The Living Data Project offers annual 3-month courses to train graduate students in Data Management, Reproducibility, Data Synthesis, and Collaboration.
Application links to be posted when applications open.
Applications for the fall course open May 25 to June 30, 2026
Applications for the spring course open Sept 8 to October 8, 2026
Applications for the fall course open May 25 to June 30, 2026
Applications for the spring course open Sept 8 to October 8, 2026
Course Descriptions
reproducible data management
2026-2027 Instructors and mentors: Diane Srivastava (UBC), Sally Taylor (UBC), David Hunt (McGill), Bryan Currinder (UBC)
This course will develop best practices in data management in ecology and evolution research and will include practical experience rescuing a dataset as part of a small team. This is one of the first reproducible data management courses in Canada specifically geared to ecology, evolution and environmental science. We will use a combination of instruction, in-class activities, and small groups to examine all parts of the research data lifecycle, starting with the collection and storage of data, progressing through the organizing of data (database design, “tidy” data principles, data versioning), the cleaning of data (quality assessment, geospatial and taxonomic data standards), and ending with the sharing of data (metadata and documentation, and archiving and accessing data in digital repositories following the new FAIR principles). Each student will work progressively in developing an individual data management plan for their own research data, and will also work in small groups to prepare an existing biological dataset for archiving using R scripts. It is anticipated that students will be able to include the doi for archived datasets on their professional CV. Trainees will also learn how to integrate recommended practices in Open Science into their individual and collaborative research workflows, and use digital platforms and tools to facilitate collaboration, ensure transparency, enable pre-registrations, and implement version control and provenance tracking.
This course will develop best practices in data management in ecology and evolution research and will include practical experience rescuing a dataset as part of a small team. This is one of the first reproducible data management courses in Canada specifically geared to ecology, evolution and environmental science. We will use a combination of instruction, in-class activities, and small groups to examine all parts of the research data lifecycle, starting with the collection and storage of data, progressing through the organizing of data (database design, “tidy” data principles, data versioning), the cleaning of data (quality assessment, geospatial and taxonomic data standards), and ending with the sharing of data (metadata and documentation, and archiving and accessing data in digital repositories following the new FAIR principles). Each student will work progressively in developing an individual data management plan for their own research data, and will also work in small groups to prepare an existing biological dataset for archiving using R scripts. It is anticipated that students will be able to include the doi for archived datasets on their professional CV. Trainees will also learn how to integrate recommended practices in Open Science into their individual and collaborative research workflows, and use digital platforms and tools to facilitate collaboration, ensure transparency, enable pre-registrations, and implement version control and provenance tracking.
collaborative data synthesis
2026-2027 Instructors and mentors: Kerri Finlay (U of Regina), Andrea Paz (UdeM), David Hunt (McGill), Bryan Currinder (UBC), Diane Srivastava (UBC)
This course prepares students to work effectively in small, highly collaborative research teams (working groups) to address complex questions in ecology, evolution, and environmental science.The first part of this course will provide an introduction and overview of approaches for synthesizing the highly structured, multi-sourced datasets that typify ecology, evolution, and environmental research, including data collation, integration, analysis, and visualization. Some of the methods covered will include hierarchical models, meta-analysis, model integration, and model updating, providing students a guide to navigating these methods and identifying methods to use in their research. This course also covers important concepts for effective team science, including: cross-discipline and cross-cultural communication, meeting facilitation, negotiating roles, conflict resolution, team workflow, digital collaboration, authorship, and working group organization. Particular attention will be paid to acknowledging power imbalances and supporting diversity in collaboration. In the second part of the course, students will put these skills into practice in either an in-person or virtual working group. Note that in-person working groups will require full-time attendance at a five day meeting somewhere in Canada, with all travel expenses covered by the CIEE and other funders. Working groups often result in co-authored publications after the course has concluded. Students will also develop skills in R programming, Git version control, collaborative research, reproducible workflows, and data analysis.
This course prepares students to work effectively in small, highly collaborative research teams (working groups) to address complex questions in ecology, evolution, and environmental science.The first part of this course will provide an introduction and overview of approaches for synthesizing the highly structured, multi-sourced datasets that typify ecology, evolution, and environmental research, including data collation, integration, analysis, and visualization. Some of the methods covered will include hierarchical models, meta-analysis, model integration, and model updating, providing students a guide to navigating these methods and identifying methods to use in their research. This course also covers important concepts for effective team science, including: cross-discipline and cross-cultural communication, meeting facilitation, negotiating roles, conflict resolution, team workflow, digital collaboration, authorship, and working group organization. Particular attention will be paid to acknowledging power imbalances and supporting diversity in collaboration. In the second part of the course, students will put these skills into practice in either an in-person or virtual working group. Note that in-person working groups will require full-time attendance at a five day meeting somewhere in Canada, with all travel expenses covered by the CIEE and other funders. Working groups often result in co-authored publications after the course has concluded. Students will also develop skills in R programming, Git version control, collaborative research, reproducible workflows, and data analysis.
Dates and Times
REPRODUCIBLE DATA MANAGEMENT
Fall Session (Sept 8- Dec 3, 2026) (no class Nov 9-13)
*For students in British Columbia and Saskatchewan, the lecture time will be one hour later after clocks change in the rest of Canada at the end of Daylight savings.
- Tue/Thu 09:00 - 10:30 Pacific Time / 10:00 - 11:30 after 01 November*
- Tue/Thu 10:00 - 11:30 Mountain Time
- Tue/Thu 10:00 - 11:30 Saskatchewan Time / 11:00 - 12:30 after 01 November*
- Tue/Thu 11:00 - 12:30 Central Time (Manitoba)
- Tue/Thu 12:00 - 13:30 Eastern Time
- Tue/Thu 13:00 - 14:30 Atlantic Time
- Tue/Thu 13:30 - 15:00 Newfoundland Time
*For students in British Columbia and Saskatchewan, the lecture time will be one hour later after clocks change in the rest of Canada at the end of Daylight savings.
COLLABORATIVE DATA SYNTHESIS
Spring Session (Jan 11 – Apr 7, 2027) (In person working group Feb 15-19) (no class Mar 1-5)
*For students in British Columbia and Saskatchewan, the lecture time will be one hour earlier after clocks change in the rest of Canada at the end of Daylight savings.
- Mon/Wed 09:00 - 10:30 Pacific Time / 8:00 - 9:30 after 14 March*
- Mon/Wed 10:00 - 11:30 Mountain Time
- Mon/Wed 10:00 - 11:30 Saskatchewan Time / 9:00 - 10:30 after 14 March*
- Mon/Wed 11:00 - 12:30 Central Time (Manitoba)
- Mon/Wed 12:00 - 13:30 Eastern Time
- Mon/Wed 13:00 - 14:30 Atlantic Time
- Mon/Wed 13:30 - 15:00 Newfoundland Time
*For students in British Columbia and Saskatchewan, the lecture time will be one hour earlier after clocks change in the rest of Canada at the end of Daylight savings.
Frequently asked questions (FAQs)
Am I eligible to apply?
These courses are open to graduate students in ecology, evolution and environmental sciences affiliated with CIEE member organizations, including specific universities and the Canadian Society for Ecology and Evolution (CSEE) and Canadian Rivers Institute (CRI). You can find a list of CIEE member organizations here. In order to apply through affiliation with the CSEE or CRI, either you or your Faculty Supervisor must be a member of these organizations.
Are these courses virtual?
The ‘Reproducible Data Management’ course is entirely virtual.
The ‘Collaborative Data Synthesis’ course is online/hybrid. Most of this course is delivered online and includes participation in either an in-person five day working group or a virtual working group of similar duration. The in-person working groups will require full-time attendance at a five day meeting somewhere in Canada, with all travel expenses covered by the CIEE and other funders.
The ‘Collaborative Data Synthesis’ course is online/hybrid. Most of this course is delivered online and includes participation in either an in-person five day working group or a virtual working group of similar duration. The in-person working groups will require full-time attendance at a five day meeting somewhere in Canada, with all travel expenses covered by the CIEE and other funders.
How do I get credit for these courses?
Each course is 3-credits. At some universities a 3-credit course may be applied as a Directed Studies. Contact your CIEE University representative to apply for a directed studies credit. A list of CIEE representatives can be found here.
If your application is managed by the Canadian Society for Ecology and Evolution (CSEE) or the Canadian Rivers Institute (CRI), rather than your home university, you will need to find a Faculty member at your university to arrange for Directed Studies credits.
If your application is managed by the Canadian Society for Ecology and Evolution (CSEE) or the Canadian Rivers Institute (CRI), rather than your home university, you will need to find a Faculty member at your university to arrange for Directed Studies credits.
How do I apply?
Click on the link at the top of this page to apply. There is limited enrolment in these courses and participating students will be selected from the pool of applicants; candidates may be waitlisted. Applications will not be accepted after the application close date. Candidates will be contacted regarding the status of their application approximately two weeks after applications close and successful applicants will then receive further instructions on course registration.
Is there a course prerequisite?
You must be comfortable coding in R at an intermediate level which you will be able to demonstrate to us in an online R quiz that you submit at the time of registration. If you are not yet at this level in R, we are happy to suggest some resources for self-study so that you can take these courses next year. We do ask for an intermediate level of R as this is key for efficient collaboration with other students in this course.
At the intermediate R level, you can read in data from csv files, create new variables, and use logical tests (e.g. >, !=). You can run generalized linear models and create and customize a wide variety of plot types. You can also use tidyverse commands to rearrange, summarize and join data tables easily. You can build for loops and use if-else commands. We do not expect that you can write your own functions, use regular expressions or work with lists and apply functions.
At the intermediate R level, you can read in data from csv files, create new variables, and use logical tests (e.g. >, !=). You can run generalized linear models and create and customize a wide variety of plot types. You can also use tidyverse commands to rearrange, summarize and join data tables easily. You can build for loops and use if-else commands. We do not expect that you can write your own functions, use regular expressions or work with lists and apply functions.