Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Conference object . 2016
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2016
License: CC BY
Data sources: ZENODO
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

A Framework For Metadata Management And Automated Discovery For Heterogeneous Data Integration

Authors: Gouripeddi, Ramkiran; Mo, Peter; Madsen, Randy; Warner, Phillip; Butcher, Ryan; Wen, Jingran; Shao, Jianyin; +4 Authors

A Framework For Metadata Management And Automated Discovery For Heterogeneous Data Integration

Abstract

Current approaches to metadata discovery are dependent on time consuming manual curations. To realize the full potential of Big Data technologies in biomedicine, enhance research reproducibility and increase efficiency in translational sciences it is critical to develop automatic and/or semiautomatic metadata discovery methods and the corresponding infrastructure to deploy and maintain these tools and their outputs. Towards such a discovery infrastructure: We conceptually designed a process workflow for Metadata Discovery and Mapping Service, for automated metadata discovery. Based on steps taken by human experts in discovering and mapping metadata from various biomedical data, we designed a framework for automation. It consists of a 3-step process: (1) identification of data file source and format, (2) followed by detailed metadata characterization based on (1), and (3) characterization of the file in relation to other files to support harmonization of content as needed for data integration. The framework discovers and leverages administrative, structural, descriptive and semantic metadata, and consists of metadata and semantic mappers, along with uncertainty characterization and provision of expert review. As next steps we will develop and evaluate this framework using workflow platforms (e.g. Swift, Pegasus). In order to store discovered metadata about digital objects, we enhanced OpenFurther’s Metadata Repository (MDR). We configured the bioCADDIE metadata specifications (DatA Tag Suite (DATS) model) as assets in the MDR for harmonizing metadata of individual datasets (e.g. different protein files) for data integration. This method of metadata management provides a flexible data resource metadata storage system that supports versioning metadata (e.g. DATS 1.0 to 2.1) and data files mapped to different versions, enhance descriptors of resources (DATS) with descriptions of content within resources, and translations to other metadata specifications (e.g. schema.org). Also, this MDR stored metadata is available for various data services including data integration.

Related Organizations
Keywords

BD2K_AHM

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 30
    download downloads 7
  • 30
    views
    7
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
30
7
Green