To address this, we propose creating a structured digital table as part of an overall effort in developing machine-readable, structured digital literature. However, the extraction of high-quality information from the corpus of scientific literature has been hampered by the lack of machine-interpretable content, despite text-mining advances. In parallel to the growth in bioscience databases, biomedical publications have increased exponentially in the past decade. This paper provides an overview of DISCO's current capabilities and discusses a number of the challenges and future directions related to the process of coordinating the integration of neuroscience data within the NIF Federation. In the past several years, DISCO has greatly extended its functionality and has evolved to play a central role in automating the complex, ongoing process of harvesting, validating, integrating, and displaying neuroscience data from a growing set of participating resources. A central component is the NIF Federation, a searchable database that currently contains data from 231 data and information resources regularly harvested, updated, and warehoused in the DISCO system. The NIF is an NIH Neuroscience Blueprint initiative designed to help researchers access the wealth of data related to the neurosciences available via the Internet. This paper describes how DISCO, the data aggregator that supports the Neuroscience Information Framework (NIF), has been extended to play a central role in automating the complex workflow required to support and coordinate the NIF's data integration capabilities. Our approach should be generalizable across many types of biomedical information. Our implementation uses the RDF Data Model in Oracle Database 10g for data retrieval, integration, and inference. We have converted a subset of the BrainPharm database into RDF and integrated it with SWAN hypothesis and publication data extracted from Alzforum and made available in RDF as the upper ontology. We present a Semantic Web approach to building this e-Neuroscience data integration framework, which involves using RDF as a standard data model to facilitate representation and integration of data. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of diverse types and levels of neuroscience data. Agreement upon a domain ontology is typically useful for querying diverse data sets, but is insufficient for integrating neuroscience data spanning multiple domains. However, most of these databases are neither integrated nor interoperating, which creates a barrier in answering complex neuroscience research questions. Presently, neuroscientists have access to a wide range of neuroscience databases through the Internet.
Universal database vs eav modeling software#
By presenting our data management system and making the software available, we aim to support related phenotyping projects. The strict use of controlled vocabularies and the application of web-access technologies proved vital to the successful data exchange between diverse institutes and data management concepts and infrastructures. Our data warehouse concept combines central data storage in databases and a file server and integrates existing and specialised database solutions for particular data types with new, project-specific databases. This project involves 11 groups from academia and breeding companies, 11 sites and four analytical platforms. We discuss the associated technical challenges and demonstrate adequate solutions exemplified in a data management pipeline for a project to identify markers for drought tolerance in potato. Data access must be provided on a long-term basis and be independent of organisational barriers without endangering data integrity or intellectual property rights. For a meaningful data evaluation and statistical analysis, standardised data storage is required. In plant breeding, plants have to be characterised precisely, consistently and rapidly by different people at several field sites within defined time spans.
0 kommentar(er)
