18 Free Datasets for Data Science Projects: Uncommon but Useful

Wondering where to find free and not-so-commonly used data sets that should be useful??

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Fortunately, or unfortunately, the Internet is awash with data sets that are very commonly used by every other data science professional.

These datasets, while perhaps not as commonly utilized as a Kaggle data set, offer a wealth of information across a wide array of fields. They present unique opportunities for exploration, analysis, and application in data science projects that seek to break new ground or address underexplored questions.

Hence, in this post, we’ll highlight a few first-rate repositories where you can find free data sets that are not so common. 

Free Datasets for Data Science Projects

#1 Global Biodiversity Information Facility (GBIF)

Website: https://www.gbif.org/ 

GBIF is a comprehensive database of information on global biodiversity. It includes species occurrence data, such as the presence of a specific species at a particular time and place, sourced from museums, research institutions, and citizen scientists worldwide.

• Applications: The dataset can be used for ecological and environmental research, including species distribution modeling, climate change impact studies, and biodiversity conservation strategies. It's also valuable for educational purposes, promoting awareness of biodiversity issues.

• Value: The extensive geographical and temporal coverage makes it an invaluable resource for understanding global biodiversity patterns and changes over time.

#2 OpenStreetMap (OSM) Data Extracts

• Website: https://www.openstreetmap.org/ 

• Data Extracts via Geofabrik: http://download.geofabrik.de/ 

OpenStreetMap offers detailed geographical data, including roads, buildings, waterways, and natural features, contributed by volunteers globally. The data extracts can include everything from street names and types to points of interest.

• Applications: Useful for urban planning, disaster response, navigation, and GIS projects, allowing for detailed spatial analysis and mapping in virtually any location on the planet.

• Value: The open and collaborative nature ensures the data is continually updated, providing an accessible and versatile resource for spatial analysis.

#3 CMU Pronouncing Dictionary

Website: http://www.speech.cs.cmu.edu/cgi-bin/cmudict 

This dictionary provides a list of words and their phonetic transcriptions in North American English, including variant pronunciations.

• Applications: It's used in text-to-speech (TTS) systems, speech recognition, rhyming dictionaries, and phonetic studies. It can also assist in linguistic research and education, especially in phonology.

• Value: As a free, machine-readable resource, it offers extensive support for applications requiring phonetic data, facilitating advancements in speech technology and linguistic analysis.

  

  "When I wanted to join the data science domain, someone told me to start my learning journey with Python, and I ended up learning HTML. That's how bad it was for me; I couldn't understand a thing..." Kriti thinks back.

  

  #4 Enron Email Dataset

  Website: https://www.cs.cmu.edu/~./enron/ 

  Contains a large collection of email data from the Enron Corporation, made public during the legal investigation following the company's collapse. It includes email content, metadata, and attachments.

  • Applications: Beyond machine learning for email classification, it serves as a rich source for social network analysis, corporate communication studies, and fraud detection research.

  • Value: The dataset's real-world, unfiltered nature provides unique insights into corporate communication and organizational behavior.

  #5 GDELT Project

  Website: https://www.gdeltproject.org/ 

  GDELT monitors global news media, providing data on events, their locations, involved parties, and related broadcast, print, and web content. It includes emotional tone and thematic elements of news coverage.

  • Applications: Ideal for geopolitical analysis, media bias studies, and global event prediction. Researchers and analysts can track trends, conflicts, and relationships between countries and organizations.

  • Value: Its vast scope and real-time update capability make it unparalleled for studying global news coverage and its impacts.

  #6 Internet Archive

  Website: https://archive.org/

  A digital library of Internet sites and other cultural artifacts in digital form. Includes texts, audio, moving images, and software as well as archived web pages.

  • Applications: Research in digital humanities, historical trends in web content, cultural studies, and machine learning projects involving natural language processing and image recognition.

  • Value: Provides a historical archive of the digital age, enabling research that requires temporal analysis of web content and digital culture.

  #7 RxNorm

  Website: https://www.nlm.nih.gov/research/umls/rxnorm/index.html 

  RxNorm provides standardized names for clinical drugs and aggregates information from various U.S. sources. It includes data on ingredients, dosages, and forms of medications.

  • Applications: Used in healthcare information systems for prescribing, dispensing, and billing. Supports clinical decision support systems, electronic health records, and patient safety initiatives.

  • Value: Facilitates interoperability between health systems, improving the accuracy and efficiency of healthcare delivery and research.

  #8 Twinword Ideas Keyword Database

  Website: https://www.twinword.com/

  Offers a database of keywords with associated data such as user intent, search volume, and SEO competition scores. It's designed to provide insight into how users search for information online.

  • Applications: Useful for market research, SEO strategies, content creation, and understanding consumer behavior. It can also support linguistic research into search patterns.

  • Value: The inclusion of user intent and competitive data makes it a powerful tool for optimizing online content and understanding market trends.

  #9 Human Protein Atlas

  Website: https://www.proteinatlas.org/

  The Human Protein Atlas provides a comprehensive map of the human proteins in cells, tissues, and organs. It includes high-resolution images and information on protein expression levels across different human tissues and organs.

  • Applications: This dataset is crucial for biomedical research, particularly in understanding human biology at the molecular level, disease mechanisms, and drug development.

  • Value: Offers an extensive collection of protein expression data, making it invaluable for research in proteomics, molecular biology, and medicine.

  #10 Zindi Africa

  Website: https://zindi.africa/

  Zindi is a platform that hosts data science competitions with a focus on solving Africa's most pressing problems. It provides datasets related to various issues such as healthcare, agriculture, and financial services in African contexts.

  • Applications: Participants can use these datasets to develop models that address real-world problems, gaining insights into unique challenges faced in African countries.

  • Value: Promotes the development of data science solutions tailored to African needs, fostering local innovation and providing researchers with context-specific datasets.

  #11 Linguistic Data Consortium (LDC)

  Website: https://www.ldc.upenn.edu/ 

  The LDC offers a variety of linguistic resources, including corpora of speech, text, and video data in multiple languages. It's a treasure trove for researchers in computational linguistics and natural language processing (NLP).

  • Applications: Useful for language model training, speech recognition, machine translation, and other NLP tasks.

  • Value: Provides access to a diverse range of linguistic data, supporting research and development in language technologies.

  #12 FAOSTAT

  Website: http://www.fao.org/faostat/en/#home 

  Maintained by the Food and Agriculture Organization of the United Nations, FAOSTAT offers statistics on global food, agriculture, and nutrition. It includes data on agricultural production, trade, land use, and greenhouse gas emissions.

  • Applications: Ideal for research on food security, agricultural economics, environmental impact assessment, and policy-making.

  • Value: As the most comprehensive database of its kind, it's instrumental for global food system analysis and sustainability studies.

  #13 Microsoft Research Open Data

  Website: https://msropendata.com/

  A collection of datasets from various domains, including computer science, biology, economics, and social sciences, curated by Microsoft Research.

  • Applications: Supports a wide range of research projects in machine learning, natural language processing, social science, and more.

  • Value: The datasets are provided with the aim of advancing collaborative research and are often accompanied by tools and resources to facilitate their use.

  #14 NOAA Climate Data Online

  Website: https://www.ncdc.noaa.gov/cdo-web/ 

  The National Oceanic and Atmospheric Administration (NOAA) provides comprehensive climate and weather data, including historical weather observations, satellite data, and climate model projections.

  • Applications: Useful for climate research, weather forecasting, disaster preparedness, and environmental science.

  • Value: Offers an extensive archive of climate and weather data essential for understanding climate change and its impacts.

  The Cutting-Edge Technology At The Climate Crisis Warfront

  # 15 Harvard Dataverse

  Website: https://dataverse.harvard.edu/ 

  An open-source repository of datasets across a wide range of academic fields, hosted by Harvard University. It includes data from research studies, publications, and projects.

  • Applications: Supports data sharing and preservation, enabling replication of research findings and further analysis across disciplines.

  • Value: Facilitates interdisciplinary research and collaboration by providing access to a diverse collection of academic datasets.

  #16 Gapminder

  Website: https://www.gapminder.org/data/ 

  Gapminder compiles global statistical data on economics, health, and environment, aiming to debunk common misconceptions about global development.

  • Applications: Ideal for educational purposes, data visualization projects, and research on global development trends.

  • Value: Provides easy-to-understand, visual data resources that challenge preconceptions and promote data literacy.

  #17 Securities and Exchange Commission (SEC) EDGAR Database

  Website: https://www.sec.gov/edgar.shtml 

  Contains financial statements and other formal documents submitted by publicly traded companies to the U.S. Securities and Exchange Commission.

  • Applications: useful for financial analysis, market research, and studying corporate governance practices.

  • Value: Offers an in-depth look into the financial and operational workings of publicly traded companies, valuable for investors, researchers, and policymakers.

  #18 Project Gutenberg

  Website: https://www.gutenberg.org/

  A library of over 60,000 free eBooks, including classic literature and historical texts. It provides plain text files that are easy to analyze computationally.

  • Applications: Can be used for natural language processing, textual analysis, and machine learning projects focused on literature.

  • Value: Enables access to a wide range of literary works for computational analysis, supporting research in digital humanities and linguistics.

  

  Now, when I look back, it's hard to believe that it is me who created 10-15 data science projects and the knowledge that I hold; this wouldn't have been possible without the industry-vetted curriculum and the experts at OdinSchool's Data Science Course.

  

  What will you do after finding the right dataset?

  If you’re anything like me, you’ll lose hours simply browsing these vast repositories. 
  
  So what do you do once you’ve found your dataset and analyzed it? If you want to feature your analysis as a project in your portfolio, there are certain steps you’ll need to follow,

  Step 1: Select Your Projects

  1. Diversity and Complexity: Choose 3-5 projects that showcase a range of skills, such as data cleaning, analysis, visualization, machine learning, and statistical modeling. Ensure the projects cover different applications or industries (e.g., finance, healthcare, environmental science) to demonstrate versatility.

  2. Originality: Include at least one project that is uniquely yours, such as an original analysis of an uncommon dataset or a novel application of machine learning. This will help you stand out.

  3. Relevance: Align your projects with the types of roles you're seeking. If you're interested in a specific sector, make sure your portfolio reflects relevant projects.

  Step 2: Build Your Projects

  1. Dataset Selection: For each project, choose datasets that are interesting and have real-world applicability. Utilize the datasets mentioned previously or find new ones that spark your interest.

  2. Clear Objectives: Start each project with a clear question or problem statement. What are you trying to discover or predict? What value does your analysis provide?

  3. Thorough Analysis: Conduct a thorough analysis, including data cleaning, exploration, and visualization. Use statistical or machine learning models where appropriate. Show your thought process and how you overcome challenges.

  4. Documentation: Document your code and analysis thoroughly. Use comments in your code and Markdown cells in Jupyter notebooks to explain your reasoning, methods, and conclusions.

  5. Results and Interpretation: Present your results in a clear and compelling manner. Use visualizations to support your findings and discuss the implications of your work.

  Step 3: Showcase Your Projects

  1. Create a GitHub Repository: Use GitHub to host your portfolio. Create a separate repository for each project, including all relevant code, datasets (if not too large, or provide links), and a detailed README file that summarizes the project, its objectives, methods, findings, and how to run the code.

  2. Write a Blog Post: For each project, write a blog post that narrates the story of your project. Use platforms like Medium, LinkedIn, or a personal blog. Explain the problem, your approach, the challenges you faced, and the solutions you devised. Embed visualizations and link back to your GitHub repository.

  3. Build a Personal Website: Create a simple website to serve as the landing page for your portfolio. Include a brief bio, a CV or resume, and links to your projects. Tools like GitHub Pages, WordPress, or Wix can be used to create a professional-looking site with minimal effort.

  Step 4: Engage with the Community

  1. Share on Social Media: Share your projects on LinkedIn, Twitter, and relevant Reddit communities. Engage with feedback and questions from the community.

  2. Contribute to Open Source: Contribute to open-source projects related to data science. This can be an excellent way to demonstrate your skills and collaborate with others.

  3. Participate in Competitions: Engage in online competitions on platforms like Kaggle. Even if you don't win, you can include the project in your portfolio and discuss what you learned.

  Step 5: Continuous Learning and Updating

  1. Keep learning: Data science is a rapidly evolving field. Stay updated with the latest tools, techniques, and best practices by taking online courses and attending workshops or webinars.

  2. Update Regularly: Regularly update your portfolio with new projects or enhancements to existing ones. Reflect on feedback and incorporate it into your work.

  A strong data science portfolio is more than a collection of projects; it's a testament to your skills, creativity, and passion for the field.

  

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  Frequently Asked Questions on Data Sets

  

  How can I merge multiple datasets effectively?

  To merge multiple datasets effectively, use a common unique identifier shared across all datasets, like a primary key. Then employ data manipulation tools like pandas in Python or SQL queries to join the datasets based on this identifier. Ensure compatibility in data types and formats for smooth merging.

  What techniques can I use to handle large datasets efficiently?

  To handle large datasets efficiently, use techniques like sampling, parallel processing, data compression, partitioning, indexing, incremental processing, out-of-core processing, data summarization, data filtering, and distributed file systems.

  How do I choose the right dataset for my project?

  Choose a dataset that's relevant, high-quality, appropriately sized, credible, contains necessary features, has suitable licensing, respects privacy and ethics, and has been explored through exploratory data analysis.

  1. Relevance: Ensure the dataset aligns with the goals and requirements of your project.

  2. Quality: Assess the quality of the dataset in terms of accuracy, completeness, and consistency.

  3. Size: Consider the size of the dataset relative to your project needs. Larger datasets may provide more insights but require more resources to process.

  4. Source: Evaluate the credibility and reliability of the dataset source to ensure data integrity.

  5. Features: Determine if the dataset contains the necessary features and variables for your analysis or modeling.

  6. Licensing: Check the dataset's licensing terms to ensure it can be used legally for your project.

  7. Privacy and Ethics: Consider ethical implications and privacy concerns associated with the dataset, especially if it contains sensitive information.

  8. Exploratory Analysis: Perform exploratory data analysis (EDA) to understand the characteristics and patterns within the dataset before committing to it for your project.

  What are the challenges of working with real-time datasets?

  Real-time datasets can be big and come in different shapes, making them hard to manage quickly. Ensuring they're accurate is tough, and it's important to process them fast without delays. As more data comes in, systems need to be able to handle the load and work well with other existing systems.

  How can I assess the bias in a dataset?

  Assessing bias in a dataset involves several steps:

  1. Define Bias: Clearly define the types of bias relevant to your analysis, such as selection bias, measurement bias, or demographic bias.

  2. Examine Data Collection Methods: Evaluate how the data was collected, including sampling techniques and data sources, to identify any inherent biases in the data collection process.

  3. Analyze Data Distribution: Examine the distribution of key variables across different demographic groups or categories to identify potential disparities.

  4. Compare to External Benchmarks: Compare the dataset's demographics or characteristics to external benchmarks, such as census data or industry standards, to detect any discrepancies or underrepresentation.

  5. Explore Missing Data: Investigate patterns of missing data and consider whether they might introduce bias, such as certain groups being more likely to have missing values.

  6. Use Statistical Tests: Employ statistical tests to quantify and identify bias, such as chi-square tests for categorical variables or t-tests for continuous variables across different groups.

  7. Consult Domain Experts: Seek input from domain experts or stakeholders who are familiar with the subject matter to identify potential sources of bias and assess their impact on the analysis.

  8. Mitigate Bias: Once identified, consider strategies to mitigate bias, such as data augmentation, reweighting, or adjusting analysis methods to account for bias.

  Are there any legal or ethical considerations I should be aware of when using public datasets?

  When using public datasets, it's crucial to:

  1. Check Data Licensing: Review the dataset's license terms to ensure you comply with usage restrictions and attribution requirements. Some datasets may have specific terms for commercial use or redistribution.

  2. Protect Privacy: Be mindful of any personally identifiable information (PII) in the dataset and comply with privacy regulations such as GDPR or HIPAA. Anonymize or de-identify data when necessary to protect individuals' privacy.

  3. Respect Data Ownership: Respect the intellectual property rights of dataset creators and providers. Obtain permission if you plan to use the data for commercial purposes or if there are specific usage restrictions.

  4. Address Bias: Assess the dataset for potential biases, such as sampling bias or demographic bias. Take steps to mitigate bias to ensure fair and unbiased analysis outcomes.

  5. Ensure Informed Consent: Ensure that data subjects have provided informed consent for data collection and usage, especially in research involving human subjects. Follow ethical guidelines for data collection and usage.

  6. Maintain Transparency: Be transparent about your data analysis methods and results. Document your processes to facilitate reproducibility and accountability.

  7. Secure Data: Take measures to secure the dataset and prevent unauthorized access or disclosure of sensitive information. Use encryption, access controls, and other security measures to protect data integrity and confidentiality.

  8. Use Responsibly: Use the data responsibly and ethically, avoiding activities that could harm individuals or communities represented in the dataset. Consider the potential social impact of your analysis and take steps to mitigate any negative consequences.