Scientific Data
A peer-reviewed, open-access journal for descriptions of datasets, and research that advances the sharing and reuse of scientific data.
A reference dataset helps researchers and policymakers understand the individual higher education institutions and their heterogeneity in Europe
Our new paper in Scientific Data presents the reference dataset on European Higher Education Institutions in Europe, now with up to 10 years of data and nearly 3,500 HEIs in about 40 European countries
Treasuring marine robotic data to observe the ocean
Data are vital to advance the understanding of our ocean. Emerging marine robots work in the operational gap left by commercial devices, hence by definition their data treatment is left in the hands of each research group.
Harnessing home technology for people living with dementia
Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living.
Unveiling Central Asia's Hidden Hydrological Treasures: CA-discharge, a Novel Data Compilation
A collection of geo-located river runoff time series in mountainous Central Asia.
Sharing Personal Health Data in the EU: Lessons from Successfully Implemented Open European ICU Databases
Empowering Healthcare through Data Democratization: Privacy Protection, Data Anonymization, and Trust-Building in Open European ICU Databases
Ten years of morphodynamic data for sustainable beach management
Sustained high quality data of nearshore waves and beach morphology are crucial to understand morphodynamic processes that determine beach evolution. We present a free and unrestricted dataset to support adaptation and mitigation actions under different global change scenarios in urban beaches.
VISEM-Tracking, a human spermatozoa tracking dataset
Manual sperm motility assessment is challenging; hence, computer-assisted sperm analysis (CASA) is used. We introduce VISEM-Tracking dataset with expert annotations, enabling deep learning models like YOLOv5 for accurate sperm analysis, improving accuracy and reliability in sperm motility evaluation
