Live notes, so an incomplete, partial record of what actually happened.
Tags: dst4l
My asides in {}
Stream/Deck: http://www.dst4l.info/schedule.html
Not just tutorials, but also talks that frame learning tools/techniques.
Goals:
- gain an understanding of the research data lifecycle
- train librarians with skills
- create a data-centric culture in libraries
- grow a community
- empower librarians (so IT people don't assume librarians can't do stuff)
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>#dst4l 2016 kickoff - what a great start! looking forward to 3 exciting days of data wrangling :) pic.twitter.com/UwxeYphVvh
— Rainer Mesi (@raineralias) December 7, 2016
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>Countdown to #DST4L 2016 is over, watch the stream of the #datascience program https://t.co/FymD0fDR1H #dst4l #libraryfutures
— DST4L (@DST4L) December 7, 2016
Challenges: colleagues not getting why you need to know this; you may being separated off from other people. So, management are an important target.
But also, DST4L as a service.
There are many working with data: such as on the internet. Access to datasets is important then. The importance of letting go: because if you don't, people won't use your locked up data.
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>#dst4l @HenrietteRoued speaking about levels of openness https://t.co/rhqUC1LHn4
— Chris Erdmann (@libcce) December 7, 2016
Things to consider:
- make metadata that enables stuff to be found online
- make your licence obvious
- does the institution actively support reuse
- available in a machine readable format
- available via a well-described API or web service
Much of this is about making experimentation possible, of making daring to fail forward possible.
From Counting to Connecting: exploring academia as a complex socio-technical system of information transformation - Pedro Parraguez Ruiz
@parraguezr
How do we go from counting (eg a census) to connecting (via complex analysis). Not something that was, until recently, possible.
Perfection is not the point when you just want to get a sense of the landscape.
Run through of the first two parts of http://data-lessons.github.io/library-shell/
Tidy Data .. Massive work goes into curating datasets before a workshop, leaving attendees poorly situated to reapply tools/approaches on their data .. So OpenRefine is a way of doing the data prep ..
- remove inconsistencies
- split multi-variable fields
- fix inconsistent terminology
Data from data .. useful to think about affordances .. value of the data about the thing doesn't negate the value of the thing, it adds to it ..
http://thomaspadilla.org/projects/scaredtodeath/#/5
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