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125 results for “automated detection”
SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). </p> <p>A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a></p>
Seals from the Staatsbibliothek zu Berlin and their automated detection
<p>This repository is a first step towards the compilation of an Islamic seals database comprising the following components:</p> <p>- digital photos of pages with seals, retraceable to the original artifact.</p> <p>- cut-outs of seals.</p> <p>- scripts for aggregating these resources.</p> <p>- scripts for automatically identifying similar/same seals.</p> <p>This repository took the collection at the StaBi as a first case. As such, this repository can be used as a data set for training purposes.</p> <p><strong>Scripts for seal detection</strong></p> <p>This is very basic still, and the examples show how it can be build out in different directions. The seal was actually taken from an entirely different collection. It seems the seal itself is not in this collection (or not in this orientation?) but clearly it can already function somewhat as an archetype to catch any stamps. Note that it only highlights the most likely candidate on a page, hence its singling out of only one seal.</p> <p><strong>Images from the Staatsbibliothek zu Berlin</strong></p> <p>These images were extracted from the Staatsbibliothek zu Berlin digital collections website. They can be retraced to their origin as follows: the PPN number is an identification for the object. The following number identifies the page.</p> <p>For a direct verification of the image, use the IIIF server by reconstructing the URL with this formula: `https://content.staatsbibliothek-berlin.de/dms/` then the PPN number, then `/full/0/` then the page number ending with `.jpg`</p> <p>The associated catalog page and manuscript viewer can be found by reconstruction the URL with this formula: `https://digital.staatsbibliothek-berlin.de/werkansicht?PPN=` followed by the PPN number.</p> <p>These images were assumed to be published by the StaBi and/or Stiftung Preußischer Kulturbesitz in the public domain per https://digital.staatsbibliothek-berlin.de/nutzungsbedingungen They have been brought together here strictly for research purposes with no further rights claimed.</p> <p><strong>Scripts for the StaBi</strong></p> <p>I used two scripts to automatically get 570 pages that supposedly contain Islamic seals. I also did some additional things to get everything working, for getting the right URLs and massaging the URLs into usable shapes.</p>
Automated sarcomere detection Matlab tool
<p>The Matlab code presented was developed by Dr. Ian Estabrook in 2019-2023 to automatically detect sarcomeres in multi-channel z-stack images of developing myofibrils, as used in the preprint: "A tension-driven sarcomere division mechanism facilitates muscle growth" by Clement Rodier, Ian Estabrook, Vincent Loreau, Dirk Görlich, Benjamin M. Friedrich, Frank Schnorrer. We thank Yasmin Magdy Emadeldin Mohamed Abdelghaffar for help with preparing this repository and the documentation of the code. An example data set with corresponding sarcomere tracking is included.</p>
Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy
<p>Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021). </p> <p>The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a></p>
Quality Assessment in DevOps: Automated Analysis of a Tax Fraud Detection System
<p>The dataset includes the results of the performance analysis of Big Blu case study under different workloads, number of available resources and execution demand of activities</p>
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica
<p>This repository contains the waveform data used by Ho et al. (2024), along with all generated fine-tuned models and event catalogs. See the README file for a summary. The corresponding software packages are available on GitHub at <a href="https://github.com/jakewalter/easyQuake.git">https://github.com/jakewalter/easyQuake.git</a>, <a href="https://github.com/seisbench/seisbench">https://github.com/seisbench/seisbench</a>, and <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a>. See the README.md file at <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a> for additional details.</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>
Convolutional neural network for automated surface crack detection using inductive thermography
<p>Two phase images of the samples AIT_01 and AIT_08, analysed in the publication "Convolutional neural network for automated surface crack detection using inductive thermography", submitted to the Journal of Electronic Imaging.</p>
Versailles, Urban driving, Automated Driving and VRU detection
<p>Scenario description: cyclist and pedestrian detection during automated driving.</p> <p>Session description: Automated Driving part of the trip only. Two rounds: the first one without IoT (VRUs not connected) then with IoT (pedestrian with smartphone/watch and cyclist with connected bike).</p> <p>Datasets descriptions:</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_Vehicle: </strong>Data generated from the vehicle sensors</p> <p>Vehicle datasets generated by the vehicle sensors during urban driving at Versailles. This includes the data coming from the CAN bus and GPS. It includes following kind of datasets: Vehicle: general data (speed, battery), PositioningSystem: data from GPS, VehicleDynamics: data about dynamic (acceleration...), Accel: acceleration data, EnvironmentSensorsAbsolute: environment sensors in absolute coordinates</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_V2X: </strong>V2X messages during uraban driving sessions</p> <p>Data exchanged with other vehicles and pedestrian during urban driving. SortOfCam: messages sent or received from bicycles</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_IoT: </strong>Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by urban driving and car sharing application with vehicle, across oneM2M platform. oneM2M: car sharing status data</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_CAM: </strong>CAM messages</p> <p>This dataset refers to messages captured inside the vehicle during car sharing.</p>
Livorno, Urban driving, Automated vehicle detects fallen bicycle
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching a fallen bicycle.</p> <p><strong>Session description</strong>:</p> <p>The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car publishes the detected event to the oneM2M and safely reduces its speed until to stop. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</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 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>
ABR raw data and results from automated hearing threshold detection
<p><strong>This repository contains: </strong></p> <ol> <li><strong>data.tar.gz</strong>: raw data from Auditory Brainstem Response (ABR) measurements performed at over 4,000 mice at the German Mouse Clinic. It also contains suitably pre-processed ABR raw data of over 8,000 mice from another public data repository (10.5061/DRYAD.CV803RV) of the Wellcome Sanger Institute. All data is intended to be used by and compatible with code published on GitHub (https://github.com/ExperimentalGenetics/ABR_thresholder). <br> </li> <li><strong>results.tar.gz</strong>: results and visualisations - comparison of two new, independent automatic hearing threshold finding methods and comparison with the manual gold standard method.</li> </ol>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 11. Activated Neuro-Symbols for Detecting that a Person walks around in the Room
<p>With the example of Figure 11, also the function of feedback connections can be explained.<br> According to the existing feedforward connections, the neuro-symbol “object stands” would be<br> activated together with the neuro-symbol “object moves” whenever the neuro-symbols “motion”<br> and “object moves” are active, because it is activated by a subset of the neuro-symbols that activate<br> the neuro-symbol “object moves”. This activation would however be undesired in this concrete<br> case. For this reason, an inhibitory feedback connection exists from the neuro-symbol “object<br> moves” to the neuro-symbol “object stands” that inhibits the activation of the neuro-symbol “object<br> stands”.</p>
Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>This paper mainly focuses on automated detection of White Matter Lesions of brain using<br> fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the<br> representation of an image into a meaningful image and makes it easier to analyze. As a first step,<br> MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient<br> image enhancement techniques. The pre-processed image is subjected to clustering. The clustering<br> algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC)<br> and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to<br> initialization and are easily trapped in local optima. In order to obtain an optimized result, the<br> clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic<br> global optimization tool which is used in many optimization problems. Figure 2 represents overall<br> process of automatic detection of WMLs of brain. Since MS lesions present different characteristics<br> from lesions in elderly individuals there are many clustering models to determine the accuracy but<br> those methods are not directly applicable to predict the accurate lesions because of the decreased<br> contrast between White Matter and Grey Matter in elderly people. The proposed clustering models<br> are derived by extending the objective functions of FCM and Possibilistic clustering with a<br> Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and<br> is shown to be more robust to noise and other artifacts than competing approaches.</p>
Figure 9. Performance analysis of FCM-PSO, GPC-PSO and GFCM-PSO-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9.</p>
Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC. The optimized result of GFMC<br> provides accurate detection of WMLs and it properly detects 195 images.</p>
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
Figures a, b ,c -8-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>In turn, the optimized results of<br> GFCM provide overall accuracy of 95%. Figures 8(a), 8(b) and 8(c) shows the comparison results<br> of clustering models and optimization technique in terms of Se, Sp and Acc.</p>
SI: A Guide to Automated Apoptosis Detection
<p>supplementary information of the article:</p> <p>"A Guide to Automated Apoptosis Detection:</p> <p>How to Make Sense of Imaging Flow Cytometry Data"</p> <p>D. Pischel et al., 2018</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.