Informatics underlying Data Science ists Peter Fox RPI

Modern informatics methodology • • • Use cases Stakeholders Distributed authority Access control Ontologies

But really it’s not just one field Informatics IT Cyber Infrastru cture (CI) Cyber

GIS 4 Science Data Analytics Context Experience Data Creation Gathering Information Presentation Organization Knowledge

So who are we talking about? 5 http: //images 2. fanpop. com/image/photos/9400000/Lt-Commander-Data-star-trek-the-next-generation-9406565 -1694 -2560.

Overused Venn diagram of the intersection of skills needed for Data Science (Drew Conway)

Data Science ² Anatomy (as an individual) ² Data Life Cycle – Acquisition, Curation

Data Science ² Physiology (in a group) ² Definition of Science Hypotheses, Guiding Questions

Slides: 8

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Informatics underlying Data Science (ists) Peter Fox (RPI) Sci. Data. Con 2016, Denver CO Mon. Sep. 12 2016 “Defining Data Professionals”

Modern informatics methodology • • • Use cases Stakeholders Distributed authority Access control Ontologies Maintaining Identity

But really it’s not just one field Informatics IT Cyber Infrastru cture (CI) Cyber Informatics Core Informatics Science Informatics Functional requirements Science, Benefit to others • CI = Discipline neutral, e. g. web server, database, wiki • Cyberinformatics = mapping to discipline neutral aspects • Core informatics = Reasoning engine, semantics, computer science • Science (X) informatics = Use cases, science domain terms, concepts in an ontology or controlled vocabulary 3

GIS 4 Science Data Analytics Context Experience Data Creation Gathering Information Presentation Organization Knowledge Integration Conversation Data Science Xinformatics Semantic e. Science 4 Web Science

So who are we talking about? 5 http: //images 2. fanpop. com/image/photos/9400000/Lt-Commander-Data-star-trek-the-next-generation-9406565 -1694 -2560. jpg

Overused Venn diagram of the intersection of skills needed for Data Science (Drew Conway) Anatomy Physiology Missing Anatomy ?

Data Science ² Anatomy (as an individual) ² Data Life Cycle – Acquisition, Curation and Preservation ² Data Management and Products ² Forms of Analysis, Errors and Uncertainty ² Technical tools and standards

Data Science ² Physiology (in a group) ² Definition of Science Hypotheses, Guiding Questions ² Finding and Integrating Datasets ² Presenting Analyses and Viz. ² Presenting Conclusions