2017
DOI: 10.3390/publications5020014
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A Trust Framework for Online Research Data Services

Abstract: Abstract:There is worldwide interest in the potential of open science to increase the quality, impact, and benefits of science and research. More recently, attention has been focused on aspects such as transparency, quality, and provenance, particularly in regard to data. For industry, citizens, and other researchers to participate in the open science agenda, further work needs to be undertaken to establish trust in research environments. Based on a critical review of the literature, this paper examines the is… Show more

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Cited by 7 publications
(6 citation statements)
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“…While these studies contribute to the understanding of the nature of trust, as well as trust factors, few studies solely focus on understanding and identifying factors of trust in data reuse. Recently Wolski, Howard, and Richardson (2017) discussed a trust framework for online data services, but the model was theoretical and without empirical support. Built on previous studies exploring different trust factors during data reuse, this study aims to quantitively examine factors of trust in data reuse from the reusers' perspectives.…”
Section: Introductionmentioning
confidence: 99%
“…While these studies contribute to the understanding of the nature of trust, as well as trust factors, few studies solely focus on understanding and identifying factors of trust in data reuse. Recently Wolski, Howard, and Richardson (2017) discussed a trust framework for online data services, but the model was theoretical and without empirical support. Built on previous studies exploring different trust factors during data reuse, this study aims to quantitively examine factors of trust in data reuse from the reusers' perspectives.…”
Section: Introductionmentioning
confidence: 99%
“…The modularisation of models (e.g., the Aquatic Ecodynamics Modelling Library "AED2", the Ecological Regional Ocean Model "ERGOM") and multiple algorithm options supported by high frequency observations (e.g., Woolway et al, 2015) of AEMs, however, make it difficult to choose a suitable model that is fit for purpose (Janssen et al, 2015). An increase in number and diversity has also been observed for supporting tools (e.g., Wolski et al, 2017) and data (e.g., LaDeau et al 2017). Accessing and finding the right data, tools and models fit for the specific project is essential and the development of a roadmap for each of these would be of great benefit to the aquatic ecosystem modelling community.…”
Section: Model Parameterisationmentioning
confidence: 90%
“…https://www.unidata.ucar.edu/software/thredds/current/tds/TDS.html) can provide a stable data set with quality assurance /quality control (QA/QC) standards and known provenance.Automated or advanced manual QA/QC procedures (e.g., Campbell et al, 2013; 'B3,' Read et al, 2016b) linked with data assimilation techniques (Hipsey et al 2015) have the potential to greatly simplify the time consuming process of data preparation.Data sharing is often associated with building trust(Wolski et al, 2017). Therefore, it is crucial in modelling studies to give credit to the provenance of the data and to work collaboratively with those who collected the data (e.g.,Bruce et al, 2018;Frassl et al 2018b).…”
mentioning
confidence: 99%
“…Existing literature stressed google scholar's ability to serve as a primary resource for a systematic review of material (Eksan, 2020). Wolski et al (2017) stated that investigating a new topic that will likely benefit from theoretical exposure requires a literature review in several situations. The author's input would stem from developing a conceptual model based on new theoretical foundations.…”
Section: Methodsmentioning
confidence: 99%