Informing curriculum with research-based knowledge Recruiting students with background in science Managing internships Monitoring the employment market Data ONE Bill Michener – Changing Community Practice and Transforming the Environmental Sciences Amber Budden – Advancing DataONE Outreach and Education Initiatives New cyberinfrastructure needs to build on existing structures and support communities of practice. DataOne components include member nodes at diverse institutions and coordinating nodes, and an investigator toolkit with commonly used tools, to be extended worldwide. Purpose is to support data-intensive science through seamless access, use, reuse, and trust of data. Categories of challenges: metadata, interoperability, data integration, representation, reasoning, trust and quality assessment, reward system through citation and use statistics, and education and training. Baseline community assessment shows that many scientists interested in sharing data, but often with conditions. Barriers can be reduced through education, training, and the proper tools. University Educators Kirk Borne, George Mason, Data Science Program – Informatics in Education and an Education in Informatics Everyone will encounter data—all professions, in citizen science, and everyday life All need skill sets and to understand ethics of data usage Problems with terminology used in hiring data managers Need for common language around data management To develop further as a profession, university administration needs to recognize the emerging profession and faculty contributions.
Informing curriculum with research-based knowledge Recruiting students with background in science Managing internships Monitoring the employment market Data ONE Bill Michener – Changing Community Practice and Transforming the Environmental Sciences Amber Budden – Advancing DataONE Outreach and Education Initiatives New cyberinfrastructure needs to build on existing structures and support communities of practice. DataOne components include member nodes at diverse institutions and coordinating nodes, and an investigator toolkit with commonly used tools, to be extended worldwide. Purpose is to support data-intensive science through seamless access, use, reuse, and trust of data. Categories of challenges: metadata, interoperability, data integration, representation, reasoning, trust and quality assessment, reward system through citation and use statistics, and education and training. Baseline community assessment shows that many scientists interested in sharing data, but often with conditions. Barriers can be reduced through education, training, and the proper tools. University Educators Kirk Borne, George Mason, Data Science Program – Informatics in Education and an Education in Informatics Everyone will encounter data—all professions, in citizen science, and everyday life All need skill sets and to understand ethics of data usage Problems with terminology used in hiring data managers Need for common language around data management To develop further as a profession, university administration needs to recognize the emerging profession and faculty contributions.
pga_063095.docx