Sharing a data source that’s surprisingly underused for fund analysis: the SEC’s N-PORT and N-CEN filings on EDGAR.

– N-PORT (quarterly, structured XML): every fund’s complete position list with weights, share counts, CUSIP/ISIN, country of domicile, ASC 820 fair-value level, monthly returns, and monthly creation/redemption flows.
– N-CEN (annual, structured XML): tracking difference vs benchmark (gross AND net of fees), securities-lending activity, in-kind creation/redemption percentages, per-broker commissions, and the full service-provider roster.

What you can pull out without any paid vendor:
– Index-fund tracking split into replication vs cost. VOO 2025 was -0.4 bps vs the S&P 500 gross of fees, -16.9 bps net.
– True per-CUSIP overlap between funds. SPY vs VOO is 476 shared holdings, ~97% by weight.
– Issuer-domicile reality checks. SPY is ~97% US, ~3% Ireland/Switzerland/Bermuda/Netherlands.

Gotchas: positions are keyed on CUSIP (not ticker), so you need a CUSIP-to-ticker map to join to anything else; unit investment trusts (like SPY) file lighter N-CEN sections than open-end funds (like VOO), so some fields are legitimately empty; and the public lag is ~60 days after quarter-end.

The StockFit API does the XML parsing and CUSIP resolution if you don’t want to build it yourself.

Not financial advice, just pointing at the filings.

Built this as part of a multilingual pretraining research project. Figured I’d share it here.

European HPLT v1 — quality-filtered from HPLT v3 web crawl data:

45M documents across 41 European languages (Germanic, Romance, Slavic, Celtic, Baltic, Finno-Ugric + more

~50.9B estimated tokens, ~190 GB raw JSONL

Every doc has a WDS quality score of 10 or higher — exact SHA-256 deduplication applied

Per-document metadata: language, URL, quality score, register/genre tag, char/word count

CC0 1.0 license — fully open, inherited from HPLT v3

Covers lower-resource languages (Maltese, Faroese, Scottish Gaelic, Occitan, Luxembourgish, Irish, Asturian) that are underrepresented in OSCAR and CulturaX.

HuggingFace: huggingface.co/datasets/ashtok897/european-hplt-v1

The goal of bacenR is to provide R functions to download and work with data from the Brazilian Central Bank (Bacen).

• The datasets available through bacenR include:
  • Normative legislation
  • Financial statements of financial institutions
  • List of financial institutions regulated by Bacen in activity
  • Ifdata resources / IFdata

Check it out: https://github.com/rtheodoro/bacenR

#bacen #financialdata #finance #rstats #datacollect #braziliandata

To the People who are gathering data for your RAG, how do you actually collect the data of your own personal information related to location history, payments and message and put it into Database.

I’m building a project where i can ask the questions to it related to my past history events. so most of the things are done through phone but the main problem is how should i send it from the device to DB.

Help me out, any suggestions related to project or any sources will be helpful.
Thanks in Advance!

I am looking for historical mandi price data for wheat across Maharashtra, India, for a minimum period of 10 years.

Sharing a dataset I built.

Disclaimer: this is my project. Free to download and use.

https://huggingface.co/datasets/fineset-io/mechanistic-interpretability-papers

Stats:

– 748 records, 2022–present

– Sources: arXiv + Semantic Scholar, cross-referenced by arxiv_id and DOI

– quality_score: 0–1, citation-normalized

Fields: id, title, abstract, authors, categories, published_date, citation_count, quality_score, has_code, code_url, venue

Built with FineSet (fineset.io).

The waitlist is open if you want daily-refreshed datasets on your own topic.

I’ve been following visa backlog updates and community-driven tracking tools recently, trying to make sense of timelines and what they might mean for my own immigration process.

It’s interesting how the same numbers can create different reactions some people feel reassured, others feel anxious, and many of us keep checking for patterns that may or may not actually exist.

It made me think about how we don’t just interpret data for accuracy we also use it for emotional grounding when outcomes feel uncertain.

As someone from a market research background, I naturally try to find patterns in data. But this experience is teaching me that not everything we track has a clear signal, even when it looks very data driven.

Maybe sometimes data is not just about prediction it also helps people sit with uncertainty.

I’m curious how do others deal with uncertainty when the “data” is incomplete and constantly changing.

Data covers 18 economies across two regions. Each run returns:

– FX momentum (30d/90d, z-scored vs own history)

– Inflation level and trend

– Commodity terms-of-trade impact (price × export share per commodity, e.g. copper +42% × 32% export share = +13.5pp impact for Peru)

– Real interest rate

– Reserve drawdown

– Structural vulnerability (debt, fiscal, banking, governance, REER)

Every signal shows the exact value, threshold, source, and reason string. No black box. Latest addition: companySignals — when a commodity tailwind or shock fires, returns the listed companies with exposure to that commodity in that country (e.g. copper tailwind in Chile → Antofagasta, BHP, Anglo American, Lundin, Teck).

Available on Apify ($1.50/run) and RapidAPI. Full methodology and schema documented in the README.

https://apify.com/malmon/african-economic-stress-monitor

https://apify.com/malmon/latam-economic-stress-monitor

​

Need field-scale data such as:

– In-situ stress (Sv, SHmax, Shmin)

– Pore pressure

– Fault parameters

– Rock mechanical properties

– Injection pressure/rate history

Interested in sites like Sleipner, In Salah, Weyburn, Otway, Decatur, etc.

Already checked CO2 DataShare and NETL EDX, but geomechanical data is limited.

Papers with tabulated field values or any datasets/repositories would be greatly appreciated.

Project Link

(Not self-promotion, unless you count open-sourcing a tool as self promotion. This is a free resource, an attempt to make a government service more available, and I don’t make any money from it.)

Howdy folks,

I wanted to share a project I’ve been working on, called Lance-TS.
It’s an opinionated TypeScript client for the U.S. Census Geocoder API, which is a free resource for geocoding U.S.-based addresses based on the TIGER/Line census geospatial database. It has no posted rate limits that I can find, and handles single and batch-address geocoding. Currently it handles address-to-coords, and I’ll implement coordinates-to-geography shortly.

My repo for this tool is attached, and the package can be installed from the npm registry with:

npm i lance-ts pnpm add lance-ts yarn add lance-ts 

Happy Geocoding! I’ve been working with map data a lot as I build some a platform for my company, and thought I would make this resource easy to access for more people.

Kindly submit any issues or edge cases you encounter while using LANCE, and I will fix them ASAP. Cheers!

I’m trying to build my own research / signal pipeline and I’m looking for something closer to Unusual Whales but without paying for a full subscription.

What I want is less dashboards and more raw data access.

Ideally:

Options / unusual flow / F&O activity

Insider trades

Politician disclosures

Hedge fund / 13F data

Dark pool / institutional signals

Near real-time or at least updated frequently

API / CSV / exportable data

Free or generous free tier

Right now I’m testing Finnhub and Tastytrade API but they don’t feel complete enough for this use case.Q

My goal is basically:

Raw data → Claude / custom filtering → synthesis → useful signals

Curious what people here actually use to assemble this stack. Open datasets, APIs, GitHub repos, hidden gems, anything.

Hey everyone,

I’m Sameer, a Business Analytics graduate currently building my data portfolio. I’m offering one free project to anyone who has messy or disorganized data they’ve been meaning to fix.

Here’s what I can do for you, completely free:

Clean and organize your Excel/CSV data (remove duplicates, fix formats, fill gaps)

Build a simple Power BI or Excel dashboard so you can actually see what’s in your data

Deliver everything back to you in a clean, usable format

All I ask in return is a short testimonial once we’re done.

Ideal if you’re a small business owner, logistics/supply chain manager, or anyone sitting on data they don’t know what to do with.

Drop a comment or DM me if you’re interested. I’ll respond quickly.

Sharing a data angle in case it’s useful.

US public companies disclose disaggregated revenue (by product and by geography) in their 10-K/10-Q/20-F filings, tagged as XBRL dimensional facts. It’s all free and public on SEC EDGAR, but it’s genuinely hard to use raw:

the geography axis is tagged inconsistently (some filers use ISO country codes, some US state codes, some their own “rest of world” catch-alls), companies mix subtotals and leaves on the product axis, and 10-Qs report cumulative half-year/nine-month figures instead of standalone quarters.

If you’re assembling this yourself, the things that bit me: keep single-axis facts only (the filings rarely tag product×geography as one crossed fact), preserve subtotal members rather than pruning them, and reconstruct standalone quarters by subtracting the cumulative periods. Period-classify each fact against the company’s real fiscal-year end, not the calendar.

I maintain a cleaned-up version of this as the StockFit API, but the underlying data is all on EDGAR if you want to parse it yourself with Arelle.

Happy to answer any questions.

So, a city where I live has recently decided to quadruple public transport fares and me and my friend group from university are making a study of consequences of rapid transport fares increase. We hope to get a credible correlation model or a heuristic at best. We have already acquired a list of 106 cities with close population density and now we need to get data on the price history of public transportation fare to then see which ones have seen comparable increase. Any additional advises are welcome.

I’ve been spending more time thinking about the dataset side of AI development and wondering where most teams encounter the biggest challenges.

A lot of discussions focus on model architecture and training techniques, but many production issues seem to trace back to the data itself:

• inconsistent annotations between labelers
• difficulty collecting rare edge cases
• balancing dataset diversity without introducing noise
• maintaining quality as datasets grow larger
• keeping training data aligned with real deployment environments

For those who work with datasets regularly:
• What is your biggest bottleneck today?
• How do you measure annotation quality?
• At what scale do dataset management problems become significant?

Interested in hearing real-world experiences from people dealing with data collection, labeling, and dataset maintenance.

Explanation and link to more datasets there. Actual data is at https://huggingface.co/datasets/ThGaskin/Migration_flows

All right, I’ve read ten articles on this and I still don’t think I understand it.

I have a small project to scrape product prices from some sites.

Nothing crazy, just e-commerce stuff. someone told me i need residential proxies but they’re like 5x the price of datacenter and i don’t understand why i would need them for something this basic Like, what actually happens if i just use datacenter? Will I be blocked right away or is it fine for most normal sites? And what the hell is an ISP proxy? is different again.

I’m just trying to not spend money on something I don’t need. any help appreciated.

Looking for free English audio datasets which I can use for transcription purposes.

I have searched on hugging face but didnt find any useful most had audio less than 10 seconds.

I have created a transcription tool and want to test it on longer audios like 5 mins and also with multiple speakers so i can test diarization as well.

Any help is appreciated.

I’ve spent the last year maintaining a public longitudinal self-tracking archive covering wearables, sleep, recovery, training, body composition, biomarkers, and weekly reporting.

​

The repository includes:

– raw and processed datasets

– longitudinal sleep and wearable records

– weekly reports

– audit trails

– prediction tracking and model-error analysis

– changelog and governance documentation

​

My goal isn’t optimization as much as documenting what long-term observation of a single subject looks like when treated like a data project.

​

I’m particularly interested in feedback on:

– dataset structure

– governance

– reproducibility

– longitudinal analysis opportunities

– potential blind spots in methodology

​

Current archive size: ~1 year of daily observations, weekly reports, wearable records, biomarker snapshots, and prediction-tracking artifacts.

​

Repository:

​

https://github.com/CDHughett/daniel-longitudinal-public

hi everyone!

i want to share with you a little project i created a few months ago to solve a problem i was having with function calling. whenever i needed a good quality and specific dataset to train my models on function calling i couldn’t find a good repo for generation. i wanted a dataset that teaches the model not only how to call the tool but also when, in different contexts. i also wanted to have maniacal control on the results, i wanted to control how many tools in each convo, when the tool is called, errors in tool callings and in particular i wanted something that was flexible enought to include *PERSONALIZED* tools with personalized mock answers!!!

for example you can find some tools i made for the sample below in the repo under

synthfc/tools/eng

and

synthfc/tools/ita

i also wanted a way to check the results and auto-correct the pieces of data that have problems. here is the repo:

https://github.com/pierpierpy/FC-synth

here some examples i created with an open source model:

https://huggingface.co/datasets/pierjoe/function-calling-synthetic-2000

hope you find it useful!

happy tool calling!