Google Scholar is a wonderful service that tracks citations of individual artricles around the web. In contrast to the ISI Thompson Web of Science Citation Index, Google Scholar is not only free, but it collects citations from a much wider variety of journals, as well as from sources like books, arXiv, PhD and MS theses, and conference abstracts; in my opinion, these are all legitimate citations. It also seems to do corrections for double counting, at least to some degree, i.e. if the same-titled paper on arXiv and in a journal cite you, the citation won’t be counted twice.
One great element of Google Scholar is the ability to setup your own Google Scholar Profile, with a pic and summary of papers and the corresponding citations. Part of Google Scholar Citation profile is that you can set up alert (i.e. follow) to your own or someone else’s record. Here is an example:
Scholar calculates the ever-important Hirsch or h-index (if you have N papers that have been cited N times or more, then your h-index equals N). Increasingly, people are looking up citations when evaluating job candidates. Unfortunately, one thing with the h-index is that we must never forget that it scales with the size of the field. For instance, h-indices in the biomedical sciences tend to be higher than in many physical sciences simply because there are many more people working in these areas, so more papers are published per unit time. For instance, a brilliant mathematician may not have a high h-index simply because there are few people working on these problems and each publishes maybe 1-2 papers per year, so the publication rate of the whole field is not very high.
I work on theory/computation in a pretty fast-moving field. I have papers that I think are very original and technically complicated and of which I am very proud. Then I have papers that I think are good and solid but, technically, they are nothing earth-shattering, and some of them happen to be in currently fashionable areas. Which ones do you think get cited the most and pick up the citations most rapidly? *waiting for 5 seconds with bated breath* Exactly. I periodically look at my profile and I see these hot-area papers quickly shoot towards the top of my citation list; they do so because there are many other people who read them and build on them, as the topics are in vogue. In contrast, the papers that are very complicated and that may represent a real technical breakthrough get citations much more slowly. While I am well respected for these results in the communities where people understand them and I get recognition through invited talks, these communities are smaller and produce relatively fewer papers per year than others I am associated with, and the citation rates scale accordingly.
My poor beloved technically complicated papers — so great, yet so underappreciated. But at least they are a constant reminder that, when I evaluate others, I should not be lazy and rely solely on the easily obtained numerical metrics.
Which ones of your papers are your personal favorites and why? Are they also the most widely accepted and cited?
My most cited paper has probably been rarely read—it is for a tool that is frequently used, and in bioinformatics, people are expected to cite the paper that introduced the tool, even if they never actually read the paper.
One of my favorite papers is 5th on my Google Scholar list, but never appears on the Web of Science, because ISI didn’t index the journal. Google Scholar gets my h-index as 38, Web of Science gets only 25 (due to inadequate indexing in some fields, particularly of computer science archival conferences).
Self-reflection is a great way to realize how much relying solely on those numbers sucks. My favorite paper is next to last in citations, but I created a new type of testing method and data processing and simulation that’s applicable across so many fields. I had a crazy amount of fun on it. My favorite patent has to be one given to me about a year ago for a therapy we’re taking to Europe that will change my industry. That one has the rare designation of good-by-the-numbers and having been fun as a Saturday night disco