With the ready accessibility of bibliometric data and the availability of ready-to-use tools for generating bibliometric indicators for evaluation purposes, there is the danger of inappropriate use. Here we present standards of good practice for analyzing bibliometric data and presenting and interpreting the results. Comparisons drawn between research groups as to research performance are valid only if (1) the scientific impact of the research groups or their publications are looked at by using box plots, Lorenz curves, and Gini coefficients to represent distribution characteristics of data (in other words, going beyond the usual arithmetic mean value), (2) different reference standards are used to assess the impact of research groups, and the appropriateness of the reference standards undergoes critical examination, and (3) statistical analyses comparing citation counts take into consideration that citations are a function of many influencing factors besides scientific quality.

PurposeThe purpose of this paper is to provide an overview of new citation‐enhanced databases and to identify issues to be considered when they are used as a data source for performing citation analysis.Design/methodology/approachThe paper reports the limitations of Thomson Scientific's citation indexes and reviews the characteristics of the citation‐enhanced databases Chemical Abstracts, Google Scholar and Scopus.FindingsThe study suggests that citation‐enhanced databases need to be examined carefully, with regard to both their potentialities and their limitations for citation analysis.Originality/valueThe paper presents a valuable overview of new citation‐enhanced databases in the context of research evaluation.

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Abstract. Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clearsky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

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