Easy tools for acquiring, processing and exploring data
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This content is not yet complete. In the meantime, see this presentation: (, )
(quite advanced, but contains a section on a user interface tool as well)
Hand-written text transcription:
Layout and text transcription:
Keyword generation from text:
An
Twitter archiving:
Data Carpentry tutorial, not really for social science but for general cleaning up of data
Further tutorials:
(includes section on extension)
(on reconciliation)
Visualisation is the act of taking data and transforming it into visual shapes and forms. The reasoning behind this is that humans are very good at processing visual information, with a lot of the necessary shape and anomaly detection and comparison processes even happening subconsciously.
Most visualisation is explanatory.
/
Book: , particularly chapter 5 for pre-attentive processing and 6 for the gestalt laws.
section 1.5
Read & . The articles are primarily oriented around explanatory data visualisation, but most computational humanities data analysis is exploratory. How do the problems transfer into that domain, when the only one you can deceive is yourself?
tabular data → chart visualisations:
tabular data → chart visualisations:
tabular data → chart visualisations:
tabular data → interactive map/network/timeline/list/facet visualisations:
Palladio has . There are also multiple tutorials on using Palladio, for example , or which is particularly on network analysis.
tabular data → map(+timeline) visualisations:
text → interactive explorative interface for linguistic study:
big, preselected collections of text → interface for linguistic study: /
If you're feeling explorative, feel free to also dig for more tools in .
If you're short on inspiration, feel free to go through hands-on tutorial covering OpenRefine, RAW and Palladio.
based on what you want to show
of four ways to visualise the same data and how that affects what you can read from it