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1,487 results for “tags”
Recombinant Hepatitis E Viruses Harboring Tags in the ORF1 Protein
<p>Hepatitis E virus (HEV) is one of the most common causes of acute hepatitis and jaundice in the world. Current understanding of the molecular virology and pathogenesis of hepatitis E is incomplete, especially due to the limited availability of functional tools. Here, we report the development of tagged HEV genomes as a novel tool to investigate the viral life cycle. A selectable subgenomic HEV replicon was subjected to random 15-nucleotide sequence insertion using transposon-based technology. Viable insertions in the open reading frame 1 (ORF1) protein were selected in a hepatoblastoma cell line. Functional insertion sites were identified downstream of the methyltransferase domain, in the hypervariable region (HVR) and between the helicase and RNA-dependent RNA polymerase domains. HEV genomes harboring a hemagglutinin (HA) epitope tag or a small luciferase (NanoLuc) in the HVR were found to be fully functional and to allow for the production of infectious virus. NanoLuc allowed to quantitatively monitor HEV infection and replication by luciferase assay. HA-tagged replicons and full-length genomes allowed to localize putative sites of HEV RNA replication by the simultaneous detection of viral RNA by fluorescence in situ hybridization and of ORF1 protein by immunofluorescence. Candidate HEV replication complexes were found in cytoplasmic dot-like structures which partially overlap with ORF2 and ORF3 proteins as well as exosomal markers. Hence, tagged HEV genomes yield new insights into the viral life cycle and should allow to further investigate the structure and composition of the viral replication complex.</p>
EOL computer vision pipelines: Classification for Image Tagging: Flower Fruit
<p>Angiosperms: Stats from Colab:</p> <ul> <li>Number of positive identified reproductive structures: 490</li> <li>Number of possible identified reproductive structures: 4611</li> <li>Number of negative identified reproductive structures: 14833</li> </ul> <p> </p>
EOL computer vision pipelines: Classification for Image Tagging: Image Type: Anura
<p>Produced by the EOL Image Type Classifier. Classifies images as map, phylogeny, illustration, herbarium sheet, or none. Dataset generated for EOL Anura images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-vs-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</p>
EOL computer vision pipelines: Classification for Image Tagging: Image Rating: Chiroptera
<p>Produced by the EOL Image Rating Classifier. Classifies images as bad or good quality (used for image gallery sorting). Dataset generated for EOL Chiroptera images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-or-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</p>
Dataset for flavour tagging R&D
<p>This is a dataset for flavour tagging R&D.</p> <p>It consists of b-jets, c-jets and light-jets in equal number and equal distributions of transverse momentum, pseudo-rapidity and track multiplicity. </p> <p>The jets are sampled from ttbar events produced from proton-proton collisions at 14 TeV, using Pythia8. An ATLAS-like detector is parameterized using Delphes. The anti-kT R=0.4 algorithm with calorimeter inputs is used to define the jets.</p> <p>Jets are labelled as b-jets if there is a b-hadron within dR<0.3 of the jet, otherwise as c-jets if there is a c-hadron within dR<0.3, otherwise as light-jets. </p> <p>Tracks are associated to jets if they are within dR<0.4. If this condition holds for more than one jet, only the closest one is considered. Tracks are represented by their perigee parameters (d0, z0, pt, phi, cotan(theta)), which are smeared according to track pt and track eta dependent uncertainties.</p> <p>This dataset is heavily based on the <a href="../records/4044628" target="_blank" rel="noopener">Secondary Vertex Finding in Jets Dataset</a> and we thank J. Shlomi for the code and input.</p>
Protein tagged from Covid-19 trial records in ClinicalTrials.gov
<p>Top 200 proteins appeared in the Covid-19 trial records in https://clinicaltrials.gov/, grouped by CATH classification, with URL linking back to the trial record at https://clinicaltrials.gov/.</p>
Tagged Twitter timelines for users reporting SARS-CoV-2 infections and related data
<p>Twitter data was collected through the Twitter API v2.0, specifically through the timeline endpoint. Details about the inference of SARS-CoV-2 self-reports and the tagging of the full timeline of each user can be found in the <a href="https://github.com/digitalepidemiologylab/content_changes_paper">GitHub repository</a>. The larger dataset ("preprocessed_data.csv") consists of a total of 8,534,171 tweets posted by 30,856 users from January 1, 2020 to October to September 30, 2021.</p> <p>The raw data from Twitter, including tweet and user IDs, has been removed or anonymized in order to comply with the EPFL guidelines for data sharing.</p> <p>In particular, the date of the tweets was removed, the text of the tweets, URLs and URL domains have been substituted with the "text", "<URL>" and "<URL_DOMAIN>" token respectively.</p> <p>User IDs have been substitued with new IDs in the [0, number of users] range (e.g. U0, U1, ...) .</p> <p>Tweet IDs have been substitued with new IDs in the [0, number of tweets] range (e.g. T0, T1, ...) .</p> <p>In addition to self-explanatory columns about topics, emotions, URL classification and symptoms we tagged, we also share the columns:</p> <ul> <li>pdate: date of the SARS-CoV-2 infection self-report for that user (adjusted with SUTime)</li> <li>effective_date: date of the tweet adjusted with SUTime, when the SUTime columns is available.</li> <li>rel_effective_day(week, month): days (weeks, months) computed with respect to the positivity date (i.e. "pdate" column). Negative numbers refer to tweets posted before the user reported a COVID-19 infection on Twitter.</li> </ul>
Synthesis of Phenol-Tagged Ruthenium Alkylidene Olefin Metathesis Catalysts for Robust Immobilisation Inside Met-al-Organic Framework Support
<p>Data confirming the structure of the new compounds obtained within the project, published in <em>Catalysts</em> <strong>2023</strong>, <em>13</em>(2), 297; <a href="https://doi.org/10.3390/catal13020297">https://doi.org/10.3390/catal13020297</a></p> <p>The research was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860322 for the ITN-EJD “Coordination Chemistry Inspires Molecular Catalysis” (CCIMC) and by the National Science Centre, Poland (OPUS grant 2017/27/B/ST5/00941).</p>
A versatile "Synthesis Tag" (SynTag) for the chemical synthesis of aggregating peptides and proteins
<p>Raw data for the project "A versatile "Synthesis Tag" (SynTag) for the chemical synthesis of aggregating peptides and proteins".</p><p>Manuscript and supporting information available on ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-7mz2c-v2.</p>
University of Kansas Field Station: Forest demography, 1980 – 2015. On ten study plots established on three management units all live trees with a dbh > 7.5 cm (3 in) were identified to species, measured, and tagged. Trees were initially measured in 1980/1981 and re-measured in three successive time periods: 1993/95; 2002/03; and 2014/15. Trees will be measured again in 2025/26.
In 1980 researchers at the University of Kansas initiated a long-term experiment monitoring the composition of oak-hickory forest communities at the University’s field station near Lawrence, Kansas. The purpose of the study was to determine how forest species composition varied temporally across distinct habitats that varied in topography, elevation, sun exposure, management history and successional stage. Ten permanent sites were sampled approximately each decade with data collection periods of 1980/81, 1993/95, 2002/03, and 2014/15. Trees with a minimum diameter at breast height (dbh) of ≥ 7.5 cm were tagged, identified to species and measured. Trees will be measured again in 2025/26.
Dynamics of large wood in streams: Tagged log inventory, Mack Creek, Andrews Experimental Forest, 1985 to 2008
Although many studies have identified the characteristics of wood stored in streams, few have attempted to measure the long-term dynamics of large wood. From 1982-1985, we developed a long-term study of input, storage, decomposition, and redistribution of large wood in Mack Creek. Each year from 1985 to the present, we have surveyed a 1.1 km section of this stream. This annual survey has allowed us to quantify the standing stocks and characteristics of large wood within the stream and floodplain of an old-growth forest and an older (ca. 1963) clear-cut. In addition, these data allow us to measure rates of input, fragmentation and movement.
Known-fate survival information for radio-tagged snowshoe hares captured in Bonanza Creek Experimental Forest from June 2008 to November 2012
This dataset contains known-fate survival information for radio-tagged snowshoe hares captured in two 200 x 450 m live-trapping grids in Bonanza Creek Experimental Forest from June 2008 to November 2012. The data can be sorted and viewed by year, site, number at risk, and number of mortalities.
Counts of tagged striped bass at forty sites throughout Plum Island estuary conducted July-October 2009 using acoustic telemetry.
Manual survey data was collected to measure striped bass distribution in Plum Island Estuary during the time period that they are in New England during their summer foraging migration. Acoustic telemetry was used to tag and track individual fish and provide measures of abundance at sample sites distributed throughout the estuary.
Daily presence of individual tagged striped bass as measured by stationary receiver detections in Plum Island Estuary in 2009
Stationary receiver data was collected to measure striped bass distribution in Plum Island Estuary during the time period that they are in New England during their summer foraging migration. Acoustic telemetry was used to tag and detect individual fish throughout the estuary.
Figure 1. MyOntoPhotos Topic tags
<p>The researchers wanted to depict the<br> preferred topics (subjects) that “capture” the respondents,<br> with the percentage of interest in each topic (i.e. #nature<br> 82%, #best friends 75%). Simultaneously, there is another<br> correlation “is interested in” where #person –respondentrefers the #place and the #time, as retrieving tags. In the<br> ontology development, it is shown also the percentages of<br> preferences of #place and #time (i.e. #place visited 53%,<br> #year 55%). The results of the ontology, as set out, are<br> presented in the following figures, 1 and 2, and in the .owl<br> archive</p>
Datasets for Boosted W Tagging
<p>These are datasets used in "DisCo Fever: Robust Networks Through Distance Correlation" by Gregor Kasieczka & David Shih.</p> <p>They consist of simulated jets from QCD and from W boson decay used in boosted W tagging. They were produced using open source tools (Pythia8, Delphes) but were designed to be as similar as possible to the sample used in ATL-PHYS-PUB-2018-014.</p> <p> </p>
anTraX: high throughput video tracking of color-tagged insects (benchmark datasets)
<p>Datasets used to benchmark anTraX tracking software. Each dataset contains the raw videos, a configured anTraX session with all parameters required to reproduce the tracking results from the paper, as well as the tracking output for the first video in each dataset.</p> <p> </p> <p> </p>
Anonymized monthly StackOverflow activity for the Python tag
<p>CSV files containing metadata for each question, answer and comment posted on StackOverflow during 2019. Each file contains one month of activity with the body text, titles and usernames removed. User and post ids have been obscured so that they are not traceable, but are consistent throughout the dataset.</p>
A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas
<p>The dataset comprises of 10 JSON files, each containing geographic metadata and a sentiment score collected from tweets between March 20, 2020 and December 1, 2020 pertaining to the COVID-19 global pandemic for ten of the most populous cities in the United States and Canada. </p>
Supplementary Material: Fluorescent Protein‐Tagged Sindbis Virus E2 Glycoprotein Allows Single Particle Analysis of Virus Budding from Live Cells
<p>Supplementary Videos for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(12), 6182-6199; doi:10.3390/v7122926, http://www.mdpi.com/1999-4915/7/12/2926:</p> <p><strong>Video S1A</strong> BHK cells infected with mCherry-E2 virus at 3 h p.i. Glycoprotein containing vesicles are transported to the PM from where individual virions bud out. White arrow point to budding virions. Overall amount of glycoproteins present on the PM and the number of virus particles budding out are relatively reduced compared to the late stage of infection. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S1B </strong>Enlarged area of video S1A showing budding virus particles from PM. White arrow indicates single particle post-budding moving away from the cell. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S2A</strong> Virus budding and single particle movement associated with filopodial extensions observed from mCherry-E2 virus-infected BHK cells at 6 h p.i. Glycoprotein containing vesicle transport to the PM is also observed. Budded virions travel along the periphery of filopodia and are released from filopodial extensions to the surrounding media. Image acquisition was at a rate of 1 fps and 285 frames were acquired. Movie was generated using these images and played at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S2B</strong> Enlarged area of video S2A showing budding virus particles from filopodia. White arrow indicates virus budding from filopodial extensions. Images were acquired at a rate of 1 fps and the acquired 285 frames were used to generate the video at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S3</strong> BHK cells transfected with RNA from a non-budding cdE2 mutant <sub>416</sub>CC<sub>417</sub>/A2 mCherry-E2 virus. This non-budding mutant is unable to release fluorescent virus particles from the infected cells. The video shows the absence of fluorescent virus particle budding from the PM at 6 h post transfection even though the PM and filopodial extensions contain mCherry-E2. Despite the transport of glycoproteins to the PM, no fluorescent particles were released into the media. Yellow arrows point toward filopodial extensions. For the video, 304 images were acquired at a rate of 0.98 fps and the video was generated using the acquired images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S4</strong> BHK cells transfected with RNA from an E1 Fusion loop (G91D) mutant of mCherry-E2 virus at 6 h post transfection. This non-fusing mutant produces fluorescent virus particles at a slower rate compared to WT that are unable to fuse after entering a new cell. White arrow points to fluorescent particles that are releasing into the media from filopodial extensions. Yellow arrow represents a fluorescent particle that had entered an adjacent un-transfected cell. A total of 149 images were acquired at a rate of 0.98 fps. Video was generated using these images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p><strong>Video S5A</strong> Glycoprotein E2 (mCherry-E2; red) colocalizing with Golgi stain (green) in BHK cells infected with mCherry-E2 virus and stained with BODIPY FL C5 ceramide at 5 h p.i. and imaged at 6 h p.i. Glycoprotein-containing red vesicles originate from Golgi as evidenced from the colocalization of red and green and these vesicles display anterograde transport to the PM and the virus particles are released by budding from the PM. Fluorescent particles are also seen budding from filopodial extensions (white arrows). Images were acquired at a rate of 0.13 fps for 295 seconds. Video was generated using these acquired images at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S5B</strong> An enlarged area of the video S5A near the white arrow showing movement of particles on filopodial extensions between two cells. Movie was played at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p> </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.