Walkthrough: From research question to a structured literature table using Iris.ai exports.

Hey folks! I've been diving deep into Iris.ai for a few literature review projects lately, and I wanted to share a concrete workflow I've settled on. It's been a game-changer for going from a fuzzy research question to a clean, structured table of papers I can actually use. I'm focusing on the export and post-processing part, which is where the real time-saver is.

Here's my typical flow:

1. **Define & Explore:** I start with a broad research question in Iris.ai's Researcher Workspace. I let the AI map the concepts and suggest papers, refining my focus with keywords and filters.
2. **Smart Filtering:** This is key. I use the "Criteria" tool to filter by specific domains, exclude certain publication types, or focus on recent years. I end up with a curated "Collection" of, say, 50-100 relevant papers.
3. **The Magic Export:** Instead of just downloading BibTeX, I use Iris.ai's **"Export to CSV"** feature for the collection. This gives me a spreadsheet with columns for Title, Authors, Abstract, Year, DOI, and – crucially – **AI-generated summaries and keywords**.

Now, here's where a little scripting turns this into a polished literature table. The raw CSV often needs some cleaning. I wrote a quick Python script to parse the export, clean up the text, and format it into a markdown table I can drop into my notes or reports.

```python
import pandas as pd
import re

# Load the Iris.ai export
df = pd.read_csv('iris_ai_export.csv')

# Select and rename columns for a cleaner table
clean_df = df[['Title', 'Authors', 'Year', 'Summary', 'DOI']].copy()
clean_df.columns = ['Title', 'Authors', 'Year', 'Key Findings (AI Summary)', 'DOI']

# Function to clean text (remove excessive newlines common in AI summaries)
def clean_text(text):
if isinstance(text, str):
# Replace multiple newlines/spaces with a single space
return re.sub(r's+', ' ', text).strip()
return text

clean_df['Key Findings (AI Summary)'] = clean_df['Key Findings (AI Summary)'].apply(clean_text)

# Truncate summary for brevity in a table
clean_df['Key Findings (AI Summary)'] = clean_df['Key Findings (AI Summary)'].str.slice(0, 250) + '...'

# Convert to Markdown
markdown_table = clean_df.to_markdown(index=False)

# Save to file
with open('literature_overview.md', 'w') as f:
f.write(markdown_table)
print("Markdown table saved to 'literature_overview.md'")
```

This gives me a ready-to-scan overview. I can then sort by year or manually add my own notes in a new column. The AI-generated summaries are fantastic for a first-pass categorization.

**Pitfalls to watch for:**
* The export can sometimes include incomplete entries—always spot-check a few DOIs.
* The AI summaries are useful but not perfect for deep nuance. I use them as a starting point for my own annotations.
* This workflow shines for the *overview* phase. For in-depth analysis and connection-making, you still need to read the papers, but you're doing so with a much better map.

Anyone else using Iris.ai exports in an automated way? Would love to swap tips on filtering criteria or alternative output formats!

-- Weave