Activities for FYF 101J
23 September 2009

Advanced data concepts and visualization

Overview

Today's session will have two purposes

1.  To discuss advanced issues regarding data like QA/QC, metadata, informatics, meta-analyses, and data mining

2.  To discuss data visualization using tables and graphs


I. Advanced issues relating to data

A. An important activity conducted in labs is Quality Assurance / Quality Control (QA/QC). Each lab should follow a set of standards to provide confidence in the data.

See example 1, example 2, example 3.

B. Data are often described by metadata. Information that describes individual categories of data. Generating good metadata will be important for efforts to put data on-line, as metadata provides a basis for searching data among various databases.

Wikipedia definition
Discussion from University of Queensland
Good and bad metadata
See
example.
See also
this page.

C. Data are often proprietary, and subject to restrictions concerning intellectual property rights.

See discussion for engineers

D. Exchange and analysis of data in an on-line environment is termed informatics.

Wikipedia definition of informatics
See example program in informatics

Data-mining and meta-analyses will be activities that many people are pursuing and will pursue in the future.

Data-mining definition from UCLA
Data mining techniques
Meta-analysis definition from Wikipedia
How to conduct a meta-analysis

II. Graphical Presentation of Data

Premise: Humans need to make sense out of information. Raw numbers are typically difficult to grasp. Therefore, methods for depicting data in a way that is psychologically easier to understand is an important endeavor for those working in technical fields.

**Data depiction using tables - Still in form of numbers; note importance of rows and colums

Example

**Data depiction using figures

A. Charting univariate data

1. Bar charts

2. Pie charts

B. Charting relational data

1. Line graphs

2. Scatterplots (correlation)
       Example 1, Example 2

3. Regression diagrams:
        Linear: high r2, low r2
        Curvilinear

4. Time series analysis, with oscillations


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