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9,302 results for “DETECT”
Figure 4 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 4 "Dialevel" sample injection chromatogram.
Detecting stripped stars while searching for quiescent black holes
<p>Data products and input files necessary to reproduce the MESA simulations in "Detecting stripped stars while searching for quiescent black holes".</p> <p>MESA version: 12778</p> <p>MESASDK version: x86_64-linux-20.3.</p>
CADeSH Dataset: Collaborative Anomaly Detection for Smart Homes
<p>Dataset used for quantitative evaluation in the paper:</p> <p>Y. Meidan, D. Avraham, H. Libhaber and A. Shabtai, "CADeSH: Collaborative Anomaly Detection for Smart Homes," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3194813.</p> <p> </p> <p>This is a table of flow-level traffic data which was continuously captured during a period of 21 days from five real home networks which were subscribed to a smart home security service, and from our lab at Ben-Gurion University of The Negev. This security service provider shared with us these network traffic flows, plus the related DNS requests and responses, and reputation intelligence of the destination IP addresses. Each instance in this dataset represents an outbound network traffic flow (in the form of an IPFIX) which emanated from an instance of the IoT model streamer.Amazon.Fire_TV_Gen_3.</p> <p>In our lab, we infected our streamer.Amazon.Fire_TV_Gen_3 with a cryptominer and executed cryptomining from this device. To imitate a scanning activity typically performed by some botnets, we also scanned the network using Nmap. In accordance, we labeled these malicious activities as (1) `is executing cryptomining,' or (2) `being scanned by Nmap.' All of the remaining IPFIXs captured in our lab or on the home networks were labeled as `assumed benign'.</p> <p>The multitude of real home networks, and the multitude of identical source devices, enable using this dataset for quantitative evaluation of (collaborative) anomaly/attack detection methods, especially for the IoT.</p>
smallSSD: The Small Robot Company's detection dataset for agriculture
<p>The Small Robot Company's detection dataset for agriculture, consisting of images collected by the Tom robot labelled with bounding boxes to delineate wheat and weeds.</p> <p>This data is shared under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial-4.0 International</a> license.</p> <p>More information about this data is available at <a href="https://github.com/SmallRobotCompany/smallssd">https://github.com/SmallRobotCompany/smallssd</a>.</p>
Attributing agnostically-detected large reductions in road CO2 emissions to policy mixes (code and data)
<p>Data and code to reproduce the main results of the paper.</p>
Supplementary materials (s.m.) of "The first report on detecting SARS-CoV-2 inside bacteria of the human gut microbiome: A case series on asymptomatic family members and a child with COVID-19".
<p> These are Supplementary materials of the version 2 of the article published at the following link: https://f1000research.com/articles/11-135/v2. The article is: Brogna C, Cristoni S, Petrillo M <em>et al.</em> The first report on detecting SARS-CoV-2 inside bacteria of the human gut microbiome: A case series on asymptomatic family members and a child with COVID-19 [version 2; peer review]. <em>F1000Research</em> 2022, <strong>11</strong>:135 (<a href="https://doi.org/10.12688/f1000research.77421.2">https://doi.org/10.12688/f1000research.77421.2</a>) </p>
Active garment recognition and target grasping point detection using deep learning
<p>Data used in the experiments of the research paper</p> <p>E. Corona, G. Alenyà, T. Gabas, and C. Torras, “Active garment recognition and target grasping point detection using deep learning,” Pattern Recognition, vol. 74, pp. 629–641, 2018. </p>
Graph D2A: D2A Dataset for Vulnerability Detection Transformed into ECPG Format for Training GNNs
<p>The Graph D2A is based on the dataset 'D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis'. Graph D2A was created by transforming individual samples from D2A into the form of Extended Code Property Graphs (ECPGs). The resulting dataset is designed for graph neural network models (GNNs) for detection on vulnerabilities in source code.</p> <p>Graph D2A was developed as part of the Master's thesis 'Evaluating Reliability of Static Analysis Results Using Machine Learning' at BUT FIT.</p>
Data from: A support system for the detection of abusive clauses in B2C contracts
<p>Many countries employ systemic methods of protecting consumers from unfair business practices. One such practice is the use of abusive clauses in business-to-consumer (B2C) contracts, which unfairly impose additional obligations on the consumer or deprive them of their due rights. This article presents an information system that utilizes artificial intelligence methods to automate contract analysis and to detect abusive clauses. The goal of the system is to support the entire administrative process, from contract acquisition, through text extraction and the recommendation of potentially abusive clauses, to the generation of official administrative documents that can be sent to court or to the owners of firms. This article focuses on on the components that use machine learning methods. The first is an intelligent crawler that is responsible for automatically detecting contract templates on websites and retrieving them into the system. The second is a document analysis module that implements a clause recommendation algorithm. The algorithm employs transformer-based language models and information retrieval methods to identify abusive passages in text. Our solution achieved first place in a competition on the automatic analysis of B2C contracts organized by the Polish Office of Competition and Consumer Protection (UOKiK), and has since been implemented as an official tool to support the contract analysis process in Poland.</p>
Pattern detection
<p>The <a href="https://github.com/datable-be/AI4_C_datasets/tree/main/textile_pattern_dataset">textile pattern dataset</a> is a set of images that is labeled with the pattern that is printed on or woven into the textile of the object. The represented object can be a piece of textile (e.g. a sample or a piece of tissue), or a fashion object such as a dress. The fashion object can be photographed individually or in a context, e.g. as worn by a model in a fashion show. The dataset can be used to train image classification models that classify images of textiles or fashion objects by their pattern, but may also be applied to other subject areas (e.g. design). </p>
A database of detected EMIC wave activity using Van Allen Probe B magnetometer data
<p>This is a catalog of EMIC wave activity detected by the Van Allen Probe B satellite, using the high resolution magnetometer dataset. This catalog covers the time period 2013 January 1 to 2019 July 15. </p>
Detection of interstellar 1-cyanopyrene: a four-ring polycyclic aromatic hydrocarbon
<div>This is the dataset for the article:</div> <div>Detection of interstellar 1-cyanopyrene: a four-ring polycyclic aromatic hydrocarbon</div> <div> </div> <div>Authors:</div> <div>GOTHAM Collaboration</div> <div> </div> <div>DOI:</div> <div>DOI: 10.1126/science.adq6391</div> <div> </div> <div>Journal:</div> <div>Science</div> <div> </div> <div>Corresponding authors:</div> <div>Gabi Wenzel, Ilsa R. Cooke, Brett A. McGuire</div> <div> </div> <div>To cite this:</div> <div>Gabi Wenzel et al., Detection of interstellar 1-cyanopyrene: A four-ring polycyclic aromatic hydrocarbon. Science 386, 810-813 (2024). DOI:10.1126/science.adq6391</div> <div> </div> <div>#--------------------------------------------------------------------------------------</div> <div> </div> <div>Folders:</div> <div> - DATA_1-cyanopyrene_spectroscopy contains all SPCAT/SPFIT files for 1-cyanopyrene</div> <div>(Figs. 2, 3, S3, S5, S7) including the partition function in the .qpart file</div> <div> - DATA_Fig2 contains the unsmoothed GOTHAM data as presented in Fig. 2</div> <div> - DATA_SNRLinesinStack contains the unsmoothed GOTHAM data centered around each of the</div> <div>150 brightest SNR lines that are contained in the stack (Figs. 3, S5, S6, S7)</div> <div> </div> <div>#--------------------------------------------------------------------------------------</div> <div> </div>
[5G-IANA] UC1 - Data processed by the AI object detection algorithm
<p>Bounding boxes of objects detected by the artificial intelligence algorithm for each video frame received.</p>
On the detection of stellar wakes in the Milky Way: a deep learning approach
<p>The machine learning dataset used in the work of "On the detection of stellar wakes in the Milky Way: a deep learning approach". The .tar contains .h5 files for four different subhalo target masses such that each file is generated from a unique simulation seed. Each file contains data from a single simulation run and is comprised of 100 samples which are generated by sampling 1% of all star particles from a particular simulation snapshot. Prior to sampling particles, we split the simulation box into three equal slices in the Z-coordinate and bin the data onto the X-Y plane with 32 bins on each axis. Each sample is then characterised by a 2D histogram of shape (32, 32, 12) with the last dimension containing the training features which are the overdensity, mean X-Y velocity, X-Y velocity dispersion and divergence in X-Y for the three Z-slices.</p>
Figure 1 in Detection of enteropathogens and research of pesticide residues in Lactuca sativa from traditional and agroecological fairs
Figure 1. Deltamethrin calibration curve.
Figure 8 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 8. Haplotype network of Planomicrobium spp. based on 16S rDNA sequences.
Figure 5 from: Egizi AM, Robbins RG, Beati L, Nava S, Evans CR, Occi JL, Fonseca DM (2019) A pictorial key to differentiate the recently detected exotic Haemaphysalis longicornis Neumann, 1901 (Acari, Ixodidae) from native congeners in North America. ZooKeys 818: 117-128. https://doi.org/10.3897/zookeys.818.30448
Figure 5 Pictorial key to adults of Haemaphysalis spp. occurring in North America.
Figure 7 from: Egizi AM, Robbins RG, Beati L, Nava S, Evans CR, Occi JL, Fonseca DM (2019) A pictorial key to differentiate the recently detected exotic Haemaphysalis longicornis Neumann, 1901 (Acari, Ixodidae) from native congeners in North America. ZooKeys 818: 117-128. https://doi.org/10.3897/zookeys.818.30448
Figure 7 Pictorial key to larvae of Haemaphysalis spp. occurring in North America.
Figure 6 from: Egizi AM, Robbins RG, Beati L, Nava S, Evans CR, Occi JL, Fonseca DM (2019) A pictorial key to differentiate the recently detected exotic Haemaphysalis longicornis Neumann, 1901 (Acari, Ixodidae) from native congeners in North America. ZooKeys 818: 117-128. https://doi.org/10.3897/zookeys.818.30448
Figure 6 Pictorial key to nymphs of Haemaphysalis spp. occurring in North America.
Microscopy images for the endosome detection task
<p>This is the dataset images and the ground truth annotation of the locations of the endosomes</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.