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Transfer Office
Sabine Hanß, Masha Lee
use.access@dzhk.de

Structure of the Feasibility Explorer

The Feasibility Explorer is the tool for selecting collectives in order to submit a data/sample use application to the DZHK. Applicants can use the Feasibility Explorer to get an overview of the data and biomaterials available at the DZHK. Using various filter settings, the population can be restricted and a collective can be formed that is suitable for investigating the question on which the application for use is based. If the size of the selected population is sufficient to answer the research question, it is advisable to submit an application for data/sample use. The functions and use of the Feasibility Explorer are explained in more detail below using illustrations.

1. Dashboard

Figure 1 shows the overall view of the first page of the Feasibility Explorer. On the left-hand side there is a navigation area to guide you through the tool. If certain questions cannot be answered with the tool, there is also a contact option in this area to get in touch with the Feasibility Explorer. The content context of the first page is a graphical representation of the availability of biospecimens, a gender and age distribution as well as the availability of image and biosignal data. Bar charts, such as the availability of biospecimens and image/biosignal data, can be enlarged using the “Zoom” button to create detailed views. Further detailed views can be highlighted using the mouseover effect on the pie chart for gender distribution and the bar chart for age distribution. For each diagram, the “SEARCH” button leads to the filter selection page, which is described below. In addition, links in the footer of each page provide further information on the DZHK, data protection and legal information. The copyright of the entire website is also displayed here.

Figure 1: Overall view of the Feasibility Explorer

2. Data search

2.1 Filtering data

Figure 2, and thus the search page, shows the various functions that are available in the Feasibility Explorer for creating a population. Functions for selecting data from the population, removing set filters and saving filter settings are described in the following sections based on this figure.

Figure 2: Collective position

The population can be restricted using various filter options, allowing you to compile a collective that meets your own requirements. The addition of a filter causes a further restriction of the previous partial data set. The available characteristics can be selected using selection fields (see Figure 3). Adding a filter further restricts the existing partial data set. In the case of filters with sub-filtering, it is possible to select either the entire filter with all its possible characteristics or only individual characteristics. There are also fields in which values can be entered to obtain specific value ranges or different units can be selected for each filter.

Figure 3: Selection fields

2.2 Start/save/reset query

Once the filters have been selected, the query can be started, reset and saved (see Figure 4). This is done using three buttons that can be clicked at the top of the form. After starting a query, data records that match the query are searched for and a results page is created. A pop-up window (see Figure 7) opens when you click on “Save query”. A query can also be completely reset if the result was not executed as desired or filters were set incorrectly.

Figure 4: Start/save/reset entire query

Filters can also be reset for each area, i.e. filters for “Clinical data”, “Biospecimens” or “Image and biosignals”, or selected completely for each area. The specified buttons can be selected for this purpose (see Figure 5).

Figure 5: Apply/reset filter per area

If no data can be found with the selected filters after starting a query, i.e. 0 hits were found, this is indicated by a pop-up window (see Figure 6).

Figure 6: Pop-up window after start without hits

After selecting the “Save query” button, a pop-up window (see Figure 7) opens in which you can enter a title for the query and a comment. This query is then stored in a table under “Data retrieval”. At a later point in time, old queries that have already been saved can be executed again or used to add further fields. This enables a history check of all previously executed queries and simplifies the scope of work.

Figure 7: Save data query