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3,696 results for “reference”
Ulex (Ulex europaeus) UAV imagery and reference data (raw)
<p>This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =4) of Ulex europaeus acquired between 2016-2017 in Chile. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 3-4 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Ulex cover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Ulex canopies are assumed to be completely delineated (by visual interpretation).</p> <p>For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution.</p> <p>Details on the dataset are mentioned in the corresponding publication:</p> <p>Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. <em>Remote sensing of environment</em>, <em>227</em>, 61-73.</p> <p><a href="https://doi.org/10.1016/j.rse.2019.03.025">https://doi.org/10.1016/j.rse.2019.03.025</a></p> <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166">https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166</a></p>
Acacia (Acacia dealbata) UAV imagery and reference data (raw)
<p>This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =2) of Pinus radiata acquired between 2016-2017 in Chile. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 3-4 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Pinus cover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Pinus canopies are assumed to be completely delineated (by visual interpretation).</p> <p>For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution.</p> <p>Details on the dataset are mentioned in the corresponding publication:</p> <p>Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. <em>Remote sensing of environment</em>, <em>227</em>, 61-73.</p> <p><a href="https://doi.org/10.1016/j.rse.2019.03.025">https://doi.org/10.1016/j.rse.2019.03.025</a></p> <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166">https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166</a></p>
PopV pretrained models for Tabula sapiens reference and helper files
<p>This repository contains PopV pretrained models for all organs published in Tabula sapiens <a href="https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5">https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5</a>. Datasets are available from <a href="https://zenodo.org/record/7587774">https://zenodo.org/record/7587774</a>.</p> <p>The code to train all models will be released in a Github reproducibility repository.</p>
Gasterosteus aculeatus gynogenetic reference genome and functional annotations version 1 and raw PacBio and Illumina data
<p class="MsoNormal">Whole genome sequencing enables us to ask fundamental questions about the genetic basis of adaptation, population structure, and epigenetic mechanisms, but usually requires a suitable reference genome for making sense of the sequence data. While the availability of reference genomes has significantly improvement in both taxonomic coverage and overall quality, this poses a challenge for researchers in determining which reference genome best suits their data. Here we compare the use of two different reference genomes for the three-spined stickleback (<em>Gasterosteus aculeatus</em>), one novel genome from a European individual and the published reference genome of a North American individual. Specifically, we investigate the impact of using a local reference versus one generated from a differentiated population on several commonly used metrics in population genomics. Through mapping genome resequencing data of 60 sticklebacks from across Europe and North America, we confirmed genome quality is an important factor in choosing a reference genome. A local reference genome did offer increased mapping efficiency and genotyping accuracy, likely stemming from the higher similarity in genome sequence and synteny. Despite comparable distributions of the metrics generated across the genome using SNP data (i.e., π, Tajima's D, and FST), window-based statistics using different references resulted in different outlier genes and enriched gene functions. In contrast, the marker-based analysis utilising DNA methylation distributions had a considerably higher overlap in outlier genes and functions when using different reference genomes. Overall, our results highlight how using a local reference genome can increase the resolution of genome scans when multiple similar-quality reference genomes are available. Such results have implications in the detection of signatures of selection.</p>
WorldCereal open global harmonized reference data repository (CC-BY-NC licensed data sets)
<p>Within the <strong>ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY-NC license or a license similar to CC-BY-NC. See document "_In-situ-data-World-Cereal - license - CC-BY-NC.pdf" for an overview of the original data sets. Currently this publication only includes a few small data sets for Tanzania originating from a disease monitoring program of the International Maize and Wheat Improvement Center (CIMMYT). CIMMYT made more data available for countries like Kenya, Ethiopia, Rwanda and, Malawi. However due project contraints these data sets were not yet harmonized.</p>
Tree species Waititu (New Zealand) UAV imagery and reference data (raw)
<p>This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics of three plots (each plot covers approx. 100 by 100 m) acquired in November 2017 in Waititu (Fjordland) New Zealand. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 2-3 cm. The orthomosaics feature RGB information and an estimated canopy height (normalized digital surface model, nDSM). The canopy height was derived by subtracting digital surface models (DSM) with digital terrain models (DTM). The DTMs were estimated from the lowest points. Details are given in the publication below.</p> <p>The orthomosaics are partially labelled (polygon shapefiles) for the two tree species, which are <em>Metrosideros umbellata</em>, an angiosperm of the Myrtaceae and <em>Dacrydium cupressinum,</em> a gymnosperm of the Podocarpaceae. The two species are abbreviated as <em>metumb </em>and <em>daccup</em>, respectively. The covered forests are primary forests with a high species richness and endemism.</p> <p>Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI the two tree species are assumed to be completely delineated (by visual interpretation guided with insitu data; details see publication below).</p> <p>For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution.</p> <p>Details on the dataset are mentioned in the corresponding publication:</p> <p>Kattenborn, T., Eichel, J., Wiser, S., Burrows, L., Fassnacht, F. E., & Schmidtlein, S. (2020). Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery. <em>Remote Sensing in Ecology and Conservation</em>, <em>6</em>(4), 472-486.</p> <p><a href="https://doi.org/10.1002/rse2.146">https://doi.org/10.1002/rse2.146</a></p> <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166">https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.146</a></p> <p> </p> <p>Acknowledgements: The data acquisition was funded by the Catalyst: Leaders program financed by the New Zealand Ministry of Business, Innovation and Employment (MBIE) and administered by the Royal Society of New Zealand and the MBIE Strategic Science Investment Fund to Manaaki Whenua-Landcare Research</p>
The full-length Quillaja brasiliensis (Quillajaceae) reference transcriptome
<p><strong>Introduction</strong></p> <p>In this study, we used the PacBio Iso-Seq technology to uncover the full-length transcriptome of <em>Quillaja brasiliensis </em>(Quillajaceae), a tree species native to Brazil and of great importance for the extraction of bioactive saponins. Establishing a full-length transcriptome is essential for understanding the metabolic pathways leading to saponin biosynthesis in <em>Q. brasiliensis </em>and should set the stage for upcoming investigations.</p> <p> </p> <p><strong>Sample obtention and processing</strong></p> <p>We collected seeds of <em>Quillaja brasiliensis</em> (A.St.-Hil. & Tul.) Mart. (Quillajaceae) from the city of Canguçú (Rio Grande do Sul, Brazil) in March/2018. After <em>in vitro</em> germination, we explanted the seedlings and submitted them to callogenesis or transferred them to the grow room at 25°C under a 16h/day photoperiod. We detached leaves from 2.8-year-old individuals for the experiments hereby presented. After leaf cleanup, we detached approximately 15 leaves and exposed them to ultraviolet radiation (UV-C germicide lamp, ʎ maximum 254 nm) or white light (control) (Table 1). After incubation, we froze the plant material in liquid nitrogen and stored it at -80 °C until the next processing step. We obtained calluses from <em>in vitro </em>germinated explants and submitted them to cellular suspension induction. We maintained the cell cultures in MS medium supplemented with naphthaleneacetic acid (5 mg/L) and kinetin (0.1 mg/L) in the absence of light, using 250 mL culture flasks under the agitation of 120 RPM. After establishing the growth profile, we collected three flasks for each growth phase of the liquid cell cultures: lag phase (three days), log phase (seven days), and stationary phase (21 days). For the sample collection, cells were filtered using the Büchner funnel, washed with distilled water, and flash-frozen in liquid nitrogen. We stored the collected material at -80 °C until the next processing step. We extracted RNA samples from different tissues and growth conditions using the cetyltrimethylammonium bromide method (CTAB) and performed their purification using the RNeasy MinElute Cleanup Kit (QIAGEN, Hilden, Germany). We quality-controlled the samples using fluorometric and spectrophotometric methods before submitting them to sequencing by Novogene (Beijing, People's Republic of China). We made an equimolar pool of the five sample types for sequencing using the Iso-Seq strategy (PacBio, Menlo Park, US) in circular consensus sequencing mode for the obtention of long reads. The raw reads are available at the European Nucleotide Archive (ENA) under accession PRJEB58985.</p> <table> <tbody> <tr> <td> <p><strong>Sample</strong></p> </td> <td> <p><strong>Sample source</strong></p> </td> <td> <p><strong>Treatment or Growth phase</strong></p> </td> </tr> <tr> <td> <p>Leaf UV light</p> </td> <td> <p>Leaf</p> </td> <td> <p>Ultraviolet light treatment.</p> </td> </tr> <tr> <td> <p>Leaf white light</p> </td> <td> <p> </p> </td> <td> <p>White light treatment</p> </td> </tr> <tr> <td> <p>Cell suspension lag</p> </td> <td> <p>Cell suspension</p> </td> <td> <p>Lag phase (three days)</p> </td> </tr> <tr> <td> <p>Cell suspension log</p> </td> <td> <p> </p> </td> <td> <p>Log phase (seven days)</p> </td> </tr> <tr> <td> <p>Cell suspension stationary</p> </td> <td> <p> </p> </td> <td> <p>Stationary phase (21 days)</p> </td> </tr> </tbody> </table> <p><strong>Table 1</strong>. Brief description of pooled sequenced samples. </p> <p> </p> <p><strong>Zenodo repository content</strong></p> <p>This repository stores the full-length transcriptome FASTA file obtained after running the IsoSeq v3 pipeline, followed by one round of polishing by LoRDEC and transcript-collapsing by Cogent/Cupcake-ToFU (collapsed_isoforms.fa). We also performed coding sequence prediction and functional annotation using CodAn and TRAPID 2.0 (PLAZA 4.5 Dicots), respectively (transcriptome_functional_characterization.tsv). A brief description of column names for the functional characterization table is also available here.</p> <p> </p>
Joseph Lalli CHM13-T2T Pangenie reference vcf - unofficial
<p>hprc-jun1-mc-chm13.pangenie_panel.bcf.gz: Draft pangenome (Jun1-mc version) in CHM13-T2T coordinates</p> <p>hprc-jun1-mc-chm13.fasta: CHM13 reference paths from Jun1-mc draft pangenome.</p> <p>hprc-jun1-mc-chm13.fasta.fai: Reference index.</p> <p>pangenie.def and pangenie.sif: Singularity container to run Pangenie 2.1</p> <p>run_pangenie.sh: Bash script to run pangenie on HTCondor servers. Specifically, the script downloads a specified 1KGP unaligned cram file, extracts the reads locally, creates a checkpoint .jf kmer count table, and then runs Pangenie on the .jf file. The resulting vcf file is compressed to a bcf.gz file, and is saved in the staging directory.</p> <p>HG02030.pangenie.bcf.gz: An example of the output from the 1KGP sample HG02030.</p>
Viral reference genomes to disentangle the recombinant phylogenetic history of the potyviruses
<p>Potyviruses are a large genus of plant-infecting RNA viruses in the family Potyviridae. Due to their rapid diversification and frequent recombination, reconstructing the phylogenetic history of the potyviruses has proven difficult. Phylogenies reconstructed from different protein-coding regions of the viral genome often reveal conflicing or discordant relationships. But the extent to which discordance is due to interspecific recombination versus phylogenetic noise or errors in reconstruction is unclear. </p> <p>To explore the recombinant history of the potyviruses, we assembled a dataset containing referece genomes for 131 species of potyviruses. High-quality, full-length reference genomes for all species were obtained form NCBI GenBank. Viral genomes were carefully aligned at the codon-level and screened for recombination. The full alignment was then partitioned into several sub-alignments between each detected recombination event, such that each sub-aligment corresponds to a non-recombinant block (NRB) free of detected recombination events. Local phylogenetic trees for each NRB were then reconstructed to explore how phylogenetic relationships varied across different regions of the potyvirus genome. </p> <p>We then used our program Espalier to disentangle the phylogenetic history of the potyviruses. Espalier reconciles and removes discordances between phylogenetic trees that are likely attributable to phylogenetic error while retaining recombination events that are strongly supported by the sequence data. Applying Espalier to the potyviruses revealed that most phylogenetic discordace between local trees is likely attributable to phylogenetic noise. Removing the discordance attributable to phylogenetic error allows us to much more clearly visualize the phylogenetic history of the potyviruses.</p>
SARS-COV-2 genomic sequences used as references for the NASCarD method
<p>A set of 9 SARS-CoV-2 genome sequences used as reference in the NASCarD process.</p>
Trained Models for "Segmentation of human functional tissue units in support of a Human Reference Atlas"
<p>This repository contains all trained models for five algorithms as documented in the paper "Segmentation of human functional tissue units in support of a Human Reference Atlas".</p>
Data for "Segmentation of human functional tissue units in support of a Human Reference Atlas"
<p>This repository contains the data documented in the paper "Segmentation of human functional tissue units in support of a Human Reference Atlas".</p> <p>The directories contain:</p> <ul> <li>The HuBMAP and HPA image data, ground truth segmentation masks, and predicted segmentation masks, as documented in the paper. </li> <li>The source data for recreating the figures 3 and 4 in the paper.</li> </ul>
Tables for "Specimen, biological structure, and spatial ontologies in support of a Human Reference Atlas"
<p><strong>Table 1. CCF Specimen, Biological Structure, and Spatial Ontology. </strong>For each ontology, we provide all class names, property names, entity Internationalized Resource Identifier (IRI), definition when available, and entity type.</p> <p><strong>Table 2. 3D Reference Objects. </strong>The 3rd HRA release features 53 <em>Spatial Entities</em> of type <em>Spatial Object Reference</em> that are linked to anatomical structures in the ASCT+B tables plus 43 <em>Spatial Entities</em> of type <em>Extraction Site</em>. For each entity, we provide the spatial entity<em> </em>name, 3D object file name, ID and version number, organ label, sex, and type. Note that a landmark is a <em>Spatial Entity</em> that has been tagged as an <em>Extraction Set</em> for a specific <em>Extraction Site</em>, see <strong>Table 3</strong>.</p> <p><strong>Table 3. Registered Tissue Data. </strong>This table shows 364 <em>Extraction Site </em>entities that either represent the registration location for <em>Tissue Blocks</em> or <em>Registration Sites</em>. For each, we provide consortium name and entity ID, HuBMAP ID if available, and experimental data donor sex as well as HRA data such as organ name, sex, Uberon ID, and GLB file. The data comes from four consortia. HuBMAP data includes 306 tissue blocks, and many of these have derived tissue sections with one or more associated datasets. The 29 well-defined GTEx extraction set sites link to 5,397 datasets. The 26 SPARC blocks link to one paper while the three KPMP tissue blocks link to two data publications. </p>
Stability of iridium single atoms on Fe3O4(001) in the mbar pressure range - Published and reference data
<p>Collection of data used in the publication in title, sorted for different conditions and pressures. Baselines included for pristine Fe3O4(001), in addition to Ir1-deposited surfaces.</p>
PBMC CITE-seq reference
<p>This PBMC CITE-seq reference object was constructed using Seurat v5.</p>
Vocabulary tools list. Referred to within the 2022 Vocabulary workshop
<p><strong>Initially created during 2021. Discussed during the 2022 Vocabulary Workshop</strong></p> <p>"This catalogue is a copy of a draft output of a 2021 Vocabulary Workshop. Participants were interested in putting together a catalogue of vocabulary tools. To address questions like...<br> "If you want to do something with vocabularies, and there are lots of tools to choose from, how do you decide which one to use?"</p> <p>The first iteration of this resource was created by Nick Car (SurroundAustralia), Edmond Chuc (TERN), Kheeran Dharmawardena (DAWE), Michael Lawley(CSIRO) and Simon Cox (CSIRO) "</p>
Global reference data set for validating ESA WorldCereal temporary cropland extent
<p>IIASA team has created a new validation data set, which is completely independent from all other existing maps or reference data sets, and which is in line with the cropland definitions and mapping period of the WorldCereal products. To decide if this is an active cropland in each period, the experts looked at very high-resolution Google historical imagery and Google Street level images, Microsoft Bing images, ESRI imagery, Planet historical data, Sentinel-2 time series, and Modis NDVI time series. The experts were asked to label 5 by 5 Sentinel pixels (each pixel 10m by 10m) either as winter crops, or as summer crops, or as maize (if this was possible), or as active crops (where it was not possible to confirm a growing season, e.g. overlap between seasons was too big or crop fields were too small in size), or as no crops, or as not sure where was too little information available for 2021. There was additional question on irrigation system, either circle, or other irrigation, or rainfed, or not sure. </p> <p>Fields:</p> <p>"rowid" -unique row identifier</p> <p>"submissionid" – unique submission id, which consists of 25 single pixels (rows)</p> <p>"timestamp" – time stamp of each submission</p> <p>"sampleid" – unique sample site, which could have a few submission ids</p> <p>"submission_itemid" – unique single pixel submission id</p> <p>"enhancement" – unique legend identifier in the Geo-Wiki database</p> <p>"question" – question asked (either on crop presence or irrigation)</p> <p>"answer" – answer to a question asked</p> <p>"sub_pixel_x","sub_pixel_y" – centroids of a single pixel in WGS84</p>
Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples
<p>We present a <strong>D</strong>iagnostic dataset of <strong>IA</strong> <strong>Ref</strong>erences in a <strong>Pento</strong>mino domain (Pento-DIARef) that ties extensional and intensional definitions more closely together, insofar as the latter is the generative process creating the former.</p> <p>We create a novel synthetic dataset of examples that pairs visual scenes with generated referring expressions; examine two variants of the dataset, representing two different ways to exemplify the underlying task; and evaluate an LSTM-based baseline, a transformer and a modified version with region embeddings on them.</p> <p>See https://github.com/clp-research/pento-diaref for more information.</p>
Guinea Pig Skin & Tick Bite Site - Widefield H&E Reference Images
<p>Underlying H&E stained guinea pig skin from control (unbitten) and tick-bitten skin. Ticks: nymphal <em>Ixodes scapularis</em>. These images are reference files for the positive and negative mode MALDI-FTICR (MSI) images collected for lipids. Collected on a Zeiss AxioImager M2 using an in-built default stitching function. Native files: .czi can be opened in Zen Lite or Zen Pro.</p>
SARS-CoV2 reference genome UCN20
<p>This genome is a majority consensus obtained from sequencing respiratory samples and first passage of dozens isolates (UCN1 to UCN20, University Hospital of Caen, data mapped on reference genome from WIV) from the early beginning of the epidemic (march - april 2020).</p>
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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)
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DANDI Archive for NWB datasets
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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.