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Management Summary: Conducted within the project “Economic Implications of New Models for Information Supply for Science and Research in Germany”, the Houghton Report for Germany provides a general cost and benefit analysis for scientific communication in Germany comparing different scenarios according to their specific costs and explicitly including the German National License Program (NLP).
Basing on the scholarly lifecycle process model outlined by Björk (2007), the study compared the following scenarios according to their accounted costs:
- Traditional subscription publishing,
- Open access publishing (Gold Open Access; refers primarily to journal publishing where access is free of charge to readers, while the authors or funding organisations pay for publication)
- Open Access self-archiving (authors deposit their work in online open access institutional or subject-based repositories, making it freely available to anyone with Internet access; further divided into (i) CGreen Open Access’ self-archiving operating in parallel with subscription publishing; and (ii) the ‘overlay services’ model in which self-archiving provides the foundation for overlay services (e.g. peer review, branding and quality control services))
- the NLP.
Within all scenarios, five core activity elements (Fund research and research communication; perform research and communicate the results; publish scientific and scholarly works; facilitate dissemination, retrieval and preservation; study publications and apply the knowledge) were modeled and priced with all their including activities.
Modelling the impacts of an increase in accessibility and efficiency resulting from more open access on returns to R&D over a 20 year period and then comparing costs and benefits, we find that the benefits of open access publishing models are likely to substantially outweigh the costs and, while smaller, the benefits of the German NLP also exceed the costs.
This analysis of the potential benefits of more open access to research findings suggests that different publishing models can make a material difference to the benefits realised, as well as the costs faced. It seems likely that more Open Access would have substantial net benefits in the longer term and, while net benefits may be lower during a transitional period, they are likely to be positive for both ‘author-pays’ Open Access publishing and the ‘over-lay journals’ alternatives (‘Gold Open Access’), and for parallel subscription publishing and self-archiving (‘Green Open Access’). The NLP returns substantial benefits and savings at a modest cost, returning one of the highest benefit/cost ratios available from unilateral national policies during a transitional period (second to that of ‘Green Open Access’ self-archiving). Whether ‘Green Open Access’ self-archiving in parallel with subscriptions is a sustainable model over the longer term is debateable, and what impact the NLP may have on the take up of Open Access alternatives is also an important consideration. So too is the potential for developments in Open Access or other scholarly publishing business models to significantly change the relative cost-benefit of the NLP over time.
The results are comparable to those of previous studies from the UK and Netherlands. Green Open Access in parallel with the traditional model yields the best benefits/cost ratio. Beside its benefits/cost ratio, the meaningfulness of the NLP is given by its enforceability. The true costs of toll access publishing (beside the buyback” of information) is the prohibition of access to research and knowledge for society.
High impact events, political changes and new technologies are reflected in our language and lead to constant evolution of terms, expressions and names. Not knowing about names used in the past for referring to a named entity can severely decrease the performance of many computational linguistic algorithms. We propose NEER, an unsupervised method for named entity evolution recognition independent of external knowledge sources. We find time periods with high likelihood of evolution. By analyzing only these time periods using a sliding window co-occurrence method we capture evolving terms in the same context. We thus avoid comparing terms from widely different periods in time and overcome a severe limitation of existing methods for named entity evolution, as shown by the high recall of 90% on the New York Times corpus. We compare several relatedness measures for filtering to improve precision. Furthermore, using machine learning with minimal supervision improves precision to 94%.