Helsinki fall 2020
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Zoom meeting for the course: . To get into the meeting, use the code 065380.
The course relies heavily on blended learning and flipped classroom techniques. Therefore, much work will happen outside of class, in interactive assignments, reading of literature, testing tools or creating presentations. Meetings will be used to give presentations, discuss, share knowledge and ensure understanding. Accordingly, after the first week, we will fall into a schedule where meetings happen on each Thursday, while each Tuesday is reserved for doing the assignments for that week.
Activatory pair discussion
Briefing of assignments
why you chose those that dataset,
what types of information does it contain,
what the structure, technical format and way of accessing the data is, and
what potential sources of problems or biases does it have. (~15-45min)
Experiment with at least one of the following tools (~30-60min + ~15-30min):
Visualization:
Data acquisition:
An automated image/video description tool
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.
Research on visualization tool development:
Read the following two articles on developing tools for particular text-based humanities research questions:
Now, think of a visualisation that would help you in your field. What information would it visualise? Prepare to discuss in class.
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? 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?
Form a group with people from your own or nearby fields. Find a computational research paper from your field. For class, prepare a presentation on the article, detailing:
Research questions - What are the human research questions? Do the projects also target computer science research questions? If so, what? What is the relationship between the CS and human research questions?
Data - How has the data used been gathered? What are the data sources used? 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?
What are your humanities research questions?
Which data will you use?
How do you plan to process, clean up and transform your data?
How do you plan to analyze your data? How will the analysis help answer the humanities research questions?
Critically analyze your data and pipeline for potential bias and problems.
.
(Tue 1.12.) Peer review two project plans of your fellow students
Be prepared to shortly (1-2 minutes max) present your project plan to the others
Answer the course background (~5min)
Look over the . Select the project that interests you the most. Be prepared to discuss why you chose those that project in class. (~20-40min)
Read up on and do the assignment mentioned there (~1-2h)
Answer the course background
Look over the . Select the project that interests you the most. Be prepared to discuss why you chose those that project in class.
Read up on and do the assignment mentioned there (~1-2h).
Find a that could be of interest to you in your final project. Be prepared to present in class (max one slide, 3 minutes):
Data cleanup: complete the . (~30-60min)
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 .
Hand-written text transcription:
Layout and text transcription:
Keyword generation from text:
An
Twitter archiving:
If you're short on inspiration, feel free to go through hands-on tutorial covering OpenRefine, RAW and Palladio. Afterwards, find other people who experimented with the same tool on Slack. Together, prepare a short demonstration (5-10 minutes) of the tool for class, describing:
For groups studying visualization tools, also read & , and reflect on how likely it is that you could use the visualizations to deceive yourself
Read up on and do the assignment mentioned there (~1-2h).
Find a that could be of interest to you in your final project. Be prepared to present in class (max one slide, 3 minutes):
Data cleanup: complete the . (~30-60min)
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 .
Hand-written text transcription:
Layout and text transcription:
Keyword generation from text:
An
Twitter archiving:
If you're short on inspiration, feel free to go through hands-on tutorial covering OpenRefine, RAW and Palladio. Afterwards, find other people who experimented with the same tool on Slack. Together, prepare a short demonstration (5-10 minutes) of the tool for class, describing:
For groups studying visualization tools, also read & , and reflect on how likely it is that you could use the visualizations to deceive yourself
Programming: Go through the and complete the assignments there.
Regular expressions: Read the section on and go through the assignments there. For the second assignment, add your solutions to the Flinga here:
Research 2: read . Try to understand on a general level what is being done on a methodological level, and how that feeds into the content argument. There will be group work relating to this in the next meeting.
Programming: Go through the and complete the assignments there.
Regular expressions: Read the section on and go through the assignments there. For the second assignment, add your solutions to the Flinga here:
Research 2: read . Try to understand on a general level what is being done on a methodological level, and how that feeds into the content argument. There will be group work relating to this in the next meeting.
language change, simulation: + . Also note that you can experiment yourself with the model described in the first paper .
twitter, sentiment analysis: +
dynamics of modern-day media: +
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
Language change, simulation: + . Also note that you can experiment yourself with the model described in the first paper .
Twitter, linguistic analysis, geographical analysis: +
dynamics of modern-day media: +
Simulation, archaeology: +
Geographic information, network analysis, archaeology: +
History, text reuse detection: + (Interestingly, first article doesn't have affiliations. Digging through, most people seem to be from )
Network analysis: +
3D/spatial analysis, archaeology: +
Image recognition of woodcut prints: /
Visual analysis, art history: /
Explore of and read the on topic modelling
Write a one to two page plan of what you'll do for your . Discuss the following:
These will be peer reviewed. Return the plan through Moodle at .
Write a one to two -page plan of what you'll do for your . Discuss the following:
These will be peer-reviewed. Return the plan through Moodle at .
