This thesis has examined a selection of Swedish universities’ views on the Swedish national publication database SwePub. The study has phenomenography as its methodology, and by means of the survey it has asked questions about the universities’ local publication databases, the national guidelines on documenting a scientific publication and how the universities regard the whole SwePub analysis project.  The purpose was to find out how the universities perceive the whole SwePub phenomenon. For selecting participants in the survey, the selection criteria were size of the university, the subjects offered there and the publishing system used. Regarding the local publication databases, the answers have focused on the difficulties and opportunities with the registration of scientific work using the own publications database. In the section on guidelines, I discuss how the universities relate to two documents on SwePub guidelines and recommendations. The analysis deals with the national guidelines related to the local practice of the universities, where national guidelines may sometimes collide with the institution's own needs and wishes. The section of the analysis that deals with the institutions' views on the SwePub analysis project at large, relates the SwePub project to the terms function and relevance. In conclusion, I discuss to what extent I have found some patterns in the answers, linked to my selection criteria for the size of the university, subject area and type of publishing system.

Classification of scientific bibliographic data is an important and increasingly more time-consuming task in a “publish or perish” paradigm where the number of scientific publications is steadily growing. Apart from being a resource-intensive endeavor, manual classification has also been shown to be often performed with a quite high degree of inconsistency. Since many bibliographic databases contain a large number of already classified records supervised machine learning for automated classification might be a solution for handling the increasing volumes of published scientific articles. In this study automated classification of bibliographic data, based on two different machine learning methods; Naive Bayes and Support Vector Machine (SVM), were evaluated. The data used in the study were collected from the Swedish research database SwePub and the features used for training the classifiers were based on abstracts and titles in the bibliographic records. The accuracy achieved ranged between a lowest score of 0.54 and a highest score of 0.84. The classifiers based on Support Vector Machine did consistently receive higher scores than the classifiers based on Naive Bayes. Classification performed at the second level in the hierarchical classification system used clearly resulted in lower scores than classification performed at the first level. Using abstracts as the basis for feature extraction yielded overall better results than using titles, the differences were however very small.