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11,177 results for “identify”
Modeling Lewy Body Disease with SNCA Triplication iPSC-Derived Cortical Organoids and Identifying Therapeutic Drugs
<p><span>This repository contains the source code for the single-cell and single-nuclei RNA sequencing data analysis for the study <strong><span>Modeling Lewy Body Disease with SNCA Triplication iPSC-Derived Cortical Organoids and Identifying Therapeutic Drugs</span></strong> by Yunjung Jin et al.<br></span></p>
An intelligent method for identifying effective reservoirs in the flood control joint operation of multi-reservoir systems
<p>The files in this record contain data for real-time optimal flood control operation of multi-reservoir systems for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Original data for reservoirs and floods;</li> <li>Source code of the MRFCMO Model;</li> <li>Results for the criteria value and the MRFCMO Model.</li> </ul>
Compounds of interest identified by screening a focused library against USP5 Zf-UBD with a 19F NMR assay
<p>Screening a set of commercial compounds selected from computational docking studies against USP5 zinc finger ubiquitin binding domain (Zf-UBD) using <sup>19</sup>F NMR spectroscopy. You can find details of preliminary <sup>19</sup>F NMR experiments <a href="https://zenodo.org/record/1246807#.WxV8HkgvzIV">here</a>.</p>
Identifier Refinery Conversion Matrixes
<p><strong>identifier-refinery</strong></p> <p>Tools and assets for easy and reproducable gene identifier conversion.</p> <p><strong>Methods</strong></p> <p>This repository is used to build matricies which can convert between different gene identifiers.</p> <p>These conversion matricies are built by:</p> <ul> <li>Randomly choosing raw CEL files from NCBI GEO for a given platform accession code (in <code>/cels</code>)</li> <li>Reading the CEL header and joining Brainarray (e.g., <code>hgu133plus2hsensgprobe</code>) and Bioconductor (e.g., <code>hgu133plus2.db</code>) (x, y) coordinates</li> <li>Finding intersecting probe identifiers</li> <li>Extracting supported identifiers and probe IDs from the Bioconductor package</li> <li>Filtering on probe IDs and Ensembl Gene IDs in Brainarray</li> <li>Writing the output to a conversion TSV file</li> <li>Check that all output conversion TSV files have a shared SHA1</li> </ul> <p><strong>Repository Contents</strong></p> <p><strong>Source Files</strong></p> <p>The <code>cels</code> directory contains raw CEL files taken from GEO. The list of supported platforms is in <code>supported_microarray_platforms.csv</code>. Source files can be acquired by running the <code>acquire_cels.py</code> script.</p> <p><strong>Docker Image</strong></p> <p>The conversion scripts are run on custom Docker images.</p> <p>Two Dockerfiles are provided in this repository - <code>base</code> Docker image, which is used to install the quire R dependancies, and the <code>pd</code> image, which is used to build the required databases for a given platform.</p> <p><strong>Conversion Scripts</strong></p> <p>A <code>build_and_convert.py</code> script is provided, which build a unique Docker image for each package, mount the downloaded CEL files as a volume, and then run the gene conversion script <code>R/gene_convert.R</code> inside the image and output the master conversion matrix. Output TSV files live in <code>cels/out/</code>.</p> <p><strong>Reproducing</strong></p> <p>The entire process can be reproduced by running the following command script from a fresh checkout of this repository. It will take some time:</p> <pre><code>$ ./generate_matricies_from_scratch.sh </code></pre> <p>You can also choose to only build a specific platform, ex.,:</p> <pre><code>$ ./generate_matricies_from_scratch.sh celegans </code></pre> <p><strong>Identifiers</strong></p> <p>Released assets in this repository are availble under the DOI, <code>xyz:1.2.3.4</code>, which can be seen on Zenodo <a href="https://link.todo">here</a>.</p> <p><strong>Related Projects</strong></p> <ul> <li><a href="https://github.com/AlexsLemonade/refinebio">AlexsLemonade/refinebio</a></li> </ul> <p><strong>Copyright</strong></p> <p><code>identifier-refinery</code> output assets are released under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0 1.0 Universal</a> license. All code is released under the BSD 3-clause license. Input assets are property of the original providers to NCBI GEO, but may be <a href="https://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html">freely downloaded and redistributed</a> unless otherwise noted.</p> <p> </p> <p>https://github.com/AlexsLemonade/identifier-refinery</p>
Research Data Management - Persistent identifier (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Association mapping identified novel candidate loci affecting wood formation in Norway spruce
<p>Genotypic data set for the association mapping in Norway spruce for wood formation and tracheid traits.</p>
Help wanted identifying hams in Swarm-E (e-POP) RRI data
<p>In the spirit of making data from the <a href="https://epop.phys.ucalgary.ca/rri/">Radio Receiver Instrument (RRI)</a> onboard <a href="http://www.esa.int/Our_Activities/Observing_the_Earth/Swarm/Swarm_trio_becomes_a_quartet">Swarm-E</a> (formally known as <a href="https://epop.phys.ucalgary.ca/">e-POP</a>) more accessible to the ham radio community via the Ham Radio Science Citizen Investigation (<a href="http://hamsci.org/about-hamsci">HamSCI</a>), we have converted RRI's data into a ".raw" format so that it can be ingested into open source software such as <a href="http://gqrx.dk/">Gqrx</a> or <a href="https://www.gnuradio.org/">GNU Radio</a>. We have done this for all RRI data related to the 2015, 2017, and 2018 ARRL Field Days.</p> <p>We encourage everyone to help us identify hams in RRI's signal. You can use the Gqrx tool discussed here, or you can use your own technique. If you decode a ham's call sign, if you would like to share your technique, or if you have any comments or suggestion <a href="mailto:perry@phys.ucalgary.ca?subject=HamSCI%3A%20RRI%20ARRL%20%22raw%22%20data%20dump">contact us</a> and let us know! </p> <p><strong>Data Rules of the Road</strong></p> <p>Please acknowledge the HamSCI project and Gareth Perry when using data from this archive in presentations and publications.</p> <p><strong>Swarm-E RRI</strong></p> <p>Swarm-E RRI is a digital radio receiver with 4 3-m monopole antennas. In most cases, the monopoles are electronically configured into a crossed-diople configuration. In this configuration, RRI records I/Q samples for the two dipoles. RRI has a sampling rate of 62500.33933 Hz, and a ~40 kHz bandpass, and can be tuned to anywhere between 10 Hz and 18 MHz. More information on Swarm-E RRI can be found in the <a href="https://link.springer.com/article/10.1007/s11214-014-0130-y">Swarm-E RRI instrument paper</a> or Gareth Perry's recent <a href="http://hamsci.org/article/field-day-cassiope-2015-results-published-radio-science">Radio Science article</a>.</p> <p><strong>Data Format</strong></p> <p>Each data file contains raw 32 bit complex I/Q samples for a given RRI dipole at a given frequency. The samples are interleaved, e.g., IQIQIQIQ... The data files do not contain any metadata. Any information regarding the time, frequency, and corresponding RRI dipole is in the file name. </p> <p><strong>Filename Format</strong></p> <p>The filename format gives information about the time and data of the recording, the tuned frequency, and which of RRI's dipoles the recording corresponds too. For example, <em>gqrx_20150628_011614_3525000_62500_RRI_Dipole1 </em>contains data recorded on Dipole 1, starting at 01:16:14 UT on June 28, 2015, at 3525000 Hz (3.525 MHz), at a sampling rate of 62500 Hz (RRI's 62500.33933 Hz sampling rate).</p> <p><strong>Gqrx</strong></p> <p>We have opted to convert the data into the .raw format so that it can be ingested into <a href="http://gqrx.dk/">Gqrx</a>. There are other ways of analyzing RRI's data; this is just one way which we felt was as easy first step. We are open to posting about other techniques on the HamSCI site as well. To help get started with Gqrx, we have developed a <a href="http://hamsci.org/resource/how-play-rri-raw-iq-file-gqrx"><em>How to play an RRI raw IQ file on Gqrx</em></a> page.</p>
dStruct: identifying differentially reactive regions from RNA structurome profiling data, Datasets.
<p>Datasets, code and results supporting the manuscript:</p> <p>Choudhary, K., Lai, Y.-H., Tran, E. J. & Aviran, S. dStruct: identifying differentially reactive regions from RNA structurome profiling data.</p>
Single Nucleotide Polymorphisms (SNPs) identified from the whole genome sequences of hilsa shad (Tenualosa ilisha) of the Bay of Bengal
<p>The data file contains 792,939 isolated SNPs identified by discoSnp++ v2.3.x (Uricaru et al., 2015) from the whole genome sequence of T. ilisha of the Bay of Bengal. The central sequence of length 2k-1 is seen in upper case, while the flanking sequences are seen in lower case. SNP_higher/lower: one of the two alleles. id: id of the SNP (each SNP has a unique id).</p> <p>FOR SNPs:</p> <p>P_i:pos_Alt1/Alt2: Information about a ith SNP (If more than a unique SNP is found, the following format is used: P_1:pos_Alt1/Alt2,P_2:pos_Alt1/Alt2,...</p> <p>pos: position of the SNP with respect to the starting position of the bubble, i.e. the starting of the upper case sequence.</p> <p>Alt1: One of the two alleles</p> <p>Alt2: the other</p> <p>FOR INDELs:</p> <p>P_1:pos_size_repeatSize</p> <p>pos: predicted position of the indel with respect to the starting position of the bubble, i.e. the starting of the upper case sequence.</p> <p>size: predicted size of the indel</p> <p>repeatSize: Size of the longest sequence both prefix of the indel and prefix of the sequence located just after the insertion.</p> <p>high/low: sequence complexity. If the sequence if of low complexity (e.g. ATATATATATATATAT) this variable would be low</p> <p>nb_pol: number of polymorphism.</p> <p>left_unitig_length: size of the full left extension.</p> <p>right_unitig_length: size of the right extension.</p> <p>left_contig_length: size of the full left extension.</p> <p>right_contig_length: size of the right extension.</p> <p>C1: number of reads mapping the central upper case sequence from the first read set.</p> <p>C2: number of reads mapping the central upper case sequence from the second read set.</p> <p>Q1 [if reads were given in fastq]: average phred quality of the central nucleotide from the mapped reads from the first read set.</p> <p>Q2 [if reads were given in fastq]: average phred quality of the central nucleotide from the mapped reads from the second read set.</p> <p>G1: Genotype of the variant in the first read set.</p> <p>G2: Genotype of the variant in the second read set.</p> <p>rank: ranks the predictions according to their read coverage in each condition favoring SNPs that are discriminant between conditions.</p>
RZooRoH: an R package to characterize individual genomic autozygosity and identify homozygous-by-descent segments
<p>these are two files used to test the RZooRoH package in the manuscript:<br> RZooRoH: an R package to characterize genomic autozygosity and identify homozygous-by-descent segments<br> A.R. Bertrand, N.K. Kadri, L. Flori, M. Gautier and T. Druet</p> <p>Each row represents a SNP. The files contain first 5 information columns followed by the genotyes (1 row per individual):<br> 1 Chromosome number<br> 2 Marker name<br> 3 Position in bp<br> 4 Allele 1<br> 5 Allele 2</p> <p>Genotypes are coded as 0, 1 and 2 for AA, AB and BB and 9 for missing.</p> <p>The file geno_sim15_5col.txt contains the simulated genotypes: 25000 markers and 500 individuals.<br> The file filtered_soay_5col.txt contains the sheep genotype data: 47365 markers and 110 individuals.</p> <p>The origin of the files is described in the paper.<br> The simulated data set is from Druet and Gautier (2017) whereas the sheep data comes from Kijas et al. (2012).</p>
PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity
<p><strong>Introduction</strong></p> <p>These are the <em>Summary Level-data</em> as presented in:</p> <p>"PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity". <em>Nature Communications </em>volume 9, Article number: 2904 (2018) doi: 10.1038/s41467-018-04766-9 </p> <p>If you use these data please cite the corresponding manuscript, which can be downloaded here: https://www.nature.com/articles/s41467-018-04766-9. When you have any questions or comments regarding this study or these files, please contact me via:</p> <p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p> <p> </p> <p><strong>Files and description</strong></p> <p>The file PR_interval_July2018_summary_results.txt is gzipped and contains the PR interval GWAS meta-analysis summary results of 92,000 European samples. The imputation reference panel used for most studies was HapMap2 (please note: hg18, build36). </p> <ul> <li><em>SNP</em> - variantID (rsID).</li> <li><em>CHR</em> - chromosome numbers [1-22 and X].</li> <li><em>POS</em> - base pair position, hg18 / build36.</li> <li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li> <li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li><em>BETA_FIXED</em> - beta from the fixed-effects model.</li> <li><em>SE_FIXED </em>- standard error from the fixed-effects model.</li> <li><em>P_FIXED </em>- P-value from the fixed-effects model.</li> </ul>
Supplementary Data for "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation"
<p>DFT-optimized structures, energies, and computed physicochemical properties of metal-organic frameworks that correspond with work in "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation" (DOI: <a href="https://pubs.acs.org/doi/10.1021/acscatal.8b05178">10.1021/acscatal.8b05178</a>).</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Shorthouse, David Peter, <a href="https://orcid.org/0000-0001-7618-5230">https://orcid.org/0000-0001-7618-5230</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Zebrafish behavioral profiling identifies ligands, targets, and neurons related to sedation and paradoxical excitation
<p>Anesthetics are generally associated with sedation, but some anesthetics can also increase brain and motor activity — a phenomenon known as paradoxical excitation. Previous studies have identified GABAA receptors as the primary targets of most anesthetic drugs, but how these compounds produce paradoxical excitation is poorly understood. To identify and understand such compounds, we applied a behavior-based drug profiling approach. Here, we show that a subset of central nervous system depressants cause paradoxical excitation in zebrafish. Using this behavior as a readout, we screened thousands of compounds and identified dozens of hits that caused paradoxical excitation. Many hit compounds modulated human GABAA receptors, while others appeared to modulate different neuronal targets, including the human serotonin-6 receptor. Ligands at these receptors generally decreased neuronal activity, but paradoxically increased activity in the caudal hindbrain. Together, these studies identify ligands, targets, and neurons affecting sedation and paradoxical excitation in vivo in zebrafish.</p>
UK Biobank GWAS of WHRadjBMI and WHR in combined, women only, and men only (unrelated, self-identified white British)
<p>GWAS of WHRadjBMI and WHR in UK Biobank, using unrelated self-identified white British samples only.</p> <p>GWAS was performed in BOLT-LMM and full details can be found here: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30239722">https://www.ncbi.nlm.nih.gov/pubmed/30239722</a> (the paper is open access).</p> <p>Columns:</p> <ul> <li>SNP, SNP identifier</li> <li>CHR, chromosome</li> <li>BP, position (hg19)</li> <li>GENPOS, genetic position</li> <li>ALLELE1, tested allele</li> <li>ALLELE0, other allele</li> <li>A1FREQ, frequency of the tested allele</li> <li>INFO, imputation quality (info score)</li> <li>CHISQ_LINREG, chi-square from linear regression</li> <li>P_LINREG, p-value from linear regression</li> <li>BETA, beta (effect) from the linear mixed model</li> <li>SE, standard error from the linear mixed model</li> <li>CHISQ_BOLT_LMM_INF, chi-square from linear mixed model</li> <li>P_BOLT_LMM_INF, p-value from the linear mixed model</li> </ul>
Supplementary tables for "Theme Enrichment Analysis: A Statistical Test for Identifying Significantly Enriched Themes in a List of Stories with an Application to the Star Trek Television Franchise"
<p>Supplementary tables for the manuscript "Theme Enrichment Analysis: A Statistical Test for Identifying Significantly Enriched Themes in a List of Stories with an Application to the Star Trek Television Franchise".</p> <p>Supplementary Information File 1 contains a table of Star Trek TOS/TAS/TNG television series episodes featuring the Klingon alien race. The criterion for inclusion is that the Klingons were deemed by the authors to have been featured throughout the episode in a way that is central to the story plot.</p> <p>Supplementary Information File 2 contains tables of over-represented Literary Theme Ontology version 0.1.1 literary themes in Star Trek TOS/TAS/TNG television series storysets as identified by the hypergeometric test.</p> <p>Supplementary Information File 3 contains tables of over-represented Literary Theme Ontology version 0.1.1 literary themes in Star Trek TOS/TAS/TNG television series storysets as identified by the TF-IDF statistic.</p>
Identifying Hot Spots in Rural Community Resilience in Scotland - database
<p>Database of nearest resilience infrastructure from each Scottish postcode, commissioned and funded by the National Centre for Resilience.</p> <p>Data are in <a href="http://www.geopackage.org/">geopackage format</a>, which is curated by the open geospatial consortium. The data format can be read by GDAL, and hence all major analytical and spatial software (e.g. R, Python, QGIS). The database has two main tables:</p> <ul> <li>datazones - a spatial table of boundary polygons. Available from <a href="https://data.gov.uk/dataset/ab9f1f20-3b7f-4efa-9bd2-239acf63b540/data-zone-boundaries-2011">data.gov.uk</a> under the <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">open government licence</a>.</li> <li>postcode_to_POI - a table of postcode to POI type distances with lookups for a range of administrative boundaries.</li> </ul> <p>An example query (in R) is shown in 10.5281/zenodo.3386179 to access data in the geopackage.</p> <p>The database was built using the following datasets:</p> <ul> <li><a href="https://www.ordnancesurvey.co.uk/business-and-government/products/os-open-roads.html">Ordnance Survey Open Roads</a></li> <li><a href="https://www.ordnancesurvey.co.uk/business-and-government/products/code-point-open.html">Ordnance Survey Code-Point Open</a></li> <li><a href="https://digimap.edina.ac.uk/webhelp/os/data_information/os_products/points_of_interest.htm">Ordnance Survey Points of Interest</a></li> </ul> <p>The following copyright licences apply to this dataset:</p> <p>© Crown Copyright and Database Right 2019. Ordnance Survey (Digimap Licence).<br> This material includes data licensed from PointX Database Right/Copyright 2019.<br> Contains NRS data © Crown copyright and database right 2019.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Lundberg, Johannes, <a href="https://orcid.org/0000-0003-4316-9183">https://orcid.org/0000-0003-4316-9183</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Garrett, Gary, <a href="https://orcid.org/0000-0003-4888-2578">https://orcid.org/0000-0003-4888-2578</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Baider, Claudia, <a href="https://orcid.org/0000-0002-2203-2076">https://orcid.org/0000-0002-2203-2076</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
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.