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Data science pipelines inform and influence many daily decisions, from what we buy to who we work for and even where we live. When designed incorrectly, these pipelines can easily propagate social inequity and harm. Traditional solutions…
Scientometric data is used to investigate empirically the emergence of search regimes in Biotechnology, Genomics, and Nanotechnology. Complex regimes can emerge when three independent sources of variance interact. In our model, researchers…
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph benchmark datasets that are widely used by the community…
Data analysis is a powerful tool in all experimental sciences. Statistical methods, such as sampling theory, computer technologies necessary for handling large amounts of data, skill in analysing information contained in different types of…
The prediction of exceptional or surprising growth in research is an issue with deep roots and few practical solutions. In this study we develop and validate a novel approach to forecasting growth in highly specific research communities.…
The Bootstrap Project's Data Science curriculum has trained about 100 teachers who are using it around the country. It is specifically designed to aid adoption at a wide range of institutions. It emphasizes valuable curricular goals by…
Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more…
Rapidly evolving technology, data and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills including data literacy is particularly prominent given both the potential of novel…
Over the last 20 years, there has been an explosion of genomic data collected for disease association, functional analyses, and other large-scale discoveries. At the same time, there have been revolutions in cloud computing that enable…
"Data" is becoming an indispensable production factor, just like land, infrastructure, labor or capital. As part of this, a myriad of applications in different sectors require huge amounts of information to feed models and algorithms…
We discuss several issues of statistical design, data collection, analysis, communication, and decision making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This…
Nowadays, science has been coming into a new paradigm, called data-intensive science. While current studies of the new phenomenon focused on building up infrastructure for this new paradigm, yet a few studies concern users of scientific…
FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems…
Every scientific endeavour consists of (at least) two components: A hypothesis on the one hand and data on the other. There is always a more or less abstract level - some theory, a set of concepts, certain relations of ideas - and a…
With the transformative technologies and the rapidly changing global R&D landscape, the multimedia and multimodal community is now faced with many new opportunities and uncertainties. With the open source dissemination platform and…
Digital twin (DT) technology holds immense potential for transforming healthcare systems through real-time monitoring, predictive analysis, and agile interventions to support various decision-making needs. However, its successful…
Open data are characterized by a number of economic, technological, innovative and social benefits. They are seen as a significant contributor to the city's transformation into Smart City. This is all the more so when the society is on the…
The predicted increase in demand for data-intensive solution development is driving the need for software, data, and domain experts to effectively collaborate in multi-disciplinary data-intensive software teams (MDSTs). We conducted a…
A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific…
In this work we introduce declarative statistics, a suite of declarative modelling tools for statistical analysis. Statistical constraints represent the key building block of declarative statistics. First, we introduce a range of relevant…