five

Metadata Schema of the Common Data Elements for Mental Health (v5)

收藏
DataCite Commons2021-07-20 更新2025-04-15 收录
下载链接:
https://kg.ebrains.eu/search/instances/Dataset/e5aad7e8-b42d-4cd2-8cac-90998a4dd6ff
下载链接
链接失效反馈
官方服务:
资源简介:
Data in the Medical Informatics Platform (MIP) resides in hospital servers and never leaves the hospital. Analyses and experiments are executed with respect to that principle while preserving patients’ anonymity making it infeasible for their identity to be inferred. Hospitals importing their data to the Medical Informatics Platform join a federation so as to run analyses on data from other hospital nodes as well. Each federation in the MIP refers to a specific Medical Condition. Here the metadata for the federation of hospitals that studies mental health is presented. This metadata schema, consisting of a total of 191 variables, has the following main variable categories: **1. Demographic** - 2 variables for basic demographic information that does not reveal patient’s identity **2. Separation anxiety** - 30 multinomial variables for anxiety concerning several reasons and situations **3. Fears of specific things or situations** - 23 multinomial variables for fears of several objects, people or situations **4. Brain anatomy** - 135 volumetric variables based on a brain atlas. Values have been generated from the brain feature extraction pipeline which uses SPM12 The metadata viewing and management is done by Data Catalogue, a central web portal of MIP. Data Catalogue offers presentation, search and hierarchical visualisation of metadata information for datasets imported into the MIP while providing metadata management features to authorized users. One of its features is parsing metadata descriptions in XLSX files and generating their equivalent in a hierarchical JSON format which the MIP uses. In this repository we upload metadata in both XLSX and the generated JSON format.
提供机构:
EBRAINS
创建时间:
2020-06-18
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作