Nor are one-dimensional citation counts the best we can do, metrically. There are many other research metrics waiting to be tested and validated: publication counts themselves are metrics. The number of years that a researcher has been publishing is also a potentially relevant and informative metric. (High citations later in a career are perhaps less impressive than earlier, though that no doubt depends on the field.) Total citations, average citations per year, and highest individual-article citation counts could all carry valid independent information, as could the average citation count (‘impact factor’) ²⁶ of the journal in which each article is published. But not all citations are equal. By analogy with Google’s PageRank algorithm, citations can also be recursively weighted in terms of how highly cited the citing article or author is. Co-citations can be informative too: being co-cited with a Nobel Laureate may well mean more than being co-cited with a postgraduate student. Downloads can be counted in the online age and could serve as early indicators of impact.

Citation metrics today are based largely on journal articles citing journal articles – and mostly just those 8000 journals that are indexed by ISI’s Web of Science. That only represents a third (although probably the top third) of the total number of peer-reviewed journals published today, across all disciplines and all languages. OA self-archiving can make the other two-thirds of journal articles linkable and countable too. There are also many disciplines that are more book-based than journal based, so book-citation metrics can now be collected as well (and Google Books and Google Scholar are already a potential source for book citation counts). Besides self-archiving the full-texts of their published articles, researchers could self-archive a summary, the bibliographic metadata, and the references cited by their books. These could then be citation-linked and harvested for metrics too. And of courses researchers can also self-archive their data (D-OA), which could then also begin accruing download and citation counts. And web links themselves provide a further metric that is not quite the same as a citation link.

Many other data could be counted as metrics too. Co-author counts may have some significance and predictive value (positive or negative: they might just generate more spurious self-citations). It might make a difference in some fields whether their citations are from a small, closed circle of specialists, or broader, crossing subfields, fields, or even disciplines: an ‘inbreeding/outbreeding’ metric can be calculated. Web link analysis suggests investigating ‘hub’ and ‘authority’ metrics. Patterns of change across time, ‘chronometrics,’ may be important and informative in some fields; the early rate of growth of downloads and citations, as well as their later rate of decay. There will be fast-moving fields where quick uptake is a promising sign, and there will be longer-latency fields where staying power is a better sign. ‘Semiometrics’ can also be used to measure the degree of distance and overlap between different texts, from unrelated works on unrelated topics all the way to blatant plagiarism.