In this project, you will apply or extend existing tools to serve the specific information needs of a real world client. You will document results in a research paper. Please work in self-selected teams of three to five. Build on the work you did as part of the IVMOOC (http://ivmooc.cns.iu.edu).

Most projects will have a data selection, data cleaning & preparation, data analysis, and data visualization phase followed by a detailed examination and discussion of results.Please do make sure that your client knows the data you are analyzing/visualizing by heart and is interested/available to work with you.

This project has the following parts/deadlines:
(1) Re-define your project. Handin due Mon 03-25-2013 at noon.
(2) Data analysis and visualization.
(3) Validation.
(4) Documentation and discussion of data analysis & visualization as well as validation results. Handin as 'Client Project Draft' by Mon 04-08-2013, noon.
Give a 10 minute presentation of your results and next steps to get feedback from everybody in Lab of Class 14.
(5) Documentation of project results. Handin as 'Client Project' by Mon 04-22-2013 at noon.
(6) Project/Peer rating. Email due Mon 04-22-2013 at noon.
(7) Final presentation during Lecture and Lab of class 16 on 04-23-2013.
All parts are detailed subsequently.

Submit your results via the https://oncourse.iu.edu/portal/site/SP13-BL-SLIS-S637-22378 Oncourse workspace.

(1) Re-define your project - Due Mon 03-25-2013, noon
First, you will need to re-define the project that you would like to work on. Build on the work you did as part of the IVMOOC (http://ivmooc.cns.iu.edu).

You will need to define the scope of the project, e.g., data sets used, data analysis to be performed - algorithms needed for this analysis, initial ideas and sketches for the data visualization part (we will learn more data analysis and visualization algorithms over time). On the Handin web page provide information on your project such as

• Title.
• Clients or collaborators.
• Explain the visualization goal/need (e.g., describe personal encounters with the problem/existing interface, relevant psychological or other theories, etc.) and why this project is important.
• Discuss related work. Here you will have to do a literature/web search to find out what theories/approaches/systems exist. You are not interested to re-invent.
• Decide if you want to compare, develop, or apply algorithms.
• Data sets used - simple statistics (e.g., number of entities, entity attributes, etc. Please provide links to the raw data.
• Data analysis/visualization (algorithms) to be applied.
• First sketches of the visualization and a discussion of the applied visual perception or design principles.
• Expected results.
• Link to prior work you did as part of the IVMOOC.

(2) Data analysis and visualization
Based on a detailed user and talk analysis, start analyzing your data set. Make sure you satisfy your clients' information needs.
Please examine and discuss the most interesting properties of your data. Discuss at least two alternative visualizations of your data. Be creative.
Use Paint, Photoshop, Fireworks or any other drawing program to enhance the expressiveness and legibility of your visualization(s), e.g., add a legend or additional comments. Make sure to apply visual perception and cognitive processing principles. Add a section that discusses what principles you applied and why.

(3) Validation
Validate the data analysis/visualization results by having your target user group comment on them.  Do they understand the visualization (elements)? What do they miss? Ask at least two users. In addition, validate your results by comparing them with related work if available.

(4) Documentation and discussion of first data analysis & visualization as well as validation results - Due Mon 04-08-2013, noon, Handin as 'Client Project Draft'
Report the results of your user and task analysis. Explain the data analysis/visualization you performed -- give links to initial, intermediate, and final result data files and any software you used. Make sure that if somebody else reads this explanation s/he is able to obtain the very same results you got. Provide several views of your data set(s). Clickable thumbnail images which lead to full resolution images are best for web based documentation. Add a brief discussion of interesting properties. Last but not least, document your validation results. The documentation should print on not more than 6 pages on a letter size printout--including key visualizations.

(5) Documentation of final project results - Due Mon 04-22-2013 at noon, Handin as 'Client Project'
Revise your visualization design based on your clients' and class feedback. As part of your final project documentation, update your data analysis, visualization, and validation results as needed. Prepare a section on 'Challenges and Opportunities' in which you address complexity & scaling issues, etc. as well as desirable modifications & extensions of your work (you have only a few weeks for this project restricting the amount of work that can be done considerably. However, I would like to know what promising avenues you see for the project you selected.) Don't forget to include an 'Acknowledgment' section in which you thank people who provided resources or helped you with this project. Please list complete data, software, literature references to all work you use or discuss. Do provide links to papers if they are available online.

Please aim for quality and not quantity. The final documentation should be no longer than 6 pages and conform to the IEEE VIS paper submission guidelines at http://ieeevis.org/year/2013/info/call-participation/paper-submission-guidelines, using the templates at http://www.cs.sfu.ca/~vis/Tasks/camera_tvcg.html.

Link the final paper from a web page. Feel free to add links to supplementary material such as raw and compiled data, code, screen shots, animations of visual interaction sequences, etc. as appropriate. Please do keep in mind that I will grade the paper not the supplementary material. However, the latter will provide an excellent resource for you and others to follow up on this work.

Handin the link to the web page and place a complete copy of your handin in Katy's SLIS mailbox.

(6) Project/Peer Rating - Due Mon 04-22-2013 at noon
Please rate on a scale of 1 (lowest) to 20 (highest) the following factors related to your group project.

• Rate the overall quality of the work in your project.
• Rate the overall contribution of each project team member (including yourself) to the project as a whole. In making your assessment include issues such as intellectual contribution, sharing of workload, dependability, responsibility, and promotion of team morale.
• If you have any other comments or considerations I should be aware of in your project team, please add them as well.

Your input will remain confidential, so please be honest in your appraisals. The rating will influence the class participation (20% of the total grade for the course) as well as the peer review portion of the final project grade (2 points out of 20 points) and it may even influence individual scores for the final project.
Please send your rating via mail to katy@indiana.edu and make sure to use the subject header "S637: Peer Rating".

(7) Final presentation
Present the results of your project during Lecture and Lab on 04-23-2013.

Handing in
Submit your results via the https://oncourse.iu.edu/portal/site/SP13-BL-SLIS-S637-22378 Oncourse workspace.

Grading
Your project will be graded out of 20 points and the points will be awarded for

• Quality of data selection, cleaning, preparation, and documentation.
• Appropriate selection of data analysis/visualization/interaction algorithms that match your users and their tasks.
• Quality of data analysis and visualization results and their discussion.
• Completeness and quality of validation.  
• Overall quality of content, including the accuracy and completeness of information, the expressiveness and clarity in communication of ideas using text and screenshots, and the appropriateness of references to/attribution(s) for the work of others.
• If you decide to implement or extend an existing algorithm, then I will grade the reliability, ease of use, internal consistency, and performance of the implementation.