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.
1,199
datasets available to search
ShareScore release 0.9.0
Dataset results
1,199 results for “Alignement”
Aligned DNA sequence matrix for phylogenetic analyses in the article "A new glassfrog of the genus Nymphargus (Anura: Centrolenidae) from Cordillera del Cóndor, Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "<span>A new glassfrog of the genus Nymphargus</span> (Anura<span>:</span> Centrolenidae) <span>from Cordillera del Cóndor, Ecuador</span>"</p> <p>The matrix is in NEXUS format and has 6613 bp and 102 terminals.</p> <p>Partitions are as follows:</p> <div> <div>charset 12S = 1-944;</div> <div>charset 16S = 945-2093;</div> <div>charset BNDFcodonPos1 = 2096-2792\3;</div> <div>charset BNDFcodonPos2 = 2094-2793\3;</div> <div>charset BNDFcodonPos3 = 2095-2791\3;</div> <div>charset ND1codonPos1 = 2795-3749\3;</div> <div>charset ND1codonPos2 = 2796-3750\3;</div> <div>charset ND1codonPos3 = 2794-3751\3;</div> <div>charset CXCR4codonPos1 = 3753-4107\3;</div> <div>charset CXCR4codonPos2 = 3754-4105\3;</div> <div>charset CXCR4codonPos3 = 3752-4106\3;</div> <div>charset cmyccodonPos1 = 4108-4507\3;</div> <div>charset cmyccodonPos2 = 4109-4508\3;</div> <div>charset cmyccodonPos3 = 4110-4509\3;</div> <div>charset POMCcodonPos1 = 4511-5120\3;</div> <div>charset POMCcodonPos2 = 4512-5121\3;</div> <div>charset POMCcodonPos3 = 4510-5122\3;</div> <div>charset RAG1codonPos1 = 5123-5576\3;</div> <div>charset RAG1codonPos2 = 5124-5577\3;</div> <div>charset RAG1codonPos3 = 5125-5578\3;</div> <div>charset SLC8A1codonPos1 = 5580-6120\3;</div> <div>charset SLC8A1codonPos2 = 5581-6118\3;</div> <div>charset SLC8A1codonPos3 = 5579-6119\3;</div> <div>charset SLC8A3codonPos1 = 6122-6587\3;</div> <div>charset SLC8A3codonPos2 = 6123-6585\3;</div> <div>charset SLC8A3codonPos3 = 6121-6586\3;</div> </div>
Campanulaceae alignment and tree
<p>Alignment(.fasta) and full tree with bootstraps (.tre) for "Compensating for the corolla? Pollen exposure is not associated with pollen-collecting hair length"</p> <p> </p> <p>Alignment modified from: Crowl, A.A., Miles, N.W., Visger, C.J., Hansen, K., Ayers, T., Haberle, R. and Cellinese, N. (2016), A global perspective on Campanulaceae: Biogeographic, genomic, and floral evolution. American Journal of Botany, 103: 233-245. <a href="https://doi.org/10.3732/ajb.1500450">https://doi.org/10.3732/ajb.1500450</a></p>
Alignment and tree files for archaeal NusA2 phylogeny
<p>These are the data underlying the phylogenetic analysis of archaeal NusA2 in Figure 3D in our manuscript:</p> <h1><strong>Archaeal NusA2 is the evolutionary ancestor of ribosomal protein eS7 in eukaryotes</strong></h1> <p>Duy Khanh Phung, Simona Pilotto, Dorota Matelska, Fabian Blombach, Nikos Pinotsis, Ladislav Hovan, Francesco Luigi Gervasio, and Finn Werner</p> <p>(under revision)</p>
Aligned files and custom code for Janssens et al. 2024 MLL oncoprotein levels influence leukemia lineage identities.
<p>This zenodo publication corresponds to Janssens et al. 2024. "MLL oncoprotein levels influence leukemia lineage identities." published in Nature Communications. This zenodo publication includes all of the code for data processing and figure generation which is also available though GitHub at https://github.com/DerekJanssens/MLL_oncoprotein_levels_NatComms. For instructions running the code see the README.md file. In addition, this zenodo publication includes the hg38 aligned bed files and bigwig files for seven of the patient samples (p160, p179, p186, p214, p247, p279 and p318) for which permissions for sharing of primary DNA sequening data was not specifically obtained. Aligned file descriptions can be found in the the Sample_Information_For_Zenodo_09.19.24 spreadsheet. </p>
Reference Spectra, Laboratory Mixture Spectra, list of NIMS cubes used in this study and the manual corrections to these NIMS cubes for better alignment.
<p>Reflectance spectra for the endmember library, including both the public data and data made in this study. <br><br>Reflectance spectra for the 100% SAO, 10% SAO, 25% SAO, 80% SAO, and 100% Water ice mixtures.<br><br>Text File of the PDS IDs of NIMS data cubes analyzed in this study.</p> <p>(X,Y) offsets applied to each data cube for better alignment between NIMS data and the Global Mosaic.</p>
Repository for the Method Article "Oriented artificial nanofibers and laser induced periodic surface structures as substrates for Schwann cells alignment"
<p>Repository containg the underlaying and extended data for the paper "Oriented artificial nanofibers and laser induced periodic surface structures as substrates for Schwann cells alignment".</p>
Transitive prediction of small molecule function through alignment of high-content screening resources
<p>This dataset supports the development of CLIP<sup>n</sup>, a contrastive-learning framework designed to align heterogeneous high-content screening (HCS) profile datasets.</p> <p><strong>GitHub link</strong>: https://github.com/AltschulerWu-Lab/CLIPn</p> <h2>Directory Structure</h2> <h3>Data Files</h3> <ul> <li>HCS_datasets.pkl: Contains 13 high-content screening (HCS) datasets from multiple studies across 20 years.</li> <li>Hypoxia.pkl: Contains 8 profile datasets using different assays and treated under diverse hypoxia durations.</li> <li>Expression.pkl: Contains 2 transcriptional profile datasets and 6 image profile datasets for multimodal analysis.</li> </ul> <h3>Folders</h3> <p><strong>raw_profiles</strong>:<br>HCS13/<br>- Contains raw data from 13 high-content screening (HCS) datasets. Each dataset includes meta and feature files. </p> <p>L1000/<br>- CDRP_feature_exp.csv: Raw L1000 expression data from the CDRP dataset.<br>- CDRP_meta_exp.csv: Metadata associated with the CDRP expression data.<br>- LINCS_feature_exp.csv: Raw L1000 expression data from the LINCS dataset.<br>- LINCS_meta_exp.csv: Metadata associated with the LINCS expression data.</p> <p>RxRx3/<br>- RxRx3_feature_final.csv: Profile data from the RxRx3 dataset.<br>- RxRx3_meta_final.csv: Metadata from the RxRx3 dataset.</p> <p>Uncharacterized_compounds/<br>- NCI_cpnData.csv: Feature data for uncharacterized compounds from the NCI dataset.<br>- NCI_cpnInfo.csv: Information about uncharacterized compounds in the NCI dataset.<br>- Prestwick_UTSW_cpnData.csv: Feature data for uncharacterized compounds from the Prestwick UTSW dataset.<br>- Prestwick_UTSW_cpnInfo.csv: Information about uncharacterized compounds from the Prestwick UTSW dataset.</p> <h2><br>Usage</h2> <p><br><br><code>import pickle</code><br><code>with open('data.pkl', 'rb') as f:</code><br><code> data = pickle.load(f)</code></p> <p><code>X = data['X']</code><br><code>y = data['y']</code><br><br></p> <h2>Data Reference</h2> <p><br>For raw datasets from 13 HCS database, data and analysis pipeline for dataset 1 was obtained from https://www.science.org/doi/suppl/10.1126/science.1100709/suppl_file/perlman.som.zip; for datasets 2-3, data were shared by authors; For datasets 4-5, analysis code was downloaded from https://static-content.springer.com/esm/art%3A10.1038%2Fnbt.3419/MediaObjects/41587_2016_BFnbt3419_MOESM21_ESM.zip and data were shared by authors; For datasets 6-7, processed dataset was downloaded from AWS following instructions from https://github.com/carpenter-singh-lab/2022_Haghighi_NatureMethods, and replicate_level_cp_normalized.csv.gz features were used. For project datasets 8-13, datasets and analysis results were downloaded from https://zenodo.org/records/7352487. For RxRx3, dataset was obtained from https://www.rxrx.ai/rxrx3. L1000 transcript datasets were downloaded using the same link as datasets 6-7 and the processed transcript data files (named “replicate_level_l1k.csv”) were used. </p>
Data from: APPLES: Scalable distance-based phylogenetic placement with or without alignments
<p>Placing a new species on an existing phylogeny has increasing relevance to several applications. Placement can be used to update phylogenies in a scalable fashion and can help identify unknown query samples using (meta-)barcoding, skimming, or metagenomic data. Maximum likelihood (ML) methods of phylogenetic placement exist, but these methods are not scalable to reference trees with many thousands of leaves, limiting their ability to enjoy benefits of dense taxon sampling in modern reference libraries. They also rely on assembled sequences for the reference set and aligned sequences for the query. Thus, ML methods cannot analyze datasets where the reference consists of unassembled reads, a scenario relevant to emerging applications of genome-skimming for sample identification. We introduce APPLES, a distance-based method for phylogenetic placement. Compared to ML, APPLES is an order of magnitude faster and more memory efficient, and unlike ML, it is able to place on large backbone trees (tested for up to 200,000 leaves). We show that using dense references improves accuracy substantially so that APPLES on dense trees is more accurate than ML on sparser trees, where it can run. Finally, APPLES can accurately identify samples without assembled reference or aligned queries using kmer-based distances, a scenario that ML cannot handle. APPLES is available publically at github.com/balabanmetin/apples.</p>
Highlighting altruism in geoscience careers aligns with diverse US student ideals better than emphasizing working outdoors
<p>We surveyed students enrolled in College Algebra at a large, urban, Hispanic-serving, R-1 public university in the southwestern United States. The data span five semesters, from Fall 2018 through Spring 2020. The survey was composed of demographic questions as well as questions using the Likert scale, in which students rated how much they agreed or disagreed with a particular statement regarding descriptions of their “ideal career.” Students were also asked to rate statements about careers in different science fields and engineering. Demographic questions were included at the end of the survey in order to mitigate stereotype threat.</p> <p>Our study tests (i) whether altruistic factors, personal achievement, or work environment are most important to college students (early in their undergraduate program) for their future careers, (ii) whether the ratings of these ideals differ between male/female, URM/non-URM, and first-generation/non-first-generation college students, and (iii) how student perceptions of the geosciences are in regard to those ideals compared to other STEM fields. </p>
group-5-NF alignment and SNPs
<p>Alignment of reads for the BSA bulk 'non-flowering' (NF) against the <em>B. oleracea</em> reference genome (as BAM file), and subsequent SNP calling results (as compressed VCF file).</p> <p> </p>
group-3-LF alignment and SNPs
<p>Alignment of reads for the BSA bulk 'late flowering' (LF) against the <em>B. oleracea</em> reference genome (as BAM file), and subsequent SNP calling results (as compressed VCF file).</p>
group-2-IF alignment and SNPs
<p>Alignment of reads for the BSA bulk 'intermediate flowering' (IF) against the <em>B. oleracea</em> reference genome (as BAM file), and subsequent SNP calling results (as compressed VCF file).</p> <p> </p>
DNA sequences of transgenes detected via environmental DNA (raw ABI files, processed FASTA files, and reference alignments)
We demonstrate that simple, non-invasive environmental DNA (eDNA) methods can detect transgenes of genetically modified (GM) animals from terrestrial and aquatic sources in invertebrate and vertebrate systems. We detected transgenic fragments between 82-234 bp through targeted PCR amplification of environmental DNA extracted from food media of GM fruit flies (<i>Drosophila melanogaster</i>), feces, urine, and saliva of GM laboratory mice (<i>Mus musculus</i>), and aquarium water of GM tetra fish (<i>Gymnocorymbus ternetzi</i>). With rapidly growing accessibility of genome-editing technologies such as CRISPR, the prevalence and diversity of GM animals will increase dramatically. GM animals have already been released into the wild with more releases planned in the future. eDNA methods have the potential to address the critical need for sensitive, accurate, and cost-effective detection and monitoring of GM animals and their transgenes in nature.
List of known SNP positions (based on SNP chip data) for base quality score recalibration of alignments for whole-genome resequencing and whole-genome bisulfite sequencing data from great tits (Parus major)
<p>The profiling of epigenetic marks like DNA methylation has become a central aspect of studies in evolution and ecology. Bisulfite sequencing is commonly used for assessing genome-wide DNA methylation at single nucleotide resolution but these data can also provide information on genetic variants like single nucleotide polymorphisms (SNPs). However, bisulfite conversion causes unmethylated cytosines to appear as thymines, complicating the alignment and subsequent SNP calling. Several tools have been developed to overcome this challenge, but there is no independent evaluation of such tools for non-model species, which often lack genomic references. Here, we used whole-genome bisulfite sequencing (WGBS) data from four female great tits (<i>Parus major</i>) to evaluate the performance of seven tools for SNP calling from bisulfite sequencing data. We used SNPs from whole-genome resequencing data of the same samples as baseline SNPs to assess common performance metrics like sensitivity, precision, and the number of true positive, false positive, and false negative SNPs for the full range of variant and genotype quality values. We found clear differences between the tools in either optimizing precision (Bis-SNP), sensitivity (biscuit), or a compromise between both (all other tools). Overall, the choice of SNP caller strongly depends on which performance parameter should be maximized and whether ascertainment bias should be minimized to optimize downstream analysis, highlighting the need for studies that assess such differences.</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "Our unknown neighbor: a new species of rain frog of the genus Pristimantis (Amphibia: Anura: Strabomantidae) from the city of Loja, southern Ecuador"
<p>The aligned matrix is in fasta format. Genes are arranged as follows:</p> <p>12S = 1–909</p> <p>16S = 910–1787</p> <p>RAG-1 = 1788–2399</p>
AliSim: Ultrafast and Realistic Sequence Alignment Simulator for Phylogenetics - Supplementary Data
<p>This supplementary data contains testing scripts, input/output data for validating and benchmarking AliSim.</p>
DNA alignment and resulting bayesian trees of Epithelantha and sister species
<p><span> The use of environmental variables to explain the evolution of lineages has gained relevance in recent studies. Additionally, it has allowed the recognition of species by adding more characters to morphological and molecular information. This study focuses on identifying environmental and landscape variables that have acted as barriers that could have influenced the evolution of <i>Epithelantha</i> species and its close genera.</span></p> <p><span>Our results show that soil pH, isothermality, temperature seasonality, and annual precipitation have a significant phylogenetic signal for <i>Epithelantha</i>. Soil type and landforms are also relevant as ecological barriers that maintain the identity of <i>Epithelantha</i> species.</span></p> <p><span>The variables associated with the soil (pH) have influenced the evolution of <i>Epithelantha</i> and probably in other genera of Cactaceae. Additionally, <i>Epithelantha</i> is frequent in the piedmont and haplic kastanozems. Bioclimatic variables reinforce the recognition of <i>E. micromeris</i> and <i>E. cryptica</i> as independent species. Therefore, ecology can be considered as a factor to explain the high level of endemism in Cactaceae.</span></p>
Data for "From Langmuir Turbulence to Supercells: the Role of Longitudinal Alignment between Surface and Bottom Forcing"
<p>The files here contains the data shown in the article. The main part of data can be find in v4. v5 include a new result of conditional averaged turbulent kinetic energy.</p>
Genome alignments for 'Machine-driven parameter-space exploration of biochemical reactions'
<p>The development of complex, multi-step <em>omics</em> methods in molecular biology is a laborious, costly, iterative and often intuition-bound process where an optimum is sought in a parameter space through step-by-step optimisations. The the difficulty of miniaturising assays and the cost of the experiments limit the dynamic range and the number of parameters that can be explored. However, because of non-linearities of the response of biochemical systems to their reagent concentrations, a broad dynamic range is necessary. Here we demonstrate the use of a high-performance nanoliter handling platform (Labcyte Echo 525) and computer generation of liquid transfer programs to explore in quadruplicates more than 600 combination of 4 parameters of a biochemical reaction, which lead us to uncover non-linear responses, parameter interactions and novel mechanical insights. With the increased availability of « <em>cloud biology</em> » computer-driven laboratory platforms, our results participate in changing methods development for biotechnology towards reproducible, computer-aided exhaustive characterisation of biochemical systems.</p> <p>This dataset contains the sequence alignments and other processing files produced by running the raw data (10.5281/zenodo.1680999) through a processing pipeline using the MOIRAI workflow manager. The most important output is the "CAGEscan_fragments" directories and represent the alignment of single mRNA molecules, which can be further analysed using the "CAGEr" software package available from Bioconductor.</p> <p>Run IDs: 171227_M00528_0321_000000000-B4GLP, 180123_M00528_0325_000000000-B4PCK, 180326_M00528_0346_000000000-B4GJR, 180403_M00528_0348_000000000-B4GP8, 180411_M00528_0351_000000000-BN3BL, 180501_M00528_0359_000000000-B4PJY, 180517_M00528_0364_000000000-BRGK6 180606_M00528_0367_000000000-BN3FG, 180607_M00528_0368_000000000-BN9KM</p>
Alignments and phylogenetic tree from: A new endemic species of Loasa ser. Macrospermae from northern Chile
<p><span></span></p> <p>Alignments used for the phylogenetic work and raw phylogenetic trees obtained.</p> <p>A new species of Loasa, endemic to the northern Andes of Chile is described and evaluated, under the IUCN criteria for conservation, as critically endangered. Molecular analyses based on plastid markers place the new species within the Loasa ser. Macrospermae, with high support, and specifically as sister to Loasa acerifolia. A key to and comparative plates including all the 13 known species of Loasa ser. Macrospermae, are provided.</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.