Hi everyone! I’m new to ResearchRabbit and still figuring things out. 😅
I’ve started adding papers for a couple different projects, but they’re all getting mixed together in my collections. How do you keep projects separate and organized? Do you use tags, multiple collections, or something else? I’m worried about losing track of which paper belongs to which project, especially when the topics overlap a bit. Any tips for a clean setup would be super helpful!
I'd make separate collections for each main project, that's been the easiest for me. Tags are useful too for cross-project themes, but starting with collections keeps things clean.
Do you use the folder structure inside collections much? I'm still trying to figure out if that's overkill or helpful for big projects.
CloudNewbie
Totally agree that collections are the foundation. I treat them like separate Salesforce campaigns - each project gets its own for clean attribution.
On folders inside collections, I only add them once a pass 50-60 papers. Under that, tags within the collection work fine for subtopics. Folders become helpful when you're prepping a literature review section and need to group supporting papers for a specific claim. But yeah, they can be overkill early on.
What's your threshold for when a project feels "big enough" to need folders? I'm curious how others gauge that.
Show me the pipeline.
Hey, welcome! Starting with separate collections is definitely the way to go. Think of each collection as its own project database.
I also lean hard into tags, especially for those overlapping themes. For example, if two projects both touch on "sentiment analysis," I'll tag papers with that in both collections. That way, you can filter within a project later without mixing up the sources. It saved me when I was pulling data for separate case studies that shared a few key methodologies.
One extra tip: I rename the papers right in the collection to include a tiny project code at the start, like "[P1]" for Project 1. It's a lifesaver if you ever use the "Similar Work" feature and get a recommended paper - you'll instantly know which project it was meant for.
Comparing tools one review at a time.
Separate collections are non-negotiable for project isolation, but the real key is defining a consistent naming and metadata schema upfront. Treat each collection as a dedicated schema in a database, where the collection name is the project identifier. For overlapping papers, I'd strongly recommend against duplicating the entry into both collections, as that creates versioning issues. Use a single 'master' collection for foundational or shared literature, then employ tags as foreign keys to link to your project-specific collections. This maintains referential integrity.
Your concern about overlapping topics is valid. This is where a controlled tagging vocabulary becomes essential. Establish a flat list of methodology and theme tags before you begin heavy tagging, and apply them uniformly across all project collections. You can then use filtered views or saved searches within ResearchRabbit to see, for example, all papers tagged "BERT" across multiple projects without commingling the core project datasets. It's akin to building a fact table with conformed dimensions.
The paper renaming tip from user595 is a practical, low-tech fallback, but it's a symptom of a weak metadata layer. If you must do it, use a standard prefix format like `[PROJ-001]` and document it. For advanced workflows, export your collections periodically to a structured format (CSV/JSON) and use a simple script to merge and deduplicate based on DOI, giving you a single queryable view of your entire corpus.
Query first, ask questions later.
The master collection concept is clever, but I'm skeptical it solves the versioning problem it claims to. You've now introduced a single point of truth that needs its own governance. Who updates tags or metadata in the master when a project's understanding of a shared paper evolves? That sounds like a distributed systems consistency problem dressed up in academic clothing.
Your filtered view approach is the correct, if optimistic, end state. It assumes perfect tag discipline, which in my experience has the same failure rate as meticulously maintained runbooks during a Sev-1. The paper renaming hack is indeed a weak metadata layer, but it's a compensating control for when the ideal schema inevitably drifts.
Have you actually run this master collection setup through a full research lifecycle, from literature review to manuscript drafting? I'd be interested in the p95 time spent on metadata upkeep versus actual analysis.
P99 or bust.