The Irish National Election Study (INES) is an extensive five-wave panel survey of (initially) 2663 respondents carried out by the ESRI through the period 2002-2007 and encompassing the Irish general elections of 2002 and 2007 as well as the local and European Parliament elections of 2004. This was the first ever such study of electoral behaviour in the Republic of Ireland. It was funded initially by a grant to TCD/UCD under the PRTLI/National Development Plan. This part of the research was directed by a team led by Michael Marsh (TCD) and Richard Sinnott (UCD) as principal investigators, assisted by Dr John Garry and Dr Fiachra Kennedy who were post-doctoral students attached to the project. Kenneth Benoit, Michael Laver, Michael Gallagher, Gail McElroy (all then TCD) and John Coakley (UCD) were associate investigators. This grant covered a post election face-to-face survey in 2002, and mail follow-ups with the same sample in 2003, 2004 and 2006. An infrastructure programme grant by the Irish Research Council for the Humanities and Social Sciences to Michael Marsh allowed a second face-to-face survey, again with the same sample, after the 2007 election, along with a supplementary sample to provide for a more representative sample for that year.

User content curation is becoming an important source of preference data, as well as providing information regarding the items being curated. One popular approach involves the creation of lists. On Twitter, these lists might contain user accounts relevant to a particular topic, whereas on a community site such as the Internet Movie Database (IMDb), this might take the form of lists of sharing common characteristics. While list curation implicitly involves substantial combined effort on the part of users, researchers have rarely looked at mining the outputs of this kind of crowdsourcing activity. Here we study a large collection of movie lists from IMDb. We apply network analysis methods to a graph that reflects the degree to which pairs of movies are "co-listed", that is, assigned to the same lists. This allows us to uncover a more nuanced categorisation of movies that goes beyond simple metadata, such as genre or era.