Open Standards-Based Data Extraction Web Tool for Complex Longitudinal Datasets

描述（由申请人提供）：NIH资助了许多长期运行的纵向研究，这些研究收集了大量数据。这些调查继续增加额外的数据收集波到他们的数据集，增加了收集的信息的财富。不幸的是，随着更多的波继续增加，数据对研究人员来说变得更加复杂。当研究包含数千个变量时尤其如此。研究人员通常对与其研究问题相关的数据子集感兴趣，但必须遍历多个数据文件和许多页的文档才能找到与其主题相关的变量。复制研究也变得具有挑战性，因为它多次涉及到同样繁重的步骤。为了应对这一挑战，研究人员需要先进的工具来导航和提取大型纵向研究的数据。该项目旨在创建一个基于开放标准的网络工具，以提供从大型公共使用纵向调查中提取的数据。该工具将允许研究人员选择变量和变量组来创建数据提取。该工具还将为摘录创建码本文件和标准化的数据文件倡议3元数据，以便能够使用DDI标准引用摘录。通过使用DDI社会科学研究开放标准，该工具也将被推广到多项研究，这是对当今在每个研究基础上开发的一次性工具的创新。第一阶段可行性研究的目的是分析准备在线数据提取研究所需的数据准备和元数据创建工作流程，验证数据文件编制倡议DDI 3标准作为此类工具基础的使用，并创建基于网络的数据提取软件原型。虽然重点是纵向调查，但拟议的系统也将处理横截面，时间序列和非重复研究。其目的是通过简化用于发现，检索和分析与研究人员调查相关的数据的过程来改进研究方法。 公共卫生相关性：希望使用来自纵向研究的公共使用数据或复制他人研究的研究人员目前必须在多个波和数据集上导航数千个变量，以回答简单的分析问题。拟议的网络工具允许研究人员创建与其查询直接相关的数据提取，从而将更多的时间花在公共卫生研究问题上，而不是数据管理上。

DESCRIPTION (provided by applicant): The NIH funds many long running longitudinal studies that have collected massive amounts of data. These surveys continue to add additional data collection waves to their datasets which increases the wealth of information collected. Unfortunately, as additional waves continue to be added the data becomes more complex for researchers to work with. This is especially true when the study contains many thousands of variables. Researchers are often interested in a subset of the data pertaining to their research question, but have to traverse multiple data files and many pages of documentation to find the variables associated with their topic. It also becomes challenging to replicate research, since it many times involves going through these same burdensome steps. To address this challenge, advanced tools for researchers are needed to navigate and extract data from large longitudinal studies. This project aims to create an open standards based web tool to provide data extracts from large public use longitudinal surveys. The tool will allow researchers to select variables and variable groups to create data extracts. The tool will also create codebook documentation and standardized Data Documentation Initiative (DDI) 3 metadata for the extracts, enabling citation of the extract using the DDI standard. The tool will also be generalized to work for multiple studies by using the DDI open standard for social science research, which is an innovation over today's generation of one-off tools developed on a per study basis. This Phase I feasibility study aims to analyze to data preparation and metadata creation workflow needed to prepare a study for online data extraction, to validate the use of the Data Documentation Initiative's DDI 3 standard for the basis of such a tool, and to create prototype web-based data extraction software. While the focus is on longitudinal surveys, the proposed system would also handle cross-sectional, time-series, and non-repeated studies. The aim is to improve research methodologies through a simplification of the process used for discovering, retrieving, and analyzing data relevant to a researcher's investigation. PUBLIC HEALTH RELEVANCE: Researchers who wish to use public use data from longitudinal studies or replicate other's research must currently navigate thousands of variables across multiple waves and datasets to answer simple analysis questions. The proposed web tool allows researchers to create data extracts that are directly related to their queries, allowing more time to be spent on public health research questions instead of data management.