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Here's how to make your imaginary website a reality. Starting a project. Vlan. To begin, click the 'Create a New Rapidweaver Project' icon on the opening screen. You'll see a blank project. To add your first text, click the '+' sign at top left and choose from the drop-down menu. This will create your site's first page. This is what windows 8,1 and 10 currently do, though their may be a way around it. Last time I tried the windows 10 beta, which was yesterday, I got it installed, only to have the built in screen reader, narrator, focus on a window that was not accessible, and windows refused to let me continue until I'd completed setup. These templates are intended as a service to our community of Data Producers, and are also being used internally at NCEI in our own data development efforts. We hope the templates will serve as good starting points for Data Producers who wish to create preservable, discoverable, accessible, and interoperable data.

NCEI NetCDF Templates v2.0

Purpose

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The NOAA National Centers for Environmental Information (NCEI) have developed netCDF templates based on what are called 'feature types' by Unidata and CF. These templates conform to Unidata's netCDF Attribute Convention for Dataset Discovery (ACDD) and netCDF Climate and Forecast (CF) conventions. Adding to these established conventions, NCEI also provides several recommendations for both netCDF variables and attributes. These best practices capture NCEI's experience in providing long-term preservation, scientific quality control, product development, and multiple data re-use beyond its original intent. Fruitjuice 2 3 0 – maximize your macbooks battery life.

These templates are intended as a service to our community of Data Producers, and are also being used internally at NCEI in our own data development efforts. We hope the templates will serve as good starting points for Data Producers who wish to create preservable, discoverable, accessible, and interoperable data. It is important to note that these templates do not represent an attempt to create a new standard, and they are not absolutely required for archiving data at NCEI. However, we do hope that you will see the benefits in structuring your data following these conventions and NCEI stands ready to assist you in doing so.

We welcome your comments, questions, and any suggestions you might have for recommended attributes. Please submit your concerns to: NCEI.NetCDF@noaa.gov - we will evaluate any feedback received and will update the templates as needed, likely on an annual basis for the next few years and then less frequently thereafter.

Key principles to keep in mind while using the templates

A few key 'principles' appear to be emerging based on initial use of these templates:

1. Be explicit! Do your best to provide accurate values for the attributes, and if you don't know them, state that clearly. Avoid 'NA' or 'N/A' as they can have multiple meanings.
2. Keep your attributes focused on the content of the file, not the overall collection to which it might belong. For broader information about the collection, consider providing an FGDC or an ISO 19115-2 (preferred by NCEI) metadata record.
3. Also remember that ACDD and CF tend to focus on what are sometimes known as 'discovery' and 'use' metadata. Basically, this kind of information focuses on helping you find the file, then using it in your application. The attributes don't try to capture every possible detail of the provenance or processing of the data, for example. Again, for richer information your best bet is to look to ISO 19115-2 as your metadata transfer standard.
4. Our templates don't try to cover every possibility. We focused on the templates that we felt would cover most situations involving environmental observations, primarily of the ocean. If your situation doesn't match one of the templates, that is okay! Just use as much of the templates as you can and consider using some of the more complicated representations detailed in the full CF documentation.

As we move forward we will refine our thinking about these 'principles' and look at ways they may help us to better explain how to implement these templates for specific data projects.

Background on CF Feature Types

The netCDF conventions identify eight feature types, each one describing a way of storing environmental data. These feature types are not based on the kind of observing system, instrument type, or variable collected. Instead, they are based on fundamental relationships among the spatiotemporal coordinates. This approach allows for CF to provide appropriate standards for representing very nearly all kinds of environmental observations. Six of these are known as 'discrete sampling geometries' or 'point observation types'. These are supplemented by the swath and grid feature types used for non-discrete data.

For most of the discrete sampling geometries, the relationship between the data can be categorized in two ways:

1. The first representation is referred to as an Orthogonal Multidimensional Array representation, in which variables of a dataset contain identical coordinate values along an axis. An example could be the vertical coordinates of variables measured on moored buoys with identical depth levels . The depth coordinate variable would store the array of the identical depth levels from the buoys. Therefore, if the depth levels for the first buoy are [5,10,15, 20] and the second buoy are [5,10,15,20,25], then the depth coordinate variable Z in the Orthogonal Multidimensional Array representation would be a single dimensional array Z = [5,10,15,20,25], and data value for the first buoy at z = 25 would be marked as a missing value.
2. The second representation is referred to as the Incomplete Multidimensional Array representation. This representation is used when the variables of a dataset contain different coordinate values along an axis. This is a useful approach, for example, if multiple XBT profiles with different vertical resolution or levels are to be stored in the same netCDF file. A variable would need to be generated that would act as an indexed array to the depth levels of all the profiles combined. For example, if the depth levels for the first XBT are [11, 23, 35, 47, 59] and for the second XBT are [12, 24, 36, 48, 50, 61, 65], then the new depth variable containing data from both the XBTs would be two dimensional array Z = [[11, 23, 35, 47, 59, _, _] [12, 24, 36, 48, 50, 61]] where '_' is a missing value, and the data values for the first XBT would be padded with missing values for indices i = 5 & i = 6.

Apart from Orthogonal multidimensional array and incomplete multidimensional array, CF allows two additional methods of packing data in an array – contiguous ragged array and indexed ragged array. While there are benefits to using these representations over multidimensional array representations, they are generally more complex. Multidimensional representations can instead be used in all the cases where these representations could be used, but are space inefficient. Where the storage efficiency is important, contiguous ragged array representation could be used instead. In this version, we have chosen to focus on the multidimensional array approaches. We wanted to start with the simplest representations first that we believe will handle most of the data that we receive at NCEI, and then move to more complex representations. Classic apple keyboard. Later versions of the templates might include the contiguous ragged array representation. So, if you feel that ragged arrays are the more appropriate way to represent your data then please feel free to use them.

A Word on netCDF Versions and Data Models

While CF does not assume the usage of netCDF as the underlying choice of data format being used, netCDF is a highly recommended format. Within netCDF, there are two broad versions – netCDF 3 and netCDF 4. NetCDF-3 implements netCDF classic data model and netCDF-4 implements both classic and enhanced data models. NCEI recommends the use of netCDF-4, classic data model. The ability to internally compress or 'chunk' data in netCDF-4 is a significant advantage over netCDF-3, especially for larger files. Chunking and internal compression allow for typically much faster access to the contents of the file, since only the relevant parts need to be decompressed. The best chunking and compression parameters for given a dataset is highly usage dependent. It is recommended that data creator play with different choices of the parameters before deciding on a good set of parameters. The classic data model does not take advantage of new features in the netCDF-4 enhanced data model, in particular the ability to use what are known as 'groups' and 'structures'. These can be very useful features but we have found that many applications can not yet take advantage of them and the CF conventions have not yet incorporated them. So, our current approach could be phrased like this: 'Start with netCDF-4 classic data model. If it works for your data, then you are all set and your files will be immediately useful in a wide range of applications. If you find yourself making too many concessions, or can see that groups or structures would greatly simplify your data, then go ahead and use them. Eventually the applications and conventions will catch up.'

Decision Tree for Selecting the Right Template for Your Data

Downcast v2 9 14 download free. Please refer to our Feature Type Decision Tree if you are unsure of which template to use. The decision tree is designed to get you to the right template by asking a few yes/no questions.

Feature Type Templates and Examples

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The table below lists the 8 feature types and provides templates for 13 of the 14 most common combinations of feature type (point, profile, etc.) and array representation (orthogonal, incomplete) expected for oceanographic observations. We are waiting on conclusions of the CF-satellite group before publishing a swath template and examples. The table provides the templates themselves in the 'CDL Template' column. For many of the templates, we have one or more real-world examples as well. The CDL representations of those examples are listed in the 'Specific CDL Examples' column, and links to the corresponding netCDF files on the NCEI THREDDS Data Server (TDS) are in the last column on the right. Following those links, you can explore the numerous services the TDS provides.

Standard Attributes and Guidance Table

The table below lists the global and variable-level attributes recommended and required by the ACDD and CF conventions, with a very small number of additional NCEI recommended attributes. For many of the attributes, we also provide advice and guidance on what information should be put into those attributes. You can also refer directly to the guidance of ACDD and CF for definitions.

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