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.
294
datasets available to search
ShareScore release 0.9.0
Dataset results
294 results for “data discovery”
Data from: high repeat content in the genomes of sparrows: the importance of genome assembly completeness for transposable element discovery
<p>Transposable elements (TE) play critical roles in shaping genome evolution. However, the highly repetitive sequence content of TEs is a major source of assembly gaps. This makes it difficult to decipher the impact of these elements on the dynamics of genome evolution. The increased capacity of long-read sequencing technologies to span highly repetitive regions of the genome should provide novel insights into patterns of TE diversity. Here we report the generation of highly contiguous reference genomes using PacBio long read and Omni-C technologies for three species of sparrows in the family Passerellidae. To assess the influence of sequencing technology on TE annotation, we compared these assemblies to three chromosome-level sparrow assemblies recently generated by the Vertebrate Genomes Project and nine other sparrow species generated using a variety of short- and long-read technologies. All long-read based assemblies were longer in length (range: 1.12-1.41 Gb) than short-read assemblies (0.91-1.08 Gb). Assembly length was strongly correlated with the amount of repeat content, with longer genomes showing much higher levels of repeat content than typically reported for the avian order Passeriformes. Repeat content for the Bell's sparrow (31.2% of genome) was the highest level reported to date for a songbird genome assembly and was more in line with woodpecker (order Piciformes) genomes. CR1 LINE elements retained from an expansion that occurred 25-30 million years ago were the most abundant TEs in the song sparrow genome. Although the other five sparrow species also exhibit evidence for a spike in CR1 LINE activity at 25-30 million years ago, LTR elements stemming from more recent expansions were the most abundant elements in these species. LTRs were uniquely abundant in the Bell's sparrow genome deriving from two recent peaks of activity. Higher levels of repeat content (79.2-93.7%) were found on the W chromosome relative to the Z (20.7-26.5) or autosomes (16.1-30.9%). These patterns support a dynamic model of transposable element expansion and contraction underpinning the seemingly constrained and small sized genomes of birds. Our work highlights how the resolution of difficult-to-assemble regions of the genome with new sequencing technologies promises to transform our understanding of avian genome evolution.</p>
Supplementary data for: simultaneous species detection and discovery with environmental DNA metabarcoding: a freshwater mollusk case study
<p>Environmental DNA (eDNA) sampling is a powerful tool for rapidly characterizing biodiversity patterns for specious, cryptic taxa with incomplete taxonomies. One such group that are also of high conservation concern are North American freshwater gastropods. In particular, springsnails of the genus <em>Pyrgulopsis</em> (Family: Hydrobiidae) are prevalent throughout the western United States where >140 species have been described. Many of the described species are narrow endemics known from a single spring or locality and it is believed that there are likely many additional species which have yet to be described. The distribution of these species across the landscape is of interest because habitat loss and degradation, climate change, groundwater mining, and pollution have resulted in springsnail imperilment rates as high as 92%. Determining distributions with conventional sampling methods is limited by the fact that these snails are often <5 mm in length with few distinguishing morphological characters, making them both difficult to detect and to identify. In order to facilitate detection of <em>Pyrgulopsis</em> we developed an eDNA metabarcoding protocol that is both inexpensive and capable of rapid, accurate detection of all known <em>Pyrgulopsis</em> species. When compared with conventional collection techniques, our pipeline consistently resulted in detection at sites previously known to contain <em>Pyrgulopsis </em>springsnails and at a cost per site that is likely to be substantially less than the conventional sampling and individual barcoding that has been done historically. Additionally, because our method uses eDNA extracted from filtered water it is non-destructive and suitable for the detection of endangered species where "no take" restrictions may be in effect. This effort represents both a tool which is immediately applicable to a group of high conservation concern across western North America and a case study in the broader application of eDNA sampling for landscape assessments of cryptic taxa of conservation concern.</p>
Data-Driven Discovery of Carbonyl Organic Electrode Molecules: Machine Learning and Experiment
<p>Prediction model of organic molecular electrode</p>
Data supporting article: "Data-Driven Discovery of the Origins of UV Absorption in Alpha-3C Protein"
<p>The uploaded folder contains data and a python notebook containing code used to plot the figures presented in the article "Data-Driven Discovery of UV Absorption in Alpha-3C Protein"</p>
Simulated data used in "AIDE: annotation-assisted isoform discovery with high precision"
<p>This repository contains the eight simulated RNA-seq samples (sample1, sample2, ..., sample8 with coverages10x, 20x, ..., 80x) simulated from the annotation file "truth.gtf" by R package polyester. The simulation procedure is described in the Methods section "Simulation for comparing isoform reconstruction methods". For example, sample 1 has mapped and sorted reads in "sample1.sorted.bam".</p> <p>When we generate the benchmarking results in the Results section "AIDE outperforms state-of-the-art methods on simulated data", we supply each isoform identification/quantification method (AIDE, Cufflinks, StringTie, or SLIDE) with one synthetic annotation file (in "aGTF.zip") and one bam file (e.g., sample1.sorted.bam).</p>
Code and data for "Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer".
<p>Code and data for "<strong>Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer</strong>".</p> <ul> <li> <p>The publicly available datasets supporting the conclusions of this article are available in the Gene Expression Omnibus repository (<a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>) and UCSC Xena Browser (TCGA, <a href="https://xena.ucsc.edu/">https://xena.ucsc.edu/</a>).</p> </li> </ul>
Training Data for 'somatic variants discovery'
<p>The data provided here are part of a Galaxy Training Network tutorial created by Björn Grüning to detect hCNVS in defined WES chromosomal regions.</p>
Improving genome-wide association discovery and genomic prediction accuracy in biobank data
<p>Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ<sup>2</sup> value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.</p>
Data for: An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
<p><span>Soybean [<em>Glycine max (L.) Merr.</em>] </span><span>serves as one of the most economically valuable crops globally, but it is sensitive to low temperatures during the crop growing season. Currently, agriculture around the world has faced more serious abiotic stresses due to climate change, so there is an urgent need to breed cold-tolerant cultivars to resist the changing environment. The cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. A total of 56 soybean samples were used, including 28 resistant varieties and 28 susceptible varieties, in the field experiments. We selected 55 SNPs (which were mapped to 39 CTgenes) from the CTgenes for distinguishing cold-tolerant lines from cold-susceptible lines. The SNP data can be applied for soybean's cold-tolerant experiment, such as soybean marker-assisted selection, soybean varieties clustering, the systems biology analysis, and further validation. </span></p>
Data and Files for Zito, Rigon and Dunson (2022): "Inferring taxonomic affiliation from DNA barcoding aiding in discovery of new taxa"
<p>This folder contains the data and the R code to reproduce the figures and tables in the paper Zito, Rigon and Dunson (2022) - "Inferring Taxonomic placement from DNA barcoding aiding in discovery of new taxa", accepted as open access publication in Methods in Ecology and Evolution.</p> <p>The file "main_FinBOL.R" reproduces the tables in the main document and in the Supporting information available online for the analysis of the FinBOL data, while "main_Simulation_Section4_SI.R" reproduces the simulation in Section 4 of the Supporting information. </p> <p>All data are saved in the folder "data". For replicability purposes, we added version 2.13 of the RDP classifier to the repository, in the folder "RDP/java". This has been downloaded from https://sourceforge.net/projects/rdp-classifier/. </p> <p>For questions, contact the author at alessandro.zito@duke.edu<br> </p>
Data from: Discovery of prolactin-like in lamprey: Role in osmoregulation and new insight into the evolution of the growth hormone/prolactin family
<p class="MsoNormal"><span>These data were generated to characterize prolactin-like (PRL-L) and growth hormone (GH) in sea lamprey (<em>Petromyzon marinus</em>) in hormone-receptor binding, tissue distribution, and gene expression patterns at various lifecycle stages, in metamorphosis, and in acclimations to hyperosmotic and hypoosmotic conditions, as well as to detect the hormonal effects on their signaling systems and branchial osmoregulation-related ion transporters. For the hormone-receptor binding assay, recombinant PRL-L and GH were radioactively labelled with <sup>125</sup>iodine and applied for determination of binding affinities with recombinant sea lamprey GH receptor and PRL receptor expressed by HEK293 cell line. Relative abundance of each gene in the cDNA samples synthesized upon the RNAs of various tissues of sea lamprey was assessed using quantitative real-time PCR (qPCR). Semiquantification of immunoreactive staining in percent area of proximal pars distalis was conducted to compare the hormone levels in the pituitary gland of sea lamprey under hypoosmotic condition.</span></p>
RDA IG Data Discovery Paradigms IG: Use Cases data
<p>The RDA Data Discovery Paradigms IG (https://www.rd-alliance.org/groups/data-discovery-paradigms-ig) aims to provide a forum where representatives from across the spectrum of stakeholders and roles pertaining to data search can discuss issues related to improving data discovery. The goal is to identify concrete deliverables such as a registry of data search engines, common test datasets, usage metrics, and a collection of data search use cases and competency questions.</p> <p>In order to identify the key requirements evident across data discovery use-cases from various scientific fields and domains, the Use Cases Task Force (https://www.rd-alliance.org/group/data-discovery-paradigms-ig/wiki/use-cases-prototyping-tools-and-test-collections-task-force) was initiated. Direct outcome of this task force is this collection of use cases outlining what users might wish to search for data and what supports they would expect a data repository should provide.</p>
Data for paper "Automated Structure Discovery for Scanning Tunneling Microscopy"
<p>Contents of the dataset:</p> <ul> <li>band.h5 -- keys are molecule indices, each molecule has the following keys:<br> <ul> <li>eigs: KS eigenvalues for each state</li> <li>coefs: KS eigenvectors for each basis set</li> <li>xyz: atomic positions</li> <li>Z: atomic species</li> <li>qs: mulliken point charges</li> </ul> </li> <li>rotations_210611.pickle -- keys train/val/test <ul> <li>Each set is a dict containing id -- rotation pairs</li> <li>rotations are 3x3 numpy arrays</li> </ul> </li> <li>disks.pt -- a pretrained model for Atomic Disks predictions</li> </ul>
The code and data for the paper entitled 'Facilitating Efficient Discovery: A GUI-Oriented Approach for Exploring Functionality using Machine Learning Model'
<h2><strong>Code</strong></h2> <p><strong><span>dataCollection.py</span></strong></p> <p><span>The collection of app data is primarily accomplished by processing and storing XML files and screenshots of the app. Firstly, the uiautomator is utilized to connect to the smart device and obtain screenshots and XML files. Subsequently, the XML files are analyzed to identify clickable functions within the GUI, and the textual information contained within the functions, along with their specific locations, is extracted. Finally, the Python file also includes handling of interface elements, such as determining if elements are obscured and verifying the legitimacy of the text.</span></p> <p><strong><span>tagData.py</span></strong></p> <p><span>The main implementation involves volunteers annotating app functionalities, including HTML generation, user data analysis, and retrieval. Flask framework is employed, presenting one GUI to the user each time while randomly prompting them to click on three functionalities. Ultimately, the time taken by users to locate these three functionalities is collected.</span></p> <p><strong><span>userPersonalization.py</span></strong></p> <p><span>Separating out the data annotated by each user facilitates personalized analysis. This process involves extraction, storage, and loading of individual user annotations.</span></p> <p><strong><span>dataPreprocessing.py</span></strong></p> <p><span>For a user-annotated functionality, completing the conversion from user time to either "hard-to-find" or "easy-to-find" involves several steps. First, the functionalities are vectorized, extracting relevant parameters from the XML files and computing their correlation with the time users spent searching for the functionalities. These parameters are then normalized to obtain feature vectors for the functionalities. Additionally, an initial determination is made regarding whether the annotated functionalities are "hard-to-find" or "easy-to-find" for each user. Subsequently, clustering is performed on all annotated data from users, and based on the clustering results, the outcomes are filtered and adjusted.</span></p> <p><strong><span>difficultFindClassifier.py</span></strong></p> <p><span>Train the classifier and use it to predict "hard-to-find" functionalities, then display the results.</span></p> <h2><span>Data</span></h2> <p><span>The data is located in the "static" folder:</span></p> <p><span>- The "persistentData" folder contains the trained classifier.</span></p> <p><span>- The "picture" folder contains screenshots of the app.</span></p> <p><span>- The "requestTime" folder stores data for when volunteers annotate only one function in a GUI.</span></p> <p><span>- The "threeResponseTime" folder saves data for when volunteers annotate three functions in a GUI.</span></p> <p><span>- The "userData" folder stores personalized user data.</span></p> <p><span><span>- The "xml_information" folder stores XML files of the app.</span></span></p>
Systematic discovery of subcellular RNA patterns in the gut epithelium - Mass spectrometry data
<p>This collection contains the raw data created from the mass spectrometry experiment and the spectral counts that were used in the manuscript for protein abundance comparisons.</p>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
Metagenomics education in a modular CURE format positively affects students' scientific discovery perception and data analytical skills
<p>The targeted metagenomics study performed by Pollock et al. <em>(Pollock 2018)</em> was part of the Global Coral Microbiome Project <em>(Vega Thurber Lab 2014)</em>. Since the original publication of Pollock et al. <em>(Pollock 2018) </em>was written for a highly specialized academic public, we provided Bsc. students with a short introduction of the paper and guided them through the metadata table stored in the <em>gcmp16S_map_r25.txt </em>file under the <em>GCMP Australia sequence data, OTU tables, and metadata</em> folder (<a href="https://doi.org/10.6084/m9.figshare.c.3855466.v2">https://doi.org/10.6084/m9.figshare.c.3855466.v2</a>).</p> <p>In order to process the fastq-files and obtain OTU tables, a custom-made pipeline on a Galaxy instance <em>(Galaxy Community)</em> was used. This pipeline contained data analytical tools obtained from the Naturalis Biodiversity Center GitHub environment (<a href="https://github.com/naturalis">https://github.com/naturalis</a>). In order to align with the newest Galaxy best practices, we updated the tools and links to the original as well as the updated GitHub and toolshed versions are included in the references list.</p> <p>The names of the original raw fastq-files contain many dots which can hamper file-type recognition during downstream analyses in Galaxy. Therefore, prior to distributing the fastq-files to the students, these names were renamed using the ManageZIP tool <em>(The BLFS Development Team 1999-2023)</em>. Dots were replaced with underscores, with an exception made for the last 2 file type extension dots. For example, the file name <em>E1.2.Tur.pelt.1.20140814.M_S71_L001_R1_001.fastq.gz</em> was changed into <em>E1_2_Tur_pelt_1_20140814_M_S71_L001_R1_001_1.fastq.gz</em>. Similarly, the filenames in the metadata table were adjusted accordingly. The total collection of renamed file names is available through this zenodo repository as wel as the accompanying metadata file.</p>
Preprocessing scripts and data for study: Pathway-Based Subnetworks Enable Cross-Disease Biomarker Discovery
<p>Supplementary File 1 containing preprocessing scripts and data for converting pathway databases into subnetworks.</p>
Link Discovery on AIS and Contextual data sets
<p>This data set contains the links discovered between AIS synopses provided in https://zenodo.org/record/2576152 and contextual data sets available at https://zenodo.org/record/2576584 . Specifically, the detected relations and the contextual data sets used are:</p> <p>a) C1 ports of Brittany, World Port Index and SeaDataNet fishing ports for proximity relation "nearto", stored in AIS_nearto_ports.ttl.7z</p> <p>b) C4 fishing areas (European Commission) for "within" relation stored in AIS_within_fishingAreas.ttl.7z</p> <p>c) C5 fishing interdiction for "within" relation stored in AIS_within_FishingConstraints.ttl.7z</p> <p>d) C5 Natura2000 for "within" relation stored in AIS_within_natura2000.ttl.7z</p> <p>Please notice that the attached data sets contain only the detected links. Description of the resources is provided in the corresponding TTL files.</p>
Data-driven discovery of photoactive quaternary oxides: dataset
<p>Dataset required for the analysis at <a href="https://github.com/WMD-group/Solar_oxides_data">this repository</a>. The link to the original publication pertaining to this dataset is available in the repository. The dataset consists of:</p> <ul> <li>Quaternary oxide compositions generated by SMACT and featurized using the Matminer package (All_oxide_comps_dataframe_featurized.csv)</li> <li>Quaternary oxide compositions predicted to have a bandgap within the target window of 1.75 +/- 0.02 eV by a gradient boosting regression model, as used in the original publication. (Useful_BGs_published.csv)</li> <li>Crystal structures upon which first-principles (DFT) calculations were carried out in the original publication (structures_published.json)</li> <li>VASP files for compounds upon which first-principles (hybrid DFT) calculations were carried out in the original publication</li> </ul>
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.