130,463 research outputs found
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Repetitive transcranial magnetic stimulation (r-TMS) and selective serotonin reuptake inhibitor-resistance in obsessive-compulsive disorder: A meta-analysis and clinical implications
No full textIntroduction: Despite promising results from several randomized controlled trials (RCTs) and meta-analyses, the efficacy of r-TMS as a treatment for OCD remains controversial, at least in part owing to inconsistency in the trial methodologies and heterogeneity in the trial outcomes. This meta-analysis attempts to explain some of this heterogeneity by comparing the efficacy of r-TMS in patients with or without resistance to treatment with selective serotonin reuptake inhibitors (SSRI), defined using standardized criteria. Methods: We conducted a pre-registered (PROSPERO ID: 241381) systematic review and meta-analysis. English language articles reporting blinded RCTs were retrieved from searches using MEDLINE, PsycINFO, and Cochrane Library databases. Studies were subjected to subgroup analysis based on four stages of treatment resistance, defined using an adaptation of published criteria (1 = not treatment resistant, 2 = one SSRI trial failed, 3 = two SSRI trials failed, 4 = two SSRI trials failed plus one or more CBT trial failed). Meta-regression analyses investigated patient and methodological factors (age, duration of OCD, illness severity, stage of treatment-resistance, or researcher allegiance) as possible moderators of effect size. Results: Twenty-five independent comparisons (23 studies) were included. Overall, r-TMS showed a medium-sized reduction of Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores (Hedge's g: -0.47; 95%CI: - 0.67 to −0.27) with moderate heterogeneity (I2 = 39.8%). Assessment of publication bias using Trim and Fill analysis suggested a reduced effect size that remained significant (g: -0.29; 95%CI: −0.51 to −0.07). Subgroup analysis found that those studies including patients non-resistant to SSRI (stage 1) (g: -0.65; 95%CI: −1.05 to −0.25, k = 7) or with low SSRI-resistance (stage 2) (g:-0.47; 95%CI: −0.86 to −0.09, k = 6) produced statistically significant results with low heterogeneity, while studies including more highly resistant patients at stage 3 (g: −0.39; 95%CI: −0.90 to 0.11, k = 4) and stage 4 (g: -0.36; 95%CI: −0.75 to 0.03, k = 8) did not. Intriguingly, the only significant moderator of the effect size found by meta-regression was the severity of baseline depressive symptoms. All trials showed evidence of researcher allegiance in favour of the intervention and therefore caution is required in interpreting the reported effect sizes. Conclusion: This meta-analysis shows that r-TMS is an effective treatment for OCD, but largely for those not resistant to SSRI or failing to respond to only one SSRI trial. As a consequence, r-TMS may be best implemented earlier in the care pathway. These findings would have major implications for clinical service development, but further well-powered RCTs, which eliminate bias from researcher allegiance, are needed before definitive conclusions can be drawn
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund
No full textAt the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far
The R&D Tax Incentives
No full textThis article sets out some background information and reflections of the author on the R&D tax incentive schemes included in the Common Corporate Tax Base (CCTB) Proposal. In particular the author analyzes the stimulus to private R&D through ad hoc tax incentives included in the CCTB Proposal and dives into the actual provisions included in the Proposal highlighting the most relevant issues connected with their design and interpretation. Moreover, the author explores the interaction between the CCTB Proposal and the granting by Member States of domestic R&D tax incentives
Facing the “new normal”: How adjusting to the easing of COVID-19 lockdown restrictions exposes mental health inequalities
No full textBackground: Re-establishing societal norms in the wake of the COVID-19 pandemic will be important for restoring public mental health and psychosocial wellbeing as well as economic recovery. We investigated the impact on post-pandemic adjustment of a history of mental disorder, with particular reference to obsessive-compulsive (OC) symptoms or traits. Methods: The study was pre-registered (Open Science Framework; https://osf.io/gs8j2/). Adult members of the public (n = 514) were surveyed between July and November 2020, to identify the extent to which they reported difficulties re-adjusting as lockdown conditions eased. All were assessed using validated scales to determine which demographic and mental health-related factors impacted adjustment. An exploratory analysis of a subgroup on an objective online test of cognitive inflexibility was also performed. Results: Adjustment was related to a history of mental disorder and the presence of OC symptoms and traits, all acting indirectly and statistically-mediated via depression, anxiety and stress; and in the case of OC symptoms, also via COVID-related anxiety (all p < 0.001). One hundred and twenty-eight (25%) participants reported significant adjustment difficulties and were compared with those self-identifying as “good adjusters” (n = 231). This comparison revealed over-representation of those with a history or family history of mental disorder in the poor adjustment category (all p < 0.05). ‘Poor-adjusters’ additionally reported higher COVID-related anxiety, depression, anxiety and stress and OC symptoms and traits (all p < 0.05). Furthermore, history of mental disorder directly statistically mediated adjustment status (p < 0.01), whereas OC symptoms (not OC traits) acted indirectly via COVID-related anxiety (p < 0.001). Poor-adjusters also showed evidence of greater cognitive inflexibility on the intra-extra-dimensional set-shift task. Conclusion: Individuals with a history of mental disorder, OC symptoms and OC traits experienced greater difficulties adjusting after lockdown-release, largely statistically mediated by increased depression, anxiety, including COVID-related anxiety, and stress. The implications for clinical and public health policies and interventions are discussed
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