Find research datasets worth reusing

Arab Pangenome Reference

Pangenomes represent a significant shift from relying on a single reference sequence to a robust set of assemblies, Arab populations remain significantly underrepresented; hence, we present the first Arab Pangenome Reference (APR) utilizing 53 individuals of diverse Arab ethnicities. We assembled nuclear and mitochondrial pangenomes using 35.27X high-fidelity long reads, 54.22X ultralong reads and 65.46X Hi-C reads yielded contiguous haplotype-phased de novo assemblies of exceptional quality, with an average N50 of 124.28 Mb. We discovered 111.96 million base pairs of novel euchromatic sequences absent from existing human pangenomes, the T2T-CHM13, GRCh38 reference human genomes, and other public datasets. We identified 8.94 million population-specific small variants and 235,195 structural variants within the Arab pangenome. We detected 883 gene duplications including 15.06% associated with recessive diseases and 1,436 bp of novel mitochondrial pangenome sequence. Our study provides a valuable resource for future genomic medicine initiatives in Arab population and other global populations.

opencc-by-4.0Sep 2023View details →

The result of variants calling with the Japonica reference

We used two major varities of the rice genome, Indica and Japonica, to build different variant calling models that differ in the composition of samples from the two varities.

opencc-by-4.0Sep 2023View details →

The result of variants calling with the Indica reference

We used two major varities of the rice genome, Indica and Japonica, to build different variant calling models that differ in the composition of samples from the two varities.

PkA1HT reference genome assembly (version 1.0)

PkA1HT reference genome assembly Long-read PacBio HiFi sequencing was performed on two distinct P. knowlesi piggyBac clones (PkBc38 and PkBc44) to fully investigate potential structural variation (including gene duplication or deletion) amongst mutants that may have been overlooked by short-read sequencing. The piggyBac insertion was cut out of the PkBc38 clone assembly to generate a reference genome for our parental PkA1-H.1 line which we’ve named PkA1HT. The final PkA1HT reference genome was annotated with Companion (v2.2.8) using the P. knowlesi H strain as reference, specifying the assembly option with no contiguation. The new PkA1HT reference genome has 18 sequences (comprised of 14 chromosomes, the mitochondrial genome, the apicoplast genome, and two unordered contigs). It has no sequencing gaps and is 25.29MB long, compared to 142 gaps and a smaller 24.32Mb for PkA1-H.1. Sequences for ten chromosomes reach into the telomeric heptamer repeats on both ends, and sequences for the four remaining chromosomes reach into the telomeric repeats for just one end (the two unordered contigs contain the remaining two unassembled chromosome ends).   Additional methods Long-read PacBio HiFi sequencing was performed on two distinct P. knowlesi piggyBac clones (PkBc38 and PkBc44) to fully investigate potential structural variation (including gene duplication or deletion) amongst mutants that may have been overlooked by short-read sequencing. High molecular weight (HMW) DNA was extracted from parasite-infected human RBCs using the MagAttract HMW DNA Kit (Qiagen #67563), following the manufacturer’s protocol for the manual purification of genomic DNA from whole blood. DNA quality and quantity were assessed using a Qubit fluorometer and agarose gel electrophoresis, with samples meeting the criteria of a Qubit concentration >50 ng/µL and intact bands on the gel. Two µg of HMW genomic DNA was then sheared to an average size of ~15-20 kb, and SMRTbell libraries were prepared using the PacBio SMRTbell Prep Kit 3.0 (PacBio #102-141-700), which included DNA end-repair, adapter ligation, and nuclease treatment to remove incomplete molecules. An additional gel-based size selection step on the PippinHT was performed to remove fragments smaller than 10 kb. The final library was purified, quantified, and sequenced on the PacBio Revio platform (1x Revio Cell) to generate long-read data. Samples were generated at the University of South Florida and sequenced at the Wellcome Sanger Institute. Sequencing data were processed for quality control using standard PacBio workflows and a genome assembly was generated for each clone. We performed our long-read assemblies using the Canu assembler (v2.2) with default parameters, followed by polishing with ILRA (v1.5.1). The ILRA workflow included running ABACAS against the current PkA1-H.1 reference genome (v. 55) followed by two iterations of short-read correction using Pilon with Illumina reads of the parental line. We performed manual finishing with the Artemis Comparison Tool (ACT), informed by long reads mapped back against the draft assemblies. We obtained >500x coverage from our PacBio sequencing with a median read length of 15kbp, and we had telomere-to-telomere completion on several chromosomes. Assemblies were then compared against each other using ACT. We found no significant structural variation between the two clonal lines, with near complete co-linearity save for each transposon insertion, indicating as expected that the piggyBac transposon insertion introduces no wider genomic changes. To compare differences between our parental line and the PkA1-H.1 reference, we used ACT to identify possible regions of recombination, followed by a mapping approach and manual analysis in Artemis for validation. We found a duplication of 14 genes on chromosome 7 in both clones. We further found several synteny breaks between our de novo assemblies and the current PkA1-H.1 reference. Analyzing those “breakpoints” more closely in ACT, we found that they are actually misassemblies in the current reference  This finding of misassemblies motivated us to generate a more complete reference genome (see next section). We otherwise found no evidence of recombination or large indels vs. the reference for either clone. It should be noted that our PkA1HT assembly also supersedes both the PkH1 and PkA1 assemblies in terms of stats (details to be reported elsewhere).

Reference Spectra, Laboratory Mixture Spectra, list of NIMS cubes used in this study and the manual corrections to these NIMS cubes for better alignment.

Reflectance spectra for the endmember library, including both the public data and data made in this study.  Reflectance spectra for the 100% SAO, 10% SAO, 25% SAO, 80% SAO, and 100% Water ice mixtures. Text File of the PDS IDs of NIMS data cubes analyzed in this study. (X,Y) offsets applied to each data cube for better alignment between NIMS data and the Global Mosaic.

opencc-by-4.0Dec 1989View details →

Tabl. 1 in The Jordan theropod (Maastrichtian, Montana, U. S. A.) referred to the genus Aublysodon

Tabl. 1 - Length and width measurements for Aublysotlon mirandus teeth. Mesures des longueur et largeur des dents de Aublysotlon mirandus.<table><tbody><tr><th>Specimen Number</th><th>anterioposterior length</th><th>width</th></tr></tbody><tbody><tr><th>KU 12919</th><td>11.5min</td><td>11.5mm</td></tr><tr><th>UCM 43447</th><td>30</td><td>25</td></tr><tr><th>UCM 38OÓO</th><td>80</td><td>45</td></tr><tr><th>UCMP 64143</th><td>60</td><td>45</td></tr><tr><th>UCMP 7309I</th><td>65</td><td>4o</td></tr><tr><th>UCMP 73l0l</th><td>50</td><td>25</td></tr><tr><th>UCMP 85141</th><td>35</td><td>25</td></tr><tr><th>UCMP 124367</th><td>25</td><td>20</td></tr><tr><th>UCMP 124398</th><td>45</td><td>25</td></tr><tr><th>UCMP 124971</th><td>40</td><td>25</td></tr><tr><th>UCMP 124980</th><td>35</td><td>30</td></tr><tr><th>UCMP 129981</th><td>35</td><td>25</td></tr><tr><th>UCMP 124982</th><td>25</td><td>20</td></tr><tr><th>UCMP 124993</th><td>30</td><td>20</td></tr><tr><th>UCMP 124999</th><td>45</td><td>30</td></tr><tr><th>UCMP 124995</th><td>75</td><td>45</td></tr><tr><th>UCMP 124996</th><td>55</td><td>10</td></tr><tr><th>USNM 8355</th><td>60</td><td>10</td></tr></tbody></table>

Reference files and small demo files for MisMatchFinder

This Zenodo repository contains the reference files and small demo files for running MisMatchFinder.  MisMatchFinder is a fast computational tool for detecting variants and infering mutational signatures from plasma low-coverage, next generation sequencing data of cancer patients without using sequencing from matched normal tissues.  MisMatchFinder is developed by the Molecular Biomarkers and Translational Genomics Lab in Peter MacCallum Cancer Centre (Australia). The tool and instructions are available at <a href="https://atlassian.petermac.org.au/bitbucket/projects/SJDAW/repos/mismatchfinder/browse">the following Bitbucket repository</a>. To run the tool, it is highly recommanded to activate the germline filter to filter out common germline variants and use a whitelist which helps to only detect variants in high mappability regions of the human genome.  For example:  $ mismatchfinder --germline_file /path/to/echtvarfile/gnomad.v3.1.2.echtvar.v2.zip --whitelist_bed /path/to/whitelist.bed -o /path/to/outputfolder/ --strict_overlaps --only_overlaps /path/to/plasma_DNA_demo.bam The files included in this dataset are described below: gnomad.v3.1.2.echtvar.v2.zip: the pre-built gnomad data used for the MisMatchFinder germline filter. GCA_000001405.15_GRCh38_full_analysis_set.100mer.highMappability.bed: the high mappability regions file for the GRCh38 human genome, used as the MisMatchFinder whitelist.  Note, the plasma_DNA_demo.bam is simulated human plasma sequencing data of chromosome 19. This file and the corresponding MisMatchFinder output (plasma_DNA_demo_bamsites.vcf.gz) should only be used for the sanity testing of MisMatchFinder.  clean_10x_rmd.bam and clean_3x_rmd.bam are two bam files which used to assess the effectiveness of MisMatchFinder quality filters. These data are computationally simuated by ART (Weichun Huang, Bioinformatics, 2012), which are supposed to contain sequencing errors similar to Illumina platforms. These data can be used to understand how high quality filters, such as applying high qulity base & mapping qualites and selecting variants only from overlapped regions of reads, can help to reduce the errors in input data (similar to Fig 1b in MisMatchFinder manuscript).

opencc-by-4.0Apr 2024View details →

Simulated EEG and EMG data for Reference Phase Analysis evaluation.

This dataset comprises simulated EEG (Electroencephalography) signals recorded from 29 channels along with an EMG (Electromyography) signal. The signals have been artificially generated using real EEG and EMG signals as the basis. Five distinct source models were generated, each comprising a different number of sources. We created models with 2, 3, 4, 5, and 6 sources. Furthermore, we add additive noise to the signals encompassing SNRs ranging from 90 to 0 dB and a phase jitter ranging from 0.1 to 1.5 radians. We generated EEG and EMG signals using MATLAB and employed a forward model, created with FieldTrip, to convert signals from the source space to the electrode space. EEG measures were computed utilizing a linear mixture model. The simulated dataset aims to mimic the characteristics of real EEG and EMG signals, including their temporal dynamics, noise, and frequency spectra. It serves as a valuable resource for developing and testing signal processing algorithms, and brain-computer interfaces in the fields of neuroscience and biomedical engineering.

Demarcation of boundaries are for biological references only. in SUHAIB FIRDOUS YATOO, AIJAZ AHMAD WACHKOO, KAVYA G. PILLAI & CHRIS M. COHEN (2024) Catalog of the Robber Flies of India (Diptera: Asilidae). Zootaxa, 5509 (1): 001-088.

Demarcation of boundaries are for biological references only.

opencc-by-4.0Aug 2024View details →

An analysis of peak fitting in reference material spectra for calibration of Raman spectroscopy instruments (Dataset)

tbd NeXus format

Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking - Dataset

This is the dataset of "Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking".  <a href="https://doi.org/10.1515/nanoph-2024-0222" target="_blank" rel="noopener">https://doi.org/10.1515/nanoph-2024-0222</a> Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.

Reference interval of optimal vitamin D level for an adult population of Bangladesh

<div> <div> <h3>Description of the data and file structure</h3> The study collected data from healthy fishermen in Bangladesh to determine the reference interval for vitamin D levels in this population. Additionally, data from an urban population were gathered to identify the parathyroid hormone (PTH) deflection point in relation to vitamin D levels <h4>Files and variables</h4> <h5>File: Urban_final.sav</h5> Description: This file contains data from an urban population aimed at identifying the parathyroid hormone (PTH) deflection point in relation to vitamin D levels. <h5>File: Fishermen_final.sav</h5> Description: This file contains data from healthy fishermen in Bangladesh, collected to determine the reference interval for vitamin D levels in this population. <h3>Code/software</h3> IBM SPSS v25 with Python plug-in software was used to analyse the data. Medcalc version-14 statistical software was used to calculate reference intervals. </div> </div>

opencc-by-sa-4.0May 2024View details →

Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization

<h2>INTRODUCTION</h2> This document outlines the creation of a global inventory of reference samples and Earth Observation (EO) / gridded datasets for the Global Pasture Watch (GPW) initiative. This inventory supports the training and validation of machine-learning models for GPW grassland mapping. This documentation outlines methodology, data sources, workflow, and results. Keywords: Grassland, Land Use, Land Cover, Gridded Datasets, Harmonization   <h2>OBJECTIVES</h2> <ul> <li> Create a global inventory of existing reference samples for land use and land cover (LULC); </li> <li> Compile global EO / gridded datasets that capture LULC classes and harmonize them to match the GPW classes; </li> <li> Develop automated scripts for data harmonization and integration. </li> </ul>   <h2>DATA COLLECTION </h2> Datasets incorporated: <table> <tbody> <tr> <td>Datasets</td> <td> Spatial distribution </td> <td>Time period</td> <td>Number of individual samples</td> </tr> <tr> <td>WorldCereal</td> <td>Global</td> <td>2016-2021</td> <td>38,267,911</td> </tr> <tr> <td>Global Land Cover Mapping and Estimation (GLanCE)</td> <td>Global</td> <td>1985-2021</td> <td>31,061,694</td> </tr> <tr> <td>EuroCrops</td> <td>Europe</td> <td>2015-2022</td> <td>14,742,648</td> </tr> <tr> <td>GeoWiki G-GLOPS training dataset</td> <td>Global</td> <td>2021</td> <td>11,394,623</td> </tr> <tr> <td>MapBiomas Brazil</td> <td>Brazil</td> <td>1985-2018</td> <td>3,234,370</td> </tr> <tr> <td>Land Use/Land Cover Area Frame Survey (LUCAS)</td> <td>Europe</td> <td>2006-2018</td> <td>1,351,293</td> </tr> <tr> <td>Dynamic World</td> <td>Global</td> <td>2019-2020</td> <td>1,249,983</td> </tr> <tr> <td>Land Change Monitoring, Assessment, and Projection (LCMap)</td> <td>U.S. (CONUS)</td> <td>1984-2018</td> <td>874,836</td> </tr> <tr> <td>GeoWiki 2012</td> <td>Global</td> <td>2011-2012</td> <td>151,942</td> </tr> <tr> <td>PREDICTS</td> <td>Global</td> <td>1984-2013</td> <td>16,627</td> </tr> <tr> <td>CropHarvest</td> <td>Global</td> <td>2018-2021</td> <td>9,714</td> </tr> </tbody> </table> Total: 102,355,642 samples   <h2>WORKFLOW</h2> <h3>Harmonization Process</h3> We harmonized global reference samples and EO/gridded datasets to align with GPW classes, optimizing their integration into the GPW machine-learning workflow. We considered reference samples derived by visual interpretation with spatial support of at least 30 m (Landsat and Sentinel), that could represent LULC classes for a point or region. Each dataset was processed using automated Python scripts to download vector files and convert the original LULC classes into the following GPW classes:        0. Other land cover        1. Natural and Semi-natural grassland        2. Cultivated grassland        3. Crops and other related agricultural practices We empirically assigned a weight to each sample based on the original dataset's class description, reflecting the level of mixture within the class. The weights range from 1 (Low) to 3 (High), with higher weights indicating greater mixture. Samples with low mixture levels are more accurate and effective for differentiating typologies and for validation purposes. The harmonized dataset includes these columns: <table> <tbody> <tr> <td>Attribute Name</td> <td>Definition</td> </tr> <tr> <td>dataset_name</td> <td>Original dataset name</td> </tr> <tr> <td>reference_year</td> <td>Reference year of samples from the original dataset</td> </tr> <tr> <td>original_lulc_class</td> <td>LULC class from the original dataset</td> </tr> <tr> <td>gpw_lulc_class</td> <td>Global Pasture Watch LULC class</td> </tr> <tr> <td>sample_weight</td> <td>Sample's weight based on the mixture level within the original LULC class</td> </tr> </tbody> </table>   <h2>ACKNOWLEDGMENTS</h2> The development of this global inventory of reference samples and EO/gridded datasets relied on valuable contributions from various sources. We would like to express our sincere gratitude to the creators and maintainers of all datasets used in this project.   <h2>REFERENCES</h2> <ul> <li> Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Van Tricht, K. et al. Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping. Earth Syst. Sci. Data 15, 5491–5515, 10.5194/essd-15-5491-2023 (2023) </li> <li> Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963 </li> <li> d’Andrimont, R. et al. Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Sci. data 7, 352, 10.1038/s41597-019-0340-y (2020) </li> <li> Fritz, S. et al. Geo-Wiki: An online platform for improving global land cover, Environmental Modelling & Software, 31, https://doi.org/10.1016/j.envsoft.2011.11.015 (2012) </li> <li> Fritz, S., See, L., Perger, C. et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data 4, 170075 https://doi.org/10.1038/sdata.2017.75 (2017) </li> <li> Schneider, M., Schelte, T., Schmitz, F. & Körner, M. Eurocrops: The largest harmonized open crop dataset across the european union. Sci. Data 10, 612, 10.1038/s41597-023-02517-0 (2023) </li> <li> Souza, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote. Sens. 12, 2735, 10.3390/rs12172735 (2020) </li> <li> Stanimirova, R. et al. A global land cover training dataset from 1984 to 2020. Sci. Data 10, 879 (2023)  </li> <li>Stehman, S. V., Pengra, B. W., Horton, J. A. & Wellington, D. F. Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017. Remot Sensing environment 265, 112646, 10.1016/j.rse.2021.112646 (2021).</li> <li> Tsendbazar, N. et al. Product validation report (d12-pvr) v 1.1 (2021). </li> <li>Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.</li> </ul>

hg19/GRCh37 and hg38 reference genome

Open the record for dataset details and reuse information.

opengpl-3.0-or-laterSep 2024View details →

Unique Property Reference Number (UPRN) indicator: Flood risk (UPRN_1_1)

This indicator covers three main flooding risks in the UK: fluvial (rivers and streams), coastal and surface water. The indicators were computed for all the Unique Property Reference Numbers (UPRNs) in the NHS Cheshire and Merseyside Integrated Care Board region (North West England). This region is formed by the following Local Authorities: Chester and Cheshire East, Cheshire West, Halton, Knowsley, Liverpool, Sefton, St. Helens, Warrington and Wirral.

opengpl-3.0-or-laterOct 2024View details →

Unique Property Reference Number (UPRN) indicator: Tree canopy coverage (UPRN_3_1)

This is an indicator with the tree canopy coverage for various distances (15m, 25m, 150m, 300m and 500m) from Unique Property Refence Numbers (UPRNs) in the Office for National Statistics (ONS) UPRN directory v2024.07 (Epoch 111). This metric is based on Bluesky’s National Tree Map™, which is the only tree dataset to include trees in Great Britain and the Republic of Ireland that are 3 metres and taller. <h3>Available files</h3> <ul> <li>UPRN_3_1_tree_canopy_coverage_cm_with_coords.csv: covers the Cheshire and Merseyside region (England) and includes latitude and longitude for each UPRN.</li> <li>UPRN_3_1_tree_canopy_coverage_cm.csv: covers the Cheshire and Merseyside region (England).</li> <li>UPRN_3_1_tree_canopy_coverage_lothian_with_coords.csv: covers the Lothian region (Scotland) and includes latitude and longitude for each UPRN.</li> <li>UPRN_3_1_tree_canopy_coverage_lothian.csv: covers the Cheshire and Merseyside region (England).</li> </ul>   <h3>Fields</h3> <table> <tbody> <tr> <td> Column name </td> <td> Description </td> </tr> <tr> <td> UPRN </td> <td> Unique Property Reference Number as per the Office for National Statistics UPRN Directory (ONSUD) v2024.07 (Epoch 111). </td> </tr> <tr> <td> n_trees_15m </td> <td> Number of trees (above 3m) within 15m from the UPRN. </td> </tr> <tr> <td> tree_canopy_cover_15m </td> <td> Percentage of area  covered by tree canopy within 15m from the UPRN. From a maximum area of approximately 707m2. </td> </tr> <tr> <td> n_trees_25m </td> <td> Number of trees (above 3m) within 25m from the UPRN. </td> </tr> <tr> <td> tree_canopy_cover_25m </td> <td> Percentage of area covered by tree canopy within 25m from the UPRN. From a maximum area of approximately 1963m2. </td> </tr> <tr> <td> n_trees_150m </td> <td> Number of trees (above 3m) within 150m from the UPRN. </td> </tr> <tr> <td> tree_canopy_cover_150m </td> <td> Percentage of area  covered by tree canopy within 150m from the UPRN. From a maximum area of approximately 70686m2. </td> </tr> <tr> <td> n_trees_300m </td> <td> Number of trees (above 3m) within 300m from the UPRN. </td> </tr> <tr> <td> tree_canopy_cover_300m </td> <td> Percentage of area  covered by tree canopy within 300m from the UPRN. From a maximum area of approximately 282743m2. </td> </tr> <tr> <td> n_trees_500m </td> <td> Number of trees (above 3m) within 500m from the UPRN. </td> </tr> <tr> <td> tree_canopy_cover_500m </td> <td> Percentage of area  covered by tree canopy within 500m from the UPRN. From a maximum area of approximately 785398m2. </td> </tr> <tr> <td> latitude (only version with coordinates) </td> <td> Latitude of the UPRN, given in decimal degrees, where N is positive and S is negative. </td> </tr> <tr> <td> longitude (only version with coordinates) </td> <td> Longitude of the UPRN, given in decimal degrees, where E is positive and W is negative. </td> </tr> </tbody> </table>   <h3>Regions</h3> Cheshire and Merseyside (Endgland) Includes the following local authorities: <ul> <li>Cheshire East</li> <li>Cheshire West and Chester</li> <li>Halton</li> <li>Knowsley</li> <li>Liverpool</li> <li>Sefton</li> <li>St. Helens</li> <li>Warrington</li> <li>Wirral</li> </ul>   Lothian (Scotland) Includes the following local authorities: <ul> <li>East Lothian</li> <li>City of Edinburgh</li> <li>Midlothian</li> <li>Westlothian</li> </ul>

opencc-by-4.0Nov 2024View details →

Azimuth Reference - Mouse PanSci

Here we provide the reference data files used to run the Azimuth Mouse PanSci web application. For a full description of the reference data structure, please see the wiki at <a href="https://github.com/satijalab/azimuth/wiki/Azimuth-Reference-Format">https://github.com/satijalab/azimuth/wiki/Azimuth-Reference-Format</a>.

opencc-by-4.0Nov 2024View details →

Kimtech and Paper Towel Microscope References

These are transmitted light microscope images of fibers from Kimtech wipes and paper towels. References to check for contamination.

Human Retina Cell Atlas reference model

This dataset hosts files needed to reproduce the Human Retina Cell Atlas (HRCA) reference model using scArches. The HRCA data can be accessed through several interactive browsers, including <a href="https://data.humancellatlas.org/hca-bio-networks/eye/atlases/retina-v1-0">HCA Data Portal</a>, <a href="https://cellxgene.cziscience.com/collections/4c6eaf5c-6d57-4c76-b1e9-60df8c655f1e">CELLxGENE</a>, <a href="https://retina.cells.ucsc.edu/">UCSC Cell Browser</a>, and <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2805">the Broad Single Cell Portal</a>. Please use these browsers for atlas exploration and visualization. For more information on HRCA, please refer to the HRCA paper (<a href="https://doi.org/10.1101/2023.11.07.566105" rel="nofollow">Li et al.</a>, bioRxiv 2023) and the Github repository at <a href="https://github.com/RCHENLAB/HRCA_reproducibility">https://github.com/RCHENLAB/HRCA_reproducibility</a>. This dataset has been used in the tutorial for the HRCA reference model at <a href="https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches">https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches</a>. Data description: 1. HRCA_snRNA_allcells_rawcounts.h5ad This file contains the cell-by-gene count matrix for over 3.1 million single nuclei and more than 36,000 gene features of the HRCA. Gene features are represented by gene symbols. Please refer to the interactive browsers for atlas exploration, where gene features are mapped to Ensembl IDs. In the cell metadata, "sampleid" indicates sample batches of cells, and "celltype" specifies 123 retina cell types. 2. model.pt This file is the trained reference model using scArches, incorporating 10,000 highly variable features from the full count matrix. It can be directly used for cell type annotation of new retina samples. 3. HRCA_snRNA_allcells_rawcounts_latent.h5ad This file contains the embeddings of all 3.1 million reference single nuclei generated by the trained reference model using scArches. These embeddings can be used to compare with the embeddings of query data for exploration. 4. HRCA_reference_model_gene_id_and_symbol.csv This file contains the mapping of Ensembl IDs to gene symbols for the 10,000 features used in the reference model. This mapping can be used to convert the gene features in a query .h5ad file from gene IDs to gene symbols, allowing cell type labels to be predicted using the trained reference model, which uses gene symbols as gene features. 5. query.h5ad This file contains a cell-by-gene count matrix for a query dataset, designed to support reproducibility in the HRCA reference model tutorial. The "majorclass" column includes pre-annotated major cell classes. Additional details on the tutorial are available at <a href="https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches">https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches</a>. 6. query_latent.h5ad This file contains the embeddings of the query data against the trained reference model. These embeddings can be compared with the reference data embeddings for exploration and visualization.