Workshop on Translational Data Science (TDS 17)

生物、物理和社会科学正被大量数据淹没：1)来自新一代仪器和传感器，成本迅速下降，分辨率迅速提高；Ii)创建大规模仪器化环境；以及iii)使用产生大型数据集的高性能模拟。数据科学是一个新兴领域，它将相关学科的领域知识与统计学/数学和计算机科学/信息学相结合，正在发展以应对这一挑战。转化数据科学是一个新兴领域的新术语，它将新的数据科学原理、技术和技术应用于具有挑战性的科学问题，这些问题有望对人类或社会福利产生重要影响。当数据科学原理、技术和技术被应用于不同领域的问题时，也会使用这个术语，包括但不限于科学和工程研究。该团队将于2017年6月26日至27日在芝加哥举办第一届转化数据科学研讨会（TDS 17），作为促进转化数据科学学科创建的第一步，也是重要的一步。本次研讨会将汇集来自学术界、工业界、基金会和联邦资助机构的约50名科学家，讨论重要问题、挑战和机遇，包括转化数据科学的范围。研讨会的一个重要成果将是一份关于转化数据科学的白皮书。另一个关键成果将是规划未来围绕转化数据科学的研讨会，并开始创建转化数据科学社区。

The biological, physical and socials sciences are being overwhelmed by the large amounts of data: i) from new generations of instruments and sensors, with rapidly decreasing costs and rapidly increasing resolutions; ii) the creation of large scale instrumented environments; and iii) the use of high performance simulation that produces large datasets. Data Science is an emerging field that is developing to meet this challenge, which integrates domain knowledge from the relevant disciplines with statistics/mathematics and computer science/informatics. Translational data science is a new term that is being used for an emerging field that applies new data science principles, techniques and technologies to challenging scientific problems that hold the promise of having an important impact on human or societal welfare. The term is also used when data science principles, techniques and technologies are applied to problems in different domains in general, including,but not restricted to,science and engineering research. The team will hold the first Workshop on Translational Data Science (TDS 17) in Chicago on June 26-27, 2017 as a first, important step to foster the creation of the discipline of translational data science. This workshop will bring together about 50 scientists from academia, industry, foundations, and federal funding agencies to discuss important issues, challenges and opportunities, including the scope of translational data science. A key outcome from the workshop will be a white paper about translational data science. Another key outcome will be planning for future workshops around translational data science and the beginning of the creation of Translational Data Science Community.