Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database

The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment. The nanosafety data is predominantly recorded in Excel spreadsheet files. Although the spreadsheets are quite convenient for the experimentalists, they also pose great challenges for the consequent processing into databases due to variability of the templates used, specific details provided by each laboratory and the need for proper metadata documentation and formatting. In this paper, we present a workflow to facilitate the conversion of spreadsheets into a FAIR (Findable, Accessible, Interoperable, and Reusable) database, with the pivotal aid of the NMDataParser tool, developed to streamline the mapping of the original file layout into the eNanoMapper semantic data model. The NMDataParser is an open source Java library and application, making use of a JSON configuration to define the mapping. We describe the JSON configuration syntax and the approaches applied for parsing different spreadsheet layouts used by the nanosafety community. Examples of using the NMDataParser tool in nanoinformatics workflows are given. Challenging cases are discussed and appropriate solutions are proposed.

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Designing an Engineering Database for Telephone Networks

ASME 1991 5th Annual Database Symposium: Engineering Databases — An Enterprise Resource ◽

10.1115/edm1991-0179 ◽

1991 ◽

Author(s):

Scott G. Danielson

Keyword(s):

Data Structure ◽

Data Model ◽

Database Modeling ◽

Telephone Networks ◽

Semantic Data ◽

Engineering Data ◽

Engineering Database ◽

Engineering Work ◽

Entity Relationship ◽

Semantic Data Model

Abstract An engineering database modeling telephone outside plant networks is developed. Semantic and relational database design methodologies are used with the semantic data model developed based on an extended entity-relationship approach. This logical model is used to generate a normalized relational data structure. This database holds engineering data supporting engineering analyses, engineering work order generation procedures, and network planning activities. The database has been linked to separate network analysis programs and CAD-based network maps by a database application.

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The fact model: A semantic data model for complex databases

Information Systems ◽

10.1016/0306-4379(88)90023-3 ◽

1988 ◽

Vol 13 (1) ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Giovanni M. Sacco

Keyword(s):

Data Model ◽

Semantic Data ◽

Semantic Data Model

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Towards evolutionary knowledge representation under the big data circumstance

The Electronic Library ◽

10.1108/el-11-2020-0318 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xuhui Li ◽

Liuyan Liu ◽

Xiaoguang Wang ◽

Yiwen Li ◽

Qingfeng Wu ◽

...

Keyword(s):

Big Data ◽

Knowledge Base ◽

Data Model ◽

Directed Acyclic Graph ◽

Conceptual Models ◽

Structural Features ◽

New Approach ◽

Content Type ◽

Semantic Data ◽

Semantic Data Model

Purpose The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data. Design/methodology/approach A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail. Findings MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance. Originality/value The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

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