An Open Repository Model for Acquiring Knowledge About Scientific Experiments

O’Connor, Martin J.; Martínez-Romero, Marcos; Egyedi, Attila L.; Willrett, Debra; Graybeal, John; Musen, Mark A.

doi:10.1007/978-3-319-49004-5_49

Cited by 6 publications

(6 citation statements)

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“…Our findings guide the implementation of a software system that aims to complement the way scientists author metadata to ensure standardization, completeness, and consistency. The Center for Expanded Data Annotation and Retrieval (CEDAR) 30 is developing a suite of tools-the CEDAR Workbench [31][32][33][34] -that allows users to build metadata templates based on community standards, to fill in those templates with metadata values that are appropriately authenticated, to upload data and their metadata to online repositories, and to search for metadata and templates stored in the CEDAR repository. The goal of CEDAR is to improve significantly the quality of the metadata submitted to public repositories, and thus to make online scientific datasets more FAIR.…”

Section: Discussionmentioning

confidence: 99%

The variable quality of metadata about biological samples used in biomedical experiments

Gonçalves

Musen

2019

Sci Data

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We present an analytical study of the quality of metadata about samples used in biomedical experiments. The metadata under analysis are stored in two well-known databases: BioSample—a repository managed by the National Center for Biotechnology Information (NCBI), and BioSamples—a repository managed by the European Bioinformatics Institute (EBI). We tested whether 11.4 M sample metadata records in the two repositories are populated with values that fulfill the stated requirements for such values. Our study revealed multiple anomalies in the metadata. Most metadata field names and their values are not standardized or controlled. Even simple binary or numeric fields are often populated with inadequate values of different data types. By clustering metadata field names, we discovered there are often many distinct ways to represent the same aspect of a sample. Overall, the metadata we analyzed reveal that there is a lack of principled mechanisms to enforce and validate metadata requirements. The significant aberrancies that we found in the metadata are likely to impede search and secondary use of the associated datasets.

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Section: Discussionmentioning

confidence: 99%

The variable quality of metadata about biological samples used in biomedical experiments

Gonçalves

Musen

2019

Sci Data

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“…The service is known as CEDAR's Value Recommender service. (31). This model represents templates and metadata using JSON-LD constructs (32), making it possible to restrict the types and values of fields to terms from ontologies.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 5 shows the architecture and workflow of CEDAR's Value Recommender service and the main integration points with CEDAR's Metadata Editor and the BioPortal ontology repository. Metadata authors use the Metadata Editor to generate metadata instances based on templates.Entered metadata are stored in CEDAR's Metadata Repository as a JSON document using an open, standards-based model(31). This model represents templates and metadata using JSON-…”

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confidence: 99%

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

et al. 2019

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Metadata—the machine-readable descriptions of the data—are increasingly seen as crucial for describing the vast array of biomedical datasets that are currently being deposited in public repositories. While most public repositories have firm requirements that metadata must accompany submitted datasets, the quality of those metadata is generally very poor. A key problem is that the typical metadata acquisition process is onerous and time consuming, with little interactive guidance or assistance provided to users. Secondary problems include the lack of validation and sparse use of standardized terms or ontologies when authoring metadata. There is a pressing need for improvements to the metadata acquisition process that will help users to enter metadata quickly and accurately. In this paper, we outline a recommendation system for metadata that aims to address this challenge. Our approach uses association rule mining to uncover hidden associations among metadata values and to represent them in the form of association rules. These rules are then used to present users with real-time recommendations when authoring metadata. The novelties of our method are that it is able to combine analyses of metadata from multiple repositories when generating recommendations and can enhance those recommendations by aligning them with ontology terms. We implemented our approach as a service integrated into the CEDAR Workbench metadata authoring platform, and evaluated it using metadata from two public biomedical repositories: US-based National Center for Biotechnology Information BioSample and European Bioinformatics Institute BioSamples. The results show that our approach is able to use analyses of previously entered metadata coupled with ontology-based mappings to present users with accurate recommendations when authoring metadata.

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“…The primary goal of CEDAR is to generate high-quality metadata describing scientific data that are semantically enriched with terms from ontologies. To that end, we developed a model that serves as a common, standards-based format for describing templates, fields, and metadata [16]. The resources in the CEDAR Workbench¾templates, fields, and metadata¾are represented as JSON-LD documents that conform to our model.…”

Section: System Architecturementioning

confidence: 99%

“…The primary goal of CEDAR is to generate high-quality metadata describing scientific data that are semantically enriched with terms from ontologies. To that end, we developed a model that serves as a common, standards-based format for describing templates, fields, and metadata [16]. For interoperability on the Web, it is crucial that all resources be represented using an open model that can be serialized to widely accepted formats such as JSON-LD or any RDF syntax.…”

Section: System Architecturementioning

confidence: 99%

The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments

Gonçalves

O’Connor

Martínez-Romero

et al. 2017

Lecture Notes in Computer Science

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The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developed¾the CEDAR Workbench¾is a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templates to generate high-quality metadata, and to share and manage these resources. The CEDAR Workbench provides a versatile, RESTbased environment for authoring metadata that are enriched with terms from ontologies. The metadata are available as JSON, JSON-LD, or RDF for easy integration in scientific applications and reusability on the Web. Users can leverage our APIs for validating and submitting metadata to external repositories. The CEDAR Workbench is freely available and open-source.

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An Open Repository Model for Acquiring Knowledge About Scientific Experiments

Cited by 6 publications

References 10 publications

The variable quality of metadata about biological samples used in biomedical experiments

The variable quality of metadata about biological samples used in biomedical experiments

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments

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