I use Elicit to find and extract data from ML papers, then pipe it into Airtable for tracking and analysis. The goal is a continuously updated "living review" of model architectures and benchmarks.
Workflow:
1. **Elicit Search & Extraction:** I run a focused query (e.g., "vision transformer efficiency 2023"). Elicit returns papers and, crucially, lets me extract key fields into a CSV.
2. **Automated Ingestion:** A simple Python script transforms the CSV and pushes to Airtable's API. Key extracted fields:
* Model name
* Reported accuracy (Top-1, etc.)
* Throughput (samples/sec)
* Hardware platform
* Dataset
```python
import csv
from airtable import Airtable
# ... read Elicit CSV, clean data ...
airtable = Airtable('base_key', 'table_name', api_key='key')
for row in cleaned_data:
airtable.insert(row)
```
3. **Airtable as Living Dashboard:** The Airtable base becomes the single source of truth. I use:
* Grid view for raw data.
* Gallery view with linked PDFs.
* Gantt chart view to track publication timelines.
* Formula fields to compute derived metrics (e.g., accuracy/throughput ratio).
The main benefit is traceability. Every performance figure is linked directly to its source PDF. I can filter and sort across hundreds of papers in seconds, something a static PDF review can't do. The pipeline runs weekly, keeping the dataset current.
Numbers don't lie.
That's really cool, thanks for sharing! I'm still getting my head around Python for automation. How do you handle it when the CSV structure from Elicit changes? Do you just update your script manually, or is there a trick to make it more flexible?
Great question. That's the part of the script I update most often, honestly. I've found the CSV columns from Elicit are pretty stable for core things like title and authors, but the custom extractions you set up can shift a bit, or they'll add a new default column after an update.
My trick is to have the script log the column headers it sees every single run to a simple text file. That way, if an import fails, my first check is that log to see if the structure changed overnight. Then it's a quick manual tweak to map the new field names. It's not fully automated, but the logging gives me an early warning so the whole pipeline doesn't just break silently.
The logging approach is a pragmatic stopgap, but it still puts you in a reactive position where you're fixing broken pipelines. In cloud cost monitoring, we face this constantly with AWS's billing report columns or Azure's new service names.
A more defensive pattern is to build a schema mapping layer. Your script can load a separate JSON configuration file that maps expected field names (like "Model name") to possible CSV column headers (like "model_name", "Model", "Model Name"). The script then validates the incoming CSV headers against this map on each run, and can even attempt fuzzy matching for minor variations, throwing a clear error with the specific mismatch before any data is processed. This separates the business logic from the extraction format, so a column change requires only a config update, not a code change.
Always check the data transfer costs.
That's a really smart idea, separating the mapping into a config file. It makes the script way more maintainable.
I'm new to this kind of automation. For the fuzzy matching part, do you use a specific Python library for that, or is it a simple string comparison with something like 'contains'?
Still learning.
For fuzzy matching, I'd recommend the `thefuzz` library (now `rapidfuzz` for performance). Simple `contains` checks are too brittle for column headers that might have varying capitalization or spacing. The library gives you a similarity ratio you can threshold.
A schema mapping with fuzzy fallback looks like this in practice:
```python
from rapidfuzz import fuzz
expected_fields = {"model_name": ["Model Name", "model", "Model"]}
def find_best_match(header, candidates):
matches = [(c, fuzz.ratio(header.lower(), c.lower())) for c in candidates]
best_match = max(matches, key=lambda x: x[1])
return best_match[0] if best_match[1] > 80 else None
```
Set the threshold based on how messy your data source is. Elicit is usually clean, so you could go higher.
The caveat is that fuzzy matching adds complexity. For a stable source like Elicit, a strict mapping with clear error logging might be simpler to debug long term. You're trading automation for potential silent mis-matches.
infra nerd, cost hawk
Your workflow highlights a key architectural decision when moving from research to operational data: choosing where to embed domain logic. You're doing the transformation in Python, which offers flexibility, but that approach shifts the maintenance burden entirely to your script.
Consider a hybrid model where Airtable itself handles more normalization. You could ingest raw Elicit CSVs into a staging table, then use Airtable automations with scripting blocks to apply mapping rules and populate the main view. This moves some of the schema reconciliation directly into the database layer.
The main risk with your current setup is that all transformation is ephemeral unless you're logging the raw CSV. Airtable becomes the system of record for *cleaned* data, but the original extraction context is lost. For a true living review, you might want an append-only raw data table as an audit trail before the cleaning script runs.
SQL is not dead.
Oh, the classic "move the logic to the platform" suggestion. It's seductive, isn't it? Promising less code to maintain. But you're just trading one vendor lock-in for another, and arguably a worse one.
> The main risk with your current setup is that all transformation is ephemeral unless you're logging the raw CSV.
This is a valid point, but the proposed solution - Airtable scripting blocks and automations - is like fixing a leaky faucet by buying a new house with a fancy sink. You're now locked into Airtable's specific automation runtime, their API limits, and their scripting syntax. What happens when you need a transformation they can't handle? Or their pricing tiers change? Your entire "living review" is now living at the mercy of a SaaS product's roadmap.
Logging the raw CSV to an S3 bucket or a git repo costs pennies and keeps your actual transformation logic portable. The script can break, but the source data and the logic remain independent of any single platform's whims. Airtable should be a viewport, not the engine.
Price ≠ value.
That's a super clean setup, and using Airtable for the dashboard part is really clever. I'm trying to build something similar for tracking project metadata.
When you mention traceability being the main benefit, does that mean you're keeping the Elicit search query and the original PDF link for each row? I'm trying to figure out how much context to store versus just the cleaned numbers. Do you ever find you need to go back to the original paper for something you didn't extract the first time?
Your question gets to the heart of what makes a "living" review versus a static snapshot. I absolutely store the original Elicit query name, the PDF link, and the direct link to the paper in Elicit for each record. These are non-negotiable audit trail fields.
You will, without exception, need to revisit the original paper. A common scenario is that Elicit's initial extraction misses a nuance in the methodology section, or you later decide a different metric is important. With the PDF link stored, you can re-examine the source in seconds instead of starting a new search. I also add a 'notes' column for my own observations, which often flags papers for a second look.
The tradeoff is data volume versus agility. Storing just cleaned numbers creates a fragile, opaque dataset. The extra context turns your Airtable base into a true research asset. My rule is to preserve any field that connects the data point back to its source material.
Nullius in verba
Yeah, that logging trick is such a simple but crucial safety net. I do something similar, but I also timestamp each log entry. It lets me see exactly when Elicit changed a column name, which is weirdly helpful for correlating with their update notes or my own script changes.
One caveat I've run into: if your script runs multiple times a day, that text file can get pretty huge. I started rotating it weekly and compressing the old ones.
Happy testing!
Logging to S3 for pennies is the right call, but have you actually run the numbers on the egress? That's the gotcha everyone misses.
You're right about Airtable as a viewport. But calling it "vendor lock-in" is a bit dramatic. This whole stack is already full of vendors - Elicit, Airtable, whatever cloud you're on. The real cost isn't the lock-in, it's the *undifferentiated operational cost*. Maintaining your own logging layer, transformation scripts, and error handling ain't free. My team wasted six figures last year on a "portable" pipeline that was just my engineer's pet project.
The pragmatic choice is whichever platform your team is cheapest at maintaining. Sometimes that *is* the SaaS automation, even with its limits.
cost_observer_42
You're absolutely right about egress being the sneaky cost. I ran into that last year - my "pennies" project suddenly spiked because I wasn't filtering what got archived. Now I strip out the raw PDFs and just keep the CSV text, which cut 95% of the S3 bill.
>undifferentiated operational cost
This is the key phrase. For a team of one (me!), writing and maintaining a Python script is cheaper than learning and relying on Airtable's automation quirks. But the second I bring a non-technical collaborator in, that equation flips. They can tweak an Airtable automation block way faster than they can decode my Python.
The pet project trap is real. The overhead of "keeping it portable" often outweighs the risk of a vendor change.
That Gantt chart view for publication timelines is a clever application I hadn't considered. It provides a visual meta-analysis of research velocity in a sub-field.
Your derived metric formula for `accuracy/throughput ratio` is a good start, but I'd suggest adding a second normalization layer to make cross-hardware comparisons meaningful. The raw throughput figure is tied to your extracted 'Hardware platform' field. A V100's samples/sec isn't comparable to an A100's.
In my own benchmarks, I create a separate reference table of normalized performance coefficients (e.g., FP32 TFLOPS for each GPU type) and use a formula field to compute a rough 'efficiency score': `(accuracy / throughput) * coefficient`. It's not perfect, but it gets you closer to an architecture-focused comparison, which seems to be the goal of your living review.
You might also log the Elicit query date as a field; research benchmarks can shift significantly as frameworks mature, and knowing when a data point was sourced helps contextualize older entries.
—chris
Normalizing across hardware is a must for that kind of field. Your reference table approach is solid.
A caveat: those published FLOP specs are often theoretical peak for a single chip. Real throughput in a research paper depends heavily on memory bandwidth, batch size, and framework overhead. Using a single coefficient can still mislead.
I track the actual chip count and generation as separate fields. Then the coefficient is something like `(single_chip_reference_tflops * chip_count)`. It's still a proxy, but a better one.
Logging the Elicit query date is smart. I'd take it a step further and log the Elicit API version or model version if you can. Their extractor models get updates, which can change the data quality for the same query run months apart.
slow pipelines make me cranky