In mid-December 2009, around 50 science colleagues assembled for what was tipped to be a veritable bibliometric wonderland. Attended by George Hirsch and Henry Small among others, the event offered a practical workshop rather than one-way theoretical presentations.

New usage metrics: recurring themes, fresh challenges

Small beginnings: it took centuries for citation structure to develop; technologies are only now available to make new metrics possible.

Incentives work both ways: people need incentives to adopt new metrics, while metrics incentivize both positive and negative behavior.

Availability of raw data: usage data can be proprietary, fragmented, and not overtly displayed.

Metrics are only part of the answer: peer review continues to play a role.

Jumping on the interdisciplinary bandwagon, the speakers and attendees represented many differing points of view: government vs. academic vs. corporate; evaluator vs. proposer; funding vs. policy vs. scientist; metric theorists vs. practitioners. But while debates were spirited, discussions were collegial and focused on advancing work on new metrics.

Two particular questions occupied participants, to which all discussions of new metrics circled back. Herbert van de Sompel of Los Alamos National Laboratory, the first speaker and one of the event organizers, asked attendees: “What are the qualities which make a metric acceptable to all stakeholders? And how do we move from conception to acceptance?” The workshop centered on projects investigating or proposing new metrics, including the MESUR project, Eigenfactor, h-bar index, and PLoS ONE’s article-level metrics. Many of these new metrics center on usage data.

Usage-based versus article-level metrics
Metrics based on usage data are central to the MESUR (MEtrics from Scholarly Usage of Resources) project. Johan Bollen from Indiana University, and principal investigator for the MESUR project, presented his findings to date. When comparing traditional citation-based metrics with usage-based metrics, he observed that usage data are very good indicators of prestige, but that evaluating scholars solely on rate metrics and total citations is “like saying Britney Spears is the most important artist who ever existed because she's sold 50 million records.”

In contrast, Peter Binfield of PLoS ONE presented the journal’s work on article-level metrics. In PLoS ONE, article views, downloads, star ratings, bookmarks and comments join the traditional citation counts. There are, however, downsides to article-level metrics like the star-rating system: Peter cautions that it is not yet widely used, and there is a propensity to give articles a five-star rating. The full scale is rarely used, meaning that it can be hard to infer much from these ratings.

Missing from the current metrics available, claims Peter, include those that predict an article’s impact from day one; ratings by reviewers, editors and other experts in an article’s particular field; mainstream media coverage; publicly available usage metrics that track article downloads, views of abstracts, re-posts of articles online and so on; tracking of “conversations” (comments, forum discussions and so on) outside the original place of publication; and the reputation of metrics among commentators.

Moving with the times
Recognizing that citation analysis has a history hundreds of years in the making, the discussion of new usage indicators has only been possible in the last decade or two. It will take a long time before scholarship catches up with these new (technological) metrics; and we are only just beginning to understand what the impact of these technologies will be.

Will Jorge Hirsch’s h-bar index take hold with the speed of the h-index? Will collaboration between the MESUR and Eigenfactor projects deliver MESUR-able results? Which approaches to network analysis will become mainstream in identifying influence, prestige and trust? When will measuring re-use of data sets become commonplace? Will metrics ever replace peer review? Whatever the answers, we look forward to the next workshop to carry the debate forward.

The burning questions

Cart before the horse: new usage-based metrics require the collection of new data for future analysis. But what data with what standards and for which metrics?

Variances in usage-tracking systems: without a central repository, how to measure usage across databases for the same article?

Power of simplicity: simple calculations, such as the h-index and impact factor, have high adoption rates; will relatively complex, computer-dependent network analyses ever achieve the same rate?

Scholarly vs. public attention: when analyzing usage data of publicly available articles, can scholarly attention be distinguished from general curiosity? Does it need to be?

What’s “new”: do existing systems for scholarly attention and funding decisions drive attention to the norm, to the detriment of breakthrough research that pushes the boundaries of science?

No single metric: while one metric will never suffice, which set of metrics will serve as a standard group?

Metrics for non-article research output: how can re-purposing mathematical formulas or re-using data sets be tracked?

Useful links

Scholarly Evaluation Metrics: Opportunities and Challenges
Scholars Seek Better Metrics for Assessing Research Productivity
Visualizations on PLoS
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