VIDEO GAMES HANDBOOK (Annotated & Illustrated)

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The file format exemplified above opens up for a number of issues described as follows. Each row is intended to describe an entity e. The unique identifier for that entity is provided in the first column. In order for information about this entity to be reconcilled with information from other sources about the same entity, the local identifier needs to be mapped to a globally unique identifier such as a URI. After each triple, there is a variable number of annotations representing the provenance of the triple and, occasionally, its certainty.

This information has to be properly identified and managed. It would be useful to identify the resources that these references represent. How do we know which controlled vocabulary it is a member of and what its authoritative definition is? How can one make the identifier an unambiguous URI?

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A similar requirement regards the provenance annotations. These are composed by document e. Page number ranges are clearly valid only in the context of the preceding document identifier. The interesting assertion about provenance is the reference document plus page range. Thus we might want to give the reference a unique identifier comprising from document ID and page range e. D Besides the entities, the table presents also some values.

Some of these are strings e. It would be useful to have an explicit syntactic type definition for these values. Moreover, a single row in the table comprises a triple subject-predicate-object , one or more provenance references and an optional certainty measure. The provenance references have been normalised for compactness e.

However, each provenance statement has the same target triple so one could unbundle the composite row into multiple simple statements that have a regular number of columns see the two equivalent examples below. Requires: TableNormalization. Lastly, since we already observed that rows comprise triples, that there is a frequent reference to externally defined vocabularies, that values are defined as text literals , and that triples are also composed by entities, for which we aim to obtain a URI as described above , it may be useful to be able to convert such a table in RDF.

Our user wants to be able to embed a map of these locations easily into my web page using a web component , such that she can use markup like:. To make the web component easy to define, there should be a native API on to the data in the CSV file within the browser. Requires: CsvToJsonTransformation. All of the data repositories based on the CKAN software, such as data. JSON has many features which make it ideal for delivering a preview of the data, originally in CSV format, to the browser.

Javascript is a hard dependency for interacting with data in the browser and as such JSON was used as the serialization format because it was the most appropriate format for delivering those data.

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As the object notation for Javascript JSON is natively understood by Javascript it is therefore possible to use the data without any external dependencies. The values in the data delivered map directly to common Javascript types and libraries for processing and generating JSON, with appropriate type conversion, are widely available for many programming languages. Beyond basic knowledge of how to work with JSON, there is no further burden on the user to understand complex semantics around how the data should be interpreted.

The user of the data can be assured that the data is correctly encoded as UTF-8 and it is easily queryable using common patterns used in everyday Javascript. When providing the in-browser previews of CSV-formatted data, the utility of the preview application is limited because the server-side processing of the CSV is not always able to determine the data types e. As a result it is not possible for the in-browser preview to offer functions such as sorting rows by date.

Note that the underlying data begins with:. The header line here comes below an empty row, and there is metadata about the table in the row above the empty row. The preview code manages to identify the headers from the CSV, and displays the metadata as the value in the first cell of the first row.

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Moreover, some of the values reported may refer to external definitions from dictionaries or other sources. It would be useful to know where it is possible to find such resources, to be able to properly handle and visualize the data, by linking to them. Lastly, the web page where the CSV is published presents also useful metadata about it.

It would be useful to be able to know and access these metadata even though they are not included in the file. NetCDF is a set of binary data formats, programming interfaces, and software libraries that help read and write scientific data files.

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NetCDF provides scientists a means to share measured or simulated experiments with one another across the web. What makes NetCDF useful is its ability to be self describing and provide a means for scientists to rely on existing data model as opposed to needing to write their own. The classic NetCDF data model consists of variables, dimensions, and attributes.

Among the tools available to the NetCDF community, two tools: ncdump and ncgen. The ncdump tool is used by scientists wanting to inspect variables and attributes metadata contained in the NetCDF file. It also can provide a full text extraction of data including blocks of tabular data representing by variables.

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The ncgen tool parses the text file and stores it in a binary format. The CDL syntax as shown below contains annotation along with blocks of data denoted by the "data:" key. For the results to be legible for visual inspection the measurement data is written as delimited blocks of scalar values. As shown in the example below CDL supports multiple variables or blocks of data. The blocks of data while delimited need to be thought of as a vector or single column of tabular data wrapped around to the next line in a similar way that characters can be wrapped around in a single cell block of a spreadsheet to make the spreadsheet more visually appealing to the user.

The next example shows a small subset of data block taken from an actual NetCDF file.


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Lastly, NetCDF files are typically collected together in larger datasets where they can be analyzed, so the CSV data can be thought of a subset of a larger dataset. CSV is by far the commonest format within which open data is published, and is thus typical of the data that application developers need to work with. The UK Government policy paper "Open Data: unleashing the potential" outlines a set of principles for publishing open data. Within this document, principle 9 states:. Release data quickly, and then work to make sure that it is available in open standard formats, including linked data formats.

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The open data principles recognise how the additional utility to be gained from publishing in linked data formats must be balanced against the additional effort incurred by the data publisher to do so and the resulting delay to publication of the data. Data publishers are required to release data quickly - which means making the data available in a format convenient for them such as CSV dumps from databases or spread sheets. One of the hindrances to publishing in linked data formats is the difficulty in determining the ontology or vocabulary e.

Whilst it is only reasonable to assume that a data publisher best knows the intended meaning of their data, they cannot be expected to determine the ontology or vocabulary most applicable to to a consuming application! Furthermore, in lieu of agreed de facto standard vocabularies or ontologies for a given application domain, it is highly likely that disparate applications will conform to different data models.

How should the data publisher choose which of the available vocabularies or ontologies to use when publishing if indeed they are aware of those applications at all! In order to assist data publishers provide data in linked data formats without the need to determine ontologies or vocabularies, it is necessary to separate the syntactic mapping e.

As a result of such separation, it will be possible to establish a canonical transformation from CSV conforming to the core tabular data model [ tabular-data-model ] to an object graph serialisation such as JSON. This use case assumes that JSON is the target serialisation for application developers given the general utility of that format. In doing so this enables CSV-encoded tabular data to be published in linked data formats as required in the open data principle 9 at no extra effort to the data publisher as standard mechanisms are available for a data user to transform the data from CSV to RDF.

Public bodies should publish relevant metadata about their datasets […]; and they should publish supporting descriptions of the format, provenance and meaning of the data.