cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13139906.v2
title: Supporting data for Tradeoff of CO2 and CH4 emissions from global peatlands under water-table drawdown

description: data_revision1.csv collects flux measurements from water table manipulation experiments listed in Reference.doc. site_chara_filled_revision1.csv collects site characteristics, soil, climate, management and topographic information. data_in_shape_permu_revision1.r is the script that combines previous two csv datasets and reorganize it into high-low water table pair for each flux. data_in_shape_permu_revision1.r generate files XXXX_ final_data_revision1.csv that are used in analysing and plotting. pred_nee_future_map_rev2_full.py and pred_ch4_future_map_rev2_long_full.py generate gridded global predictions of future CO<sub>2</sub> and CH<sub>4</sub> in response to water table drawdown through machine learning models (random forest) trained by the collected water table manipulation experimental datasets. Code in the uncertainty folder combines bootstrap resampling and ensemble predictions to generate 95% confidence intervals. For the response of CO<sub>2</sub>, the code randomly sampled 80% of observation samples to build one random forest model. This random model was then used to make future predictions with different combinations of predictor datasets. This bootstrap resampling, random forest model building and future prediction was repeated 200 times. In total, the code generated 25200 (200 x 21 CO<sub>2,initial</sub> datasets x 2 WTD<sub>initial</sub> datasets x 3 Climate datasets) ensemble members and estimated the 95% interval as the indicator of prediction uncertainty. For the response of CH4, the code generated 8400 ensemble members through 200 times bootstrap resampling, 7 CH<sub>4,initial</sub> datasets, 2 WTD<sub>initial</sub> datasets and 3 climate datasets. Scripts in the “Figures” folder are used to generate figures in the main text and the supplementary information with, FigureS referring to figures in supplementary information. delta_rcp85_figshare.nc stores means and the 95% confidence intervals of future CO<sub>2</sub> and CH<sub>4 </sub>emissions in response to water table drawdown across the globe under RCP8.5 climate conditions, in unit of mg CO<sub>2</sub>-eq m<sup>-2</sup> h<sup>-1</sup>. delta_rcp26_figshare.nc stores means and the 95% confidence intervals of the response under RCP2.6 conditions. We assume CH<sub>4</sub> has a global warming potential that is 25 times CO<sub>2</sub> over a 100-year time horizon. Note for site_chara_filled_revision1.csv WT: &gt;0 below soil surface; &lt;0 above soil surface Flux: positive sign = release gas to the atmosphere Manipulation length: s: within 1 year; m, 1-10 years; l, &gt;10 years Arctic: North of 66.5N; Boreal, 50-66.5; Temperate, 30-50; tropical, -30 – 30 CH<sub>4</sub>, unit, µg m<sup>-2</sup>h<sup>-1</sup> CO<sub>2</sub>, unit, mg m<sup>-2</sup>h<sup>-1</sup> <br>
DOI: 10.5281/zenodo.4585664
title: Supplementary dataset 1 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 1st part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2, and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585663
title: Supplementary dataset 1 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 1st part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2, and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585703
title: Supplementary dataset 2 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 2nd part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2, and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585702
title: Supplementary dataset 2 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 2nd part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2, and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585720
title: Supplementary dataset 3 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 3rd part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2, and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585719
title: Supplementary dataset 3 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 3rd part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2, and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585740
title: Supplementary dataset 4 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 4th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585739
title: Supplementary dataset 4 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 4th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585866
title: Supplementary dataset 5 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 5th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585865
title: Supplementary dataset 5 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 5th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585933
title: Supplementary dataset 6 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 6th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585932
title: Supplementary dataset 6 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 6th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585996
title: Supplementary dataset 7 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 7th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4585995
title: Supplementary dataset 7 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 7th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.6084/m9.figshare.14176526
title: SHARK data.pzfx

description: Raw pre-processed data for Figs. 1–6 and Supplementary Figs. 1–17 for SHARK
DOI: 10.6084/m9.figshare.14176526.v1
title: SHARK data.pzfx

description: Raw pre-processed data for Figs. 1–6 and Supplementary Figs. 1–17 for SHARK
DOI: 10.5281/zenodo.4586075
title: Supplementary dataset 8 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 8th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4586074
title: Supplementary dataset 8 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 8th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.6084/m9.figshare.14176529
title: ELISA

description: Enzyme-linked immunosorbent assay analysing protein levels of IL1b, IL6 and TNF in the small intestines after exposure to chemotherapy.
DOI: 10.6084/m9.figshare.14176529.v1
title: ELISA

description: Enzyme-linked immunosorbent assay analysing protein levels of IL1b, IL6 and TNF in the small intestines after exposure to chemotherapy.
DOI: 10.5281/zenodo.4587679
title: LCL problems

description: A number of classified LCL problems on tree and cycle graph families
DOI: 10.5281/zenodo.4586287
title: Supplementary dataset 9 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 9th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4586286
title: Supplementary dataset 9 - The effects of model complexity and size on metabolic flux distribution and control. Case study in Escherichia coli.

description: 9th part of Supplementary dataset 1-15: contains populations of 50'000 control coefficients (Metabolic Control Analysis) for three models D1, D2 and D3 of different network size. These populations of control coefficients were used to study the effects of model complexity - in terms of network size - on the conclusions drawn from MCA. A metric for measuring the sensitivity of MCA control coefficients to these structural changes was proposed.
description: This work was supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DOI: 10.5281/zenodo.4587681
title: LCL problems

description: A number of classified LCL problems on tree and cycle graph families