Comments on: Notes http://dc2014.thatcamp.org at GWU on April 26 Sat, 26 Apr 2014 19:38:46 +0000 hourly 1 https://wordpress.org/?v=4.9.12 By: Annie Dempsey http://dc2014.thatcamp.org/notes/#comment-74 Sat, 26 Apr 2014 19:38:46 +0000 http://dc2014.thatcamp.org/?page_id=199#comment-74 Room 210, Session 4: Creating Crowd Sourcing Transcription for oral and video history

– Smithsonian transcriptions
– Crowdsourcing: cost efficient
– Question: how do you translate this to oral and video history?
– Video history: a lot of it is restricted, but some examples of video content:
– Manhattan Project
– Small arms history (including history of AK-47)

– Take reel-to-reel material on digital recorders
– Audio cassettes
– Sent to contractor to be transcribed

– Funds: sketchy

Challenges of oral transcriptions:
– Ensuring accuracy: accents, misheard words
– Tedious: pause, rewind, play, pause, rewind, play, repeat…

How do we accommodate for these problems?

– becomes more manageable if you break it up into segments: 30 seconds every 10 minutes
^ improves accuracy
– in a crowdsource context, divide it up by time stamps and assign people minute blocks
– recognizable people and recordings: entices people to participate
– have a notes portion: people can make notes of time stamps and confusion about language understanding
– But if there are unknown figures, contextualize them: e.g. why was this person important to xyz movement? How did this person influence xyz public figure?
– time stamps: makes it really clear where a project stopped
– tools to slow down speech

Potential tools:
– iTunes, Express Scribe, YouTube, iMovie (Mac; can alter pitch, speed, tone)

Data visualization:
– If you get demographics of digital volunteers, you can map where they are and what content they tend to be interested in
^ future donors and fundraisers can come from this

Why do you transcribe oral histories?
– Accessibility
– data mining: make it accessible to new research techniques
– social network analysis: transcribe and make conclusions about relationships
– preservation: if it’s recorded on a cassette or something that might be hard to digitize, you have the words forever
^ also an argument for keeping cassettes and similar media; notion that this is valuable oral content

Is there a way to build an online tool where once an oral history exists online, it can automatically enter a data mining program?
– there should be a way once an oral history is approved, you can send it directly through a code which submits it in a data context
– could be multistep, but you could build a single online forum for it

Failure is success: you rule out dead ends and apply it to your next effort

]]>
By: Alex DeLarge http://dc2014.thatcamp.org/notes/#comment-73 Sat, 26 Apr 2014 19:35:25 +0000 http://dc2014.thatcamp.org/?page_id=199#comment-73 DH SHOW & TELL
What are some useful tools for academic research?
TurboScan
Pocket
Evernote
Perseus.Tufts.edu
Prezi
Newsdiffs
Dropbox
SpiderOak
Carbonite
MediaFire
Lynda.com (tutorials)
PDF Expert
Writecheck
Safeassign

]]>
By: Alex DeLarge http://dc2014.thatcamp.org/notes/#comment-72 Sat, 26 Apr 2014 18:40:27 +0000 http://dc2014.thatcamp.org/?page_id=199#comment-72 DH in the classroom (digital pedagogy)

Group Bibliographies
– annotated bibliographies
– shared bibliographies (e.g., through Zotero)

Transcriptions
– permanency
– skill-neutral

]]>
By: Annie Dempsey http://dc2014.thatcamp.org/notes/#comment-71 Sat, 26 Apr 2014 16:07:29 +0000 http://dc2014.thatcamp.org/?page_id=199#comment-71 207 Session 1: Sunlight Foundation: Data Visualization
Amy Ngai, Amy Cesal, Ben Chartoff

– “Make gov transparent and accountable through data, tools, policy and journalism”
– free, open-source; staff of designers social scientists, reporters, policy, developers, comms
sunlightfoundation.com/api/community: projects to assist with!

What not to do:
– Data can be pretty, but does it SHOW anything
– No key, no introductory texts, no clear message of the data
– “Number art” pretty, but doesn’t tell you information
– No scales, labels, titles

“Data pervs”
– Notion that something is pretty, so it must “mean” something
– But this can mean that just because something is aesthetically appealing, people might not look deeply into it/question it
– “WTF Visualizations: Visualizations that make no sense” Tumblr page

“Squint test”
– Graphics that tell the story on the first glance
e.g. informative headlines, notable images like red lines indicating increase/decrease
– Use context: what are themes that people commonly understand? E.g. take numerical data and put it in the context of how it would fill a football field
^ most people have an estimate of the size of a football field; makes abstract data more accessible

Disseminating data
– Reporters can take screenshots of data graphics
– Reporting for reporters; making it more bite-sized for reporters to quickly understand information
– Social media shares

TOOLS:

Maps
– Show geospatial trends e.g. where are political fundraisers happening?
www.mapbox.com

The R Project
– Data visualization coding tool online
R-project.org

Tableau
– Basic coding

Developers/Coders:
github.com/sunlightlabs

Sunlight Foundation Data Visualization Style Guide

Be open to and flexible with whatever tools are available! If it gets the job done, use it

]]>