• Dryad Digital Repository

  Dryad is a nonprofit membership organization that is committed to making data available for research and educational reuse now and into the future.

• Harvard Dataverse

  A free data repository open to all researchers from any discipline, both inside and outside of the Harvard community, where you can share, archive, cite, access, and explore research data. (Includes the data from other Dataverse repositories, e.g. UCSD)

• Zenodo

  To ensure no one is left behind through lack of access to the necessary tools and resources, Zenodo makes the sharing, curation, and publication of data and software a reality for all researchers.

• Figshare

  Figshare is a repository where users can make all of their research outputs available in a citable, shareable, and discoverable manner.

• Google Dataset Search

  Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher’s site, a digital library, or an author’s web page.

• ICPSR (endorsed by APA)

  ICPSR maintains a data archive of more than 250,000 files of research in the social and behavioral sciences.

• openAFRICA

  Searchable datasets maintained by Code for Africa, with support from the World Bank and AWS. “All the data on openAFRICA is intended to be actionable. The data is meant to help you change the world.”

• Registry of Research Data Repositories

  Offers a way to search over 2,000 worldwide data repositories.

• City of Seattle

  The Open Data Program makes the data generated by the City of Seattle openly available to the public to increase the quality of life for our residents; increasing transparency, accountability, and comparability; promoting economic development and research; and improving internal performance management.

• King County

  Welcome to King County’s Open Data portal, where you can discover, analyze, and connect with publicly available datasets published by King County agencies.

• Curate Science

  A database of studies published in the sciences (and particularly social sciences/psychology); many with open data. Search by topic (e.g., personality, romantic relationships, etc.) and find papers with accompanying datasets.

• Iris Data Set

  The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms.
  The Iris Data Set contains three classes of 50 instances each, where each class refers to a type of iris plant.

• Kaggle

  Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.

• Kaggle – Titanic Survivor Data

  This classic dataset includes training and testing data (to practice building models and seeing if they cross-validate) trying to predict whether people survived the Titanic disaster in 1912.

• Kaggle – World Happiness Data

  This is a classic dataset for analysis. Over several years, it looks at national happiness and other data by country. It contains categorical and continuous data and has data nested within countries over time.

• Linear Regression Data Sets for Machine Learning

  Here there are open linear regression datasets for free downloading. Some of the datasets include sample regression tasks to complete with the data.

• Project Implicit – Open Data for Implicit Racial Bias and More

  Project Implicit was founded to foster the dissemination and application of implicit social cognition. The site contains many gigabytes of datasets looking at IAT data in psychology (fast, automatically learned associations with race, gender, etc.) and is an important repository of datasets for research in psychology.

Should I Trust This Data Source?

First, consider the overall reputation of the source for your data. At the end of the day, datasets are created by humans, and those humans may have specific agendas or biases that can translate through your work.

All the data sources we’ve listed here are reputable, but there are more sources for data that aren’t as reputable. The one caveat to our list here is that community-contributed collections, like data. world or GitHub, may vary in their quality. When in doubt about your data source’s reputation, compare it to similar sources that cover the same topic.

Is the Data Skewed?

Understanding how your dataset skews will help you choose the right data to analyze. It’s good to use visualizations to see how your dataset skews because it’s not always obvious by looking at the numbers alone.

For numeric columns, use a histogram to see which type of distribution each column has (normal, left, right, uniform, bimodal, etc.). Here’s a quick visualization of a few distributions you might see:

This visual understanding will help you avoid outliers and be aware of general trends as you perform your data analysis. Strict recommendations on what to do next depend on your dataset, but, overall, how it skews will provide a general idea of the data’s quality and hint at which columns to use in an analysis. You can then use this general idea to avoid misrepresenting your data.

For non-numeric columns, use a frequency table to check how many times a value appears. In particular, you might check if there is mostly one value present. If that’s the case, your analysis might be limited because the variety of values is small. Again, this is just to provide a general idea of data quality and indicate which relevant columns to use.

You can create these visuals and frequency tables within Excel or Google Sheets using CSVs, but you might want to look at a business intelligence (BI) tool for complex datasets.

Start Using Your Free Datasets

Once you have your data and are confident of its quality, it’s time to put it to work. You can go a long way with the likes of Excel, Google Sheets, and Google Data Studio, but if you want good practice for your data career, you should cut your teeth with the real deal: a BI platform like Chartio.

A BI platform will provide powerful data visualization capabilities for any dataset, from small CSVs to large datasets hosted in data warehouses, like Google BigQuery or Amazon Redshift. You can play with your data to create dashboards and even collaborate with others.