Effective Unsupervised Author Disambiguation with Relative Frequencies

This work addresses the problem of author name homonymy in the Web of Science. Aiming for an efficient, simple and straightforward solution, we introduce a novel probabilistic similarity measure for author name disambiguation based on feature overlap. Using the researcher-ID available for a subset of the Web of Science, we evaluate the application of this measure in the context of agglomeratively clustering author mentions. We focus on a concise evaluation that shows clearly for which problem setups and at which time during the clustering process our approach works best. In contrast to most other works in this field, we are sceptical towards the performance of author name disambiguation methods in general and compare our approach to the trivial single-cluster baseline. Our results are presented separately for each correct clustering size as we can explain that, when treating all cases together, the trivial baseline and more sophisticated approaches are hardly distinguishable in terms of evaluation results. Our model shows state-of-the-art performance for all correct clustering sizes without any discriminative training and with tuning only one convergence parameter.Comment: Proceedings of JCDL 201

How reliable are unsupervised author disambiguation algorithms in the assessment of research organization performance?

The paper examines extent of bias in the performance rankings of research organisations when the assessments are based on unsupervised author-name disambiguation algorithms. It compares the outcomes of a research performance evaluation exercise of Italian universities using the unsupervised approach by Caron and van Eck (2014) for derivation of the universities' research staff, with those of a benchmark using the supervised algorithm of D'Angelo, Giuffrida, and Abramo (2011), which avails of input data. The methodology developed could be replicated for comparative analyses in other frameworks of national or international interest, meaning that practitioners would have a precise measure of the extent of distortions inherent in any evaluation exercises using unsupervised algorithms. This could in turn be useful in informing policy-makers' decisions on whether to invest in building national research staff databases, instead of settling for the unsupervised approaches with their measurement biases

Analysing Scientific Mobility and Collaboration in the Middle East and North Africa

This study investigates the scientific mobility and international collaboration networks in the Middle East and North Africa (MENA) region between 2008 and 2017. By using affiliation metadata available in scientific publications, we analyse international scientific mobility flows and collaboration linkages. Three complementary approaches allow us to obtain a detailed characterization of scientific mobility. First, we uncover the main destinations and origins of mobile scholars for each country. Results reveal geographical, cultural and historical proximities. Cooperation programs also contribute to explain some of the observed flows. Second, we use the academic age. The average academic age of migrant scholars in MENA was about 12.4 years. The academic age group 6-to-10 years is the most common for both emigrant and immigrant scholars. Immigrants are relatively younger than emigrants, except for Iran, Palestine, Lebanon, and Turkey. Scholars who migrated to Gulf Cooperation Council countries, Jordan and Morocco were in average younger than emigrants by 1.5 year from the same countries. Third, we analyse gender differences. We observe a clear gender gap: Male scholars represent the largest group of migrants in MENA. We conclude discussing the policy relevance of the scientific mobility and collaboration aspects.Comment: 37 pages, 9 figures, 5 table

MOVING: A User-Centric Platform for Online Literacy Training and Learning

Part of the Progress in IS book series (PROIS)In this paper, we present an overview of the MOVING platform, a user-driven approach that enables young researchers, decision makers, and public administrators to use machine learning and data mining tools to search, organize, and manage large-scale information sources on the web such as scientific publications, videos of research talks, and social media. In order to provide a concise overview of the platform, we focus on its front end, which is the MOVING web application. By presenting the main components of the web application, we illustrate what functionalities and capabilities the platform offer its end-users, rather than delving into the data analysis and machine learning technologies that make these functionalities possible

e-Science

This open access book shows the breadth and various facets of e-Science, while also illustrating their shared core. Changes in scientific work are driven by the shift to grid-based worlds, the use of information and communication systems, and the existential infrastructure, which includes global collaboration. In this context, the book addresses emerging issues such as open access, collaboration and virtual communities and highlights the diverse range of developments associated with e-Science. As such, it will be of interest to researchers and scholars in the fields of information technology and knowledge management

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute