ERA distribution of information systems journals

The Excellence in Research for Australia (ERA) initiative being conducted by the Australian Research Council (ARC), mandates a single journal and conference ranking scheme over every academic discipline in Australia. A universal publication outlet ranking list mandated by a government agency is unique and has attracted interest and comment both within Australia and overseas. Equally, the interest shown has come from all sectors involved in academic publishing &ndash; authors, reviewers, publishers &ndash; and from commercial and open access publishers. This paper investigates the distribution of information systems journals over the various ERA parameters and comments on a claim of bias whereby the ranking of a journal is positively influenced by the number of years it has been in existence in the areas of information systems and business journals. Clear evidence of the diversity of the information systems discipline is observed. The benefits of a multidisciplinary foundation for information systems is also noted. Longer established journals are shown to attract higher rankings and possible reasons for and implications flowing from this are discussed.<br /

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance

Predicting future service use in Dutch mental healthcare:A machine learning approach

Item does not contain fulltextA mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare. This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual predictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Nevertheless, uncertainty in the prediction of high-cost patients remains a challenge.9 p

Deep learning for surface electromyography artifact contamination type detection

The quality of surface Electromyography (sEMG) signals could be an issue if highly contaminated by Power Line Interference (PLI), Electrocardiogram signal (ECG), Movement Artifact (MOA) or White Gaussian Noise (WGN), that could lead to unsafe operation of devices that is controlled by sEMG data, such as electro-mechanical prothesis. There are some mitigation methods proposed for some specifics sEMG contaminants and to use these methods in an efficient way is important to identify the contaminant in the sEMG signal. In this work we propose the use of a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) units in the hidden layer with no need of features extraction with the objective to classify the signal directly from sequences of the band-pass filtered data. The method proposed use the NinaPro database with amputee and non-amputee subjects. Only non-amputee subjects are used for parameters selection and then tested on both databases. The results show that 98% of the non-contaminated sEMG data was corrected classified and more than 95% of the contaminants were identified inside the training SNR range. Also, in this work is presented a SNR sensibility control and the contamination analysis in the range from −40 dB to 40 dB in 10 dB steps. The conclusion is that is possible to classify the contamination type in sEMG signals with a RNN-LSTM with a 112.5 ms time window and to predicted with a small error the classification hit rate for each SNR level in some cases

The dawn of a new ERA?: Australian Library & Information Studies (LIS) researchers further ranking of LIS journals

The Australian federal government’s Excellence in Research Australian (ERA) (Excellence in research (ERA), 2009) policy initiative has given Australian LIS researchers the opportunity to review their listings of preferred journal titles that will be a component of measured research activity in the new federal government funding regimes. The Australian research environment and university reliance on ranking meant that the importance of ranking journal titles could not be ignored. The ranking of journal titles as submitted to the Research Quality Framework (RQF) exercise in 2007-8, was reviewed in a tight timeframe with a collegial response to calls for feedback. The results are reported and the anomaly of the place of Australian LIS in the Field of Research (FoR) category as assigned by the Australian Bureau of Statistics is discussed, as is the potential relevance of this categorisation regarding the choice of journal titles by these members of the LIS discipline