In this “Coffee & Learn” session, enjoy some coffee (or bring your beverage of choice from the coffee shop downstairs!) and join us for a 45-minute discussion on completing a data inventory.
Join us for a 1 hour crash course in data cleaning basics with Excel.
Through this workshop, participants will learn*:
- Simple and advanced filtering
- Creating histograms through the Data Analysis Toolpak
- Splitting multi-valued cells and trimming whitespace
- Data validation and custom lists
* Disclaimer: This class is not a general Excel class, but rather a specialized application of Excel. Galter Health Sciences Library & Learning Center does not provide technical support for any Microsoft Products.
Join us for a 45-minute discussion on best practices for file organization.
Through this workshop, participants will:
- Learn best practices for file naming
- Learn best practices for digital folder organization
- Gain tips for adding searchable keywords and tags to files
An introduction to basic concepts in research data management including University retention requirements, data management plan requirements, data documentation, file naming conventions, metadata, and sharing research data.
Upon completion of this one-hour workshop, participants will:
- Understand and be able to apply best practices for file naming and documentation
- Be familiar with basic tidy data best practices
- Be familiar with metadata best practices
- Understand and be able to locate online Federal funder requirements for data sharing
- Be familiar with publication and data sharing tools available both at Northwestern and through the Web
An introductory class in OpenRefine, a free, open-source tool for cleaning data in spreadsheets. No coding knowledge is needed. Familiarity with concepts such as data records and values is helpful.
Upon completion of this 90-minute workshop, participants will:
- Understand how to facet and transform data values
- Understand how to write simple data transformations
- Understand how to retrieve data from APIs
- Understand how to reconcile data against controlled data sources
This one-hour class will provide basic advice and tips for effectively setting up and starting your Excel-based data projects. Through this workshop you will learn:
- Documentation tips
- Blank and duplicate removal
- Conditional calculations
- An introduction to Pivot Tables
Microsoft Excel or a similar spreadsheet program is required for participation.
*Disclaimer: This class is not a general Excel class, but rather a specialized application of Excel. Galter Health Sciences Library & Learning Center does not provide technical support for any Microsoft Products.
Whether building on skills from Galter’s introductory “Cleaning Spreadsheet Data with OpenRefine,” or approaching the powerful data-wrangling tool OpenRefine for the first time, you can gain many new skills in this 90-minute class, including:
- Removing duplicate records
- Joining two OpenRefine projects
- Installing extensions
- Transposing columns to rows
The National Institutes of Health’s (NIH) Final policy for Data Management and Sharing goes into effect January 25, 2023. The policy requires submission of a Data Management and Sharing Plan (DMSP) with all NIH funding applications that outlines how project data and metadata will be managed and shared. In this class, tips will be shared for generating a 1-2 page DMSP for compliance with the NIH policy.
This workshop provides an introduction to databases and the Structured Query Language (SQL) for clinical researchers. Topics covered include:
- Introduction to databases
- Normalization and data modeling
- Introduction to SQL
- Recognizing bad data
- Documentation and metadata
- In-class programming exercises using a mock clinical database
Prior to attending this workshop, participants should install one of the following SQL clients to run example queries in real time:
- DBeaver: Available for Windows, Mac, and *NIX
- SQL Server Management Studio (SSMS): Windows only
The procedure for connecting to the sandbox database will be given by the instructor.
Through this class you will learn about the characteristics of FAIR data (Findable, Accessible, Interoperable, and Reusable) and how your data can be made FAIR in practical ways through institutional repositories such as the one hosted by the Feinberg School of Medicine. Motivations for FAIR data sharing, such as the NIH's Policy for Data Management and Sharing, will also be discussed.