|API|| If you want to use OpenDataCommunities’ data to make your own app, the API (Application Programming Interface) lets you get to the data programmatically.
For detail on how to use the API go to http://opendatacommunities.org/help
An Application Programming Interface (API) is a set of functions, procedures, methods or classes used by computer programs to request services from the operating system, software libraries or any other service providers running on the computer. A computer programmer uses the API to make application programs. (source: https://simple.wikipedia.org/wiki/Application_programming_interface)
|Area|| Data can be explored by differently sized and differently organised geographical areas on the site, depending on your need. A full diagram and a further list can be viewed at http://opendatacommunities.org/areas
The area diagram contains links to the following area definitions: Country, Region, English Upper Tier Authorities (for inner and outer London, counties, metropolitan counties, English unitary authorities), English Lower Tier Authorities (London boroughs, non metropolitan districts, metropolitan districts, English unitary authorities), Westminster parliamentary constituencies, parishes, 2001 lower super output areas, 2011 lower super output areas.
The area list includes the above areas plus other areas including: national parks, local enterprise partnerships, NUTS (nomenclature of territorial units for statistics) 1, 2 and 3 areas, providers of social care, providers of social care by district, help-to-buy postcode district by authority, help-to-buy postcode sectors by authority
|Concept Scheme|| A concept scheme is a collection of concepts about a topic.
A concept scheme often acts as a list of possible values for a certain property of a resource (i.e. possible objects for RDF triples)
On opendatacommunities.org there is a list of concept schemes here: http://opendatacommunities.org/vocabularies
Select the concept scheme of interest to get to related datasets.
At time of writing, there are over 100 concept schemes. Examples include:
|CSV|| CSV stands for comma-separated values. On opendatacommunities.org you have the option to download data as a csv file. A CSV file stores tabular data (numbers and text) in plain text. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. The use of the comma as a field separator is the source of the name for this file format. (source: https://en.wikipedia.org/wiki/Comma-separated_values)
Any text editor, including Excel, can open a csv file.
|Cube Utilities|| To understand cube utilities, we need to understand what a cube is first. View the datacube definition below.
Cube utilities are tools relating to a datacube. These are for developers and can be found here: http://opendatacommunities.org/cubetool
|Data Cart|| The Data Cart helps you compare data across datasets. You can add rows and columns from different datasets to create your own, bespoke spreadsheet. An example of when this is useful is when you’re comparing data across authorities.
For example, the data cart makes it possible to select the LSOAs in a district and download deprivation data at LSOA level for just that district. This is a fairly common requirement which is made easy with the data cart and area browsing.
For more information on how to use the data cart go to: http://opendatacommunities.org/cart?tab=help
|Datacube|| A datacube (cube) is a way of describing the different dimensions of data.
For example, in a table of Olympic data for 2012, countries could form rows and number of gold, silver and bronze medals the columns. Add other years though (like 2004, 2008) and you need other spreadsheets (we can imagine them stacked back to back like a Rubik’s cube).
A datacube, though, can have even more dimensions than a regular 3D cube. For example, other dimensions for data relating to the olympics might also include gender and Paralympic data.
For more on this, a useful tutorial for this can be found here: https://medium.swirrl.com/how-the-olympics-explains-multidimensional-data-8e58b127edb2
|Dataset||A dataset is a collection of related data. Statistical datasets (stored on OpenDataCommunities as datacubes) contain observations with common variables (dimensions of the cube).|
|Deprecated Dataset||Some datasets may be highlighted with a flag saying “Deprecated Dataset”. This occurs where a dataset has been replaced by an updated version, but the original dataset has to be retained, in case other people have linked to the dataset or are accessing it via the APIs.|
|Dereference URI|| See also “URI”.
To dereference a URI means to go to it on the Web and get back the information held about the resource identified by the URI.
|Multidimensional Data||Multidimensional data is data that has more than two dimensions. Examples of dimensions include: reference area, reference period, gender, age, measure type, indices of deprivation etc. For more on understanding multidimensional data, a useful tutorial can be found here: https://medium.swirrl.com/how-the-olympics-explains-multidimensional-data-8e58b127edb2. See also: Datacube.|
|Ontologies|| An ontology is a set of classes and properties about a certain topic area. These can be used for resources' types and properties (predicates) respectively. On opendatacommunities.org, a list of ontologies can be found here: http://opendatacommunities.org/vocabularies
At the time of writing these user guides, there are almost 40 ontologies. Examples:
* An ontology of terms for use with Index of Multiple Deprivation data
* Vocabulary of terms for DCLG business plan reporting
|Organisation||On Opendatacommunities, organisations are groups which produce the data. Groups on the site include: The Department for Communities and Local Government, The Homes and Communities Agency and The National Planning Casework Unit|
|RDF||RDF stands for Resource Description Framework, and is one of the standards for representing Linked Data on the web.|
|SPARQL|| SPARQL is a computer language used to search for, and get back, information from databases.
“SPARQL is an RDF query language, that is, a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework (RDF) format.” (source: https://en.wikipedia.org/wiki/SPARQL)
|SPARQL Query||A SPARQL query is a search enquiry written in the SPARQL language and designed to extract specific information from a database.|
|URI||A Uniform Resource Identifier (URI) is a string of characters used to identify a resource. (source: https://en.wikipedia.org/wiki/Uniform_Resource_Identifier). In Linked Data, URIs are used to identify things and concepts about which we want to publish data. The URI also serves as a URL where you can look up information about that resource (see “Deference URI”).|
|Theme||Themes on OpenDataCommunities are a way to organise and browse the datasets. Each theme contains relevant datasets to it. Themes include: Energy Efficiency and Performance, Fire and Rescue Services, Geography, Homelessness, House Building, Households, Housing Market, Local Government Finance, Organisations, Planning, Societal Wellbeing and Transparency.|
|Vocabularies|| Vocabularies define the terms used to describe the data. On Opendatacommunities vocabularies are stored as concept schemes and ontologies. Go to these definitions for more detail.
|XML||XML is short for Extensible Markup Language and is a set of rules that define a way of encoding documents so that they are human-readable and machine-readable. This is a download format that may be opened in any software that can read XML files, such as the Microsoft Office suite, or OpenOffice.org|