ABSTRACT
This paper provides additional negative data regarding the paper Evaluation of thermal properties of thin films by IR radiometry using a comprehensive set of Zr-O-N thin films [1]. In that paper, a matrix of samples was prepared to evaluate the so-called Extremum method for the analysis of Infrared (IR) radiometry data. Such matrix was composed by 3 types of films with 4 different thickness in 3 types of substrates, totalizing 36 samples in total. The data of this paper can be divided into three separate categories: i) lack of adhesion of the films deposited on Teflon, simultaneously to the films deposited on other substrates. ii) Improvement of the signal and signal-to-noise ratio on samples that did not present an extremum (minimum or maximum) using the initial (more conventional) way of measurement. iii) It is also presented a failed fitting of the IR radiometry data created with entangled material parameters. All this data is relevant for researchers devoted to measurement of thermal properties of thin films by IR radiometry that employ the two layer model and Extremum Method.
ABSTRACT
INTRODUCTION: Biomedical research is increasingly becoming a data-intensive science in several areas, where prodigious amounts of data is being generated that has to be stored, integrated, shared and analyzed. In an effort to improve the accessibility of data and knowledge, the Linked Data initiative proposed a well-defined set of recommendations for exposing, sharing and integrating data, information and knowledge, using semantic web technologies. OBJECTIVE: The main goal of this paper is to identify the current status and future trends of knowledge representation and management in Life and Health Sciences, mostly with regard to linked data technologies. METHODS: We selected three prominent linked data studies, namely Bio2RDF, Open PHACTS and EBI RDF platform, and selected 14 studies published after 2014 (inclusive) that cited any of the three studies. We manually analyzed these 14 papers in relation to how they use linked data techniques. RESULTS: The analyses show a tendency to use linked data techniques in Life and Health Sciences, and even if some studies do not follow all of the recommendations, many of them already represent and manage their knowledge using RDF and biomedical ontologies. CONCLUSION: These insights from RDF and biomedical ontologies are having a strong impact on how knowledge is generated from biomedical data, by making data elements increasingly connected and by providing a better description of their semantics. As health institutes become more data centric, we believe that the adoption of linked data techniques will continue to grow and be an effective solution to knowledge representation and management.
