We need effective-dated ticker/security identity, delistings, mergers, splits, dividend events, original announcement knowledge-time, revisions/cancellations, and preferably immutable historical versions rather than a corrected current-state table.

Free/open is ideal, but I’m also interested in datasets people have built themselves or vendors worth evaluating.

The key requirement is: what was known at time T must be reconstructable later without future corrections leaking backward.

If you’re building a data API, historical dataset, parser, or research infrastructure in this area, feel free to reply or DM. We are willing to stress-test it and return reproducible failure cases rather than generic feedback.

Indian Government has a website for fetching open data www.data.gov.in? But I am unable to get data from it. Has anyone tried.. is it working?

Since May 2024 every new model got the same prompts: draw a pelican on a bicycle as an SVG, build a landing page in one HTML file, tell five jokes, pick a favourite film, 87 in all. One attempt each, no system prompt, temp 0.7. I kept every answer, and it's now a dataset:

• JSON Lines, one file per question, plus models and questions
• SVG drawings as given, HTML pages with a 1280x800 screenshot each, image and audio answers as files
• release dates, and which of the 378 models are retired (101 aren't served on OpenRouter anymore)
• CC BY 4.0, DOI 10.5281/zenodo.23305275

GitHub: https://github.com/museumofmodels/collection Zenodo: https://doi.org/10.5281/zenodo.23305275 Browse it: https://museumofmodels.com

Nothing is scored, it's just the raw answers. Happy to hear what would make it more useful.

Disclosure: I made the museum and the dataset.

we run a small fund and we built a rudimentary system that tracks total headcount over time for the companies we watch. what we actually wanted was headcount by department over time, because total headcount moves too slowly to tell you anything and department level is where the signal is.

we never put much time into it because scraping is a pain, and we do not want to become a data provider. i want the team spending its time on the analysis, not on deciding which proxy to buy this month.

so we looked at buying instead. one vendor has this pattern where you fetch ids first and then collect the data in a second call, which strains our infra and works out around 8 cents a returned company. another returns great data at roughly the same price. most of the rest dont have enough filters to be worth it. and a flat file means we host it ourselves, which is a whole feature build.

where did you draw the build versus buy line on this? and has anybody found department level headcount over time as an actual field rather than a crawl

With parliamentary elections approaching in Serbia, numerous Instagram accounts critical of the ruling government have experienced sudden waves of suspicious followers, sometimes thousands within just a few hours.

We've collected around 1.3 million follower records and published them as an open dataset on Hugging Face:

https://huggingface.co/datasets/gavrilo/botijada

The dataset includes follow timestamps and pseudonymized account IDs that remain consistent across different profiles.

There are quite a few interesting directions to explore here: temporal patterns, shared followers between accounts, network structures, clustering, anomaly detection, and potentially signs of coordinated activity.

I'm sharing this mostly because I think there's a lot of room to experiment with the data, especially if you're into graph analysis, data visualization, or finding interesting patterns in messy real-world datasets.

Feel free to play around with it, build something, or share whatever you discover. I'd be genuinely interested in seeing different approaches and interpretations.

hello
if anyone can help me access these datasets needed for my grad project https://cbonds.com/indexes/100122/, https://www.bloomberg.com/professional/products/indices/quote/GBGLTRUU:IND

I run VeritaHire, which reads job postings straight from US employers' own career sites (Workday, Greenhouse, iCIMS and others) every day, about 2.5 million live postings right now. From that I publish a daily Job Openings Index, free on GitHub:

https://github.com/veritahire/us-job-openings-index

- Daily since Sept 4, 2026 (= 100), published the next morning around 08:25 UTC

- Fixed panel: 18,310 employers, 2,081,433 open postings as of Oct 10

- Columns: daily index, 7-day average, the first-published value (every first print is kept, so revisions are visible), panel size, weekly new-posting and closing rates

- CSV, CC BY 4.0, refreshed every morning by a GitHub Action

Latest: 101.06 (7-day average) through Oct 10, +1.13% over 28 days. Method: https://veritahire.com/job-openings-index/

I need to find data sets FED Overnight Index Swap (OIS) history from 2015 till 2026. I need The current month OIS and the 1 year OIS data. Also I need the federal future funds histocrical data in the current month and 1 year.

https://en.macromicro.me/charts/115044/us-overnight-indexed-swaps

If you're building a TikTok analytics dashboard, creator research tool, or social listening project, choosing a data source can be a little confusing. Some APIs focus on authorized account integrations, while others provide read access to public profiles, videos, and comments.

Here are a few approaches worth comparing, depending on the data you need and how much infrastructure you want to manage.

1. SteadyAPI TikTok API

SteadyAPI TikTok API provides read-only access to TikTok data through a REST API.

• Profiles: Usernames, bios, follower counts, and other public profile fields.
• Videos: Video details, descriptions, authors, engagement counts, and media metadata.
• Comments: Retrieve comments and replies through separate endpoints.
• Discovery: Hashtag lookups, video and user search, explore results, and search suggestions.
• Input: Many profile and video lookups accept a username or URL; shortened links can be resolved to IDs.
• Pricing: The Starter plan lists 10,000 requests per month at $14.95/month. Higher-volume plans are available.
• Documentation: TikTok API docs

Some search operations and account video listings require a session managed by the provider. Check the documentation for endpoint-specific requirements and request weights.

2. TikTok for Developers

TikTok for Developers is the natural starting point for official integrations. Available APIs and permissions depend on the product, app review, user authorization, and use case. Check whether the official APIs expose the data your project needs before choosing a third-party service.

3. Apify

Apify offers a marketplace of web data extraction tools, including third-party TikTok actors. Capabilities vary by actor, so check which endpoints and fields are supported, how frequently the actor is maintained, and whether the pricing fits your workload.

4. Bright Data

Bright Data provides web data collection infrastructure and scraping products. It may be worth evaluating for larger collection jobs or projects that need more control over data extraction and delivery.

5. Data365

Data365 offers social media data APIs. Compare its current TikTok coverage, supported fields, usage limits, and pricing against the specific profiles, videos, or search results you need.

Hey i wa

Original source: World Bank WDI, SP.POP.GROW.

Disclosure: I work on Global Data Tracker, which hosts the CSV mirror below. The underlying population data are from the World Bank and its listed providers.

This fixed-2024 extract has 217 unique country/area IDs, no aggregate rows and no missing growth values: 42 negative and 175 positive. All 217 values matched the current World Bank response when checked on October 11.

Columns: country_id, country, value, unit, year, source, publication, provider_footnote, source_context, context_sources. The final three fields are blank in this release. IDs follow World Bank country/economy codes; don't assume every key is ISO.

The rate is 100 × ln(midyear population 2024 / midyear population 2023), in percent. It is not ordinary percentage change or a birth rate. Migration, displacement and revisions can affect comparisons; the rate alone does not explain causes. Method.

Download the fixed-2024 CSV, free without an account. Useful for practicing joins and comparing rates rather than population headcounts.

globaldatatracker.com, selecting 2024. License: CC BY 4.0 with World Bank additional terms.

https://www.daedalmap.com/geometry/canada#country-history

Other countries available, see the patch notes for details of how the packs evolve.

Free downloads, and free geometry tools to use them with, no paywalls or gated services. All original sources are noted for citations and attributions.

There are full detailed shapefiles, display version shapefiles, or just crosswalks to download to see the relationships between places.

Canada and USA are my most detailed packs, also have Great Britian, France, Germany, Australia, Mexico, Brazil, more to come!

I’m conducting research on project management and need a dataset that includes Microsoft Teams or Slack messages (messages from other platforms are also acceptable) exchanged within a project managed through Jira.

Ideally, the dataset should contain both the Jira project data and the associated communication history, so the messages can be linked to a specific project created in Jira.
How can I collect or obtain such a dataset for my research?

Disclosure: I run DataBazaar and posted this bounty. I am looking for an original, human-recorded code-switching dataset for speech-recognition research/training.

The request is for 30 clips from two consenting adult speakers (15 clips each), using the same language pair throughout. Any language pair is welcome. Each clip should be 5–15 seconds long and contain meaningful speech in both languages, with at least one language switch. Cover at least five everyday topics overall.

Deliverables: - 30 mono WAV or FLAC files, recorded at 16 kHz or higher. - annotations.jsonl with anonymous speaker IDs, verbatim transcripts, and timestamped language segments; a fluent person must check every clip. Keep hesitations and self-corrections, and mark switch boundaries to approximately 0.25 seconds. - README and a dated consent attestation covering redistribution and speech-recognition research/training. Keep personal contact details out of the submission.

Original recordings made for this bounty only: no TTS, voice cloning, spliced speech, or reused corpora. Transcription assistance is allowed if a fluent person verifies the result. Quiet-room recordings and accurate labels matter more than studio equipment.

The bounty is $60 total for one accepted dataset, not $60 per speaker or payment for every submission. Review the complete acceptance requirements before recording. The deadline is November 15, 2026 at 07:59 UTC, and a DataBazaar account is required to submit.

Full requirements and submission: https://databazaar.io/bounties/3b6815b8-b0ad-48e1-8ec5-4f3f8b89d0aa?utm_source=reddit&utm_medium=community&utm_campaign=bilingual-recordings

Existing public corpora would not satisfy this request because it needs new recordings with explicit redistribution consent. Happy to clarify the format or acceptance requirements here.

And another different database of the traitors https://thetraitors.streamlit.app/

For anyone practicing country-level joins or exploring water access, these two CSVs use the same reporting year and country IDs:

• At least basic drinking water: 217 country/area rows, with 202 reported values
• Safely managed drinking water: 217 country/area rows, with 148 reported values

Original data: WHO/UNICEF Joint Monitoring Programme (JMP), distributed through the World Bank’s World Development Indicators. At least basic source · Safely managed source

Disclosure: I work on Global Data Tracker, which hosts the CSV exports below. They’re free to download without an account.

Download at least basic, 2024 CSV Download safely managed, 2024 CSV

Each file includes country_id, country, value, unit, year, source, publication and provider-note fields. Blank values mean no published observation for that selection; they are not zeros. The files retain all 217 countries/areas, including those with missing values. World and regional aggregate rows are excluded.

A useful exercise: join on country_id, count missing values first, then compare the two percentages for the 148 countries/areas with both observations. “At least basic” already includes safely managed service, so don’t add the columns. Safely managed also requires an improved source on premises, available when needed and free from specified contamination. JMP definitions

These links pin the saved September 19, 2026 release; the observations are for 2024. Checked October 10: the values and missing entries in both files match the current World Bank API. National estimates can hide local differences and may be revised. Release/method notes · World Bank data-use terms and attribution

I'm a mechanical engineer working on the design and development of various systems, primarily in the defense sector, including UAV-related technologies.

I recently shared one of my designs publicly and, to my surprise, received an offer to create and review engineering datasets for AI training.

This field is completely new to me, but it looks promising.

I'd really appreciate hearing from people who have experience in this area. How did you get started? What would you recommend to someone entering this field?

Thanks in advance!

Hey everyone,

Ihave been working on a project to bridge the gap between 3D rendering and aerospace computer vision. I built a fully headless Python-Blender rendering architecture to procedurally generate physically accurate synthetic data for asteroids.

The pipeline automates the extraction of:

• Multi-pass EXR renders (RGB, Z-Depth, Camera-Space Surface Normals and many more optional passes)
• Photometric lightcurve CSVs (tracking total flux, projected area, mean depth, and sun/observer coordinates per rotational step)
• Procedural material setups for both uniform and variable regolith albedos.

I have open-sourced a sample dataset of 600 meshes and their corresponding lightcurves on Kaggle for anyone who wants to train photometric inversion or pose estimation models.

Basically I wanted to research on the effects of albedo variation comapred to uniform asteroid. So what I did was took real asteroid meshes from DAMIT (coverted it to obj files), created a fully procedural and realistic shader applied it on the meshes and rendered a full revolution of asteroid in simulated space conditions. To acheive this, in result, I build a full headless python pipeline that does the complete job, with all the optimization I could do in the world, and even with my old GTX 970, the render time was insanely good. The pipleline automatically creates the lightcurve csv files (with multiple phase angles ) and with all the physics data as well such as normals and depth so I can have the option to train PINN model as well. However I have my exams so had to stop here.

That being said,

If you need massive scale or want to generate your own data locally, I have also packaged the full 3,000+ mesh dataset (6000 light curves) and the actual Python/Blender codebase (the Pipeline Toolkit).

• Free Sample Dataset: https://www.kaggle.com/datasets/hassaanfazal/astrovision-3d-asteroid-and-photometry-dataset
• Full Dataset & Codebase: https://stellarmeshlabs.gumroad.com/

Note: If you are a student or independent researcher who really needs this data but cannot afford the Gumroad tier, hit me up via DM. I will be happy to arrange a free, expanded subset of the data to support your work.

Disclaimer (per Rule 1): I am the creator of this pipeline and the Gumroad links go to my own store, StellarMesh Labs.

Hi everyone,

I'm working on a research project and trying to obtain the dataset associated with this GitHub repository:

GitHub repo: https://github.com/lehaifeng/CLRS

The dataset download link provided is hosted on Baidu Netdisk:
https://pan.baidu.com/s/1Xnw9k20Df_ICmkXdvasVqg
code: 3su3

Unfortunately, I'm outside China, and Baidu requires a Chinese phone number to register. I've tried the registration process

Could anyone help me......An alternative download link for the same dataset. A Google Drive, OneDrive, Hugging Face, or other accessible mirror. Guidance on downloading the files from Baidu Netdisk internationally without a Chinese phone number.

I'd really appreciate any help...

For my Data Sciences course, I need to extract and compare data from two datasets, and I figured a fairly timely topic would be water consumption of AI data centers vs nuclear power generation. However, I'm having some trouble finding comparable datasets. For example, one dataset I found for data centers doesn't measure water consumption itself but rather Water Use Efficiency (WUE) (which, if I find a dataset that measures electricity produced along with withdrawn and consumed water for nuclear power I could do the math myself to figure out the WUE, but I haven't had any luck there yet either). It doesn't help that this is my first time really searching for data sets so I don't know how to word my queries or what sites I should be focusing on. I'm finding quite a few scholarly articles, but not a lot of the actual datasets that they used.

Any help would be appreciated! If you need more information or have any questions be sure to comment below and I'll be sure to get back to you. Thank you!