RoMEO Studies 8: self-archiving: the logic behind the colour-coding used in the Copyright Knowledge Bank

Purpose – The purpose of this research is to show how the self-archiving of journal papers is a major step towards providing open access to research. However, copyright transfer agreements (CTAs) that are signed by an author prior to publication often indicate whether, and in what form, self-archiving is allowed. The SHERPA/RoMEO database enables easy access to publishers' policies in this area and uses a colour-coding scheme to classify publishers according to their self-archiving status. The database is currently being redeveloped and renamed the Copyright Knowledge Bank. However, it will still assign a colour to individual publishers indicating whether pre-prints can be self-archived (yellow), post-prints can be self-archived (blue), both pre-print and post-print can be archived (green) or neither (white). The nature of CTAs means that these decisions are rarely as straightforward as they may seem, and this paper describes the thinking and considerations that were used in assigning these colours in the light of the underlying principles and definitions of open access. Approach – Detailed analysis of a large number of CTAs led to the development of controlled vocabulary of terms which was carefully analysed to determine how these terms equate to the definition and “spirit” of open access. Findings – The paper reports on how conditions outlined by publishers in their CTAs, such as how or where a paper can be self-archived, affect the assignment of a self-archiving colour to the publisher. Value – The colour assignment is widely used by authors and repository administrators in determining whether academic papers can be self-archived. This paper provides a starting-point for further discussion and development of publisher classification in the open access environment.

An Indirect Genetic Algorithm for a Nurse Scheduling Problem

This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.