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Abstract. The online availability of scientific-literature databases and natural-language-processing (NLP) algorithms has enabled large-scale bibliometric studies within the field of scientometrics. Using NLP techniques and Thomson ISI reports, an initial analysis of the role of Alzheimer's disease (AD) within the neurosciences as well as a summary of the various research foci within the AD scientific community are presented. Citation analyses and productivity filters are applied to post-1984, AD-specific subsets of the PubMed and Thomson ISI Web-of-Science literature bases to algorithmically identify a pool of the top AD researchers. From the initial pool of AD investigators, top-100 rankings are compiled to assess productivity and impact. One of the impact and productivity metrics employed is an AD-specific H-index. Within the AD-specific H-index ranking, there are many cases of multiple AD investigators with similar or identical H-indices. In order to facilitate differentiation among investigators with equal or near-equal H indices, two derivatives of the H-index are proposed: the Second-Tier H-index and the Scientific Following H-index. Winners of two prestigious AD-research awards are highlighted, membership to the Institute of Medicine of the US National Academy of Sciences is acknowledged, and an analysis of highly-productive, high-impact, AD-research collaborations is presented.

A new class of social web-based metrics for scholarly publications (altmetrics) has surfaced as a complement to traditional citation-based metrics. Our aim was to study and characterize those recent papers in the field of Parkinson’s disease which had received the highest Altmetric Attention Scores and to compare this attention measure to the traditional metrics. The top 20 papers in our analysis covered a variety of topics, mainly new disease mechanisms, treatment options and risk factors for the development of PD. The main media sources for these high attention papers were news items and Twitter. The papers were published predominantly in high impact journals, suggesting a correlation between altmetrics and conventional metrics. One paper published in a relatively modest journal received a significant amount of attention, reflecting that public attention does not always parallel the traditional metrics. None of the most influential papers in PD, as reviewed by Ponce and Lozano (2011) made it to our list, suggesting that recent publications receive higher attention scores, and that altmetrics may omit older, seminal work in the field.

For the biomedical sciences, the Medical Subject Headings (MeSH) make available a rich feature which cannot currently be merged properly with widely used citing/cited data. Here, we provide methods and routines that make MeSH terms amenable to broader usage in the study of science indicators: using Web-of-Science (WoS) data, one can generate the matrix of citing versus cited documents; using PubMed/MEDLINE data, a matrix of the citing documents versus MeSH terms can be generated analogously. The two matrices can also be reorganized into a 2-mode matrix of MeSH terms versus cited references. Using the abbreviated journal names in the references, one can, for example, address the question whether MeSH terms can be used as an alternative to WoS Subject Categories for the purpose of normalizing citation data. We explore the applicability of the routines in the case of a research program about the amyloid cascade hypothesis in Alzheimer’s disease. One conclusion is that referenced journals provide archival structures, whereas MeSH terms indicate mainly variation (including novelty) at the research front. Furthermore, we explore the option of using the citing/cited matrix for main-path analysis as a by-product of the software.

Abstract. BACKGROUND:Neurorehabilitation covers a large range of disorders, assessment approaches and treatment methods. There have been previous citation analyses of rehabilitation and of its subfields. However, there has never been a comprehensive citation analysis in neurorehabilitation. OBJECTIVE: The present study reports findings from a citation analysis of the top 100 most cited neurorehabilitation papers to describe the research trends in the field. METHODS: A de-novo keyword search of papers indexed in the Web of Science Core Collection database yielded 52,581 papers. A candidate pool of the 200 most-cited papers published between 2005 and 2016 was reviewed by the clinician authors. The papers in the top 100 deemed to be irrelevant were discarded and replaced by the most highly-cited articles in the second tier deemed to be clinically relevant. RESULTS:The most frequently cited neurorehablitation papers appeared in Stroke, Movement Disorders, and Neurology. Papers tended to focus on treatments, especially for stroke. Authorship trends suggest that top cited papers result from group endeavors, with 90% of the papers involving a collaboration among 3 or more authors. CONCLUSION: Treatment studies, often focused on stroke, appear to have the highest impact in the field of neurorehabilitation.