Adding new datasets

A major issue in bioinformatics research is the sheer volume of information that researchers are faced with. It is often a laborious task to find data relevant or vital to analyzing and interpreting experimental findings in a particular area of research. Data from high-profile projects are usually easily found, but there are also many smaller laboratories. Their data are harder to obtain, but may be related to and complement the research interest at hand. It would therefore be highly beneficial if it were possible to easily explore the datasets of these laboratories. In PIBAS FedSPARQL, all semantically represented data can be integrated and used for further exploring by way of the system’s feature for adding new dataset (Fig. 5).

Adding new dataset to predefined query. This figure shows the pop-up window that allows users to incorporate any new dataset not included in the predefined list of datasets for an existing template. Users need to enter the dataset name, initiative name, dataset link, a comment, the endpoint URL, pattern query and the dataset properties most relevant for the selected template and topic. Finally, they need to click the “Add” button to complete the action. Conversance with SPARQL and the underlying ontology is necessary for this step

This feature increases the flexibility of our system and opens the door to a better understanding of data, creating new opportunities for the researchers to perform more productive experiments in the future. By clicking the “Add” button, the researchers can add dataset that is not included in the predefined list of datasets for an existing template. In the pop-up window that appears, the dataset name, initiative name, dataset link, a comment, the endpoint URL, pattern query and some dataset properties that are most important for the selected template and topic have to be entered. The additional properties are used for the system’s feature for detecting similar data items. The pattern query entered should match a selected topic and template. Following our use case, the pattern query must contain the variable Target that matches the name of the running template. The pattern query variable is visible in the top-right corner of the pop-up window for adding new dataset. For testing purposes, we are using a test dataset with a test ontology and a test endpoint.⁷ After entering the basic information, the query preparation component rewrites the original query (Fig. 6) and the researchers can now run a new query. Following this, the rewritten query is evaluated and a more complete answer is returned to the end users (Fig. 7).

Rewritten predefined query after adding new dataset. This figure shows the rewritten predefined Federated SPARQL query of the template “Find targets for the drug” after incorporating a new test dataset

Result set after adding new dataset to predefined query. This figure shows the results in a table after executing the rewritten predefined Federated SPARQL query. The results table has the same layout as in Fig. ​Fig.44