Archiving and Documenting Child Health and Human Development Data Sets

在普通民众中创造关于人类行为和社会经验的大型科学研究-- 研究构成了一项巨大的科学投资--这种研究的高价值创造了高度的科学原理-- 在显著扩大这类数据的科学使用的传播工具方面具有优势。到目前为止，限制 传播美国人口数据的努力极大地限制了测试密钥外部有效性的能力 发现，增加了即使在美国人口中，来自任何特定研究人群的发现也有可能 可能不适用于其他重要群体。传播生活人口中的类似数据资源 在与美国和西欧截然不同的情况下，迫切需要这样的社会和 行为科学家可以快速、轻松地测试关键发现的外部有效性。 我们将利用NICHD在奇特万山谷家庭研究(CVFS)中的长期投资 我们希望尼泊尔能够实现这一高度优先的目标。由NICHD颁发的十几个不同奖项组成 20多年来，CVFS是来自美国以外的社会科学和健康科学的非常有价值的数据资源。 利用这一R03计划“对人口科学中现有的数据集进行存档和记录”，我们 将实施一系列数据改进以及新的传播和数据用户支持工具，以制定- 从而提高CVFS数据的可用性和可用性。CVFS是一种很好的比较数据再利用工具 来源，以尼泊尔20年来的小组研究为特色，有许多重要的特点。首先，CVFS是专门针对 旨在复制美国和欧洲最好的纵向研究的设计特点，例如 收入动态小组研究(PSID)。其次，它被设计用来衡量随时间变化的动态 在社区层面，不仅仅是个人和家庭层面。第三，CVFS衡量环境 随时间的变化，包括土地利用/土地覆盖、生境质量和生态系统的变化。第四， CVFS跟踪所有移民(包括个人和家庭)，无论他们搬到哪里，并定期重新- 对外来移民重新采集样本。最后，CVFS现在有了所有家庭成员的唾液DNA样本- 为所有儿童进行基于BERS和血液的贫血筛查。因此，CVFS是一个无与伦比的资源 研究避孕药使用和不使用的时间变化，整个生命过程的健康，以及环境因素的基因变化- 情绪相互作用。 我们将改变对这一特殊资源的访问，重点放在CVFS数据的使用上， 特别设计的便于比较的措施，构造新的数据文件以加快数据链接 以及数据分析、构建新的学习工具和新的基于网络的分析工具。这些活动包括 专门设计用于提高CVFS与来自其他设置的数据之间的可比性的透明度 以帮助科学家进行这些比较。我们还将提高数据质量和用户体验，以便 这些多层次、20年、多主题的数据的复杂性并不是数据使用的障碍。

The creation of large scientific studies of human behavior and social experience in the general popula- tion constitutes an enormous scientific investment – the high value of such studies creates a high scientific pri- ority on dissemination tools that significantly expand the scientific use of such data. To date the limitation of dissemination efforts to data on the U.S. population greatly restricts the ability to test the external validity of key findings, raising the possibility that even within the U.S. population findings from any particular study population may not apply to other important populations. Dissemination of similar data resources from populations living under circumstances quite different from the U.S. and Western Europe are urgently needed so that social and behavioral scientists can quickly and easily test the breadth of external validity of key findings. We will take advantage of NICHD's long-term investment in the Chitwan Valley Family Study (CVFS) in Nepal to achieve this high priority objective. Built from more than a dozen different awards from NICHD over 20 years, the CVFS is a very valuable data resource from outside the U.S. in social and health sciences. Harnessing this R03 program for “archiving and documenting of existing data sets in population science”, we will implement a series of data improvements and new dissemination and data-user support tools to dramati- cally enhance the availability and usability of the CVFS data. The CVFS is an excellent comparative data re- source, featuring a 20-year panel study from Nepal with many important features. First, CVFS was specifically designed to replicate design features from the best longitudinal studies in the U.S. and Europe, such as the Panel Study of Income Dynamics (PSID). Second, it was designed to measure dynamics of change over time in the community level, not just the individual and household levels. Third, the CVFS measures environmental changes over time, including changes in land use/land cover, habitat quality, and eco-system. Fourth, the CVFS follows all migrants (both individuals and households) no matter where they move and periodically re- freshes the sample with in-migrants. Finally, CVFS now has saliva based DNA samples from all family mem- bers and blood based anemia screening for all children. As a result the CVFS is an unparalleled resource for study change over time in contraceptive use and non-use, health across the life course, and gene by environ- ment interactions. We will transform access to this special resource with changes focused on use of the CVFS data, measures specially designed to facilitate comparisons, construction of new data files to speed data linking and data analysis, construction of new learning tools, and new web-based analysis tools. The activities are specifically designed to improve the transparency of comparability between CVFS and data from other settings to assist scientists making these comparisons. We will also improve data quality and user experience so the complexity of these multi-level, 20-year, multi-topic data is not an obstacle to data use.