Project 01: Data Collection Facts Label

Investigating a Dataset’s Ingredients

Published

September 28, 2026

Overview

How often have you heard the phrase “data is all around us”? You might think that something so prevalent must be easy to collect– right? Why bother talking about data collection?

Well, as it turns out, not all datasets are created equally.

Through this assignment, you will learn to think critically about how data was collected and how that impacts what it can tell us (and what it cannot).

NoteParticipatory Research

This assignment is part of Data Science Education research conducted by Kathleen Isenegger and a team of students in Computer Science and the MS in Data Science and Analytics. You are asked to complete a pre and post survey, and the research team will be evaluating the results of your project. You are allowed to opt out of the research, but not the project.

Think about a Nutrition Facts Label that you see on food items. This label tells us what went into creating a food item: how much fiber, protein, carbohydrates, and so forth. That way the consumer can make an informed decision on if they want to eat this food. In this assignment you will create a Data Collection Facts Label. This label is designed to tell you what went into creating a dataset, such as: who collected the data? When and where did they collect it? Why was it collected? What is included in the data and what is missing?

You will complete this assignment in multiple phases/checkpoints, each one described below and linked to more detailed instructions. Your final project deliverable will be a collaborative public website where you present your work, and a pre-recorded presentation. You will only be graded once on the final website and presentation using the evaluation rubric at the bottom of this page.

Additionally you will be assigned to review one (1) of your classmates videos and one (1) video from CSCI 608 - the graduate level data science course who are participating in the same activity.

Phase 1: Pick a domain

Form groups of 2-3 people. Together, select a domain of interest and come up with a data science question related to it.

Choose a topic that interests you and has real-world importance. To ease the process of finding open source data, select a topic you believe to have stakeholders in at least one of the following sectors: research/academia, nonprofit organizations, or government.

Domain Ideas:

  • Food habits or nutrition
  • Housing affordability
  • Public transportation
  • Climate change
  • Water usage
  • Sports injuries
  • School funding
  • Healthcare access
  • Air quality
  • Social media behavior
  • Campus safety
  • Agriculture
  • Criminal justice
  • Energy use
  • Public health
  • Disability access
  • Environmental pollution
Important

Do not start looking for data sources yet!

WarningSubmission Instructions
  1. Edit the readme for this Project 01 topic sign up sheet by clicking the pencil icon in the top right and adding your team information.
  2. Click the green “Commit changes” button in the top right and write “added my team” as a commit message. Only one person per team needs to create this entry.

Phase 2: Search for Data

This phase of the assignment will be completed independently. Your goal is to find an open source data set related to your research question and create a Data Collection Facts Label for this data set. You and your partner must have different data sets.

Find a Data Source

Comb the internet for a data source that could help answer your research question.

The dataset you use for this assignment must have some form of documentation on the data collection procedure. Some examples of what to look for are: metadata, codebooks, methodology notes, technical reports, and methods sections.

When you find the dataset you will use, let your partner(s) know! The assignment does not work if you select the same datasets: they must be different.

You may choose to modify your research question if you are unable to find a relevant dataset, but your new research question should be in the same domain as before. That way, your research question will remain similar to your partner’s, even if not exactly the same.

Good Places to Look for Data

Suggested Search Engine (e.g. Google) prompts

  • “[topic] open dataset”
  • “[topic] dataset methodology”
  • “[topic] data collection method”
  • “[topic] research paper data collection”
  • “[topic] survey dataset”
  • “[topic] government data”
  • “[topic] codebook”
  • “[topic] methods section”
ImportantA Word of Warning

Use caution with some sources like Kaggle or GitHub. These can be useful, but they may not include enough information about how the data were originally collected. When possible, try to trace the dataset back to its original source and check whether documentation is available..

NoteCan I Use AI?

You may use AI tools as a starting point to help search for data sources, brainstorm keywords, or identify possible datasets and research papers related to your topic. However, do not use or cite AI as a direct source in your work. Treat AI like a search tool: use it to help you find possible sources, then navigate to the original organization that produced the data. Your final sources and citations should come from reliable, traceable original sources, not from AI-generated responses.

Create a Data Collection Facts Label

WarningSetup Instructions
  1. Make a copy of this Data Collection Facts Label Template into your own Google drive.
  2. Go to the Project 01 topic sign up sheet repository and click on the data_facts_template.qmd template file. Click the down arrow in the top right corner (Hover text says “Download raw file”) to download this single file.
  3. Go to your DATA385 class folder (either locally or in R Studio) and add this .qmd file to your home directory (NOT in an existing repo)
Important

Do not change the layout of this google sheet. You will be importing this directly into R later. Changing the layout will break your code.

Use the following guide to fill out the blanks in your fact label. The label should contain only the raw facts about the data source, do not try to do an analysis yet. Save judgments about usefulness, ethics, bias, missing information, and comparison for the next phase. You likely will have to do more research and looking around at the data set to find some of this information. You may not find information on all items in the list. If, after a throughough search you can’t find this information simply state that is the case.

Source name: Title or name of the dataset, data portal, study, report, or data source. Domain/topic: Topic area the data belongs to, such as food, health, education, housing, climate, transportation, or sports.
Source type: Public dataset, government dataset, institutional data, research dataset, data repository, data portal, or student-created dataset. Collector/producer: Organization (public or private?), researchers, agency, company, community group, or person responsible for producing the data. (This helps us think about how (un)trustworthy the data is).
Original purpose: Why the data were originally collected, such as public reporting, research, monitoring, administration, recordkeeping, or planning. (This helps us think about possible ethical concerns and if the data can be appropriately applied to other contexts). Collection method: How the data were collected or produced, such as survey, interview, sensor, administrative record, mobile app, web scraping, experiment, observation, manual entry, public records, participatory sensing, or transaction records.
Record-creation event: What causes one record to be created, such as a survey being submitted, a purchase being made, a sensor reading being taken, a student enrolling, a clinic visit being recorded, a snack being eaten, or an item being discarded. Unit of observation: What one row, record, case, or entry represents, such as one person, school, product, city, survey response, app entry, hospital visit, weather reading, or snack.
Population or objects represented: People, groups, places, objects, events, or behaviors included in the data. Time period covered: Dates, years, or time range included in the data.
Geographic coverage: Location or area covered, such as a city, state, country, school district, hospital system, online platform, or GPS location. Main variables/fields: Main variables or fields in the data.
Data types included: Types of data included, such as numerical, categorical, text, image, date, time, location/GPS, audio, sensor readings, or IDs. Direct measurements: Variables that measure something directly, such as temperature, age, price, date, location, or a recorded response.
Possible proxy variables: Variables that may stand in for something harder to measure. Save the final direct/proxy judgment for the analysis step. Documentation found: Metadata, codebook, data dictionary, README file, technical report, methods section, survey instrument, or collection notes.
Stated collection details: Collection details the source clearly explains. If something is not stated, write “not stated” rather than analyzing it yet.
WarningSubmission Instructions

Render your fact sheet to PDF and upload to Canvas assignment “Checkpoint 2”.

Phase 3: Comparative Analysis

This phase of the assignment will be completed together with your group. Your goal is to answer the question: “Which source seems to fit your research question better?”

To answer this question, compare your fact labels side by side. You do not need to repeat all of the basic information from the labels. Instead, focus on noticing the important similarities and differences between the two sources, as well as limitations you may think of that both or neither source encounters.

Your goal is to analyze why the differences matter. This is where you explain what the comparison shows about data collection, representation, limitations, bias, ethics, and your research question.

Questions to consider while doing this comparative analysis are listed below. Do not try to answer every question, but touch on each area.

Trustworthiness/reputability

  • What documentation is available for each source?
  • What information is missing, unclear, or not stated in each source?
  • What limitations or data quality issues might come from how each source was collected?

Degree of relevance to research question

  • How do the collection methods affect what kinds of conclusions someone could make?
  • In order to answer your research question, would you be employing any of the variables as proxies?
  • If you could improve one source, what additional data would you add?

Ethical soundness

  • What possible bias might be present in either source?
  • What ethical concerns might be connected to either source?

Scope

  • What does one row, record, case, or entry represent in each source?
  • What populations do each dataset represent? Which is more relevant to your research question? Why?
  • What other claims could someone reasonably make using these sources?

Phase 4: Public dissemination

A Quarto Website template repository will be distributed to each team. Each person will have their own page to display their fact sheet, and there will be a combined ‘comparative analysis’ summary page.

Follow the best practices discussed in class to collaboratively build this website

Tip
  • Everyone works on branches
  • Only add/commit .qmd files
  • Submit your work to the main branch using pull requests
  • Someone other than the author handles the merging
  • One person is responsible for pulling all the final files and building the site.

Build your website

Follow the steps below to help slowly build up your site with fewest merge conflicts. This identifies one “builder” person who handles the building of the site. Everyone else are ‘contributors’. Everyone is responsible for contributing commits, and this will be tracked via PR’s and commit messages.

All members

  1. Clone the website repo to your account
  2. Create a branch to work on by typing git checkout -b yourname in the terminal
  3. Get your .qmd data fact page that you created in phase 2.
    • Rename it as yourname.qmd and copy the file into this repo.
  4. Edit the sharing settings of your data facts google sheet to add your team mates as editors.
  5. Edit the _quarto.yml page and update the page names and references under “Data set details”. That is, change it from person1.qmd or person2.qmd to yourname.qmd and change the corresponding page title.
  6. Git add/commit/push only your .qmd file and the _quarto.yml file
  7. Open a PR in github and assign someone to handle the merge.
ImportantMERGE CONFLICTS POSSIBLE

Only commit your .qmd and .yml file. If you commit the entire repo, you WILL cause merge conflicts with your team.

Builder - Initial build & publish

  • After all PR’s are handled in github, switch back to the main branch by typing git checkout main
  • git pull all changes to your local copy of the main branch.
  • Fill out info on the index.qmd page about your project.
  • Do a full page build - make sure it looks good.
    • Debug google permission problems as they occur.
  • add a .nojekyll file (because Dr.D forgot)
  • double check that all the rendered html files are in the docs folder
  • add all files, commit, push
  • Set repo publish settings

Contributor - Add your comparative analysis

  • Switch back to the main branch by typing git checkout main in the terminal.
  • Pull all changes to update your local copy of the main branch.
  • From this now updated main branch, create a new feature branch called compare-data and switch to it.
  • Create an analysis.qmd file in your main folder, and transfer your comparative analysis info from phase 3 into this file.
  • Render this page and confirm it looks okay
  • Add this page to the navbar by editing the _quarto.yml
  • Commit and only the analysis.qmd and _quarto.yml to github, open a PR.
  • Someone else resolve this PR to merge this feature onto main

Final build, push, confirm deploy

  • Switch back to main, pull, render and debug as necessary
  • Do one final push with commit message “Project 01 complete”
  • Check that all the links work on the final website

Record your presentation

Plan and record a 5-7 minute long presentation to teach the class about:

  1. What your data sets were and how they were created
  2. The main findings of your comparison analysis

Each person is responsible for sharing information about their dataset, and will be graded individually on your part of the presentation.

WarningVideo Submission Instructions

Record your presentation using any standard video recording software. If you do not have a preference, Zoom with screen sharing works wonderfully.

Name your video file with your last names, and upload to This google folder.

Submission

Submit your project information HERE for evaluation

Evaluation Rubric

NoteLearning Objectives
  1. Learn different ways of how to interact with github repositories and RStudio in the cloud (not evaluated).
  2. Find datasets with source documentation (metadata, codebooks, or data dictionaries) to explain who collected each dataset etc. why it was collected, and how it was produced.
  3. Describe what each dataset represents, including observations, variables, data types, population, place, and time period.
  4. Evaluate how well the data fit a research question. Identify limitations, missing information, possible bias, data quality issues, and ethical concerns in each dataset.
  5. Compare datasets to explain how collection methods, purposes, variables, documentation, and context shape what each source can show or miss. Communicate the comparison clearly in a presentation using evidence.
  6. List a variety of data collection methods used across domains and evaluate their strengths and limitations.
Category Weight 4 - Master 3 - Adept 2 - Proficient 1 - Novice
Domain, Research Question, and Source Selection 10% Student clearly describes a meaningful domain/topic and states a focused research question. The selected data sources are relevant to the question and work well for comparison because they show meaningful similarities and differences. Student describes a relevant domain/topic and research question. The selected data sources connect to the question and can be compared, though the connection or comparison may need more focus. Student identifies a general domain/topic, but the research question is broad, unclear, or only loosely connected to the selected sources. The sources may be difficult to compare. Student does not clearly identify a domain, research question, or usable data sources related to the assignment.
Understanding of Source Context and Documentation 15% Student clearly explains where the data came from, who produced it, why it was originally collected, how it was collected or produced, and what documentation supports those details. Student distinguishes between what the source states and what is inferred or unclear. Student explains the main source context and documentation accurately, but some details about purpose, collection method, or documentation may need more precision. Student gives a basic explanation of the source context, but important information is missing, vague, or not clearly connected to documentation. Student does not clearly explain where the data came from, who produced it, why it was collected, how it was produced, or what documentation supports the explanation.
Data Collection Facts Label 15% Student uses the Facts Label effectively to organize important information about each source, including source identity, collection context, observation/scope, variables/data structure, direct/proxy measures, and documentation. Entries are mostly factual rather than analytical. Student completes the Facts Label with most important information included. Some fields may be brief, vague, or partially incomplete, but the label still helps explain the source. Student completes parts of the Facts Label, but several important fields are missing, unclear, inaccurate, or mixed with unsupported interpretation. Student does not complete the Facts Label in a useful way, or the label shows little understanding of what the source is, what records represent, or how the data were produced.
Dataset Representation and Measurement 15% Student clearly explains what the data represent, including unit of observation, population or objects represented, time/place coverage, major variables, data types, direct measurements, and possible proxy variables. Student shows awareness of how measurement choices shape meaning. Student explains what the data represent with mostly accurate attention to observations, population/scope, variables, data types, and direct/proxy measures. Some details may need more depth. Student describes some basic features of the data, but the explanation is incomplete or confused, especially around unit of observation, population/scope, or direct versus proxy measures. Student does not clearly explain what the data represent, what one record means, what variables are included, or how the data measure the topic.
Comparison of Data Sources 15% Student makes a clear and meaningful comparison between the sources. The comparison explains how differences in purpose, collection method, population/scope, variables, documentation, and context affect what each source can show or miss. Student compares the sources across several important features. The comparison is mostly clear, but may need more explanation of why the differences matter. Student makes a limited comparison. The response may mostly list facts from the labels without fully explaining similarities, differences, or their importance. Student does not meaningfully compare the sources, or discusses each source separately without explaining how they differ or why the differences matter.
Analysis of Fit, Limitations, Bias, Ethics, and Claims 20% Student thoughtfully analyzes what the comparison reveals. Student explains how well each source fits the research question, what information is missing or unclear, what limitations or data quality issues may exist, what biases or ethical concerns may be present, and what claims someone could reasonably make or should avoid making. Student discusses fit, limitations, bias, ethics, and possible claims in a reasonable way, but some points need more evidence, depth, or connection to the data collection process. Student identifies some limitations, bias, ethical concerns, or fit issues, but the analysis is brief, general, or not clearly tied to how the data were collected or documented. Student does not meaningfully analyze fit, limitations, missing information, bias, ethics, or appropriate claims, or dismisses these issues without a reasonable explanation.
Presentation and Communication 10% Presentation is clear, organized, and focused on the assignment purpose. Student introduces the domain and sources efficiently, uses the Facts Labels as evidence, and spends most of the presentation on comparison, analysis, and what someone should know before using the data. Presentation is understandable and includes the main parts of the assignment, but may need stronger organization, clearer evidence, or more emphasis on comparison and analysis. Presentation includes some required information, but it may be hard to follow, light on evidence, or focused too much on topic summary instead of data collection and comparison. Presentation is missing major parts, difficult to follow, or does not focus on data collection, documentation, comparison, or limitations.