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404 results for “reference data”
Air and soil temperature data from the Reference Stand network at the Andrews Experimental Forest, 1971 to present
The current network of temperature measurement sites are designed to represent spatial variability of air and soil temperature in rugged mountain topography, and serve as second-level stations to capture specific microclimate temperatures in conjunction with a network of Benchmark Meteorological Stations (MS001). The air and soil thermograph network has been reduced from the historical network of 37 sites originally established. Currently there are 10 measurement sites with two of these sites measuring relative humidity in addition to air and soil temperature. An original network of 19 sites (RS01-RS19) were established during the International Biome Program in the early 1970's. Emphasis on phenology, plant moisture stress, and leaf nutrient content led to extending this network of air and soil temperature measurement. A plant community classification system (Dyrness et al., 1971) was used as a primary means of stratification, and a set of permanent vegetation plots (Reference Stands) was installed to represent forest communities with distinct vegetation and hypothesized different environments (Dyrness et al., 1974). A thermograph network was installed within the reference stands in the early 1970's (Zobel et al., 1974), and vegetation standing crop, tree growth and mortality, and plant succession were also measured. The majority of these sites were established to monitor micro-meteorological data under the canopy. The purpose of this network was to provide air and soil temperature data for modeling photosynthesis, respiration, phenology, and decomposition, and to measure environmental gradients.
EATRIS-Plus multi-omics data of a human reference cohort
<p>In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics <br>profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, the following data types are included:</p> <ul> <li>Targeted metabolomics (acylcarnitines, amino acids and very long chain fatty acids)</li> <li>Lipidomics (negative and positive ionization modes)</li> <li>Proteomics</li> <li>mRNA-seq</li> <li>miRNA-seq</li> <li>miRNA qRT-PCR</li> <li>Enzymation Methylation sequencing</li> </ul> <p>The pre-processed mult-omics data can be accessed here in the shape of a MultiAssayExperiment object (<a href="https://doi.org/10.1158/0008-5472.can-17-0344">Ramos et al. 2017</a>). Instructions on how to read the object into R can be found here: <a href="https://github.com/EATRIS/Read_MultiAssayExperiment">Read_MultiAssayExperiment</a>.</p> <p>A similar object for Python (MuData) including the same data will be added later. </p> <p> </p> <p>DATA AVAILABILITY STATEMENT:</p> <p>Full data related to the EATRIS-Plus multiomic cohort are available in the ClinData repository (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclindata.imtm.cz%2F&data=05%7C02%7CCasper.deVisser%40radboudumc.nl%7C347853c763954a30b82208dc3ebe2571%7Cb208fe69471e48c48d87025e9b9a157f%7C0%7C0%7C638454233596610669%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Jp7u%2BXblry9QNg4EQkJE4CKxMZZfMK9U84Ob7E6up90%3D&reserved=0">https://clindata.imtm.cz</a>) and include full phenotypic information, physical and laboratory examinations, multiomic data from white blood cells (whole genome sequencing, enzymatic methylation DNA sequencing, mRNA sequencing, miRNA sequencing) or plasma (miRNA qPCR profiling, proteomics, targeted metabolomics, untargeted lipidomics, Raman spectroscopy profiling). However, access is restricted due to legal, ethical, scientific and/or commercial reasons. Access to the data is subject to approval and a data sharing transfer agreement. For data access please contact <a href="mailto:data.access@imtm.upol.cz">data.access@imtm.cz</a>. </p>
Real-time deformability cytometry reference data
<p>This dataset consists of four exemplary real-time fluorescence and deformability cytometry measurements. The HDF5-files can be opened with dclab [1] or Shape-Out [2].</p> <p><strong>calibration_beads.rtdc</strong><br> The calibartion beads (8 Peaks, PolyAN) consist of eight bead populations with different mixtures of fluorophores.</p> <p><br> <strong>CD34_HSPC.rtdc</strong><br> Hematopoietic stem and progenitor cells (HSPCs) were obtained using apheresis. The cells were tagged with a fluorescently labeled antibody that binds to the CD34 transmembrane protein. CD34-positive HSPCs are gated with `fl3_max > 90`. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> <strong>leukocytes.rtdc</strong><br> The leukocyte population (white blood cells) of this blood sample can be visualized by setting `aspect < 2` and `area_ratio < 1.05`. For more information, see e.g. [4].</p> <p><br> <strong>reticulocytes.rtdc</strong><br> Blood contains mostly red blood cells (RBCs) and about 1% reticulocytes (which develop into mature RBCs). Reticulocytes contain ribosomal RNA which was stained with Syto13 for this measurement. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> [1] <a href="https://github.com/ZellMechanik-Dresden/dclab">https://github.com/ZellMechanik-Dresden/dclab</a></p> <p>[2] <a href="https://github.com/ZellMechanik-Dresden/ShapeOut">https://github.com/ZellMechanik-Dresden/ShapeOut</a></p> <p>[3] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:<a href="https://dx.doi.org/10.1038/nmeth.4639">10.1038/nmeth.4639</a>.</p> <p>[4] Toepfner et al., "Detection of human disease conditions by single-cell morpho-rheological phenotyping of whole blood". eLife, 7:e29213, 2017. doi:<a href="https://dx.doi.org/10.1101/145078">10.1101/145078</a>.</p> <p><br> SHA256 sums:<br> 08c2ef13eed903ef0f9e451727ab8484df09b5d3b39227dab726e0164dcbe244 calibration_beads.rtdc<br> 663b44a9db88d85996500045489e37a317cf115719223a531d617f8e3d450e79 CD34_HSPC.rtdc<br> 68bd538b42ffb990f1db52d5f3b21f37c9aff31208ab284f3910fd6872c40fdb leukocytes.rtdc<br> 5c323ea75bf7eeb2a28d922730772d50270dd872d6957e60d6062663f3628fb3 reticulocytes.rtdc</p>
Reference percentiles for carotid measures of subclinical atherosclerosis in children and adolescents – Data from the KiGGS study
<p>The dataset presents reference centile data for carotid intima-media thickness as well as four carotid stiffness parameters. The reference percentiles are based on data from the German Health Interview and Examination Survey for Children and Adolescents 2003-2006 (KiGGS). KiGGS started as a cross-sectional study conducted between 2003 and 2006 which was based on a nationally representative sample and aimed at obtaining comprehensive data on the health of children and adolescents aged 0 to 17 years living in Germany. Detailed information on study design and conduct has been published in peer reviewed journals as well as detailed papers on individual parameters. The documentation provides relevant references.<br> <br> 11 years later, KiGGS Wave 2 (2014-2017) was conducted as an interview and examination survey. Carotid sonography was attempted at follow-up (KiGGS2) in all 4,798 participants of the KiGGS cohort aged 14 to 28 years. Carotid intima-media thickness (CIMT) was successfully measured in 4,709 participants. Reference centiles for the distensibility coefficient, stiffness index ß, Young's elastic modulus and Peterson's elastic modulus were computed using data on 4,305 adolescents and young adults aged 14 to 28.</p>
Reference data and documentation for Skills4EOSC Deliverable D6.1 Mapping of existing professional networks
<p>This record presents the data underlying <strong>Skills4EOSC Deliverable D6.1 Mapping of existing professional networks</strong> and relevant documentation of the search string.</p>
May to July 2018 regions of interest (ROIs) of tidal marsh and tidal forest plant species to be used as ground reference data in habitat mapping
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground reference data for habitat mapping. Regions of interest (ROIs) for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were generated near ground control points (GCP) by digitizing vegetation areas in ArcGIS 10.4 based on field maps.These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
IPBES Data Management Tutorials - Session 5.5: References and citation manager: Zotero
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session, <em>References and citation manager: Zotero, </em>reviews why IPBES recommends Zotero to manage references and provides links to key resources.</p>
BAM reference data: XPS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a)
<p>The raw data are given as VAMAS-File. The measurement condtions are given in the file. The C1s-fits are provided as ascii-files.</p> <p>For further information please look at 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.</p> <p>The transmission function is obtained as ascii-file trm.dat. The energy scale is kinetic energy.</p> <p> </p> <p>Measurement conditions:</p> <p>XPS measurements were performed at an Axis Ultra DLD (KRATOS, Manchester, UK) with monochromatic Al K radiation (E = 1486.6 eV). The electron emission angle was 0° and the source-to-analyzer angle was 60°. The binding energy scale of the instrument was calibrated following a Kratos analytical procedure, which uses ISO 15472 binding energy data. The setting of the instrument was the hybrid lens mode and the slot mode with an analysis area of approximately 300x700 m². Furthermore, charge neutralization with a flood gun was used. All spectra were recorded in the fixed analyzer transmission (FAT) mode. The samples were measured as powders prepared on a special stainless-steel sample holder.</p> <p> </p> <p> </p> <p> </p>
Default SingleM reference "metapackage" data
<p>SingleM is a tool for profiling shotgun metagenomes. It has a particular strength in detecting microbial lineages which are not in reference databases. The method it uses also makes it suitable for some related tasks, such as assessing eukaryotic contamination, finding bias in genome recovery, computing ecological diversity metrics, and lineage-targeted MAG recovery.</p> <p>The data here is the singlem "metapackage" which is the reference package to be used with SingleM in e.g. "pipe" mode.</p> <p>SingleM is available at <a href="https://github.com/wwood/singlem">https://github.com/wwood/singlem</a>.</p> <p>The newest version is built from Genome Taxonomy Database (GTDB) version 10-RS226, but older versions available in the history of this record should still work with the newest SingleM software.</p> <p> </p> <h2>Changelog</h2> <p>version 5.4.0</p> <ul> <li>Updated to GTDB 10-RS226.</li> <li>The 5.x version number indicates the metapackage format. Old 4.x versions still work with the current software version.</li> </ul> <p>version 4.3.0</p> <ul> <li>Updated to GTDB 09-RS220.</li> </ul> <p>version 4.2.2</p> <ul> <li>Fixed name of .zb folder to be correct version</li> </ul> <p>version 4.2.1</p> <ul> <li>Changed name of .zb folder to be standard</li> </ul> <p>version 4.2.0</p> <ul> <li>Updated GTDB 08-RS214 package to metapackage version 5, and smafa database version 2.</li> </ul> <p>version 4.1.0</p> <ul> <li>Updated GTDB 07-RS207 package to metapackage version 5, and smafa database version 2 (this is the same as version 3.1.2, but with an updated version number).</li> </ul> <p>version 3.2.1</p> <ul> <li>Updated genome sizes for GTDB genomes (for use with `read_fraction`) corrected based on CheckM v2 estimates of completeness and contamination.</li> </ul> <p>version 3.2.0</p> <ul> <li>Updated to GTDB 08-RS214.</li> </ul> <p>version 3.1.2</p> <ul> <li>Updated GTDB 07-RS207 package to metapackage version 5, and smafa database version 2.</li> </ul>
AirMLP - SPS30 low-cost sensors and Tecora reference station PM 2.5 data
<p>The information below describes a dataset related to a study conducted in Turin, Italy, involving low-cost laser-scattering SPS30 sensors placed by Wiseair SRL and a Tecora reference station placed by Arpa Piemonte (Italian Air Quality Agency). This dataset spans two different time periods in 2022, specifically from March 1, 2022, to April 29, 2022, and from October 26, 2022, to December 30, 2022. The data in this dataset pertains to the mass concentration of PM2.5 (particulate matter with a diameter of 2.5 micrometres or less).</p><p> </p><p>The reference station's data is divided into two periods and is provided in files named "rf_x.csv." These files contain hourly data and timestamps in GMT+1. Each file has three columns:</p><ul><li>"valid_at" (in Rome local hour, GMT+1)</li><li>"valore_originale" (PM 2.5 raw mass concentration values recorded by the reference station)</li><li>"pm2p5" (PM 2.5 mass concentration validated values by the air quality agency)</li></ul><p>The low-cost sensors, referred to as "ari_xxxx.csv," provide data at approximately 15-minute frequency. These files contain the following columns:</p><ul><li>"valid_at" (in GMT)</li><li>"pm2p5" (PM 2.5 raw mass concentration measured by the SPS30 sensor)</li><li>"relative_humidity" (expressed as a percentage)</li><li>"temperature" (in degrees Celsius)</li><li>"pressure" (in hPA)</li><li>"wind_speed" (in meters per second)</li><li>"cloud_coverage" (expressed as a percentage)</li></ul><p>Notably, the "relative_humidity" and "temperature" values are gathered from sensors placed within a device containing the SPS30 low-cost sensor.</p><p> </p><p>Here's a summary of the specific data files in this dataset:</p><ul><li>"<strong>rf_1.csv</strong>": Hourly data provided by the Air Quality Agency for the first period.</li><li>"<strong>rf_2.csv</strong>": Hourly data provided by the Air Quality Agency for the second period.</li><li>"<strong>arpa_1727.csv</strong>," "<strong>arpa_1952.csv</strong>," and "<strong>arpa_1953.csv</strong>": Three low-cost sensors placed by Wiseair, which refer to the first period.</li><li>"<strong>arpa_1885.csv</strong>" and "<strong>arpa_2049.csv</strong>": Two low-cost sensors placed by Wiseair, that refer to the second period.</li></ul>
Reference data for neural retina atlas
<p>This repository contains reference files used in analysis for the manuscript 'A proteogenomic atlas of the human neural retina' by Riepe et al. (2024). The code is available at https://github.com/cmbi/Neural-Retina-Atlas.</p>
12S metabarcoding reference data from the Research Institute for Nature and Forest (INBO)
<p>This dataset contains metabarcoding reference data with 12S sequences from fish and other vertebrates for Teleo and Riaz primers suited for use with the <a href="https://git.metabarcoding.org/obitools/obitools/wikis/home/">OBITools</a> package.</p> <p><strong>Files</strong><br>- <strong>all_seqs_INBO_riaz_amplified.fasta</strong>: reference data for Riaz marker<br>- <strong>all_seqs_INBO_Valentini_teleo_amplified.fasta</strong>: reference data for Teleo marker<br>- <strong>species_INBO_riaz.csv</strong>: species for which reference data is included in Riaz dataset<br>- <strong>species_INBO_teleo.csv</strong>: species for which reference data is included in Teleo dataset</p>
Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans
<p>This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in <em>C. elegans</em>". Please see the linked preprint/publication for full details.</p> <p>The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.</p>
Fish and crayfish density and count data for Peeks Creek, Macon County, NC, USA 2005-2014, 2019, and 2022 following a catastrophic debris flow, as well as six reference streams
We followed the process of recovery of the fish and crayfish assemblage in Peeks Creek, a high-gradient second order stream in the Little Tennessee River watershed of North Carolina, after a debris flow devastated the channel and its riparian zone. After 15 years, the fish assemblage had recovered, and the channel and riparian zone had stabilized. Of the three major components of the fish assemblage, Rainbow Trout (Oncorhynchus mykiss (Walbaum)), a strong swimmer, reappeared in year 1. Longnose Dace (Rhinichthys cataractae (Valenciennes in Cuvier and Valenciennes)) reappeared in year 3. Mottled Sculpin (Cottus bairdii Girard), a weak swimmer, did not become established until year 6 and only resumed expected abundance in year 9. Appalachian Brook Crayfish (Cambarus bartonii cavatus Hay) numbers recovered quickly, though only one individual was found the year following the debris flow. Unassisted natural recovery occurred after a costly engineered restoration project had been rejected and arguably represents the preferable solution. However, recovery of the fish assemblage may not have been achieved if the stream flowed directly into an impoundment or low gradient river that lacked the source of species for recolonization, or if the stream had been located above a barrier to upstream movement.
Fine woody debris inventory data from reference stands and inventory plots in the Pacific Northwest, 1992 to 2000
These data provide an inventory of the mass of downed fine woody debris stored within various forest types. This data is used to determine total organic matter, carbon, and nutrient stores in forests.
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>
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>
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>
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>
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.