UNCOVER COVID-19 Challenge（COVID-19相关数据集）

原文：

UNCOVER COVID-19 Challenge

United Network for COVID Data Exploration and Research

Challenge Description

The Roche Data Science Coalition (RDSC) is requesting the collaborative effort of the AI community to fight COVID-19. This challenge presents a curated collection of datasets from 20 global sources and asks you to model solutions to key questions that were developed and evaluated by a global frontline of healthcare providers, hospitals, suppliers, and policy makers.

 

Dataset Description

This dataset is composed of a curated collection of over 200 publicly available COVID-19 related datasets from sources like Johns Hopkins, the WHO, the World Bank, the New York Times, and many others. It includes data on a wide variety of potentially powerful statistics and indicators, like local and national infection rates, global social distancing policies, geospatial data on movement of people, and more.

 

Challenge Details

The tasks associated with this dataset were developed and evaluated by global frontline healthcare providers, hospitals, suppliers, and policy makers. They represent key research questions where insights developed by the Kaggle community can be most impactful in the areas of at-risk population evaluation and capacity management.

To participate in this challenge, review the research questions posed in the dataset tasks and submit solutions in the form of Kaggle Notebooks.

 

We encourage participants to use the presented data and if needed, their own proprietary and non-proprietary datasets to create their submissions.

 

Timeline

The goal of the UNCOVER challenge is to connect the AI community with the frontline of responders to this global crisis. For this next round of submission the Roche Data Science Coalition will be evaluating solutions and surfacing this research to experts on June 27th. Each task will have one submission identified as the best response to the research question posed in the task. That submission will be marked as the “accepted solution” to that task.

 

Accessing the Data

Datasets have been made available here on Kaggle and are intermittently being updated from their respective sources.

 

You may also access the datasets through the Namara platform to get the most up to date version of each dataset, thanks to our collaborators at ThinkData Works.

 

Details on the provenance of each dataset are available in the file descriptions of each folder.

译：

揭开COVID-19的挑战

COVID数据探索与研究联合网络

挑战描述

罗氏数据科学联盟（RDSC）正在请求人工智能社区的合作努力来对抗COVID-19。这项挑战展示了20个全球来源的精心策划的数据集，并要求您对全球一线医疗保健提供商、医院、供应商和决策者开发和评估的关键问题的解决方案进行建模。

数据集说明

这个数据集由200多个公开可用的COVID-19相关数据集组成，这些数据集来自约翰·霍普金斯大学、世界卫生组织、世界银行、纽约时报和许多其他机构。它包括各种可能强大的统计数据和指标的数据，如地方和国家感染率、全球社会距离政策、人口流动的地理空间数据等。

挑战详细信息

与此数据集相关的任务由全球一线医疗保健提供商、医院、供应商和决策者开发和评估。它们代表了关键的研究问题，在这些问题中，Kaggle社区开发的见解在高危人群评估和能力管理领域最具影响力。

要参与这项挑战，请回顾数据集任务中提出的研究问题，并以Kaggle笔记本的形式提交解决方案。

我们鼓励参与者使用呈现的数据，如果需要，使用他们自己的专有和非专有数据集来创建他们的提交。

时间轴

“揭露挑战”的目标是将人工智能社区与应对全球危机的前线联系起来。对于下一轮提交，罗氏数据科学联盟将评估解决方案，并于6月27日将这项研究提交给专家。每个任务将有一个提交文件，确定为对任务中提出的研究问题的最佳回应。这一提交将被标记为该任务的“可接受的解决办法”。

访问数据

数据集已经在Kaggle上提供，并从各自的来源间歇性地更新。

多亏了ThinkData Works的合作伙伴，您还可以通过Namara平台访问数据集，以获得每个数据集的最新版本。

每个文件夹的文件说明中提供了每个数据集来源的详细信息。

大家可以到官网地址下载数据集，我自己也在百度网盘分享了一份。

链接：获取数据集