RIT ASA DataFest 2025

DataFest Location

Main room: Gosnell Hall - Auditorium 1250

Supplies

• We recommend that every team member have a desktop or laptop available for use during the competition. You might find it helpful to have a mix of PCs and Macs since they have different strengths. We recommend that you make sure the software you will be using throughout the weekend is installed correctly and running on your computer before the competition. You will be working with a large dataset so make sure that you have the space for it on your drive.
• You might want to have some of your favorite statistical or computational reference books ready to be used if you have them, and bookmark the pages that you regularly use.

Large Data Advice

• The dataset you will be working with is quite large.  If you type a variable name to view it, it will take a while to display. Therefore, remember these R commands: head(), tail(), str(). 
• We strongly recommend you create a small data set that you can use to test things on. Then, if it works out, you can apply your procedure to the large dataset.  Some procedures can take a long time to run on large data sets, and so it will be good to know that your procedure works (because you tested it on a smaller data set) while you wait.  We recommend taking a random sample of rows from the original data set, but there might be other approaches you find useful. 

DataFest Rules

• Before downloading the dataset you must sign the non-disclosure agreement by agreeing to the terms of use and entering your name and email address. At the end of DataFest, delete all data from thumb drives, hard drives, etc. The data is sensitive. 
• Should members of your team drop out at the last minute, you might be asked to join another team that is also missing members. 
• At all times between 9 am-12midnight there will be a consultant present. These are faculty, grad students, or other professionals with field-specific knowledge on the dataset. They all have different areas of expertise. Feel free to ask anything. This is not an exam, but a competition. Do not expect the consultants to write code for you, do data management, etc. They are there to help point you in the right direction, but you're responsible for getting there on your own. The schedule of consultants will be made available at the beginning of the event. 

DataFest Judging

• Each team will have five minutes to present their findings to the judges. 
• At some point on Friday, you might want to set aside time to think about what you want the judges to know. The five-minute time limit will be strictly enforced. At least one member must be present for the presentation.
• Your report must be submitted to the designated Google drive by 11 AM Sunday. Allowed formats: PDF. If using a web-based tool like Google Docs, please export to PDF and send the PDF as your submission.
• Your slides should be ready by 1 PM Sunday on your own computer (Zoom links will be sent to you then), when the parallel presentations start. You don't need to submit you slides. 
• Awards will be given in three categories:
  • Best Insight
  • Best Visualization 
  • Best Use of External Data

Formatting Requirements

1. Document Structure:

• Use the standard Springer journal structure:
  • Title (concise, descriptive)
  • Author/Team Name(s) and Affiliation (if applicable)
  • Abstract (150–250 words, summarizing objectives, methods, key findings)
  • Keywords (4–6 terms)
  • Introduction (context, problem statement, objectives)
  • Methodology (data sources, tools, analytical techniques)
  • Results (visualizations, statistical findings)
  • Discussion (interpretation, significance, limitations)
  • Conclusion (key takeaways, future work)
  • References (Springer-compliant citations)
  • Appendices (optional, e.g., code snippets, raw data samples).

2. Formatting Details:

• Font:
  • Main text: Times New Roman or Arial, 10–12 pt.
  • Headings: Bold, numbered hierarchically (e.g., 1, 1, 1.1.1).
• Margins: 1 inch (2.54 cm) on all sides.
• Line Spacing: 1.15 or single-spaced.
• Paragraphs: No indentation; add 6 pt spacing between paragraphs.
• Figures/Tables:
  • Centered, with captions below (figures) or above (tables).
  • Caption format: 1 / Table 1: [Descriptive title].
  • Ensure high resolution (300+ DPI) for images.
• Equations: Use an equation editor (e.g., LaTeX, MathType) and number sequentially.
• Citations:
  • Use square brackets (e.g., [1]) for in-text citations.
  • Follow Springer’s reference style (e.g., APA, IEEE, or journal-specific).
• Page Numbers: Bottom center, starting from the first page.

3. File Specifications:

• Page Size: A4 or US Letter.
• File Format: Submit as a single PDF (no Word/LaTeX files unless specified).
• Naming Convention: pdf.

Submission Process

1. Deadline: Submit by [date/time] via [portal/email link].
2. Anonymization: Do not include team names in the text (only in the filename).
3. Page Limit: Maximum 8–12 pages (including references and appendices).
4. Supplementary Materials:

• Attach datasets, code, or interactive visualizations as a ZIP file (if allowed).
• Label files clearly (e.g., zip).

Additional Tips

• Use Springer’s LaTeX or Word template (download from Springer Author Guidelines).
• rticle package from R
• Proofread for grammar, typos, and formatting consistency.
• Avoid footnotes unless critical.
• Ensure tables/figures are self-explanatory and referenced in the text.

Checklist for Participants

• Title page with team name (filename only).
• Abstract and keywords included.
• Figures/tables labeled and cited.
• Citations formatted correctly.
• PDF previewed for layout errors.

Let participants know if you’ll provide a template or sample report for reference!

How are you scored at DataFest@RIT?
For each of the categories, there is specific rubric with the five criteria of evaluation of your entry

Best Visualization

Each criterion is rated on a Likert scale (1-5)

Clarity of Message: 
1: Unclear or misleading | 3: Understandable but vague | 5: Extremely clear and intuitive

Aesthetic Design: 
1: Visually cluttered/unappealing | 3: Functional but plain | 5: Professionally designed, visually striking

Appropriateness of Visualization Type:
1: Incorrect chart/format for the data | 3: Adequate but suboptimal | 5: Perfectly matched to the data story

Creativity/Innovation: 
1: Generic or copied | 3: Some originality | 5: Highly inventive, unique approach

Technical Execution: 
1: Flawed or buggy | 3: Functional with minor issues | 5: Flawless, polished, and interactive (if applicable)

Total Score (out of 25):

Best Insight

Each criterion is rated on a Likert scale (1-5)

Significance of Insights: 
1: Superficial or obvious | 3: Moderately valuable | 5: Deep, impactful, or unexpected

Data-Driven Rigor: 
1: Opinions unsupported by data | 3: Partial evidence | 5: Fully backed by robust analysis

Novelty:
1: Conventional or recycled | 3: Some fresh angles | 5: Groundbreaking perspective

Actionability:
1: No practical application | 3: Partially actionable | 5: Clear, implementable recommendations

Methodology: 
1: Weak or flawed analysis | 3: Standard techniques | 5: Sophisticated, statistically sound approach

Total Score (out of 25):
 

Best Use of External Data

Each criterion is rated on a Likert scale (1-5)

Relevance of External Data:
1: Irrelevant or distracting | 3: Somewhat related | 5: Directly enhances the analysis

Integration with Primary Data:
1: Poorly merged or disjointed | 3: Basic linkage | 5: Seamlessly blended for richer insights

Enhancement of Analysis:
1: Adds no value | 3: Moderately improves results | 5: Transformative impact on conclusions

Data Quality/Credibility:
1: Unreliable sources | 3: Mixed credibility | 5: Authoritative, well-documented sources

Creativity in Sourcing:
1: Common/public datasets | 3: Minor unique additions | 5: Unconventional or hard-toaccess data

Total Score (out of 25):