Data Science research is undergoing a revolution fueled by the transformative
power of technology, the Internet, and an ever increasing computational
capacity. The rate at which sophisticated algorithms can be developed is
unprecedented, yet they remain outpaced by the massive amounts of data that are
increasingly available to researchers. Here we argue for the need to creatively
leverage the scientific research and algorithm development community as an axis
of robust innovation. Engaging these communities in the scientific discovery
enterprise by critical assessments, community experiments, and/or crowdsourcing
will multiply opportunities to develop new data driven, reproducible and well
benchmarked algorithmic solutions to fundamental and applied problems of
current interest. Coordinated community engagement in the analysis of highly
complex and massive data has emerged as one approach to find robust
methodologies that best address these challenges. When community engagement is
done in the form of competitions, also known as challenges, the validation of
the analytical methodology is inherently addressed, establishing performance
benchmarks. Finally, challenges foster open innovation across multiple
disciplines to create communities that collaborate directly or indirectly to
address significant scientific gaps. Together, participants can solve important
problems as varied as health research, climate change, and social equity.
Ultimately, challenges can catalyze and accelerate the synthesis of complex
data into knowledge or actionable information, and should be viewed a powerful
tool to make lasting social and research contributions.

数据科学研究正在经历一场由技术、互联网和不断增长的计算能力驱动的革命。我们在此提出，需要创造性地利用科学研究和算法开发社区作为强大创新的轴心，通过关键评估、社区实验和集众智等方式，让这些社区参与科学发现探索，从而带来发展新的数据驱动、可复现且经过充分基准测试的算法解决方案，来解决当前感兴趣的基础性和应用性问题。通过协调社区参与高度复杂和大规模数据的分析，可以找到最佳应对这些挑战的鲁棒方法学。当社区参与采用竞赛形式，也被称为挑战赛时，分析方法的验证在本质上得到解决，建立了性能基准。最后，挑战赛促进跨学科开放创新，创建能够直接或间接协作解决重要科学差距的社区。通过共同努力，参与者可以解决诸如健康研究、气候变化和社会公正等各种重要问题。最重要的是，挑战赛可以催化和加速将复杂数据合成知识或可执行信息的过程，应被视为一个产生持久社会和研究贡献的强大工具。