3,191 research outputs found
The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web
Research in life sciences is increasingly being conducted in a digital and
online environment. In particular, life scientists have been pioneers in
embracing new computational tools to conduct their investigations. To support
the sharing of digital objects produced during such research investigations, we
have witnessed in the last few years the emergence of specialized repositories,
e.g., DataVerse and FigShare. Such repositories provide users with the means to
share and publish datasets that were used or generated in research
investigations. While these repositories have proven their usefulness,
interpreting and reusing evidence for most research results is a challenging
task. Additional contextual descriptions are needed to understand how those
results were generated and/or the circumstances under which they were
concluded. Because of this, scientists are calling for models that go beyond
the publication of datasets to systematically capture the life cycle of
scientific investigations and provide a single entry point to access the
information about the hypothesis investigated, the datasets used, the
experiments carried out, the results of the experiments, the people involved in
the research, etc. In this paper we present the Research Object (RO) suite of
ontologies, which provide a structured container to encapsulate research data
and methods along with essential metadata descriptions. Research Objects are
portable units that enable the sharing, preservation, interpretation and reuse
of research investigation results. The ontologies we present have been designed
in the light of requirements that we gathered from life scientists. They have
been built upon existing popular vocabularies to facilitate interoperability.
Furthermore, we have developed tools to support the creation and sharing of
Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page
An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations
Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations
A Linked Data Approach to Sharing Workflows and Workflow Results
A bioinformatics analysis pipeline is often highly elaborate, due to the inherent complexity of biological systems and the variety and size of datasets. A digital equivalent of the âMaterials and Methodsâ section in wet laboratory publications would be highly beneficial to bioinformatics, for evaluating evidence and examining data across related experiments, while introducing the potential to find associated resources and integrate them as data and services. We present initial steps towards preserving bioinformatics âmaterials and methodsâ by exploiting the workflow paradigm for capturing the design of a data analysis pipeline, and RDF to link the workflow, its component services, run-time provenance, and a personalized biological interpretation of the results. An example shows the reproduction of the unique graph of an analysis procedure, its results, provenance, and personal interpretation of a text mining experiment. It links data from Taverna, myExperiment.org, BioCatalogue.org, and ConceptWiki.org. The approach is relatively âlight-weightâ and unobtrusive to bioinformatics users
A posteriori metadata from automated provenance tracking: Integration of AiiDA and TCOD
In order to make results of computational scientific research findable,
accessible, interoperable and re-usable, it is necessary to decorate them with
standardised metadata. However, there are a number of technical and practical
challenges that make this process difficult to achieve in practice. Here the
implementation of a protocol is presented to tag crystal structures with their
computed properties, without the need of human intervention to curate the data.
This protocol leverages the capabilities of AiiDA, an open-source platform to
manage and automate scientific computational workflows, and TCOD, an
open-access database storing computed materials properties using a well-defined
and exhaustive ontology. Based on these, the complete procedure to deposit
computed data in the TCOD database is automated. All relevant metadata are
extracted from the full provenance information that AiiDA tracks and stores
automatically while managing the calculations. Such a protocol also enables
reproducibility of scientific data in the field of computational materials
science. As a proof of concept, the AiiDA-TCOD interface is used to deposit 170
theoretical structures together with their computed properties and their full
provenance graphs, consisting in over 4600 AiiDA nodes
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