Danske OA-tidsskrifter og Directory of Open Access Journals (DOAJ)

The report describes a project to get more Danish journals included in DOAJ. The project was initiated in autumn 2020 and started in January 2021. The background has been to strengthen communication, collaboration and knowledge sharing across university libraries within the Royal Danish Library, but also to collaborate nationally with other university libraries that work with OJS servers (Aalborg University Library and Copenhagen Business School). The initiative has been initiated to increase the quality and visibility of the Royal Danish Library Open Science services as well as strengthening the OA journals in terms of visibility and traffic.Rapporten beskriver et projekt, der skulle få flere danske tidsskrifter optaget i DOAJ. Projektet blev initieret i efteråret 2020 og påbegyndt januar 2021. Baggrunden har været at styrke kommunikation, samarbejde og videndeling på tværs af universitetsbiblioteker inden for Det Kgl. Bibliotek, men også at samarbejde nationalt med andre universitetsbiblioteker, der arbejder med OJS servere (Aalborg Universitetsbibliotek og Copenhagen Business School). Initiativet er igangsat for at øge kvalitet og synlighed af Det Kgl. Biblioteks Open Science services samt styrke OA-tidsskrifterne i forhold til synlighed og trafik. 

The generalized Hierarchical Gaussian Filter

Hierarchical Bayesian models of perception and learning feature prominently in contemporary cognitive neuroscience where, for example, they inform computational concepts of mental disorders. This includes predictive coding and hierarchical Gaussian filtering (HGF), which differ in the nature of hierarchical representations. Predictive coding assumes that higher levels in a given hierarchy influence the state (value) of lower levels. In HGF, however, higher levels determine the rate of change at lower levels. Here, we extend the space of generative models underlying HGF to include a form of nonlinear hierarchical coupling between state values akin to predictive coding and artificial neural networks in general. We derive the update equations corresponding to this generalization of HGF and conceptualize them as connecting a network of (belief) nodes where parent nodes either predict the state of child nodes or their rate of change. This enables us to (1) create modular architectures with generic computational steps in each node of the network, and (2) disclose the hierarchical message passing implied by generalized HGF models and to compare this to comparable schemes under predictive coding. We find that the algorithmic architecture instantiated by the generalized HGF is largely compatible with that of predictive coding but extends it with some unique predictions which arise from precision and volatility related computations. Our developments enable highly flexible implementations of hierarchical Bayesian models for empirical data analysis and are available as open source software

Danish OA journals and Directory of Open Access Journals (DOAJ) ENGLISH version: Partly English text

Parts of this report are in English. The report describes a project to get more Danish journals included in DOAJ. The project was initiated in autumn 2020 and started in January 2021. The background has been to strengthen communication, collaboration and knowledge sharing across university libraries within the Royal Danish Library, but also to collaborate nationally with other university libraries that work with OJS servers (Aalborg University Library and Copenhagen Business School). The initiative has been initiated to increase the quality and visibility of the Royal Danish Library Open Science services as well as strengthening the OA journals in terms of visibility and traffic. This is an abbreviated version of the Danish version. Further information about the project can be obtained by contacting the Corresponding Author.Dele af rapporten er på engelsk. Rapporten beskriver et projekt, der skulle få flere danske tidsskrifter optaget i DOAJ. Projektet blev initieret i efteråret 2020 og påbegyndt januar 2021. Baggrunden har været at styrke kommunikation, samarbejde og videndeling på tværs af universitetsbiblioteker inden for Det Kgl. Bibliotek, men også at samarbejde nationalt med andre universitetsbiblioteker, der arbejder med OJS servere (Aalborg Universitetsbibliotek og Copenhagen Business School). Initiativet er igangsat for at øge kvalitet og synlighed af Det Kgl. Biblioteks Open Science services samt styrke OA-tidsskrifterne i forhold til synlighed og trafik. Dette er en forkortet udgave af den danske version. Yderligere oplysninger om projektet kan fås ved henvendelse til den primære kontaktperson