Related papers: Navigating Diverse Data Science Learning: Critical…
This paper presents a multifarious examination of natural resources and environmental scientists' adventures navigating the policy change towards open access and cultural shift in data management, sharing, and reuse. Situated in the…
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…
Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture. Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has…
In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks…
Ethics have become an urgent concern for data science research, practice, and instruction in the wake of growing critique of algorithms and systems showing that they reinforce structural oppression. There has been increasing desire on the…
AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and…
Materials science is becoming increasingly more reliant on digital data to facilitate progress in the field. Due to a large diversity in its scope, breadth, and depth, organizing the data in a standard way to optimize the speed and creative…
Emerging research frontiers and computational advances have gradually transformed cognitive science into a multidisciplinary and data-driven field. As a result, there is a proliferation of cognitive theories investigated and interpreted…
The development of data science expertise requires tacit, process-oriented skills that are difficult to teach directly. This study addresses the resulting challenge of empirically understanding how the problem-solving processes of experts…
Background: Dataset skills are used in STEM fields from healthcare work to astronomy research. Few fields explicitly teach students the skills to analyze datasets, and yet the increasing push for authentic science implies these skills…
The tremendous advances in computer science in the last few decades have provided the platform to address and solve complex problems using interdisciplinary research. In this paper, we investigate how the extent of interdisciplinarity in…
Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being…
The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While…
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a…
The rising availability of digital traces provides a fertile ground for new solutions to both, new and old problems in cities. Even though a massive data set analyzed with Data Science methods may provide a powerful solution to a problem,…
In this short review, I will summarize my research experience in three fields in applied mathematics: mathematical biology, applied probability, and applied discrete mathematics. Specifically, I will show how each project was initiated, and…
Science is facing a reproducibility crisis. Previous work has proposed incorporating data analysis replications into classrooms as a potential solution. However, despite the potential benefits, it is unclear whether this approach is…
Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology,…
Artificial Intelligence has an important place in the scientific community as a result of its successful outputs in terms of different fields. In time, the field of Artificial Intelligence has been divided into many sub-fields because of…
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and working on real-world problems. We believe that…