Perspectives
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- More journals, both OA and TA, encourage or require OA to the data underlying published articles. Major publisher associations like ALPSP and STM, which lobby against national OA policies for literature, encourage OA for data. Even when these policies don't cover peer-reviewed articles, they accelerate research, demonstrate the benefits of unrestricted sharing, and build expectations and momentum for OA in other categories.
- More journals (OA and TA) are integrating text and data with links between text and data files, tools to go beyond viewing to querying data, dynamic charts and tables to support user-driven what-if analyses, and multimedia displays. Some types of integration can be done in-house at the journal and kept behind a price wall. But other types, especially those added retroactively by third parties, require OA for both the text and data. OA invites motivated developers to use their cleverness and creativity, and the existence of motivated developers invites authors and publishers to make their work OA.
- Thomson Scientific is selecting more OA journals for Impact Factors, and more OA journals are rising to the top cohort of citation impact in their fields. For scholars and institutions using Impact Factors as crude metrics of quality, this trend legitimates OA journals by showing that they can be as good as any others. There are other gains as well. Because OA increases citation impact (studies put the differential at 40-250%, depending on the field), high-quality OA journals can use citation impact to shorten the time needed to generate prestige and submissions commensurate with their quality. OA journals with rising impact are successfully breaking a vicious circle that plagues all new journals: needing prestige to attract high-quality submissions and needing high-quality submissions to generate prestige. OA journals high in impact, quality, and prestige are improving their own fortunes and changing expectations for other OA journals. But they are also making two little-known truths better known: first, that OA can help journals, not just readers and authors, and second, that a journal's impact, quality, and prestige do not depend on its medium or business model --except insofar as OA models actually amplify impact.
- New impact measurements are emerging that are more accurate and nuanced, more inclusive, more timely, and less expensive than Impact Factors. These include Eigenfactor, h-Index, Journal Influence and Paper Influence Index, Mesur, Usage Factor, Web Impact Factor, and Y Factor. What most of them have in common is the harnessing of new data on downloads, usage, and citations made possible by OA. In this sense, OA is improving the metrics and the metrics are improving the visibility and evaluation of the literature, especially the OA literature.
- Download counts are becoming almost as interesting as citation counts. Not only are they being incorporated into impact metrics, but a CIBER study (September 2005) discovered that senior researchers found them more credible than citations as signs of the usefulness of research. A Brody-Carr-Harnad study (March 2005) found that early download counts predict later citation counts. No one thinks download counts mean the same thing as citation counts, but they're easier to collect, they correlate with citation counts, and they're boosted by OA. In turn they boost OA; repository managers have learned that showing authors their download tallies will encourage other authors to deposit their work.
- The unit of search has long since shifted from the journal to the article. Now the unit of impact measurement is undergoing the same transition. Authors and readers still care about which journal published a given article, but they care less and less about which other articles appeared in the same issue. More and more, finding a relevant article in an OA repository, separated from its litter mates, gives searching scholars all they want.
