News

​We are currently seeking senior researchers at or near retirement, or organizations, with legacy datasets to take part in our program. We will match you with a trained graduate student who will collaborate with you to archive your dataset using best practices. Contact any of the organizers to discuss the suitability of your dataset, see also criteria [below] and FAQs [below]. 

Organizers

Diane Srivastava, CIEE Director and Professor UBC Zoology 
Mary O’Connor, CIEE Deputy Director, Assoc. Professor UBC 
Sally Taylor, Research Data Librarian and Adjunct Professor, UBC 
Timothée Poisot, Assistant Professor, Biological Sciences, Université de Montréal 
Kerri Finlay, Assistant Professor, Biology Univ. of Regina 
Raymond Ng, Director Data Science Institute, Professor Computer Science, UBC
Jason Pither, Co-lead Fostering Open Science at UBC, Associate Professor, Biology, UBC Okanagan
Jennifer Sunday, William Dawson Scholar and Assistant Professor, Biology, McGill University
Laura Pollock, Assistant Professor, Biology, McGill University
Gavin Simpson, Adjunct Professor, Biology, Univ. Regina
Éric Harvey, Assistant Professor, Biological Sciences, Université de Montréal

living data project articles
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Read this article about the career and contributions of renowned ecology researcher Dr. Tony Sinclair.  As part of the Living Data Project,  PhD student Francisco Henao Diaz worked with Tony to preserve decades of Serengeti bird data.

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Read this article about the career and contributions of ecology researcher Dr. Roy Turkington.  As part of the Living Data Project,  PhD student Sandra Emry worked with Roy to preserve data on understory plants in the Yukon.
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Read this article about marine bird surveys in Stanley Park, Vancouver, BC, dating back to 1995.  As an intern of the Living Data Project PhD Student Jenny Munoz assisted with this comprehensive dataset.
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Read this article about the Green River Project which ran from 1945 to 1959 in northwestern New Brunswick following a widespread spruce budworm outbreak.   As an intern of the Living Data Project MSc Student Skye Butterson assisted with this dataset.

living data project information

Criteria for legacy datasets
1.     The dataset includes biological data that is relevant to ecology, evolution or environmental science. The dataset does not need to be entirely biological; physical and chemical data is acceptable if in the context of an ecological or evolutionary process. For example, water chemistry is an important part of understanding aquatic ecology.
 
2.     The dataset should be important in one of the follow ways: (1) extensive in either space or time (e.g. bird surveys over more than a decade, or national surveys of caribou density); (2) describes a study that was ground-breaking in the history of science; (3) concerns a species or ecosystem that is considered at risk in Canada, or of high cultural or societal value.
 
3.     Priority will be given to datasets that concern a Canadian species or ecosystem, or have been collected by a Canadian researcher or Canadian organization.
 
4.     The owner of the dataset commits the data to be permanently archived, and open and accessible. We realize that some dataset owners may need to temporarily delay the publication of their data, and in such cases we may consider a short-term embargo.​
Frequently Asked Questions by data owner
1.     Is the Living Data Project setting up its own repository?
No. There are now a large number of excellent data repositories that have been set up by government agencies, university libraries, synthesis centres, non-profit organizations and international consortia. We will work with you to decide what repository fits your dataset best – this is your data, and you can decide on its home as long as the repository meets our criteria for long-term digital archiving. We also realize that some employers (e.g. Canadian government) mandate particular repositories.
 
2.     What if the data owner is no longer alive or the data belonged to a defunct organization?
No problem. In this case, the intern will be doing a lot of data forensics, piecing together the meaning of each variable by cross-referencing other sources or by interviewing contemporaneous researchers.
 
3.     If I make my data public, won’t other people use it erroneously because they do not understand the context?
Misuse of data is a valid concern, but we have an important tool to combat it: metadata. You and your intern will work together to develop detailed descriptions (metadata) of each variable in the dataset to explain the complexities of each variable. Furthermore, we have the ability to create additional variables that describe any heterogeneity in data quality or context. The goal is to provide enough information about the data that no further explanations are needed to use it.  
 
4.     I can’t make my data public yet because I still have publications planned.
Just because your data is archived doesn’t mean that you cannot publish papers based on it. In fact, many journals now required data to be properly archived before accepting a manuscript for publication. You may also worry that another researcher will ‘scoop’ you on your own data. This is often more fear than fact. Instead, what tends to happen when other researchers see your data carefully organized is that you receive more invitations for collaborations.
 
5.     If I make my data public, won’t I lose credit for all the time I invested in collecting this data?
Not necessarily. You can decide the type of Creative Commons open licence associated with your archived dataset. If you choose a CCBY licence, future researchers are required to acknowledge you as the data collector.
 
6.     I already have planned to give my data to a younger colleague so I don’t need to archive it.
That’s great that you have found such a colleague. However, it would be much more valuable to pass on this dataset if it was already properly documented, validated, organized and archived – otherwise they will be stuck doing this without your valuable input or, even worse, not at all. We would be happy to work with both you and your junior colleague to design your data archiving project so that it meets both of your needs as well as that of the greater research community.
 
7.     If my data is in [database program X], isn’t it already archived?
Unfortunately not. If it isn’t in a data repository, in a simple machine-readable format like a .csv or a .txt file, then it isn’t futureproof. Any data held in a proprietary software package may become unreadable if that package is no longer supported by the software company or the company ceases to exist. Remember those Lotus 1-2-3 files you used to have? Good luck opening them.
 
8.     What will I get out of this?
A lasting legacy for your research career, and the satisfaction that your data will continue to inform future generations of scientists. You will also have a career summary posted on the CIEE – Living Data Project website honouring your achievements and the importance of your dataset.

Available internships
​details coming
Courses, workshops and online training
The Living Data Project offers these four one-month courses, all presented virtually in fall 2020: 

Productivity and Reproducibility in Ecology & Evolution Tu Th 9:00-10:30 PDT. Sept 28-Oct 30 (no class Oct 19-23) 

Scientific data management for ecology and evolution Tu Th  10:30-12:00 PDT. Nov2-Dec4 (no class Nov 9 -13). 

** the above two courses satisfy in part the requirements for the CIEE “Certificate in Data Management and Reproducible Research.” 

Synthesis Statistics for Ecology and Evolution Tu Th 10:30-12:00 PDT.  Sept 28-Oct 30  (no class Oct 19-23). 

Scientific collaboration in Ecology, Evolution and Environmental Science Tu Th 9:00-10:30 PDT Nov2-Dec4 excl. the week of Nov 9.  

**the above two courses satisfy in part the requirements for the CIEE “Certificate in Synthetic and Collaborative Science.”
 
Eligibility. These courses are open to graduate students conducting thesis research in ecology, evolution or environmental science at any CIEE member university (see list here).  Several courses also require basic coding skills (see syllabus for each course). Information for course registration is being circulated internally by your university.

Obtaining credit for courses. Course credits are currently being offered for students from these universities: McGill, Université de Montréal, UBC (Vancouver and Okanagan) and University of Regina. Students in other CIEE universities seeking course credits should contact their CIEE Management Board representative (here). Normally each module is 1 credit. In universities without 1-credit graduate courses, students may need to bundle three modules into 3 credits. 

Certificate program
Why?
Dramatic shifts in the scientific workflow mean that Canadian graduate students urgently require high-demand skills from data management, reproducible research, scientific synthesis and team science in order to remain globally competitive. However, there is currently no formal program for certification in these competencies. Our nationwide certificate program will provide highly sought-after qualifications for both the Canadian and global job market.

The certificates.
The CIEE offers two certificate programs: Certificate in Data Management and Reproducible Research and Certificate in Synthetic and Collaborative Science. Each Certificate program consists of two courses (12 hours each) and one immersive activity (either a data rescue internship or working group participation). Both certificates also require EDI training and participation in a workplace transition evening. You will be mentored by Living Data Project leaders throughout your certificate, and we will help you discover other customized training that can complement these elements. 
Certificate in Data
Management and
Reproducible Research
Certificate in Synthetic and
Collaborative Science
Technical skills course
(mandatory)
Scientific data management
for ecology and evolution
Synthesis statistics for ecology
and evolution
Professional skills course
(mandatory)
Productivity and
reproducibility in ecology and
evolution
Scientific collaboration in
ecology and evolution
Immersive activity
(mandatory)
Data rescue internship
Working group participation
Customized training
(university-specific
courses, QCBS, Mitacs)
Data wrangling, high
performance computing,
statistics
Scientific writing, data
visualization, project
management
EDI training (mandatory)
Unconscious bias and cross-cultural communication modules
National workshops
(optional)
Data management, synthesis, collaboration skills
Workforce transition
evenings (mandatory)
Roundtables with representatives from non-academic sectors
​Eligibility. 
Funding for data rescue internships is currently restricted to graduate students in McGill, Université de Montréal, UBC (Vancouver and Okanagan) and University of Regina. Interested students from other universities currently will need to find independent funding for the data rescue internship. Working group participation and technical and professional skills courses are open to graduate students in any CIEE member university. Students who have completed coursework in a certificate program will be prioritized for internships and working groups. We recognize that some students may have constraints (e.g. research-related, family-related, etc.) around their ability to fulfil all certificate elements in a certain order; in such cases, we can be flexible and/or consider equivalencies in training – please contact us to discuss.
​We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).
Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG).

 We acknowledge the support of this research project the by the University of British Columbia, McGill University, the Université de Montréal, and the University of Regina.