2. Data Visualisation / Astronomy Challenges to commonality Challenges to commonality  How does Astronomical visualisation differ from others? Infrastructure Requirements Infrastructure Requirements  Grid Requirements

3. Nature of Astronomical Data & Visualisation Largely Static Largely Static  2d tables (catalogues)  pixel images  Metadata (some) Exploration – largely visual Exploration – largely visual Hypothesis testing – largely mining Hypothesis testing – largely mining

4. Challenges Lots of loud astronomers Lots of loud astronomers Hard to Normalise, esp between disciplines. Yet need to retain access to ‘raw’ data. Hard to Normalise, esp between disciplines. Yet need to retain access to ‘raw’ data. Objects move… Objects move… Large images / tables  sample, aggregate Large images / tables  sample, aggregate Finding out about existing tools Finding out about existing tools

5. More Challenges Special Science Requirements for tools (eg finding distances on images)  plugins Special Science Requirements for tools (eg finding distances on images)  plugins Noisy data (but bio / meteo have same problem) Noisy data (but bio / meteo have same problem) Incomplete/high error models (bio / meteo again) Incomplete/high error models (bio / meteo again) Inherent Mk 1 eyeball limitations. Solid cubes. Make use of colours, shapes, movies. 7d on paper. Inherent Mk 1 eyeball limitations. Solid cubes. Make use of colours, shapes, movies. 7d on paper. Need pre-visualisation methods AND retain access to raw data. Need pre-visualisation methods AND retain access to raw data.

6. Grid Requirements Reliability – the right data to the right machine! Reliability – the right data to the right machine! Speed & Latency (for visualisation) Speed & Latency (for visualisation) Collaboration (not yet) Collaboration (not yet) Integration – access to eg stats services Integration – access to eg stats services Easy / simple controls – focus on science not infrastructure. Easy / simple controls – focus on science not infrastructure.

7. Summary Tools exist Tools exist  ‘generalising’ + ‘modularisation’ Expertise exists – ‘synergy’ with professional visualisors Expertise exists – ‘synergy’ with professional visualisors Astronomy data not unique – ‘synergy’ with other disciplines Astronomy data not unique – ‘synergy’ with other disciplines