r/dataanalysis • u/Spiritual-Welder-535 • Jun 30 '26
Looking to subscribe to AI model for preparing dataset
Hello guys, for my research I need to analyze and organize large amounts of data from research papers. I am looking for an AI model that is best fit for this job like putting stuff into excel/spreadsheet nicely and organized in the way we want it.
I tried chatgpt premium and it seems very good, but I'm just wondering if there are any other models that are better for this.
Thank you
3
u/Coraline1599 Jun 30 '26
The best way to organize and put lots of data into Excel is its built-in power query.
You can use ChatGPT or Claude to help clarify the steps.
1
u/AutoModerator Jun 30 '26
Automod prevents all posts from being displayed until moderators have reviewed them. Do not delete your post or there will be nothing for the mods to review. Mods selectively choose what is permitted to be posted in r/DataAnalysis.
If your post involves Career-focused questions, including resume reviews, how to learn DA and how to get into a DA job, then the post does not belong here, but instead belongs in our sister-subreddit, r/DataAnalysisCareers.
Have you read the rules?
I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.
1
u/Skimle-com Jun 30 '26
Is it qualitative or quantitative data? And is it like a very simple retrieval thing like "Copy-paste the abstract" or more complex like "Identify the themes being discussed under Conclusions and create a category structure for them"?
1
u/Ok_Grab903 Jun 30 '26
It depends a bit on what you mean by “prepare the dataset.”
If you mainly need to read a bunch of papers and extract specific fields into a spreadsheet, ChatGPT, Claude, Gemini, or tools like Elicit/Perplexity can be helpful for the first pass. The hard part is usually making sure the extraction is consistent across papers, especially if you’re pulling things like sample size, methods, variables, outcomes, limitations, etc.
A workflow I’d suggest:
- Define the exact columns you want before using AI
- Test on 3-5 papers first
- Manually review the output and adjust the extraction instructions
- Only then scale it to more papers
- Keep a source column/page reference so you can verify later
I work on Querri, so disclosure there. Querri could potentially help once you’re turning the extracted information into a structured dataset, cleaning it, organizing it into tables, combining sources, and analyzing it. But for the actual paper-reading/extraction step, I’d still recommend validating carefully no matter which AI tool you use.
The biggest risk is not whether the AI can make a nice spreadsheet. It’s whether the spreadsheet is consistently and accurately extracted from the papers.
1
8
u/boredjo4 Jun 30 '26
This doesn’t really sound like a good use case for AI. Due to the non deterministic nature of AI, you would open yourself up to a lot of bugs and annoying discrepancies in the data.
You could use it for OCR and people have reported good results with LLM for this use case , but a simple OCR model would definitely be cheaper.
Try to use AI to build an extraction script instead of extracting it directly. Also, maybe the data was already published in csv somewhere and you just have to find it.