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294 results for “Data discovery”
Meteorological data from the Discovery Tree at the Andrews Experimental Forest, 2015 to present
The upper-canopy of forests is known to experience a very different leaf wetness than the rest of the forest: it is often simultaneously brighter, hotter, windier, and drier. The upper canopy also contains most of the leaf area, and because it absorbs most of the solar radiation, it accounts for the great majority of carbon and water exchanges in most forests. Critically, this is also the zone where most climate variations and stress likely manifest. The upper canopy is also the region of the forest that is sampled by satellite imagery. Intensive canopy microclimate monitoring provides connections to satellite-based imagery at varying temporal and spatial scales in order to scale across the Andrews landscape and improves our understanding of forest function and its response to climate change. A 50 meter old growth tree, called the Discovery Tree, was instrumented with various sensors. A thermal infrared camera was installed in March 2014, which collects surface temperatures of the old-growth forest and the adjacent secondary-growth forest. Since then, the scope of information being acquired in real-time has increased to include temperature, leaf wetness, relative humidity, soil temperature, soil moisture, wind direction and speed. This suite of data serves as a glimpse into the canopy and soil processes we are unaware of when our feet are planted firmly on the ground. These measurements complement and leverage ongoing, long-term climate measurements collected in the sub-canopy and at the climate stations located across the Andrews forest, and potentially link with Lidar data on canopy structure and planned soil moisture measurements. Canopy thermal imaging and microclimate measurements have been established for ecophysiological applications such as monitoring the response of forest tree canopies to climate variations, including heat and drought stress.
Data from: A FAIR and modular image-based workflow for knowledge discovery in the emerging field of imageomics
<p>Data and results from the Imageomics Workflow. These include data files from the Fish-AIR repository (https://fishair.org/) for purposes of reproducibility and outputs from the application-specific imageomics workflow contained in the Minnow_Segmented_Traits repository (https://github.com/hdr-bgnn/Minnow_Segmented_Traits).</p> <p>Fish-AIR:<br> This is the dataset downloaded from Fish-AIR, filtering for Cyprinidae and the Great Lakes Invasive Network (GLIN) from the Illinois Natural History Survey (INHS) dataset. These files contain information about fish images, fish image quality, and path for downloading the images. The data download ARK ID is dtspz368c00q. (2023-04-05). The following files are unaltered from the Fish-AIR download. We use the following files:</p> <p>extendedImageMetadata.csv: A CSV file containing information about each image file. It has the following columns: ARKID, fileNameAsDelivered, format, createDate, metadataDate, size, width, height, license, publisher, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>imageQualityMetadata.csv: A CSV file containing information about the quality of each image. It has the following columns: ARKID, license, publisher, ownerInstitutionCode, createDate, metadataDate, specimenQuantity, containsScaleBar, containsLabel, accessionNumberValidity, containsBarcode, containsColorBar, nonSpecimenObjects, partsOverlapping, specimenAngle, specimenView, specimenCurved, partsMissing, allPartsVisible, partsFolded, brightness, <br> uniformBackground, onFocus, colorIssue, quality, resourceCreationTechnique. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>multimedia.csv: A CSV file containing information about image downloads. It has the following columns: ARKID, parentARKID, accessURI, createDate, modifyDate, fileNameAsDelivered, format, scientificName, genus, family, batchARKID, batchName, license, source, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>meta.xml: A XML file with the metadata about the column indices and URIs for each file contained in the original downloaded zip file. This file is used in the fish-air.R script to extract the indices for column headers.</p> <p>The outputs from the Minnow_Segmented_Traits workflow are:</p> <p>sampling.df.seg.csv: Table with tallies of the sampling of image data per species during the data cleaning and data analysis. This is used in Table S1 in Balk et al. </p> <p>presence.absence.matrix.csv: The Presence-Absence matrix from segmentation, not cleaned. This is the result of the combined outputs from the presence.json files created by the rule “create_morphological_analysis”. The cleaned version of this matrix is shown as Table S3 in Balk et al.</p> <p>heatmap.avg.blob.png and heatmap.sd.blob.png: Heatmaps of average area of biggest blob per trait (heatmap.avg.blob.png) and standard deviation of area of biggest blob per trait (heatmap.sd.blob.png). These images are also in Figure S3 of Balk et al.</p> <p>minnow.filtered.from.iqm.csv: Filtered fish image data set after filtering (see methods in Balk et al. for filter categories).</p> <p>burress.minnow.sp.filtered.from.iqm.csv: Fish image data set after filtering and selecting species from Burress et al. 2017.</p>
Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery
<p><strong>Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery</strong></p> <p>This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the <em>claims.csv</em> file, the <em>AirportCode</em> implies <em>AirportName</em>, as each code should be unique for a given airport.</p> <p>The file <em>ground_truth.csv</em> is a comma separated file containing approximate functional dependencies. <em>table</em> describes the relation we refer to, <em>lhs</em> and <em>rhs</em> reference two columns of those relations where semantically we found that <em>lhs</em> implies <em>rhs</em>.</p> <p>The file <em>excluded_candidates.csv</em> and <em>included_candidates.csv</em> list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the <em>g3_prime</em> value was too small.</p> <p><strong>Dataset References</strong></p> <ul> <li><em>adult.csv</em>: Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.</li> <li><em>claims.csv</em>: TSA Claims Data 2002 to 2006, <a href="https://www.dhs.gov/tsa-claims-data">published by the U.S. Department of Homeland Security</a>.</li> <li><em>dblp10k.csv</em>: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. <a href="https://hpi.de/naumann/projects/repeatability/datasets/dblp-dataset.html">Made available as DBLP Dataset 2</a>.</li> <li><em>hospital.csv</em>: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>t_biocase_...</em> files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>tax.csv</em>: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> </ul>
SNP and indel discovery and genotyping in next-generation sequencing data
<p>Code, logs and data for discovery and genotyping of SNPs and indels, in the the D.melanogaster genome, using GATK HaplotypeCaller. Code is in the zipped folder named code.zip. Run logs for this code as in the zipped folder named logs.zip. The unfiltered vcf genotypes file is named lhm_rg_HC_2015-09-15.vcf.gz. The filtered vcf genotypes file is named f1.lhm_rg_HC_raw.vcf.gz. The vcf submitted to NCBI dbSNP (filtered, and with indels >50bp and variants with null alternate alleles both removed) is named dbSNP.lhm_rg_HC_raw.vcf.gz. The folder local_reference.zip contains the reference assembly files against which genotypes were called against, and includes the code used to format the data prior to use. Also included is genotypes data from the two in-house reference line samples sequenced (BDGP6+ISO1 mito/dm6, Bloomington <em>Drosophila</em> Stock Center no. 2057)</p> <p>Samples are 220 Sussex-LH<sub>M</sub> hemiclones, and 2 RG. The first run did not include chromosome 4 and the mitochondrial genome, so these were genotyped separately, and then added to the rest of the results.</p> <p>The link for the NCBI dbSNP record is currently https://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1062461and the submitter handle is MORROW_EBE_SUSSEX.</p> <p>At the time of writting, the NCBI D.melanogaster build is still being updated, and therefore ss identifiers, but not rs identifers are available.</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>
Structural variant discovery and genotyping in next-generation sequencing data
<p>Code, logs, data, and summaries for detection and genotyping of genomic structural variants in the D.melanogaster Sussex LHM hemiclones (and one in-house reference line individual), using Genomestrip/2.0</p> <p>The unfiltered CNV pipleline results are lhm_gs.cnvs.raw.vcf.gz</p> <p>Filtered CNV results (including removal of bad samples) are filtered.goodS.lhm_gs.cnvs.raw.vcf.gz</p> <p>The file uploaded to NCBI dbVAR (which comprises of the filtered CNVs and indels >50bp from the HaplotypeCaller method) is lhm_sx16.dbVAR.vcf.gz</p> <p>The NCBI dbVAR accession number is nstd134. Code, logs and summary data are in the zipped archives, named accordingly. The archive reference_data.zip contains additional input files required for Genomestrip, including a shell script for making some of them. The file gstrip_lhm_RG_bams.list is also an input for Genomestrip, indicating bam file names and paths.</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> <p> </p>
Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. coli
<p>Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. col</p> <p>This dataset pertains to our work developing TESEC Mtb ALR, a genetically engineered strain of E. coli expressing the enzyme ALR derived from Mtb. We used the TESEC Mtb ALR strain in a high-throughput drug screen and identified benazepril as targeted inhibitor of the ALR enzyme. We then performed additional experiments to characterize the activity of benazepril against E. coli, Mtb and purified enzymes. Finally, we tested the extensibility of the platform by constructing and screening against similar strains for additional targets: Asd, CysH, DapB, and TrpD.</p> <p>These files include growth measurements, biochemical assays and other forms of biological data. They are packaged together with scripts used to analyze the data and present them in figures. Our goal in creating this archive was to present our complete analysis pipeline in the spirit of open science. It is not intended to be a stand-alone resource. Consult the associated manuscript for protocols, units of measurement and other essential technical context.</p> <p>Our scripts were written for Python 3.8. The raw data is presented as human-readable .csv files intended to be imported as Pandas DataFrames. Some data is also packaged as Python dictionaries saved with the Pickle package.</p>
Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data
<p>This is a release of the Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data.<br> <br> ERuDIte contains over 11,000 training resources on data science including courses (MOOCs), video tutorials, conference talks, and other materials. The metadata of these resources is described uniformly using schema.org. In addition, we use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology (DSEO), which we developed to further describe the contents of the training resources. Resource relevance and tags are curated by experts to ensure high quality. Finally, we map the references to people and organizations in the learning resource metadata to entities in DBpedia, DBLP, and ORCID, thus embedding our collection in the web of linked data. Our collection is continually growing. We hope that ERuDIte will provide a framework to foster open linked educational resources on the web.<br> <br> Distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/)</p>
First discovery and confirmation of PN candidates found from AI and deep learning techniques applied to VPHAS+ survey data
<div> <div> <div> <div> <div> <div> <p>Appendices: VPHAS+ detected PNG images labelled by red boxes, SHS Hα-Rband quotient images, VPHAS+ Hα-Rband quotient images, SAAO spectra with spectral lines labelled.</p> </div> </div> </div> <p>Table: Parameters of the observed PN candidates and any associated nebulosity or outflows.</p> <p>Reduced spectra.</p> </div> </div> </div>
Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors
<p>This repository contains the data used in the corresponding study:</p> <p>Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors</p> <p><strong>Abstract</strong></p> <p>Piezoresponse force microscopy (PFM) is routinely used to probe the nanoscale electromechanical response of ferroelectric and piezoelectric materials. However, many challenges remain in the interpretation of the recovered signal. Specifically, many non-ferroelectric contributions affect the measured response, ranging from electrostatics, to charge injection and trapping, and topographic cross-talk. Recently, machine learning (ML) has been utilized to identify multiple contributors within complex data systems, such as PFM response. A substantial advancement in ML approaches for PFM techniques is offered by dimensional stacking, enabling encoding of physical and/or chemical correlations within the materials’ response across different data dimensions spanning varying ranges. However, dimensional stacking requires appropriate scaling for each dimension (before ML analysis) to minimize undesired information loss. Here, the impact of clustering globally and locally scaled parameters in polarization switching experiments via resonant PFM (RPFM) are discussed. Specifically, dimensional stacking of scaled parameters can mask or enhance ferroelectric and non-ferroelectric behaviors, and aid identification of various physical phenomena contributing to the measured RPFM response. This study highlights the importance of data curation for ML, and its role in identifying signal contributors to scanning probe microscopy (SPM)-based techniques with multidimensional data, such as resonant and/or spectroscopic SPM.</p>
GE Discovery TOF MI PET NEMA IQ projector benchmark listmode data
<p>## LIST0000.BLF</p> <p>listmode file from GE Discvoery MI PET/CT containing all acquired emission events (HDF5)<br> of a single bed position NEMA IQ phantom acq.</p> <p>## corrections.h5</p> <p>file containing all quantitative corrections estimate using GE's duetto tool box (HDF5)</p> <p>- correction_lists/sens -> sensivity value for acquired events<br> - correction_lists/atten -> attenuation value for acquired events<br> - correction_lists/contam -> additive contaminations (randoms + scatter) for all acquired events<br> - all_xtals/atten -> attenuation values for all possible crystal combinations<br> - all_xtals/sens -> sensitivity values for all possible crystal combinations<br> - all_xtals/xtal_ids -> all possible crystal combinations</p> <p> </p>
Data for: Discovery of low-metallicity stars in the central parsec of the Milky Way
<p>Spectroscopic data from Stostad et al. (2015) and Do et al. (2015). This file contains K-band spectra from the Gemini NIFS instrument of stars in the central parsec of the Galactic center. The files are in FITS format. Additional descriptions are in in Stostad et al. (2015). </p> <p>If using this data, please cite Do et al. (2015) and Stostad et al. (2015)</p> <p>https://ui.adsabs.harvard.edu/abs/2015ApJ...808..106S/abstract<br>https://ui.adsabs.harvard.edu/abs/2015ApJ...809..143D/abstract</p> <p> </p> <p>Contributors to the creation of the spectra from this dataset include:</p> <p>Tuan Do</p> <p>Morten Stostad</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>
Data accompanying the manuscript "Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies"
<p>Dataset for figures 2, A6 and A8 for the manuscript: "Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies." The dataset contains the optimal protocols for Majorana transport in both the Kitaev Chain model as well as the Proximity Coupled Semiconduncting Nanowire model obtained with Differentiable Programming and Natural Evolution Strategies. Also the Simulated Annealing (SA) optimal protocols for the Kitaev chain are included.</p>
Orthology guided transcriptome assembly of Italian ryegrass and meadow fescue for single nucleotide polymorphisms discovery (data set)
<p>Transcriptome sequencing was performed on ten samples (corresponding to six genotypes) of <em>Festuca pratensis</em> and ten samples (corresponding to six genotypes) of <em>Lolium multiflorum</em> and fourteen samples of<em> Lolium perenne</em> (corresponding to fourteen genotypes). Using the OGA approach, 18,952 non-redundant <em>F. pratensis</em> transcripts were assembled by combining the contigs of all six genotypes based on orthology with the <em>Brachypodium distachyon </em>proteome. Similarly, <em>19,036</em> non-redundant<em> L. multiflorum</em> transcripts were assembled and annotated. In total, 17,455 orthologous transcripts were shared between the transcriptomes of the two species. Out of these, 16,613 orthologous transcripts overlap with the previously published<em> L. perenne</em> transcriptome containing 19,279 non-redundant transcripts(fasta files). We identified SNPs, the following criteria were used to classify it as one of following three classes (1) intraspecific SNPs (INTRA), (2) interspecific SNPs in two-way comparison (INTER-2W) and (3) interspecific SNPs in three-way comparison (INTER-3W) (GFF files).</p>
Supplementary data and scripts for the publication "Discovery of numerous novel Helitron-like elements in eukaryote genomes using HELIANO"
<p>##Information of directories. Each directory contains necessary data and scripts to do the corresponding analysis</p> <p>1. Benchmarking/: benchmarking analysis for heliano. (For Figure 3, Supplementary Table S2)<br>2. GeneCapture/: analysis for captured genes within HLEs. (For Figure 6, Supplementary Table S11)<br>3. HLE_model_species/: analysis for heliano results on three model species. (For Table 1, Figure 4, Supplementary Table S5-9, Supplementary Figure 4)<br>4. HLE_sampled_species/: analysis for heliano results on 404 sampled species. (For Figure 5, Supplementary Table S10, Supplementary Figure 5)<br>5. hmm_model/: build hmm model for Hel and Rep domains. (For RepHel.hmm that is important to detect and classify HLEs)<br>6. Phylogenetical_tree/: making phylogenetical trees. (For Figure 1, Supplementary Figure 2-3)<br>7. Sample_species/: about how to sample species from various genome assemblies. (Supplementary Table S3)<br>8. HLE_feature/: information about the position of RepHel domain and the length of HLE sequences. (Supplementary Figure 1)</p>
Supporting data for: "Data-driven discovery of cardiolipin-selective small molecules by computational active learning"
<p>This repository contains supporting data and code for the paper titled "Data-driven discovery of cardiolipin-selective small molecules by computational active learning" by Bernadette Mohr, Kirill Shmilovich, Isabel Kleinwächter, Dirk Schneider, Andrew L.Ferguson, and Tristan Bereau.</p>
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: discovery cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the discovery cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) a file mapping the SNP data subject IDs to the TCR repertoire data subject IDs (gwas_id_mapping.tsv)<br> (2) a file including the PCAir PCs, self-reported ancestry, and genomic ancestry for each subject (all_pc_air.txt)<br> (3) a file including the PCAir variance explained by each PC (all_pc_air_variance.txt)<br> (3) a file including the SNP ID, chromosome, hg19 position, allele, rsid, and quality control metrics for each SNP in the SNP array (emerson_snp_rs_data.tsv)<br> (4) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (5) a file including predicted TRBD2 allele genotypes for each subject (emerson_trbd2_alleles.tsv)<br> (6) Parsed TCRB repertoire data. These raw data were first published in Emerson et. al, <em>Nature Genetics </em>2017. (emerson_parsed_tcrb.tgz)</p> <p><strong>Corresponding discovery cohort raw TCR repertoire data is available here: </strong>https: //doi.org/10.21417/B7001Z (ImmuneACCESS database)<br> <strong>Corresponding discovery cohort SNP data is available here:</strong> https: //www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001918.v1.p1 (The database of Genotypes and Phenotypes, accession number: phs001918)<br> <br> <strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
Data for the "Discovery of Dehydroamino Acid Residues in the Capsid and Matrix Structural Proteins of HIV-1"
<p>Bottom-up mass spectrometry-based proteomic analysis (trypsin) was performed on four biological replicates of HIV-1 virions. These virions were isolated from HEK293T cells transfected with a HIV-1 proviral plasmid derived from the pNL4-3 molecular clone, rendered biosafe due to inactivating point mutations in both the env and vpr reading frames. There are 8 total spectra, 4 are from unlabeled aliquots of sample, and 4 are from aliquots of sample treated with glutathione to label dehydroamino acids (Spectra can be accessed on MassIVE (MSV000088220). All data was analyzed using MetaMorpheus version 0.0.319 (https://github.com/smith-chem-wisc/MetaMorpheus). Provided here are the results of this analysis.</p>
Interactive Causal Structure Discovery with Hyytiälä measurements (experiment code and data)
<p>This archive contains code and data required to reproduce the results presented in the following two papers.</p> <p>Interactive Causal Structure Discovery in Earth System Sciences<br> published in Proceedings of The KDD'21 Workshop on Causal Discovery, 2021.</p> <p>Technical note: incorporating expert domain knowledge into causal structure discovery workflows<br> published in Biogeosciences, 2022</p> <p>The archive contains a README markdown document detailing the contents and how to run the experiments.</p> <p> </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.