Modern research practice asks researchers to engage with information in new ways through the use of digital technologies. The landscape of this skill set is rapidly changing and difficult to pinpoint at an interdisciplinary level. The modern researcher is expected to navigate digital tools that are not unique to the work that they do on a daily basis within their discipline and to be able to share that work in meaningful ways with collaborators.
Open science has become increasingly relevant to modern scientific practice and reflects the development of the modern research skill set. There is a rising tide of policy that requires researchers to navigate open methodology in order to gain access to grant funding. Despite a shift towards support of open science in major policy making bodies, such as the OECD, the training that early career researchers receive has not caught up with this fast changing policy landscape.
This talk will define a modern research skill set, its relevance to the principles of open science, and the need for a better understanding of open practice and of tools and strategies that improve the reproducibility of scientific findings. We will show how the NCSU Libraries are preparing the next generation of researchers through basic training in core elements of the modern research skill set through the NCSU Libraries Summer of Open Science, a 12 week series of workshops and meetups for building baseline technical skills in command line usage, Python programming, web publishing, and scholarly identify management. This series was developed and taught by librarians working in instruction, open science, makerspaces, and software development. We’ll also show how we are simplifying the learning experience for novice data scientists using Vagrant and Ansible to provision reproducible computing environments for instructional use.
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Sponsored by the Laboratory for Analytic Sciences
LAS aims to bring together a multi-disciplinary group of academic, industry, and government researchers, analysts and managers together to re-engineer the intelligence analysis process to promote predictive analysis. LAS will do this by conducting both classified and unclassified research in a variety of areas of research. The research done in this area will serve as the foundation for mission effects and integrated back into the enterprise.
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