↓ Skip to main content

Arkheia: Data Management and Communication for Open Computational Neuroscience

Overview of attention for article published in Frontiers in Neuroinformatics, March 2018
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

Mentioned by

twitter
5 X users

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
37 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Arkheia: Data Management and Communication for Open Computational Neuroscience
Published in
Frontiers in Neuroinformatics, March 2018
DOI 10.3389/fninf.2018.00006
Pubmed ID
Authors

Ján Antolík, Andrew P. Davison

Abstract

Two trends have been unfolding in computational neuroscience during the last decade. First, a shift of focus to increasingly complex and heterogeneous neural network models, with a concomitant increase in the level of collaboration within the field (whether direct or in the form of building on top of existing tools and results). Second, a general trend in science toward more open communication, both internally, with other potential scientific collaborators, and externally, with the wider public. This multi-faceted development toward more integrative approaches and more intense communication within and outside of the field poses major new challenges for modelers, as currently there is a severe lack of tools to help with automatic communication and sharing of all aspects of a simulation workflow to the rest of the community. To address this important gap in the current computational modeling software infrastructure, here we introduce Arkheia. Arkheia is a web-based open science platform for computational models in systems neuroscience. It provides an automatic, interactive, graphical presentation of simulation results, experimental protocols, and interactive exploration of parameter searches, in a web browser-based application. Arkheia is focused on automatic presentation of these resources with minimal manual input from users. Arkheia is written in a modular fashion with a focus on future development of the platform. The platform is designed in an open manner, with a clearly defined and separated API for database access, so that any project can write its own backend translating its data into the Arkheia database format. Arkheia is not a centralized platform, but allows any user (or group of users) to set up their own repository, either for public access by the general population, or locally for internal use. Overall, Arkheia provides users with an automatic means to communicate information about not only their models but also individual simulation results and the entire experimental context in an approachable graphical manner, thus facilitating the user's ability to collaborate in the field and outreach to a wider audience.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 22%
Student > Ph. D. Student 6 16%
Student > Master 5 14%
Student > Bachelor 4 11%
Other 2 5%
Other 7 19%
Unknown 5 14%
Readers by discipline Count As %
Computer Science 8 22%
Neuroscience 7 19%
Engineering 3 8%
Agricultural and Biological Sciences 3 8%
Physics and Astronomy 3 8%
Other 7 19%
Unknown 6 16%
Attention Score in Context

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 25 March 2018.
All research outputs
#12,948,080
of 23,025,074 outputs
Outputs from Frontiers in Neuroinformatics
#395
of 753 outputs
Outputs of similar age
#159,131
of 332,016 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#13
of 18 outputs
Altmetric has tracked 23,025,074 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 753 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 47th percentile – i.e., 47% 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 332,016 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 51% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.