Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

1 Introduction

Research data is diverse and requires specific knowledge to be interpreted, driving user communities to create metadata recommendations. Metadata for datasets, as for any other kind of resource, requires a tradeoff between a comprehensive description and control of the production cost [8]. This is more drastic in the “long-tail of science” as institutions often lack financial resources for data curation [4]. As metadata schemas grow to encompass the needs of different groups, their descriptors may become unnecessary or irrelevant to others, even in similar domains, leading to an overall lack of interoperability [1, 2]. This motivated some research groups to adapt and combine sets of descriptors from several metadata schemas in order to suit the needs of their applications, creating Application Profiles [3] to describe research datasets.

We focus on data description in the early stages of research, much like ADMIRAL [5], and propose that researchers choose their own set of metadata descriptors from existing ontologies. Dendro, our platform, innovates by integrating research datasets in the Semantic Web and allowing users to describe them using concepts captured in ontologies. We combine this dynamic approach with the advantages of a triple-based data model proposed in the same context [6]. To simplify the workflow, we do not attempt to represent the contents of files as sets of RDF triples (as done in VoIDFootnote 1 for example) instead focusing on describing and relating the files and folders themselves.

Dendro is designed to support researchers in their daily data management activities. With a generic data model that allows on-demand metadata descriptor selection by the user, it is completely built on both generic and domain-specific ontologies. OpenLink Virtuoso and SPARQL are at the core of its data layer, enabling metadata descriptions to be exposed on the Web and queried through Virtuoso’s SPARQL endpoint.

2 Enabling Collaboration and Interoperability

Dendro was designed from the start as an user-friendly interface layer for users without data management knowledge. Users build a knowledge base using ontologies in the background, allowing them to focus on choosing the properties with the right semantics for their descriptions without being concerned with design and implementation issues that arise from ontology use. Given its collaborative nature, the solution can be classified as a semantic wiki built on a triple store. It differs from other semantic wikis like Semantic Mediawiki, for example, that stores amalgamated sets of triples as “pages” in its relational database. According to the documentationFootnote 2, Semantic Mediawiki can use a triple store to provide a SPARQL endpoint, but the synchronization between the relational database and the triple store uses dedicated business logic—a trait shared by other linked open data compatible systems.

Based on our own past developments in Semantic Mediawiki [7], we concluded that its interface is not designed to allow users to combine descriptors from several ontologies when describing a pageFootnote 3. Dendro, on the other hand, makes it easier to describe any kind of resource using combinations of descriptors not specified a priori. The ontology-based data model enables data management personnel without coding skills to contribute by building and loading additional ontologies into their Dendro, which can then be shared on the web to document the descriptions and reused by others in the Dendro instances that they manage.

3 A Walkthrough of the Solution

In this section we will provide an overview of the main features provided by Dendro in its current form. We demonstrate the usage of Dendro in the daily research data management activities within research groups from two very distinct domains—fracture mechanics experiments (mechanical engineering) and pollutant analysis (analytical chemistry)Footnote 4.

Fig. 1.
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Using Dendro to describe a mechanical engineering dataset

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Figure 1 is a composite of screenshots showing how Dendro can be used to describe a dataset from the mechanical engineering domain. Area 1 shows the project list that allows users to see the projects that they have created in the system (i.e. there is an instance of dcterms:creator in the graph, with the project as its subject and the user as its object). Area 2 shows the main description interface. Note the list of options available to the user (area 2A, from left to right: create folder, upload file(s), download folder, backup folder, restore folder, and show/hide deleted files). The file list 2B shows the contents of the current folder and allows the user to navigate in the system. The autocomplete box 2C is used to retrieve descriptors from the ontologies currently loaded in the Dendro instance, based on the values of their rdfs:label and rdfs:comment annotation properties—upon selection, the descriptor is added to the description area to be filled in. All descriptors originate from ontologies available on the web. Upon loading an ontology into Dendro, its properties become available in the search box, provided they have their own rdfs:label and rdfs:comment annotation properties.

The system also provides a set of smart descriptors 3, usually presented below 2C, which can be seen as shortcuts for fast selection of most recently used descriptors. Upon first use, the system will simply recommend the most used descriptors in the system. When the user selects a descriptor, the system will give preference to descriptors from the same ontology. When the user selects another descriptor from a different ontology, the recommendation is broadened to the descriptors from the now two active ontologies. All changes to descriptor values are versioned, as can be seen in area 4. Finally, the system supports recursive backup and restore of directory structures (including metadata) through ZIP files. Area 5 shows the contents of a complete backup of the current project—note the metadata.json file at the root, which contains all the metadata for all resources in the project’s directory tree.

Fig. 2.
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A free-text search and SPARQL query over Dendro’s graph

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Figure 2 shows the resource described in Fig. 1 among the results of a full-text search for the term “fracture mechanics” over the Dendro system (1). The search is powered by an ElasticSearch index that indexes every resource in the graph by its literals and that is continuously updated. Area 2 shows a partial view of the results of a SPARQL query used to retrieve the metadata for the same resource—SPARQL queries such as this are used internally by Dendro to retrieve and modify data in the underlying OpenLink Virtuoso graph database.

4 Conclusions and Future Work

Dendro is a research data management platform designed to provide researchers with a collaborative environment for storing and describing their datasets. Ontologies are used as sources for properties, picked by researchers to describe their research data.

Dendro differs from other research data management platforms in its “all semantic web” approach. By employing a triple-based data model and OpenLink Virtuoso, each resource can have an arbitrary set of descriptors. As they interact with the system, Dendro users are actually building a Linked Open Data graph of interconnected research-related resources, while data access is performed internally via SPARQL all accross the platform.

Dendro development is informed by the requirements of a panel of researchers from the University of Porto, and preliminary tests have shown a good match between their data management needs and the services of the platform. We regard it as an effective practical application of semantic web technologies, as well as a catalyst for the creation of domain-specific lightweight ontologies.