This python tool checks the content of Copernicus Marine in situ NetCDF files and produces a report of conformity. The NetCDF content checker is flexible, you may add your own format rules in the RULES directory. Each file format is specified in an XML rules file.

ACE2-ERA5 Sample Output

Full spatial and temporal variables output from a 2-year inference using the ACE2-ERA5 checkpoint initialized on 2001-01-01T00:00:00. The outputs have been written out as 20 segments to avoid large file sizes. The 2-year inference with 6-hourly has 2920 timesteps, so each segment has 146 timesteps. Each segment_00** folder contains a netCDF file (autoregressive_predictions.nc) containing all output variables for that segment.

This is used to show the issue of the file type metadata for netCDF file type

The global forest age dataset (GFAD) describes the age distributions of plant functional types (PFT) on a 0.5-degree grid. Each grid cell contains information on the fraction of each PFT within an age class. The four PFTs, needleaf evergreen (NEEV), needleleaf deciduous (NEDE), broadleaf evergreen (BREV) and broadleaf deciduous (BRDC) are mapped from the MODIS Collection 5.1 land cover dataset, crosswalking land cover types to PFT fractions. The source of data for the age distributions is from country-level forest inventory for temperate and high-latitude countries, and from biomass for tropical countries. The inventory and biomass data are related to fifteen age classes defined in ten-year intervals, from 1-10 up to a class greater than 150 years old. The GFAD dataset represents the 2000-2010 era.

The netCDF (network Common Data Form) file format is increasingly used to store and manage multidimensional scientific data. Although netCDF files offer multiple advanced features and functionality in their own right, workflows that involve netCDF files can be intimidating for new users due to their binary format. There are several methods to manage netCDF file data including via libraries in programming languages such as Fortran or Python. However these methods require knowledge of the programming languages as a prerequisite. Other user-interface applications such as Panoply, NetCDF Explorer, or ArcGIS have functionality to access, view, and in some cases modify or create netCDF files. Another tool to manage netCDF files is the netCDF operators (NCO). NCO is a set of command line tools developed and maintained by the original creators of the netCDF file, the Unidata program at the University Corporation for Atmospheric Research. As such NCO tools are highly optimized and flexible, allowing a myriad of netCDF workflows. This html-based tutorial aims to demystify basic functionalities and syntax of NCO commands that are useful for analysing netCDF scientific data. The tutorial contains multiple examples that focus on scientific data (e.g. climatic measurements or model output) analysis including code snippets, explanations, and figures. Specifically, part 1 covers basic concatenation and averaging of single and ensemble record variables using the ncrcat, ncecat, ncra, and ncea commands respectively. Part 2 builds on part 1 and focuses on basic and advanced uses of the weighted-averaging command ncwa. Examples of other common NCO commands including breif desctiptions on how to download or install the package, and tools for netCDF visualization are also included in the tutorial. Although the tutorial is not in depth, as it does not explicitly cover all the NCO commands nor all of their options, it is a good starting point as many other NCO commands follow similar syntax and conventions.

This resource includes different command line used to convert the test rasters as one netCDF file. The tools used are GDAL(http://www.gdal.org/), netCDF4 python(http://unidata.github.io/netcdf4-python/), and NCO (http://nco.sourceforge.net/)

This data set contains 5 km resolution Meteosat-7 Water Vapor (channel 10) satellite data over the DYNAMO region. Data are available at 30 minute intervals and are in the NetCDF file format. These data are the calibrated temperature values.

Abstract: The NetCDF file 'results.nc' contains the model output from the modeling effort described in: Crone, et al., 2011. The file contains the data necessary to recreate Figure 4 from this paper. It includes the 32 best permeability fields that were generated by the 32 genetic algorithm runs described in the paper (variable 'permeability'), and it includes the relative phase lags generated by the model when an average of these 32 permeability fields are used as input (variable 'phase'). The variables 'x' and 'z' contain the model node centers in meters, and the variable 'n' is the model run index. Funding was provided by NSF grant(s): OCE09-28181.

This dataset contains the Coded Surface Bulletin (CSB) dataset reformatted as netCDF-4 files. The CSB dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.

Each netCDF file contains one year of CSB fronts data represented as spatial map data grids. The times and geospatial locations for the data grid cells are also included. The front data is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded. The front polylines from the CSB dataset are rasterized into the appropriate data grids. Each file conforms to the Climate and Forecast Metadata Conventions.

There are two large groupings of the CSB netCDF files. One group uses a data grid based on the North American Regional Reanalysis (NARR) grid, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.

The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.

There are a number of files within each group. The files all follow the naming convention codsus_[masked]_

The codsus_

The

Within each grid group, there are five subsets of files:

• codsus_masked_
• codsus_masked_
• codsus_
• codsus_
• codsus_

The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the Iowa Environmental Mesonet.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Feature Names Relationship File contains a record for each feature name and any attributes associated with it. Each feature name can be linked to the corresponding edges that make up that feature in the All Lines shapefile (edges.shp), where applicable to the corresponding address range or ranges in the Address Range Relationship File (addr.dbf), or to both files. Although this file includes feature names for all linear features, not just road features, the primary purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute, which can be used to link to the Address Range Relationship File (addr.dbf). The linear feature is identified by the linear feature identifier (LINEARID) attribute, which can be used to relate the address range back to the name attributes of the feature in the Feature Names Relationship File or to the feature record in the Primary Roads, Primary and Secondary Roads, or All Roads shapefiles. The edge to which a feature name applies can be determined by linking the feature name record to the All Lines shapefile (edges.shp) using the permanent edge identifier (TLID) attribute. The address range identifier(s) (ARID) for a specific linear feature can be found by using the linear feature identifier (LINEARID) from the Feature Names Relationship File through the Address Range/Feature Name Relationship File (addrfn.dbf).

Each netcdf (.nc) file contains the location of atmospheric blocks over North America and its surrounding oceans. The data are gridded in latitude and longitude. The block data is simply a mask, with 1s in locations where a block has been detected and zeros elsewhere. A full description of the calculations used in generating the data is in the related journal article. If you would like the data in a different file type, contact James Booth. Contact: jbooth@ccny.cuny.edu

This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

The attached zip file contains raw data files submitted by the authors and a NetCDF file. Progressively, raw data will be imported into PANGAEA as distinct data publications related to the original sources (journal or data publications).

ntlam175/file dataset hosted on Hugging Face and contributed by the HF Datasets community

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Feature Names Relationship File contains a record for each feature name and any attributes associated with it. Each feature name can be linked to the corresponding edges that make up that feature in the All Lines shapefile (edges.shp), where applicable to the corresponding address range or ranges in the Address Range Relationship File (addr.dbf), or to both files. Although this file includes feature names for all linear features, not just road features, the primary purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute, which can be used to link to the Address Range Relationship File (addr.dbf). The linear feature is identified by the linear feature identifier (LINEARID) attribute, which can be used to relate the address range back to the name attributes of the feature in the Feature Names Relationship File or to the feature record in the Primary Roads, Primary and Secondary Roads, or All Roads shapefiles. The edge to which a feature name applies can be determined by linking the feature name record to the All Lines shapefile (edges.shp) using the permanent edge identifier (TLID) attribute. The address range identifier(s) (ARID) for a specific linear feature can be found by using the linear feature identifier (LINEARID) from the Feature Names Relationship File through the Address Range/Feature Name Relationship File (addrfn.dbf).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Feature Names Relationship File contains a record for each feature name and any attributes associated with it. Each feature name can be linked to the corresponding edges that make up that feature in the All Lines shapefile (edges.shp), where applicable to the corresponding address range or ranges in the Address Range Relationship File (addr.dbf), or to both files. Although this file includes feature names for all linear features, not just road features, the primary purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute, which can be used to link to the Address Range Relationship File (addr.dbf). The linear feature is identified by the linear feature identifier (LINEARID) attribute, which can be used to relate the address range back to the name attributes of the feature in the Feature Names Relationship File or to the feature record in the Primary Roads, Primary and Secondary Roads, or All Roads shapefiles. The edge to which a feature name applies can be determined by linking the feature name record to the All Lines shapefile (edges.shp) using the permanent edge identifier (TLID) attribute. The address range identifier(s) (ARID) for a specific linear feature can be found by using the linear feature identifier (LINEARID) from the Feature Names Relationship File through the Address Range/Feature Name Relationship File (addrfn.dbf).

albertvillanova/tmp-empty-file dataset hosted on Hugging Face and contributed by the HF Datasets community

This data set contains Cloud Condensation Nuclei (CCN) data collected aboard the NCAR C-130 as part of the VOCALS campaign, from the period 15 October 2008 to 15 November 2008. Data for each flight is supplied in its own NetCDF file.

This data file represents a time series of canopy area of giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) satellite imagery, along with relevant metadata. The kelp canopy is composed of the portions of fronds and stipes floating on the surface of the water. Canopy area (m2) data are given for individual 30 x 30 meter pixels for all coastal areas of Baja California, Mexico, California, Oregon, and Washington, including offshore islands. Biomass data (wet weight, kg) are given for individual 30 x 30 meter pixels in the coastal areas extending from near Ano Nuevo, CA through the southern range limit in Baja California (including offshore islands), representing the range where giant kelp is the dominant canopy forming species.

Data were derived from the three Landsat sensors listed above.
Observations are made on a 16 day repeat cycle, for each instrument,
but the temporal coverage is irregular because of cloud cover,
instrument failure, and the mission length of each sensor (TM: 1984
– 2011, ETM+: 1999 – present, OLI: 2013 – present). Estimates of
canopy area are derived from the fractional cover of kelp canopy
determined from satellite surface reflectance. Estimates of kelp
canopy biomass are derived from the relationship between giant kelp
fractional cover determined from satellite surface reflectance and
empirical measurements of giant kelp canopy biomass in long-term SBC
LTER study plots obtained using SCUBA. The different Landsat sensors
were calibrated to each other using simulated Landsat data derived
from hyperspectral imagery. Missing data due to the ETM+ scan line
corrector error were filled using a synchrony-based gap filling
method.


Data are organized into a single NetCDF file and contain the
quarterly area and biomass means for each Landsat pixel across the
three sensors. Relevant metadata such as number of Landsat estimates
from which the mean was derived, the number of estimates from each
sensor, standard error for each quarterly estimate, spatial
coordinates, and date are all included in the file. For assistance
with the data, please contact sbclter@msi.ucsb.edu.

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1976.

Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.

For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

Those files were created using the KrigR package.