Journals and repositories: an evolving relationship?

It is now widely accepted that there are two routes to open access (OA): OA repositories and OA journals. It is often assumed these are distinct alternative parallel tracks. However, it has recently become clear that there is potential for repositories and journals to interact with each other on an ongoing basis and between them to form a coherent OA scholarly communication system. This paper puts forward three possible models of interaction between repositories and journals; services such as arXiv and PubMed Central, and the work carried out by the RIOJA project, are working exemplars and pilot implementations of these models. The key issues associated with the widespread adoption of these models include repository infrastructure development; changing ideas of the ‘journal’, ‘article’, and ‘publication’; version management; quality assurance; business and funding models; developing value-added features; content preservation; policy frameworks; and changing roles and cultures within the research community.

The Development of an On-line Submission and Peer Review System

Online submission and peer review is emerging as the next step forward for many journal publishers in an ever increasing drive to take advantage of technological improvements in transferring data electronically over the internet. The Electronic Submission and PEer REview (ESPERE) project was initiated in 1996 as an electronic Libraries (eLib) initiative of the Higher Education Funding Council for England (HEFCE). Subsequently the project continued as a self-funding group composed of a consortium of learned society and commercial journal publishers intent on utilising the changes in technology to improve the services they provide to their authors as well as cutting their costs and increasing efficiencies.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.