Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19,342
datasets available to search
ShareScore release 0.9.0
Dataset results
19,342 results for “Sequences”
Genotyping of European Toxoplasma gondii strains by a new high-resolution next-generation sequencing-based method
<p>The data set comprises 164 FASTQ files generated with an Ion AmpliSeq-based genotyping method for <em>Toxoplasma gondii </em>and<em> </em>a BED file used for the design of the Ion AmpliSeq primer panel. The FASTA file named as "AmpliSeq-ME49-Reference" was used as a reference for mapping and data analysis of the FASTQ files. The GZ file named as "Tgondii_IonAmpliSeq_Results_SNPs_VCF" is a VCF file, which contains all SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference. The VCF file was converted into a FASTA file named as "Tgondii_IonAmpliSeq_Results_SNPs", which also contains the SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference.</p> <p>The work is published in the European Journal of Clinical Microbiology & Infectious Diseases with the title "Genotyping of European <em>Toxoplasma gondii</em> strains by a new high‑resolution next‑generation sequencing‑based method"; https://doi.org/10.1007/s10096-023-04721-7</p>
Resolving the Interpretation of Magnetic Coercivity Components from Backfield Isothermal Remanence Curves Using Unmixing of Non-linear Preisach Maps: Application to Loess-Paleosol Sequences
<p>The data set includes:</p> <p>1. Non-linear Preisach measurements for Lunca and Costinești loess-paleosol sections</p> <p>2. IRM coercivity distributions from Lunca and Costinesti interpolated on a common sequence of fields</p> <p>3. Lunca granulometry data</p> <p>4. Median Grain size for Costineși section.</p> <p>5. Magnetic susceptibility data measured at Lunca section (Constantin et al., 2015)</p> <p>6. IRM acquisition curves derived from backfield IRM data through rescaling for Costinesti section</p> <p>7. Costinesti rock magnetic data (Necula et al., 2015)</p>
Role of fluid on earthquake occurrence: Example of the 2019 Ridgecrest and the 1997, 2009 and 2016 Central Apennines sequences
<p>This repository contains files needed to reproduce the b-value times series and stress change modeling related to the Central Apennines and Ridgecrest earthquake sequences (paper under revision, preprint available at <a href="https://doi.org/10.31223/X5MH1J">https://doi.org/10.31223/X5MH1J</a>). </p>
An association sequence suitable for producing ground-state RbCs molecules in optical lattices
<p>The data that support the findings of this study are uploaded here. All the data files are self-explanatory. They contain individual column names. </p> <p>The experimental data for Fig. 4(a) are in Fig4a_experimental_data.csv under the folders Fig_4>Fig_4a. To convert to binding energy there is a fitting algorithm in the Python code Feshbach_Fit.py .</p>
Variation of and associations with the depth and evenness of sequencing coverage in a sample of archived plastid genomes
<p>Depth and evenness of sequencing coverage are considered potential indicators of genome assembly quality. In plastid genomics, where new data generation has outpaced the development of suitable assembly quality indicators, these coverage metrics could offer insights into the quality of plastomes of different sizes, structures, or taxonomic origins. However, the typical variation of sequencing depth and evenness among archived plastid genomes, their variability between plastome partitions, and any association with methodological factors have yet to be evaluated. This study explores the variation of sequencing depth and evenness across a sample of publicly accessible plastid genomes and their potential associations with plastome structure, assembly accuracy, and the methodological provenance of the genome data using statistical tests. Our results indicate significant differences in sequencing depth across the four structural partitions as well as between the coding and non-coding sections of the genomes, a significant correlation between sequencing evenness and the number of ambiguous nucleotides, and a significant difference in sequencing evenness between several DNA sequencing platforms. These findings highlight that many publicly accessible plastid genomes are based on sequence data with highly variable sequencing depth and evenness and that this variation is influenced, at least partially, by genome structure and methodological factors.</p>
Training data for 'Exome sequencing data analysis' tutorial (Galaxy Training Material)
<p>The data used in this tutorial are a subset of the data published previously in <a href="https://zenodo.org/record/3243160">Training material for the course "Exome analysis with GALAXY"</a>. Credit for uploading the original data goes to Paolo Uva and Gianmauro Cuccuru!</p> <p>Specifically, you may need the following datasets for following the tutorial:</p> <p><strong>Raw sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/father_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/father_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R2.fq.gz</a></li> </ul> <p><strong>Premapped sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_father.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_father.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_mother.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_mother.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_proband.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_proband.bam</a></li> </ul> <p><strong>Reference sequence (human chromosome 8)</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz?download=1">https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz</a></li> </ul> <p> </p> <p>If you would just like to play with GEMINI rather than work through the full tutorial, you'll find below a prebuilt GEMINI database (for GEMINI version 0.20.1) for the family trio. You can start exploring this database without having to run GEMINI load and, in fact, without having to install GEMINI's bundled annotation data.</p>
Inventory of soil prokaryotic and fungal microbiome (via 16S rRNA gene amplicons and ITS sequencing) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, February 2019 - October 2020
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased saltwater exposure, leading to change in soil microbial communities and many important biogeochemical processes. Given the high spatial and temporal heterogeneity in coastal wetlands, especially in tropical or subtropical climates characterized by seasonal temperature, precipitation, and tidal fluctuations, it remains unclear which environmental factors influence the compositions of soil microbial communities in wetlands affected by varying degrees of sea-water intrusion. To address this, a two-year survey was conducted on microbial community structure in submerged surface soils from 14 wetland sites across the Florida Everglades, representing three major ecosystem types, i.e. freshwater marshes, mangrove forests, and seagrass meadows. Bulk surface soil samples of each site were collected from February 2019 to October 2020 to cover dry and wet seasons. In addition to bulk soil samples, soil cores were collected from each site in August 2020 to assess vertical gradients of microbial communities. The dataset contains amplicon sequencing data of 16S rRNA gene (both bulk soil and soil cores) and ITS gene (only the bulk soil). The 2019 to 2020 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804243 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804243), PRJNA804246 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804246), and PRJNA804228 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804228). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-H
Catalog of NCBI sequence read archive (SRA) data for salamanders at the Hubbard Brook Experimental Forest 2012-2021
This project was designed to describe fine-scale population genetic differentiation of the stream salamander Gryinophilus porphyriticus among five study streams in the Hubbard Brook Experimental Forest. The data are paired with intensive capture-recapture data to assess direct fitness effects of individual genetic diversity, including effects of individual multilocus heterozygosity on stage-specific survival probabilities. This dataset publishes a manifest of the genomic sequence reads submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). These samples are published at NCBI under the BioProject ID 1090913 (https://www.ncbi.nlm.nih.gov/bioproject/1090913). The tables here include sample metadata and the NCBI URLs to each sample. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Mitochondrial COXI sequences and associated metazoan abundances in soils collected from the McMurdo Dry Valleys, Antarctica from 1995 to 2022
As part of an ongoing long-term sampling effort conducted by the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, the top 10 cm of soil was collected from sampling sites across the McMurdo Dry Valleys region of Antarctica. A subset of these samples representing each valley and a differing disturbance legacy from the last glacial maximum were analyzed for this data package. Samples were collected between 1995 and 2022. In each sample, the abundances of three genera of nematodes (Scottnema, Eudorylaimus, and Plectus), tardigrades, and rotifers were calculated. Following metazoan extractions, individual Scottnema lindsayae were selected for mitochondrial Cytochrome c oxidase I (COX1) Sanger dideoxy sequencing. Sequencing resulted in 249 assembled sequences of 285 base pairs after alignment and trimming.
Syllable level speech sequencing
Open the record for dataset details and reuse information.
Sequence data for Claar et al. 2020 Scientific Reports
<p>Illumina Mi-Seq sequence data of the ITS2 marker (in .fasta format) associated with Claar et al. 2020 Scientific Reports "Chronic disturbance modulates symbiont (Symbiodiniaceae) beta diversity on a coral reef".</p>
GenBank accession numbers of the four marker genes and associated voucher specimens/tissues that were used in this study. For more details see Guo et al. (2014). Sequences of species in bold are unpublished and were provided by P. Guo as personal communication in Rediscovery of Andrea's keelback, Hebius andreae (Ziegler & Le, 2006): First country record for Laos and phylogenetic placement
GenBank accession numbers of the four marker genes and associated voucher specimens/tissues that were used in this study. For more details see Guo et al. (2014). Sequences of species in bold are unpublished and were provided by P. Guo as personal communication
Intergenic sequences of Mesocricetus auratus
<p>Intergenic sequences created with the 16 libraries of Mesocricetus auratus that will be used for Bgee 15.0</p>
Data for Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery
<p><strong>Description of the datasets</strong></p> <p>Data are organized as folders and compressed with tar.gz.</p> <p>There are two compressed data folder: <strong>data </strong>which used for cattle genome graphs experiment and <strong>data_human</strong> which we used for human genome graphs experiment. </p> <p><strong>Cattle genome graphs experiments</strong></p> <p>First you need to unzip the file using command <em>tar -xvzf data.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>Utilities: contain bovine ARS-UCD 1.2 fasta reference with the accompanying index.</li> <li>Bin: contain the softwares used in the paper (vg, liftover, vcf2diploid)</li> <li>Part1: data for analysis in variant prioritization section, further subdivided into: <ul> <li>vcf_sim: variant files from four animal in each breed used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul> </li> <li>Part2: data used for analysis in the section of graph mapping with breeds-filtered variants, further subdivided into: <ul> <li>vcf_breed: variant files used to graphs construction.</li> </ul> </li> <li>Part3: data used for analysis in the section of consensus genome, further subdivided into: <ul> <li>read_sims: simulated reads as in the part1, but the coordinates are liftovered to the new consensus genomes.</li> <li>reference: contain the original reference and consensus references.</li> <li>vcf_consensus: contain major allele variants to construct consensus genomes.</li> </ul> </li> <li>Part4: data analysis in the section of whole genome graph construction and variant genotyping. <ul> <li>vcf_construct: variants from chromosome 1-29 from 82 Brown Swiss used to construct BSW whole genome graph.</li> <li>BSW_graph: whole genome Brown Swiss graph with the three accompanying indexes (xg,gcsa, and gbwt).</li> </ul> </li> </ul> <p><strong>Human genome graphs experiments</strong></p> <p>First you need to unzip the <em>data_human</em> file using command <em>tar -xvzf data</em><em>_hum.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>reference: the g1k_v37 reference used as a graph backbone</li> <li>vcf_sim: variant files from four individuals in each population used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul>
Figure 2. D in Neotypification of Drawida hattamimizu Hatai, 1930 (Annelida, Oligochaeta, Megadrili, Moniligastridae) as a model linking mtDNA (COI) sequences to an earthworm type, with a response to the 'Can of Worms' theory of cryptic species
Figure 2. D. hattamimizu unscaled habitus (from Watanabe, 2005, fig. 1 after Hatai's 1931 original).
The SV callsets of the HG002 human sample produced by cuteSV with multi long-read sequencing platforms.
<p>The SV callsets of the HG002 human sample produced by cuteSV with multi long-read sequencing platforms.</p>
SP2020_eDNA_raw_sequence_files
<p>Raw fast5 sequence files for eDNA sequencing</p>
Datasets for Itemset, Sequence and Tree Mining
<p>There are three different datasets included, that can be used for itemset, sequence and tree mining methods.</p> <p><strong>dense_db.zip</strong></p> <p>contains various real itemset datasets like <strong>chess</strong>, <strong>connect</strong>, <strong>mushroom</strong>, <strong>pumsb, T10I4D100K, T40I10D100K</strong> and so on, used in the papers on frequent, closed and maximal itemset mining. For example, <strong>Mohammed J. Zaki</strong> and Ching-Jui Hsiao. <strong>Efficient algorithms for mining closed itemsets and their lattice structure.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(4):462–478, April 2005. <a href="https://doi.org/10.1109/69.846291">doi:10.1109/69.846291</a>. Or Karam Gouda and <strong>Mohammed J. Zaki</strong>. <strong>Genmax: an efficient algorithm for mining maximal frequent itemsets.</strong> <em>Data Mining and Knowledge Discovery: An International Journal</em>, 11(3):223–242, November 2005. <a href="https://doi.org/10.1007/s10618-005-0002-x">doi:10.1007/s10618-005-0002-x</a>.</p> <p> </p> <p><strong>plandata.zip</strong>: </p> <p>Planning dataset for sequence mining. It was used in the paper <strong>Mohammed J. Zaki</strong>, Neal Lesh, and Mitsunori Ogihara. <strong>PLANMINE: predicting plan failures using sequence mining.</strong> <em>Artificial Intelligence Review</em>, 14(6):421–446, December 2000. Special issue on Applications of Data Mining. <a href="https://doi.org/https://doi.org/10.1023/A:1006612804250">doi:https://doi.org/10.1023/A:1006612804250</a>.</p> <p> </p> <p><strong>cslogs.zip</strong>: </p> <p>The CSLOGS data was used for tree mining, e.g., in <strong>Mohammed J. Zaki</strong>. <strong>Efficiently mining frequent trees in a forest: algorithms and applications.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(8):1021–1035, August 2005. Special issue on Mining Biological Data. <a href="https://doi.org/10.1109/TKDE.2005.125">doi:10.1109/TKDE.2005.125</a>.</p> <p> </p>
Subset of nucleosomal DNA sequences from mouse brain nucleus accumbens tissue (GEO dataset GSE54263)
<p>This dataset contains a subset of nucleosomal DNA sequences of +1 nucleosomes from mouse brain nucleus accumbens cells (NAC) used to analyze nucleosome positioning sequence (NPS) patterns in <a href="https://doi.org/10.1371/journal.pcbi.1007365">Pranckeviciene, Erinija and Hosid, Sergey and Liang, Nathan and Ioshikhes, Ilya (2020). Nucleosome positioning sequence patterns as packing or regulatory. In PLoS computational biology, 16 (1), pp. e1007365.</a></p> <ul> <li>controlm.fa.gz contains sequences of <strong>control</strong> mice (GSE54263 subset Con_H3 GSM1311267)</li> <li> resilientm.fa.gz contains sequences of mice <strong>resilient to social stress</strong> (GSE54263 subset Res_H3 GSM1311268)</li> <li> susceptiblem.fa.gz contains sequences of<strong> </strong>mice <strong>susceptible to social stress</strong> (GSE54263 subset Sus_H3 GSM1311269)</li> </ul> <p>This dataset originates from the GEO accession GSE54263 data from <a href="https://www.nature.com/articles/nm.3939">Sun H, Damez-Werno DM, Scobie KN, Shao NY et al. ACF chromatin-remodeling complex mediates stress-induced depressive-like behavior. <em>Nat Med</em> 2015 Oct;21(10):1146-53.</a></p>
Appendix. List of the 28S and 16S rRNA sequences recovered from GenBank. 28S = 28S rRNA GenBank accession number; 16S = 16S rRNA GenBank accession number. in Genetic and morphological evidence for cryptic species in Macrobrachium australe and resurrection of M. ustulatum (Crustacea, Palaemonidae)
Appendix. List of the 28S and 16S rRNA sequences recovered from GenBank. 28S = 28S rRNA GenBank accession number; 16S = 16S rRNA GenBank accession number.
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.