Got data questions? Book a free consultation with Cushing/Whitney Medical Library’s data librarian for yourself, group, or team to discuss data-related research questions and needs.
Most consultations occur over Zoom, but you may also send your question via email, or schedule an in-person meeting.
Examples of consultation topics
- How to find and select health sciences dataset(s), including how to reuse open public data for research or course assignments
- Best practices for research data management
- How to make your research more open, accessible, and reproducible, and how to make your data FAIR (findable, accessible, interoperable, and reusable)
- Data compliance and governance, including data use agreements, data licensing, and proper data storage methods
- Data processing, analysis, and visualization
- Data tools and software to support your research, such as Python and R
Looking for asynchronous ways of learning? Here are some of our favorite free resources for learning to work with data.
- Learn Python the Right Way [online book] — A free, comprehensive guide to Python and programming in general. If you've taken "Getting Started with Python" at the library, you will already be familiar with Replit, which this book also uses for exercises.
- Python for Non-Programmers [LinkedIn Learning] — Access this free course, aimed at those new to programming, through Yale's subscription to LinkedIn Learning. Like the above suggestion, this course also uses Replit.
- RealPython.com [online resource and tutorials] — An expansive collection of free Python tutorials, as well as other resources like forums, podcasts, and helpful articles.
- Ekmekci B, McAnany CE, Mura C (2016) An Introduction to Programming for Bioscientists: A Python-Based Primer. PLOS Computational Biology 12(6): e1004867 [open-access journal article] — Read this PLOS Computational Biology article for a step-by-step guide to getting started with Python for biological and biomedical use.
- Automate the Boring Stuff [online book] — A free, excellent introduction to all things automation, including web scraping, reminder applications, data formatting, auto-complete forms, and more.
- CS Dojo’s Python Tutorial for Absolute Beginners [YouTube videos] — If you prefer to learn through video, this is a great series.
- Python Documentation — Official Python docs are available at python.org, where you can also find a beginner's guide and many additional resources. We also recommend W3 Schools Python Tutorial as supplementary quick-reference documentation and as a learning resource.
Spreadsheet best practices
- Read this article, Data organization in spreadsheets. Full citation: Karl W. Broman & Kara H. Woo (2018) Data Organization in Spreadsheets, The American Statistician, 72:1, 2-10, DOI: 10.1080/00031305.2017.1375989.
- Bioinformatics Support Hub: provides consultations and training on various bioinformatics topics, as well as free access to popular bioinformatics software.
- StatLab: provides workshops and walk-in help on statistical tools and topics, including R, Nvivo, and data analysis and visualization.
- GIS Support: provides access to GIS software as well as consultations and workshops on GIS topics.
- Yale REDCap Team: A team of experts in data management, programming, ITS engineering, Linux, research project management, and administrative support for the Yale REDCap instance that also provides trainings and other resources.
- Yale Center for Research Computing (YCRC): supports high performance computing needs at Yale through office hours and workshops and has four on-site Linux clusters available for advanced computational projects.
- Yale Center for Biomedical Data Science (YCBDS): research and education hub for biomedical data science on Yale's Medical Campus.
- Research Core Facilities: provides Yale researchers access to scientific instrumentation.
- BD2K Foundations of Biomedical Data Science
- NIH Training Modules to Enhance Data Reproducibility
- NCBI Workshops, Webinars, and Codeathons
- Library Carpentry: training in Git, Unix Shell, Working with Data, and Open Refine
- NIH Pragmatic Trials Collaboratory