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Hardisty AR, Michener WK, Agosti D, et al. The Bari Manifesto: An interoperability framework for essential biodiversity variables. Ecological Informatics. 2018. Available at: http://www.sciencedirect.com/science/article/pii/S1574954118301961.
Hardisty AR, Michener WK, Agosti D, et al. The Bari Manifesto: An interoperability framework for essential biodiversity variables. Ecological Informatics. 2018. Available at: http://www.sciencedirect.com/science/article/pii/S1574954118301961.
Hardisty AR, Michener WK, Agosti D, et al. The Bari Manifesto: An interoperability framework for essential biodiversity variables. Ecological Informatics. 2018. Available at: http://www.sciencedirect.com/science/article/pii/S1574954118301961.
Hossain M, Muñoz H, Wu R, et al. Becoming DataONE Tier-4 Member Node: Steps taken by the Nevada Research Data Center. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP).; 2017.
Hossain M, Muñoz H, Wu R, et al. Becoming DataONE Tier-4 Member Node: Steps taken by the Nevada Research Data Center. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP).; 2017.
Hook LA, Vannan SSK, Beaty T, Wilson BE. Best Practices for Preparing Environmental Data Sets to Share and Archive. 2010.
Henebry GM. Beyond words: effective graphics and metadata are keys to concise scientific communication. Landscape Ecology. 2011;26:1355–1358.
Hampton SE, Strasser CA, Tewksbury JJ, et al. Big data and the future of ecology. 2013;11(3):156 - 162.
D
Hou C-Y, Mayernik M. Data Management Training Modules: An Initial Survey and Comparison Result. 2016.
Hernández N, Castro LA, Favela J, Michán L, Arnrich B. Data Quality in Mobile Sensing Datasets for Pervasive Healthcare. In: Khan SU, Zomaya AY, Abbas A, eds. Handbook of Large-Scale Distributed Computing in Smart Healthcare. Handbook of Large-Scale Distributed Computing in Smart Healthcare. Cham: Springer International Publishing; 2017:217–238. Available at: https://doi.org/10.1007/978-3-319-58280-1_9.
Hochachka WM, Fink D, Hutchinson RA, et al. Data-intensive science applied to broad-scale citizen science. Trends in Ecology & Evolution. 2012.
Hochachka WM, Fink D, Hutchinson RA, et al. Data-intensive science applied to broad-scale citizen science. Trends in Ecology & Evolution. 2012.
Huh T, Park G, Ahn S, Hwang S, Jung H. Design Criteria of Korean LTER Data Platform Model for Full Life-cycle Data Management. International Journal of Applied Engineering Research. 2017;12(3):336-342. Available at: http://www.ripublication.com/Volume/ijaerv12n3.htm.
Huh T, Park G, Ahn S, Hwang S, Jung H. Design Criteria of Korean LTER Data Platform Model for Full Life-cycle Data Management. International Journal of Applied Engineering Research. 2017;12(3):336-342. Available at: http://www.ripublication.com/Volume/ijaerv12n3.htm.
Hodge V, Jessop M, Fletcher M, et al. A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience. 2016;14(1):23 - 40. Available at: https://doi.org/10.1007/s12021-015-9276-3.

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