History of oxygen production in the central Congo Basin peatlands region over the Holocene: codes for geospatial data processing and analysis

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History of oxygen production in the central Congo Basin peatlands region over the Holocene: codes for geospatial data processing and analysis
Galvez, Matthieu; Wu, Shuzhuang; Garcin, Yannick; Schefuß, Enno; Gassier, Ghislain; Lebamba, Judicael; Kiahtipes, Christopher; Bokomba Bwamangele, Ferdinand; Kidebua Lutonadio, Roger; Wotzka, Hans-Peter; Adatte, Thierry; Jaccard, Samuel, 2026, "History of oxygen production in the central Congo Basin peatlands region over the Holocene: codes for geospatial data processing and analysis", https://doi.org/10.23708/8CNH4U, DataSuds, V1, UNF:6:A4yOU4d6wdyno9eUHU2rHQ== [fileUNF]
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Description
This dataset includes all the codes for the geospatial computing of O2 production in the central Congo Basin peatlands region and which are presented in the article "Hydroclimate controls on Congo peatland net oxygen release over the past 10,600 years".

The ReadMe.txt file contains information on the methodology used to produce this dataset and on the structure of the files made available.
Subject Earth and Environmental Sciences
Keyword Central Congo, Peat, Oxygen production, Carbon, Holocene
Related Publication Hydroclimate controls on Congo peatland net oxygen release over the past 10,600 years. Galvez M.E., Wu S., Garcin Y., Schefuß E., Gassier, G., Lebamba J., Kiahtipes C.K., Bokomba Bwamangele F., Kidebua Lutonadio R., Wotzka H.-P., Adatte T., Jaccard S.L., Nature Geoscience, in press.
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ReadMe.txt
Plain Text - 3.6 KB
Published Jun 1, 2026
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MD5: 6182bbf0eefd24ef7c423dbb93e6aeb1
Contents summary, information about methodology and files structuration
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ReadMe_light.txt
Plain Text - 1.1 KB
Published Jun 1, 2026
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MD5: d2dd9333e92e06ac13cfa0ab4a4a0bb4
contains the citation, description and conditions of use, or even access, depending on the case, to the dataset
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CONGO_DATA_regression.tab
Data_files/Tabular Data - 1.6 KB
Published Jun 1, 2026
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4 Variables, 80 Observations UNF:6:2DLFNDtFzhBIpsmy1q30Yw==
Square root of peat thickness and TOC % from the whole central Congo Basin peatlands. Based on the field data of Crezee et al. (2022).
Code
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TOC_vs_dO2.tab
Data_files/Tabular Data - 462 B
Published Jun 1, 2026
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2 Variables, 42 Observations UNF:6:H0/P8tj63CM91rNMaeBkow==
TOC % versus dO2 data from the IMB100 core.
Code
Preview "Data_files/TOC_vs_dO2.tab"
1_oxygen_analysis_computation.py
python_source_codes/Python Source Code - 7.7 KB
Published Jun 1, 2026
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MD5: 79fdb788c37dbc1c2aa1769f4b89e763
Script to estimate oxygen production from peat thickness using statistical regressions.
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2_oxygen_analysis_plots.py
python_source_codes/Python Source Code - 5.1 KB
Published Jun 1, 2026
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MD5: 5519f5d34ae05f3410c1f6b1cbf04bed
Script to displays two regressions (a polynomial fit between square root of peat thickness and TOC %), and then a linear fit between TOC % and the oxygen demand (dO₂)), displaying and saving the fitted curves with their uncertainties.
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3_depth_uncertainity_maps.py
python_source_codes/Python Source Code - 2.7 KB
Published Jun 1, 2026
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MD5: 3c516a94589b526f979cc3f2fd1cd913
Script to read two raster files representing median peat thickness and its relative uncertainty, then to compute and save two maps showing the estimated upper (here deeper peat depths) and lower bounds (here shallower peat depths) of thickness.
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4_lower_do2_uncertainity_map.py
python_source_codes/Python Source Code - 4.8 KB
Published Jun 1, 2026
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MD5: b2e174f368715a11429e32421a0f8f15
Script to perform a lower-bound (here derived from shallower peat depths) estimation of dO2 from peat thickness using polynomial and linear regressions, then to apply the model to raster data and to export TOC and dO2 maps with visualizations.
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5_upper_do2_uncertainity_map.py
python_source_codes/Python Source Code - 3.9 KB
Published Jun 1, 2026
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MD5: 5b1156f32886f16edd82f35c04331896
Script to perform a upper-bound (here derived from deeper peat depths) estimation of dO2 from peat thickness using polynomial and linear regressions, then to apply the model to raster data and to export TOC and dO2 maps with visualizations.
Code
environment.yaml
python_source_codes/application/x-yaml - 80 B
Published Jun 1, 2026
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MD5: bfb4aeb455b71a79041ddeb087ab8a2c
File containing a list of package to be installed
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