Institutional Research Data Management Strategy
Introduction
Acronyms and definitions are described at the end of this document.
Research data management (RDM) encompasses the processes and procedures “applied through the lifecycle of a research project to guide the collection, documentation, storage, sharing and preservation of research data.”1 The importance of research data and RDM are growing amongst the global research community. RDM best practices address developments in the research landscape such as drives for transparency and reproducibility, concerns about privacy and security, and efficient use of research funds. All those who work with research data, including researchers, students, and staff, apply RDM processes and procedures in their work to varying degrees of formality. Concordia University of Edmonton (CUE) recognizes the importance of research data and commits to implementing and supporting best practices in RDM by developing an institutional RDM strategy by March 1, 2023, as required by the Tri-Agency.
Research Excellence
Globally, research funders as well as the research community are prioritizing open scholarship, data management planning, data sharing, and broad reuse of data where possible. There is an increased interest in interdisciplinarity and regional, national, and international collaboration in research across all fields. Thus, proper research data management helps ensure that expectations of best practices; ethical, legal, and commercial obligations; timeliness; findability and accessibility (e.g. the FAIR principles);2 and acknowledgement and citation are respected and facilitated. Research excellence also requires research integrity; therefore, CUE recognizes that research data, the methods that produced them, and the metadata that describes them need to be compiled, archived, and made shareable to allow research verification and reproduction.
While openness and sharing are integral to all research, CUE further recognizes that not all data is suitable to be shared publicly. Research data is valuable; thus, considerations are made for ethics (i.e.: protection of research participant anonymity), intellectual property, and commercial agreements when stewarding data.
Indigenous Data Sovereignty
Indigenous communities have the rights to self-governance and authority to control their cultural heritage embedded in their languages, knowledges, practices, technologies, natural resources, and territories. This includes sovereignty over their data, acquired through research practices. CUE affirms that a non-standardized, context-based approach is needed to ensure that the unique rights, interests, and circumstances of the First Nations, Métis and Inuit are acknowledged, affirmed, and implemented with regards to research and research data management.
This strategy is only one step of an ongoing discussion around Indigenous data sovereignty at CUE, and Indigenous research more broadly. CUE is committed to working with Indigenous rights-holders to ensure that all research done in collaboration with Indigenous communities is community-led, and that Indigenous data will be managed in accordance with data management principles developed and approved by those communities, and on the basis of free, prior, and informed consent. The OCAP®3 and CARE4 principles are a starting point; the university acknowledges that alternate, non-standard paths may be necessary for the respectful management of Indigenous data.
Oversight and Implementation
The Office of Research is responsible for the development and execution of CUE’s institutional RDM strategy, in consultation with stakeholders across campus. Internal and external partnerships and collaborations have been and continue to be sought for the implementation of each section of this strategy.
Strategy
PART A: RDM GOVERNANCE
| Objective(s) | Current State | Gaps | Goals |
| A.1. Working Group for CUE’s RDM strategy implementation | Office of Research, specifically RDM Manager and AVPR, with support from the library, have consulted with stakeholders across campus to understand current state and needs for RDM preparedness. | No working group for the implementation of the RDM strategy has been incepted. | Strike a working group after dissemination of CUE’s RDM Strategy on or before March 1, 2023. |
| A.2. Institutional policies, procedures, and guidelines | CUE has some policies and forms related to collaborative research. | No institutional-wide policies exist that specifically target RDM practices. | Review current institutional policies and identify any that are required to manage research data, including research data acquired with and from Indigenous communities. |
| A.3. Regional and national participation in RDM networks and events | CUE provides RDM support and leadership for small Canadian institutions, primarily in Alberta, through the ARMIN network. Ad hoc participation in other networks (e.g. Borealis, DRAC, etc.) occurs through CUE’s Office of Research, Library, and others. | All participation happens on an individual basis, rather than from an institutional perspective. ARMIN’s grant funding ends February 2023. | Determine value of formal participation in RDM networks and events with an institutional perspective, including knowledge exchange. Assess the future of ARMIN post RDM strategy dissemination. |
| A.4. Periodic appraisal and risk assessment of CUE’s RDM strategy | CUE is working on the development of its first RDM strategy, which will become publicly available on March 1, 2023. | CUE is in the process of generating its RDM strategy, and therefore has not yet outlined its assessment. | Develop an appraisal and risk assessment plan that involves end-users at CUE. Update the strategy, along with associated action plans, as needed. |
PART B: RDM AWARENESS
| Objective(s) | Current State | Gaps | Goals |
| B.1. Institutional support and training | Through the Library and the Office of Research, CUE has institutional accounts for Borealis and the DMP Assistant, respectively, as support for DMPs and data deposit. Ad hoc support may be provided as requested. | CUE does not have a formal institutional RDM training program. | Develop, implement, and update (as needed) an institutional RDM training program specific to the role (i.e.: student, faculty, staff). Generate field-specific training, as needed. Outline an effective communication plan on RDM requirements, training opportunities, support systems, etc. available to the CUE community. |
| B.2. Development of data management plans (DMPs) | The Office of Research holds an administrative account on DMP Assistant (a national, online, bilingual, free data management planning tool) developed to assist researchers in preparing data management plans. | The CUE community is not currently required to generate formal DMPs, whether for internal or external funding opportunities. No CUE-specific DMP templates have been generated. | Evaluate the need for CUE-specific templates on DMP Assistant. Work with internal and external stakeholders to determine the DMP integration process, as needed, for funding and other purposes |
| B.3. Data acquisition | Research data is acquired in various ways, using a variety of tools, which may or may not be field-specific. | CUE does not have common or standard recommendations for research data acquisition, although at the department/unit level, there may be some common practices adopted by students, faculty or staff. | Assess the need for an institutional approach/ recommendation for electronic or physical data acquisition, including data storage, security, and student-specific guidelines. |
| B.4. Data curation | In CUE’s Borealis account, CUE’s Research Data Manager has curated and deposited pilot studies. | Currently, CUE has no formal guidelines or workflow for data curation. | Establish guidelines and workflows for curating data, as needed, while identifying institutional unit resources that may be required. |
| B.5. Data repositories and archiving | Through the Library, CUE has an institutional account with the Borealis research data repository. | No formal institutional guidelines or workflow for depositing and/or archiving digital and/or physical data. | Develop and implement policies (with associated procedures), as well as guidelines and workflows, for depositing data to the institutionally-supported repository (i.e.: Borealis). Determine if Indigenous research data requires its own policy(ies) or guideline(s). |
Acronyms & Definitions
CARE Principles for Indigenous Data Governance
The CARE Principles for Indigenous Data Governance are Collective Benefit, Authority to control, Responsibility, and Ethics. These principles are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles (www.go-fair.org) encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.5
Data
Data are facts, measurements, recordings, records, or observations collected by researchers and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of text, numbers, symbols, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, workflows, equipment descriptions, data files, data processing algorithms, software, programming languages, code, or statistical records.6
FAIR Guiding Principles for Scientific Data Management and Stewardship
Guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.7
The First Nations Principles of OCAP®
The First Nations principles of Ownership, Control, Access, and Possession – more commonly known as OCAP® – assert that First Nations have control over data collection processes, and that they own and control how this information can be used. OCAP® is not a four-criteria shopping list that researchers or others can check off according to their own standards. OCAP® must be understood through the lens of First Nations. This must involve consideration of First Nations governance structures, values, history, and expectations.8
Indigenous Data
Indigenous data includes any information or data collected, created, or held by an individual or organization, now or in the future, that is capable of identifying Indigenous communities, First Nations membership, Indian status, or residence in an Indigenous community. Indigenous data may also include any information or data generated by an Indigenous researcher. Data may be a wide variety of formats, inclusive of digital data and data as knowledge and information.9
Research Data
Research data are data that are used as primary sources to support technical or scientific enquiry, research, scholarship, or creative practice, and that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results. Research data may be experimental data, observational data, operational data,
third party data, public sector data, monitoring data, processed data, or repurposed data. What is considered relevant research data is often highly contextual, and determining what qualifies as such should be guided by disciplinary norms.10
Research Data Management
Research data management (RDM) refers to the processes applied through the lifecycle of a research project to guide the collection, documentation, storage, sharing and preservation of research data.
RDM is essential throughout the data lifecycle—from data creation, processing, analysis, preservation, storage, and access, to sharing and reuse (where appropriate), at which point the cycle begins again. Data management should be practiced over the entire lifecycle of the data, including planning the investigation, conducting the research, backing up data as it is created and used, disseminating data, and preserving data for the long term after the research investigation has concluded.
The agencies acknowledge the diversity of models of scientific and scholarly inquiry that advance knowledge within and across the disciplines represented by agency mandates. The agencies, therefore, recognize that significant differences exist in standards for RDM—including what counts as relevant research data—among and across the disciplines, areas of research, and modes of inquiry that the agencies support.11
Effective: March 1, 2023
1 “1d. What is research data management?” Frequently Asked Questions: Tri-Agency Research Data Management Policy, last accessed December 29, 2022, at https://www.science.gc.ca/eic/site/063.nsf/eng/h_97609.html#1d
2 Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18.
3 “The First Nations Principles of OCAP®.” First Nations Information Governance Centre, last accessed January 17, 2023 at https://fnigc.ca/ocap-training/
4 “CARE Principles for Indigenous Data Governance.” The Global Indigenous Data Alliance, last accessed January 17, 2023 at https://www.gida-global.org/care
5 Research Data Alliance International Indigenous Data Sovereignty Interest Group. “CARE Principles for Indigenous Data Governance.” The Global Indigenous Data Alliance, (September 2019). https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5d79c383e904c741c9e9cd86/1568260995760/CARE+Principles+for+Indigenous+Data+Governance_FINAL_Sept+06+2019.pdf
6 “1a. What are data?” Frequently Asked Questions: Tri-Agency Research Data Management Policy, last accessed January 17, 2023 at https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/research-data-management/tri-agency-research-data-management-policy-frequently-asked-questions#1a
7 Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
8 “The First Nations Principles of OCAP®.” First Nations Information Governance Centre, last accessed January 17, 2023 at https://fnigc.ca/ocap-training/
9 The Alberta First Nations Information Governance Centre, last accessed January 17, 2023 at https://www.afnigc.ca/main/index.php?id=home&content=home; First Nations Information Governance Centre, last accessed January 17, 2023 at https://fnigc.ca/
10 “1b. What are research data?” Frequently Asked Questions: Tri-Agency Research Data Management Policy, last accessed January 17, 2023 at https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/research-data-management/tri-agency-research-data-management-policy-frequently-asked-questions#1b
11 “1d. What is research data management?” Frequently Asked Questions: Tri-Agency Research Data Management Policy, last accessed January 17, 2023 at https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/research-data-management/tri-agency-research-data-management-policy-frequently-asked-questions#1d