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464
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ShareScore release 0.7.1
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464 results for “high density”
Data from: High male density favors maintenance over reproduction in a butterfly
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Data from: Plant reproductive strategies vary under low and high pollinator densities
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Data from: High temperature intensifies negative density dependence of fitness in red flour beetles
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Data from: Alternative reproductive tactics and inverse size-assortment in a high-density fish spawning aggregation
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Data from: Light stabilizers added to the shell of coextruded wood-high density polyethylene composites to improve mechanical and anti-UV aging properties
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Data from: Restriction site-associated DNA sequencing for SNP discovery and high-density genetic map construction in southern catfish (Silurus meridionalis)
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Data from: High-density sex-specific linkage maps of a European tree frog (Hyla arborea) identify the sex chromosome without information on offspring sex
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Data from: Microbeam two-dimensional small-angle X-ray scattering investigating the effects of reduced graphene oxide on local microstructures of high-density polyethylene/reduced graphene oxide nanocomposite bars
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Data from: High-density cultivation of microalgae continuously fed with unfiltered water from a recirculating aquaculture system
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Data from: Estimations of linkage disequilibrium, effective population size and ROH-based inbreeding coefficients in Spanish Churra sheep using imputed high-density SNP genotypes
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Data from: Demography of a high density tiger population and its implications for tiger recovery
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Aboveground Biomass Density for High Latitude Forests from ICESat-2, 2020
This dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project. Mapping forest aboveground biomass is essential for understanding, monitoring, and managing forest carbon stocks toward climate change mitigation. The AGBD estimates cover the extent of high latitude boreal forests and extend southward to 50 degrees latitude outside the boreal zone. AGBD was predicted using two modeling steps: 1) Ordinary Least Squares (OLS) regression related field plot measurements of AGBD to NASA's ICESat-2 30-m lidar samples, and 2) random forest models were used to extend estimates beyond the field plots by relating ICESat-2 AGBD predictions to wall-to-wall covariate stacks from Harmonized Landsat Sentinel-2 (HLS) and the Copernicus DEM. Per-pixel uncertainties are estimated from bootstrapping both models. Non-vegetated areas (e.g. built up, water, rock, ice) were masked out. HLS composites and ICESat-2 data were from 2019-2021; three years of conditions were aggregated into the circa 2020 map. ICESat-2 data were filtered to include only strong beams, growing seasons (June through September), solar elevations less than 5 degrees, snow free land (snow flag set to 1), and "msw_flag" equal to 0 (clear skies and no observed atmospheric scattering). ICESat-2's ATL08 product was resampled to a 30-m spatial resolution to better match both the field plots and mapped pixels. HLS data (L30HLS) were used to create a greenest pixel composite of growing season multispectral data, which was then used to compute a suite of vegetation indices: NDVI, NDWI, NBR, NBR2, TCW, TCG. These were then used, in combination with the slope and elevation data from the Copernicus DEM product, to predict 30-m AGBD per 90-km tile. Estimates of mean AGBD with standard deviation are provided in cloud-optimized GeoTIFF (CoG) format. Training data are in comma-separated values (CSV) format. A polygon map of data tiles is included as a GeoPackage file and a Shapefile.
BUV/Nimbus-4 Level 2 High-Density Ozone Data V005 (BUVN4L2HDBUV) at GES DISC
The Nimbus-4 BUV Level 2 High-Density Ozone Data collection contains the vertical distribtuion and total column amount of ozone, as well as the full set of ancillary information. Each file contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. Layer ozone amounts are provided at 12 layers: 0.24, 0.49, 0.99, 1.98, 3.96, 7.92, 15.8, 31.7, 63.3, 127, 253, and 1013 mbar (bottom of layer value). The data collection also contains quality flags, orbital information, and housekeeping data.The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data. The files consist of data records each with two hundred and seven 4-byte words. The first record is the header record, followed by a series of data records, and ends with several trailer records that pad out the original blocked records. A typical orbit file is about 96 kB in size.The BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region.This product was previously available from the NSSDC as the Total and Profile Ozone Data (HDBUV) with the identifier ESAC-00030 (old ID 70-025A-05Q).
Gene expression changes in Burkitt's lymphoma due to treatment with high density lipoprotein-like nanoparticles (HDL NP)
GEO Series GSE98028. Homo sapiens. 9 samples. Type: Expression profiling by array.
MicroRNAs are Transported in Plasma and Delivered to Recipient Cells by High-Density Lipoproteins (Mouse HDL signatures from WT and LDLR-/- high fat diet)
GEO Series GSE25150. Mus musculus. 9 samples. Type: Other.
High Density Rice Array (HDRA6.4) developed by the McCouch-Rice Lab at Cornell University
GEO Series GSE71553. Oryza sativa. 1568 samples. Type: Genome variation profiling by SNP array.
High-density spatial transcriptomics arrays for in situ tissue profiling
GEO Series GSE130682. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
Growing pains: natural stress response during growth of B. subtilis (high density time-resolved transcriptome analysis)
GEO Series GSE19831. Bacillus subtilis. 118 samples. Type: Expression profiling by array.
A High Parasite Density Environment Induces Transcriptional Changes and Cell Death in Plasmodium falciparum Blood Stages
GEO Series GSE91188. Plasmodium falciparum; Anopheles gambiae. 9 samples. Type: Expression profiling by array.
MLL-AF4 cooperates with PAF1 and FACT to drive high density enhancer interactions in leukemia [TT-Seq]
GEO Series GSE202566. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
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.