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 “sequence”
Supplementary Data: Uncovering DFG-out sequence propensity determinants of kinases with machine learning
<h2>General description</h2><p>This submission accompanies the paper "Uncovering DFG-out sequence propensity determinants of kinases with machine learning" and covers:</p><ul><li><strong>Trained models (train/models_latest)</strong>. A subset of these is also published as a part of the <a href="https://github.com/edikedik/kinactive">KinActive tool</a>; each model is of `KinactiveClassifier` type and can be loaded using this tool as well via `kinactive.io.load()` function).</li><li><strong>Patched sequences (patched_seqs)</strong>. PDB sequences, with missing regions patched by UniProt sequences. This is a collection of <a href="https://github.com/edikedik/lXtractor">lXtractor</a> ChainSequence objects.</li><li><strong>All labels, variables, and datasets</strong> (datasets and labels).</li><li><strong>Initial chains and predictions for SwissProt proteins</strong> (SP_predictions).</li></ul><h2>Note on model abbreviations</h2><p>The main text emphasized datasets that yielded more interpretable results. These were constructed from domain sequences, labeled as apo, inactive, DFG-in, or DFG-out, and further divided into TK and STK subsets. We refer to these datasets with the abbreviation <strong>AAIO</strong> (Apo All In or Out) to distinguish them from additional datasets.</p><p>In addition to <strong>AAIO</strong>, we explored two alternative labeling strategies:</p><ul><li><strong>AHAO</strong> (Apo/Holo Any Out): This dataset includes sequences from ligand-bound entries. All sequences within 95\% identity clusters are labeled as DFG-out if the cluster contains at least one sequence in this state. All others are labeled as DFG-in.</li><li><strong>AAO</strong> (Apo Any Out): This dataset excludes sequences corresponding to ligand-bound entries but includes those with conflicting conformational tendencies within 95\% identity clusters.</li></ul><p>Each of these datasets had two versions:</p><ol><li>A seed version, denoted by a "*" symbol.</li><li>A version enriched with orthologous sequences (no special designation).</li></ol><p>Together with the <strong>TkST</strong> datasets (encompassing TK and STK labels) used for testing the methodology, a total of 14 datasets were used, and both <i>RF</i> and <i>XGB</i> models were applied to each, using the same initial settings, resulting in 28 different models. This additional information is provided for completeness.</p>
Using a mobile Nanopore sequencing lab for end-to-end genomic surveillance of Plasmodium falciparum: a feasibility study
Open the record for dataset details and reuse information.
FASTA 16s gene sequencing files for Control points for design of taxonomic composition in synthetic human gut consortia
<p>FASTA file output from Illumina MiSeq. Processed tally files (reads / species used to calculate relative abundances) are available in the other Zenodo repository listed in the key resources table, under ~ BC###/rawData/tallyfile.xlsx</p>
Varieties SNPs-based sequencies (5)
Open the record for dataset details and reuse information.
Varieties SNPs-based sequencies (4)
Open the record for dataset details and reuse information.
Dataset for the paper "Largest aftershock nucleation driven by afterslip during the 2014 Iquique sequence"
<p>Dataset for the following paper </p><p>Yuji Itoh, Anne Socquet, and Mathilde Radiguet, Largest aftershock nucleation driven by afterslip during the 2014 Iquique sequence, <i>Geophysical Research Letters</i>, 2023GL104852</p><p>See readme.txt files for details about the files and the contact informaton. Uncompressing produces files with ~ 500 MB in total.</p>
Extended dataset and Mathematica notebook produced by the Integer sequence (A363743): a(n) = floor(sqrt(log_10(n!))).
<p>This integer sequence was registered and published in the On-Line Encyclopedia of Integer Sequences (OEIS.org) Database on August 17 - 2023, under the OEIS code: A363743. </p><p>This sequence can be generally expressed as follows: a(n) = floor(sqrt(log_10(n!))), where n is a non-negative integer. It should be noted that the aforementioned formula is written in accordance with OEIS specific style sheet format. On the other hand, it was possible to represent the general formula on another two forms that the following: </p><p>1) a(n) = floor(sqrt(A034886(n) - 1)).</p><p> 2) a(n) = A000196(A034886(n) -1). </p><p>This dataset verifies the reported properties in the comments section of the OEIS publication. Our evaluation ranges from 0 to n = 5000, in contrast to the publication which ranges from 0 to n = 92. These mentioned properties are the following: </p><p>* Every non-negative integer occurs at least 4-times. </p><p>* Each integer k > 14 appears fewer than k times. </p><p>* The only integers k that occur exactly k times are 11, 13 and 14. </p><p>* This sequence can produce random values between 0 and 1 if we do a(n)/a(n+m) for any non-negative integer m. </p><p>The numerical data showed on this dataset was generated by the following Mathematica program: Array[Floor@ Sqrt[Log10[#!]] &, 5000, 0] The previous program was builded on Mathematica v13.3.0.</p><p> <strong>Note:</strong> More mathematical details, graphics and technical information can be found in the notebook or .nb file provided in this dataset. </p>
COLSCREEN: 16S-sequencing of fecal microbiota from a population-based CRC screening program
<p><span>The research cohort (COLSCREEN study) was recruited among individuals that participated during 2016-2020 in the ongoing population-based CRC screening program overseen by the Catalan Institute of Oncology in L’Hospitalet del Llobregat, Barcelona, Spain. This program invites men and women between the ages of 50 and 69 to partake in the immunochemical fecal occult blood test (FIT). In the event of a positive FIT result (≥ 20 μg Hb/g feces), the participants are then recommended to undergo colonoscopy. Participants of the COLSCREEN study (N=997) were invited to participate after receiving a positive FIT result. Since CRC diagnosis is rare in screening, this cohort includes patients diagnosed clinically from the CRC Functional Unit (N=45). Furthermore, a subset of participants with a negative FIT is also included (N=140). All of them underwent a colonoscopy and participants were categorized based on the risk stratification proposal by Castells et al. after careful review of the colonoscopy and histopathology reports. All participants who agreed to take part of the COLSCREEN study provided written informed consent and donated a fecal and blood sample at recruitment (samples obtained before colonoscopy) and answered an epidemiological questionnaire. Clinically diagnosed CRC patients usually underwent a colonoscopy before recruitment, and the fecal samples were obtained prior surgery. In the present study, we excluded those participants that reported having used antibiotics or probiotics one month before sampling. </span></p>
Fig. 3 in Genetic variation in the spotted seal (Phoca largha Pallas, 1811) from the Rimsky-Korsakov Archipelago (Peter the Great Bay, western sea of Japan) as inferred from mitochondrial DNA control region sequences
Fig. 3. Consensus maximum likelihood tree demonstrating the matrilineal genealogy of some Phocidae species generated from the 460 bp mtDNA control region sequences. The numbers at branch nodes represent bootstrap support of 1000 replications for the maximum likelihood trees and posterior probabilities of 2,000,000 generations for the Bayesian trees with the same topology, respectively. The accession numbers of Phoca largha from Liaodong Bay are highlighted in bold. Scale bar indicates the relative branch lengths.
Fig. 2 in Genetic variation in the spotted seal (Phoca largha Pallas, 1811) from the Rimsky-Korsakov Archipelago (Peter the Great Bay, western sea of Japan) as inferred from mitochondrial DNA control region sequences
Fig. 2. Minimum spanning network showing the mutational relationships among Phoca largha haplotypes detected in a sample of 32 spotted seal underyearlings. The tick marks on the branches indicate mutational changes. The circle sizes correspond to the number of haplotypes. The haplotypes of groups (A) and (B) are shown in gray and white circles, respectively. The median vectors are indicated by dark dots.
VCF file of whole genome sequencing data of 163 rats mapped jointly to mRatBN7.2
<p>We analyzed whole genome sequencing data of 163 rats. These data were first mapped to mRatBN7.2, followed by variant calling using deepvariant and joint analysis using GLNexus. Sites that are likely called due to base-level errors in mRatBN7.2 are removed. </p>
A massive proteogenomic screen identifies thousands of novel human protein coding sequences
<p>Accurate annotation of genes in the human genome is fundamental for biomedical research and genomic data interpretation. The Ensembl, RefSeq, and GENCODE consortiums continuously update the human genome annotations based on new computational and experimental evidence, and new proteins were identified constantly. The Genotype-Tissue Expression (GTEx) project has generated more than 15,000 RNA sequencing dataset from multiple-tissues of more than 800 donors which allows to model almost all transcripts and proteins in the human genome. Using proteins translated from the GTEx transcript model, more than 21 million in-silico trypsin-digested peptides were generated. To identify high-confidence novel proteins with proteomic support, we screened more than 2,000 proteomic projects in the PRIDE database and selected more than 50,000 mass spectrometry (MS) runs from 923 projects. These MS data were used to validate the predicted novel peptides. With a stringent standard, we identified almost 20,000 novel peptides. </p> <p>This dataset include files used in the the above analysis. More details can be found in the GitHub page (https://github.com/ATPs/human_novo_protein_2022). </p>
BSO-GBD:Behavior Sequence-Oriented GitHub Bot Detection model
<p>Our dataset is primarily divided into three parts. Firstly, there are human accounts (Human) and bot accounts (Bot). Both of these types appear similar to regular developer accounts, lacking any distinctive markings. The second category comprises self-bot accounts, typically formed by GitHub apps. These accounts generally bear an explicit "bot" label, and we exclude them in our classification.</p> <p>The dataset is structured as follows: a total of 4786 ordinary accounts, with 4325 being Human accounts and 461 being Bot accounts. Each account internally consists of a CSV table with the following fields: user_name; repo_name; event_type_L1; event_type_L2; event_type_L3; timestamp. These data constitute our raw dataset.</p>
Sequence assembly lengths of the target used reported by HybPiper.
<p>This dataser provides detailed information on the sequence assembly lengths of the targets used in our study. We successfully recovered between 386 and 488 loci for each species, with coverage varying from 43× in M. chiriquiensis A.Vázquez (ID MA3140B) to 1174.77× in M. aff. silvioi (Lozano) Govaerts (ID MA3129B), and a mean coverage of 309×. The mean sequence length for each target ranged from 7 to 2,721 bp, with an overall mean of 79 bp per target. On average, we recovered approximately 387,557 bp for each sample. Samples with less than 250 genes with sequences above 50% of the mean target length (GenesAt50pct) were discarded. This data is crucial for understanding the depth and breadth of our sequence coverage and provides valuable context for our findings.</p>
Supplementary Material for "Detecting Automatic Software Plagiarism via Token Sequence Normalization"
<p>This repository contains additional material supporting the paper titled "Detecting Automatic Software Plagiarism via Token Sequence Normalization", presented at ICSE 2024 (research track).</p> <div> <div> <div> <p>The paper presents a defense mechanism against automated plagiarism generators utilizing program dependence graphs and demonstrates its effectiveness in countering insertion-based and reordering-based obfuscation attacks.</p> </div> </div> </div> <p>The defense mechanism was also integrated into the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a>, thus providing a widely accessible solution.</p> <p><strong>Contents Overview:</strong></p> <ul> <li> <p><strong>Datasets:</strong> Two datasets from the <a title="PROGpedia Repository" href="../record/7449056">PROGpedia</a> collection and two internal datasets. For the latter, only the metadata is available due to the sensitive nature of the data.</p> </li> <li> <p><strong>Plagiarized Submissions:</strong> Generated plagiarism instances illustrating various obfuscation methods such as insertion, reordering, and insert-after-reordering.</p> </li> <li> <p><strong>Evaluation Data:</strong> JSON files detailing calculated similarities and runtime measurements for all datasets.</p> </li> <li> <p><strong>Source Code:</strong> The implementation of our defense mechanism based on the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a> (v4.0.0). Note that JPlag is licensed under the GPL-3.0 license.</p> </li> <li> <p><strong>Evaluation Code:</strong> Python code for runtime measurements.</p> </li> <li> <p><strong>Interactive Plots:</strong> HTML visualizations of the paper's plots, offering dynamic insights into the research findings. particularly focusing on the detection of automatic software plagiarism through token sequence normalization.</p> </li> <li><strong>Demo:</strong> A packaged JAR of our implementation alongside an instruction on how to execute it.</li> </ul>
Genotype likelihoods for low-coverage whole-genome sequencing data of yellow warblers
<p>The following datasets include the required input files used to empirically test population assignment in WGSassign on Yellow Warbler data. The file "yewa.known.ind105.ds_2x.beagle.gz" includes the filtered variants of 105 Yellow Warbler individuals output as genotype likelihoods and stored in a Beagle-formatted file. The ID file, "yewa.known.ind105.reference.IDs.txt", is a tab-delimited file with 2 columns, the first being the sample ID, and the second being the known reference population. The sample order in the ID file should match that of the input beagle file. To measure the assignment accuracy of WGSassign, we used leave-one-out cross validation using the input beagle file and our ID file.</p>
Using big sequencing data to identify chronic SARS-Coronavirus-2 infections
<p>This dataset supports the "Using big sequencing data to identify chronic SARS-Coronavirus-2 infections" publication in Nature Communications. </p><p>It contains the following files and folders:<br> </p><ul><li>Readme.txt - detailed information on all folders and files.</li><li>Supplementary Dataset 1</li><li>Supplementary Dataset 2</li><li>Supplementary Dataset 3</li><li>Supplementary Dataset 4</li><li>Supplementary Dataset 5</li><li>Supplementary Dataset 6</li><li>Supplementary Dataset 7</li></ul>
cDNA sequence of E2 gene family in Arabidopsis thaliana and data of statistical analysis
<p>E2 ubiquitin-conjugating enzymes act as a heart role in the ubiquitination process and are responsible for catalysis ubiquitin transfer. Although the function of ubiquitin-protein ligases (E3s) in plant response to diverse abiotic stress by targeting specific substrates has been well studied, the E2s' involvement in environmental responses and their downstream targets are not well understood. Here, we demonstrated that the E2 ubiquitin-conjugating enzyme 18 (UBC18) regulates the stability of FREE1 to modulate iron deficiency stress. UBC18 affects the ubiquitination of FREE1 and promotes its degradation, overexpression of<em> UBC18</em> in plants decreases their sensitivity to iron deficiency by reducing the level of FREE1, and high accumulation of FREE1 in<em> </em>the<em> ubc18</em> mutant resulted in sensitivity to iron deficiency. In addition, we demonstrated the lysine residues K227, K295, K315, and K540 are required for FREE1 ubiquitination and stability regulation, and mutation of these lysines of FREE1 residues resulted in sensitivity to iron starvation in plants. Taken together, our findings reveal a mechanism of UBC18 in response to iron deficiency stress by altering the abundance of FREE1, and further elucidate the role of ubiquitination sites in FREE1 stability regulation and the plant iron deficiency response.</p>
RNA sequencing data from the guts of Drosophila wildtype and CG3740/nazo double mutant 20-day-old males
<p>Lipid dyshomeostasis has been implicated in a variety of diseases ranging from obesity to neurodegenerative disorders such as Neurodegeneration with Brain Iron Accumulation (NBIA). Here, we uncover the physiological role of Nazo, the Drosophilamelanogaster homolog of the NBIA-mutated protein – c19orf12, whose function has been elusive. Ablation of Drosophila c19orf12 homologs leads to dysregulation of multiple lipid metabolism genes. nazo mutants exhibit markedly reduced gut lipid droplet and whole-body triglyceride contents. Consequently, they are sensitive to starvation and oxidative stress. Nazo is required for maintaining normal levels of Perilipin-2, an inhibitor of the lipase – Brummer. Concurrent knockdown of Brummer or overexpression of Perilipin-2 rescues the nazo phenotype, suggesting that this defect, at least in part, may arise from diminished Perilipin-2 on lipid droplets leading to aberrant Brummer-mediated lipolysis. Our findings potentially provide novel insights into the role of c19orf12 as a possible link between lipid dyshomeostasis and neurodegeneration, particularly in the context of NBIA.</p>
MRI Raw Data of Cardiac Radial Real-Time BSSFP Sequence (Part 1/5)
<p>Other parts of this dataset are available at:</p> <div> <div>* Part 1: 10.5281/zenodo.10492333</div> <div>* Part 2: 10.5281/zenodo.10912299</div> <div>* Part 3: 10.5281/zenodo.10492343</div> <div>* Part 4: 10.5281/zenodo.10492455</div> <div>* Part 5: 10.5281/zenodo.10493095</div> </div>
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.