https://www.dropbox.com/scl/fi/vm7zztz460hfgb0sxy633/bounty-columns-offset-data-sample.csv?rlkey=ytsp9dcuabxhywhun5tbs1lm6&e=2&st=ogqkbbez&dl=0

this is the provided data set and i need someone to predict the next half of the dataset with either 90% or 100% accuracy please

I don’t care how you solve it, only that you provide proof of the solve, and the algo code that solved it. Must provide full code to replicate.

The data is multi-dimensional, and catalogued. I have both halves of the data, to compare against.

Thanks, dm me if you are interested, i am ready to offer upwards of 150 USD for the solution

Hi everyone,

I’m working on a side project and need real-time gas/fuel price data in Canada.

I know GasBuddy and Waze get theirs from crowdsourcing. GasBuddy also used to have a GraphQL API, but that seems shut down. I already emailed OPIS but got no response.

Ideally, I’m looking for:

• Station-level data with location
• Prices by fuel type (regular, premium, diesel, etc.)
• Search by postal code or lat/long
• Brand filtering if possible
• Fuel price based on the type of fuel – Petrol, Diesel and also the price for Regular, Premium etc.

Are there any real-time APIs or datasets available for this? Or is scraping the only realistic option here for real-time data for the daily fuel price?

Thanks! 🙏

Researching ML data pipeline pain points. For production ML builders: what’s your biggest training data prep frustration?

🔍 Data quality? ⏱️ Labeling bottlenecks? 💰 Annotation costs? ⚖️ Bias issues?

Share your real experiences!

Spent weeks trying to find realistic e-commerce data for AI/BI testing, but most datasets are outdated or privacy-risky. Ended up generating my own synthetic datasets — users, products, orders, reviews — and packaged them for testing/ML. Curious if others have faced this too?

https://youcancallmedustin.github.io/synthetic-ecommerce-dataset/

Hi all,

I’ve been working on a side project where I crawled and AI-enriched over 2.6 million company websites across 111 industries worldwide.

What’s inside:

• Company name, website, industry
• Long + short descriptions (AI-generated)
• Enriched metadata (socials, emails, locations where available)
• Website screenshots
• Delivered in JSONL, Parquet, and CSV formats

Access:

• A free sample explorer with 150 companies is live here: https://ctxdb.ai/sample-dataset
• Full dataset available for purchase (Q3 2025 edition + Q4 coming soon).
• A yearly “Momentum Plan” also refreshes the dataset quarterly with new companies + updated profiles.

Why I built this:

I wanted an up-to-date, structured dataset useful for:

• Lead generation / prospecting
• Market research & competitive tracking
• AI/ML model training
• Academic or investment research

Happy to hear your thoughts / feedback / need for API access? – also curious how you’d use a dataset like this.

Hey everyone! I’ve been working on a project to make SEC financial data more accessible and wanted to share what I just implemented. https://nomas.fyi

**The Problem:**

XBRL taxonomy names are technical and hard to read or feed to models. For example:

– “EntityCommonStockSharesOutstanding”

These are accurate but not user-friendly for financial analysis.

**The Solution:**

We created a comprehensive mapping system that normalizes these to human-readable terms:

– “Common Stock, Shares Outstanding”

**What we accomplished:**

✅ Mapped 11,000+ XBRL taxonomies from SEC filings

✅ Maintained data integrity (still uses original taxonomy for API calls)

✅ Added metadata chips showing XBRL taxonomy, SEC labels, and descriptions

✅ Enhanced user experience without losing technical precision

**Technical details:**

– Backend API now returns taxonomy metadata with each data response

– Frontend displays clean chips with XBRL taxonomy, SEC label, and full descriptions

– Database stores both original taxonomy and normalized display names

– Caching system for performance

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Hello there !

I am trying to find dataset for autism detection using EEG.
Can anyone link any source or anything.

Thanks…

Hey everyone!

As a side project, I started collecting and structuring data on recently funded startups (updated daily). It includes details like:

1. Company name, industry, description
2. Funding round, amount, date
3. Lead + participating investors
4. Founders, year founded, HQ location
5. Valuation (if disclosed) and previous rounds

Right now I’ve got it in a clean, google sheet, but I’m still figuring out the most useful way to make this available.

Would love feedback on:

1. Who do you think finds this most valuable? (Sales teams? VCs? Analysts?)
2. What would make it more useful: API access, dashboards, CRM integration?
3. Any “must-have” data fields I should be adding?

This started as a freelance project but I realized it could be a lot bigger, and I’d appreciate ideas from the community before I take the next step.

Link to dataset sample – https://docs.google.com/spreadsheets/d/1649CbUgiEnWq4RzodeEw41IbcEb0v7paqL1FcKGXCBI/edit?usp=sharing

Hey everyone, I’ve published a freshly-built anime ratings dataset that I’ve been working on. It covers 1.77M users, 20K+ anime titles, and over 148M user ratings, all from engaged users (minimum 5 ratings each).

This dataset is great for:

• Building recommendation systems
• Studying user behavior & engagement
• Exploring genre-based analysis
• Training hybrid deep learning models with metadata

🔗 Links:

• Kaggle Dataset: https://www.kaggle.com/datasets/tavuksuzdurum/user-animelist-dataset (inference notebook available)

• Hugging Face Space: https://huggingface.co/spaces/mramazan/AnimeRecBERT

• GitHub Project (AnimeRecBERT Hybrid): https://github.com/MRamazan/AnimeRecBERT-Hybrid

I’ve released a new dataset built from the EU’s Tenders Electronic Daily (TED) portal, which publishes official public procurement notices from across Europe.

• Source: Official TED monthly XML package for August 2025
• Processing: Parsed into a clean tabular CSV, normalized fields, and enriched with CPV 2008 labels (Common Procurement Vocabulary).
• Contents (sample):
  • notice_id — unique identifier
  • publication_date — ISO 8601 format
  • buyer_id — anonymized buyer reference
  • cpv_code + cpv_label — procurement category (CPV 2008)
  • lot_id, lot_name, lot_description
  • award_value, currency
  • source_file — original TED XML reference

This free sample contains 100 rows representative of the full dataset (~200k rows).
Sample dataset on Hugging Face

If you’re interested in the full month (200k+ notices), it’s available here:
Full dataset on Gumroad

Suggested uses: training NLP/ML models (NER, classification, forecasting), procurement market analysis, transparency research.

Feedback welcome — I’d love to hear how others might use this or what extra enrichments would be most useful.

The article outlines several fundamental problems that arise when teams try to store raw media data (like video, audio, and images) inside Parquet files, and explains how DataChain addresses these issues for modern multimodal datasets – by using Parquet strictly for structured metadata while keeping heavy binary media in their native formats and referencing them externally for optimal performance: reddit.com/r/datachain/comments/1n7xsst/parquet_is_great_for_tables_terrible_for_video/

It shows how to use Datachain to fix these problems – to keep raw media in object storage, maintain metadata in Parquet, and link the two via references.

Specifically I am hoping to find a dataset that I can use to determine how often the favorites, or favored outcome occurs.

I’m curious about the comparison between sports betting sites and prediction markets like Polymarket.

Here’s a dataset I built on Polymarket diving into how accurate it is at prediction outcomes: https://dune.com/alexmccullough/how-accurate-is-polymarket

I want to be able to get data on sports betting lines that will allow me to do something similar so I can compare the two.

Anyone know where I can find one?

Hey, guys, I bought this book through a second hand book store and finding it a really good place to start statistics. However, the access card inside the book is not working thus I can’t access the resources from the internet. I tried googling it and finding the datasets for an hour but no luck. Just wondering if anyone here would have access to the dataset and would love to share.
Thank you in advance.

Anybody here interested in Ethiopia’s trade data import&export spanning from 2020 to 2025?

Guys, I’ve been working on few datasets lately and they are all the same.. I mean they are too synthetic to draw conclusions on it… I’ve used kaggle, google datasets, and other websites… It’s really hard to land on a meaningful analysis.

Wt should I do? 1. Should I create my own datasets from web scraping or use libraries like Faker to generate datasets 2. Any other good websites ?? 3. how to identify a good dataset? I mean Wt qualities should i be looking for ? ⭐⭐

Hey everyone,

I’m a medical officer in Bengaluru, India, working on a non-funded network meta-analysis on the comparative efficacy of new-generation anti-obesity medications (Tirzepatide, Semaglutide, etc.).

I’ve finalized my search strategies for the core databases, but unfortunately, I don’t have institutional access to use the “Export” function on the Cochrane Library and Embase.

What I’ve already tried: I’ve spent a significant amount of time trying to get this data, including building a Python web scraper with Selenium, but the websites’ advanced bot detection is proving very difficult to bypass.

The Ask: Would anyone with access be willing to help me by running the two search queries below and exporting all of the results? The best format would be RIS files, but CSV or any other standard format would also be a massive help.

1. Cochrane Library (CENTRAL) Query:

(obesity OR overweight OR “body mass index” OR obese) AND (Tirzepatide OR Zepbound OR Mounjaro OR Semaglutide OR Wegovy OR Ozempic OR Liraglutide OR Saxenda) AND (“randomized controlled trial”:pt OR “controlled clinical trial”:pt OR randomized:ti,ab OR placebo:ti,ab OR randomly:ti,ab OR trial:ti,ab)

1. Embase Query:

(obesity OR overweight OR ‘body mass index’ OR obese) AND (Tirzepatide OR Zepbound OR Mounjaro OR Semaglutide OR Wegovy OR Ozempic OR Liraglutide OR Saxenda) AND (term:it OR term:it OR randomized:ti,ab OR placebo:ti,ab OR randomly:ti,ab OR trial:ti,ab)

Getting these files is the biggest hurdle remaining for my project, and your help would be an incredible contribution.

Thank you so much for your time and consideration!

Would love to have dataset for that for my thesis as cs student

Hi

I am meant to investigate the ENRON Dataset for a study but the large file and its messiness proves to be a challenge. I have found via Reddit, Kaggle and github ways that people have explored this dataset, mostly regarding fraudulent spam (I assume to delete these?) or created scripts that allow investigation of specific employees (e.g. CEOs that ended up in jail bc of the scandal).
For instance here: Enron Fraud Email Dataset
Now, my question is whether anyone has the Enron Dataset CLEAN version i.e free from spam OR has cleaned the Enron data set so that you can look at how some fraudulent requests were made/questionable favours were asked etc.

Any advice in this direction would be so helpful since I am not super fluent in Python and coding so this dataset is proving challenging to work with as a social science researcher.

Thank you so much

Talia

https://www.kaggle.com/datasets/darrenlang/all-movies-earning-100m-domestically

*Domestic gross in America

Used BoxOfficeMojo for data, recorded up to Labor Day weekend 2025

check it: formulabot.com/madde

Hi,

I’m prototyping a PhD project on feminist discourse in France & Québec. Goal: build a multi-source corpus (academic APIs, activist blogs, publishers, media feeds, Reddit testimonies).

Already tested:

• Sources: OpenAlex, Crossref, HAL, OpenEdition, WordPress JSON, RSS feeds, GDELT, Reddit JSON, Gallica/BANQ.
• Scripts: Google Apps Script + Python (Colab).

Main problems:

1. APIs stop ~5 years back (need 10–20 yrs).
2. Formats are all over (DOI, JSON, RSS, PDFs).
3. Free automation without servers (Sheets + GitHub Actions?).

Looking for:

• Examples of pipelines combining APIs/RSS/archives.
• Tips on Pushshift/Wayback for historical Reddit/web.
• Open-source workflows for deduplication + archiving.

Any input (scripts, repos, past experience) 🙏.

Hey folks – I’m working on a research project around eDiscovery workflows and ran into a gap with the datasets that are publicly available.

Most of the “open” collections (like the EDRM Micro Dataset) are useful for testing parsers because they include many file types – Word, PDF, Excel, emails, images, even forensic images – but they don’t reflect how discovery actually feels. They’re kinda just random files thrown together, without a coherent story or links across documents.

What I’m looking for is closer to a realistic “mock case” dataset:
• A set of documents (emails, contracts, memos, reports, exhibits) that tell a narrative when read together (even if hidden in a large volume of files)
• Something that could be used to test workflows like chronology building, fact-mapping, or privilege review
• Public, demo, or teaching datasets are fine (real or synthetic)

I’ve checked Enron, EDRM, and RECAP, but those either don’t have narrative structure or aren’t really raw discovery.

Does anyone know of (preferably free and public):
• Law school teaching sets for eDiscovery classes
• Vendor demo/training corpora (Relativity, Everlaw, Exterro, etc.)
• Any academic or professional groups sharing narrative-style discovery corpora

Thanks in advance!