Okay, so I finally pushed myself to take a bunch of Elicit exports and actually visualize them. We all know the platform is great for extracting data from papers, but I feel like the real story starts *after* you hit "Export CSV."
I took a corpus of about 100 papers on a specific niche in behavioral economics I've been tracking. The goal was simple: spot the trend lines that aren't obvious from just reading the table in Elicit. Things like: which methodologies are gaining traction over time? Are certain outcome measures being phased out?
Here’s a quick rundown of my process and some immediate insights:
* **The Export:** Obviously, started with the CSV export of the "paper table." The key columns for me were `Publication Year`, `Methodology`, `Main Findings` (for keyword scraping), and `Interventions Studied`.
* **The Tool:** I used Python (pandas + seaborn) for this, but you could do similar in R or even Sheets with some pivot tables. The point isn't the tool, it's asking the right questions of the data.
* **The Visuals:** Created a few simple charts:
* A timeline showing the frequency of specific methodology terms (e.g., "RCT," "quasi-experimental," "systematic review") across the years.
* A bar chart of the most common outcome measures, filtered to the last 5 years vs. the 5 years before that.
* A network graph (this was the fun part) linking commonly co-occurring interventions and outcomes.
What jumped out? The shift from purely survey-based methods to mixed-methods designs after 2018 was way steeper than I expected. Also, seeing "agent-based modeling" pop up in the last two years in this field was a surprise—it wasn't on my radar at all from just reading abstracts.
The big thing missing for me? I wish Elicit's export included the **structured data from the "Summary of Main Results"** in a more parse-able way. Having that in a separate column, even as a semi-structured list, would be a game-changer for this kind of analysis. Right now, I'm manually tagging that from the abstract.
Has anyone else done something similar? I'm curious about:
* How you're cleaning and structuring the exported data.
* What "aha" trends you've found in your own literature sets.
* If you've found a smooth way to incorporate the "Interventions" or "Outcomes" data into a proper time-series analysis.
Spreadsheets > marketing slides.
Love the direction of this. Moving from the raw extracted data to visual trends is exactly where the value gets unlocked.
One thing that's worked well for me is bringing that cleaned CSV into Grafana, especially for time-series trends like methodology adoption. You can set up a dashboard that updates if you add more papers, turning this from a one-off analysis into a living view of your research niche.
Did you run into any issues with the consistency of the text data, like `Methodology` or `Main Findings`? Normalizing those free-text fields is usually the hardest part before you can visualize anything meaningful.
- GG
I've been down this road, and my first question is always about the quality of the source extraction before we get to the fancy visualizations. You're assuming the exported fields like `Methodology` and `Interventions Studied` are consistent enough to graph meaningfully across 100 papers.
In my experience, Elicit's extractions are only as good as the paper abstracts and the model's interpretation that day. You'll get "RCT," "Randomized Controlled Trial," and "randomized clinical trial" all as separate methodology entries, not to mention the creative paraphrasing in the `Main Findings` column. Before seaborn even gets imported, you're staring at days of manual normalization or building a fragile keyword classifier. Did you have to do a massive text-cleaning operation, or are you just graphing the raw strings and calling a spurious trend "insight"?
—MB