Managing your informative networks

One of the main sources of "fresh information" in science is the email. More precisely, mailing lists. Depending on your area of expertise, you can find several email lists, where is possible to searching for:

• Interesting scientific discussions (like where can I find the first reference of algorithm X? is there a better way to do this? where is the best source of this topic?).
• Postdoctoral positions: jobs that you can take if you already got a PhD and you want to make a lot of moneynew exciting research for a short period (2 or 3 years).
• Special numbers of scientific journals: maybe the opportunity to publish a paper in a highly recognized journal in your area, or at least, getting a good feedback on your research.
• Conferences: the main source of getting information about new or classic conferences, specially the important dates and the list of interesting topics.

As a researcher between the fields of Information Retrieval and Machine Learning, I used to read the webir list for having the last news on the IR field. Unfortunately, Einat Amitay stop managing the list after 10 years of being there (the list is now closed). Recently, following the advice of my supervisor, I joined the list ML-news which seems to be a very active forum on the area of Machine Learning, but I am still looking for a substitute for webir... Any good suggestion for a mailing list in the field of IR?

On the other hand, what do you think of mailing lists? What lists do you belong to? Do you think email is very 90-ish? Do you trust more in facebook groups? Are you running a Machine Learning twitter account? Feel free to answer, please.

H-Index and so

The Hirsch Index (or in a sort way, the h-Index) is a way to measure scientific popularity by one number. A scientific with an h-Index of n, will have n papers with at least n citations each one. Note that I am saying that it can measure "scientific popularity", neither "scientific excelence" nor "scientific quality" (although there is often a correlation between those three facts).

h-index is sometimes a way to self-glorification, other times it hides a collaborative mafia ("I will cite you if you cite me"), but I like it (and so scientific community do). It is only a metric, but, as in other metrics, is a quantitative way to measure the importance of a scientific in its community.

As you can see, h-index grows in an exponential manner: when you get your first citation, you get your h=1. To get h=2 you need, either to get (al least) one more citation on that paper, and (at least) two more in a different one, or get (at least) two citations in different papers. That means that, stepping from an h-index of n-2 to n-1 is quite easy than doing the same from n-1 to n (because in every step you need more and more citations).

A tool which is helpful to compute this index is "Publish or Perish", available here. Using google scholar and other similar services, this tool can easily compute the h-index of a researcher, in a semi-automatic way (you often have to discard manually several publications that are not coming from that author, and of course, self-citations).

By the way, at the present day (2/12/2008) my h-index is 1. It is not so bad for a PhD student, but I hope it would be improved next year...