Event Abstract

The CARMEN (Code Analysis Repository and Modelling for E-Neuroscience) project for collaborative sharing of data and analysis tools in electrophysiology: reviewing an early co-laboratory.

  • 1 University of Stirling, Computing Science and Mathematics, United Kingdom

The CARMEN portal (developed under a UK EPSRC grant from 2006, and under a UK BBSRC grant until 2015) launched in 2008 (https://portal.carmen.org.uk). It allows researchers to store and share electrophysiology datasets and analysis tools, with a sophisticated security system, enabling researchers to collaborate and share datasets and tools. It was intended as a place where researchers could work collaboratively on data as though the analysis and modelling software, and the datasets were local to the researchers. Considerable effort was put into developing the metadata system [1] and the internal data format, Neurophysiology Data Translation Format (NDF) [2]. Metadata is critical for allowing re-use of datasets and tools. Nonetheless, users report that inputting metadata discourages them from uploading data. This problem has two aspects. Firstly, neurophysiology metadata is complex and multi-faceted, starting from the relatively simple structural metadata (about the detailed file format) and ranging through more complex issues (e.g. the type and precise geometrical arrangement of electrodes and the detailed description of the neural tissue being recorded from) to even more difficult descriptions of the stimuli being used while recordings are being made (if they are from a live animal). Secondly, neurophysiologists do record this information, but often in a physical lab book. Uploading it feels like duplication of effort, and this is made worse because of the constraints imposed by the (generally) form-based input system for metadata failing to match the text in the lab book. The obvious solution to this is the use of an electronic lab book, so that the data is only entered once, but here too, standardisation is critical, particularly for extracting metadata. Because most electrophysiology research is performed on equipment manufactured by a number of companies, and because they do not agree on standard formats, there are many different data formats for electrophysiology. Making analysis tools work across the plethora of formats for electrophysiology datasets entails an internal data format: this is critical, because the analysis and visualisation tools need to be able to interpret data without having to interrogate the format first. Services were provided to translate user data into this format. However, the format used [2] was designed at a time when overall formats were still changing. For example, HDF5 was not used, unlike the more recent formats proposed by Teeters et al [3] in their recent Neuroscience Without Borders project paper. Much CARMEN usage has been by groups collaborating geographically: the security system allows sharing datasets and analysis tools with a high degree of granularity. These users have made little use of workflow technology. However, this may be because there were problems in providing workflow capability. The capability now provided includes a graphical editor, but this does not permit loops in the workflow, seriously reducing its usefulness (for example, it does not currently support applying a workflow over a range of parameters). This is an example of ease of use taking precedence over usefulness: the intended users were thought not to be programmers, so a visual interface for building workflows was provided. However, it is very difficult to provide the range of capabilities in a visual programming system that can be provided by a textual system – and this can be seen in the rarity of visual programming languages for general-purpose programming. In particular, providing loops and if-branches that allow for alteration of parameters is much easier in a scripting language. The unintended effect was to make sophisticated use of the workflow facility essentially impossible. The recently released integrated capability to eyeball datasets has been useful, more so than the original tool used for data visualisation, which required the use of a separate program. This is an example of how developments in the technology alter the way in which one writes systems: in this case, the inclusion in HTML5 of an integrated (and cross-browser) capability for graph drawing changed the way in which graphics was best presented. Retinal neurophysiologists have been the most effective users of the CARMEN collaboratory. A cross-animal study of excised retinal development has re-used data from different animals, using workflows to perform identical analysis on the raw data from different experiments: all the data and tools are publicly available [4]. Aspects of CARMEN’s implementation are now out-of-date. As noted earlier, a new internal data format would use HDF5, and the more recent attempts at standardisation. Issues of storing metadata have not been entirely solved, although there is more recent work on metadata [5]. However, the issue of ensuring single entry metadata is not yet solved. On the more technical side, services currently run on single cores, and much of the client-side was coded as Java applets (for example, data uploading). A new implementation would change these early decisions. Very recently, a new collaboratory system has been unveiled by the Human Brain Project [6]. This has come from a large project, and covers much more than neurophysiology. It includes much of what CARMEN attempted in its SP5 Neuroinformatics Platform. The release to non-members of HBP is very recent, and as yet no comparisons have been made. It is, however, clear that the collaboratory concept remains very much alive, but that the technology to support it has moved on considerably since the CARMEN project was implemented.


EPSRC grant EP/E002331/1, BBSRC grant BB/I001042/1


[1] F. Gibson, P.G. Overton, T.V. Smulders, S.R. Schultz, S.J. Eglen, C.D. Ingram, S. Panzeri, P. Bream, M. Whittington, E. Sernagor, M. Cunningham, C. Adams, C. Echtermeyer, J. Simonotto, M. Kaiser, D.C. Swan, M. Fletcher, P. Lord, Minimum Information about a Neuroscience Investigation (MINI): Electrophysiology, Nature Precedings, 2008, hdl:10101/npre.2009.1720.2
[2] See http://www.carmen.org.uk/?page_id=282 for the specification and MATLAB toolbox implementations.
[3] J.L. Teeters, K. Godfrey, R. Young, C. Dang, C. Friedsam, B. Wark, H. Asari, S. 
Peron, N. Li, A. Peyrache, G. Denisov, J.H. Siegle, S. R. Olsen, C. Martin, M. Chun, S. Tripathy, T.J. Blanche, K. D. Harris, G. Buzsaki, C. Koch, M. Meister, K. Svoboda, F.T. Sommer: Neurodata Without Borders: Creating 
a common data format for neurophysiology. Neuron 88:629-634 (2015)
[4] S.J. Eglen, M. Weeks, M. Jesso., J. Simmonotto, T. Jackson, E. Sernagor, A data repository and analysis framework for spontaneous neural activity recordings indeveloping retina. GigaScience, 3(3), 1–12, (2014).
[5] Y. Le Franc, A. Bandrowski, P. Bruha, V. Papez, J. Grewe, R. Moucek, S.J. Tripathy, T. Wachtler, Describing neurophysiology data and metadata with OEN, the Ontology for Experimental Neurophysiology, Frontiers in Neuroinformatics 44, Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014
[6] https://www.humanbrainproject.eu/en_GB/platforms-overview

Keywords: neurophysiology portal, Collaboratory, CARMEN, Neural Data Format, metadata

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Infrastructural and portal services

Citation: Smith LS (2016). The CARMEN (Code Analysis Repository and Modelling for E-Neuroscience) project for collaborative sharing of data and analysis tools in electrophysiology: reviewing an early co-laboratory.. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00036

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 29 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Prof. Leslie S Smith, University of Stirling, Computing Science and Mathematics, Stirling, Scotland, FK9 4LA, United Kingdom, l.s.smith@cs.stir.ac.uk

© 2007 - 2020 Frontiers Media S.A. All Rights Reserved