Bringing Data Science, Xinformatics and Semantic eScience into the Graduate Curriculum (solicited) EGU (EOS 6/ ESSI2.3) April 25, 2012, Vienna Peter Fox (RPI) Tetherless World Constellation

tw.rpi.edu Themes Future Web Web Science Policy Social Xinformatics Data Science Semantic eScience Data Frameworks Semantic Foundations Knowledge Provenance Ontology Engineering Environments Inference, Trust Hendler Fox McGuinness Multiple depts/schools/programs ~ 35 (Post-doc, Staff, Grad, Ugrad)

Application Themes Govt. Data Open Linked Apps Env. Informatics Ecosystems Sea Ice Ocean imagery Carbon Health Care/ Life Sciences Population Science Translational Med Health Records Hendler/ Erickson Fox McGuinness/Luciano Platforms: Bio-nano tech center Exp. Media and Perf. Arts Ctr. Comp. Ctr. Nano. Innov. Data Intensive

4 DataInformationKnowledge Context Presentation Organization Integration Conversation Creation Gathering Experience Data ScienceXinformaticsSemantic eScience Web Science

Also at RPI Data Science Research Center and Data Science Education Center acenter.htmlhttp:// acenter.html –Over 35 research faculty, 5 post-docs, ? grad students Data is one of Rensselaer Plans’ five thrusts Other key faculty –Fran Berman (VPR) –Jim Myers (Director CCNI)

Curriculum Web Science and IT – undergrad, and MSc. and PhD. (with science concentrations) Environmental Science with Geoinformatics concentration Bio, geo, chem, astro, materials - informatics GIS for Science Master of Science – Data Science (pending) Multi-disciplinary science program (2012) PhD in Data and Web Science

E.g. IT with Env. Sci. ERTH-1200 Geology II (4 credits) - spring CHEM-2250 Organic Chemistry I (4 credits) - spring ERTH-2210 Field Methods (2 credits) - fall IENV-1920 Environmental Seminar (2 credits) - spring BIOL-2120 Intro. to Cell and Molecular Biology (4 credits) - spring IENV-4500 Global Environmental Change (4 credits) - fall ERTH-4180 Environmental Geology (4 credits) – spring ERTH-4963 Xinformatics (4 credits) – spring IENV-4700 One Mile of the Hudson River (4 credits) - fall

Geoinformatics concentration CSCI Computer Science I CSCI Data Structures CSCI Introduction to Algorithms or ERTH Geographic Information Systems in the Sciences CSCI4380 – Databases CSCI Data Science CSCI4960 – Xinformatics ERTH 4980 – Senior Thesis

Web Science Learning Objectives Students will demonstrate knowledge and be able to explain the three different "named" generations of the web (a/k/a Web 1.0, Web 2.0, and Web 3.0) from mathematical, engineering, and social perspectives Students will demonstrate the ability to use the dynamic programming language Python to develop programs relating to Web applications and the analysis of Web data. Students will be able to understand and analyze key Web applications including search engines and social networking sites. Students will be able to understand and explain the key aspects of Web architecture and why these are important to the continued functioning of the World Wide Web. Students will be able to analyze and explain how technical changes affect the social aspects of Web-based computing. Students will be able to develop "linked data" applications using Semantic Web technologies.

Data Science Objectives To instruct future scientist how to sustainably generate/ collect and use data for their research as well as for others: data science. To instruct future technologists how to understand and support essential data and information needs of a wide variety of producers and consumers For both to know tools, and requirements to properly handle data and information Will learn and be evaluated on the full life- cycle of data and relevant methods, technologies and best practices. 10

Learning Objectives Develop and demonstrate skill in data collection and management Know how to develop and apply data models and metadata models Demonstrate knowledge of data standards Develop and demonstrate the application of skill in data science tool use and evaluation Demonstrate the application of data life-cycle principles and data stewardship Demonstrate proficiency in data and information product generation 11

Xinformatics Objectives To instruct future information architects how to sustainably generate information models, designs and architectures To instruct future technologists how to understand and support essential data and information needs of a wide variety of producers and consumers For both to know tools, and requirements to properly handle data and information Will learn and be evaluated on the underpinnings of informatics, including theoretical methods, technologies and best practices. 12

Learning Objectives Through class lectures, practical sessions, written and oral presentation assignments and projects, students should: –Develop and demonstrate skill in development and management of multi-skilled teams in the application of informatics –Demonstrate ability to develop conceptual and logical information models and explain them to non-experts –Demonstrate knowledge and application of informatics standards –Demonstrate skill in informatics tool use and evaluation 13

Modern informatics enables a new scale-free framework approach

Semantic eScience Objectives Ontology Development, Merging and Validation Semantic Language and Tool Use and Evaluation Use Case Development and Elaboration Semantic eScience Implementation and Evaluation via Use Cases Semantic Application Development and Demonstration Group Project and Team Development, Use Case Implementation and Evaluation

Discussion… Science and interdisciplinary from the start! –Not a question of: do we train scientists to be technical/data people, or do we train technical people to learn the science –It’s a skill/ course level approach that is needed Education and research semi-coupled We must teach methodology and principles over technology * Data science must be a skill, and natural like using instruments, writing/using codes Team/ collaboration aspects are key ** Foundations and theory must be taught ***

18 Progression after progression ITCyber Infrastru cture Cyber Informatics Core Informatics Science Informatics Science, Societal Benefit Areas Informatics Example: CI = OPeNDAP server running over HTTP/HTTPS Cyberinformatics = Data (product) and service ontologies, triple store Core informatics = Reasoning engine (Pellet), OWL Science (X) informatics = Use cases, science domain terms, concepts in an ontology Requirements