Comparative Politics Dataset Award 

 As you know, Alan Greenspan retired from Fed about a month ago (and already has an $8M book deal, but I digress...).  Jens' post  below  reminded me of one of my favorite Greenspan quotes: "I suspect greater payoffs will come from more data than from more technique."  He was speaking to economics about models for forcasting economic growth, but I suspect his comments apply at least as strongly to political science and other social sciences.  You might have the most cutting-edge, whiz-bang, TSCS-2SLS-MCMC evolutionary Bayesian beta-beta-beta-binomial model that will tell you the meaning of life and wash your car at the same time, but if the data that you put in is either non-existent or garbage, it isn't going to do you a lot of good.  Unfortunately, the incentives in the profession do not seem sufficient to reward the long, tedious efforts required to collect high-quality data and to make it publicly available to the academic community.  Most scholars would surely like to have better data; they would just prefer that someone else collect it. 


 Having said all that, it is worth noting efforts that make data collection and dissemination a more rewarding pursuit.  One such effort is the Dataset Award given by the APSA Comparative Politics section for "a publicly available data set that has made an important contribution to the field of comparative politics."  This year's request for nominations hits the nail on the head:  
  
The interrelated goals of the award include a concern with encouraging development of high-quality data sets that contribute to the shared base of empirical knowledge in comparative politics; acknowledging the hard work that goes into preparing good data sets; recognizing data sets that have made important substantive contributions to the field of comparative politics; and calling attention to the contribution of scholars who make their data publicly available in a well-documented form. 
  
The section is currently accepting nominations for the 2006 award, with a deadline of April 14.  Information about nominating a dataset can be found  here .