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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series

Authors: Rajala, Tuomas;

Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series

Abstract

A set of datacubes where z-dimension is time, thus each (x,y,.) is a timeseries. The idea is to detect sudden changes in each series, assuming 1) the series can be quite short and noisy 2) the change occurs in spatial patches. The set has synthetic examples with known change-events, and a real-world dataset with unknown change-events. The data files are related to the pape T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, "Journal of Agricultural, Biological and Environmental Statistics", https://doi.org/10.1007/s13253-023-00546-1 More of the NFI data is available from Natural Resources Institute Finland, https://kartta.luke.fi/index-en.html See `data/00data_readme.txt` for further details.

{"references": ["https://doi.org/10.1007/s13253-023-00546-1"]}

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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