How To Use Art Open Data
Interface With APIs
Web APIs provide read access to data and may allow data to be written back to share as well.
This allows data to be accessed and published more quickly than with downloadable datasets, often instantaneously.
Datasets can be loaded into applications and programming environments directly.
This makes social network analysis and statistical analysis much more efficient.
Perform Statistical Analysis
Datasets can be analysed to find statistical features such as averages and outliers. This can direct further analysis or suggest subjects for critical consideration.
Perform Knowledge Discovery
Text and images can be processed to discover patterns, similarities between different works, relationships between subjects, and even limited kinds of aesthetic and affective qualities.
Perform Social Network Analysis
The interactions of individuals in the artworld over time can be analysed to model relationships and the relative importance or position of individuals within their social cliques.
Create Data Visualisations
Static or interactive graphical presentations data can be useful for finding interesting properties of a dataset or for better understanding the features or relationships within a dataset. It can also be art in its own right.
Where To Find Data Sources And Tools
CKAN is a directory of datasets.
archive.org is an online media repository. It contains scans of many important and useful art historical primary documents.
gutenberg.org is an online library of electronic texts. It includes books and lectures by John Ruskin, William Morris, and many others.
freebase is an online database that extracts information from Wikipedia and makes it available for download. It has datasets on artworks, artists, and other art-related subjects.
Culture24 provides and API to access data about exhibitions and other current events at UK galleries and museums.
The Culture Grid API provides access to aggregated information from UK museum websites.
flickr commons provides images from museum collections tagged by volunteers, searchable and taggable through an API.
Wikimedia Commons provides images uploaded by volunteers, searchable through an API.
Wordle is an online service that creates “word clouds” from text. This can be useful for visually getting the feel for a text quickly.
Many Eyes is an online data repository and graphing service. It can be a convenient way of sharing data and visualisations.
SocNetV is a social network analysis application. It allows social network data to be analysed and visualised in various useful ways. http://socnetv.sourceforge.net/
GNUPlot is a data graphing utility. It supports many different kinds of graphs and can be a useful tool for plotting data.
R is a statistical analysis programming language. It is useful for statistical analysis, machine learning, and for drawing high-quality graphs of the results. http://www.r-project.org/
Python is a general-purpose programming language. It is useful for accessing APIs, text processing, machine learning, and data visualisation. http://www.python.org/
Processing is a simple data visualisation programming language. It can easily be extended to use more advanced facilities and is useful for creating interactive information graphics. http://processing.org/
PD is a graphical programming language popular with digital artists. Using it with art open data helps to include artists in the analysis and visualisation of that data. http://puredata.info/
How To Proceed
Locate And Index Data
Find new APIs and new sources of data, and explore existing sources to find new datasets, then add them to CKAN.
Digitise Primary Sources
If you have physical access to an out-of-copyright primary source, photograph or scan it and upload the results to archive.org.
Extract Data From Primary Sources
Once primary sources have been scanned, more structured data can be extracted from them. Text scans can be cleaned up and converted to machine readable formats using Optical Character Recognition (OCR). Artwork scans can be cleaned up, be tagged or categorized or otherwise have metadata added, or be processed algorithmically to find features or extract aesthetic information such as palettes.
Produce Interfaces To APIs
Web APIs are no good if people can’t use them. Creating libraries of code in a programming language you use to access an art open data API opens that API up to all the users of that language.
Combining multiple datasets can add information that is missing from a main dataset or extend its coverage of dates or regions.
Add Non-Art Open Data
Using geodata from OpenStreetMap, bibliographic data from the British Museum, economic data from OpenEconomics, and other sources of Open Data can complement Art Open Data. Combining data from diverse fields can provide context and reveal or explain unexpected features of the original dataset.
Having got all this data, it’s time to explore it and to find interesting things that are hidden in it. Theories can be suggested, supported or undermined by the data, and it’s here that the traditional skills of art history or art theory can come into play.
Data visualisation of art data is where art and data truly join together. Whether a simple chart or a complex interactive animation, making data about art visual can provide inspiration to both the study and production of art.