Helsinki fall 2018
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For 2.11.:
Answer the course background
Look over the . Select the project that interests you the most. Post a short message on the channel on the course Slack to introduce yourself and to describe why you chose those that project.
Read up on . Be ready to discuss in groups on the next lecture.
(, )
Answer the course background
Look over the . Select the project that interests you the most. Post a short message on the channel on the course Slack to introduce yourself and to describe why you chose those that project.
Read up on . Be ready to discuss in groups on the next lecture.
(, )
Find a dataset that could be of interest to you in your final project. Post a message on on Slack giving a link to the dataset and a note on why you selected it.
Read & as preparation for next week, learning to not trust visualisations blind.
Visualisation: Experiment with at least one of the following tools:
What is the tool good for?
What kind of data do you need for the tool to be useful?
What information does the data need to contain?
What format does it have to be in?
Your experience with the tool.
If someone has already posted on the tool you tested, don't repeat them. Instead, add to what they've said in a thread. (also be prepared to discuss the tools in class)
Read on some small, actual work:
Read on some small, actual work:
Select (at least) one of the following sets of paired articles based on your own interests:
Form a group with all the other people who selected the same articles. For class, prepare a presentation on them, detailing:
How do the two articles relate to each other?
Research questions - What are the humanities research questions? Do the projects also target computer science research questions? If so, what? What is the relationship between the CS and humanities research questions?
Data - How has the data used been gathered? What are the data sources used? How has the data been processed? Is the data available for others to use?
Methods - What methods do the projects apply? How do the methods support answering the research questions?
Partners - What is the make-up of the projects? Which disciplines are represented by the participants?
Data cleanup: complete the .
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.
Afterwards, post a message on on Slack detailing:
Programming: Go through the and complete the assignments there.
Regular expressions: Read the section on and go through the assignments there.
In preparation for the lecture on 21.11., read .
language change, simulation: + . Also note that you can experiment yourself with the model described in the first paper .
twitter, sentiment analysis: +
simulation, archaeology: +
geographic information, network analysis, archaeology: +
history, text reuse detection: + (Interestingly, first article doesn't have affiliations. Digging thrhough, most people seem to be from this project)
network analysis: +
3D/spatial analysis, archaeology: +
Image recognition of woodcut prints: /
Check out the site, and especially
The of the DHH15 key concepts of socialism group
The of the DHH15 Finnair Blue Wings multimodality group
If you understand Finnish, the
For 5.12., explore of and read the on topic modelling
Check out the site, and especially
The of the DHH15 key concepts of socialism group
The of the DHH15 Finnair Blue Wings multimodality group
If you understand Finnish, the
language change, simulation: + . Also note that you can experiment yourself with the model described in the first paper .
twitter, sentiment analysis: +
simulation, archaeology: +
geographic information, network analysis, archaeology: +
history, text reuse detection: + (Interestingly, first article doesn't have affiliations. Digging thrhough, most people seem to be from this project)
network analysis: +
3D/spatial analysis, archaeology: +
Image recognition of woodcut prints: /
Explore of and read the on topic modelling
Find a computational humanities research paper that interests you. Post a message on on Slack shortly describing why you picked the paper.