@article{0812f7e6c3654f2f8c8f1148424aa32d,
title = "Towards automatic generation of portions of scientific papers for large multi-institutional collaborations based on semantic metadata",
abstract = "Scientific collaborations involving multiple institutions are increasingly commonplace. It is not unusual for publications to have dozens or hundreds of authors, in some cases even a few thousands. Gathering the information for such papers may be very time consuming, since the author list must include authors who made different kinds of contributions and whose affiliations are hard to track. Similarly, when datasets are contributed by multiple institutions, the collection and processing details may also be hard to assemble due to the many individuals involved. We present our work to date on automatically generating author lists and other portions of scientific papers for multi-institutional collaborations based on the metadata created to represent the people, data, and activities involved. Our initial focus is ENIGMA, a large international collaboration for neuroimaging genetics.",
keywords = "Neuroinformatics, Semantic metadata, Semantic science",
author = "Jang, {Mi Hyun} and Tejal Patted and Yolanda Gil and Daniel Garijo and Varun Ratnakar and Jie Ji and Prince Wang and Aggie McMahon and Thompson, {Paul M.} and Neda Jahanshad",
note = "Funding Information: As scientific collaborations become more complex, documenting the details of the datasets used becomes increasingly challenging since it involves gathering information from dozens or hundreds of individuals across many institutions. We have shown that a semantic metadata repository for a collaboration enables the creation of tools to generate automatically author lists and tables that summarize key information about data collection and other data characteristics that are important to an article. We have an initial implementation of a semantic repository and associated generation tools for the ENIGMA neuroimaging genetics collaboration, which we continue to extend both in content and capabilities. Acknowledgements. We are very grateful to the KAVLI foundation for their support of ENIGMA Informatics (PIs: Jahanshad and Gil). We also acknowledge support from the National Science Foundation under awards IIS-1344272 (PI: Gil), ICER-1541029 (Co-PI: Gil), and IIS-1344272 (PI: Gil), and from the National Institutes of Health{\textquoteright}s Big Data to Knowledge Grant U54EB020403 for support for ENIGMA (PI: Thompson). We thank the members of the Organic Data Science and Linked Earth projects for their contributions to the design of the framework. We also thank the many participants of the ENIGMA collaboration for their feedback to this work.; 1st Workshop on Enabling Open Semantic Science, SemSci 2017 ; Conference date: 21-10-2017",
year = "2017",
language = "English",
volume = "1931",
pages = "63--70",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR-WS",
}