query for Aristotle:







To get these data into Mathematica we could just use:

data = WolframAlpha["Aristotle", {{"PopularityPod:WikipediaStatsData", 1}, "ComputableData"}];

DateListPlot[data, Joined -> True, AspectRatio -> 1/6]

We can obviously see sharp deeps or "negative" peaks at the end of every year and some wider deeps in the vicinity of August. My guess is winter and summer school vacations lead to the drop of student queries on Aristotle. This means that major source of those queries is from academia. Could this be true? Also notice how similar are the data for 2008 and 2009 - another argument towards some rather systematic data access through the year. But I would be really curious to find out what are those strong peaks in 2011 and 2012 landing in the same month approximately. Will we get the same peak in 2013?



To start thinking about these questions it would be helpful to be able to locate peak positions in the data - basically determine their time stamp. Any ideas on an efficient implementation?



But most importantly, where those peaks are coming from? Especially in the light that compared, for example, to Plato, - the totality of data are strongly correlated, confirming the academia hit source idea. But those peaks are definitely of charts.





I have noticed that Wikipedia popularity data from Wolfra|Alpha can give some very curios cases. For example, this is aTo get these data into Mathematica we could just use:We can obviously see sharp deeps or "negative" peaks at the end of every year and some wider deeps in the vicinity of August. My guess is winter and summer school vacations lead to the drop of student queries on Aristotle. This means that major source of those queries is from academia. Could this be true? Also notice how similar are the data for 2008 and 2009 - another argument towards some rather systematic data access through the year. But I would be really curious to find out what are those strong peaks in 2011 and 2012 landing in the same month approximately. Will we get the same peak in 2013?To start thinking about these questions it would be helpful to be able to locate peak positions in the data - basically determine their time stamp. Any ideas on an efficient implementation?