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 two weeks, 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
Assignments given
Meeting contents
Assignments given

For Thu 29.10. (in two days):

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

  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. (~20-40min)

For Tue 3.11. (in one week, 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, Different types of data, data quality, available open datasets

Assignments due
Meeting contents
Assignments given
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

  • Briefing of assignments on data and tools

Assignments given

For Tue 3.11. (in five days):

  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 (one slide, max 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)

For Thu 5.11. (in one week):

  1. 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 image/video description tool

      5. 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

Tue 3.11. — Debriefing of assignments

Assignments due
Meeting contents
Assignments given
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 (one slide, max 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)

Meeting contents
  • Group discussion on the history of humanities computing

  • Presentations on datasets

  • Group discussion on OpenRefine

Assignments given

For 5.11. (in two days):

  1. 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 image/video description tool

      5. 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
Meeting contents
Assignments given
Assignments due
  1. 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 image/video description tool

      5. 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 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.

  3. 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.

  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, Statistics

Assignments due
Meeting contents
Assignments given
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.

  3. 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.

  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

  • Briefing of assignments

Assignments given
  1. (Do the assignments on statistics (not yet ready, but will contain the following in addition to other stuff):

    1. Check out the Explained Visually site, and especially PCA explained visually)

  2. 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:

    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?

Thu 19.11. — Debriefing, Computational analysis

Assignments due
Meeting contents
Assignments given
Assignments due
  1. 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:

    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?

Meeting contents
Assignments given

For Thu 26.11. (in one week):

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

  2. 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?

Thu 27.11. — Debriefing, Computational analysis

Assignments due
Meeting contents
Assignments given
Assignments due
  1. Explore this topic model of CEEC and read the explanation on topic modelling

  2. 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?

Meeting contents
Assignments given

For Tue 1.12. (in five days):

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

    1. What are your human 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 assignments at https://moodle.helsinki.fi/course/view.php?id=36622

For Thu 3.12. (in one week):

  1. Peer review two project plans of your fellow students

  2. Be prepared to shortly present your project plan to the others

Thu 3.12. — Debriefing, Open, reproducible research

Assignments due
Meeting contents
Assignments given
Assignments due
  1. Find (in groups if you like) a computational humanities research paper that interests you. Prepare to present it in class.

  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 assignments at https://moodle.helsinki.fi/course/view.php?id=36622

Meeting contents
Assignments given

For Thu 17.12. (in two weeks):

Thu 17.12. — Deadline for returning final project