# Linked Data for Life Sciences
## 3 Proposed Framework  
We propose an approach to couple the above-mentioned databases to facilitate the three tiers of the research process in a continuous fashion. Figure 1 depicts a general overview of our proposed framework. As the input, (open) data from sources--which can be in any of proprietary formats such as database, CSV, XML, or documents--are considered. We propose to convert this data into Linked Data using existing tools such as the RDF Mapping Language [25] (RML) ([[http://semweb.mmlab.be/rml/spec.html](http://semweb.mmlab.be/rml/spec.html)]([http://semweb.mmlab.be/rml/spec.html](http://semweb.mmlab.be/rml/spec.html))), Sparqilify ([[https://github.com/AKSW/Sparqilify](https://github.com/AKSW/Sparqilify)]([https://github.com/AKSW/Sparqilify](https://github.com/AKSW/Sparqilify))), OpenRefine's RDF extension ([[http://refine.deri.ie/](http://refine.deri.ie/)]([http://refine.deri.ie/](http://refine.deri.ie/))), etc. RML is a language for specifying customized mappings from heterogeneous data structures (including databases, XML, CSV) to the RDF data model. An example of mapping a CSV file to RDF is shown using the RML mapping language ([[http://rml.io/spec.html#example-CSV](http://rml.io/spec.html#example-CSV)]([http://rml.io/spec.html#example-CSV](http://rml.io/spec.html#example-CSV))). Essentially, the table name is the resource, the column names map to properties, and the values in each cell correspond to the object value in the triple. Additionally, each resource is typed by associating it to a class in an (existing) ontology.  
After conversion of data sets into the RDF format, they are not only machine-readable but also more interoperable since they are represented using a standardized vocabulary. To interlink the different data sets, there are several tools available such as LIMES ([[http://aksw.org/Projects/LIMES.html](http://aksw.org/Projects/LIMES.html)]([http://aksw.org/Projects/LIMES.html](http://aksw.org/Projects/LIMES.html))), SILK ([[http://silkframework.org/](http://silkframework.org/)]([http://silkframework.org/](http://silkframework.org/))), etc., which perform lexical matches over the object values to (semi-)automatically find matches between data sets which are in RDF format.  
As a result, with the data sets including the textual data annotated using classes from standard ontologies, retrieving similar studies to perform (1) systematic reviews and (2) meta-analysis becomes easier. Moreover, with the different data sets interlinked with one another, the (3) discovery of novel links between these data sets is now possible by querying this integrated data space.  
In the proposed framework, it is then mandated that the outputs of these three analyses should be made available using the FAIR principles such that not only the data but also the metadata associated with it be maximally findable, accessible, interoperable, and reusable. This will greatly impact the reusability of data, metadata, and results to be applied in different use cases. Moreover, it is possible to make the data available via an API, specifically using the smartAPI specification. This will facilitate finding and exploring data sets relevant to the particular use case by querying the smartAPI  
Figure 1: Proposed Framework for describing the flow of data from sources to outputs.  
registry. In the following sections, we describe each of the three use cases in detail that follow the proposed approach.