This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Classification". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

Database of neuronal cell types based on multimodal characterization of single cells to enable data-driven approaches to classification. It includes data such as electrophysiology recordings, imaging data, morphological reconstructions, and RNA and DNA sequencing data.

Sixty one soils (soil types) represent the range of soils found across South Australia’s agricultural lands. Mapping shows the most common soil within each map unit, while more detailed proportion data are supplied for calculating respective areas of each soil type (spatial data statistics).

Soil Type Classification

## Overview

Soil Type Classification is a dataset for semantic segmentation tasks - it contains Soil Types annotations for 343 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Bureau of Ocean Energy Management (BOEM) researchers often require detailed information regarding emergent marsh vegetation types (i.e., fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey, in collaboration with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates decision-tree analyses to classify emergent marsh vegetation types using two existing vegetation surveys and independent variables such as Landsat and high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2007 and 2013 National Agriculture Imagery Program (NAIP) color-infrared aerial photography. The final classification consists of three 10-m raster datasets that were produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classifications are dated 2007 and 2013 because the dates of the two vegetation surveys and of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.

A global digital data base of soil properties is available at 1 degree longitude resolution. For each land cell, the data base includes major and associated soil units, surface texture, and slope; phase and miscellaneous land units are included where available. The data base was compiled as part of an effort to improve modeling of the hydrologic cycle in the GISS Genreal Circulation Model.

The atlas map serves the knowledge of the distribution and properties of soils in Dresden. Conductive soil types and conductive soil types are characteristics derived from the prevailing soil societies that allow a simplified and summarized presentation. Soil types characterize soil formation, soil types document the grain mixtures that occur.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (SSURGO). The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. The map units delineated on the detailed soil maps in a soil survey represent the soils or miscellaneous areas in the survey area. The map unit descriptions in this report, along with the maps, can be used to determine the composition and properties of a unit. A map unit delineation on a soil map represents an area dominated by one or more major kinds of soil or miscellaneous areas. A map unit is identified and named according to the taxonomic classification of the dominant soils. Within a taxonomic class there are precisely defined limits for the properties of the soils. On the landscape, however, the soils are natural phenomena, and they have the characteristic variability of all natural phenomena. Thus, the range of some observed properties may extend beyond the limits defined for a taxonomic class. Areas of soils of a single taxonomic class rarely, if ever, can be mapped without including areas of other taxonomic classes. Consequently, every map unit is made up of the soils or miscellaneous areas for which it is named and some minor components that belong to taxonomic classes other than those of the major soils.The Map Unit Description (Brief, Generated) report displays a generated description of the major soils that occur in a map unit. Descriptions of non-soil (miscellaneous areas) and minor map unit components are not included. This description is generated from the underlying soil attribute data. To see the Non-Technical description of the soil types, click here.

For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620 U.S. Department of Agriculture USDA Natural Resources Conservation Service p: 1-833-ONE-USDA e: askusda@usda.gov

This is a set of data type definitions created in the Data Type Registry (DTR) and used for the FAIRCORE4EOSC SSH Case Study.

• 0acb62608006dbbbcf88.json: ExtendedMimeType type definition.
• 710b1a3647d431e205e0.json: application/tei+xml type instance.
• fe61e4792b37f2bbb26e.json: application/tei+xml taxonomy node.
• f33c32fa8246e2ca6d5c.json: text+xml taxonomy node.
• 372ea08cab33e71c02c6.json: text taxonomy node.

This dataset contains the digitized treatments in Plazi based on the original book chapter Jarvis, Charlie (2007): Chapter 7: Linnaean Plant Names and their Types (part I). In: Order out of Chaos. Linnaean Plant Types and their Types. London: Linnaean Society of London in association with the Natural History Museum: 586-598, ISBN: 978-0-9506207-7-0, DOI: https://doi.org/10.5281/zenodo.291971

NWI digital data files are records of wetlands location and classification as defined by the U.S. Fish Wildlife Service. This dataset is one of a series available in 7.5 minute by 7.5 minute blocks containing ground planimetric coordinates of wetlands point, line, and area features and wetlands attributes. When completed, the series will provide coverage for all of the contiguous United States, Hawaii, Alaska, and U.S. protectorates in the Pacific and Caribbean. The digital data as well as the hardcopy maps that were used as the source for the digital data are produced and distributed by the U.S. Fish Wildlife Service's National Wetlands Inventory project.

Dataset

This dataset was created by kulvanth

Contents

This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Mental Process". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder characterized by substantial heterogeneity. To identify the convergence of disease pathology on common pathways, it is essential to understand the correlations among ASD candidate genes and study shared molecular pathways between them. Investigating functional interactions between ASD candidate genes in different cell types of normal human brains may shed new light on the genetic heterogeneity of ASD. Here we apply cell type-specific gene network-based analysis to analyze human brain nucleus gene expression data and identify cell type-specific ASD-associated gene modules. ASD-associated modules specific to different cell types are relevant to different gene functions, for instance, the astrocytes-specific module is involved in functions of axon and neuron projection guidance, GABAergic interneuron-specific modules are involved in functions of postsynaptic membrane, extracellular matrix structural constituent, and ion transmembrane transporter activity. Our findings can promote the study of cell type heterogeneity of ASD, providing new insights into the pathogenesis of ASD. Our method has been shown to be effective in discovering cell type-specific disease-associated gene expression patterns and can be applied to other complex diseases.

A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler's (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution.

Learn about the different types of fertilizers available in the market, including organic, inorganic, slow-release, liquid, granular, and water-soluble fertilizers. Discover their nutrient compositions and roles in plant nutrition to choose the right fertilizer for your plants.

Forecast: Re-Import of Sets of Articles of Mixed Types of Pens or Pencils to France 2018 - 2022 Discover more data with ReportLinker!

Cell motility data were calculated from tracked trajectories of ∼50 cells from each cell type at low cell density.*PFK: primary goldfish keratocyte.

Alternative polyadenylation (APA) in 3’ untranslated regions (3’ UTR) plays an important role in regulating transcript abundance, localization, and interaction with microRNAs. Length-variation of 3’UTRs by APA contributes to efficient proliferation of cancer cells. In this study, we investigated APA in single cancer cells and tumor microenvironment cells to understand the physiological implication of APA in different cell types. We analyzed APA patterns and the expression level of genes from the 515 single-cell RNA sequencing (scRNA-seq) dataset from 11 breast cancer patients. Although the overall 3’UTR length of individual genes was distributed equally in tumor and non-tumor cells, we found a differential pattern of polyadenylation in gene sets between tumor and non-tumor cells. In addition, we found a differential pattern of APA across tumor types using scRNA-seq data from 3 glioblastoma patients and 1 renal cell carcinoma patients. In detail, 1,176 gene sets and 53 genes showed the distinct pattern of 3’UTR shortening and over-expression as signatures for five cell types including B lymphocytes, T lymphocytes, myeloid cells, stromal cells, and breast cancer cells. Functional categories of gene sets for cellular proliferation demonstrated concordant regulation of APA and gene expression specific to cell types. The expression of APA genes in breast cancer was significantly correlated with the clinical outcome of earlier stage breast cancer patients. We identified cell type-specific APA in single cells, which allows the identification of cell types based on 3’UTR length variation in combination with gene expression. Specifically, an immune-specific APA signature in breast cancer could be utilized as a prognostic marker of early stage breast cancer.