Towards automatic generation of portions of scientific papers for large multi-institutional collaborations based on semantic metadata. Jang, M., Patted, T., Gil, Y., Garijo, D., Ratnakar, V., Ji, J., Wang, P., McMahon, A., Thompson, P., & Jahanshad, N. In CEUR Workshop Proceedings, volume 1931, 2017.
abstract   bibtex   3 downloads  
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.
@inproceedings{
 title = {Towards automatic generation of portions of scientific papers for large multi-institutional collaborations based on semantic metadata},
 type = {inproceedings},
 year = {2017},
 keywords = {Neuroinformatics,Semantic metadata,Semantic science},
 volume = {1931},
 id = {3be499a6-1405-31bf-a751-0a2cf6832393},
 created = {2017-12-18T22:22:28.039Z},
 file_attached = {false},
 profile_id = {a4dd3107-a343-3164-8df4-d46fe9e15cfc},
 last_modified = {2017-12-18T22:22:28.039Z},
 read = {false},
 starred = {false},
 authored = {true},
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 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.},
 bibtype = {inproceedings},
 author = {Jang, M. and Patted, T. and Gil, Y. and Garijo, D. and Ratnakar, V. and Ji, J. and Wang, P. and McMahon, A. and Thompson, P.M. and Jahanshad, N.},
 booktitle = {CEUR Workshop Proceedings}
}
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