60 research outputs found
Adventures in Semantic Publishing: Exemplar Semantic Enhancements of a Research Article
Scientific innovation depends on finding, integrating, and re-using the products of
previous research. Here we explore how recent developments in Web technology,
particularly those related to the publication of data and metadata, might assist that
process by providing semantic enhancements to journal articles within the mainstream
process of scholarly journal publishing. We exemplify this by describing semantic
enhancements we have made to a recent biomedical research article taken from
PLoS Neglected Tropical Diseases, providing enrichment to its
content and increased access to datasets within it. These semantic enhancements
include provision of live DOIs and hyperlinks; semantic markup of textual terms, with
links to relevant third-party information resources; interactive figures; a
re-orderable reference list; a document summary containing a study summary, a tag
cloud, and a citation analysis; and two novel types of semantic enrichment: the
first, a Supporting Claims Tooltip to permit “Citations in
Context”, and the second, Tag Trees that bring together semantically
related terms. In addition, we have published downloadable spreadsheets containing
data from within tables and figures, have enriched these with provenance information,
and have demonstrated various types of data fusion (mashups) with results from other
research articles and with Google Maps. We have also published machine-readable RDF
metadata both about the article and about the references it cites, for which we
developed a Citation Typing Ontology, CiTO (http://purl.org/net/cito/). The
enhanced article, which is available at http://dx.doi.org/10.1371/journal.pntd.0000228.x001, presents a
compelling existence proof of the possibilities of semantic publication. We hope the
showcase of examples and ideas it contains, described in this paper, will excite the
imaginations of researchers and publishers, stimulating them to explore the
possibilities of semantic publishing for their own research articles, and thereby
break down present barriers to the discovery and re-use of information within
traditional modes of scholarly communication
Interacting with robots as performers and producers of music
Is it really so strange to think about a robot as something, or perhaps someone that can produce music, as a performer or even as a composer? What happens when robots perform on stage to live audiences, and when they are perceived as intelligent? In this abstract we start to unpack and explicate some of the issues that emerge when the fields of music technology and robotics come together. The aim of this piece of writing is to prompt the Digital Music Research community to engage in debate, in order develop this emerging field of research
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
Workflow forever: Semantic Web Semantic models and tools for preserving and digitally publishing computational experiments
The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web
Using Metadata Actively
Almost all researchers collect and preserve metadata, although doing so is often seen as a burden. However, when that metadata can be, and is, used actively during an investigation or creative process, the benefits become apparent instantly. Active use can arise in various ways, several of which are being investigated by the Collaboration for Research Enhancement by Active use of Metadata (CREAM) project, which was funded by Jisc as part of their Research Data Spring initiative. The CREAM project is exploring the concept through understanding the active use of metadata by the partners in the collaboration. This paper explains what it means to use metadata actively and describes how the CREAM project characterises active use by developing use cases that involve documenting the key decision points during a process. Well-documented processes are accordingly more transparent, reproducible, and reusable.
Workflow-centric research objects: First class citizens in scholarly discourse.
A workflow-centric research object bundles a workflow, the provenance of the results obtained by its enactment, other digital objects that are relevant for the experiment (papers, datasets, etc.), and annotations that semantically describe all these objects. In this paper, we propose a model to specify workflow-centric research objects, and show how the model can be grounded using semantic technologies and existing vocabularies, in particular the Object Reuse and Exchange (ORE) model and the Annotation Ontology (AO).We describe the life-cycle of a research object, which resembles the life-cycle of a scienti?c experiment
Structuring research methods and data with the research object model:genomics workflows as a case study
Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e. g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as "which particular data was input to a particular workflow to test a particular hypothesis?", and "which particular conclusions were drawn from a particular workflow?". Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well. Availability: The Research Object is available at http://www.myexperiment.org/packs/428 The Wf4Ever Research Object Model is available at http://wf4ever.github.io/r
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