2,077 research outputs found
Towards structured sharing of raw and derived neuroimaging data across existing resources
Data sharing efforts increasingly contribute to the acceleration of
scientific discovery. Neuroimaging data is accumulating in distributed
domain-specific databases and there is currently no integrated access mechanism
nor an accepted format for the critically important meta-data that is necessary
for making use of the combined, available neuroimaging data. In this
manuscript, we present work from the Derived Data Working Group, an open-access
group sponsored by the Biomedical Informatics Research Network (BIRN) and the
International Neuroimaging Coordinating Facility (INCF) focused on practical
tools for distributed access to neuroimaging data. The working group develops
models and tools facilitating the structured interchange of neuroimaging
meta-data and is making progress towards a unified set of tools for such data
and meta-data exchange. We report on the key components required for integrated
access to raw and derived neuroimaging data as well as associated meta-data and
provenance across neuroimaging resources. The components include (1) a
structured terminology that provides semantic context to data, (2) a formal
data model for neuroimaging with robust tracking of data provenance, (3) a web
service-based application programming interface (API) that provides a
consistent mechanism to access and query the data model, and (4) a provenance
library that can be used for the extraction of provenance data by image
analysts and imaging software developers. We believe that the framework and set
of tools outlined in this manuscript have great potential for solving many of
the issues the neuroimaging community faces when sharing raw and derived
neuroimaging data across the various existing database systems for the purpose
of accelerating scientific discovery
Sharing brain mapping statistical results with the neuroimaging data model
Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html
Micropublications: a Semantic Model for Claims, Evidence, Arguments and Annotations in Biomedical Communications
The Micropublications semantic model for scientific claims, evidence,
argumentation and annotation in biomedical publications, is a metadata model of
scientific argumentation, designed to support several key requirements for
exchange and value-addition of semantic metadata across the biomedical
publications ecosystem.
Micropublications allow formalizing the argument structure of scientific
publications so that (a) their internal structure is semantically clear and
computable; (b) citation networks can be easily constructed across large
corpora; (c) statements can be formalized in multiple useful abstraction
models; (d) statements in one work may cite statements in another,
individually; (e) support, similarity and challenge of assertions can be
modelled across corpora; (f) scientific assertions, particularly in review
articles, may be transitively closed to supporting evidence and methods.
The model supports natural language statements; data; methods and materials
specifications; discussion and commentary; as well as challenge and
disagreement. A detailed analysis of nine use cases is provided, along with an
implementation in OWL 2 and SWRL, with several example instantiations in RDF.Comment: Version 4. Minor revision
Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications
For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes
Making open data work for plant scientists
Despite the clear demand for open data sharing, its implementation within plant science is still limited. This is, at least in part, because open data-sharing raises several unanswered questions and challenges to current research practices. In this commentary, some of the challenges encountered by plant researchers at the bench when generating, interpreting, and attempting to disseminate their data have been highlighted. The difficulties involved in sharing sequencing, transcriptomics, proteomics, and metabolomics data are reviewed. The benefits and drawbacks of three data-sharing venues currently available to plant scientists are identified and assessed: (i) journal publication; (ii) university repositories; and (iii) community and project-specific databases. It is concluded that community and project-specific databases are the most useful to researchers interested in effective data sharing, since these databases are explicitly created to meet the researchers’ needs, support extensive curation, and embody a heightened awareness of what it takes to make data reuseable by others. Such bottom-up and community-driven approaches need to be valued by the research community, supported by publishers, and provided with long-term sustainable support by funding bodies and government. At the same time, these databases need to be linked to generic databases where possible, in order to be discoverable to the majority of researchers and thus promote effective and efficient data sharing. As we look forward to a future that embraces open access to data and publications, it is essential that data policies, data curation, data integration, data infrastructure, and data funding are linked together so as to foster data access and research productivity
NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use
Background Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability. To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets. Methods Building on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets. Results The NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments. ConclusionThe NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research
The Deployment of an Enhanced Model-Driven Architecture for Business Process Management
Business systems these days need to be agile to address the needs of a
changing world. Business modelling requires business process management to be
highly adaptable with the ability to support dynamic workflows,
inter-application integration (potentially between businesses) and process
reconfiguration. Designing systems with the in-built ability to cater for
evolution is also becoming critical to their success. To handle change, systems
need the capability to adapt as and when necessary to changes in users
requirements. Allowing systems to be self-describing is one way to facilitate
this. Using our implementation of a self-describing system, a so-called
description-driven approach, new versions of data structures or processes can
be created alongside older versions providing a log of changes to the
underlying data schema and enabling the gathering of traceable (provenance)
data. The CRISTAL software, which originated at CERN for handling physics data,
uses versions of stored descriptions to define versions of data and workflows
which can be evolved over time and thereby to handle evolving system needs. It
has been customised for use in business applications as the Agilium-NG product.
This paper reports on how the Agilium-NG software has enabled the deployment of
an unique business process management solution that can be dynamically evolved
to cater for changing user requirement.Comment: 11 pages, 4 figures, 1 table, 22nd International Database Engineering
& Applications Symposium (IDEAS 2018). arXiv admin note: text overlap with
arXiv:1402.5764, arXiv:1402.5753, arXiv:1502.0154
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