied Dom-WDS</li> <li>E-Dom-WS: Entropy of Dom-WS </li> <li>E-Dom-WDS: Entropy of Dom-WDS</li> <li>E-Dom-W2: Entropy of Dom-W2</li> <li>E-Dom-W3: Entropy of Dom-W3</li> </ul>
Dataset of BattLeDIM: Battle of the Leakage Detection and Isolation Methods
<p>Drinking Water Distribution Networks (DWDN) are susceptible to infrastructure failures, which may lead to water losses. Typically, these water losses are due to background leakages and pipe bursts which may occur anywhere within the distribution network. Background leakages are normally difficult to detect due to their small size, whereas pipe bursts are easier to locate as they are of larger size and may appear on the surface. The early detection and localization of some leakage event is extremely important, as this would reduce the time required for accommodating the event and therefore reducing the risk of further infrastructure degradation, contamination events and consumer complaints.</p> <p>In previous years, a number of methodologies have been proposed to detect and isolate the location of leakage events using various types of sensor measurements. These methods were commonly evaluated on private commercial datasets, and as a result, it is not possible to objectively compare these methods in their ability to detect and isolate leaks. In the past year, a leakage detection dataset has been proposed, LeakDB, based on benchmark networks and created using the WNTR tool, using pressure-driven demands and realistic leakage modelling. Inspired by the “BATtle of the Attack Detection ALgorithms” (BATADAL), which focused on the detection of cyber-physical attacks, our team decided to organize a similar “battle” focusing on leakage events.</p> <p>The Battle of the Leakage Detection and Isolation Methods (BattLeDIM), aims at objectively comparing the performance of methods for the detection and localization of leakage events, relying on SCADA measurements of flow and pressure sensors installed within water distribution networks. Participants may use different types of tools and methods, including (but not limited to) engineering judgement, machine learning, statistical methods, signal processing, and model-based fault diagnosis approaches.</p>
Dataset for Overscan Detection in Digitized Analog Films by Precise Sprocket Hole Segmentation
<p>This repo includes the self-generated dataset as well as the pre-trained models .</p> <p>ISVC 2020 - 15th International Symposium on Visual Computing</p> <p>Paper: Overscan Detection in Digitized Analog Filmsby Precise Sprocket Hole Segmentation</p> <p> </p> <p>Acknowledgement:</p> <p>Visual History of the Holocaust: Rethinking Curation in the Digital Age. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Grant Agreement 822670.</p> <p>https://www.vhh-project.eu</p> <p> </p>
MARSIS Transient Layer Detections during Comet Siding Spring Event 2014
<p><strong>This data is used in the publication, "Prolonged Lifetime of the Transient Ionized Layer in the Martian Atmosphere Caused by Comet Siding Spring" by Luppen et al. 2020.</strong></p> <p>MARSIS_transient_layer_detections.csv: The data contained in this file are measurements from the Mars Express spacecraft's MARSIS instrument, collected during the Comet Siding Spring flyby event in 2014. The comet is known to have deposited a large amount of material into Mars' ionosphere, producing a transient layer of plasma that was noticed by multiple spacecraft instruments immediately following the comet's closest approach to the planet. The layer was originally thought to have had a lifetime of 1-2 days. Follow-up analysis, represented in this data file, indicate that this layer could have lasted at least 7 days and as many as 32 days after closest approach, much longer than previously reported. Our results help to constrain models of metal ion chemistry in the Martian upper atmosphere.</p> <p>MARSIS_regular_ionospheric_measurements.csv: The data contained in this file are measurements of the regular ionosphere during the comet Siding Spring event. These data are plotted in Figures 3 and 4 of the manuscript.</p> <p>MAVEN_normalized_euv_irradiance.csv: The data contained in this file are measurements collected from the Extreme UltraViolet (EUV) monitor that is part of the Langmuir Probe and Waves (LPW) instrument on the MAVEN spacecraft collected during the comet Siding Spring event. These data are plotted in Figure 5 of the manuscript.</p> <p>FISM_normalized_euv_irradiance.csv: The data contained in this file are measurements collected from the Flare Irradiance Spectral Model (FISM) collected during the comet Siding Spring event. These data are plotted in Figure 5 of the manuscript.</p> <p>MARSIS_averaged_data_figure4.csv: The data contained in this file are derived products from the MARSIS_regular_ionospheric_measurements.csv data. A running mean was calculated with 2 degree SZA bins, the SZA centers of those bins were identified, and a standard deviation was calculated for each bin. These data are plotted in Figure 4 of the manuscript. Note that we make use of a shaded region signifying a 3 sigma standard deviation.</p>
Addressable Nanoantennas with Cleared Hotspots for Single-Molecule Detection on a Portable Smartphone Microscope
<p>The advent of highly sensitive photodetectors and the development of photostabilization strategies made detecting the fluorescence of single molecules a routine task in many labs around the world. However, to this day, this process requires cost-intensive optical instruments due to the truly nanoscopic signal of a single emitter. Simplifying single-molecule detection would enable many exciting applications, <em>e.g.</em> in point-of-care diagnostic settings, where costly equipment would be prohibitive. Here, we introduce addressable NanoAntennas with Cleared HOtSpots (NACHOS) that are scaffolded by DNA origami nanostructures and can be specifically tailored for the incorporation of bioassays. Single emitters placed in the NACHOS emit up to 461-fold (average of 89±7-fold) brighter enabling their detection with a customary smartphone camera and an 8-US-dollar objective lens. To prove the applicability of our system, we built a portable, battery-powered smartphone microscope and successfully carried out an exemplary single-molecule detection assay for DNA specific to antibiotic-resistant <em>Klebsiella pneumonia</em> „on the road “. Here we demonstrate the raw data on which our findings based on.</p>
Supplementary Material: "A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data"
<p><strong>Supplementary Material</strong></p> <p>This material regards the paper entitled "<em>A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data</em>".</p> <p>The Readme.txt file explains all the contents of the data package, which consists of the data supporting the paper and the MATLAB script for the Individual Tree Detection and Measurement (ITDM).</p> <p>Please cite the related article if using the data or the script.</p> <p>Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322.</p>
UAV Imagery Dataset for Paddy Rice Panicle Detection
<p>Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based on high spatial resolution images provide a potential solution to increase the throughput as well as the accuracy of panicle identification. The quality and volume of the dataset are crucial to training an accurate and robust deep learning model. Panicle segmentation tasks require particularly costly annotations. Here we open a paddy rice panicle dataset, acquired by DJI Mavic Pro in 2018, to public use for rice panicle phenotyping. </p> <pre>@article{wang2021paddy, title={Paddy Rice Imagery Dataset for Panicle Segmentation}, author={Wang, Hao and Lyu, Suxing and Ren, Yaxin}, journal={Agronomy}, volume={11}, number={8}, pages={1542}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }</pre>
Simulated NGS datasets for real-time detection of novel pathogens
<p>Datasets based on the <a href="https://doi.org/10.5281/zenodo.3678563">bacterial</a> and <a href="https://doi.org/10.5281/zenodo.4312525">viral</a> simulated NGS datasets. Fastq files correspond to tests sets of those datasets. Basecall files were generated based on the fastq files with an 8nt simulated barcode between the mates of a read pair. The "rn" datasets containg random length subreads (25-250bp) of the original validation and training reads.</p> <p>The Nanopore datasets were resimulated with <a href="https://github.com/liyu95/DeepSimulator">DeepSimulator 1.5</a> (Li et al., 2020) based on the original datasets (i.e. using the same species composition as the original data). The test Nanopore dataset contains full reads (target average length: 8kb) and the training and validation datasets - 250bp subreads.</p>
Traffic Detection Datasets
<p><em>Public (anonymized) road traffic detection datasets from Huawei Munich Research Center.</em></p> <p>Traffic detection datasets from a variety of traffic sensors (i.e. induction loops). The data is useful for traffic indexing, dominant flows detection, forecasting traffic patterns, and adjusting stop-light control parameters, i.e. cycle length, offset and split times.</p> <p>There are three datasets:</p> <ul> <li><strong>RM </strong>is a real-life dataset collected in the period of March 2020 from traffic detectors in an area of a Chinese city. The detectors were located in 6 intersections monitoring the traffic on 44 road edges. Each detector was collecting data every 1 second for all the road edges in its radius. In total, 2894174 detections were collected, from 337089 different vehicles.</li> <li><strong>COM </strong>is a synthetic dataset that generated in the same road network as the real-life data RM and RD, with the difference that we included detectors in the 2 intersections where the real scenario didn’t have. Then, we generated equally random trips over the road network. In total we generated 18910 detections from 7500 vehicles.</li> <li><strong>GRID </strong> was created using the SUMO Simulation of Urban Mobility [2]. We randomly generated trips on a 10𝑥10 intersections grid road network using a utility from SUMO that equally generates trips over the road network. Then, running the simulation and using TraCI Traffic Control Interface library [3] we read the simulation data and collect the detections. The simulation collected data include 1806141 detections from 135618 different vehicles.</li> </ul> <p>The datasets were used in the Querying Top-k Dominant Traffic Flows on Large Urban Road Networks [1] paper.</p> <p>[1] Stella Maropaki, Paolo Sottovia, and Stefano Bortoli. 2021. Querying Top-k Dominant Traffic Flows on Large Urban Road Networks. In 2021 24th International Conference on Extending Database Technology (EDBT).</p>
NoSyms: A neural network approach to detecting data structures in raw memory
<p>This data was used for a experiments with graph convolutional neural networks for memory forensics as part of a bachelor thesis (included as pdf).<br> <br> Abstract:<br> <br> This work presents a neural network based approach for data structure detection in raw memory that does not require an entirely matching description of the target data structure. Instead, it’s merely necessary to provide multiple descriptions of data structures similar to the target as training data in the form of debugging symbols. The core contribution of this work is a formal description and implementation of encoding data structure definitions as well as raw memory contents such that they can be processed by graph convolutional neural networks. A description and implementation of a neural network meant to detect data structures in the memory contents of a Linux Kernel demonstrates the practical applicability of the described approach.<br> <br> The Code is available on GitHub <a href="https://github.com/NiklasBeierl/nosyms">https://github.com/NiklasBeierl/nosyms</a>.<br> <br> nokaslr_dump is the qemu memory snapshot used to test the model.<br> nokaslr.raw is the "raw" form of the snapshot as produced by Volatility 3's layerwriter plugin.<br> symbols-training-data contains the Volatility symbol JSON files from which training data was derived.<br> nokaslr_pointers.csv lists the kernel space pointers in the snapshot and<br> nokaslr_tasks.csv lists task structs in the snapshot. Both were extracted via a Volatility plugins that are included in the GitHub Repo.<br> vmlinux-5.4.0-58-generic.json is the symbol file for the kernel the snapshot was taken from.<br> other-symbols.zip contains symbol files I generated vor various other kernels but did not end up using, use at your own discretion.</p>
Detection of Functionally Similar Code Clones: Data, Analysis Software, Benchmark
<p>We analysed 2,800 programs in Java and C for which we knew they are functionally similar. We checked if existing clone detection tools are able to find these functional similarities and classified the non-detected differences. We make all used data, the analysis software as well as the resulting benchmark available here.</p>
The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves
<p>We distribute complete gravitational-wave signals in the Advanced LIGO band (10 Hz - 8192 Hz) of the inspiral and merger of two neutron stars. These waveforms been constructed by hybridizing numerical-relativity data obtained with the WhiskyTHC code [1] with tidal effective-one-body waveforms [2,3]. More details on the procedure used to generate these waveforms are given in [4]. </p> <p>The waveforms are distributed as HDF5 files containing the amplitude and phase of the -2 spin-weighted spherical harmonics multipoles of the strain:</p> <p><span class="math-tex">\(( h_+ - \mathrm{i} h_\times )_{l,m} = \frac{A_{l,m}}{D_{\rm cm}} \exp(-\mathrm{i} \phi_{l,m} )\)</span></p> <p>where <span class="math-tex">\(D_{\rm cm}\)</span> is the distance in cm from the source.</p> <p>The data files include a machine readable "/metadata" group with:</p> <ul> <li>/metadata/EOS: name of the equation of state</li> <li>/metadata/M_{A|B}: mass in isolation of star A (or B) in grams</li> <li>/metadata/R_{A|B}: radius of star A (or B) in cm</li> <li>/metadata/k2T: tidal coupling constant of the binary (see [3])</li> <li>/metadata/kl_{A|B}: l=2,3,4 dimensionless Love numbers of star A (or B)</li> </ul> <p>We store amplitude and phase for multipoles modes up to l=4 as time series sampled at 16384 Hz.</p> <p>We make these waveforms freely available in the hope that they will be useful. We kindly ask you to cite [3] and [4] in any publication resulting from the use of these waveforms.</p> <p>---<br /> [1] http://www.tapir.caltech.edu/~david_e/whiskythc.html<br /> [2] https://eob.ihes.fr/<br /> [3] S. Bernuzzi, A. Nagar, T. Dietrich, T. Damour; Modeling the Dynamics of Tidally Interacting Binary Neutron Stars up to the Merger; Phys.Rev.Lett. 114 (2015) 16, 161103.<br /> [4] D. Radice, S. Bernuzzi, C. D. Ott; The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves; arXiv:1603.05726.</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (Transp. Res. Part C, 2014)
<p>This dataset contains and describes the data used in</p> <p>Danalet, A., Farooq, B., & Bierlaire, M. (2014). A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. <em>Transportation Research Part C: Emerging Technologies</em>, <strong>44</strong>, 146-170. doi:10.1016/j.trc.2014.03.015</p> <p>Specifically it contains WiFi traces, pedestrian Semantically-Enriched Routing Graph (SERG), and Potential Attractivity measure (PAM).</p>
Data set to article "Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink"
<p>The file Data_Marxetal2014_AB.csv contains the data to the paper<br> Marx, S., Hansen-Goos, O., Thrun, M., & Einhäuser, W. (2014). Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink. Journal of Vision, 14(14):4, 1-18, http://www.journalofvision.org/content/14/14/4, doi:10.1167/14.14.4.<br> as comma-separated value (csv) file</p> <p>Each row contains the data of one trial, represented by the following columns</p> <p>1 - number of the line<br> 2 - subject ID<br> 3 - experiment number<br> 4 - color condition (1: gray inverted, 2: gray original, 3: color inverted, 4: color original)<br> 5 - number of targets<br> 6 - SOA in ms<br> 7 - serial position of first target (0 if absent)<br> 8 - serial position of second target (0 if absent)<br> 9 - category of first target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 10 - category of second target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 11 - response to "How many animals?" (detection)<br> 12 - response to first category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)<br> 13 - response to second category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)</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.