Dataset Description. Owner agrees to transfer the LAMIS-DMDB dataset to Recipient: - Name of the Dataset: LAMIS-DMDB - Description: A full field digital mammography database for breast cancer AI-CAD researches
Dataset Description. Briefly describe the data captured for this project and its purpose. This should have sufficient detail that enables readers to quickly understand whether the project or dataset is of interest to them.
Dataset Description. The data following anonymization has been provided by Xx. Xxxxx Xxxxx, Champalimaud Breast Unit, Lisbon, Portugal within the framework of the BOUNCE EU funded project. A short summary is provided here.
Dataset Description. The data, following anonymization, has been provided by Prof. Xxxx Xxx-Horenczyk, THE HEBREW UNIVERSITY OF JERUSALE, Israel, within the framework of the BOUNCE EU funded project. The study details and outcome have been previously described [Xxxxxx-Xxx et al. 2012, 2016, Xxx-Xxxxxxxxx et al 2015, 2016]. A short summary is provided here.
Dataset Description. There are not formal requirements for the dataset description formulated yet. In particular, the description will depend on the type of a dataset. However, it is recommended to publishers of dataset to provide following information: • The nature of the dataset (scope of data) • The scale of the dataset (amount of data) • To whom could the dataset be useful • Whether the dataset underpins a scientific publication (and which publications) • Information on the existence (or not) of similar datasets • Possibilities for integration with other datasets and reuse It is also possible that the description will have additional internal structure (XML).
Dataset Description. The data set used to model mental health (MH) trajectories of HADS total scores over 7 measurement points (M0 to M18) comprised 474 patients who had no more than 1 missing value. Imputation, was implemented using the Multiple Imputation through Chained Equations (MICE) algorithm from the R package ‘MICE’ for 1, 14, 7, 12, 18, 17, and 19 observations, respectively for data at M0, M3, M6, M9, M12, M15, and M18. For modelling of global QoL trajectories across the 7 measurement points the available data set comprised 472 patients who had no more than 1 missing value. Imputation, was implemented for 9, 15, 8, 13, 21, 17, and 24 observations, respectively for data at M0, M3, M6, M9, M12, M15, and M18.
