sits.rep: Reproducible Search on Land Use and Land Cover Classifications
Abstract
The reproducibility of research has been a topic of great discussion in the scientific community. This question has motivated international journals to create good practices documents that help researchers to organize data, source codes, and artifacts of their publications to ensure the reproduction of publications. Consequently, several computational tools have been developed with the aim of dealing with issues of scientific reproducibility. This paper presents a tool to obtain reproducibility of scientific experiments in the context of land use and land cover classifications based on machine learning techniques with the R package called sits (Satellite Image Time Series). This tool, called sits.rep, assists researchers in all the steps of their experiments, thus increasing the productivity of the teams that develop land use classifications, once they can focus exclusively on producing better classifications.
Keywords:
Reproducibility of research, land use and land cover maps, R language
References
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N¨ust, D., Konkol, M., Pebesma, E., et al. (2017). Opening the publication process with executable research compendia. D-Lib Magazine, 23(1/2).
Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060):1226–1227.
Prinz, F., Schlange, T., and Asadullah, K. (2011). Believe it or not: how much can we rely on published data on potential drug targets? Nature reviews Drug discovery, 10(9):712.
Soille, P., Burger, A., Marchi], D. D., et al. (2018). A versatile data-intensive computing platform for information retrieval from big geospatial data. Future Generation Computer Systems, 81:30 – 40.
Vasilevsky, N. A., Brush, M. H., Paddock, H., et al. (2013). On the reproducibility of science: unique identification of research resources in the biomedical literature. PeerJ, 1:e148.
Beaulieu-Jones, B. K. and Greene, C. S. (2017). Reproducibility of computational workflows is automated using continuous analysis. Nature biotechnology, 35(4):342–346.
Camara, G., Assis, L. F., Ribeiro, G., et al. (2016). Big earth observation data analytics: Matching requirements to system architectures. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial ’16, page 1–6, New York, NY, USA. Association for Computing Machinery.
Camara, G., Simoes, R., Andrade, P. R., et al. (2018). e-sensing/sits: Version 1.12.5.
Chirigati, F., Shasha, D., and Freire, J. (2013). Reprozip: Using provenance to support computational reproducibility. In Proceedings of the 5th USENIX Conference on Theory and Practice of Provenance, TaPP’13, page 1, USA. USENIX Association.
Di Tommaso, P., Chatzou, M., Floden, E.W., et al. (2017). Nextflow enables reproducible computational workflows. Nature biotechnology, 35(4):316–319.
Gentleman, R. and Lang, D. T. (2007). Statistical analyses and reproducible research. Journal of Computational and Graphical Statistics, 16(1):1–23.
Gertler, P., Galiani, S., and Romero, M. (2018). How to make replication the norm (vol 554, pg 417, 2018). Nature, 555(7698):580–580.
Gil, Y., David, C. H., Demir, I., et al. (2016). Toward the geoscience paper of the future: Best practices for documenting and sharing research from data to software to provenance. Earth and Space Science, 3(10):388–415.
Govoni, M., Munakami, M., Tanikanti, A., et al. (2019). Qresp, a tool for curating, discovering and exploring reproducible scientific papers. Scientific data, 6:190002.
Greff, K., Klein, A., Chovanec, M., et al. (2017). The sacred infrastructure for computational research. In Proceedings of the 15th Python in Science Conference (SciPy 2017), volume 28, pages 49–56.
Group on Earth Observations (2020). Group on earth observations - FAQ - Accessed: 2020-02-20. https://www.earthobservations.org/g_faq.html.
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLOS Medicine, 2(8).
Landau, W. M. (2018). The drake r package: a pipeline toolkit for reproducibility and high-performance computing. Journal of Open Source Software, 3(21).
McNutt, M. (2014). Reproducibility. Science, 343(6168):229–229.
Nosek, B. A., Alter, G., Banks, G. C., et al. (2015). Promoting an open research culture. Science, 348(6242):1422–1425.
N¨ust, D., Konkol, M., Pebesma, E., et al. (2017). Opening the publication process with executable research compendia. D-Lib Magazine, 23(1/2).
Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060):1226–1227.
Prinz, F., Schlange, T., and Asadullah, K. (2011). Believe it or not: how much can we rely on published data on potential drug targets? Nature reviews Drug discovery, 10(9):712.
Soille, P., Burger, A., Marchi], D. D., et al. (2018). A versatile data-intensive computing platform for information retrieval from big geospatial data. Future Generation Computer Systems, 81:30 – 40.
Vasilevsky, N. A., Brush, M. H., Paddock, H., et al. (2013). On the reproducibility of science: unique identification of research resources in the biomedical literature. PeerJ, 1:e148.
Published
2020-06-30
How to Cite
MARIANO, Rafael; QUEIROZ, Gilberto; ANDRADE, Pedro; SANTOS, Rafael.
sits.rep: Reproducible Search on Land Use and Land Cover Classifications. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 11. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 51-60.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2020.11019.
