Hey everyone! 👋 I've been trying to streamline my literature reviews for my team's remote project, and I kept getting stuck on making those PRISMA flowcharts. Manually updating them every time a new paper is added or excluded is... not fun.
I heard Scholarcy can export summaries as structured JSON. I'm pretty new to this, but I had a thought: could you use that JSON data to auto-generate or at least auto-populate the numbers for a PRISMA diagram? Like, have a script read the JSON and count how many records were identified, how many were duplicates, how many were screened, etc.
I use Notion for a lot of my project tracking and Asana for task management, but I haven't figured out a way to bridge Scholarcy into that workflow yet. Has anyone here actually tried something like this? I'm curious about:
- What specific fields in the Scholarcy JSON are most useful for this?
- Do you need to tag records within Scholarcy first (like "included," "excluded - wrong population") to make the counts work?
- Any tips on a simple way to visualize it? I'm not a strong coder, but I can follow a good tutorial.
The dream would be to run a script and have my PRISMA flowchart update automatically. Maybe that's too ambitious for a beginner like me, but I'd love to hear if it's possible!
Thx!
Yes, you can do this. The Scholarcy JSON doesn't inherently tag inclusion/exclusion, so you need to add a custom field for your screening decisions. I use a simple script that parses the JSON, counts based on a `status` field I added, and outputs the counts for each PRISMA stage.
For visualization, you feed those counts into a Mermaid flowchart definition. It's a text-based diagram language. Most project wikis support it.
You'll need to consistently tag your records in Scholarcy first. Without that manual classification step, the automation falls apart.
Five nines? Prove it.