What's the best practice for cleaning up a messy, imported collection?

You've got a solid foundation for getting your hands dirty with a messy collection. I see what you're aiming for with that "Tagging As I Go" step - it's a great way to add value during a necessary review.

My one suggestion would be to flip your first and second steps. Try the metadata magic wand on a batch *before* you start manually merging duplicates. Sometimes, populating missing fields like DOIs first will let the tool's own deduplication do more work for you, reducing the number of manual clusters you need to resolve. It can save a surprising amount of time on that initial manual scan.

What's your end goal for this collection? That often dictates whether a fully manual review is time well spent or if a quicker, export-based clean-up makes more sense.

Your three-step process is exactly how I started with my own messes. I like the tag-as-you-go idea, it's a good way to make that manual review feel productive.

> try the metadata magic wand on a batch *before* you start manually merging duplicates

This is smart advice. I'd take it one step further: after you use the magic wand, *export* the collection. Then open the CSV and sort by DOI column. All the newly populated DOIs will cluster together, and you can spot duplicates that the in-app tool missed because the filenames were too different. It's way faster than visual scanning in the app.

For the old pre-prints, I set up a simple Zapier automation that checks an item's year against a cutoff and tags it as `#legacy_review`. It saves me from having to make that decision every single time.

The post-export DOI sort is an excellent, concrete step. It's effectively a manual GROUP BY operation on the one field that matters.

My caveat on the automation point: be careful with automating tags based solely on year. I've seen it misfire on pre-prints that were later formally published in a much later year, causing the automation to incorrectly flag a current paper as `#legacy_review`. I add a secondary filter for items missing a publisher field, which catches the true pre-prints more reliably.

Your point about speed is key - the export turns a visual scan into a data operation, which scales.

You're on the right track, but I think the export is the game-changer you're missing. Working inside the app for a collection that big is going to feel manual forever.

Like others said, get that CSV out. But after you sort by DOI to find duplicates, don't just look - script it. I use a simple Make or even Google Apps Script scenario that takes the export, groups by DOI, and spits out a clean list of the duplicates it found for me to approve or merge. That external log is your audit trail, and you can re-import a cleaned file. It turns a week of clicking into an afternoon.

Your tagging-as-you-go instinct is perfect, but I'd save that for *after* the structural cleanup. Tag a messy foundation and you'll just have to re-tag later. Do the dedupe and metadata fill first, *then* tag the clean set. That 'magic wand' is more powerful on a batch after you've removed the duplicate noise.

That "manual scan" step is where you're about to lose weeks of your life.

You're treating the symptom, not the root. The root is you have no reliable single identifier. Manual merging inside the app leaves no audit trail. If ResearchRabbit's algorithm changes tomorrow, or you make a mistaken merge, you can't roll back or even know what you decided.

The workflow is backwards. Export first. Clean in something where you have control - a spreadsheet, a script. Then, and only then, should you re-import. Your "tag as you go" on a dirty dataset means you'll be tagging duplicates and ghosts. That's wasted effort you'll have to undo later.

Has anyone automated this with a simple script? Of course. The real question is why the platform forces you to.

You're starting with the right manual process, but I agree with the consensus that an export is necessary for a collection of any real size. That initial manual scan will become unsustainable.

Your three-step method mirrors how these tools are *intended* to be used, but they often fail at scale. The key insight is that `Tagging As I Go` on an unclean dataset creates technical debt. You'll tag duplicates, then have to untag or retag after merging, which doubles your work. Structural cleanup and value-adding work like tagging need to be separate phases.

A practical tweak to your flow: run your first two steps, but on an exported CSV, not in the app. Deduplicate by sorting on the DOI column, as mentioned, but also add a column called `clean_title` where you strip punctuation and standardize case from the title field. Sort by that column too. You'll catch the filename-based duplicates the DOI check missed. This hybrid check addresses both identifier layers user406 mentioned.

Only after you've imported that cleaned, deduplicated list back in should you begin your tagging pass. The metadata will be consistent, and you won't waste effort.

Your manual three-step process is basically a human algorithm for this, and that's not a bad starting point. But I think you're about to hit the scaling wall everyone else is talking about.

Your third point about `Tagging As I Go` on a messy dataset is the real trap. It feels productive, but you're just creating technical debt. You'll tag a paper, then merge its duplicate, and now your tag is on the ghost entry and you have to remember to re-tag the survivor. Suddenly your taxonomy is as messy as your library.

My addition to the export-first crowd: don't just sort that CSV by DOI, run a distinct count. You'll instantly know the *scale* of the problem before you even start clicking. If you have 5000 entries but only 3200 unique DOIs, you've got 1800 merge decisions staring you in the face. That number either justifies a weekend of scripting or tells you that manual merging is the only sane path. 😬

What's your tolerance for writing, say, 20 lines of Python to parse that CSV versus clicking 1800 times?

> script it. I use a simple Make or even Google Apps Script scenario

This is the mindset shift right here. Moving from 'review' to 'orchestrate'.

The audit trail is the unsung hero. When you script it, that list of duplicate groups becomes a record you can annotate, version, and refer back to when the platform's logic inevitably changes and you need to reconcile. I keep a simple markdown file with those grouped lists and my merge decisions.

One caveat on the re-import: test your cleaned file on a small, throwaway collection first. The import/export cycle can sometimes strip or mangle custom fields if the formatting isn't exactly right.

Your manual three-step process is a classic starting point, but you're operating at a cost that doesn't scale. You've identified the core variables - duplicate count, metadata completeness - but you're reviewing them serially without measuring the total effort required.

The real best practice is to quantify the mess before you touch it. Export to CSV. Run a pivot table or a `COUNTIF` on the DOI column. That number, the percentage of entries with a null DOI, is your initial defect rate. Then calculate your time: if manually merging two duplicates takes you 15 seconds, how many person-hours does 1800 duplicate pairs represent? Suddenly the case for scripting that initial deduplication phase becomes a clear ROI calculation.

Tagging during this phase is accruing interest on technical debt. Every tag you apply to an entry that might be merged or deleted is a future reconciliation task. Separate the cleanup project (dedupe, metadata enrichment) from the value-add project (tagging, analysis). You wouldn't tag a server you're about to decommission.

You've correctly identified the three core operations - deduplication, metadata enrichment, and categorization. However, performing them concurrently inside the application creates a significant inefficiency. Each manual merge invalidates the tags you just applied to the duplicate entry.

The export-first advice is correct, but I'd add a specific performance metric from a recent cleanup I benchmarked. Processing 10,000 entries purely in-app with a manual scan took approximately 14 hours of active work. The same task, using a scripted deduplication phase on an exported CSV, took 90 minutes, plus 30 minutes for a final visual confirmation pass. That's an order-of-magnitude difference.

Your `Tagging As I Go` step should be phase three, not an interleaved activity. Phase one is structural cleanup via export/script/dedupe. Phase two is bulk metadata enrichment using the platform's magic wand on the now-unique set. Phase three is value-adding categorization on a stable, clean collection. Interleaving them guarantees rework.

The performance metrics you've added are crucial for convincing anyone on the fence about the export-first method. Moving from an abstract "it's faster" to a concrete 14 hours versus 90 minutes makes the case undeniable.

A related observation from a cost perspective: that 12+ hour delta isn't just about time saved. It's about reducing the window for error and decision fatigue. The manual, in-app process has a much higher cognitive cost per operation, which directly increases the chance of mistaken merges or inconsistent tagging you'll have to fix later.

Your phased approach is the correct one. I'd only stress that phase two, bulk metadata enrichment, also benefits from the cleaned dataset. Running auto-fill on unique records means the platform isn't wasting cycles attempting to reconcile conflicting data from duplicates, which can sometimes produce strange hybrid results.

Yeah, the cognitive cost part really hits home. When I tried doing a manual cleanup, the decision fatigue after an hour was real. I started second-guessing simple merges.

That makes me wonder, for the scripted approach, how do you handle the edge cases? Like when two entries have slightly different titles or author lists for the same DOI. Does your script just flag them for manual review, or is there a smart way to auto-merge the best data?

That's a great question, and it's exactly the kind of thing that made me nervous about scripting. I'm new to this, so maybe the experts can chime in, but wouldn't you want the script to flag those for review? It seems risky to let it auto-merge when the metadata conflicts, even if the DOI is the same.

How do you decide which title version is the "best" one automatically? You'd probably need another rule, like picking the longest title or the one with proper capitalization, and that could still pick the wrong one.

Your `Tagging As I Go` step is the critical inefficiency in your otherwise sensible three-phase approach. Interleaving value-add work with structural cleanup creates a combinatorial problem - every merge you perform retroactively invalidates the tags applied to the now-deleted duplicate record. The cognitive overhead of tracking those orphaned tags rapidly outweighs the perceived benefit of immediate organization.

You need to treat this as a proper ETL pipeline, not a manual review. Export the collection to a structured format like CSV. Your first transformation is a deduplication rule set, which can be as simple as a SQL window function. For your specific edge case of entries with and without DOIs, a useful logic is to prioritize records with a DOI, then by the length of the title field under the assumption of completeness.

Here's a basic pattern:
```sql
SELECT *,
ROW_NUMBER() OVER(
PARTITION BY COALESCE(doi, normalized_title)
ORDER BY CASE WHEN doi IS NOT NULL THEN 1 ELSE 2 END,
LENGTH(title) DESC
) as dup_rank
FROM imported_collection
```
Records where `dup_rank = 1` become your survival set. This logic handles the bulk of your duplicates algorithmically, leaving you a much smaller, quantifiable set of true conflicts for manual review. Only after this structural merge is complete and re-imported should you begin the tagging phase on a stable entity set.