2025 FWS Data Management Workshop

Are you interested in learning how to improve the quality, reliability, and usability of your ecological data? If so, then please join us as we walk through elements of systematic planning – a quality management framework promoted by the U.S. EPA to assist scientists, managers, and practitioners in the development and achievement of established data quality performance standards. This interactive course provides participants the opportunity to engage in discussion and hands-on exercises on the concepts and applications of data quality planning, implementation, and assessment, fundamental to environmental research and monitoring activities. This half-day training session has been designed to enhance competency in both career- and entry-level professionals on the practical applications of quality assurance (QA) and quality control (QC) best practices. Support to this training was funded by the Great Lakes Restoration Initiative. 

Upon completion of this course, participants will be able to:   

• Describe the Purpose of Systematic Planning 
• Describe the Value of a Data Management Plan 
• Develop Meaningful Goals and “SMART” Project Objectives 
• Define Performance/Acceptance Criteria for Indicators of Data Quality (i.e., data quality indicators) 
• Describe the Distinction Between Implementation and Effectiveness Monitoring 
• Describe Why it’s Important to Incorporate “Independent” Oversight 
• Describe Quality Control Procedures Used During Monitoring 
• Describe Why it’s Important to Conduct Data Quality Review 
• Describe the Process Involved in Verifying and Validating Project Data 
• Describe the Purpose of Certifying Your Data 

The intent of this course is to provide an introduction to Quarto for intermediate to advanced R users seeking to develop automated reports. We will cover the basics of Quarto, followed by a walkthrough of the three primary components of a Quarto file: the YAML metadata, text, and code blocks. After discussing each component, we will offer brief exercises that allow participants to explore how these components can customize documents. Our goal is for participants to leave with foundational skills necessary to create a Quarto report.

Learning Objectives 

• Introduce Quarto and its applications.   
• Describe the three components of a Quarto file: YAML metadata, text, and code blocks.  
• Provide opportunities for hands-on exploration of Quarto components through in-class exercises.  

Prerequisites: 

• Intermediate knowledge of R 
• Our course will require having R and R Studio installed on computers. 

Join us to explore how Digital Object Identifiers (DOIs) and Open Researcher and Contributor IDs (ORCIDs) can increase the visibility of your research.  We will cover the basics of obtaining and managing these identifiers, focusing on their benefits for tracking research outputs and improving data sharing.

Audience: Service employees not familiar with scholarly publishing and public access requirements in the Fish and Wildlife Service.  

Objective: Service employees will better understand permanent identifiers for tracking research outputs and improving data sharing with an overall goal of increased open science practices.  

Skills Learned:  Service employees will have a scholarly profile and be able to mint DOI’s for research products  

Materials/Resources:  
Computer, come with one research product: a dataset, a publication, etc. 

This course will cover the fundamentals of Lean, Six Sigma, Theory of Constraints and Continuous Process Improvement (CPI).  

Innovation through CPI is a key skill for effective program and project management and ensuring products remain on time and on budget. These methodologies are applied to risk management, data management, performance improvement, and evidence-based decision making.

The course will be led by Billy Skaradek, a Lean Six Sigma Black Belt with numerous federal process improvements to his credit.

Learning Objectives: Upon completion, you will:

• Understand the key concepts of Lean, Six Sigma, Theory of Constraints, and CPI.
• Have a rudimentary understanding of some tools used in improving processes.
• Assist with CPI process improvement projects as a sponsor or subject matter expert.
• Assist with identification of improvement opportunities.

Target Audience: FWS employees that manage or use routine processes, want to improve efficiency and effectiveness, and are challenged to “do more with less”.

Tidy Data - A Strategy & Mindset - Bonnie Campbell 
FWS gathers a tsunami of data daily. To be usable this data must first be tidied. Tidying allows us to process data more efficiently for understanding and use. This course aims to encourage consideration and use of the tidy data strategy & mindset for the data we use in FWS.

Combines presentation & hands on activities.

Learning Objectives:

• Recognize the difference between tidy & messy data
• Identify & fix issues that make data messy vs. tidy
• Add tools to your quiver for making data tidy

Learn about DOI GitHub Enterprise Cloud (DGEC), related policies, its origin and implementation. DGEC features, access requirements, collaboration, tie-in with code.gov and more. Combines presentation & hands on activities. Learning Objectives: + Know why DGEC is important for your work + Know where to go for DGEC information & support + Know how to obtain access, establish repositories, leverage features + Become familiar with internal & sponsored collaborators workflows Prerequisites: None. Familiarity with code development (including versioning & control) Git, GitHub is a plus. Lab computers require: Git, R, RStudio