Towards an e-infrastructure for Open Science in Soils Security
Resumo
Soils Security is a critical and growing global concern. The OpenSoils' objective is to host, connect and share large amounts of curated soil data and knowledge at the Brazilian and South America level. The e-infrastructure consists of several layers of services, a database of soil profiles, a cloud-based computational framework to compute and share soil data integrated with a map visualization tools. OpenSoils is open, elastic, provenance-oriented and lightweight computational e-infrastructure that collects, stores, describes, curates, harmonizes and directs to various soil resource types: large datasets of soils profiles, services/applications, documents, projects and external links. OpenSoils is the first open science-based computational framework of soils security in the literature.
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Yamson, D. O., et al., Putting Soils Security on the Policy Agenda: Need for a Familiar Framework. Challenges. 4:2 15 pages. 2016.
van Es, H., A New Definition of Soil CSANews 62:20-21, 2017.
Baker, M., 1,500 scientists lift the lid on reproducibility. Nature. 533:7604, 2016.
Buneman, P., Khanna, S., Tan, W-C. Data Provenance: Some Basic Issues. In: Kapoor S., Prasad S. (eds) FST TCS 2000: Foundations of Software Technology and Theoretical Computer Science. FSTTCS 2000. Lecture Notes in Computer Science, vol 1974. Springer, Berlin, Heidelberg.
BDSolos, Banco de Dados de Solos. [link], (acessado em 9.3.2018).
Caracciolo, C. et al. The AGROVOC Linked Dataset. Semantic Web, 4, 3, pages. 341-348. 2013.
Cruz, S.M.S, Campos, M. L. M and Mattoso, M. Towards a Taxonomy of Provenance in Scientific Workflow Management Systems. In: SERVICES I, pages. 259-266, USA, 2009.
Cruz, S.M.S, Nascimento, J.A.P. SisGExp: Rethinking Long-Tail Agronomic Experiments. IPAW 2016.
DBPedia, [link] (acessado em 24.3.2018).
Deelman, E. et al., Workflows and e-Science: An overview of workflow system features and capabilities. Future Generation Computer Systems, 25:5, pages. 528–540, 2009.
Fanelli, D. Opinion: Is science really facing a reproducibility crisis, and do we need it to? Proceedings of the National Academy of Sciences of the USA, March 2018.
FeBR, Repositório de dados de solos. [link], (acessado em 9.3.2018).
Freire, J., Koop, D., Santos, E., Silva C.T. Provenance for Computational Tasks: A Survey. Computing in Science and Engineering, 10:3, pages 11–21, 2008.
Freire, J. Chirigati, F. Provenance and the Different Flavors of Computational Reproducibility. IEEE Data Engineering Bulletin, 41(1), pages. 15-26, 2018.
Fecher, B. and Friesike, S. Open Science: One Term, Five Schools of Thought. Opening Science, pages. 17-47, 2013.
Foster, E. D., Deardorff, A. Open Science Framework (OSF). J Med Libr Assoc. 105:2, pages. 203–206. 2017.
Gyrard, A., Serrano, M., Atemezing G. A. Semantic Web Methodologies, Best Practices and Ontology Engineering Applied to Internet of Things. 2nd IEEE World Internet of Things, 2015.
Hey, T., Tansley, S., Tolle, K. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, 2009.
Hutson, M., Artificial intelligence faces reproducibility crisis. Science, 359: 6377, pp. 725-726, 2018.
Koch, A. et al., Soil Security: Solving the Global Soil Crisis. Global Policy, 4:4 ages 434-441. 2013.
Lockers, R. et al., Analysis of Big Data technologies for use in agro-environmental science. Environmental Modelling & Software, 84, pp. 494-504, 2016.
Mattoso, M. et al., Towards supporting the life cycle of large-scale scientific experiments. International Journal of Business Process Integration and Management, 5:1, pages 79-92, 2010.
McBratney, A., Field, D. J and Koch, A. The dimensions of soil security. Geoderma. 213, pages 203213, 2014.
Melo, A. A. B. et al., Spatial distribution of organic carbon and humic substances in irrigated soils under different management systems in a semi-Arid zone in Ceará, Brazil. SEMINA: CIENCIAS AGRARIAS, 37:4, pages 1845-1856, 2016.
Moreau. L., Missier, P. PROV-DM: The PROV Data Model. [link] (acessado em 24.3.2018).
Munafò, M. Open Science and Research Reproducibility. Ecancer medical science. 10, ed56. 2016.
Nascimento, J. A. P. RFLOW: uma arquitetura para execução e coleta de proveniência de workflows estatísticos. Dissertação de Mestrado, UFRRJ, 2015.
Neves V. C. et al., Managing Provenance of Implicit Data Flows in Scientific Experiments. ACM ACM Transactions on Internet Technology. Volume 17 Issue 4, Article No. 36, 2017.
OGP, Open Government Partnership, 2017. [link] (acessado em 9.3.2018).
Pansu, M., Gautheyrou, J., Handbook of Soil Analysis, Springer, 2006.
Pimentel, J. F et al., noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts 2017. Proceedings of the VLDB. vol 10:12, pages 1841-1844, 2017.
Rizzo, G.S.C, Ceddia, M. B., Cruz, S. M. S. Banco de Dados Pedológico: Primeiros Estudos. V RAIC – UFRRJ, 2017.
Wilkinson, M. D. et al., The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, Article number: 160018, 2016.
Worlfert, S. et al., Big Data in Smart Farming – A review. Agricultural Systems, v. 153, pages 69-80, 2017.
Yamson, D. O., et al., Putting Soils Security on the Policy Agenda: Need for a Familiar Framework. Challenges. 4:2 15 pages. 2016.
van Es, H., A New Definition of Soil CSANews 62:20-21, 2017.
Publicado
26/07/2018
Como Citar
DA CRUZ, Sérgio Manuel Serra et al.
Towards an e-infrastructure for Open Science in Soils Security. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 12. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 49-56.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2018.3273.
