From 2006-2012, I had a NSF CAREER Award to collect data on online protest across 20 different issue areas. That effort produced two time-series datasets: a panel dataset tracking about 1,200 websites across 5 years, and a cross-sectional dataset tracking new samples of websites each year for five years. Each of these datasets is really two nested sets: one on the overall websites and one on all protest actions that were hosted or linked to from study websites.
After discussions with potential users at the CBSM pre-conference in Las Vegas, several data collection team members and I designed a data release process based directly on potential user input that is engineered to develop a strong and informed user base and reviewing community for the dataset. The first step in that data release process is a limited use period in which potential users can apply to use the data while it is still embargoed. In exchange for early access, these early users will agree to support the development of a user community around the dataset in a variety of ways. (For folks that don’t want to support a user community and just want their hands on the data, they will get that chance when the data is publicly released at the end of 2015.)
I invite you to read about the data release program at: http://jearl.faculty.arizona.edu/node/11
That page contains a call for proposals, with the first deadline on October 1st (with other deadlines following on a quarterly basis). Other pages linked from that page will take you to pages on how the data is being used already, potential collaborators from the data collection team, and detailed information on the dataset and documentation.
I hope many of you will consider contributing to this effort by submitting proposals and agreeing to join the user community. It’s a complicated dataset that over 60 people have put a lot of blood, sweat, and tears into building. We are eager to help develop a community that appreciates the complexity of these data and is prepared to use these data in scientifically appropriate ways. By doing so, we hope to not just introduce new data, but introduce the needed skills to use the data wisely.