Funding information in Web of Science: An updated overview. Despite the limitations of funding acknowledgment (FA) data in Web of Science (WoS), studies using FA information have increased rapidly over the last several years. Considering this WoSu0027recent practice of updating funding data, this paper further investigates the characteristics and distribution of FA data in four WoS journal citation indexes. The research reveals that FA information coverage variances persist cross all four citation indexes by time coverage, language and document type. Our evidence suggests an improvement in FA information collection in humanity and social science research. Departing from previous studies, we argue that FA text (FT) alone no longer seems an appropriate field to retrieve and analyze funding information, since a substantial number of documents only report funding agency or grant number information in respective fields. Articles written in Chinese have a higher FA presence rate than other nonEnglish WoS publications. This updated study concludes with a discussion of new findings and practical guidance for the future retrieval and analysis of funded research.

Toward Automatic Identification of Dataset Names in Scholarly Articles As the number of scholarly articles we can access is increasing, it becomes possible to read them freely. However, it is difficult to understand scholarly articles since they are basically written for experts. Our big goal is, developing methods to extract essential elements of articles, to facilitate open innovation. To this end, this paper is devoted to considering automatic identification of dataset names in articles. Because a dictionary of datasets is necessary for evaluation, existing methods have focused on some specific discipline. To achieve applicability to any disciplines, we adopt a machine learning approach with a huge amount of scholarly papers. Because we treat papers in multidisciplines, it is challenging how to evaluate experimental results. To solve it, we quantitatively evaluate experimental results with precisionN, which does not require to know all the dataset names in the papers we use, and qualitatively check if candidate tokens are dataset names or not using a GUI tool we have developed. While about 13 tokens of the top 20 output by our method were dataset names, the other ones are names of methods, models, or organizations. So it is important future work to remove such noise results, using additive compositionality of word vectors.