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3,696 results for “Reference”
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Genomes and full-length 16S reference sequences for 27 Alpha- and Gamma-Proteobacterial isolates from Red Sea Acropora corals
<p>Coral-associated bacteria contribute to the biology of their host, but the underlying molecular interactions are largely unknown. To further our functional understanding, we obtained 27 alpha- and gamma-proteobacterial isolates, many of which are Rhodobacteraceae, from three coral species of the genus <em>Acropora </em>and assembled/annotated their genomes as a resource for further functional studies. Our results reveal the immense taxonomic and genetic diversity of common alpha- and gamma-proteobacterial coral-associated bacteria. We hope these data provide a framework to study the function of specific bacteria in the coral holobiont. Isolates are available upon request.</p>
BAM reference data: EDS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a)
<p>The EDS spectra are given in the EMSA/MAS format as defined by ISO 22029:2012 Microbeam analysis — EMSA/MAS standard file format for spectral-data exchange. The exact locations of the sample areas measured with EDS are indicated in the SEM images.</p> <p>For further information please look at:</p> <p>- Radnik, J. Kersting, R., Hagenhoff, B., Bennet, F., Ciornii, D.; Nymark, P., Grafström R. and Hodoroaba, V.- D. <em>Nanomaterials </em><strong>2021</strong>, <em>11</em>, 639. https://doi.org/10.3390/nano11030639, and</p> <p>- Radnik, Jörg. (2021). BAM reference data: XPS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a) [Data set]. Nanomaterials. Zenodo. http://doi.org/10.5281/zenodo.4986068</p> <p>Measurement conditions:</p> <p>The EDS analysis in the present study has been performed with a QUANTAX 400 EDS system (BRUKER, Berlin, Germany), which is equipped with an SDD (Silicon Drift-Detector) of the 10 mm2 nominal area. An excitation of 10 keV was applied for the analysis of the titania samples prepared as a thick dry powder layer on an aluminum stub, so that the substrate cannot be coexcited. The analysis reas were selected as large as 5 x 5 µm<sup>2</sup> on sample agglomerates of about 1eng0 µm size.</p>
Liftover of Drosoophila melanogaster genotypes, DGRP sample, from dm3 to dm6 reference assembly.
<p>Genotype data for Drosophila melanogaster DGRP collection.<strong> </strong>Variant positions relative to reference genome version dm6.<strong> </strong>Variant identifiers are in NCBI format.<strong> </strong>Processing code (including original data, reference sequences, perl scripts, etc).</p> <p>The initial intention of performing this 'liftover' was to allow comparison with data from a different melanogaster sample which had been analysed against a dm6 reference assembly. At the time of writing I am not aware that a dataset like this one exists.</p> <p>I've uploaded it in case it is useful to anyone in the future - and also as a trial of zenodo.</p> <p>See README.txt and flylifter.sh for more info.</p> <p> </p>
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
Audio files referred to in Roettger, Timo B. (2017). Tonal Placement in Tashlhiyt - How an intonation system accommodates to adverse phonological environments.
<p>Audio files referred to in Roettger, Timo B. (2017). Tonal Placement in Tashlhiyt - How an intonation system accommodates to adverse phonological environments. Berlin: Language Science Press (DOI 10.5281/zenodo.814472)</p>
INDIGO Graffiti Reference Database
<p>There is an extensive body of popular and scholarly literature on ancient and recent graffiti. The <strong>INDIGO Graffiti Reference Database</strong> was created to collect much of this graffiti-related literature in one place. It also contains literature about street art because contemporary graffiti and street art are hard to disentangle, and their relationship and hierarchy depend upon the source one consults. The database is a product of the academic graffiti project INDIGO.</p> <p>This collection of references is made available in two forms:</p> <ul> <li>a closed-source <a href="https://www.citavi.com/en">Citavi</a> database (the *.ctv6archive file);</li> <li>an open-source <a href="https://www.zotero.org">Zotero</a> database <ul> <li>either <a href="https://www.zotero.org/groups/5192206/indigo_graffiti_reference_database/library">consultable online</a> (reading only),</li> <li>or downloadable here as a Zotero RDF or BibTex file (*.rdf and *.bib, respectively) for import into Zotero or other reference managers.</li> </ul> </li> </ul>
Global Pasture Watch - Grassland reference samples based on visual interpretation of VHR imagery and harmonized datasets (2000–2024)
<p>Reference point samples used in the production of the <a href="https://doi.org/10.5281/zenodo.13890401">global maps of annual grassland class and extent for 2000—2022</a><strong> </strong>within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Wath</a> initiative. </p> <p>The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises <strong>2.3M points</strong> visually classified (<em>using Very High Resolution imagery</em>) in:</p> <ol> <li><strong>Cultivated grassland,</strong></li> <li><strong>Natural/semi-natural grassland</strong></li> <li><strong>Other land cover</strong></li> </ol> <p>The file <code>gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:</p> <ul> <li>cluster_id: Cluster id defined by k-means (FSCS),</li> <li>cluster_distance: Distance from the sample tile to center of the cluster (FSCS),</li> <li>cluster_size: Size of cluster (strata) defined by the FSCS,</li> <li>priority: Priority used by the visual interpretation,</li> <li>tile_id: Sample tile id,</li> <li>imagery: VHR reference images used by the visual interpretation,</li> <li>min_year: Minimum of year covered by the reference samples,</li> <li>max_year: Maximum of year covered by the reference samples,</li> <li>n_years: Number of years covered by the reference samples,</li> <li>n_samples_c1: Number of reference samples for "Cultivated grass" (1),</li> <li>n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),</li> <li>n_samples_c3: Number of reference samples for "Open Shrubland" (2),</li> <li>n_samples_c4: Number of reference samples for "Not grass" (3),</li> <li>n_samples_all: Total number of reference samples,</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_point.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides individual points (with 60-m spatial support) and include the follow collumns:</p> <ul> <li>sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,</li> <li>x: Longitude in WGS84 (EPSG:4326),</li> <li>y: Latitude in WGS84 (EPSG:4326),</li> <li>vi_tile_id: 1-km tile id,</li> <li>tile_id: GLAD tile id (1x1 degree)</li> <li>imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),</li> <li>ref_date: Reference date of GPW samples (based on VHR image) and of other existing datasets,</li> <li>year: Reference year of GPW samples (based on VHR image) and of other existing datasets,</li> <li>class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Open shrubland; 4: Other land cover) ,</li> <li>class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Open shrubland; Other land cover) ,</li> <li>dataset_name: Existing dataset names (CGLS-LC, EuroCrops, GeoWiki, GeoWiki-feedback, LCMap-Conus, LUCAS, MapBiomas, WorldCereal, GPW) <br>dataset_class: Original land cover class provided by the maintainer of existing dataset</li> <li>esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,</li> <li>glad_glcluc_yyyy: Land cover class labels extracted from UMD GLAD GLCLUC for the reference date,</li> <li>glc_fcs30d_yyyy: Land cover class labels extracted from GLC_FCS30D for the reference date,</li> <li>gpw_fscs_cluster: K-Means output ranging from 0—9999 according to Feature Space Coverage Sampling (FSCS),</li> <li>ml_cv_group: spatial block CV group (based on vi_tile_id),</li> <li>ml_type: specify if the sample was used for (1) training or (2) calibration.</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides the grid samples (with 10-m spatial support) and include the follow collumns:</p> <ul> <li>tile_id: 1-km tile id,</li> <li>bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,</li> <li>bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,</li> <li>bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,</li> <li>google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,</li> <li>google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,</li> <li>google_image_end_date: End date of the Google Maps Images used in the visual interpretation,</li> <li>missing_image_date: No images available,</li> <li>same_image_bing_google: Images from the same date available in Google and Bing Maps.</li> </ul> <p>The dataset was produced through the <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS plugin Fast Grid Inspection</a>.</p> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
Reference data bundle for PacificBiosciences/HiFi-human-WGS-WDL
<p>Static input files to support alignment, variant calling, filtering, and annotation for human HiFi WGS using the GRCh38 reference.</p> <p>https://github.com/PacificBiosciences/HiFi-human-WGS-WDL</p> <p><code>hifi-wdl-resources-v3.1.0</code><br><code>├── GRCh38</code><br><code>│ ├── annotation</code><br><code>│ │ ├── GRCh38.oddRegions.bed.gz</code><br><code>│ │ ├── GRCh38.oddRegions.bed.gz.tbi</code><br><code>│ │ ├── GRCh38.repeats.bed.gz</code><br><code>│ │ ├── GRCh38.repeats.bed.gz.tbi</code><br><code>│ │ ├── GRCh38.segdups.bed.gz</code><br><code>│ │ ├── GRCh38.segdups.bed.gz.tbi</code><br><code>│ │ └── README.md</code><br><code>│ ├── ensembl.GRCh38.101.reformatted.gff3.gz</code><br><code>│ ├── human_GRCh38_no_alt_analysis_set.fasta</code><br><code>│ ├── human_GRCh38_no_alt_analysis_set.fasta.fai</code><br><code>│ ├── methbat</code><br><code>│ │ ├── cpgIslandExt.sorted.hg38.tsv</code><br><code>│ │ └── README.md</code><br><code>│ ├── pharmcat</code><br><code>│ │ ├── pharmcat_positions_2.15.4.vcf.bgz</code><br><code>│ │ ├── pharmcat_positions_2.15.4.vcf.bgz.csi</code><br><code>│ │ └── README.md</code><br><code>│ ├── README</code><br><code>│ ├── sawfish</code><br><code>│ │ ├── annotation_and_common_cnv.hg38.bed.gz</code><br><code>│ │ ├── annotation_and_common_cnv.hg38.bed.gz.tbi</code><br><code>│ │ ├── expected_cn.hg38.XX.bed</code><br><code>│ │ ├── expected_cn.hg38.XY.bed</code><br><code>│ │ └── README</code><br><code>│ ├── slivar_gnotate</code><br><code>│ │ ├── buildGnomad_v4</code><br><code>│ │ ├── CoLoRSdb.GRCh38.v1.2.0.deepvariant.glnexus.zip</code><br><code>│ │ ├── gnomad.hg38.v4.1.custom.v1.zip</code><br><code>│ │ └── README.md</code><br><code>│ ├── sv_pop_vcfs</code><br><code>│ │ ├── CoLoRSdb.GRCh38.v1.2.0.pbsv.jasmine.vcf.gz</code><br><code>│ │ ├── CoLoRSdb.GRCh38.v1.2.0.pbsv.jasmine.vcf.gz.tbi</code><br><code>│ │ ├── gnomad.v4.1.sv.sites.pass.vcf.gz</code><br><code>│ │ ├── gnomad.v4.1.sv.sites.pass.vcf.gz.tbi</code><br><code>│ │ └── README.md</code><br><code>│ └── trgt</code><br><code>│ ├── adotto_strchive_20250827.hg38.bed.gz</code><br><code>│ └── README.md</code><br><code>├── GRCh38.ref_map.v3p1p0.template.tsv</code><br><code>├── GRCh38.tertiary_map.v3p1p0.template.tsv</code><br><code>└── slivar</code><br><code> ├── clinvar_gene_desc.20250618T144412.txt</code><br><code> ├── get_lof_gnomadv4.sh</code><br><code> ├── lof.gnomadv4p1.lookup</code><br><code> ├── README</code><br><code> ├── README.lof.md</code><br><code> └── slivar-functions.v0.2.8.js</code></p> <p><code>9 directories, 40 files</code></p>
Towards sustainable food consumption: reference list
<p>This dataset includes all the bibliographic references from the SAPEA evidence review report 'Towards a sustainable food system'.</p>
Dataset of publication "Derivation and validation of a reference data-based real gas model for hydrogen"
<p>In this repository, a new real gas model for hydrogen based on the Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) v10.0 is provided for the use in the simulation software OpenFOAM v2012. The model is valid in a temperature and pressure range of 150-400 K and 0.1-1000 bar, respectively. Usage beyond this range is not recommended as it may lead to unrealistic results.</p>
Reference data bundle for CoLoRSdb
<p>Static input files to support alignment, quality control, variant calling, and ancestry estimation using the GRCh38 and CHM13 references in the CoLoRSdb workflow.</p><p><a href="https://github.com/juniper-lake/CoLoRSdb">https://github.com/juniper-lake/CoLoRSdb</a></p><p><a href="https://colorsdb.org/">https://colorsdb.org/</a></p><p> </p><p>colorsdb_resources/</p><p>├── CHM13</p><p>│ ├── human_chm13v2.0_maskedY_rCRS.fasta</p><p>│ ├── human_chm13v2.0_maskedY_rCRS.fasta.fai</p><p>│ ├── human_chm13v2.0_maskedY_rCRS.trf.bed</p><p>│ ├── somalier.sites.chm13v2.T2T.vcf.gz</p><p>│ └── vcfparser.CHM13.ploidy.txt</p><p>└── GRCh38</p><p> ├── hificnv.cnv.excluded_regions.hg38.bed.gz</p><p> ├── hificnv.cnv.excluded_regions.hg38.bed.gz.tbi</p><p> ├── hificnv.female_expected_cn.hg38.bed</p><p> ├── hificnv.male_expected_cn.hg38.bed</p><p> ├── human_GRCh38_no_alt_analysis_set.fasta</p><p> ├── human_GRCh38_no_alt_analysis_set.fasta.fai</p><p> ├── human_GRCh38_no_alt_analysis_set.trf.bed</p><p> ├── peddy.GRCH38.sites</p><p> ├── peddy.GRCH38.sites.bin.gz</p><p> ├── somalier.sites.hg38.vcf.gz</p><p> ├── trgt.adotto_repeats.hg38.bed</p><p> ├── trgt.pathogenic_repeats.hg38.bed</p><p> ├── trgt.repeat_catalog.hg38.bed</p><p> └── vcfparser.GRCh38.ploidy.txt</p><p>2 directories, 19 files</p><p> </p>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
Reference images for pytroll
<p>This record holds reference images for pytroll/satpy automated image production testing. The code used to produce the imagery, including code to obtain necessary input data, is included with the Satpy repository and the <a href="https://zenodo.org/records/14056667">Pytroll/Satpy record</a> (from version 0.54).</p> <p>The satellite images included in this record were produced with Satpy. The input data used to produce the images was obtained from various sources, that should be credited where imagery are used. Those are:</p> <table> <tbody> <tr> <td><strong>Pattern</strong></td> <td><strong>Operator</strong></td> </tr> <tr> <td>*GOES*</td> <td>NOAA</td> </tr> </tbody> </table> <p> </p>
Reference Mean and Low Streamflow for all Brazilian Catchments Generated Using Machine Learning Models
<p>This dataset provides comprehensive hydrological information for river networks in Brazil, focusing on reference streamflows, specifically long-term mean flows (qm) and low flows exceeded 95% of the time (q95). Covering over 400,000 ungauged river points, the dataset was developed using advanced machine learning models trained on environmental descriptors and validated against data from 1,069 gauging stations spread across the country. The machine learning pipeline evaluated six regression models to achieve high predictive accuracy (R² > 0.8 for qm and > 0.7 for q95). The 62 environmental descriptors - encompassing climate, topography, land cover, lithology, and water storage characteristics - that were used as features for the models are also included.</p> <p>Key features:</p> <ul> <li><strong>Spatial Coverage:</strong> Brazilian territory and the Amazon River basin, based on the <a href="https://metadados.snirh.gov.br/geonetwork/srv/api/records/f7b1fc91-f5bc-4d0d-9f4f-f4e5061e5d8f" target="_blank" rel="noopener">BHO 5k</a> dataset of officially adopted river networks.</li> <li><strong>Outputs:</strong> Predicted qm and q95 values for each river stretch, with 90% and 75% confidence intervals to account for prediction uncertainty.</li> <li><strong>Environmental Descriptors:</strong> Aggregated from upstream catchment area.</li> </ul> <p> </p>
R package n2khab: providing preprocessed reference data for Flemish Natura 2000 habitat analyses
The n2khab package is an R package with preprocessing functions and standard reference data, useful for analyses regarding Flemish Natura 2000 habitats and regionally important biotopes (RIBs). URL: <a href="https://inbo.github.io/n2khab">https://inbo.github.io/n2khab</a>.
The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding
<div>PN40024, a highly homozygous inbred line originating from ‘Helfensteiner’, was used for T2T genome assembly. In total, 21 Gb (21 024 461 524 bp, ∼42× coverage) HiFi reads were generated by the PacBio platform. For the preliminary assembly, hifiasm was used to assemble the HiFi reads. We then used MUMmer and the 12X.v0 genome version (V. vinifera genome assembly 12X.v0 to order the 38 contigs into 19 chromosomes.</div> <p> The PN_T2T genome size was finally generated (494.87 Mb), being 69 Mb longer than 12X.v0 using the same statistical method. The k-mer metric was used to evaluate genomic homozygosity, estimated at 99.8%. The BUSCO for this genome is up to 98.5%.</p> <p>The PN40024.T2T genome assembly: PN.fa</p> <p>The PN40024.T2T gene annotation: PN_T2T.v5.1.gff3</p> <p>The PN40024.T2T TE annotation: PN_T2T_TE.gff</p> <p>The PN40024.T2T centromere annotation: PN.trf.gff3</p> <p>The PN40024.T2T protein sequence: PN_protein.fa</p> <p>The PN40024.T2T cds sequence: PN40024.cds.fa</p> <p>Comparison of gene annotation among PN_T2T and PN_T2T.v5.1, 12X.v0, 12X.v2, PN40024.v4, PN40024.v4.1: correlation.list.txt</p> <p>Mitochondrial assembly sequence of PN40024: PN_T2T_mit.fa</p> <p>Annotation of mitochondrial assembly for PN40024:PN_T2T_mit.gff3</p> <p>Chloroplast assembly sequence of PN40024: PN_T2T_chl.fa</p> <p>Annotation of chloroplast assembly for PN40024: PN_T2T_chl.gff3</p> <p>Citation: </p> <p>Please cite this paper when using the data of PN_T2T for your publications.</p> <p>Xiaoya Shi, Shuo Cao, Xu Wang, Siyang Huang, Yue Wang, Zhongjie Liu, Wenwen Liu, Xiangpeng Leng, Yanling Peng, Nan Wang, Yiwen Wang, Zhiyao Ma, Xiaodong Xu, Fan Zhang, Hui Xue, Haixia Zhong, Yi Wang, Kekun Zhang, Amandine Velt, Komlan Avia, Daniela Holtgräwe, Jérôme Grimplet, José Tomás Matus, Doreen Ware, Xinyu Wu, Haibo Wang, Chonghuai Liu, Yuling Fang, Camille Rustenholz, Zongming Cheng, Hua Xiao, Yongfeng Zhou, The complete reference genome for grapevine (<em>Vitis vinifera</em> L.) genetics and breeding, <em>Horticulture Research</em>, Volume 10, Issue 5, May 2023, uhad061, <a href="https://doi.org/10.1093/hr/uhad061">https://doi.org/10.1093/hr/uhad061</a></p>
Reference dataset for comparison of cloud detection algorithms for Sentinel-2 imagery
<p>Sentinel-2 cloud mask reference dataset generated and analyzed as part of Tarrio, K., Tang, X., Masek, J.G., Claverie, M., Ju, J., Qiu, S., Zhu, Z. and Woodcock, C.E., 2020. Comparison of cloud detection algorithms for Sentinel-2 imagery. Science of Remote Sensing, 2, p.100010. [https://www.sciencedirect.com/science/article/pii/S2666017220300092](https://www.sciencedirect.com/science/article/pii/S2666017220300092)</p> <p><strong>1. Reference masks</strong></p> <p><strong>Algorithms:</strong></p> <ul> <li>Fmask 1.x</li> <li>Fmask 2.x</li> <li>Fmask 4.x</li> <li>Tmask</li> <li>Sen2Cor</li> <li>MAJA</li> <li>LaSRC</li> </ul> <p><strong>Locations:</strong></p> <ul> <li>South Africa (35JPM)</li> <li>Senegal (28PDC)</li> <li>Switzerland (32TLT)</li> <li>France (31TCJ, 31TFJ)</li> <li>Morocco (29RNQ)</li> </ul> <p><strong>Standardized legend:</strong></p> <p>Original algorithm outputs were standardized to the same categorical legend.</p> <ul> <li>0 = clear land</li> <li>1 = clear water</li> <li>2 = cloud shadow</li> <li>3 = snow/ice</li> <li>4 = cloud</li> </ul> <p>All reference masks processed to both 10m and 30m resolution, with the exception of Tmask, which is available only at a 30m resolution.</p> <p><strong>Mask naming convention:</strong></p> <p>All processed masks are named according to the following convention:<br> M<*resolution*><*S2 MGRS tile ID*><*YYYY*><*DOY*><*algorithm*><br> e.g. **M30T28PDC2016351TMASK**</p> <p><br> <strong>2. Interpreted sample points</strong></p> <p>Sample points were selected based on agreement among different map products. This record includes a shapefile with the final interpretations for each of the sampled sites. (See publication for additional information.)</p>
Reference sequences and analyses
<p>Reference sequences used to train PlasX. Includes contig sequences, gene calls, and gene functional annotations.</p>
Reference Measurements of Pavement Samples
<p>Data for MDPI Sensors article <em>Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete </em>(doi: <a href="https://doi.org/10.3390/s21248190">10.3390/s21248190</a>). Contains data related to Sections 2.3 and 3.1 of the paper. Data describe reference measurements of pavement samples.</p> <p>Data are registered point clouds in ASCII text XYZ format in an arbitrary coordinate system.</p>
ScienceDex guides
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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)
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.