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9,302 results for “DETECT”
Supplementary material 2 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
ITS multiple sequence alignment : Explanation note: A multiple sequence alignment in the NEXUS format (Maddison et al. 1997) comprising all 63 matching ITS sequences, plus the three newly generated ones (KU356730, KU356731, and KU356732). The alignment was produced in MAFFT without manual adjustment (Katoh and Standley 2013). The alignment is composed of partial nSSU (bases 1-34 in the alignment), the full ITS1 (bases 35-678), the full 5.8S (bases 679-838), the full ITS2 (bases 839-1395), and partial nLSU (bases 1396-end). The SRP RNA occupies position 203-474 in the alignment. The alignment is provided for overview purposes only; the two-order nature of the taxa (Boletales and Russulales) coupled with the high variability of the ITS region jointly mean that the alignment will not be suited for phylogenetic inference.
Supplementary material 1 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
Output from cmsearch and primers used : Explanation note: A) The output from cmsearch showing all 63 relevant matches to the three ectomycorrhizal lineages. B) Detail of the primers used to re-amplify the specimens.
Detection of novel astroviruses MLB1 and MLB2 in the sera of febrile Tanzanian children.
<p>Demultiplexed and human-filtered sequencing data files (fastq.gz).</p>
Data for "A New Method of Three-Dimensional Location for Low-frequency Electric Field Detection Array"
<p>In a manuscript entitled “A New Method of Three-Dimensional Location for Low-frequency Electric Field Detection Array”, the lightning location data obtained by the low-frequency electric field detection array (LFEDA) were analyzed. The data of our results are including in the Data_for_Fig_0x.ogw. These files can be opened by Origin 9.0(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citation.</p>
Figure S1: Two-tailed intergroup Pearson analysis between patient age, COVID-19 detection, de-livery time, gravida, and termination week (Y-axis) with maternal and neonatal anti-COVID-19 immunoglobulins (IgG and IgM) (X-axis), depicted as a three-point color map (lowest = blue, 0 = white, highest = red) showed no significant correlation (at p>0.05).
<p>Figure S1: Two-tailed intergroup Pearson analysis between patient age, COVID-19 detection, de-livery time, gravida, and termination week (Y-axis) with maternal and neonatal anti-COVID-19 immunoglobulins (IgG and IgM) (X-axis), depicted as a three-point color map (lowest = blue, 0 = white, highest = red) showed no significant correlation (at p>0.05).</p>
VULDEFF: Vulnerability detection method based on function fingerprints and code differences
<p>The dataset of VULDEFF. For more information, visit our <a href="https://github.com/das-lab/Vuldeff" target="_blank" rel="noopener">GitHub</a>.</p> <p>Due to the lack of large-scale, real, and uniformly formatted vulnerability datasets in the field of software security, we realize the automatic collection and processing of vulnerability patches and vulnerability source code and build a reliable and continuously updated patch and vulnerability source code dataset. The dataset includes 9,546 patch files of CVE and 24,920 unpatched vulnerable source code files, covering 464 programs, and can be extended to support other open source projects.</p>
Landslide Detection: Faster-RCNN
<p>Our dataset comprises a collection of high-resolution satellite imagery annotated for landslide detection using the Fast RCNN (Region-based Convolutional Neural Network) model. Each image in the dataset is meticulously labeled to identify regions prone to landslides, providing crucial insights for disaster prevention and mitigation efforts. Leveraging the Fast RCNN architecture, which excels in accurately delineating object boundaries, our dataset offers precise localization of landslide-prone areas, aiding researchers and policymakers in proactive risk assessment and management strategies. By sharing this dataset with the ISPRS community, we aim to foster collaboration and facilitate advancements in landslide detection methodologies for enhanced disaster resilience.</p>
MFMET webinar - 04. Leakage in Microfluidic Devices – detection and quantification
<p><span>This video is shows how leakage testing can be evaluated in microfluidic devices. <br></span></p> <p>The leakage tests include:</p> <ul> <li><span><span>·<span> </span></span></span>Visual inspection test</li> <li><span><span>·<span> </span></span></span>Pressure decay test </li> <li><span><span>·<span> </span></span></span>Flow rate measurement test</li> </ul> <p> </p> <p>Further readings can be found:</p> <p><a href="https://mfmet.eu/publications">https://mfmet.eu/publications</a></p> <p>• Deliverable 1 – Guidelines and a test protocol for flow control evaluating leakage and burst pressure in microfluidic devices. <a href="https://mfmet.eu/publications%20%20%20%20&bull; Deliverable%201%20&ndash;%20Guidelines%20and%20a%20test%20protocol%20for%20flow%20control%20evaluating%20leakage%20and%20burst%20pressure%20in%20microfluidic%20devices.%20Link%20%20%20&bull; A1.2.3%20Documented%20example%20of%20the%20test%20protocol%20for%20leakage%20and%20burst%20pressure.%20Link%20%20%20&bull; The%20MFA%20&%20MFMET%20&ndash;%20Protocols%20for%20leakage%20testing.%20Link%20%20%20&bull; Overcoming%20Technological%20Barriers%20in%20Microfluidics:%20Leakage%20Testing.%20Front.%20Bioeng.%20Biotechnol.%2010:%20958582 https://doi.org/10.3389/fbioe.2022.958582">Link</a></p> <p>• A1.2.3 Documented example of the test protocol for leakage and burst pressure. <a href="https://mfmet.eu/publications%20%20%20%20&bull; Deliverable%201%20&ndash;%20Guidelines%20and%20a%20test%20protocol%20for%20flow%20control%20evaluating%20leakage%20and%20burst%20pressure%20in%20microfluidic%20devices.%20Link%20%20%20&bull; A1.2.3%20Documented%20example%20of%20the%20test%20protocol%20for%20leakage%20and%20burst%20pressure.%20Link%20%20%20&bull; The%20MFA%20&%20MFMET%20&ndash;%20Protocols%20for%20leakage%20testing.%20Link%20%20%20&bull; Overcoming%20Technological%20Barriers%20in%20Microfluidics:%20Leakage%20Testing.%20Front.%20Bioeng.%20Biotechnol.%2010:%20958582 https://doi.org/10.3389/fbioe.2022.958582">Link</a></p> <p>• The MFA & MFMET – Protocols for leakage testing. <a href="https://mfmet.eu/publications%20%20%20%20&bull; Deliverable%201%20&ndash;%20Guidelines%20and%20a%20test%20protocol%20for%20flow%20control%20evaluating%20leakage%20and%20burst%20pressure%20in%20microfluidic%20devices.%20Link%20%20%20&bull; A1.2.3%20Documented%20example%20of%20the%20test%20protocol%20for%20leakage%20and%20burst%20pressure.%20Link%20%20%20&bull; The%20MFA%20&%20MFMET%20&ndash;%20Protocols%20for%20leakage%20testing.%20Link%20%20%20&bull; Overcoming%20Technological%20Barriers%20in%20Microfluidics:%20Leakage%20Testing.%20Front.%20Bioeng.%20Biotechnol.%2010:%20958582 https://doi.org/10.3389/fbioe.2022.958582">Link</a></p> <p>• Overcoming Technological Barriers in Microfluidics: Leakage Testing. Front. Bioeng. Biotechnol. 10: 958582 <a href="https://doi.org/10.3389/fbioe.2022.958582">https://doi.org/10.3389/fbioe.2022.958582</a> </p> <p>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Data availability: Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies
<p>Manuscript data "Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies" published in Journal of Applied Ecology. R code to replicate the simulations can be found in the supplementary materials of the manuscript.</p>
Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating, interpreting, and extending the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
Peptidoform analysis of IP-MS data allows detection of differentially present bait proteoforms
<p>Datasets supporting iPTMs manuscript (https://doi.org/10.1101/2024.01.23.576810)</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 2 - Geared Caliper Base
Open the record for dataset details and reuse information.
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - NanoVise part 1
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - NanoVise part 2
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
<p>This repository includes the code and all relevant files used throughout our study. These encompass everything from the initial sheets of the whole acoustic dataset utilised for selecting the annotated dataset to the notebook (.ipynb) and the recordings employed in training the model.</p> <p>The .wav file are compressed in the file: <a href="../api/records/11220741/draft/files/wind-noise-detection-main.7z/content" target="_blank" rel="noopener noreferrer">wind-noise-detection-main.7z</a>/data/208_file_recordings_paper_wind.tar</p> <p> </p> <p> </p>
Data for "Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure"
<p>New York City contact network data used in the publication “<a href="https://doi.org/10.1016/j.socnet.2024.06.003">Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure</a>” (LA-UR-23-26868). This contact network comes in the form of a weighted edge list. Each row describes an edge, with the first and second column containing the labels of the nodes connected by the edge, and the third column contains the corresponding weight of the edge. In this network, an edge encodes an interaction between two individuals and the weight describes the duration of the interaction in seconds. In total the edge list describes 6,376,729,847 interactions among 6,813,615 individuals; the first 10 interactions are listed below as an example.</p> <p>2, 1, 84121<br>4, 3, 83654.4<br>5, 3, 79591.4<br>5, 4, 87642<br>6, 3, 79853<br>6, 4, 81604<br>6, 5, 79146<br>8, 7, 80604<br>10, 9, 84259.6<br>12, 11, 68990.8</p> <p> </p> <p>This work is approved for public distribution under LA-UR-24-25046.</p>
Figure 6. Burned area per month detected between 1985 and 2020 in Breeding biology of the Ochre-breasted pipit (Anthus nattereri), with notes on its conservation in the Upper Rio Grande Grasslands, south-eastern Brazil
Figure 6. Burned area per month detected between 1985 and 2020 in São João del-Rei, south-eastern Brazil. Data from the MapBiomas (2022). The dotted line indicates the average across months.
Detection history of 175 adult Plain-crested Elaenia (Elaenia cristata) throughout nine sessions between 2010 and 2014 in the northeastern Brazil.
<p>The input file for analysing the detection history of 175 adult Plain-crested Elaenia (<em>Elaenia cristata</em>) throughout nine sessions between 2010 and 2014 in the northeastern Brazil.</p>
Dataset fOr herding and predatoR detectIon with the use of robotS (DORIS)
<p>Dataset with images and annotations (semantic masks for YOLO and other architectures) of sheep, wolves, persons and depth images in order to carry out experiments of robots used for herding sheep and detect potential predators as wolves. It is divided into two different URLs since it is greater than 50GB. This dataset contains images of persons, wolves, sheep and the depth images that can be used to simulated environments and they have the depth estimated from the images using Depth Anything. We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR".</p> <p>Since the original dataset exceeds 50 GB (https://doi.org/10.57967/hf/2059), there are three datasets associated:</p> <ul> <li>a dataset that contains YOLO annotations for these images.(this dataset)</li> <li>a second dataset contains images of Sheep, (10.5281/zenodo.11313800)</li> <li>a third dataset contains Person, Wolf classes, and depth maps generated using Depth Anything, which can be utilized for simulating external environments or outdoor navigation. ( 10.5281/zenodo.11313966)</li> </ul> <p> </p>
Detecting and Handeling Sensors Errors in Stormwater Detention Ponds
<p>This is a bachelor project written by software students at Aalborg university.<br><br>It is a python project for analysing experiment data with introduced faults.<br>Read the README.md inside the .zip file. </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.