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.
1,569
datasets available to search
ShareScore release 0.9.0
Dataset results
1,569 results for “read until”
Draft genome assembly of a Japanese Oikopleura dioica male individual (O3), using Nanopore long reads.
<p>This draft assembly was used to validate the chrY scaffolds of the OSKA2016 reference genome in the publication “A genome database for a Japanese population of the larvacean Oikopleura dioica”, Development Growth and Differentiation, Wang and coll., 2020 (in press). It is provided as supplemental data for the reproducibility of this work; please note that no further polishing has been done to correct sequencing errors.</p> <p>Genome sequence reads were produced on a MinION sequencer (Oxford Nanopore Technologies) using high-molecular weight DNA from a male individual of the Oikopleura dioica species of zooplankton. The individual was related to the laboratory strain established from a western Japanese population that was used to produce the OSKA2016 reference genome. The raw reads were basecalled with the Guppy software version 3.3.0 using its dna_r9.4.1_450bps algorithm, and deposited in the European Nucleotide Archive (Study ID: PRJEB38559). The draft assembly was made with the Flye software version 2.7 with the options --genome-size 65m and --min-overlap 3000.</p>
Metagenomics assemblies and high-quality MAGs for "Long-read metagenomics to retrieve high-quality metagenome-assembled genomes from canine feces"
<p>This dataset includes the different metagenomics assemblies analyzed and its summary (_info.txt file):</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/100_assembly.fasta">100_assembly.fasta</a> is the Flye 2.7 metagenomics assembly merging HMW and non-HMW datasets</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/75_assembly.fasta">75_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 75% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/50_assembly.fasta">50_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 50% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/HMW_assembly.fasta?versionId=749ff6fd-2642-4ad1-971a-7f3404baa595">HMW_assembly.fasta</a> is the Flye 2.7 metagenomics assembly for HMW dataset.</p> <p>Moreover, it also includes the eight frameshift-corrected high-quality MAGs analyzed in the manuscript. </p>
Sistematic Mapping review of literature about public policies innovation and reading competences
<p>The systematic mapping method makes possible to identify what is known about a topic, what has been researched, what aspects remain unknown, and to obtain information about current trends and future challenges regarding this topic. In this Excel , the following articles were included: studies on innovative public policies related to reading skills, those published only in journals that can be found in two databases: Web of Science (WOS) and Scopus, published from January 2015 to December 2019, and related to the educational sector (social sciences, psychology, multidisciplinary, educational research, medical education, environmental education).</p>
Mapped read data and files and scripts from: Vicariance followed by secondary gene flow in a young gazelle species complex
<p>Grant's gazelles have recently been proposed to be a species complex comprising three highly divergent mtDNA lineages (<em>Nanger granti</em>, <em>N. notata</em> and <em>N. petersii</em>). The three lineages have non-overlapping distributions in East Africa, but without any obvious geographical divisions, making them an interesting model for studying the early stage evolutionary dynamics of allopatric speciation in detail. Here we use genomic data obtained by restriction site-associated (RAD) sequencing of 106 gazelle individuals to shed light on the evolutionary processes underlying Grant's gazelle divergence, to characterize their genetic structure and to assess the presence of gene flow between the main lineages in the species complex. We date the species divergence to 134,000 years ago, which is recent in evolutionary terms. We find population subdivision within <em>N. granti</em>, which coincides with the previously suggested two subspecies, <em>N.g. granti</em> and <em>N.g. robertsii</em>. Moreover, these two lineages seem to have hybridized in Masai Mara. Perhaps more surprisingly given their extreme genetic differentiation, <em>N. granti</em> and <em>N. petersii</em> also show signs of prolonged admixture in Mkomazi, which we identified as a hybrid population most likely founded by allopatric lineages coming into secondary contact. Despite the admixed composition of this population, elevated X-chromosomal differentiation suggests that selection may be shaping the outcome of hybridization in this population. Our results therefore provide detailed insights into the processes of allopatric speciation and secondary contact in a recently radiated species complex.</p>
Dataset and trained models belonging to the article 'Distant reading patterns of iconicity in 940.000 online circulations of 26 iconic photographs'
<p>Quantifying Iconicity - Zenodo</p> <p><br> ## The Dataset<br> This dataset contains the material collected for the article "Distant reading 940,000 online circulations of 26 iconic photographs" (to be) published in New Media & Society (DOI: 10.1177/14614448211049459). We identified 26 iconic photographs based on earlier work (Van der Hoeven, 2019). The Google Cloud Vision (GCV) API was subsequently used to identify webpages that host a reproduction of the iconic image. The GCV API uses computer vision methods and the Google index to retrieve these reproductions. The code for calling the API and parsing the data can be found on GitHub: https://github.com/rubenros1795/ReACT_GCV.</p> <p>The core dataset consists of .tsv-files with the URLs that refer to the webpages. Other metadata provided by the GCV API is also found in the file and manually generated metadata. This includes:<br> - the URL that refers specifically to the image. This can be an URL that refers to a full match or a partial match<br> - the title of the page<br> - the iteration number. Because the GCV API puts a limit on its output, we had to reupload the identified images to the API to extend our search. We continued these iterations until no more new unique URLs were found<br> - the language found by the ``langid`` Python module [link](https://github.com/saffsd/langid.py), along with the normalized score.<br> - the labels associated with the image by Google<br> - the scrape date</p> <p>Alongside the .tsv-files, there are several other elements in the following folder structure:</p> <p>```<br> ├── data<br> │ ├── embeddings<br> │ └── doc2vec<br> │ └── input-text<br> │ └── metadata<br> │ └── umap<br> │ └── evaluation<br> │ └── results<br> │ └── diachronic-plots<br> │ └── top-words<br> │ └── tsv<br> ```</p> <p>1. The ```/embeddings``` folder contains the doc2vec models, the training input for the models, the metadata (id, URL, date) and the UMAP embeddings used in the GMM clustering. Please note that the date parser was not able to find dates for all webpages and for this reason not all training texts have associated metadata.<br> 2. The ```/evaluation``` folder contains the AIC and BIC scores for GMM clustering with different numbers of clusters.<br> 3. The ```/results``` folder contains the top words associated with the clusters and the diachronic cluster prominence plots.</p> <p>## Data Cleaning and Curation<br> Our pipeline contained several interventions to prevent noise in the data. First, in between the iterations we manually checked the scraped photos for relevance. We did so because reuploading an iconic image that is paired with another, irrelevant, one results in reproductions of the irrelevant one in the next iteration. Because we did not catch all noise, we used Scale Invariant Feature Transform (SIFT), a basic computer vision algorithm, to remove images that did not meet a threshold of ten keypoints. By doing so we removed completely unrelated photographs, but left room for variations of the original (such as painted versions of Che Guevara, or cropped versions of the Napalm Girl image). Another issue was the parsing of webpage texts. After experimenting with different webpage parsers that aim to extract 'relevant' text it proved too difficult to use one solution for all our webpages. Therefore we simply parsed all the text contained in commonly used html-tags, such as ```<p>```, ```<h1>``` etc.</p>
NewsEye / READ OCR training dataset from French Newspapers (18th, 19th, early 20th C.)
<p>The dataset comprises French newspaper pages from 18th, 19th and early 20th century with carefully corrected text. The page images were provided by the <a href="https://www.bnf.fr/en">French National Library</a> and comprise 127 pages (training set) and 8 pages (validation set). The data are formed according to the PAGE format (cf. Cf. <a href="https://github.com/PRImA-Research-Lab/PAGE-XML/">https://github.com/PRImA-Research-Lab/PAGE-XML/</a>) and were produced with the <a href="http://read.transkribus.eu/">Transkribus </a>platform with support of the <a href="http://newseye.eu/">NewsEye</a> and the <a href="http://read.transkribus.eu/">READ </a>project.</p>
Simulated NGS read datasets for novel human virus prediction
<p>This repository contains simulated Illumina read datasets for novel human virus prediction and associated metadata extracted from the Virus Host Database (<a href="https://www.genome.jp/virushostdb/">https://www.genome.jp/virushostdb/</a>). The reads are 250bp long and were simulated with Mason (<a href="https://www.seqan.de/apps/mason/">https://www.seqan.de/apps/mason/</a>) from genomes downloaded from NCBI. The training-validation-test split was done on whole viral sequences to ensure "novelty" of validation and test viruses. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. The negative class sets contain reads simulated from chordate-infecting ("cho"), metazoan-infecting ("met"), eukariote-infecting ("euk") and all-nonhuman viruses. The positive class contains human-infecting viruses. The stratified dataset ("strat") contains an equal number of reads from "cho", "met but not cho", "euk but not met" and "all but not euk". </p> <p>Species-level datasets ("humspec", "allspec" and "chospec", with the corresponding fasta and *_species.rds files) are constructed analogously, but ensuring that all viruses of a given species were assigned to either training, val or test set. This is a stricter setting modelling a "novel viral species" scenario while reflecting within-species phenotype diversity.</p> <p>blast_hits.gz contains blast hits of human virome reads form Moustafa et al., 2017 (https://doi.org/10.1371/journal.ppat.1006292) blasted against our training database (see paper for details). In the second column you can find the matched label and the accession number of the matched reference. blast_labels_complete.gz contains extracted labels for all virome reads, including those without any matches. Note: one of the read headers (>3c8ac47039d32b11c8fe23f588e444e9) from Moustafa et al. is slightly corrupted with null characters. You can remove them with sed 's/\x0//g' or equivalent.</p> <p> </p>
R script for identification and localisation of prophage within bacterial genomes using outward-oriented paired-end reads.
<p>This R script shows an analysis example of using outwards-oriented paired-end reads (OPRs), identified using the OPR finder function in the mVIRs package, to identify p22 in <em>S</em>. Tm LT2 as described in the publication "<strong>High throughput sequencing provides exact genomic locations of inducible prophages and accurate phage-to-host ratios in gut microbial strains" </strong>by Zünd et al. Microbiome (2021)</p>
The Marginal Latin (LaM) readings of 2 Samuel - The collation file
<p>The collected cases of Marginal Latin (LaM) readings in 2 Samuel. Made at the time of sending the draft of the article to a publisher for an evaluation.</p>
RNA-Seq read counts from monocyte- and ips-derived macrophages
<p>This file contains raw RNA-Seq read counts from monocyte-derived macrophages (MDMs) and induced pluripotent stem cell-derived macrophages (IPSDMs) before and after 6 hours stimulation after LPS.</p> <p>The file was created using featureCounts. The first two columns are:</p> <ul> <li>gene_id: Ensembl 74 gene id.</li> <li>length: Length of the gene in bp.</li> </ul> <p>The MDM samples are:</p> <ul> <li>B1_ctrl</li> <li>B1_LPS</li> <li>B4_ctrl</li> <li>B4_LPS</li> <li>B5_ctrl</li> <li>B5_LPS</li> <li>B2_ctrl</li> <li>B2_LPS</li> <li>B3_ctrl</li> <li>B3_LPS</li> </ul> <p>The IPSDM samples are:</p> <ul> <li>CRL1_ctrl</li> <li>CRL1_LPS</li> <li>S7RE_ctrl</li> <li>S7RE_LPS</li> <li>FSPS10C_ctrl</li> <li>FSPS10C_LPS</li> <li>FSPS11B_ctrl</li> <li>FSPS11B_LPS</li> </ul> <p> </p>
A survey of the sorghum transcriptome using single-molecule long reads
<p>Alternative splicing and alternative polyadenylation (APA) of pre-mRNAs greatly contribute to transcriptome diversity, coding capacity of a genome and gene regulatory mechanisms in eukaryotes. Second-generation sequencing technologies have been extensively used to analyze transcriptomes. However, a major limitation of short-read data is that it is difficult to accurately predict full-length splice isoforms. Here we sequenced the sorghum transcriptome using Pacific Biosciences single molecule real time long-read isoform sequencing and developed a pipeline called TAPIS (Transcriptome Analysis Pipeline for Isoform Sequencing) to identify full-length splice isoforms and APA sites. Our analysis reveals transcriptome-wide full-length isoforms at an unprecedented scale with over 11,000 novel splice isoforms. Additionally, we uncover APA of ~11,000 expressed genes and more than 2,100 novel genes. These results greatly enhance sorghum gene annotations and aid in studying gene regulation in this important bioenergy crop. The TAPIS pipeline will serve as a useful tool to analyze Iso-Seq data from any organism.</p>
Data for: Improving Kieker's Scalability by Employing Linked Read-Optimized and Write-Optimized NoSQL Storage
<p>We show, how polyglot persistence increase Kieker's scalability by employing separate read-optimized and write-optimized noSQL storage. For this purpose we extended Kieker to store its monitoring output in Apache Cassandra , which is a write-optimized wide-column noSQL database. For the analysis of the generated monitoring output we are using ElasticSearch, a read-optimized document store noSQL storage. We are interlinking read-optimized and write-optimized noSQL storage within our Regression Benchmarking Execution Environment (RBEE). To ensure scalability we are employing a container infrastructure. The mentioned noSQL storages and the linker are operating within one single Docker container which scales horizontally.</p> <p>For generating reference values, we instrumented a Java SE application with Kieker's file system writer and measured throughput and method's execution times. Consecutively, we instrumented the same Java SE application with our Apache Cassandra writer and measured throughput and method's execution time. Finally, we compared measurement results of Kieker's file system writer with the measurement results of our Apache Cassandra writer.</p>
Read-mapping for next-generation sequencing data (Drosophila melanogaster)
<p>Code, logs and quality-control data for whole-genome resequencing of Sussex-LH<sub>M</sub> and RG <em>Drosophila melanogaster</em>.</p> <p>Mapping code is in the archive lhm_mapping_scripts.zip</p> <p>Mapping logs are in the in the archive lhm_mapping_logs.zip</p> <p>Other zip archives contain the quality control data.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Read-mapping for next-generation sequencing data (Wolbachia)
<p>Code, log files and QC data. NCBI SRA accession number SRP091004. Note that sequencing Wolbachia was not a central aim of the project, and was undertaken in order to maximise the amount of information that could be extracted from the raw genome sequence data targetted at the fruit-fly host.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
FMR1 Iso-Seq intermediate files: FLNC and nFL reads
<p>Over 40% of males and ~16% of female carriers of <em>FMR1</em> premutation allele (55-200 CGG repeats) are at risk for developing Fragile X-associated Tremor/Ataxia Syndrome (FXTAS), an adult onset neurodegenerative disorder. On the other hand, about 20% of female carriers will develop Fragile X-associated Primary Ovarian Insufficiency (FXPOI), in addition to a number of adult-onset clinical problems (<em>FMR1</em> associated disorders). Marked elevation in <em>FMR1</em> mRNA levels have been observed with premutation alleles resulting in RNA toxicity. This molecular mechanism has been proposed as the leading molecular mechanism to explain the phenotypes observed in premutation carriers.</p> <p>The <em>FMR1</em> gene, as many housekeeping genes, undergoes alternative splicing. Using Single Molecule, Real-Time (SMRT) sequencing and qRT-PCR we have recently reported that the relative abundance of all <em>FMR1</em> mRNA isoforms is significantly increased in the premutation group compared to controls. In this study, we have further investigated the transcriptional <em>FMR1</em> isoforms distribution pattern in different tissues including muscle, brain, heart and testes from 3 individuals with premutation allele and FXTAS and compared them to the isoform profiles of age-matched controls. Here we report on the identification of novel isoforms, some of which are observed only in premutation carriers and might play a role in the pathogenesis of FXTAS.</p> <p>Our findings suggest that the characterization of expression levels of the different <em>FMR1</em> isoforms is fundamental for understanding the regulation of the <em>FMR1</em> gene as well as for elucidating the mechanism(s) by which “toxic gain of function” of the <em>FMR1</em> mRNA may play a role in FXTAS and/or in the other <em>FMR1</em>-associated conditions. In addition to the elevated levels of <em>FMR1</em> isoforms, the altered abundance/ratio of the corresponding FMRP isomers may affect the overall function of FMRP in premutations.</p>
read_dataset_german_konzilsprotokolle
<p>This dataset arises from the READ project (Horizon 2020).<br> <br> Images were provided and enriched under the lead of Dr. Dirk Alvermann (Universitätsarchiv Greifswald - Germany).<br> All in all this dataset contains 8770 trainscribed textlines of handwritten historical documents from the late 18th century.</p> <p>Besides the images and page-files (containing geometric textline information and transcripts), lists dividing the dataset in train and test data are provided (each list element contains the corresponding image, textregion and textline identifiers and therefore an explicit mapping of a list element to a textline is possible). Furthermore sublists of the train list are given.<br> </p>
MinION Reads From a Tomato Source: David Eccles' TEDxWellington 2016 Dataset
<p>This is a dump of the data (i.e. MinION reads) that were produced during an on-stage sequencing run for the TEDxWellington 2016 conference. The data dump also includes presentation slides, associated videos, and a script of the talk that I gave during the conference.</p>
Train-B dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR). Batch 1 and Batch 2.
<p>Train-B Dataset. Dataset of pages without any layout or text line information. The corresponding transcripts are provided at page level with line breaks. It has 10k pages, though for convenience it is divided into two 5k page batches. This information is provided in PAGE format. </p> <p>This dataset is complementary to this other dataset:</p> <p>https://zenodo.org/record/439807#.WOIBZ3WLSkA</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p> <p> </p>
Train-A dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p>Train-A Dataset of pages with manually revised baselines and the corresponding transcripts associated to them. This batch is small, 50 pages. Please, keep in mind that only the baselines have been manually corrected, The polygons associated to each line have not been manually reviewed. </p> <p>This dataset is complementary to this other dataset:</p> <p>https://zenodo.org/record/439811#.WOIF9HWLSkA</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p>
Data from: CIDER-Seq: unbiased virus enrichment and single-read, full length genome sequencing
<p>Raw and finished sequence data produced in the study: </p> <p>Mehta D, Hirsch-Hoffmann M, Patrignani A, Gruissem W, Vanderschuren H (2017) CIDER-Seq: unbiased virus enrichment and single-read, full length genome sequencing. <em><strong>bioRxiv</strong></em>. doi: https://doi.org/10.1101/168724</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.