In an attempt to tackle abuses of self-citation and ‘citation farms’ (relatively small clusters of authors massively citing each other’s papers), a team at Stanford University, led by Professor John P.A. Ioannides, recently produced a list of the world’s most-cited scientists based on more accurate standardized citation metrics.
It is divided into two categories — career-long citation impact and single year citation impact (2019) — which follow an extensive analysis of research data from the mid-1990s through to 2019, covering millions of scientists in 22 fields and 176 subfields of study. In a total of 159,683 scientists, 385 are from Portugal.
IT researchers have been featured across four main fields: Information & Communication Technologies, Engineering, Physics & Astronomy and Enabling & Strategic Technologies.
Also worth noting is that out of the 37 most-cited Portuguese researchers within Information & Communication Technologies, 11 are from IT.
Ranked for career-long citation impact are: Mário Figueiredo, José Bioucas-Dias, Fernando Pereira, Mário Silveirinha, Joel Rodrigues, Ana Fred, Hugo Proença, José Pedro, Nuno Carvalho, Sérgio Cruz, João Sobrinho, Jonathan Rodriguez, Stanislav Maslovski, Adolfo Cartaxo, Carlos Fernandes, José Brandão Faria, Octavian Postolache, Francisco Alegria and Paulo André.
For single year citation impact, IT names include Nuno Garcia, Shahid Mumtaz and Filipe Clemente.
The global rankings, published in PloS Biology, build on the number of citations, H-Index, co-authorship and a composite indicator — all of which were measured using data from the SCOPUS database.
It comes at a time when funding agencies, journals and others are focusing more on potential issues that arise from excessive self-citation.
“Use of citation metrics has become widespread but is fraught with difficulties. Some challenges relate to what citations and related metrics fundamentally mean and how they can be interpreted or misinterpreted as a measure of impact or excellence”, the authors state.
Labelled by Nature as “the largest collection of self-citation metrics ever published”, the article reflects broader concerns about an over-reliance on citation metrics for making decisions about hiring, promotions and research funding.
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IT researchers are delving into AI and ‘deep learning’ techniques to extract useful diagnostic information from cardiac sounds.
The DigiScope2 team, led by IT researchers Miguel Coimbra and Francesco Renna, aims to create a field-ready prototype of a computer-assisted decision system for cardiac auscultation, by incoporating new artificial intelligence algorithms into a machine learning framework.
DigiScope2 is based on the success of past projects (DigiScope, FutureHealth, HeartSafe, SmartHeart, NanoSTIMA) which, during the last decade, have produced a vast database of cardiac sounds and associated clinical information, new knowledge on cardiac signal processing and solid technology for collecting auscultation signals.
This has laid the groundwork for the development of new algorithms, which are able to automatically recognize sounds from healthy patients and distinguish those who might need more in-depth examinations.
According to Francesco Renna: “The developed algorithms, which have been presented at international conferences and published in some of the best magazines in the field, use in particular new techniques of ‘deep learning’. These techniques have already proven to be the most effective in analyzing biomedical images and signals, reaching levels of accuracy even higher than those of human specialists.
“Our work focused on the design of specific methods in line with the characteristics of cardiac sound signals. These present a series of technical challenges that elicit the use of the most advanced AI technologies, given the strong presence of noise in auscultations performed in clinical environments, and given the wide range of variables in sounds collected from patients of different ages, physical shapes, etc.”, he added.
Nearly a decade ago, the first DigiScope project resulted in a software architecture, compatible with digital stethoscopes already available on the market, which allowed doctors to collect cardiac sounds quickly and intuitively, without hindering the clinical practice.
Now the team is relying on AI to take future cardiac pathology screening tools to a new level, making them more accurate and reliable, which ultimately avoids patients having to go through useless, more complicated tests.
“At the moment, the team is focused on the integration phase, so we’re using our software architecture to program the newly designed algorithms, which were developed and tested in large databases throughout the project”, the researcher concluded.
The DigiScope2 project is co-funded by COMPETE 2020 within the scope of SAICT — Sistema de Apoio à Investigação Científica e Tecnológica — involving an investment of 239 thousand euros, which resulted in an ERDF incentive of 200 thousand euros.https://www.compete2020.gov.pt/noticias/detalhe/29200-DigiScope2-FrancescoRenna-NL29102020