@article{
 title = {The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data},
 type = {article},
 year = {2019},
 volume = {arXiv:1902},
 websites = {http://arxiv.org/abs/1902.11162},
 month = {2},
 day = {26},
 id = {f39a4f13-5b8c-3244-bf01-a974ea4ad345},
 created = {2019-09-06T09:36:15.367Z},
 accessed = {2019-03-01},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2020-07-10T11:55:06.930Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Wittenburg2019},
 private_publication = {false},
 abstract = {There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.},
 bibtype = {article},
 author = {Wittenburg, P. and Sustkova, H. Pergl and Montesanti, A. and Bloemers, S. M. and de Waard, S. H. and Musen, M. A. and Graybeal, J. B. and Hettne, K. M. and Jacobsen, A. and Pergl, R. and Hooft, R. W. W. and Staiger, C. and van Gelder, C. W. G. and Knijnenburg, S. L. and van Arkel, A. C. and Meerman, B. and Wilkinson, M. D. and Sansone, S-A and Rocca-Serra, P. and McQuilton, P. and Gonzalez-Beltran, A. N. and Aben, G. J. C. and Henning, P. and Alencar, S. and Ribeiro, C. and Silva, C. R. L. and Sayao, L. and Sales, L. and Veiga, V. and Lima, J. and Dib, S. and Xavier, P. and Murtinho, R. and Tendel, J. and Schaap, B. F. and Brouwer, P. M. and Gavai, A. K. and Bouzembrak, Y. and Marvin, H. J. P. and Mons, A. and Kuhn, T. and Gambardella, A. A. and Azevedo, R. de Miranda and Muhonen, V. and van der Naald, M. and Smit, N. W. and Buys, M. J. and de Bruin, T. F. and Schoots, F. and Goodson, H. J. E. and Rzepa, H. S. and Jeffery, K. G. and Shanahan, H. P. and Axton, M. and Tkachenko, V. and Maya, A. D. and Meyers, N. K. and Conlon, M. and Haak, L. L. and Schultes, E. A.},
 journal = {arXiv}
}

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Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.},\n bibtype = {article},\n author = {Wittenburg, P. and Sustkova, H. Pergl and Montesanti, A. and Bloemers, S. M. and de Waard, S. H. and Musen, M. A. and Graybeal, J. B. and Hettne, K. M. and Jacobsen, A. and Pergl, R. and Hooft, R. W. W. and Staiger, C. and van Gelder, C. W. 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