Omkar2118/datasets huggingface dataset:
Fingertip Video Dataset of HB Estimation
Full name: Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia

This dataset comprises 1-minute fingertip video recordings collected from 150 anemic patients, ranging from 6 months to 32 years of age, with hemoglobin levels between 4.3 gm/dL and 12.4 gm/dL. The videos were recorded using a smartphone’s camera and flashlight, designed to capture PPG (Photoplethysmography) signals, which are essential for non-invasive hemoglobin level estimation.
Key Features:

Size: 150 videos (1-minute each)
Age Range: 6 months to 32 years
Hemoglobin Levels: 4.3 gm/dL - 12.4 gm/dL
Format: MP4 video files
Applications: Non-invasive anemia diagnosis, PPG signal analysis, deep learning-based medical diagnostics.

Please cite our work as:
Sabir, H., Khan, K.U., Ishaq, O., Alazeb, A., Aljuaid, H., Algarni, A., & Park, J. (2024). Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier. IEEE Access. DOI: 10.1109/ACCESS.2024.3398353

Introducing paper: Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier
User Story NeoDataset
Full name: User Story NeoDataset

This dataset includes User Story (or Issue) text descriptions, User Story titles, and Story Points from 33 software development projects, comprising a total of 20,479 User Stories (or issues) extracted from GitLab repositories, amounting to 12,262.7 Story Points. The mining process focused on GitLab’s top open-source projects that use agile software development methodologies and record task sizes in Story Points. Only tasks with the **State** attribute set to **Closed** and with the **Weight** attribute filled in were collected. The **Weight** field in GitLab is used to record the effort in Story Points. The data was mined between January 2023 and April 2023. The projects in the dataset have diverse characteristics, covering different programming languages, business domains, and geographic locations of the teams.

The paper below describe shortly this dataset in **section 4.1 Dataset**  
Citation Suggestion
Neo, G. S.; Moura, J.; Almeida, H.; Neo, A. and Freitas Júnior, O. (2024). User Story Tutor (UST) to Support Agile Software Developers. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-697-2; ISSN 2184-5026, SciTePress, pages 51-62. DOI: 10.5220/0012619200003693

Introducing paper: User Story Tutor (UST) to Support Agile Software Developers