9,433 research outputs found
DEEP: a provenance-aware executable document system
The concept of executable documents is attracting growing interest from both academics and publishers since it is a promising technology for the dissemination of scientific results. Provenance is a kind of metadata that provides a rich description of the derivation history of data products starting from their original sources. It has been used in many different e-Science domains and has shown great potential in enabling reproducibility of scientific results. However, while both executable documents and provenance are aimed at enhancing the dissemination of scientific results, little has been done to explore the integration of both techniques. In this paper, we introduce the design and development of DEEP, an executable document environment that generates scientific results dynamically and interactively, and also records the provenance for these results in the document. In this system, provenance is exposed to users via an interface that provides them with an alternative way of navigating the executable document. In addition, we make use of the provenance to offer a document rollback facility to users and help to manage the system's dynamic resources
Designing Traceability into Big Data Systems
Providing an appropriate level of accessibility and traceability to data or
process elements (so-called Items) in large volumes of data, often
Cloud-resident, is an essential requirement in the Big Data era.
Enterprise-wide data systems need to be designed from the outset to support
usage of such Items across the spectrum of business use rather than from any
specific application view. The design philosophy advocated in this paper is to
drive the design process using a so-called description-driven approach which
enriches models with meta-data and description and focuses the design process
on Item re-use, thereby promoting traceability. Details are given of the
description-driven design of big data systems at CERN, in health informatics
and in business process management. Evidence is presented that the approach
leads to design simplicity and consequent ease of management thanks to loose
typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International
Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore
July 2015. arXiv admin note: text overlap with arXiv:1402.5764,
arXiv:1402.575
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.
Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
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
Rice Galaxy: An open resource for plant science
Background: Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non−computer savvy rice researchers. Findings: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice−bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Conclusions: Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science
- …