When you are doing research, you tend to collect a lot of papers. I remember that at the end of m PhD, when I moved to another continent to do a postdoc, I dumped a huge box of photocopies in my parents’ basement. A few years ago, I had collected two cupboards full of photocopies. It was getting seriously out of hand. Then, of course, journals started putting everything online as PDFs and the same process started all over again but this time filling up hard disk folders instead. I used to have subject-based folders, which sort of worked until something fit within 2 or 3 or 4 of my subjects. Searching for some old paper you had read a few years back became more and more nightmarish. Then somebody showed me Papers.

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The other day I met a professor from a pretty good department and he said something along the lines of “of course the h-index is not important but it is funny that all the hires we have made over the years had a normalised h-index of 1 or above and when they didn’t, questions were asked”. So the conclusion really is that to be hired you need a normalised h-index greater or equal than 1.

What’s this all about? First the h-index, in case you have been in the bush and haven’t heard about it yet. There’s good explanation on the wiki page on the h-index. Briefly, if you order your publications by the number of citations in decreasing order and index them 1,2,3, etc., when the index becomes equal to the number of citations, you have found your h-index. Thus, an h-index of 10 means that you have 10 papers with 10 citations or more. It filters for outliers such as one paper with a 1000 citations. The normalised h-index (hbar?) is the h-index divided by the number of years that have passed since your first publication. Supposedly (I can’t find where I read this now), an h-index of 20 when you have been in your career for 20 years qualifies you to become a full professor.

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I used to go to the library. Every couple of weeks or so I would go and check the journals, browse through their tables of content (TOCs), and flip through the pages. You would find odd articles in areas there weren’t quite your own. Slowly over time journals got bigger and were published more frequently. Then they started emailing TOCs out, which seemed like a pretty good improvement as you could just read them on your computer. Gradually, I started to run out of time: the TOCs were just too long and there were too many of them. Virtual journals (such as the Virtual Journal of Ultrafast Science) offered some hope but they don’t cover all journals such as Elsevier journals, so you would still end up having to read a bunch of emailed TOCs.

For a couple of years, I tried an RSS reader. A free reader, such as NetNewsWire for the Mac or FeedDemon for Windows (http://www.newsgator.com/individuals/) can collect RSS feeds containing TOCs from you chosen journal. FeedDemon has to possibility of searching through your feeds. That worked pretty well until I switched to a Mac and found that NetNewsWire couldn’t search through feeds. By the time the TOCs were simply to huge to read them “raw”.

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Every scientist has to cope with the problem of managing references (or citations, or notes, or literature, or whatever you call it.)  When writing his second paper he discovers that he has to type a number of references that he already typed in when preparing his first paper. This repetitive action calls for a repository of references. In an ideal world many group members submit their references to this repository and after some time a very efficient storage medium has been created.

Pitfalls
Alas. The real world is never like this. And for many reasons. Typos in entries will live for ever, or will give rise to duplicate entries. Incomplete entries will downgrade the usefulness of the database. Inconsistent use of case (uppercase, lowercase, title case) is causing a mess. Different spelling of names will lead to duplicate entries, or to angry readers when they see their name misspelled in a list of references in an article in a high-impact journal. Many programs (or ‘wizards’) that import references cannot deal with extended characters (leave alone Unicode).  Names with diacritics (like umlauts) are dealt with either inconsistently or wrongly.  Partitioning of names into initials, first names and last names is full of traps and many import filters fall in those traps. In this respect the following error in the book Latex by Leslies Lamport (an excellent book and excellent macro package, of course) is typical: on page 141 (Chapter on “The Bibliography Database”) Lamport discusses “von Beethoven, Ludwig”. The name of course is Ludwig van Beethoven, as the name is of Flemish origin. And indeed “Van” is not his middle name.

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