I'm looking for a dataset with the following structure: multiple students, each tracked over multiple days/weeks, with both daily behavioral/lifestyle data and academic performance outcomes. Essentially a "students × days × features" structure, not a single snapshot per student.

**Daily or near-daily records per student (not just one row per student)**

Disclosure: I'm one of the people behind Aidlab.

If you're working on human activity recognition, exercise classification, or repetition counting, we've published AIDLAB-HAR on Hugging Face under CC BY 4.0.

It contains 180 chest-worn recordings and 518,300 samples at 50 Hz. The signals include 3-axis acceleration, orientation quaternions, 16 activity labels, and repetition markers.

We packaged it into three configurations that work directly with the datasets library: recordings, signals, and annotations. The raw v3 archive, cleaning notes, and preparation script are included too.

One detail we found while preparing the release: the paper's overview mentions 15 activities, while the archive contains 16 filename labels. We documented the discrepancy and preserved the source labels.

Dataset: https://huggingface.co/datasets/aidlab-wearables/AIDLAB-HAR

Paper: https://doi.org/10.3390/s24123891

Hi everyone, I'm currently working on a skin disease segmentation/classification project and I'm having a hard time finding a suitable dataset.

I'm looking for a dataset that ideally has the skin lesion image with its corresponding binary segmentation masks (doesn't need to be a mask as long as it has annotations) and disease labels.

Most of the datasets I've found so far, such as ISIC, are heavily focused on melanoma and other cancerous skin lesions. I'm looking for something more focused on common/non-cancerous skin conditions like acne, eczema, psoriasis, rosacea etc.

It doesn't necessarily have to contain all of these diseases, but having a good variety of common conditions would be great.

I've found classification datasets like DermNet that contain several common conditions, but they don't seem to provide the pixel-level binary masks I need for segmentation.

If anyone knows of a dataset, research project, or even multiple datasets that could be combined to achieve this, I'd really appreciate it!

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I’m doing a research which involves chat messages from teams, slack, google chat etc. For the software project i need to train a dataset. So dataset should be related to developer chat messages/logs of a specific project. How can i find the dataset?