130,530 research outputs found
L' Impostore smascherato. Excerpts - Don Mus.Ms. 1936 : S, orch
No full textGiacomo TrittoQuelle: manuscript, manuscript. - Provenienz: Fürstlich Fürstenbergische Hofbibliothek, Donaueschingen[score:] L'Impostore smascherato - Teatro Nuovo | Dolce amabile nel viso | Aria | Del Sig:re D. Giacomo Tritto | 1794 | F:5 [S:] Aria Del Signore Giacomo Tritt
Le Nozze in garbuglio. Excerpts - Don Mus.Ms. 1935 : V, orch
No full textGiacomo TrittoQuelle: manuscript, manuscript. - Provenienz: Fürstlich Fürstenbergische Hofbibliothek, Donaueschingen[score:] Le Nozze in garbuglio = Teatro Nuovo | Spesso vedrai un volto | Aria | Del Sig:re D. Giacomo Tritto | 1793 | F:6 [parts:] Spesso vedrai un Volto | Aria | a | Due Violini | Due Oboe | Due Corni | Viola e Basso | Del Signore Giacomo Tritt
Valutazione farmacologica e stimolazione elettrica di campo del tubulo epididimario di ratto dopo tubulo-vasostomia termino-terminale
No full textDiagnostic imaging of Tietze's syndrome. Comparison of computerized tomography and ultrasonography]
No full text
Autonomic receptors distribution in the detrusor muscle and bladder sphincter of newborn
No full textValutazione farmacologica e stimolazione elettrica di campo del deferente di ratto dopo vaso-vasostomia termino-terminale
No full textPharmacological and histological investigations on the sensory-motor apparatus of the human foetal bladder
No full textMeSH 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
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