Community Research Education and Engagement for Data Science (CREEDS)

 描述（由申请人提供）：数据丰富的生物医学科学的发展速度在一定程度上受到以下因素的限制：缺乏对大数据集和工具的认识和专业知识，缺乏有效交叉不同领域的方法，以及劳动力中不同群体的代表性不足。为了解决这些差异，我们提出了CREEDS：数据科学的社区研究教育和参与。这个头衔代表了我们对培养早期职业研究人员的国家，多元化和跨学科社区的实用技能的总体目标的承诺。我们的三个具体目标是：（1）为生物医学研究人员提供所需的实践技能和洞察力，以利用大数据科学的力量并推进其承诺，以加速科学发现，（2）开发在线社交环境，以促进新手和专家之间的大数据想法，方法和技术的交流，以及（3）通过有针对性地招聘和保留弱势和代表性不足的学生群体，增强生物医学大数据劳动力的多样性。我们将亲自参与150名研究生通过密集的，自我定制的，为期两周的暑期学校在纽约市，将展示有趣的，当前的，在整个纽约市的学校活动的合作案例研究。它还将包括负责任的研究行为的培训。参与者将采用主动学习技术，通过在现实生活中的大型数据集上的个人和小组任务来发展他们对特定新方法和工具的技能。培训将提高不同背景的学生的技能，使其达到足够的水平，以进行更多的研究生研究，并且不需要任何先前的计算经验。学生还将获得经验和材料，以帮助他们教别人，当他们回到自己的家乡机构。此外，我们还将指导另外30名纽约大学的研究生参加为期四个月的梦想挑战赛。梦想挑战团队将帮助发展解决困难的计算技能，现实生活中的生物医学问题。Kipin Hall在线社交环境将使参与者能够发现工作机会，找到数据源和工具，并与同行以及大数据专家进行交流。我们将利用我们与服务于代表性不足的群体的组织的现有关系，以促进参与并加强这一令人兴奋的领域。为了不断改进我们的计划，我们将积极寻求和回应我们的参与者，计划教师和我们的顾问小组的反馈。作为这个项目的结果，我们将培训ovr 180人来发展团队技能，以理解，选择和使用基因组学数据工具和方法。我们将通过将暑期学校放在Coursera上来接触更多的人。有了这些知识，我们计划下一代基因组学科学家将更好地设计，分析和解释高通量基因组学数据集。这是研究教育的重要需求，我们的目标是与CREEDS解决。

 DESCRIPTION (provided by applicant): The speed of advancement in data-rich biomedical sciences is limited in part by the lack of awareness and expertise in big data sets and tools, the absence of approaches that productively intersect disparate domains, and the underrepresentation of diverse groups in the workforce. To address these disparities, we propose CREEDS: Community Research Education and Engagement for Data Science. This title represents our commitment to our overall goal of fostering practical skills for a national, diverse and interdisciplinary community of early career researchers. Our three specific aims are: (1) to provide biomedical researchers with the practical skills and insight needed to harness the power and advance the promise of big data science to accelerate scientific discovery, (2) to develop an online social environment to facilitate the exchange of big data ideas, approaches and techniques between novices and experts, and (3) to enhance the diversity of the biomedical big data workforce through targeted recruitment and retention of disadvantaged and underrepresented student populations. We will personally engage 150 graduate students through an intensive, self-tailored, two-week summer school in NYC that will showcase interesting, and current, collaborative case studies in activities at schools throughout NYC. It wil also include training in the Responsible Conduct of Research. Participants will employ active learning techniques to develop their skills of specific new methods and tools through both individual and group tasks on real-life large data sets. The training will raise the skills of students of varied backgrounds to a sufficient level for additional graduate research and will not require any prior computing experience. Students will also receive experience and materials to help them teach others when they return to their home institutions. Additionally, we will mentor another 30 NYC-based graduate students for team participation in four month long DREAM challenges. The DREAM challenge teams will help develop computational skills on solving difficult, real-life biomedical problems. An online social environment, Kipin Hall, will enable participants to discover job opportunities, find data sources and tools and communicate with peers as well as big data experts. We will leverage our extant relationships with organizations serving underrepresented groups to boost participation and enhance this exciting field. To continually improve our program we will actively seek and respond to feedback from our participants, program faculty and our advisory panel. As a result of this project, we will train ovr 180 people to develop team skills to understand, select and use genomics data tools and approaches. We will reach more individuals by placing the summer school on Coursera. Armed with this knowledge, we plan that the next generation of genomics scientists will be better placed to design, analyze and interpret high-throughput genomics datasets. This is the important need for research education that we aim to address with CREEDS.