Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,302
datasets available to search
ShareScore release 0.9.0
Dataset results
9,302 results for “DETECT”
Supplementary material 2 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of the Red-breasted Toucan, Ramphastos dicolorus, consuming an egg of the Ruddy Quail-dove, Geotrygon montana. Seven days later, a second egg present in this nest was also consumed by a Red-breasted Toucan. :
Supplementary material 1 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of the Collared Forest-falcon, Micrastur semitorquatus, depredating a nest of White-necked Thrush, Turdus albicollis, containing three eggs. In this cloudy day, the infrared LEDs were activated. :
Dataset from the study: "Brain Haemorrhage Detection using a SVM Classifier with Electrical Impedance Tomography Measurement Frames"
<p>This repository contains the scripts and other resources used in:</p> <p>“Brain Haemorrhage Detection using a SVM Classifier with Electrical Impedance Tomography Measurement Frames”, McDermott B et al. 10.1371/journal.pone.0200469</p> <p> </p> <p>>> THIS DATA IS FREE FOR USE IN ANY SCIENTIFIC RESEARCH WITH CITATION (PLEASE USE THE DOI FOR PRECISE CITATION): DOI 10.1371/journal.pone.0200469</p> <p> </p> <p>>> IF YOU USE THESE RESOURCES, CITE!!!</p> <p> </p> <p>The repository contains the code and resources to generate the EIT Measurement Frames used in Numerical Models (STL models, code to mesh, code to generate frames)</p> <p> </p> <p>Also included are the Measurement Frames generated from the Phantom Studies.</p> <p> </p> <p><strong>=> This is all the data (how to replicate it for numerical studies and the actual data for the phantom part) used in the study</strong></p> <p> </p> <p>Classifier generation, training, testing, cross validation etc uses standard MATLAB methods and resources: fitcsvm() etc.</p> <p> </p> <p>The subfolders are ordered numerically in the logical order of use</p> <p>The subfolders contain text files with more details and description relevant to the contents of the respective subfolder.</p> <p> </p> <p>Note:</p> <p> </p> <p>Software used in this study:</p> <p>At a minimum the first 2 will be needed to be installed on your computer</p> <p>(other methods etc needed are detailed within the folders)</p> <p> </p> <p><strong>MATLAB 2017B</strong> (<a href="https://uk.mathworks.com/products/matlab.html">https://uk.mathworks.com/products/matlab.html</a>):</p> <p>The primary software tool used throughout</p> <p> </p> <p><strong>EIDORS </strong>(<a href="http://eidors3d.sourceforge.net/index.shtml">http://eidors3d.sourceforge.net/index.shtml</a>)<strong>: </strong></p> <p>Open Source software package, compatible with MATLAB, with tools for use in Electrical Impedance Tomography studies.</p> <p> </p> <p><strong>Autodesk Fusion 360</strong> (<a href="https://www.autodesk.com/products/fusion-360/overview?mktvar002=685209&mkwid=sMHGuhLcc%7Cpcrid%7C209188650131%7Cpkw%7Cfusion%20360%7Cpmt%7Ce%7Cpdv%7Cc%7Cslid%7C%7Cpgrid%7C34851227038%7Cptaid%7Ckwd-11029869505%7C&intent=&utm_medium=cpc&utm_source=google&utm_campaign=GGL_Fusion+360_IE_BR_SEM&utm_term=fusion%20360&utm_content=sMHGuhLcc%7Cpcrid%7C209188650131%7Cpkw%7Cfusion%20360%7Cpmt%7Ce%7Cpdv%7Cc%7Cslid%7C%7Cpgrid%7C34851227038%7Cptaid%7Ckwd-11029869505%7C&gclid=Cj0KCQjw1NzZBRCoARIsAIaMwut5zqUE_d7oieOvw6H3SpU45Jw7vwL5KAKqyRYW20Be6QWE-V3HaB0aAnW0EALw_wcB">https://www.autodesk.com/products/fusion-360/overview?mktvar002=685209&mkwid=sMHGuhLcc|pcrid|209188650131|pkw|fusion%20360|pmt|e|pdv|c|slid||pgrid|34851227038|ptaid|kwd-11029869505|&intent=&utm_medium=cpc&utm_source=google&utm_campaign=GGL_Fusion+360_IE_BR_SEM&utm_term=fusion%20360&utm_content=sMHGuhLcc|pcrid|209188650131|pkw|fusion%20360|pmt|e|pdv|c|slid||pgrid|34851227038|ptaid|kwd-11029869505|&gclid=Cj0KCQjw1NzZBRCoARIsAIaMwut5zqUE_d7oieOvw6H3SpU45Jw7vwL5KAKqyRYW20Be6QWE-V3HaB0aAnW0EALw_wcB</a>):</p> <p>Used to generate the head, brain and lesion STL files. These final STL files are provided in this repository.</p>
Finding an antibody to detect EZH1 protein expression – Part 1
<p>Finding an antibody to determine EZH1 expression in AML patient cells.</p>
FIGURE 6 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 6. Comparisons of male body: dorsum (A), ventrum (B), ventral views of right foot (C) and hand (D) of Leptophryne javanica sp. nov. (MZB Amph 15943: I), Leptophryne cruentata (MZB Amph 22257: II), and Leptophryne borbonica (MZB Amph 17126: III). Scale bars = 5 mm.
FIGURE 2 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 2. Maximum Likelihood tree of ~473 bp fragment of 16S rRNA gene for Leptophryne javanica sp. nov., along with representatives all Leptophryne species. Values above and below branch indicate Bayesian Posterior Probabilities (BPP), Maximum Likelihood Bootstrap Proportion (MLBP), and Neighbour Joining Bootstrap Proportion (NJBP).
FIGURE 7 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 7. Comparisons of lateral (I) and dorsal (II) views of head of (A) Leptophryne javanica sp. nov. (MZB Amph 15943), (B) Leptophryne cruentata (MZB Amph 22257), and (C) Leptophryne borbonica (MZB Amph 17126).
FIGURE 1 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 1. Map of Java island, showing known localities of Leptophryne javanica sp. nov. (stars, 1=Mt. Slamet, 2=Mt. Ciremai) and Leptophryne cruentata (diamonds, 3=Mt. Gede-Pangrango, 4=Mt. Halimun-Salak).
FIGURE 5 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 5. Dorsal body, ventral hand, ventral foot and ventral body of a female paratype Leptophryne javanica sp. nov. (MZB Amph 28564). Scale bars = 5 mm. Photos by Farist Alhadi.
FIGURE 4 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 4. Dorsal and ventral views of preserved male holotype Leptophryne javanica sp. nov. (MZB Amph 15946) (Above). Lateral view of head, ventral surfaces of left foot and right hand, and dorsal surface of right hand of the male holotype (MZB Amph 15946) (Below). Scale bars = 5 mm.
FIGURE 3 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 3. Photos in life of (A) male holotype (MZB Amph 15946) and (B) Female paratype (MZB 28564) of Leptophryne javanica sp. nov., (C) male (MZB Amph 22257) (photographs by Farist Alhadi), and (D) female (uncatalogued) (photographs by Arief Tajali), of Leptophryne cruentata from Cibereum waterfall, Mt. Gede.
FIGURE 8 in Detection of Cryptic taxa in the genus Leptophryne (Fitzinger, 1843) (Amphibia; Bufonidae) and the description of a new species from Java, Indonesia
FIGURE 8. Advertisement calls of a male paratype of Leptophryne javanica sp.nov. (MZB Amph 28566). (A) Oscillograms of all four calls. Oscillograms (B) and sonograms (C) of a single call, showing variation in call type.
Seasonal variation of mortality, detectability and body condition in a population of the adder (Vipera berus)
<p>Raw data used in paper by Dirk Bauwens & Katja Claus. 2019. Seasonal variation of mortality, detectability and body condition in a population of the adder (<em>Vipera berus</em>). Ecology and Evolution 9:5821–5834. DOI: 10.1002/ece3.5166 </p> <p>CH_data_Season_males.INP</p> <p>A text file with extension .INP for input in the program MARK (White & Burnham, 1999; Cooch & White, 2015), containing the capture-recapture histories of individual adult male adders (n = 1146). Each 52-digit string contains a 1-digit score (1/0 ; captured / not captured) per season (1-4) and year of study (2005 – 2017).</p> <p>CH_data_Season_females.INP</p> <p>A text file with extension .INP for input in the program MARK (White & Burnham, 1999; Cooch & White, 2015), containing the capture-recapture histories, including breeding status of individual adult female adders (n = 832). Each 39-digit string contains a 1-digit score (B/N/0 ; Breeding / Non-breeding / not captured) per season (1-3) and year of study (2005 – 2017).</p>
Datasets for : High-resolution detection and differential expression analysis of transcription start sites using MAPCap
<p>This dataset corresponds to the study: High-resolution detection and differential expression analysis of transcription start sites using MAPCap (Bhardwaj et. al. 2018)</p> <p>It includes:</p> <p> - TSS identified using MAPCap in stage 15 embryos and larvae.</p> <p> - Differentially expressed TSS using MAPCap (FDR < 0.05) in larvae.</p> <p> - Common and stage-specific enhancer TSS identified in this study</p>
HaterNet a system for detecting and analyzing hate speech in Twitter
<p>This dataset consists of two corpuses used in the paper "Detecting and analyzing hate speech in Twitter: HaterNet a system in the Spanish prevention of hate crime office". A first one based on tweets collected at different random dates between February 2017 and December 2017 with a final size of 2 million tweets. A second one with 6,000 tweets labeled as described in the paper as hate containing or not.</p>
Dataset for Evaluating Sustain-Pedal Detection from Polyphonic Piano Music
<p>To evaluate methods of sustain-pedal detection from polyphonic piano music, we built a dataset consisting of ten well-known passages of Chopin's music. Ground-truth annotations of this dataset represent sustain-pedal on/off states at every 0.1 second. This annotation was based on the sustain-pedal movement tracked by a dedicated measurement system.</p> <p>Music scores of the ten passages were saved in PDF files. They were performed by a pianist using a Yamaha baby grand piano situated in the studios at Queen Mary University of London. The audio were recorded at 44.1 kHz and 24 bits using the spaced-pair stereo microphone technique. A pair of Earthworks QTC40 omnidirectional condenser microphones was positioned about 50 cm above the strings. </p> <p>We have developed a transfer learning method such that the on/off state of the sustain pedal can be detected at every 0.1 second. The ground-truth annotation and our detection results were saved in <em>transfer-learning-y_segment.npz</em>, which can be loaded using <em>numpy.load </em>in Python. The key for passage name, ground-truth annotation and our detection results is <em>filename_record</em>, <em>y_true</em> and <em>y_pred</em>, respectively.</p>
ROS-Specific Huntingtin Interactions: Optimization of Poly ADP Ribose Detection by Immunofluorescence
<p>Optimization of conditions for the detection of poly ADP ribose by immunofluorescence.</p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - raw figures of all quantities from all simulations
<p>We performed many simulations in many conditions, calculated many quantities and reported them in the main paper. However, the journal manuscript did not place for so many figures. Nevertheless, the reported values of all simulations rely on the results that were also summarized in the figures. These figures were produced by the Jupyter notebooks analyzing the simulation results.</p> <p>Each figure file has the following nomenclature:</p> <p>TypeOfFigure_concentration_diffusionCoefficient_1or2pop_simulationDuration(long)_Type of PSF.</p> <p>For example:</p> <p><em>molecule_positions_pure_and_impure_bursts_varying_F_C62_D22_5_psfNumeric</em> - report on the molecular position histograms of pure and impure bursts for varying F values, in the simulation with C=62pM, D=22.5 um^2/s, a NUmerical PSF model and 60s duration of simulation (if nothing appears, it was 60s; if the word <em>long</em> appears, it was 180s).</p>
Sound event localization and detection (SELDnet) results
<p>This package is part of the work - <a href="https://github.com/sharathadavanne/seld-metric">Joint Measurement of Localization and Detection of Sound Events</a> presented in WASPAA 2019.</p> <p>This package consists of results from the <a href="https://arxiv.org/abs/1905.08546">SELDnet method</a> for joint sound event localization and detection. The results corresponding to different training states of 5, 25 and 75 epochs are provided here. These results are for the four cross-validation splits of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset. The sound events in this dataset consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. This <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset is part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a> and can be downloaded <a href="https://zenodo.org/record/2599196#.XT_RmHUzaCg">here</a>.</p> <p>Each of the results folders consists of 400 files, corresponding to the results of the individual recordings of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset.</p> <p>This data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>mic_dev_5</em></strong>,<strong><em> mic_dev_25, </em></strong>and <strong><em>mic_dev_75</em></strong>, correspond to SELDnet results at 5, 25 and 75 epochs.</p> <p>Download the zip files and use your favorite compression tool to unzip these split zip files.</p>
Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins
<p>Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decision-making.<br> Despite intensive research on models of<br> collective decision-making, the implementation in multi-robot systems is still challenging.<br> Here, we advance the state of the art by introducing more plasticity to the decision-making process and by increasing the task difficulty.<br> Most studies on large-scale multi-robot decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence.<br> We investigate a dynamic environment that requires constant collective monitoring of option qualities.<br> Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly.<br> This is only possible by preventing lock-ins, a global consensus state of no return.<br> In addition, we introduce a task of increased difficulty as the robots must locate themselves to assess the quality of an option.<br> Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame.<br> We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.</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.