Introduction to Scientific Data (P01344)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : PHY-P-ISD

This course introduces a number of issues relating to the use of data in e-Science, through a combination of case studies and consideration of generic principles and problems. The course will cover the passage of data from its rawest state to its publication and curation in archival form. General issues to be addressed in this process include the tracking of provenance and the versioning of both data products and data analysis software, as both contribute to the concepts of integrity, validation and reproducibility, which are crucial to the use of data as scientific evidence. This course will also cover a variety of formats and structures used to store scientific data (e.g. relational database management systems and files), the ways in which they can be accessed and integrated, and the responsibilities of the curators of archival data. Throughout this course an emphasis will be maintained on those scientific data issues which are most relevant to e-Science applications, which tend to be characterised by the integration of distributed data sources of significant size and/or complexity.

Entry Requirements

? This course is not available to visting students.

? Pre-requisites : Only postgraduates or final year MPhys students with suitable computational background, subject to space restrictions and agreement with relevant Programme Coordinator.

Subject Areas

Home subject area

Postgraduate (School of Physics), (School of Physics, Schedule Q)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 1 (Blocks 1-2)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	12:10	13:00	KB
Lecture	Friday	12:10	13:00	KB

Summary of Intended Learning Outcomes

On completion of this course, students should be able to:
1. Demonstrate an understanding of the salient issues relating to the use of data in the scientific process.
2. Compare and contrast a range of formats and structures used to store scientific data and the ways that they can be accessed and integrated.
3. Illustrate how the generic problems of data curation and manipulation are addressed within several scientific disciplines.
4. Demonstrate an awareness of standard representations for data and metadata.
5. Design relational and XML schemas for scientific data and metadata.