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11,177 results for “identify”
FIGURE 2 in Contribution of ITS towards identifying mites of the family Macrochelidae (Acari: Mesostigmata) collected from cattle manure in São Paulo, Brazil
FIGURE 2. Phylogenetic trees inferred from the alignments of eight ITS sequences in the family Macrochelidae through the maximum likelihood (ML), maximum parsimony (MP) and neighbour-joining (NJ) methods.
Crowd-Annotation Results: Identifying and Classifying User Requirements in Online Feedback
<p>Results from the Figure Eight experiment conducted as part of the paper "Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing" published at REFSQ 2020.</p>
Research data supporting article "Recurrent horizontal transfer identifies mitochondrial positive selection in a transmissible cancer"
<p>Gene expression data supporting analyses described in the article "Recurrent horizontal transfer identifies mitochondrial positive selection in a transmissible cancer" (Strakova et al., 2020).</p>
Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra
<p>The Paper: <a href="https://arxiv.org/abs/1909.10963">https://arxiv.org/abs/1909.10963</a></p> <p>Abstract: We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue which did not have existing spectroscopic observations. Our new catalogue contains 50.4 million galaxies, 2.1 million quasars, and 58.8 million stars. We provide individual classification probabilities for each source, with 6.7 million galaxies (13%), 0.33 million quasars (15%), and 41.3 million stars (70%) having classification probabilities greater than 0.99; and 35.1 million galaxies (70%), 0.72 million quasars (34%), and 54.7 million stars (93%) having classification probabilities greater than 0.9. Precision, Recall, and F1 score were determined as a function of selected features and magnitude error. We investigate the effect of class imbalance on our machine learning model and discuss the implications of transfer learning for populations of sources at fainter magnitudes than the training set. We used a non-linear dimension reduction technique (Uniform Manifold Approximation and Projection: UMAP) in unsupervised, semi-supervised, and fully-supervised schemes to visualise the separation of galaxies, quasars, and stars in a two-dimensional space. When applying this algorithm to the 111 million sources without spectra, it is in strong agreement with the class labels applied by our random forest model.</p> <p>When using this dataset, please reference our paper via the journal (<a href="https://arxiv.org/abs/1909.10963">https://arxiv.org/abs/1909.10963</a>) and this DOI (10.5281/zenodo.3459293). If you make use of our scripts please reference our Github repository DOI (10.5281/zenodo.3855160).</p> <p>File descriptions:</p> <p>All of these files are Pandas Dataframes, saved as pickle files. df_spec_classprobs.pkl contains the spectroscopically observed sources used for training and testing. This has been cleaned, and has the results of the random forest classifier added as additional columns (sources used for training have NaNs in the class_pred column). SDSS-ML-all contains the 111 million photometrically observed sources, with our class labels and probabilities added. SDSS-ML-galaxies/quasars/stars is the same file broken up by assigned class for convenience.</p>
Identifying and Prioritizing Chaos Experiments by Using Established Risk Analysis Techniques - Artifacts
<p>This is the supplementary material for the following publication: </p> <p>Dominik Kesim, André van Hoorn, Sebastian Frank, and Matthias Häussler. Identifying and prioritizing chaos experiments by using established risk analysis techniques. In Proceedings of the 31st International Symposium on Software Reliability Engineering (ISSRE 2020). IEEE, 2020</p>
Catch me if you can. Can human observers identify insiders in asset markets?
<p>Supplementary material</p> <p>This documentation provides information on the files and folders contained in the supplementary material. Note that all .do and .R files in the supplementary material are meant to be run directly from the folder containing them.</p> <p>1.contracts\</p> <p>1.1.contracts\RegulationContractsModPas.do</p> <p>Contains calculations for the results reported in section 3.2 and for Table 5 of the paper.</p> <p>2.globals\</p> <p>Contains data potentially used in analysis.</p> <p>3.infodata\</p> <p>Contains data potentially used in analysis.</p> <p>4.predictions\</p> <p>4.1.Predictions\Regulation_predictions.do</p> <p>Contains calculations for Tables 2-4 of the paper.</p> <p>4.2.Predictions\Regulation_predictions_DetectionProb.do</p> <p>Contains calculations for the results reported in section 3.1 of the paper.</p> <p>5.subjects\</p> <p>Contains data potentially used in analysis.</p> <p>6.timelog\</p> <p>6.1.timelog\Regulation_timelog.do</p> <p>Contains calculations for Figure 4 and Figure A1 of the paper.</p> <p>7.z-Tree\</p> <p>This folder contains the experimental software.</p> <p>7.1.z-Tree\*.zdata</p> <p>Parameter files for group assignment, market assignment, subject identifiers etc. used in the experimental software.</p> <p>7.2.z-Tree\z-Tree_Documentation.pdf</p> <p>Documentation of all variables used in the z-Tree experimental software.</p> <p>8.DetectionProbability.xlsx</p> <p>Contains data for Table 2 in the paper. Based on 4.1 Predictions\Regulation_predictions.do, section stata02.</p> <p>9.groups.zdata</p> <p>Overview of group IDs. Used In some analyses.</p> <p>10.Insider_regulation_data.do</p> <p>Stata .do file which processes the raw z-Tree output data (see section 12 of this document) in preparation for all analyses.</p> <p>11.PowerAnalysis.R</p> <p>Contains all post-hoc power analyses reported in sections 3.1 and 3.2 of the paper.</p> <p>12.PUN*_C*.xls</p> <p>Original z-Tree output files containing all experimental results. These are processed into Stata format in Insider_regulation_data.do.</p> <p>13.ResultsWithReferences.pdf</p> <p>The file “ResultsWithReferences.pdf” contains the results section of the paper, including (in blue) references to the analysis scripts described in the present document.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p>Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Wikipedia Citations: A comprehensive dataset of citations with identifiers extracted from English Wikipedia
<p>The dataset is composed of <strong>3 parts</strong>:</p> <p>1. The dataset of 29.276 million citations from 35 different citation templates, out of which 3.92 million citations already contained identifiers, and approximately 260,752 citations were equipped with identifiers from Crossref. This is under the filename: <strong>citations_from_wikipedia.zip</strong></p> <p>2. A minimal dataset containing a few of the columns from the citations from Wikipedia dataset. These columns are as follows: 'type_of_citation', 'page_title', 'Title', 'ID_list', metadata_file', 'updated_identifier'. This is under the filename: <strong>minimal_dataset.zip. </strong>The 'metadata_file' column can be used to refer to the metadata collected from CrossRef and page title, the title of the citation can be used to refer to the 'citations_from_wikipedia.zip' dataset and get more information for a particular citation (such as author, periodical, chapter).</p> <p>3. Citations classified as a journal and their corresponding metadata/identifier extracted from Crossref to make the dataset more complete. This is under the filename: <strong>lookup_data.zip</strong>. This zip file contains a CSV file: <strong>lookup_table.gzip</strong> (a parquet file containing all citations classified as a journal) and a folder:<strong> metadata_extracted </strong>(a folder containing the metadata from CrossRef for all the citations mentioned in the table)</p> <p><br> The data was parsed from the Wikipedia XML content dumps published in May 2020.</p> <p>The source code to extract and getting used to the pipeline can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki</strong></p> <p>The taxonomy of the dataset in (1) can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki/wiki/Taxonomy-of-the-parent-dataset</strong></p>
Single-cell Transcriptomic Analysis Identifies Extensive Heterogeneity in the Cellular Composition of Mouse Achilles Tendons
<p>Tendon is a dense connective tissue that stores and transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, yet little is known about the diversity of cell types that populate tendon. To address this, we determined the heterogeneity of cell populations within mouse Achilles tendons using single-cell RNA sequencing. In assembling a transcriptomic atlas of Achilles tendons, we identified 11 distinct types of cells, including 3 previously undescribed populations of tendon fibroblasts. This table contains differential gene expression for specific genes identified in distinct populations of cells within tendon tissue. </p>
Quit attempts amongst tobacco users identified in the Tamil Nadu Tobacco Survey of 2015-16: A 3 year follow-up mixed methods study
<p><span><b>Objectives</b></span></p> <p><span>To determine current tobacco use in 2018-19, quit attempts made and to explore the enablers and barriers in quitting tobacco among tobacco users identified in the Tamil Nadu Tobacco Survey (TNTS) in 2015-16. </span></p> <p><span><span><b>Setting</b></span></span></p> <p><span><span>TNTS was conducted in 2015-16 throughout the state of TN in India covering 111363 individuals. Tobacco prevalence was found to be 5.2% (n=5208) </span></span></p> <p><span><span><b>Participants</b></span></span></p> <p><span><span>All tobacco users in eleven districts of TN identified by TNTS (n=2909) were tracked after three years by telephone. In-depth interviews (n=26) were conducted in a sub-sample to understand the enablers and barriers in quitting. </span></span></p> <p><span><span><b>Primary and Secondary Outcomes</b></span></span></p> <p><span><span>Current tobacco use status, any quit attempt and successful quit rate were the primary outcomes, while barriers and enablers in quitting were considered as secondary outcomes.</span></span></p> <p><span><span><b>Results </b></span></span></p> <p><span><span>Among the 2909 tobacco users identified in TNTS 2015-16, only 724 (24.9%) could be contacted by telephone, of which 555 (76.7%) consented. Of those who consented, 210 (37.8%) were currently not using tobacco (i.e. successfully quit) and 337 (60.7%) continued to use any form of tobacco. Of current tobacco users, 115 (34.1%) never made any quit attempt and 193 (57.3.8%) have made any attempt to quit. Those using smoking form of tobacco products (aRR=1.2, 95% CI: 1.1-1.4) and exposure to smoke at home (aRR=1.2, 95% CI: 1.1-1.3) were found to be positively associated with continued tobacco use (failed or no quit attempt). Support from family and perceived health benefits are key enablers, while peer influence, high dependence and lack of professional help are some of the barriers to quitting. </span></span></p> <p><span><b>Conclusion</b></span></p> <p><span>Two-thirds of the tobacco users continue to use tobacco in the last 3 years. While tobacco users are well aware of the ill-effects of tobacco, various intrinsic and extrinsic factors play a major role as a facilitator and lack of the same act as a barrier to quit.</span></p>
Identifying Implicit Vulnerabilities through Personas as Goal Model: Case study model
<p>CAIRIS model to accompany 'Identifying Implicit Vulnerabilities through Personas as Goal Model: Case study model' SECPRE 2020 paper.</p> <p>Instructions for importing the model package: https://cairis.readthedocs.io/en/latest/io.html#importing-models</p> <p>Instructions for working with user goal models: https://cairis.readthedocs.io/en/latest/usergoals.html#user-goals-and-user-goal-models</p>
Datasets associated with "Improving Small Molecule Force Fields by Identifying and Characterizing Small Molecules with Inconsistent Parameters"
<p>This provides associated datasets for a paper/preprint forthcoming soon, by Ehrman et al., "Improving small molecule force fields by identifying and characterizing small molecules with inconsistent parameters". A poster describing an earlier portion of the work is available at https://doi.org/10.5281/zenodo.3385278. The AllMolsAllData.csv.gz file provides a Pandas dataframe for the full set of molecules considered, as described in the paper, whereas the 25andunderarchive.tar.gz provides optimized geometries for 265,847 molecules with four or more difference flags.</p>
Sequenced-based paternity analysis to improve breeding and identify self-incompatibility loci in intermediate wheatgrass (Thinopyrum intermedium)
<p>In outcrossing species such as intermediate wheatgrass (IWG, Thinopyrum intermedium), polycrossing is often used to generate novel recombinants through each cycle of selection, but it cannot track pollen-parent pedigrees and it is unknown how self-incompatibility (SI) genes may limit the number of unique crosses obtained. This study investigated the potential of using next-generation sequencing to assign paternity and identify putative SI loci in IWG. Using a reference population of 380 individuals made from controlled crosses of 64 parents, paternity was assigned with 92% agreement using Cervus software. Using this approach, 80% of 4158 progeny (n = 3342) from a polycross of 89 parents were assigned paternity. Of the 89 pollen parents, 82 (92%) were represented with 1633 unique full-sib families representing 42% of all potential crosses. The number of progeny per successful pollen parent ranged from 1 to 123, with number of inflorescences per pollen parent significantly correlated to the number of progeny (r = 0.54, p < 0.001). Shannon's diversity index, assessing the total number and representation of families, was 7.33 compared to a theoretical maximum of 8.98. To test our hypothesis on the impact of SI genes, a genome-wide association study of the number of progeny observed from the 89 parents identified genetic effects related to non-random mating, including marker loci located near putative SI genes. Paternity testing of polycross progeny can impact future breeding gains by being incorporated in breeding programs to optimize polycross methodology, maintain genetic diversity, and reveal genetic architecture of mating patterns.</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.