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514 results for “North Carolina”
FIG. 18. Morphotype 16, VMNH 211462 in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 18. Morphotype 16, VMNH 211462. Scale bar: 1 mm.
FIG. 13. Morphotype 11, VMNH 51912 in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 13. Morphotype 11, VMNH 51912. Scale bar: 0.5 mm.
FIG. 24. Morphotype 21, VMNH 51491 in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 24. Morphotype 21, VMNH 51491. Scale bar: 1 mm.
FIG. 43. Morphotype 38, VMNH 49659 in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 43. Morphotype 38, VMNH 49659. Scale bar: 0.5 mm.
FIG. 12. Morphotype 10, AMNH 04-88 in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 12. Morphotype 10, AMNH 04-88. Scale bar: 0.5 mm.
FIG. 16. Morphotype 14, VMNH 51944. A, B. Habitus. C in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 16. Morphotype 14, VMNH 51944. A, B. Habitus. C. Antennae. Scale bars: A, B: 1 mm; C: 0.5 mm.
FIG. 42. Morphotype 37, VMNH 129484 in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 42. Morphotype 37, VMNH 129484. Scale bar: 0.5 mm.
FIG. 28. Morphotype 25, AMNH 04-81 in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 28. Morphotype 25, AMNH 04-81. Scale bar: 1 mm.
FIG. 27. Morphotype 24, VMNH 49593. A, B. Habitus. C. Head. D in Remarkable Diversity Of Beetles (Coleoptera) In The Late Triassic (Norian) "Solite Deposit" Of Virginia And North Carolina
FIG. 27. Morphotype 24, VMNH 49593. A, B. Habitus. C. Head. D. Hind leg. Scale bars: 1 mm.
Figure 8 in Six new feather mite species (Acari: Astigmata) from the carolina parakeet Conuropsis carolinensis (Psittaciformes: Psittacidae), an extinct parrot of North America
Figure 8. Protonyssus proctorae sp. n., female. (A) Dorsal view; (B) ventral view.
Figure 2 in Six new feather mite species (Acari: Astigmata) from the carolina parakeet Conuropsis carolinensis (Psittaciformes: Psittacidae), an extinct parrot of North America
Figure 2. Lopharalichus beckeri sp. n., female. (A) Dorsal view; (B) ventral view.
Figure 9 in Six new feather mite species (Acari: Astigmata) from the carolina parakeet Conuropsis carolinensis (Psittaciformes: Psittacidae), an extinct parrot of North America
Figure 9. Fainalges gracilitarsus sp. n., male. (A) Dorsal view; (B) ventral view.
Figure 6 in Six new feather mite species (Acari: Astigmata) from the carolina parakeet Conuropsis carolinensis (Psittaciformes: Psittacidae), an extinct parrot of North America
Figure 6. Chiasmalges carolinensis sp. n., male. (A) Dorsal view; (B) ventral view; (C) tarsus IV.
Education Services for the Deaf and Blind of the North Carolina Department of Public Instruction (ESDB dataset)
<p>The second dataset used in this study was collected by our group jointly with the Education Services for the Deaf and Blind (ESDB) of the North Carolina Department of Public Instruction under IRB 14046 of NC State University. For the ESDB data, the 14 sessions collected were grouped into pairs to ease a granular analysis, hence ending with 7 sessions. The characteristics of each dataset can be found in website.</p>
North Carolina Building Features
<p>Raw data used in the <a href="https://github.com/geo-smart/flood-risk-ml-tutorial">GeoSmart Flood Risk Machine Learning Tutorial</a>. These data were used to create the machine learning ready dataset for the tutorial. Data are stored by county with one zip file per county. Filenames are the <a href="https://www.nccourts.gov/assets/documents/publications/County-Codes-Numbers_07132021.pdf?VersionId=eLzhZKgNV9i8BOQPBi6Zfddbsi__LSkU">FIPS county codes</a>. See the tutorial link above for Python examples of how to read and utilize these data.</p>
Predicted Spatially Complete Zoning Map of North Carolina
<p>Spatially-complete zoning map of North Carolina, USA. The <strong>results </strong>folder contains results of a machine learning (random forest) model predicting 3 core district zones (residential, non-residential, and mixed use) and 13 sub-district zones (open space, industrial, commercial, office, planned use, high-density residential, medium-high-density residential, medium-density residential, medium-low-density residential, low-density residential, agricultural residential, mixed use, and downtown). Results are provided as 30-m rasters (.tif) with each value corresponding to a zoning district. Table containing zone district ID (number) and zone district name (character string) is included in <strong>zone_classification.csv</strong>. Final (spatially complete statewide maps) can be found in the <strong>final_predicted </strong>folder. This folder includes Statewide core district results in <strong>NC_predicted_core.tif</strong> and statewide sub-district results in <strong>NC_predicted_sub.tif</strong>. </p> <p>Zoning was generalized and reclassified into 3 core district zones and 13 sub-district zones (described above). Reclassified zoning data, collected from 39 counties in North Carolina is provided in the <strong>observed </strong>folder with core districts in <strong>core_district_observed_zones.tif</strong> and sub-districts in <strong>sub_district_observed_zones.tif</strong>. Also in this folder is <strong>zoning_implementation_NC.csv</strong> which includes links to the source data (zoning map and zoning ordinance) for all collected data.</p> <p>Two models were created to predict zones under different data availability scenarios (i.e., scenarios that assume different levels of data availability). Predictions labeled “within_county” utilized the within-county model which predicts zoning districts in areas where zoning data is partially available for that county. To approximate scenarios of incomplete data accessibility, 20% of the data was randomly removed from training and reserved for independent performance assessments. Predictions labeled “between-county” utilized the between-county model which predicts zoning districts in areas where zoning data is inaccessible. To approximate this scenario, multiple between-county model iterations were computed by randomly removing entire counties from the training dataset and computing performance metrics on the removed (test) counties. Predictions are provided for both core districts and sub-districts (described above). Results from these models can be found in the <strong>predicted </strong>folder. This folder contains four subfolders: <strong>core_district_within_county</strong>, <strong>sub_district_within_county</strong>, <strong>core_district_between_county</strong>, and <strong>sub_district_between_county</strong>. Within each of these folders are predicted maps 30-m raster (.tif), performance reports including precision, recall, and f1 score overall and per district (.csv), and accuracy maps (3-km grid shapefile [.shp, .shx, .prj, .dbf]) with values corresponding to the proportion of misclassified pixels within a grid cell. Multiple randomized testing county samples were conducted for the between-county models. Each random sample is labeled <strong>r*_</strong> where * is replaced with a number between 1 and 15.</p>
Teachers Leading the Front Lines - North Carolina (Tealeaf-NC)
ClinicalTrials.gov study NCT06587230. IPD Sharing: YES. Countries: 1. Publications: 16.
North Carolina Newborn Exome Sequencing for Universal Screening
ClinicalTrials.gov study NCT02826694. IPD Sharing: YES. Countries: 1. Publications: 1.
University of North Carolina-Healthwise Partnership Project on Birth Options Decision Aid
ClinicalTrials.gov study NCT04053413. IPD Sharing: YES. Countries: 1. Publications: 1.
Patient Priorities Care-North Carolina
ClinicalTrials.gov study NCT04233554. IPD Sharing: YES. Countries: 1. Publications: 1.
ScienceDex guides
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.