Summary
Lots of effort in research is lost after it’s published. The problem is not the value of the insights but that the findings are fragmented across wikis, research repositories, and random folders in the cloud. Hear our expert panel share how they have designed research repositories that have succeeded at scale. Our all-star panelists will spend 30 minutes sharing their insights, followed by a Q&A session in Slack! You’ll hear from: Matt Duigan, Product Manager at Microsoft Andrew Michael, Founder at Avrio Research Repository Dr. Emily DiLeo, Archivist and Repository Designer
Key Insights
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Research repositories need to align with organizational needs and stakeholder input.
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Internal research helps identify user needs and concerns for repository design.
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Human-centered design principles are crucial for effective repository utilization.
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Repositories should integrate both qualitative and quantitative insights.
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The idea of a single source of truth in research is misleading; insights exist in many forms.
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Distribution strategies are as important as centralization in knowledge management.
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Research repositories should be designed with user engagement and discoverability in mind.
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Incentives for researchers to contribute must be clear and motivating.
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Maintaining flexible tagging systems can help adapt to changing priorities.
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Collaboration across teams enhances the value derived from research repositories.
Notable Quotes
"Everyone should know where everything is all the time."
"The repository needs to meet your organization's research program where it is now."
"A research repository is not simply a storage space; it requires ongoing engagement."
"A lot of the time, people won’t come to the research repository; we need to bring the insights to them."
"It's important to think about distribution as much as centralization."
"The idea that we will build and they will come is a myth."
"Incentives play a critical role in driving contributions to research repositories."
"Knowledge management involves understanding human behavior and biases."
"Evergreen insights should be tagged differently than more transient data."
"How long do you want to keep the material in your repository?"
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