Characteristics of Collaboration in the Emerging Practice of Open Data Analysis

2017 ◽  
Author(s):  
Joohee Choi ◽  
Yla Tausczik
Keyword(s):  
Data Analysis ◽  
Open Data

scholarly journals Application-Based COVID-19 Micro-Mobility Solution for Safe and Smart Navigation in Pandemics

2021 ◽  
Vol 10 (8) ◽  
pp. 571
Author(s):  
Sumit Mishra ◽  
Nikhil Singh ◽  
Devanjan Bhattacharya
Keyword(s):  
Data Analysis ◽  
Routing Algorithm ◽  
Open Data ◽  
Route Planning ◽  
Contact Tracing ◽  
Residential Areas ◽  
Environment Exposure ◽  
Micro Mobility ◽  
Spatial System ◽  
Mobility Solution

Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity.

Open data, trials and new ethics of using others' work

2020 ◽  
pp. medethics-2019-105898
Author(s):  
Nicholas W Carris ◽  
Byron Cheon ◽  
Jay Wolfson
Keyword(s):  
Blood Pressure ◽  
Data Analysis ◽  
Systolic Blood Pressure ◽  
Research Productivity ◽  
Preliminary Analysis ◽  
Data Use ◽  
Open Data ◽  
Third Party ◽  
Intervention Trial ◽  
Using Data

Data and ideas are the capital of research productivity. Is it ethical to preempt the publication of another researcher’s unpublished data or preliminary analysis, perhaps without citation? The long-established answer is ‘certainly not’—but recent ‘open data’ use suggests otherwise. A research competition was held using data from The Systolic Blood Pressure Intervention Trial (SPRINT). This SPRINT Data Analysis Challenge created a novel environment for using open data as data became open early. This allowed third-party researchers the opportunity to assess some of the trial’s outcomes before trialists. Could this infringe on trialists’ right to analyse their data? Simultaneously, trialists had access to analyses from submissions to the competition that were not formally ‘published’ with a typical author credit or citation. Therefore, trialists had the opportunity to view the competition submissions and published on those ideas first without a typical way to cite the source of that idea. Could this infringe on researchers’ right to be credited for their ideas? This is not intended as a criticism of open data, the SPRINT Data Analysis Challenge, or similar systems/ventures, but is an effort to objectively note what may be remediable flaws in the worthwhile, growing and dynamic uses of open data. We offer preliminary analytics to shed more light and provide fodder for additional discussion.

Reconciling stakeholder interests in the era of open data

2018 ◽  
Vol 82 (3) ◽  
pp. 253-259
Author(s):  
Alessandro S. De Nadai
Keyword(s):  
Mental Health ◽  
Data Analysis ◽  
Data Sharing ◽  
Health Research ◽  
Open Data ◽  
Secondary Data ◽  
Mental Health Research ◽  
Secondary Data Analysis ◽  
Stakeholder Interests ◽  
Funding Agencies

While there is great enthusiasm about new data sharing initiatives in mental health research, some concerns have recently been expressed that reflect tension between those who generate data and those who engage in secondary data analysis. While many aspects of data sharing have been considered, some of this tension has not been fully addressed. If this tension continues to go unresolved, enthusiasm for data sharing initiatives may be hindered. The author suggests solutions to these issues after carefully considering respective stakeholder interests (including those of patients, researchers, and funding agencies).

scholarly journals Analysing Open Data in Virtual Research Environments

2017 ◽  
Vol 13 (4) ◽  
pp. 76-92 ◽  
Author(s):  
Anneke Zuiderwijk
Keyword(s):  
Data Analysis ◽  
Policy Making ◽  
Open Data ◽  
Societal Issues ◽  
Societal Challenges ◽  
Research Environments ◽  
Insight Into

This article describes how virtual research environments (VREs) offer new opportunities for researchers to analyse open data and to obtain new insights for policy making. Although various VRE-related initiatives are under development, there is a lack of insight into how VREs support collaborative open data analysis by researchers and how this might be improved, ultimately leading to input for policy making to solve societal issues. This article clarifies in which ways VREs support researchers in open data analysis. Seven cases presenting different modes of researcher support for open data analysis were investigated and compared. Four types of support were identified: 1) ‘Figure it out yourself', 2) ‘Leading users by the hand', 3) ‘Training to provide the basics' and 4) ‘Learning from peers'. The author provides recommendations to improve the support of researchers' open data analysis and to subsequently obtain new insights for policy making to solve societal challenges.

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