## 1 Introduction  
The XPRIZE Foundation [1; 2; 3] designs and operates multi-million-dollar, global competitions to incentivize the development of technological breakthroughs that accelerate humanity toward a better future. The foundation's mission is to inspire and empower a global community of problem-solvers to positively impact our world. XPRIZE believes solutions to the world's problems can come from anyone, anywhere. As part of that philosophy, XPRIZE looked to develop a Data Collaborative that will support the resolution of complex, global problems. It had three broad goals:  
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2. Comprising a collection of unique datasets and AI tools, this Data Collaborative would democratize data and the tools to analyze them, enabling virtually anyone, anywhere to use data to solve the world's grandest challenges. 3. It would provide access to the massive amounts of data that have been organized and cleaned so that it can be accessed in an accountable, transparent, and responsible manner. 4. Data owners across industries and topic areas will feel safe in sharing their data while maintaining ownership, privacy, and security. Data scientists, as well as Machine Learning and Artificial Intelligence systems, will all have access to the Data Collaborative, resulting in enhanced problem-solving models and innovative approaches to solving Grand Challenges posed by XPRIZE competitions. Implementation of the Data Collaborative started in late 2019 but paused when the COVID-19 pandemic struck in early 2020. The XPRIZE Foundation decided to leverage existing Data Collaborative plans to make COVID-19 relevant data available from the XPRIZEPandemic Alliance [4; 5], which is a cooperative of different organizations sponsored by the foundation that would contribute data for analysis. The Data Collaborative would first be used for COVID-19 efforts and then be reused for other datacentric XPRIZE competitions and challenges. A few existing environments and services have similarities to the Data Collaborative. Google's Kaggle [6] is a web-based environment that provides an analytics environment to users at large along with a large collection of publicly datasets. Similar to the Data Collaborative, Kaggle is used for hosting competitions [7], focusing on machine learning-oriented problems. Google also maintains Colab [8; 9], which provides a Python-oriented environment. Available commercial services for hosting analytics environments include CoCalc [10; 11] and Nextijournal [12]. The XPRIZE Foundation differs from these operations by not focusing on individual data scientists or consumers and instead focusing on fewer, higher impact technology challenges run by teams of people. We present the design and implementation of the XPRIZE Data Collaborative as a descriptive case study [13; 14] of a Cloud Native Application (CNA) [15; 16; 17; 18], from requirements, design, and implementation to operating experiences. Many case studies and surveys of Cloud implementations focus on particular categories of Cloud Technology, such as microservices and container technologies [17; 19; 20] and serverless computing [21; 22; 23], but our examination asks the question of whether real CNAs are actually so narrowly focused and if they would use other Cloud technologies, such as Software Defined Infrastructure (SDI) [24] and even older, seemingly deprecated technologies such as Virtual Machines (VMs). As we describe our implementation choices, we match those choices with common, well-known design Cloud Design patterns [18; 19; 22; 23; 25]. Our case study also looks at whether the COVID-19 pandemic affected the Data Collaborative's design, development, and implementation. In this introduction, we have described critical background information such as the XPRIZE Foundation's mission and goals for the Data Collaborative. We have listed similar and related services that currently exist and provided context regarding Cloud Native Applications with which we will frame our case study.