Call for papers: Open data for discovery science
Guest Editors: Andreas Holzinger, Institute for Medical Informatics, Medical University Graz, Austria and Philip Payne, Washington University in St. Louis, USA.
The use of open data for discovery science has gained much attention recently as its full potential is unfolding and being explored in projects spanning all areas of healthcare research. A plethora of data sets are now available thanks to drives to make data universally accessible and usable for discovery science. However, with these advances come inherent challenges with the processing and management of ever expanding data sources. The computational and informatics tools and methods currently used in most investigational settings are often labor intensive and rely upon technologies that have not been designed to scale and support reasoning across multi-dimensional data resources. In addition, there are many challenges associated with the storage and responsible use of open data, particularly medical data, such as privacy, data protection, safety, information security and fair use of the data. There are therefore significant demands from the research community for the development of data management and analytic tools supporting heterogeneous analytic workflows and open data sources. Effective anonymisation tools are also of paramount importance to protect data security whilst preserving the usability of the data.
The purpose of this thematic series is to bring together articles reporting advances in the use of open data including the following:
· The development of tools and methods targeting the reproducible and rigorous use of open data for discovery science, including but not limited to: syntactic and semantic standards, platforms for data sharing and discovery, and computational workflow orchestration technologies that enable the creation of data analytics, machine learning and knowledge extraction “pipelines”.
· Practical approaches for the automated and/or semi-automated harmonization, integration, analysis, and presentation of “data products” to enable hypothesis discovery or testing.
· Theoretical and practical approaches for solutions to make use of interactive machine learning to put a human-in-the-loop, answering questions including: could human intelligence lead to general heuristics that we can use to improve heuristics?
· Frameworks for the application of open data in hypothesis generation and testing in projects spanning translational, clinical, and population health research.
· Applied studies that demonstrate the value of using open data either as a primary or enriching source of information for the purposes of hypothesis generation/testing or for data-driven decision making in the research, clinical, and/or population health environments.
· Privacy preserving machine learning and knowledge extraction algorithms that can enable the sharing of previously “privileged” data types as open data.
· Evaluation and benchmarking methods that can be used to demonstrate the impact of results generated through the primary or secondary use of open data.
· Socio-cultural and policy issues and frameworks relevant to the sharing, use, and dissemination of information and knowledge derived from the analysis of open data.
Submission is open to everyone, and all submitted manuscripts will be peer-reviewed through the standard BMC Medical Informatics and Decision Making review process. Manuscripts should be formatted according to our submission guidelines and submitted via the online submission system. Please indicate clearly in the covering letter that the manuscript is to be considered for the ‘Open data for discovery science’ collection.
For further information, please email the in-house editor Dirk Krueger at email@example.com.