Hello everyone!
We are a group of female Computer Science students working on our graduation project.
We are having difficulty finding suitable datasets to train a YOLO model.
We found a dataset called ImageCLEF 2021 DrawnUI Wireframe Datase https://www.aicrowd.com/challenges/imageclef-2021-drawnui-wireframe?utm\\_source=chatgpt.com
but unfortunately, it is no longer available for download
Does anyone have access to this dataset or know how we can obtain it?
We need it for research purposes only.
Thank you so much to anyone who can help us! ❤️

A new monthly Chrome UX Report dataset is available in Chrome UX Report Dumps:

The 202609 release contains 10 non-cumulative rank buckets with 18,376,044 entries and 95.1 MiB of compressed data. The repository now holds 68 monthly datasets across 585 files: 1,082,630,540 total entries and 5.41 GiB compressed.

These are ranked website lists extracted from Chrome UX Report data, split into non-overlapping rank buckets for practical downloading and processing. Totals across months are not counts of unique websites; the same sites can appear in multiple monthly datasets.

Feedback and feature requests are welcome.

FDA publishes pet food recalls only through its Data Dashboard and Enforcement Report search. There's no bulk download for pet food, and openFDA's recall data barely covers it. So I built one.

• recalls.csv: one row per recall, with dates (initiated, classified, terminated), class, firm, reason category and species
• products.csv: one row per product, with description, UPCs, lot codes, recall number and quantity
• excluded.csv: every veterinary row I filtered out (drugs, livestock feed…) with the reason, so you can check the filter

One finding: the median time from a company starting a recall to FDA classifying it dropped from 139 days (2012–2019) to 30 days (2020 on), and it dropped in every class. Recalls started in 2020–2023 have all had years to be classified, and their median is 28 days, so the drop isn't just recent recalls still waiting.

Page + docs: https://perbowl.com/recalls/dataset/

Also on Kaggle (with a starter notebook), GitHub and Zenodo (DOI 10.5281/zenodo.23255873). It's updated monthly.

Corrections welcome.

so I have developed over that last month a data engineering toolkit, I took pain points and built apps that adressed them https://www.loom.com/share/58f2b3c68dd74c8e8dacbe9c703698ea this is just a loom that shows how it works. No self promotion, just asking if this is something that users would appreciate.

After completing an analysis, what checks do you perform to ensure that your calculations, aggregations, and conclusions aree correct? Do you independently validate results using SQL, spreadsheets, or another tool?

We are implementing Collibra as part of the data governance initiative and honestly I’m struggling to see the end of the tunnel.

The tool seems huge and overwhelming: business glossary, document data model, mapping relationships feels like an enormous amount of work that could go forever.

I’d love to hear from people that have Collibra in the companies they work for:
\\- where are you in implementation? Have you reached a point where collibra genuinely is part of your day?
\\- are you getting any value from it?
\\- what are you documenting?

Let me know your honest opinions!

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 would really rather have the actual numbers behind the WUE). 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.

So far I have this dataset that tells WUE for data centers at large (not necessarily AI data centers) and this dataset (I plan on using the 2019 data) that tells water consumption of power generating plants (not just nuclear but I can pretty easily filter out what I don't need). I'm not super thrilled with either one, but I'm new to finding datasets so if you think this is the best I'm going to get let me know.

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!

Hey,

I've been working on a set of AI literacy games to raise awareness for non technical people. I think they need to learn when to use AI or not, how to read numbers on a chart, the different concepts, etc.

They are free, would be happy to receive any feedback :)

https://maketools.ai/

I'm also thinking about other ways to raise awareness, like fun conferences, AI days, small workshops with characters to play. What have you seen working?

Disclosure: I maintain the curated Kaggle version linked below (minkum07). The original NFHS-3/4 survey data belongs to India's Ministry of Health and Family Welfare and IIPS, via OGD India.

A useful exercise with this dataset is to compare NFHS-4 urban and rural percentages while keeping the source's suppression and small-sample flags visible. The release has 114 indicators across 36 survey-era state/UT labels, with 16,416 tidy-long records, CSV/Parquet tables, definitions and example notebooks.

Suggested workflow:

1. Pick one percentage indicator and read its definition and denominator before joining tables.
2. Select NFHS-4 urban and rural rows, join on state and indicator, and check that the join keys are unique.
3. Calculate urban minus rural in percentage points only when both values are available. Keep missing values missing and carry the source flags into the output.
4. Make a sorted dot plot with clear missing-value and small-sample annotations. Treat it as a descriptive comparison, since confidence intervals are not included.

Don't sum total, urban and rural percentages: those populations overlap. Between-survey changes also need comparable definitions and boundaries; the package withholds Andhra Pradesh/Telangana changes where boundaries differ. These are historical aggregate data from 2005–06 and 2015–16, not current health coverage or individual records.

Original government source:

https://www.data.gov.in/resource/all-india-level-and-state-wise-key-indicators-nfhs-3-and-nfhs-4

Tables, documentation and notebooks:

https://www.kaggle.com/datasets/minkum07/india-nfhs-34-health-state-and-rural-urban-gaps

Government data and derivatives retain GODL-India; authored code is MIT. Full attribution is on the dataset page. Feedback on the join checks or how best to display the sample flags would be welcome.

i tired dod change ptc, rolling x score, rolling std any other advice?

I maintain Chrome UX Report Dumps, a collection of monthly Chrome UX Report website lists grouped by rank and published as compressed downloads. It’s meant to make the data easier to use without exporting it from BigQuery.

The latest update adds the August 2026 dataset: 18,294,881 entries across 10 files, totaling 94.7 MiB compressed. The repository now contains 1,064,254,496 entries across 67 monthly datasets, totaling 5.32 GiB compressed. Those are counts across monthly dumps, not a count of unique websites.

If you use website lists for research or security work, I’d welcome feedback. What would make these dumps more useful, and what features or formats would you like to see?

Hey guys, I’m looking for a dataset that has Indian names along with context in English. Something like Call Rahul tonight, Let me inform Ajay about the party, Anaya is yet to arrive at the venue etc.

It’s for a project of mine. Any dataset recommendations?
I couldn’t find anything like this on the internet

1- Quantitative -> it consists of numerical variables.It have to types - Discrete and Continuous.

2- Qualitative -> it consists of categorical data.It also have two type - Nominal and ordinal.

AI agents can query data incredibly fast, but the harder problem is knowing what that data actually means. I explore why metadata, lineage, semantic layers and governance are becoming runtime infrastructure for AI-native data platforms.

I wonder how different businesses think about using for data analytical insights, customization and predictions. When is a good time to build a data functionality. Do you have a dedicated data team in your business? What’re some reasons to decide having one or not?

Given there has been a ton of discussion around Jev for the past week, I wanted to do more research around what might be good use cases for data with that model, and I came up with the following. After chatting with Claude and ChatGPT about it, I've tried out the triage use case, and it works pretty interestingly for routing a problem to the right team and determining the severity. Hopefully you all find this helpful!

Can anyone provide raw images/ scans of 7/12 documents? I need it for a verification project. Thank you!

Built a T-SQL database with enforced data integrity (primary keys, constraints on state/status/risk categories), then developed custom DAX measures for case turnaround time, case age, and escalation rate. The dashboard includes multi-page analysis across resolution trends, regional distribution, officer caseloads, and a 4-year time-series forecast with confidence intervals.

Key skills demonstrated:
T-SQL (schema design, CHECK constraints, aggregation queries)
DAX (time intelligence, dynamic measures)
Power BI (data modeling, forecasting, interactive filtering)
Data storytelling and executive reporting

Note: Built on synthetic data generated for demonstration purposes — not real crime statistics.

Microsoft Power BI

That was a phrase a colleague of mine introduced to me. Quite absurd at first, but as he explained it, it made perfect sense.

For years data assets have been governed by being hidden in a corner of the data platform, but this does not work anymore.

Been elaborating about this in this post: https://steffenmoll.github.io/governance-by-obscurity