Scholarly Database

The Scholarly Database (SDB) aims to serve the needs of researchers and practitioners interested in the analysis, modeling, and visualization of large-scale scholarly datasets. The database currently provides access to 11 major datasets such as Medline, U.S. patents, National Science Foundation and National Institutes of Health funding awards - a total of about 20 million records. The books, journals, proceedings, patents, grants, technical reports, doctoral and master theses can be cross searched. Results can be downloaded as data dumps for further processing. The online interface at https://sdb.School of Library and Information Science.indiana.edu provides full-text search for four databases (MEDLINE, NSF, NIH, USPTO) using Solar. Specifically, it is able to search and filter the contents of these databases using many criteria and search fields, particularly those relevant for scientometric research and science policy practice

Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches

We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models--BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE.PubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts

113 Years of Physical Review: Using Flow Maps to Show Temporal and Topical Citation Patterns

We visualize 113 years of bibliographic data from the American Physical Society. The 389,899 documents are laid out in a two dimensional time-topic reference system. The citations from 2005 papers are overlaid as flow maps from each topic to the papers referenced by papers in the topic making intercitation patterns between topic areas visible. Paper locations of Nobel Prize predictions and winners are marked. Finally, though not possible to reproduce here, the visualization was rendered to, and is best viewed on, a 24 ” x 30 ” canvas at 300 dots per inch (DPI). Keywords---network analysis, domain visualization, physical review 1

Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text- Based Similarity Approaches

Background: We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents. Methodology: We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models – BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compare