The document discusses tools that encourage criticism in digital humanities research. It covers three main themes: digital tool criticism which involves reflecting on tool use; data scopes which provide a framework for conceptualizing data transformations; and documenting the research process by tracing tool interactions and choices. Specific topics discussed include the need for transparency in search tools and other interfaces, conducting workshops for collaborative tool criticism, using the data scopes concept to document how data is processed, and challenges of documenting the research process and data provenance. The goal is to promote infrastructure that invites reflection on tools and reveals how data is transformed through the research process.

1. Tools that Encourage Criticism
Digital Humanities Infrastructures and Research
Marijn Koolen - KNAW Humanities Cluster
Research & Development
Symposium on Tools Criticism - Leiden University - 21-11-2019

3. Tools & Reflection In Three Themes
● Digital Tool Criticism
○ Reflection on tool use
● Data Scopes
○ Conceptual framework for data transformations
● Documenting Research Process
○ Tracing tool interactions, choices and decisions

4. Digital Tool Criticism

7. ● Fast and easy to use
● Aim to hide technical details but search fields are not ‘technical’ detail
● Most interfaces not made with tool criticism in mind
○ But, there are too many details!
○ Which should be brought to our attention?
Search Engines: Experts in Retrieval, Masters at Hiding

8. Communicating Choices and Options

11. ● Inspection tool in CLARIAH Media Suite is first attempt
○ What are other ways to identify and flag issues in data and tools?
○ Input from researchers and developers needed!
● How do/can other often used search interfaces deal with transparency?
○ Nederlab - research portal for linguistic analysis
○ Delpher - Dutch digitized newspaper archive
○ WorldCat
○ Europeana
○ HathiTrust Research Center
○ Google
Reflection and Transparency in Tool Interfaces

12. Use of digital tools in the context of research: criticism should incorporate all elements of research, e.g. RQ, methods,
data and tools. (Koolen, van Gorp & van Ossenbruggen, DSH 2019)
More later in Jasmijn van Gorp’s presentation
Tool Criticism as Reflection

13. ● Switch from close to distant reading
○ Puts us in a new, less familiar environment and paradigm
○ Unknown pitfalls, what are the reflective questions that signal where to expect these pitfalls?
● Perceived conflict of investing time
○ Acquiring new ‘technical’ skills vs. doing the ‘actual’ research.
○ Self-defeating beliefs about own possibilities to learn. (Fook 2015, p. 445)
● What are ways to break out of this impasse?
Critical Reflection and Technical Knowledge

14. ● Tool Criticism through Collaboration and Experimentation
○ Workshops: Tool Crit. 2015, DH Benelux 2017, DH Benelux 2018, Utrecht University 2018
● Collaboratively using tools prompts discussions
○ Face-to-face: collaboratively looking under the hood and its consequences
○ Explaining how you think it works is a great way to bring out gaps in your own understanding
(Sloman and Fernbach 2017)
● Many research questions require huge number of skills
○ Need to collaborate to ensure at least someone involved understands specific tool details
● Experimentation with tools to deepen understanding
○ “We learn differently when we are learning to perform than when we are learning to
understand what is being communicated to us.” (Mezirow 1991, p.1)
● But what level of skills do we need?
○ Ben Schmidt (2016): not algorithmic steps, but data transformations
Digital Tool Criticism Workshops

15. Data Scopes

16. ● Data needs processing to offer insights for research questions
○ Making data transformations offers different perspectives or scopes on data
■ Often left out of publications, outsourced as “technical detail”
■ But details matter, process is intellectual effort!
● Data Scopes concept (Hoekstra and Koolen 2018)
○ Conceptual tool for thinking and communicating about research data processing
○ Especially for combining data from different sources
Data Scopes

17. Data Scopes
● Data needs processing to offer insights for research questions
○ Making data transformations offers different perspectives or scopes on data
■ Often left out of publications, outsourced as “technical detail”
■ But details matter, process is intellectual effort!
● Data Scopes concept (Hoekstra and Koolen 2018)
○ Conceptual tool for thinking and communicating about research data processing
○ Especially for combining data from different sources
● Five types of transforming activities:
○ Selecting
○ Modeling
○ Normalizing
○ Linking
○ Classifying
● A form of scholarly primitives (Unsworth 2000, Anderson et al. 2010)

19. ● Online book response corpus (Boot 2017)
○ ~400,000 book reviews in Dutch
○ From different review sites (Bol, Hebban, Dizzie, Wat Lees Jij Nu, …)
● Research questions
○ What impact does reading fiction have on readers?
■ How do reviewers describe impact of book?
■ Are there differences across genres/authors?
Use Case: Analyzing Online Book Reviews

20. Reading Impact
“Je gaat Stijn eigenlijk een beetje begrijpen, …”
“You start to understand Stijn, …”
“Helaas is de schrijfstijl bedroevend, presenteert Kluun zich als een
pseudo-intellectueel …”
“Unfortunately the writing style is pathetic, Kluun presents himself as a pseudo-intellectual …”

21. Reading Impact Rules
● 349 rules
○ Identifying 4 types of impact: general, narrative, style, reflection
● Term: boeiend
○ Rule 2: Style impact: boeiend + style term - e.g. “boeiend taalgebruik” (engaging use of
language)
○ Rule 3: Reflection: boeiend + topic term - e.g. “boeiende thematiek” (engaging themes)
● Phrase: in één ruk * uitlezen
○ Rule 79: General impact - “Ik heb het boek in één ruk helemaal uitgelezen.” (finish in one go)
○ Many variants
■ één/een/1
■ adem/avond/dag/keer/middag/ruk/stuk/zucht/...
■ uitlezen/uit

22. ● Gather review data
○ Select review sites
○ Model review from web page (book title, author, ISBN, reviewer, date, rating, website, …)
○ Link author and book title to WorldCat record (for missing data)
■ Select ISBN, publisher, publication year
○ Link ISBN to record in boek.nl database
■ Select genre classification (NUR code)
Data Scopes for Reading Impact Analysis

26. Data Scopes for Reading Impact Analysis (2/2)
● Extract impact expressions
○ Select individual sentences from book reviews
○ Normalise words in sentences to their lemmas
○ Select all sentences that match an impact rule
○ Classify sentences by impact rule
● Analyse impact
○ Select impact matches by book genre or author or book ID or reviewer ID

27. Entanglement of Data and Tools

28. Entanglement of Data and Tools
Each step changes the underlying data!

29. Documenting Research Process

30. ● We should develop/ask for tools that
○ Show or describe implementation choices
○ Allow data inspection
○ Self-document interactions or encourage documenting
● Examples
○ Open Refine: spreadsheets on steroids, powerful data cleaning, enrichment and analysis
○ Notebooks: e.g. Jupyter Lab and Hub
● Workshop Documenting Research Practices (DH Benelux 2019)
○ Research practice and teaching
○ 20 participants, none had systematic approach
○ Assignments aimed at increasing awareness of reasons and methods
Documenting Tool Interactions

31. Data Has No Memory
● Through linking, our review dataset now has complete set of ISBNs
○ Allows comparing reviews of different editions of a book
○ E.g. does plain edition affect readers differently from critical edition?
● Each edition has own ISBN
○ Modeling: group reviews by ISBN, group ISBNs by title+author or NTSC

32. Data Has No Memory
● Through linking, our review dataset now has complete set of ISBNs
○ Allows comparing reviews of different editions of a book
○ E.g. does plain edition affect readers differently from critical edition?
● Each edition has own ISBN
○ Modeling: group reviews by ISBN, group ISBNs by title+author or NTSC
● Banana peel: we’ve hidden uncertainty!
○ Some reviews don’t specify ISBN (we looked them up separately)
○ So we don’t know which edition is reviewed
○ But transformed dataset implies we do!
● Possible solution: add provenance info on data and process
○ How can tools help with this?

35. Open Refine

39. Wrap Up
● Pragmatic approach to discuss transparency in DH research and infrastructure
○ Digital Tool Criticism: Reflection, checklist + questions
○ Data Scopes: Understanding data transformations in research process
○ Document Research Practices: Data has no memory
● Infrastructure should
○ Invite us to collaborate, experiment, question, reflect
○ Reveal and document transformations

40. Humanities scholar at the CLARIAH Toogdag:
“Our students are too stupid to write queries in a structured query
language!”

41. ● A lot of this work is a collaboration with:
○ Rik Hoekstra
○ Jasmijn van Gorp
○ Jacco van Ossenbruggen
○ Antske Fokkens
○ Liliana Melgar
○ Peter Boot
○ Ronald Haentjens Dekker
○ Marijke van Faassen
○ Lodewijk Petram
○ Jelle van Lottum
○ Marieke van Erp
○ Adina Nerghes
○ Melvin Wevers
Acknowledgements

42. Thank You!
Questions?

43. References
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