Thursday, April 12, 2012

Overview of the NCAR/UCAR Data Citation Workshop up in Boulder

It has now been a week since the first day of the NCAR/UCAR Data Citation Workshop.  They recently put up all of the slides, so it is good to go back and see them again.  Here are my main take aways.

Purpose of Data Citation (Page 7)
• Aid scientific reproducibility through direct, unambiguous connection to the precise data used
• Credit for data authors and stewards
• Accountability for creators and stewards
• Track impact of data set
• Help identify data use (e.g., trackbacks)
• Data authors can verify how their data are being used.
• Users can better understand the application of the data.

“Tracking Dataset Citations Using Common Citation Tracking Tools Doesn’t Work”—Heather Piwowar, DataONE (Page 13)
• Traditional fields such as author and date too imprecise
• Web of Science, Scopus, and other tools don’t handle identifiers

Surprise, many scientists do not know how to cite data. We need to better advertise this guide from the Digital Curation Center.

Something basic like this could be used.
Author(s). ReleaseDate. Title, [version]. [editor(s)]. Archive and/or Distributor. Locator. [date/time accessed]. [subset used].
In addition to DOIs, there are other identifier systems, such as ARKs, and EZIDs. EZID is used by many organizations.

Data citations in NCAR/UCAR Whitepaper.

Barbara Losoff at CU-Boulder did a good job of explaining the libraries role in data curation? (Start at page 5.)

Love this quote - "Data preservation is communicating with the future."

Dr. Tim Killeen from the NSF provided a good overview of their vision (start at page 20) concerning data citation and curation. This book, Cyber Infrastructure Framework for the 21st Century, is worth a read.

I am still not quite sure what the NSF Earth Cube Project is all about.

NOAA gets a good amount of social media views and uses.(See page 10 of 11. For example, YouTube: ~ 715 subscribers, ~ 88,000 video views.)  This may be where the action is headed.

This observatory tracks how people cite their data in articles through WoS and Google Scholar.  We could do something like that here.

The big finale from Matthew Mayernik

We need to make things "Simple, Weak, Open, and Together"

Simple – Don’t overload citations
Weak – Make it easy for users, data users and data submitters
Open – easier to address problems and new cases
Together – Lots of interest in multiple communities
The idea came from From John Wilbanks – Designing for Emergence.

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