Text and Graph Based Approach for Analyzing Patterns of Research Collaboration: An analysis of the TrueImpactDataset

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper, we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how frequently have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset (Herrmannova et al., 2017) (Herrmannova et al., 2017), a new dataset containing two types of publications – publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information

Research Collaboration Analysis Using Text and Graph Features

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how much have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset [11], a new dataset containing two types of publications - publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information

Quality Assurance Based Healthcare Information System Design

Despite decades of research, health information systems have been characterised by cost over-runs, poor specifications and lack of user uptake. We propose an alternative approach to their design. By viewing health care as a process and quality as continuously seeking iterative improvements to processes, an objectoriented analysis reveals a class model, which supports quality assurance (QA). At the heart of the model is the ability to store actions for comparison with intentions. Measurement of the proportion of planned tasks that are executed provides a basis for identifying when to alter a process. We show that the model is able to represent medical and administrative procedures and argue that it forms an electronic record suitable for health care organisations. Were this record to become a standard, software could be developed close to the point of use, in harmony with the needs of stakeholders, so avoiding many criticisms of health information systems

On Quality and Communication: The Relevance of Critical Theory to Health Informatics

Health information systems require long-term investment before they provide a socio-economic return, yet their implementation remains problematic, possibly because the claims made about them appear not to sit well with healthcare professionals’ practice. Health informatics should address these issues from a sound conceptual base, such as might be provided by critical theory, which seeks to identify hidden assumptions and ideologies. This discipline can provide a better understanding of the inner workings of socio-technical systems, with a view to improving them through the promotion of emancipation (allowing people to fulfill their potential). Critical theory can also shed light on the problems with health information systems and offer insight into remedies, for example, by relating Habermas’ theories about communication to feedback, a concept central to quality assurance (QA). Such analysis finds that QA’s principal practices can be interpreted as emancipatory but requires organizations to substantially change their behavior. An alternate approach is to install health information systems designed to support QA. Applying critical theory to these systems shows that they could become an active part of service delivery rather than static repositories of data, because they may encourage standardized conversations between all stakeholders about the important features of health care. Success will depend on access for all participants to data entry and analysis tools, integration with work practice, and use by staff and management in QA. These ideas offer new directions for research into and the development of health information systems. The next step will be to implement them and observe their technical and emancipatory properties

Citations and Readership are Poor Indicators of Research Excellence: Introducing TrueImpactDataset, a New Dataset for Validating Research Evaluation Metrics

In this paper we show that citation counts and Mendeley readership are poor indicators of research excellence. Our experimental design builds on the assumption that a good evaluation metric should be able to distinguish publications that have changed a research field from those that have not. The experiment has been conducted on a new dataset for bibliometric research which we call TrueImpactDataset. TrueImpactDataset is a collection of research publications of two types -- research papers which are considered seminal work in their area and papers which provide a survey (a literature review) of a research area. The dataset also contains related metadata, which include DOIs, titles, authors and abstracts. We describe how the dataset was built and provide overview statistics of the dataset. We propose to use the dataset for validating research evaluation metrics. By using this data, we show that widely used research metrics only poorly distinguish excellent research