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Orchestrating differential data access for translational research: a pilot implementation

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2017
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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3 tweeters

Citations

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6 Dimensions

Readers on

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46 Mendeley
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2 CiteULike
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Title
Orchestrating differential data access for translational research: a pilot implementation
Published in
BMC Medical Informatics and Decision Making, March 2017
DOI 10.1186/s12911-017-0424-6
Pubmed ID
Authors

Marco Brandizi, Olga Melnichuk, Raffael Bild, Florian Kohlmayer, Benedicto Rodriguez-Castro, Helmut Spengler, Klaus A. Kuhn, Wolfgang Kuchinke, Christian Ohmann, Timo Mustonen, Mikael Linden, Tommi Nyrönen, Ilkka Lappalainen, Alvis Brazma, Ugis Sarkans

Abstract

Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data. In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements. Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility. Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 17%
Student > Master 6 13%
Student > Bachelor 5 11%
Student > Ph. D. Student 5 11%
Librarian 4 9%
Other 10 22%
Unknown 8 17%
Readers by discipline Count As %
Computer Science 13 28%
Medicine and Dentistry 7 15%
Engineering 6 13%
Social Sciences 3 7%
Nursing and Health Professions 2 4%
Other 8 17%
Unknown 7 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 August 2017.
All research outputs
#6,100,019
of 11,564,189 outputs
Outputs from BMC Medical Informatics and Decision Making
#529
of 1,067 outputs
Outputs of similar age
#109,013
of 263,748 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 30 outputs
Altmetric has tracked 11,564,189 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,067 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,748 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.