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24 results for “sqlite”
SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). </p> <p>A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a></p>
Research Organization Registry (ROR) Data in Sqlite Database
<p>Version 1.1 of the Research Organization Registry data translated into a SQLite database using ROR2DB (https://github.com/Metadata-Game-Changers/ROR2DB).</p> <p>This version adds country, country_code, and status to the ror table.</p>
Data from the ENTSO-E Transparency Platform - SQLite files
<p>Data extracted from the ENTSO-E Transparency Platform (transparency.entsoe.eu/).</p> <p> </p> <p>## AggregatedGenerationPerType (UPDATED 5/1/2021)</p> <p>Data in SQLite format. Timestamps (fields Datetime and Submission) are stored as seconds since 1970-1-1.</p> <p>CREATE TABLE gen_data<br> (Datetime INTEGER,<br> Submission INTEGER,<br> Resolution TEXT,<br> Area TEXT,<br> Type TEXT,<br> Generation REAL,<br> Consumption REAL,<br> PRIMARY KEY (Datetime, Area, Type))<br> </p>
gnomAD SQLite database WGS v4.0
<p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of <100G and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc.). (A query containing 300.000 variants takes ~40s.)</p><p>Find more information on <a href="https://github.com/KalinNonchev/gnomAD_DB">here</a>.</p><p>gnomAD SQLite database WGS v4.0</p>
gnomAD SQLite database WES v4.0
<p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of <100G and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc.). (A query containing 300.000 variants takes ~40s.)</p><p>Find more information on <a href="https://github.com/KalinNonchev/gnomAD_DB">here</a>.</p><p>gnomAD SQLite database WES v4.0</p>
ArchiHeart - Dataset : Software containers, SQlite database and DGRP data
<p><strong>Title : </strong>Genetic architecture of natural variation of cardiac performance in flies</p> <p><strong>Abstract : </strong>Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP)1. Genome Wide Associations Studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were extensively validated with <em>in vivo</em> cardiac-specific gene manipulation<em>.</em> Specifically, non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict Transcription Factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart specific knockdown. Although rarely studied, the genetic control of phenotypic variability is of primary importance, with both medical and fundamental implications. We showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping with those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability is also associated with gene regulatory network deviations. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans.</p> <p><strong>Data</strong> :</p> <ul> <li>phenosnip_singleageanalysis.img : singularity v2.6 image with R 3.4.4 and python 2.7, used for preliminary statistical analysis.</li> <li>phenosnip_singleagegwas.img : singualrity v2.6 image with FastLMM and plink, used for GWAS analysis</li> <li>phenosnip_singleageepistasis.img : singualrity c2.6 image with fastEpistasis and plink, used for epistasis analysis</li> <li>Phenosnip.sqlite.tar.gz : SQlite database containing the genotype information from DGRP consortium and the phenotype data of the study (1 week aged drosophila)</li> <li>dgrp2.tar.gz : BED, BIM and FAM files with genetic information of the DGRP lines (data issued from the DGRP consortium)</li> <li>datasets_saha_et_al.zip : raw data (individual phenotypes of DGRP lines / variants identified by GWAS / individual phenotypes from validation experiments) and large scale datasets used for analyses (PPI and genetic interactions / regulatory variants) </li> </ul>
GEOROC data as SQLite database derived from GEOROC Compilations provided by DIGIS Team
<p>Cite the sources of this dataset as:</p> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Archaean Cratons", https://doi.org/10.25625/1KRR1P, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Complex Volcanic Settings", https://doi.org/10.25625/1VOFM5, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Continental Flood Basalts", https://doi.org/10.25625/WSTPOX, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Convergent Margins", https://doi.org/10.25625/PVFZCE, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Intraplate Volcanic Rocks", https://doi.org/10.25625/RZZ9VM, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Ocean Basin Flood Basalts", https://doi.org/10.25625/AVLFC2, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Ocean Island Groups", https://doi.org/10.25625/WFJZKY, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Oceanic Plateaus", https://doi.org/10.25625/JRZIJF, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Rift Volcanics", https://doi.org/10.25625/KAIVCT, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Seamounts", https://doi.org/10.25625/JUQK7N, GRO.data, V3</p> </blockquote>
gnomAD SQLite database V3.1.2
<p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 98G for WGS v3.1.2 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - <a href="https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml">here</a> you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.)</p> <p>It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields <a href="https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml">here</a>.</p> <p>gnomAD SQLite database V3.1.2</p>
CuffDiff SQLite database
<p>This SQLite database collects the CuffDiff RNA-Seq results that are needed to run the Galaxy RNA-Seq tutorial visualization using CummeRbund</p>
gnomAD SQLite database WGS v4.1
<div> <div> <div> </div> </div> </div> <div> <p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of <100G and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc.). (A query containing 300.000 variants takes ~40s.)</p> <p>Find more information on <a href="https://github.com/KalinNonchev/gnomAD_DB">here</a>.</p> <p>gnomAD SQLite database WGS v4.1</p> </div>
gnomAD SQLite database WES v4.1
<div> <div> <div> </div> </div> </div> <div> <p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of <100G and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc.). (A query containing 300.000 variants takes ~40s.)</p> <p>Find more information on <a href="https://github.com/KalinNonchev/gnomAD_DB">here</a>.</p> <p>gnomAD SQLite database WES v4.1</p> </div>
2.4 million GLOVE word/phrase vectors SQLite database trained on PubMed abstracts
<p>This is a 2.4 million GLOVE word/phrase vectors SQLite database trained on PubMed 2021 abstracts that can be used as word/phrase embeddings in machine learning applications.</p>
Wikipedia Compressed SQLite database
<p>Starting point:</p> <ul> <li><a href="http://static.wiki/">static.wiki</a></li> <li><a href="https://news.ycombinator.com/item?id=28012829">HN post</a></li> </ul> <p>We downloaded the 40.3 GiB SQLite database of English Wikipedia and created a compressed version of it with <a href="https://github.com/mlin/sqlite_zstd_vfs/">sqlite_zstd_vfs</a>, our read/write <a href="https://facebook.github.io/zstd/">Zstandard</a> compression layer for SQLite3. The compressed version is 10.4 GiB (26%) and remains randomly accessible over the web in the spirit of the original (although we don't yet have a WebAssembly build of the compression layer).</p> <p>sqlite_zstd_vfs is a building block of our <a href="https://mlin.github.io/GenomicSQLite/">Genomics Extension for SQLite,</a> which bundles it along with other domain-specific features. This demonstrates a general-purpose use case of the compression layer intended for large-scale genomics data storage.</p>
gnomAD SQLite database V2.1.1
<p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 99G for WGS v3.1.1 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - <a href="https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml">here</a> you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.)</p> <p>It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields <a href="https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml">here</a>.</p> <p>gnomAD SQLite database V2.1.1</p>
gnomAD SQLite database V3.1.1
<p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 99G for WGS v3.1.1 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - <a href="https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml">here</a> you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.)</p> <p>It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields <a href="https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml">here</a>.</p> <p>gnomAD SQLite database V3.1.1</p>
Result of Show View Updates on sqlite db fetch call on non indexed columns of table with large data
<p>Result of Show View Updates on sqlite db fetch call on non indexed columns of table with large data</p>
Result of Show View Updates on sqlite db fetch call on indexed columns of table with large data
<p>Result of Show View Updates on sqlite db fetch call on indexed columns of table with large data</p>
SRAmetadb XML SQLite
<p>This SQLite Database stores all XMLs from the SRA metadata dump file in an easily accessible SQLite format. The database was generated using <strong>DRAmetaDBB</strong>, an experimental tool developed during Biohackathon 2024.</p> <ul> <li><strong>Project Description</strong>: This SQLite database contains all the XML files from the SRA metadata dump, making it easier to query and access SRA metadata. The database is part of ongoing experimental efforts to explore the potential of using SQLite to store and query extensive SRA metadata. The records are stored in the original SRA XML format.</li> <li><strong>Tool Used</strong>: The database was generated using a tool developed during Biohackathon 2024. This tool is still in its early stages and should be considered experimental. For more information about the tool used to generate this database, please visit the <a href="https://github.com/nishad/srameta-dbb">SRAmeta-DBB GitHub repository</a>.</li> <li><strong>Status</strong>: This database is a proof of concept and has not been thoroughly tested. It is not recommended for use in production environments or for critical applications.</li> </ul> <p>⚠️ <strong>Warning</strong></p> <p>This database is a proof of concept created using an experimental tool. It has not been thoroughly tested, and significant changes may occur. Use it with caution, especially in any critical or production environments.</p>
SRA Accessions SQLite Database
<p>This is a <strong>SQLite Database</strong> for SRA Accession IDs derived from an SRA Accessions TAB file. The database was generated using DRAmetaDBB, an <strong>experimental tool</strong> developed during <a href="http://2024.biohackathon.org">Biohackathon 2024</a>.</p> <ul> <li><strong>Database Description</strong>: This SQLite database contains SRA Accession IDs based on SRA Accession data. It is a proof of concept and part of ongoing experimental efforts to explore the potential of using SQLite to store and query SRA metadata.</li> <li><strong>Tool Used</strong>: The database was generated using a tool developed during Biohackathon 2024. This tool is still in its early stages and should be considered experimental. For more information about the tool used to generate this database, please visit the <a href="https://github.com/nishad/srameta-dbb">SRAmeta-DBB GitHub repository</a>.</li> <li><strong>Status</strong>: This database is a proof of concept and has not been thoroughly tested. It is not recommended for use in production environments or for critical applications.</li> </ul> <p>⚠️ <strong>Warning</strong><br>This database is a proof of concept created using an experimental tool. It has not been thoroughly tested, and significant changes may occur. Use it with caution, especially in any critical or production environments.</p>
gnomAD Allele Frequency SQLite database V2.1.1
<p>This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 56G for WGS v3.1.1 (about 760.000.000 variants), and allows scientists to look for minor allele frequencies of variants really fast (A query containing 300.000 variants takes ~40s.) Look here for more information: https://github.com/KalinNonchev/gnomAD_MAF</p> <p>Genomic variant database</p> <p>gnomAD Allele Frequency SQLite database V2.1.1 for hg19</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.