Graphics for Exploratory Analysis and Data Discovery in the Life Sciences

Revealed competition between cluster organizations: An exploratory analysis of the European life sciences sector

Environment and Planning A Economy and Space ◽

10.1177/0308518x18802325 ◽

2018 ◽

Vol 51 (3) ◽

pp. 705-723

Author(s):

Bas Karreman ◽

Martijn J. Burger ◽

Fred van Eenennaam

Keyword(s):

Life Science ◽

Life Sciences ◽

Policy Research ◽

Exploratory Analysis ◽

European Regions ◽

Cluster Policy ◽

Geographic Space ◽

Funding Sources

Extant cluster policy research has largely ignored the particular role that cluster organizations play in reifying cluster policies in practice. Based on a survey of 163 cluster organizations in the European life sciences sector, this study explores the heterogeneity of cluster organizations in geographic space and examines whether and where revealed competition – defined as the combined overlap in service offerings, sub-sectoral focus and funding sources – between life science cluster organizations within European regions is most apparent. The findings indicate that the degree of functional and sectoral substitutability of cluster organizations differs substantially across Europe, though some regions, particularly in Spain, Denmark, France and Estonia, are more prone to revealed competition.

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DATS: the data tag suite to enable discoverability of datasets

10.1101/103143 ◽

2017 ◽

Author(s):

Susanna-Assunta Sansone ◽

Alejandra Gonzalez-Beltran ◽

Philippe Rocca-Serra ◽

George Alter ◽

Jeffrey S Grethe ◽

...

Keyword(s):

Big Data ◽

Scientific Literature ◽

Journal Article ◽

Life Sciences ◽

National Institutes Of Health ◽

Collaborative Effort ◽

Data Types ◽

Data Discovery ◽

Core Set ◽

Independent Model

Today's science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)'s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMed's goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of datasets, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as a Schema.org annotated serialization to be used beyond DataMed, for example, in projects like DataCite.

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