Helsinki fall 2021

Zoom meeting for the course: https://helsinki.zoom.us/j/61379040642. To get into the meeting, use the code 852773.

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 Wednesday, while each Monday is reserved for doing the assignments for that week.

Meetings and assignments at a glance

Mon 1.11. — Introduction

Meeting contents

• Activatory breakout group discussion

• Introduction, practicalities -lecture

• Flinga questionnaire on background and interests

• Briefing of assignments

Assignments given

For Wed 3.11. (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)

4. Watch this 4 minute video on problems with non-standard data. Alternatively, watch the 17 minute version or read this article.

For Wed 10.11. (in 1½ weeks, but more assignments will be given on this Wednesday, so start already):

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

Wed 3.11. — Debriefing of assignments, data

Assignments due

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

2. Answer the course background questionnaire

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.

4. Watch this 4 minute video on problems with non-standard data. Alternatively, watch the 17 minute version or read this article

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 (by end of Monday 8.11.). Together, prepare a short demonstration (max 5-7 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

Wed 10.11. — 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 (by end of Monday 8.11.). Together, prepare a short demonstration (max 5-7 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

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/EPVE75B

3. Research: 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.

Wed 17.11. — Debriefing, Statistics

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/EPVE75B

3. Research: 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

• Spill-over presentations from the week before

• Debriefing of programming and regular expression assignments

• Group work on the Old Bailey research

• Short introduction to statistics

• 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

   • Custom visualization tool building: Visualizing Mouvance: Toward a visual analysis of variant medieval text traditions / Rule‐based Visual Mappings – with a Case Study on Poetry Visualization

   Post which article you've selected on #research in Slack by Monday the 22nd at the latest. Form a group with all the other people who selected the same articles. For class, prepare a max 5-minute presentation on them, detailing:

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

   2. Questions - What are the research questions tackled in the articles?

   3. Methods - What means are used in the articles to answer the research questions?

   4. Data - What data are used in the articles as the bases for answering the research questions?

   5. Partners - Which disciplines are represented by the authors of the articles?

   Hint: when thinking of what to put in the presentation, look at the figures and tables included in the articles. Often, these make very good focal points around which to build your explanation of what the articles are about.

Wed 24.11. — 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, the 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

   • Custom visualization tool building: Visualizing Mouvance: Toward a visual analysis of variant medieval text traditions / Rule‐based Visual Mappings – with a Case Study on Poetry Visualization

   Form a group with all the other people who selected the same articles. For class, prepare a max 5-minute presentation on them, detailing:

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

   2. Questions - What are the research questions tackled in the articles?

   3. Methods - What means are used in the articles to answer the research questions?

   4. Data - What data are used in the articles as the bases for answering the research questions?

   5. Partners - Which disciplines are represented by the authors of the articles?

   Hint: when thinking of what to put in the presentation, look at the figures and tables included in the articles. Often, these make very good focal points around which to build your explanation of what the articles are about.

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

Meeting contents

• Group presentations on research articles

• Group assignment on topic modelling

• Lecturer presentation on research using computational analysis

• Briefing of assignments

Assignments given

1. Form a group with people from your own or nearby fields. Find a computational research paper from your field. For class, prepare a max 5-minute presentation on the article, detailing:

   1. Questions - What are the research questions tackled in the article?

   2. Methods - What means are used in the article to answer the research questions?

   3. Data - What data are used in the article as the bases for answering the research questions?

   4. Partners - Which disciplines are represented by the authors of the article?

   Hint: when thinking of what to put in the presentation, look at the figures and tables included in the article. Often, these make very good focal points around which to build your explanation of what the article is about.

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 or social science 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 human 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 (you should be able to self-enrol).

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

Wed 1.12. — 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 max 5-minute presentation on the article, detailing:

   1. Questions - What are the research questions tackled in the article?

   2. Methods - What means are used in the article to answer the research questions?

   3. Data - What data are used in the article as the bases for answering the research questions?

   4. Partners - Which disciplines are represented by the authors of the article?

   Hint: when thinking of what to put in the presentation, look at the figures and tables included in the article. Often, these make very good focal points around which to build your explanation of what the article is about.

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 or social science 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 human 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 (you should be able to self-enrol).

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

Meeting contents

• Group presentations on research

• Lecture on computational analysis

• Briefing of assignments

Assignments given

For Mon 6.12. (in five days):

1. Peer review two project plans of your fellow students

For Wed 8.12. (in one week):

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

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

Wed 8.12. — Debriefing, Computational analysis, Open, reproducible research

Assignments due

1. (Mon 6.12.) 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

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

Meeting contents

• Project plan presentations

• Lecture on topic modelling and other computational analysis approaches as well as open, reproducible research

Assignments given

For Wed 22.12. (in two weeks):

1. Final project

2. Read the new teaching material on the fundamental concepts of statistics. Give feedback to Eetu on Slack about it: what is understandable, what is not, etc.

3. Remember to fill in the course feedback form!

Wed 22.12. — Deadline for returning the final project

Remember to fill in the course feedback form!