Difference between revisions of "1.4.3 Quality in collaborative research"
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** Do collaborators have laboratory notebooks? | ** Do collaborators have laboratory notebooks? | ||
* Data management practices | * Data management practices | ||
− | ** Are practices compliant with FAIR principles? | + | ** Are practices compliant with FAIR and ALCOAplus principles? |
* Platform for data sharing with collaborating parties | * Platform for data sharing with collaborating parties | ||
** Does it support transparent data sharing? | ** Does it support transparent data sharing? | ||
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* Reporting of results (presentation of research data between collaborating parties) | * Reporting of results (presentation of research data between collaborating parties) | ||
** Are there any measures necessary to ensure complete reporting including all replicates? | ** Are there any measures necessary to ensure complete reporting including all replicates? | ||
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'''RISK ASSESSMENT''' | '''RISK ASSESSMENT''' | ||
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== C. Resources == | == C. Resources == | ||
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− | The FAIR Guiding Principles for scientific data management and | + | EQIPD guidance on different collaborative settings: |
+ | * [[1.4.3.1 Industry-academia: Research as service]] | ||
+ | * [[1.4.3.2 Industry-academia: Research as collaboration]] | ||
+ | * [[1.4.3.3 Academia-academia: Research as service]] | ||
+ | * [[1.4.3.4 Academia-academia: Research as collaboration]] | ||
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+ | [https://www.nature.com/articles/sdata201618 The FAIR Guiding Principles for scientific data management and stewardship] | ||
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+ | Webinar on Research Collaborations by Clemence Veauvy from UBC: | ||
+ | {{#ev:youtube|https://youtu.be/CkTUbQ5qUJY}} | ||
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back to [[Toolbox]] | back to [[Toolbox]] | ||
− | Next item: [[1. | + | Next item: [[1.4.3.1 Industry-academia: Research as service]] |
Latest revision as of 06:38, 25 March 2021
A. Background & Definitions
Research collaboration, in the context of this Toolbox article, refers to any mode of collaboration between two or more researchers or research organizations where one collaborating party depends on quality of results generated by another collaborating party. The collaboration modes range from a fee-for-service relationships to research projects executed jointly by members of a consortium where each member contributes towards shared goals.
B. Guidance & Expectations
It is strongly recommended that:
- each collaborating party defines research quality expectations prior to entering any formal collaboration agreements and certainly before initiating any experimental work;
- if a collaboration is supported by a formal collaboration agreement, research quality expectations are specified as an attachment to the agreement;
- all factors that can bias the research conduct (e.g. time pressure) are defined and discussed between parties;
- if appropriate, individuals responsible for specific aspects of research quality are explicitly identified.
Research quality expectations may also include on measurements to ensure data integrity, traceability and security:
- Data generation and documentation practices
- Will raw data be properly handled and stored?
- Do collaborators have laboratory notebooks?
- Data management practices
- Are practices compliant with FAIR and ALCOAplus principles?
- Platform for data sharing with collaborating parties
- Does it support transparent data sharing?
- Is it secure?
- Reporting of results (presentation of research data between collaborating parties)
- Are there any measures necessary to ensure complete reporting including all replicates?
RISK ASSESSMENT
- Is there any risk that inadequate quality of research practices (e.g. documentation) will endanger intellectual property rights?
PLEASE DO NOT FORGET
- To check whether research at the collaborating party meet required ethical standards
C. Resources
EQIPD guidance on different collaborative settings:
- 1.4.3.1 Industry-academia: Research as service
- 1.4.3.2 Industry-academia: Research as collaboration
- 1.4.3.3 Academia-academia: Research as service
- 1.4.3.4 Academia-academia: Research as collaboration
The FAIR Guiding Principles for scientific data management and stewardship
Webinar on Research Collaborations by Clemence Veauvy from UBC:
back to Toolbox
Next item: 1.4.3.1 Industry-academia: Research as service