Curating Scientific Information in Knowledge Infrastructures

Authors : Markus Stocker, Pauli Paasonen, Markus Fiebig, Martha A. Zaidan, Alex Hardisty

Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures.

Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations.

The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps.

In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures.

For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.

URL : Curating Scientific Information in Knowledge Infrastructures

DOI : http://doi.org/10.5334/dsj-2018-021

Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape

Author : Yi Shen

This paper presents the results of a research data assessment and landscape study in the institutional context of Virginia Tech to determine the data sharing and reuse practices of academic faculty researchers.

Through mapping the level of user engagement in “openness of data,” “openness of methodologies and workflows,” and “reuse of existing data,” this study contributes to the current knowledge in data sharing and open access, and supports the strategic development of institutional data stewardship.

Asking faculty researchers to self-reflect sharing and reuse from both data producers’ and data users’ perspectives, the study reveals a significant gap between the rather limited sharing activities and the highly perceived reuse or repurpose values regarding data, indicating that potential values of data for future research are lost right after the original work is done.

The localized and sporadic data management and documentation practices of researchers also contribute to the obstacles they themselves often encounter when reusing existing data.

URL : Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape

Alternative location : http://www.ijdc.net/index.php/ijdc/article/view/10.2.157

Open data : Empowering the empowered or …

Open data : Empowering the empowered or effective data use for everyone? :

“This paper takes a supportive but critical look at “open data” from the perspective of its possible impact on the poor and marginalized and concludes that there may be cause for concern in the absence of specific measures being taken to ensure that there are supports for ensuring a wide basis of opportunity for “effective data use”. The paper concludes by providing a seven element model for how effective data use can be achieved.”

URL : http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3316/2764