Analysing design-oriented dialogues: a case study in conceptual data modelling

The aim of this article is to demonstrate that data modelling is becoming a crucial, if not dominant, vector for our understanding of animal populations and is consequential for how we study the affective relations between individual bodies and the communities to which they belong. It takes up the relationship between animal, body and data, following the datafication of starling murmurations, to explore the topological relationships between nature, culture and science. The case study thus embodies a data journey, invoking the tactics claimed by social or natural scientists, who generated recent discoveries in starling murmurations, including their topological expansions and contractions. The article concludes with thoughts and suggestions for further research on animal/data entanglement, and threads the concept of databodiment throughout, as a necessary dynamic for the formation and maintenance of communities.

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A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽

10.3233/sw-200416 ◽

2020 ◽

pp. 1-16

Author(s):

Francesco Beretta

Keyword(s):

Semantic Analysis ◽

Historical Research ◽

Data Modelling ◽

Fine Grained ◽

Fair Principles ◽

Active Research ◽

New Research ◽

High Level ◽

Conceptual Data ◽

Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

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A Framework of Local Geospatial Data Infrastructure for Sustainable Urban Development

Jurnal Teknologi ◽

10.11113/jt.v71.3834 ◽

2014 ◽

Vol 71 (4) ◽

Author(s):

Azman Ariffin ◽

Nabila Ibrahim ◽

Ghazali Desa ◽

Uznir Ujang ◽

Hishamuddin Mohd Ali ◽

...

Keyword(s):

Urban Development ◽

Spatial Data ◽

Geospatial Data ◽

Sustainable Urban Development ◽

Data Infrastructure ◽

Shape Category ◽

Key Dimensions ◽

Data Layers ◽

Conceptual Data

This paper addresses the need to develop a Local Geospatial Data Infrastructure (LGDI) for sustainable urban development. This research will highlight the effective and efficient framework for the development of local infrastructure. This paper presents a framework (a combination of domain based and goal based frameworks) for developing a Local Geospatial Data Infrastructure. The basis of this research is on a case study conducted in a Malaysian city. The main focus of the case study was on measuring and assessing sustainability. Six conceptual frameworks were produced based on 6 key dimensions of sustainability. The developed framework consists of 6 conceptual data models and 6 conceptual data structures. It was concluded that 30 spatial data layers are needed of which 12 data layers are categorized as point shape, 17 data layers are categorized as polygon shape and 1 data layer as line shape category.

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