Course Overview

Synthesis Skills for Early Career Researchers (SSECR; [SEE-ker]) is a newly-designed course organized by the Long Term Ecological Research (LTER) Network. This course aims to address the need for more comprehensive interpersonal and data skills in ecology. You can find more information on SSECR at the LTER Network page for the course.

If instead you’re interested in joining as a team project mentor you can find more information–and apply–here.

Course Priorities

  • Surface and test new synthesis ideas for feasibility
  • Prepare more graduate students to be effective participants in/leaders of the synthesis projects
  • Connect LTER graduate students across sites
  • Develop intergenerational linkages around synthesis research

Course Description

The course is structured around small group synthesis projects, so that lessons are immediately applied to a synthesis problem that is relevant to learners and will likely result in a publication. The course starts with an in-person launch to establish cohort cohesion and ensure that any setup issues are resolved. Participants will pitch projects to the group and assemble a team of collaborators.The ideal configuration would be 6 project teams of 4-5 students each. Prior to the start of the course, the Network Office will recruit a corps of potential project mentors, who will be matched with participant projects and who agree to meet with students approximately 4-5 times per year.

Applicants will propose modest or exploratory synthesis projects as part of the application process. In addition to ideas stemming from participants’ own work, course mentors will make available a small library of synthesis ideas in search of execution. The in-person kickoff week will focus on cohort-building, pitching projects and assembling project teams, identifying relevant data, and getting set up on servers and collaboration tools.

Course Scheduling

Course participants meet for three hours, weekly, at the same time each week. Sessions alternate between hands-on instruction in technical and soft skills that are relevant for inclusive synthesis and team work on the chosen group projects.

Each three-hour session will include 2 components:

  1. Inspiration (~60 minutes): Presentation by an experienced synthesis scientist, describing why and how they conduct synthesis. This diverse group of researchers will be recruited from across the field and course participants will have ample time for discussion with each presenter.
  2. Instruction (~120 minutes): Each session will focus on a specific instructional topic, with technical skills, team-science skills, and communication topics interspersed throughout the year. The discussion will be limited to official course participants, but instructional materials for each topic will be available online, allowing individuals or site- or topic-based groups to follow along independently.
    • Technical skills will build on earlier lessons and are not intended to be stand-alone modules. The course will include social and leadership skills required to bring a synthesis project from idea to completion (or, for larger projects to completed proposal) and will include techniques for ensuring that multiple thinking and learning styles are respected and valued.

Project groups will also meet at a time of their own choosing to work on projects. Project mentors are encouraged to participate in work sessions at least 4 times throughout the year.

Date Module Primary Instructor(s)
9/5/24 Version Control Nick Lyon; Angel Chen
9/16-20/24 Reproducibility Best Practices Nick Lyon
10/3/24 Data Discovery & Management Li Kui; Marty Downs
10/17/24 Team Sciences Practices Marty Downs; Carrie Kappel
10/31/24 Inclusive Facilitation Carrie Kappel
11/14/24 Data Visualization & Exploration Sarah Elmendorf; Nick Lyon
12/5/24 Project Management Marty Downs
12/19/24 Supporting Divergent, Emergent, and Convergent Thinking Carrie Kappel
1/16/25 Data Wrangling Nick Lyon
1/30/25 Authorship & Intellectual Credit Marty Downs
2/13/25 Analysis & Modeling Nick Lyon
2/27/25 Next Steps & Logic Models Marty Downs
3/13/25 Communicating Findings Gabriel de la Rosa
3/27/25 Reproducible Reports Nick Lyon
4/10/25 Optional Module TBD
4/24/25 Optional Prep TBD
5/8/25 Optional Prep TBD
5/22/25 Final Project Symposium SSECR Participants!

Course Policies

Attendance

If you get sick, observe a religious holiday unaccounted for by the SSECR schedule, have to miss class for an interview, or simply don’t think you can handle class on a given day, please email the course instructors as early as possible to let us know that you won’t be in class with a (brief) explanation. This will help us to share resources we’ll cover in class with you and plan for a smaller in-class community while you are out. Our hope is that this course will be somewhere you want to attend, but we totally understand that you have many demands on your time and sometimes life happens!

Please keep in mind that your presence in and contributions to class are important both to your understanding of the material and the creation and maintenance of an in-class community.

Usability, Accessibility, and Design

We are committed to creating a course that is inclusive in its design. If you encounter barriers, please let the instructors know immediately so that we can determine if there is a design adjustment that can be made or if an accommodation might be needed to overcome the limitations of the design. We are always happy to consider creative solutions as long as they do not compromise the intent of the learning activity. We welcome feedback that will assist us in improving the usability and experience for all students.

Artificial Intelligence Tools

Artificial intelligence (AI) tools are increasingly well-known and widely discussed in the context of data science. AI products can increase the efficiency of code writing and are becoming a common part of the data science landscape. For the purposes of this course, we strongly recommend that you do not use AI tools to write code. There is an under-discussed ethical consideration to the use and training of these tools in addition to their known practical limitations. However, the main reason we suggest you not use them for this class though is that leaning too heavily upon AI tools is likely to negatively impact your learning and skill acquisition.

You may have prior experience with some of the quantitative skills this course aims to teach but others are likely new to you. During the first steps of learning any new skill, it can be really helpful to struggle a bit in solving problems. Your efforts now will help refine your troubleshooting skills and will likely make it easier to remember how you solved a given problem the next time it arises. Over-use of AI tools can short circuit this pathway to mastery. Once you have become a proficient coder, you will be better able to identify and avoid any distortions or assumptions introduced by relying on AI.

AI Resources

Name, Gender Identity, and/or Gender Expression

You provided a name when you first applied to be a part of the course but we will gladly honor your request to be addressed by an alternate name. We will also use whichever pronouns you identify with. Please advise us of your pronouns and/or chosen name early in the course so that we can ensure that we treat you respectfully throughout the course.

Code of Conduct

Group work is a significant part of this course explicitly in the synthesis project facet as well as implicitly by the collaborative nature of many of the modules. We expect that you will be mutually respectful with one another both in and outside of class time. We will ask you questions during the course and during class is also an ideal time for you all to ask us questions that you have on course topics or policies. We don’t believe that “dumb questions” exist, and expect that you treat your peers’ questions with the respect you’d like your questions to be with. We will learn more together in an environment where we build one another up than we would in one where we fail to support one another.