Helsinki fall 2018

Timetable for fall 2018

Wednesday 31.10. — Introduction

Assignments given

For 2.11.:

1. Answer the course background questionnaire

2. Look over the final projects from last year. Select the project that interests you the most. Post a short message on the #introductions channel on the course Slack to introduce yourself and to describe why you chose those that project.

3. Read up on the history of humanities computing. Be ready to discuss in groups on the next lecture.

Friday 2.11. — Different types of data, data quality, available open datasets

(pdf, gd)

Assignments due

1. Answer the course background questionnaire

2. Look over the final projects from last year. Select the project that interests you the most. Post a short message on the #introductions channel on the course Slack to introduce yourself and to describe why you chose those that project.

3. Read up on the history of humanities computing. Be ready to discuss in groups on the next lecture.

Assignments given

Assignments for 7.11.:

1. Find a dataset that could be of interest to you in your final project. Post a message on #datasets on Slack giving a link to the dataset and a note on why you selected it.

2. Read Perception deception & Common visualization mistakes as preparation for next week, learning to not trust visualisations blind.

Wednesday 7.11. — Easy tools for processing and exploring data

(pdf, gd)

Assignments due

1. Find a dataset that could be of interest to you in your final project. Post a message on #datasets on Slack giving a link to the dataset and a note on why you selected it.

2. Read Perception deception & Common visualization mistakes as preparation for next week, learning to not trust visualisations blind.

Assignments given

Two full weeks to do these, need to be done only by 21.11.:

1. Data cleanup: complete the OpenRefine tutorial.

2. Visualisation: Experiment with at least one of the following tools:

   • 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.

   If you're short on inspiration, feel free to go through this hands-on tutorial covering OpenRefine, RAW and Palladio.

   Afterwards, post a message on #tools on Slack detailing:

   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.

   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)

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

4. Regular expressions: Read the section on regular expressions and go through the assignments there.

5. In preparation for the lecture on 21.11., read this research article.

Friday 9.11. — No lecture

Wednesday 14.11. — Clinic for support in the assignments

Friday 16.11. — No lecture

Wednesday 21.11. — Fundamental concepts of statistics

Assignments due

1. Data cleanup: complete the OpenRefine tutorial.

2. Visualisation: Experiment with at least one of the following tools:

   • 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.

   If you're short on inspiration, feel free to go through this hands-on tutorial covering OpenRefine, RAW and Palladio.

   Afterwards, post a message on #tools on Slack detailing:

   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.

   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)

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

4. Regular expressions: Read the section on regular expressions and go through the assignments there.

5. In preparation for the lecture on 21.11., read this research article.

Assignments given

In preparation for next Wednesday's (28.11.) lecture, 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

• 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

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? How has the data been processed? 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?

Friday 23.11. — Fundamental concepts of statistics / Computational data analysis literacy

Assignments given

1. Explore bootstrapping

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

3. Read on some small, actual work:

   1. The presentation of the DHH15 key concepts of socialism group

   2. The presentation of the DHH15 Finnair Blue Wings multimodality group

   3. If you understand Finnish, the election questionnaire analysis and visualisation

4. For 5.12., explore this topic model of CEEC and read the explanation on topic modelling

Wednesday 28.11. — Computational data analysis literacy, part 2

Assignments due

1. Explore bootstrapping

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

3. Read on some small, actual work:

   1. The presentation of the DHH15 key concepts of socialism group

   2. The presentation of the DHH15 Finnair Blue Wings multimodality group

   3. If you understand Finnish, the election questionnaire analysis and visualisation

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

• 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

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? How has the data been processed? 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?

Presentations

• Plundering philosophers

• Twitter

• 3D archaeology

Friday 30.11. — No lecture

Wednesday 5.12. — Computational data analysis literacy, part 3

Assignments due

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

Assignments given

1. Find a computational humanities research paper that interests you. Post a message on #research on Slack shortly describing why you picked the paper.

2. Prepare to shortly (max 2 minutes) present your current idea for your final project on the lecture on

Friday 7.12. — Open, reproducible research and publishing / Final project

Wednesday 12.12. — No lecture, remote support for final project

Friday 14.12. — No lecture, remote support for final project

Friday 21.12. — Deadline for returning final project