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Cross Dataset Query Interface

The goal of NSRR Cross Dataset Query Interface is to be directly used by clinical researchers, for activities such as data exploration seeking to formulate, clarify, and determine the availability of support for potential hypotheses as well as for cohort identification across multiple datasets.

Click here to go to NSRR Cross Dataset Query Interface

NSRR Cross Dataset Query Interface has three main components:

  1. Query Builder, with terminology support and visual controls such as slider bar and checkboxes to compose query critera;
  2. Query Manager, which stores and labels queries for reuse;
  3. Graphical Exploration, which allows exploration of graphical distribution for core terms across multiple datasets.

Tutorials (click here to view a tutorial video)

  • Tutorial 1 - Query Builder
  • Tutorial 2 - Query Manager
  • Tutorial 3 - Graphical Exploration

Learn More!

Tutorial 1 - Query Builder

Go to NSRR Cross Dataset Query Interface website https://www.x-search.net[Fig01].

Fig01: The NSRR Cross Dataset Query Interface homepage.

You can take a tour by clicking the "Query Guide" button to learn how to construct queries and explore graphical distribution. Clicking the "Build Query" button or the sub menu QUERY BUILDER under the menu QUERY will lead you to the Query Builder page[Fig02].

  • In the "Area for data source selection" (blue box 1), you can select datasets of interest such as SHHS (Sleep Heart Health Study) and CHAT (Childhood Adenotonsillectomy Study).
  • In the "Area for searching core terms" (blue box 2), you can look up a specific core term in two ways. The first way is "browse" (red circle), where you can browse core terms in terms of categories. For example, clicking the first category "01 - Demographics" (Arrow A) will pop up a sub menu with all the core terms (e.g., "Age", "Ethnicity", "Gender", "Race") or sub categories under this category. The second way is "search" (red circle in Fig03), where you can search for core terms like "gender" in the text box. Then a list of candidate variable terms, which partially match the search term, will be automatically displayed.
  • Clicking an appropriate candidate term (Arrow B or Arrow C in Fig02 or Arrow D in Fig03) will add a corresponding individual query widget to the "Area for query composition" (blue box 3), where you can specify the query based on the type of the term and dataset variables mapped to this term. If the term is a categorical type, then checkboxes will be provided for you to select. If the term is a numeric type, then a slider bar along with two text boxes specifying range will be provided so that you can either slide the bar or enter a range in the text boxes.
  • Clicking the "Query" button on the bottom left corner of the "Area for query composition" (blue box 3) will generate the number of unique subjects satisfying the composed query criteria. For example, both Fig02 and Fig03 show a sample query for unique number of female subjects in SHHS and CHAT with age between 10 and 50.

Fig02: Query Builder by Browsing Terms. This shows a sample query for unique number of female subjects in SHHS and CHAT with age between 10 and 50.

Fig03: Query Builder by Searching Terms. This shows a sample query for unique number of female subjects in SHHS and CHAT with age between 10 and 50.

While you are exploring or done with the query composition, you can also scroll down and save the constructed query[Fig04]. Be sure to sign up or sign in before saving a query. The Name and Description of the query are automatically generated for your convenience, and you can customize them as you want.

Fig04: Save Query Widget.

Clicking the "Save Query" button in Fig04 will lead you to the Query Manager page, which allows you to keep track of saved queries and retrieve record information on the queries.

Tutorial 2 - Query Manager

The Query Manager allows researchers to keep track of saved queries and retrieve record information on the queries. Clicking the sub menu QUERY MANAGER under the menu QUERY will lead you to the Query Manager page https://www.x-search.net/queries/manager[Fig05]. You will need to sign up or sign in to access this page. Clicking a name of a saved query (blue box) will link to the Query Builder page with query widgets automatically loaded for the saved query, where you can review and update query criteria. Clicking a name of a saved query (blue box) will link to the Query Builder page with query widgets automatically loaded for the saved query, where you can review and update query criteria.

Fig05: Query Manager.

Tutorial 3 - Graphical Exploration

There are two ways to explore graphical distribution of multiple datasets according to a single core term and any two core terms.

3.1 Graphical Exploration for a Single Core Term

The first way is to view graphical distribution for a single core term across all the dataset variables mapping to it. Box plots are generated for numeric terms and bar plots for categorical terms. For example[Fig06], clicking the core term "Body mass index" under the category "02 - Anthropometry" (blue arrow) will generate a query widget, where you can click the button "View Box Plots" (red circle) to view the box plots[Fig07] generated for each dataset variable mapping to the core term "Body mass index".

Fig06: Generating Query Widget for Body Mass Index.

As shown in Fig07, there are four box plots corresponding to four variables in datasets SHHS and CHAT mapping to the core term "Body mass index". The summary statistics of these variables are also reported in the table below the box plots. Hovering over a box plot will show the summary statistics for the corresponding variable.

Fig07: Graphical Distribution of Body Mass Index across Multiple Datasets.

3.2 Graphical Exploration for Any Two Core Terms

The second way is to view graphical distribution by choosing any two core terms. For example, clicking the sub menu GRAPHICAL EXPLORATION under the menu QUERY or clicking the tab Graphical Exploration will lead you to the Graphical Exploration page https://www.x-search.net/queries/graph[Fig08]. You will need to choose one core term for X axis and another for Y axis (e.g., Race for X axis and Systolic blood pressure for Y axis in blue boxes). Box plots will be generated if a numeric core term is chosen for Y axis and bar plots will be given if a categorical core term is chosen for Y axis.

Fig08: Graphical Exploration Interface: choose core terms for X axis and Y axis.

Clicking the button "Graph Distribution" (red circle in Fig08) will generate multiple graphs[Fig09]. Each graph in Fig09 is rendered for a variable in a dataset mapping to Y axis against a variable mapping to X axis in the same dataset. For example, the first graph in Fig09 shows the box plots for the baseline-visit variable "Average Systolic BP" in the dataset SHHS against the baseline-visit variable "Race" in SHHS. Here each box plot is rendered for the "Average Systolic BP" data under a specific option of "Race" (e.g., White, Black, or Other).

Fig09: Graphical Exploration Interface: bar plots for "Systolic blood pressure" against "Race".