• “Gnomes and elves problem” – invisible labor in special collections and other fields
• What is invisible labor?
  • Thread on Twitter a few weeks ago stating person found this hidden gem; no, a librarian found it, catalogued it, shouted it out, you didn’t discover it
  • Invisibility – things difficult to assess (like course surveys); direct vs. indirect measures, skills that don’t manifest until later on, hard to measure and communicate
    • Big issue for funding – erasure of labor has financial implications
  • Technology may have exasperated this issue of erasing labor
    • Physical card catalog bank vs. online – mass quantity better indicates labor behind it
  • Librarian older model seen as support for researchers, not co-researchers
  • DH asking for more and different support – role as librarian changing based on tasks of researchers, which plays into idea of visibility
    • Visibility obvious as providing a service, but less so in other ways
  • Be more conscious about delivering products and benefits to get away from being seen as a call center
    • Ex. Mukurtu Project – active collaboration with indigenous peoples; normally invisible labor but support highlighted because of direct engagement with this community
    • Linked the product with the process
    • Engage with invisible voices that want to interpret themselves
  • Public transcription can help as well
    • Ex. transcription center at Smithsonian
  • Use the word “product” – makes things visible, but also still invisible
    • Everything in libraries being “projectized”
    • Hard to make something like knowledge a commodity
    • Creating end products like transcribed Diller jokes, which is important to show what labor is doing, but then worried that we are going to be judged off of that
      • Turns things into assets, money-makers (look what we can do in 5 months, how much $ we can make from it)
      • McDonalds vs. working with a chef
    • Product view also erases maintenance issue
  • Library as servant
    • I’m going to go to library and librarians going to find what I want vs. librarians teaching me to find something I need
    • View librarians as partners
  • Don’t necessarily want to be more of a partner by making invisible labor visible
    • Show the demands, need for funding, etc.
    • DH is helping uncover hidden labor
  • Flexibility and prioritization
    • If we have different levels of say, describing millions of collection objects
    • Triage and categorize objects as demonstration of value
    • And different levels of support
    • Counter: some don’t want to value different things differently
    • Doesn’t mean priorities won’t change, just see immediate demand in current environment
  • Important to define our goals – is it # of papers scanned into internet, or more indirect measures that may take longer to manifest down the road
    • Quantitative AND qualitative – qualitative harder to put in grant report, for example
    • Subjective harder to quantify
  • Need a big, broad picture narrative
  • Librarians as experts vs. community collaboration/ownership
    • Use librarians as starting point to get academic project off the ground – like a guide for best practices and best tools
    • Write them into your budgets
  • MFA becoming the new MBA – we are critical
  • Transcribing – undergraduate students present learned how much effort it takes
  • Crowdfunding – often seen as solution to all labor issues (unpaid labor)
    • But also sense of community and getting people involved
  • Want to bring digital documents into physical space – show and tell model
    • See what digital looks like in person – seeing that would give one a different awareness

How to approach DH?

-Text analysis
-Social network analysis
-Geo-spatial mapping
-Distance reading / content analysis
-Visual/sound analysis
-Visualization

Resources

Dirt Directory (dirtdirectory.org)
-comprehensive website/registry listing resources to help you conduct research
-can be categorized by your approach (text analysis, numeric data, etc.)

Tags (for twitter date collection)
-allows you to collect any tweet you want by the minute
-only need twitter and gmail account
-using twitter’s API including location, vast amounts of data

Voyant (voyant-tools.org) for text analysis
-load your own dataset
-enables you to quantify the humanities into datasets just as scientists and social scientists do
-shows (from left to right) a word cloud, an automatic summary (including words per sentence, frequent words, distinctive words, vocabulary density, etc.), the top five words, and words preceding and following specific words
-tool to exclude phrases you do not want to count as words

Programming Historian (programminghistorian.org)
-valuable especially for isolated regions where resources may be more limited
-always looking for contributors
-tutorials are well-written
-using regular expression to clean OCR text

Open Refine (openrefine.org)

Text grid labs – downloadable application for text analysis
-upload photos of manuscript
-can embed links, etc.

Gephi (gephi.org) for visualization

Palladio (hdlab.standford.edu/palladio) for visualizing historical data
-perfect for exploring and catered to be user-friendly
-partially funded by NEH

Google nGram

Social network analysis
-lots of statistics
-all you need is two columns of two related persons
-difference from Palladio – shows nodes (persons beyond the first degree of separation)
-analysis includes:
-maximum geodesic distance – diameter (“hops” of degrees of separation from one side of the chart to the other side)
-centrality (how many times people have go through you to get to another relation)
-exemplifies “power law curve”
-Eigenvector unit – “proximity to power” (how close you are to people with high scores of centrality)

Oxygen

Omeka
-omeka.org and omeka.net
-free, easy, nice to use
-really good at presenting all the metadata, making it very accessible
-comprehensive source for manuscript, images, audio, video

Zotero
-good for articles, books, embedding
-create things in zotero and you can embed on Omeka using a connecting tool

Wikis and Wikithons

Transcribed by: Hope Gillespie

• There is a widespread perception that Wikipedia isn’t a “trustworthy” source
  • But crowdsourcing is helping
• Initially, anyone could edit
  • They took on a group of editors
  • NOT primary source driven,  must be able to cite source of information
• Wiki ambassadors
  • Can train students to edit pages
• Editing
  • If the format exists already, It’s easy to follow
• VisualEditor
  • Gives you the ability to edit, but you must have an account and you can only work on one at a time
• Wikipedia Tutorial
  • Shows you how to edit
  • en.wikipedia.org/wiki/wikipediatraining
• Adam Lewis ( DC Wiki Ambassador)
• Sandboxes
  • Not meant to be permanent, low risk

Notes:

-Best way to find sources- is the source credible? How to verify digital sources

-Marketing skills for the job market in information literacy

-Teaching skills to students: biggest issue- authority is contextual when it comes to sources

-Sources validity depends on the project

-“Authority for sources exists on a spectrum”

-In the classroom- evaluating authority in sources activity — evaluate biases

-Facebook and blogposts can be useful if used in the correct way

-Making research “student central”

-Credibility is variable depending on topic

-Papers and learning how to acquire sources has become less central to learning in college classrooms

-Continuing the process and information literacy through-out entire collegiate careers and as resources throughout professional lifetime

-How can we implement information literacy as an every-day tool?

-Expanding information literacy beyond just English courses

-Gap in conversation between disciplines

-Not just humanities students need these skills— not accepting what is put in front of you, establishing critical thinking skills

-Creating stronger library presences on campuses

-Should we require scopes and methods classes?

-Start information literacy classes younger

-Changing the attitude of educational institutions on the importance of information literacy embedded in course curriculum

-Connection between digital and information literacy: should they be taught as different or the same field of study?

Helpful tools for research:

-Voyant Tools voyant-tools.org

-J-STOR Text Analyzer www.jstor.org/analyze/

-Each library has different digital database which makes teaching different depending on institution

-Strong database presence to counter Google- databases algorithms are not like Google which is hard to teach and be affordable for most libraries and campuses