Helsinki fall 2020

Zoom meeting for the course: https://helsinki.zoom.us/j/68027991035. 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.

Meetings and assignments at a glance

Tue 27.10. — Introduction

Meeting contents

• Activatory pair discussion

• Introduction, practicalities -lecture

• Flinga questionnaire on background and interests

• Briefing of assignments

Assignments given

For Thu 29.10. (in two days):

1. Join the course Slack and optionally the hypothes.is group

2. Answer the course background questionnaire (~5min)

3. Look over the final projects from previous years. Select the project that interests you the most. Be prepared to discuss why you chose those that project in class. (~20-40min)

For Thu 5.11. (in 1½ weeks, but more assignments will be given on Thursday, so start already):

1. Read up on the history of humanities computing and do the assignment mentioned there (~1-2h)

Thu 29.10. — Debriefing of assignments, data

Assignments due

1. Answer the course background questionnaire

2. Look over the final projects from previous years. Select the project that interests you the most. Be prepared to discuss why you chose those that project in class.

Meeting contents

• Group discussion of projects from previous years

• Lecture on data

• Briefing of assignments on data and tools

Assignments given

For Thu 5.11. (in one week):

1. Read up on the history of humanities computing and do the assignment mentioned there (~1-2h).

2. Find a dataset that could be of interest to you in your final project. Be prepared to present in class (max one slide, 3 minutes):

   1. why you chose those that dataset,

   2. what types of information does it contain,

   3. what the structure, technical format and way of accessing the data is, and

   4. what potential sources of problems or biases does it have. (~15-45min)

3. Data cleanup: complete the OpenRefine tutorial. (~30-60min)

4. Experiment with at least one of the following tools (~30-60min + ~15-30min):

   1. Visualization:

      • tabular data → chart visualisations: RAW​

      • tabular data → chart visualisations: Voyager

      • tabular data → chart visualisations: ​Tableau​

      • tabular data → ​interactive map/network/timeline/list/facet visualisations: Palladio​

        • Palladio has help pages. There are also multiple tutorials on using Palladio, for example this one, or this one which is particularly on network analysis.

      • tabular data → map(+timeline) visualisations: ​Carto​

      • ​text →​ interactive explorative interface for linguistic study: Voyant tools​

      • ​big, preselected collections of text → interface for linguistic study: Korp / corpus.byu.edu​

      • If you're feeling explorative, feel free to also dig for more tools in TAPoR.

   2. Data acquisition:

      1. Hand-written text transcription: Transkribus

      2. Layout and text transcription: OCR4all

      3. Keyword generation from text: Annif

      4. An automated sound transcription tool

      5. An automated image/video description tool

      6. Twitter archiving: TAGS

   If you're short on inspiration, feel free to go through this 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:

   1. What is the tool good for?

   2. What kind of data do you need for the tool to be useful?

      1. What information does the data need to contain?

      2. What format does it have to be in?

   3. Your experience with the tool.

   4. For groups studying visualization tools, also read Perception deception & Common visualization mistakes, and reflect on how likely it is that you could use the visualizations to deceive yourself

Thu 5.11. — Debriefing of assignments, Programming

Assignments due

1. Read up on the history of humanities computing and do the assignment mentioned there (~1-2h).

2. Find a dataset that could be of interest to you in your final project. Be prepared to present in class (max one slide, 3 minutes):

   1. why you chose those that dataset,

   2. what types of information does it contain,

   3. what the structure, technical format and way of accessing the data is, and

   4. what potential sources of problems or biases does it have. (~15-45min)

3. Data cleanup: complete the OpenRefine tutorial. (~30-60min)

4. Experiment with at least one of the following tools (~30-60min + ~15-30min):

   1. Visualization:

      • tabular data → chart visualisations: RAW​

      • tabular data → chart visualisations: Voyager

      • tabular data → chart visualisations: ​Tableau​

      • tabular data → ​interactive map/network/timeline/list/facet visualisations: Palladio​

        • Palladio has help pages. There are also multiple tutorials on using Palladio, for example this one, or this one which is particularly on network analysis.

      • tabular data → map(+timeline) visualisations: ​Carto​

      • ​text →​ interactive explorative interface for linguistic study: Voyant tools​

      • ​big, preselected collections of text → interface for linguistic study: Korp / corpus.byu.edu​

      • If you're feeling explorative, feel free to also dig for more tools in TAPoR.

   2. Data acquisition:

      1. Hand-written text transcription: Transkribus

      2. Layout and text transcription: OCR4all

      3. Keyword generation from text: Annif

      4. An automated sound transcription tool

      5. An automated image/video description tool

      6. Twitter archiving: TAGS

   If you're short on inspiration, feel free to go through this 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:

   1. What is the tool good for?

   2. What kind of data do you need for the tool to be useful?

      1. What information does the data need to contain?

      2. What format does it have to be in?

   3. Your experience with the tool.

   4. For groups studying visualization tools, also read Perception deception & Common visualization mistakes, and reflect on how likely it is that you could use the visualizations to deceive yourself

Meeting contents

• Group discussion on the history of humanities computing

• Debriefing of the OpenRefine assignment

• Group presentations of tools

• Briefing of assignments on programming and research

slides

Assignments given

For Thu 12.11. (in one week):

1. Programming: Go through the fundamental concepts of programming for humanists and complete the assignments there.

2. Regular expressions: Read the section on regular expressions and go through the assignments there. For the second assignment, add your solutions to the Flinga here: https://edu.flinga.fi/s/ESFEH28

3. Research on visualization tool development:

   Read the following two articles on developing tools for particular text-based humanities research questions:

   • Visualizing Mouvance: Toward a visual analysis of variant medieval text traditions

   • Rule‐based Visual Mappings – with a Case Study on Poetry Visualization

   Now, think of a visualisation that would help you in your field. What information would it visualise? Prepare to discuss in class.

4. Research 2: read this research article. 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.

Thu 12.11. — Debriefing, Recap

Assignments due

1. Programming: Go through the fundamental concepts of programming for humanists and complete the assignments there.

2. Regular expressions: Read the section on regular expressions and go through the assignments there. For the second assignment, add your solutions to the Flinga here: https://edu.flinga.fi/s/ESFEH28

3. Research on visualization tool development:

   Read the following two articles on developing tools for particular text-based humanities research questions:

   • Visualizing Mouvance: Toward a visual analysis of variant medieval text traditions

   • Rule‐based Visual Mappings – with a Case Study on Poetry Visualization

   Now, think of a visualisation that would help you in your field. What information would it visualise? Prepare to discuss in class.

4. Research 2: read this research article. 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.

Meeting contents

• Debriefing of programming and regular expression assignments

• Group discussion on the visualization research

• Group work on the Old Bailey research

• Recap of the course thus far

• Briefing of assignments

slides

Assignments given

1. Select (at least) one of the following sets of paired articles based on your own interests:

   • language change, simulation: Social networks and intraspeaker variation during periods of language change + Utterance selection model of language change. Also note that you can experiment yourself with the model described in the first paper here.

   • twitter, sentiment analysis: What a Nasty day: Exploring Mood-Weather Relationship from Twitter + A Biased Review of Biases in Twitter Studies on Political Collective Action

   • dynamics of modern-day media: Using the president’s tweets to understand political diversion in the age of social media + Emotive, evaluative, epistemic: a linguistic analysis of affectivity in news journalism

   • simulation, archaeology: Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley + Understanding Artificial Anasazi

   • geographic information, network analysis, archaeology: Exploring the dynamics of transport in the Dutch limes + Testing the Robustness of Local Network Metrics in Research on Archeological Local Transport Networks

   • history, text reuse detection: Plundering Philosophers:Identifying Sources of the Encyclopédie + The Use and Abuse of the Digital Humanities in the History of Ideas: How to Study the Encyclopédie (Interestingly, first article doesn't have affiliations. Digging thrhough, most people seem to be from this project)

   • network analysis: Protestant Letter Networks in the Reign of Mary I: A Quantitative Approach + Automated analysis of the US presidential elections using Big Data and network analysis

   • 3D/spatial analysis, archaeology: A Survey of Geometric Analysis in Cultural Heritage + A GIS-based viewshed analysis of Chacoan tower kivas in the US Southwest: were they for seeing or to be seen?

   • Image recognition of woodcut prints: Image-matching technology applied to Fifteenth-century printed book illustration / Wormholes record species history in space and time

   Post which article you've selected on #research in Slack by Tuesday the 17th at the latest. Form a group with all the other people who selected the same articles. For class, prepare a presentation on them, detailing:

   1. How do the two articles relate to each other?

   2. 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?

   3. Data - How has the data used been gathered? What are the data sources used? Is the data available for others to use?

   4. Methods - What methods do the projects apply? How do the methods support answering the research questions?

   5. Partners - What is the make-up of the projects? Which disciplines are represented by the participants?

2. Explore this topic model of CEEC and read the explanation on topic modelling

Thu 19.11. — Debriefing, Computational analysis

Assignments due

1. Select (at least) one of the following sets of paired articles based on your own interests:

   • Language change, simulation: Social networks and intraspeaker variation during periods of language change + Utterance selection model of language change. Also note that you can experiment yourself with the model described in the first paper here.

   • Twitter, linguistic analysis, geographical analysis: What a Nasty day: Exploring Mood-Weather Relationship from Twitter + Mapping Lexical Innovation on American Social Media

     A Biased Review of Biases in Twitter Studies on Political Collective Action

   • dynamics of modern-day media: Using the president’s tweets to understand political diversion in the age of social media + Emotive, evaluative, epistemic: a linguistic analysis of affectivity in news journalism

   • Simulation, archaeology: Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley + Understanding Artificial Anasazi

   • Geographic information, network analysis, archaeology: Community structure of copper supply networks in the prehistoric Balkans: An independent evaluation of the archaeological record from the 7th to the 4th millennium BC + Testing the Robustness of Local Network Metrics in Research on Archeological Local Transport Networks

   • History, text reuse detection: Plundering Philosophers:Identifying Sources of the Encyclopédie + The Use and Abuse of the Digital Humanities in the History of Ideas: How to Study the Encyclopédie (Interestingly, first article doesn't have affiliations. Digging through, most people seem to be from this project)

   • Network analysis: Protestant Letter Networks in the Reign of Mary I: A Quantitative Approach + Automated analysis of the US presidential elections using Big Data and network analysis

   • 3D/spatial analysis, archaeology: A Survey of Geometric Analysis in Cultural Heritage + A GIS-based viewshed analysis of Chacoan tower kivas in the US Southwest: were they for seeing or to be seen?

   • Image recognition of woodcut prints: Image-matching technology applied to Fifteenth-century printed book illustration / Wormholes record species history in space and time

   • Visual analysis, art history: A Quantitative Approach to Beauty. Perceived Attractiveness of Human Faces in World Painting / Against Digital Art History

   Form a group with all the other people who selected the same articles. For class, prepare a presentation on them, detailing:

   1. How do the two articles relate to each other?

   2. 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?

   3. Data - How has the data used been gathered? What are the data sources used? Is the data available for others to use?

   4. Methods - What methods do the projects apply? How do the methods support answering the research questions?

   5. Partners - What is the make-up of the projects? Which disciplines are represented by the participants?

2. Explore this topic model of CEEC and read the explanation on topic modelling

Meeting contents

• Group presentations on research articles

• Lecturer presentation on research using computational analysis

• Briefing of assignments

Assignments given

For Thu 26.11. (in one week):

1. 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:

   1. 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?

   2. Data - How has the data used been gathered? What are the data sources used? Is the data available for others to use?

   3. Methods - What methods do the projects apply? How do the methods support answering the research questions?

   4. Partners - What is the make-up of the projects? Which disciplines are represented by the participants?

2. Write a one to two page plan of what you'll do for your final project. Discuss the following:

   1. What are your humanities research questions?

   2. Which data will you use?

   3. How do you plan to process, clean up and transform your data?

   4. How do you plan to analyze your data? How will the analysis help answer the humanities research questions?

   5. Critically analyze your data and pipeline for potential bias and problems.

   These will be peer reviewed. Return the plan through Moodle at https://moodle.helsinki.fi/course/view.php?id=36622.

Thu 26.11. — Debriefing, Computational analysis

Assignments due

1. 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:

   1. 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?

   2. Data - How has the data used been gathered? What are the data sources used? Is the data available for others to use?

   3. Methods - What methods do the projects apply? How do the methods support answering the research questions?

   4. Partners - What is the make-up of the projects? Which disciplines are represented by the participants?

2. Write a one to two -page plan of what you'll do for your final project. Discuss the following:

   1. What are your humanities research questions?

   2. Which data will you use?

   3. How do you plan to process, clean up and transform your data?

   4. How do you plan to analyze your data? How will the analysis help answer the humanities research questions?

   5. Critically analyze your data and pipeline for potential bias and problems.

   These will be peer-reviewed. Return the plan through Moodle at https://moodle.helsinki.fi/course/view.php?id=36622.

   .

Meeting contents

• Group presentations on research

• Group assignment on topic modelling

• Lecture on computational analysis

• Briefing of assignments

Assignments given

For Tue 1.12. (in five days):

1. Peer review two project plans of your fellow students

For Thu 3.12. (in one week):

1. Be prepared to shortly (1-2 minutes max) present your project plan to the others

Thu 3.12. — Debriefing, Open, reproducible research

Assignments due

1. (Tue 1.12.) Peer review two project plans of your fellow students

2. Be prepared to shortly (1-2 minutes max) present your project plan to the others

Meeting contents

• Project plan presentations

• Lecture on open, reproducible research

Assignments given

For Thu 17.12. (in two weeks):

1. Final project

Thu 17.12. — Deadline for returning final project