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A new quantitative method is introduced to analyze the collaboration among different organizations. The method defines the collaboration score based on the number of people involved in collaboration, and then the collaboration strength is obtained by summing up the collaboration scores with this method. We choose "Project 985" universities, which represent the top universities in China, as an example to study the collaboration network, strength in leading collaboration and strength in participating collaboration. Results based on Scopus show some characteristics of such collaboration and verify the feasibility of the new approach.

This paper studies how missing data in the PageRank algorithm influences the result of papers ranking and proposes PrestigeRank algorithm on that basis. We make use of PrestigeRank to give the ranking of all papers in physics in the Chinese Scientific and Technology Papers and Citation Database (CSTPCD) published between 2004 and 2006. We compared PrestigeRank result with PageRank and citation ranking. We found PrestigeRank is significantly correlated with PageRank and citation counts. We also used paper citation networks to rank journals, and compared the result with that of journal citation networks. We proposed PRsum, PRave, and compared both of them with citation counts and impact factor. It indicates PRsum, PRave can reflect journal's authority favorably. We also discuss the advantages and disadvantages, application scope and application prospects of PrestigeRank in the evaluation of papers and journals.

This paper proposes a new method for indicator selection in panel data analysis and tests the method with relevant data on agricultural journals provided by the Institute of Scientific & Technical Information of China. An evaluation exercise by the TOPSIS method is conducted as a comparison. The result shows that panel data analysis is an effective method for indicator selection in scholarly journal evaluation; journals of different disciplines should not be evaluated with the same criteria; it is beneficial to publish all the evaluation indicators; unavailability of a few indicators has a limited influence on evaluation results; simplifying indicators can reduce costs and increase efficiency as well as accuracy of journal evaluation.

Three major principles for the selection of indicator data normalization methods in multi-attribute evaluation are presented in this paper. Principle 1: The relative gap between the data for the same indicator should remain constant; Principle 2: The relative gap between different indicators should remain variable ; and Principle 3: The maximum values after normalization should be equal. According to these three major principles, a normalization method for positive indicators is screened out from several alternatives, and a new normalization method for negative indicators is proposed. These two methods are very good for the comparison among panel data,. The requirement for data normalization methods is different when the evaluation goals are different, Ranking-order-based evaluation is insensitive to data normalization methods.