NSERC-CSE Research Communities grants
Call for joint NSERC-CSE Research Community projects focused on Exploratory analysis for unstructured data

Overview

Policy on Sensitive Technology Research and Affiliations of Concern

To ensure that the Canadian research ecosystem is as open as possible and as safeguarded as necessary, the Government of Canada has introduced the Policy on Sensitive Technology Research and Affiliations of Concern (STRAC Policy). The STRAC Policy addresses risks related to Sensitive Technology Research Areas performed with research organizations and institutions that pose the highest risk to Canada’s national security. The STRAC Policy applies to this funding opportunity.

At the Step 2 - Full application stage, applicants must identify whether the grant application aims to advance a Sensitive Technology Research Area (STRA). If so, the submission of attestation forms will be required from researchers with named roles (applicants, co-applicants, and collaborators) to certify that they are not currently affiliated with, nor are in receipt of funding or in-kind support from, a Named Research Organization (NRO). Refer to the relevant FAQ for instructions on how to submit the relevant Attestations for Research Aiming to Advance Sensitive Technology Research Areas.

The Tri-agency guidance on the STRAC Policy provides more information on applicable procedures and requirements, including new responsibilities of researchers and responsibilities of institutions. For more information about research security at the granting agencies, refer to the Tri-agency guidance on research security.

On this page

• Background
• Objectives
• Context
• Funding value and duration
• Applicants
• Application
• Equity, diversity, and inclusion
• Award and reporting
• Conflict of Interest (COI)
• Resources
• Contact

Background

The Natural Sciences and Engineering Research Council of Canada (NSERC) and the Communications Security Establishment Canada (CSE) are partnering to fund Research Communities to conduct unclassified research on cutting-edge technologies in areas of strategic importance to CSE and the Government of Canada. A Research Community is defined as a group of researchers – and their research personnel (i.e., students, post-doctoral fellows, and research professionals) – from multiple Canadian universities working in related domains and sharing NSERC‑CSE funds awarded to their project(s). The focus of this funding call is on exploratory analysis for unstructured data. CSE is the sole partner organization; no additional partner organizations will be accepted.

CSE is Canada’s foreign signals intelligence agency and technical authority for cyber security and information assurance. CSE is home to the Tutte Institute for Mathematics and Computing (TIMC), where researchers work with government, academia, and industry to tackle scientific challenges related to CSE’s mission. TIMC’s data science team is focused on the underlying mathematical theory of exploratory data analysis and the implementation of that theory in scalable, broadly applicable tools. In support of CSE's mandate, these tools support the analysis of cyber defence and signals intelligence data.

Objectives

• Advance research in fundamental algorithms and tools for exploratory analysis of large unstructured datasets.
• Develop a sustainable, open-source software ecosystem for exploratory analysis of large unstructured datasets.
• Grow and expand an interdisciplinary community that includes, for example, mathematicians, computer scientists, visualization, and computer interaction experts to work together on exploratory analysis of unstructured datasets.
• Establish Canada and Canadian Universities as leaders in unstructured data analysis.
• Support the development of highly qualified personnel working in the field of exploratory data analysis.

Context

Exploratory data analysis is crucial to gain insights into the data used to build a model. Effective data curation and data quality assessment — both essential prerequisites for robust data science and machine learning inquiry — require flexible methods for exploratory data analysis. For classical tabular data, the analysis can be achieved through various summary statistics and statistical visualizations. In the modern era of unstructured data, such as text, images, and other diverse data types, this approach to exploratory data analysis is insufficient. Fortunately, recent advances in vector representations of unstructured data offer a potential path forward.

Vector representations, combined with effective tools for search, clustering, and visualization, can provide an effective workflow for exploratory analysis of unstructured data. Much of the work in this area is new, and integrating different components into a cohesive workflow remains challenging. Through this grant, CSE is seeking new research and development in both the building blocks for tasks required to perform exploratory analysis of unstructured data and the integration of those tasks into an overarching workflow.

The techniques developed through this grant should be general and have the potential to work with many data types (e.g., text, image, audio, video, computer system log data, source code, compiled binaries). Moreover, CSE would like to foster the growth of an open-source software ecosystem implementing these techniques.

Below are some of the components that CSE envisions as crucial to an effective workflow for exploratory analysis of unstructured data. Proposals that cover the interaction of several components or provide other holistic solutions to exploratory analysis will be favoured.

• Vectorization of unstructured data to enable downstream work, including both deep learning-based approaches (already effective for natural language, images, and audio) and more traditional methods applicable to unique or novel data types such as computer process logs, software binaries (especially malware), and software source code. Multi-modal approaches that can work across and fuse diverse data types are also of interest.
• Approximate nearest neighbour (ANN) search to enable efficient searching of large vector stores, with a particular focus on scalability, distributed systems, and Graphics Processing Unit (GPU) acceleration. This can include approaches to extending ANN in various ways, including constrained search, adaptive-k search, and hybrid search using ANN-based techniques.
• Dimension reduction for both clustering and visualization with a particular interest in neighbour-based methods that can scale to very large datasets and still provide good clustering and/or effective visualizations for exploration, such as FiT-SNE, UMAP and Minimum-Distortion Embedding.
• Clustering and outlier detection for summarization and simplification, such as techniques that are noise tolerant, hierarchical, or multi-resolution, and highly scalable (e.g., DBSCAN and HDBSCAN). Areas of particular interest are effective clustering techniques for high-dimensional data from vectorization/embedding methods that do not require dimension reduction. Support for other features, such as soft or overlapping clusters or fast cluster inference on new data, are also viewed as valuable.
• Interactive Visualization of entire datasets that allows for easy exploration. This requires interactivity and benefits from annotation methods and summarization-on-demand. Such visualization methods should also be able to receive feedback, which can be incorporated into earlier component computations.
• Disposable local models for rapid hypothesis testing. These models need to be simple, robust, explainable, and almost parameter free to provide a simple interface and rapid feedback, ideally within the visualization tool. Early work in this direction includes tools like Snorkel. Simpler and robust models that can be tightly integrated into the workflow are particularly interesting.

CSE is seeking solutions to exploratory analysis of unstructured data that are both grounded in mathematical theory while also being practical on realistic data. Consequently, CSE’s objective is the development of a new, novel, and interdisciplinary Research Community that includes, for example, teams composed of machine learning experts, software engineers, mathematicians and visualization and human-computer-interaction experts. Applications with a clear plan to grow a broader, sustainable community of interest that will endure beyond the conclusion of the grant period will be of particular interest.

Funding value and duration

Individual projects can request either $700,000 (small project) or $1,400,000 (large project) per year over four years from NSERC. Grant funding is provided in equal parts by NSERC and CSE. The aim is to fund one large or two small projects per call. The NSERC-CSE Research Communities grant(s) focused on exploratory analysis for unstructured data will be awarded in May 2025.

First, applicants will be asked to submit a letter of intent (LOI) that will be screened by NSERC and CSE. Based on the screening process, selected applicants will be invited to submit a full proposal to the Alliance grants program. NSERC will administer Alliance grant applications and subsequent funding. See Application for details.

Funds will be administered according to NSERC’s use of grant funds guidelines, outlined in the Tri-agency Guide on Financial Administration.

Applicants

This Research Communities grant(s) will support unclassified research focusing on exploratory analysis for unstructured data. However, the funding is conditional on the applicant or one of the co-applicants and some of the Research Community members (i.e., professors, students, and post-doctoral fellows) travelling to Ottawa to conduct classified research at CSE throughout the grant period. See Classified research for details.

Unclassified research

Canadian university researchers must be eligible to receive NSERC funds. To qualify as a Research Community for the purposes of this grant offering, the list of co-applicants must include eligible academic researchers from multiple Canadian universities working in related domains. However, only one application per researcher will be accepted under this call for proposals (as either applicant or co-applicant).

CSE reserves the right to review and approve the list of Research Community members, including collaborators, prior to their engagement with the research team.

Classified research

In collaboration with CSE researchers, the applicant or one of the co-applicants and some of the Research Community members will be required to work on classified research at CSE facilities in Ottawa. However, the Alliance proposal will only describe the activities and the budget related to the unclassified component of the research.

Applicants must ensure that they can respect the following conditions, at the risk of having their LOI rejected or their grant terminated:

• When submitting the LOI, the applicant, or at least one of the co-applicants, must be a Canadian citizen, eligible for a TOP SECRET security clearance, and willing to work in Ottawa at CSE for a minimum of two weeks per year. The identified researcher(s) must maintain their security clearance for the entire grant period.
• Within one year of the grant award, and in the subsequent years in which the grant is held, the grant recipient must submit the names of additional Research Community members (i.e., professors, students, and post-doctoral fellows) who are Canadian citizens, eligible for a TOP SECRET security clearance, and willing to work in Ottawa at CSE.
  • Students are welcome under CSE’s Student Program and will be expected to complete at least one semester of work at CSE. See Additional details.
  • Post-doctoral fellows and professors are welcome through CSE’s Interchange Program; the length of their visits will be variable. See Additional details.

Application

Step 1 – Letter of intent (LOI)

To engage CSE in supporting their research project, applicants must submit an LOI via NSERC’s online system using the LOI template.

LOIs must be received by NSERC by October 16, 2024, before 8:00 p.m. (ET). Important: Institutions may have an earlier deadline for applicants to submit their LOIs in the system in order to forward them by NSERC's deadline. Applicants should contact their research grants office for that internal deadline to submit the LOI.

The LOI must not exceed four pages, excluding references, and must:

• Describe the research team and identify the researcher(s) (applicant and/or co-applicant(s)) who are eligible for a TOP SECRET clearance. The research team must include researchers (co-applicants) from multiple Canadian universities. It is important to note that applicants who will be invited to submit a full application (see Step 2) will not be allowed to make any changes to the research team (applicant and co-applicants) once it has been reviewed and approved by NSERC and CSE at the LOI stage.
• Outline the proposed research and explain how the project will respond to the specific research objectives of this call.
• Include a summary of the proposed research project’s main objectives and challenges and the expected outcomes and benefits for Canada. Also, describe how the collaboration with CSE will have a positive impact on the proposed research.

In addition to a completed LOI template, applicants must provide the following documents and information to NSERC via the online system:

• A preliminary budget (in Canadian dollars) and accompanying justification to cover the direct cost of the proposed research according to eligible expenses listed in the Tri-agency Guide on Financial Administration. The total Amount requested from NSERC can be either $700,000 (small project) or $1,400,000 (large project) per year over four years. These amounts include CSE’s contribution to the grant funding.
• A completed and up-to-date personal data form with CCV attachment (NSERC form 100A) for the applicant and all co-applicants. The Contributions to research and training must be addressed in the three parts as per the instructions.

Instructions for submitting documents and information to NSERC:

• Log in to NSERC’s online system and select Create a new form 101
• Select Research partnerships programs, then Alliance grants
• For the Proposal type field, select Letter of Intent
• For the Type of call field, select CSE – Research Communities from the drop-down menu

NSERC will review eligibility requirements, including eligibility of the proposed expenses and eligibility of the applicant and the co-applicants to receive funds from NSERC under the Alliance grant program. The NSERC eligibility criteria for faculty apply. CSE will review the LOI to determine which proposals meet the objectives of the call based on the following criteria:

• Novelty of the proposed research
• Alignment of the proposed research with the call
• Potential to create a new, novel, interdisciplinary and impactful Research Community
• Practical applicability of expected outcomes
• Collaboration with CSE

Step 2 – Full application (by invitation only)

Based on the review of the LOI, applicants may be invited to proceed with a full application. NSERC will send a letter of invitation to applicants and only applications that have been invited to submit will be accepted.

The letter of invitation will provide instructions on how to apply to this call under the Alliance grants program. Applicants invited to submit a full application will also receive a template to use for their proposal, as well as the established merit indicators that will be used to assess the merit evaluation criteria listed below. Full applications must be submitted via NSERC’s online system.

Invited university researchers can apply for a four-year Alliance grant. Individual projects can request either $700,000 (small project) or $1,400,000 (large project) per year over four years from NSERC. Grant funding is provided in equal parts by NSERC and CSE. CSE is the sole partner organization; no additional partner organizations will be accepted on the application.

The deadline to submit the full application is February 26, 2025, before 8:00 p.m. (ET).

Full applications will first undergo an administrative assessment by NSERC to ensure they are complete and comply with all requirements. Once the administrative assessment is satisfactorily completed, NSERC will conduct a merit assessment of the applications through an Evaluation Committee of external peer reviewers with expertise directly related to this call. The merit of the full applications will be assessed against the following four equally weighted evaluation criteria:

1. Relevance and expected outcomes
   • Significance of the intended outcomes and benefits for Canada
   • Innovativeness of the proposed research and its potential to lead to advancements or new knowledge
   • Alignment with the objectives and/or research topics of the call
2. Proposal
   • Clarity of the objectives and deliverables; appropriateness of the scope and size of planned research activities to achieve the expected outcomes; quality and feasibility of the research proposal
   • Appropriateness of, and justification for, the planned expenditures
3. Project team
   • Appropriateness of the expertise of the proposed Research Community for carrying out the planned research activities, as well as for managing the project and providing training
   • Clarity of the individual contributions to the research effort and added value of the proposed Research Community in achieving the desired outcomes
   •  Steps taken to ensure effective collaboration with CSE
4. Training plan
   • Opportunities for enriched learning experiences for research trainees (undergraduates, graduates, post-doctoral fellows) to develop relevant research skills as well as professional skills such as leadership, communication and collaboration
   • Consideration of equity, diversity and inclusion in the training plan (assessed by NSERC staff; for guidance, consult the Equity, diversity and inclusion in your training plan web page)

Upon completion of the external peer review, NSERC will determine the merit of the proposal by applying merit indicators based on the assessment of the evaluation criteria by the Evaluation Committee and establish a list of fundable applications from which CSE will select the one(s) to be funded based on the following additional criteria:

• Alignment of the proposed research project with topic of the call, including practical applicability of expected outcomes
• Commitment to collaborate with CSE and alignment with CSE activities
• Potential to create a new, novel, interdisciplinary and impactful Research Community

The information provided in the application is collected under the authority of the Natural Sciences and Engineering Research Council Act. NSERC is subject to the Access to Information Act and the Privacy Act. The information provided is stored in a series of NSERC data banks as described in Information about programs and information holdings.

Equity, diversity, and inclusion

NSERC is acting on the evidence that achieving a more equitable, diverse, and inclusive Canadian research enterprise is essential to creating the excellent, innovative, and impactful research necessary to advance knowledge and understanding, and to respond to local, national, and global challenges. This principle informs the commitments described in the Tri-agency Statement on Equity, Diversity, and Inclusion (EDI) and is aligned with the objectives of the Tri-agency EDI Action Plan.

Excellent research considers EDI both in the research environment (forming a research team, student training) and in the research process. For Alliance grants, EDI considerations are currently evaluated in the training, mentorship and professional development opportunities for students and trainees. The aim is to remove barriers to the recruitment and promote full participation of individuals from underrepresented groups, including women, Indigenous Peoples (First Nations, Inuit, and Métis), persons with disabilities, members of visible minority/racialized groups and members of 2SLGBTQI+ communities. Applicants are encouraged to increase the inclusion and advancement of underrepresented groups as one way to enhance excellence in research and training. For additional guidance, applicants should refer to Alliance grants: Equity, diversity and inclusion in your training plan and the NSERC guide on integrating equity, diversity and inclusion considerations in research.

Award and reporting

NSERC and CSE aim to announce funding decisions by May 2025.

Those awarded grants will be required to provide periodic progress reports on the financial use of grant(s) and on research progress/results, and a final report once the project is completed. Details on reporting requirements, including scheduling, will be provided with the award letter and the terms and conditions of the award.

Conflict of Interest (COI)

Researchers must transparently disclose any COI to their institution and all participants in the proposed research activities, including trainees. When necessary, researchers must take steps to minimize and manage these conflicts according to their institution’s COI policy and disciplinary standards. All participants should always strive to avoid COI.

To document and mitigate any potential COI, any prospective Research Community members must disclose any previous partnerships (e.g., research, contractual) with CSE when submitting the LOI.

Resources

• NSERC-CSE Research Community grants terms and conditions of applying for applicants
• NSERC-CSE Research Community grants terms and conditions of award

Contact

Email: alliance_cse-cst@nserc-crsng.gc.ca