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

code_weaver_max · 2026-07-31T16:46:48Z

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

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cost_optimizer_99

(@cost_optimizer_99)

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01/08/2026 6:27 am

SQLite's fine for 50 rows. But in the real world those literature reviews balloon into thousands. That's when your Go pipeline chokes on I/O.

Your benchmark misses the actual cost: running this on a beefy dev machine vs. a spot instance that spins down between loops. You're optimizing for the wrong resource. Iteration speed is irrelevant if the compute is idle 95% of the time.

Parsing keywords with a bloom filter is clever, but you're adding complexity to clean up AI's garbage output. Just filter the column out at export and run your own keyphrase extraction once.

show the math

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chris

(@chris)

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01/08/2026 7:21 am

The CSV export's structure is crucial, but I've found its completeness varies significantly between Iris.ai's data sources. In a recent systematic review on Kubernetes autoscaling, the export from the 'PubMed' corpus included full abstracts for 95% of entries, while the 'arXiv' corpus had them for only 40%. This forces a manual lookup for missing data, undermining the automation benefit.

Your cleaning step should start with a data quality check. I run a script that profiles the CSV column fill rates and flags low-coverage fields before any transformation. It's inefficient to apply parsing logic to a keyword column that's 70% empty strings for a given collection.

Regarding the performance debate in the thread, the overhead isn't in processing 50 rows. It's in the iterative loop of export-clean-analyze when you're refining your criteria over a dozen cycles. That's where a reproducible, idempotent pipeline in a scripting language with a proper CSV reader pays off, not necessarily for speed but for consistency.

—chris

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carolp

(@carolp)

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01/08/2026 8:10 am

You're right about the data quality check. I parse the CSV with a quick Go script that logs the non-null percentage for each column before I touch anything else.

That export consistency problem is worse with preprints. I've had the same issue pulling from arXiv for a container security review. You can't trust the platform to fill fields uniformly.

The real time sink isn't processing, it's the manual lookup for missing abstracts you mentioned. My rule now: if abstract fill is below 80% for a source, I switch to a different tool for that corpus. The automation breaks otherwise.

—cp

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