This dataset is associated with a publication currently under peer review (DOI and link to the publication will be updated upon its publication).Permafrost serves as a significant carbon reservoir, storing up to 1700 petagrams of carbon accumulated over millennia. As global warming accelerates permafrost thaw, this carbon can be mobilized, with a fraction being transformed into volatile organic compounds (VOCs). These VOCs can influence atmospheric oxidizing capacity and contribute to the formation of secondary organic aerosols.In this study, active layer soils—the seasonally unfrozen layer above the permafrost—were collected from two contrasting Greenlandic permafrost locations (Disko Island, and Kangerlussuaq) and incubated to investigate their role in soil-atmosphere VOC exchange. Laboratory incubations were conducted under controlled conditions, where a VOC mixture gas was continuously purged through jars containing the soil samples. Gas concentrations were monitored at the inlet and outlet using a PTR-ToF-MS, allowing for the estimation of VOC uptake rates based on the differences in VOC concentrations.The results demonstrated that these soils actively function as VOC sinks, despite variations in their physicochemical properties. Soils from upper active layers showed relatively higher uptake capacities, with soil moisture, organic matter, and microbial carbon content identified as key factors influencing uptake rates. Additionally, uptake coefficients for several major VOC species were calculated, providing valuable data for future model development. Correlation analysis and varying uptake coefficients suggest that the sink is likely biotic, with selective preferences for different VOCs. The findings indicate that the development of a deeper active layer under climate change could enhance the soil’s sink capacity and mitigate net VOC emissions from permafrost thaw.Detailed methods and interpretations of the results can be found in the associated publication.

This dataset is associated with a publication currently under peer review (DOI and link to the publication will be updated upon its publication).Permafrost serves as a significant carbon reservoir, storing up to 1700 petagrams of carbon accumulated over millennia. As global warming accelerates permafrost thaw, this carbon can be mobilized, with a fraction being transformed into volatile organic compounds (VOCs). These VOCs can influence atmospheric oxidizing capacity and contribute to the formation of secondary organic aerosols.In this study, active layer soils—the seasonally unfrozen layer above the permafrost—were collected from two contrasting Greenlandic permafrost locations (Disko Island, and Kangerlussuaq) and incubated to investigate their role in soil-atmosphere VOC exchange. Laboratory incubations were conducted under controlled conditions, where a VOC mixture gas was continuously purged through jars containing the soil samples. Gas concentrations were monitored at the inlet and outlet using a PTR-ToF-MS, allowing for the estimation of VOC uptake rates based on the differences in VOC concentrations.The results demonstrated that these soils actively function as VOC sinks, despite variations in their physicochemical properties. Soils from upper active layers showed relatively higher uptake capacities, with soil moisture, organic matter, and microbial carbon content identified as key factors influencing uptake rates. Additionally, uptake coefficients for several major VOC species were calculated, providing valuable data for future model development. Correlation analysis and varying uptake coefficients suggest that the sink is likely biotic, with selective preferences for different VOCs. The findings indicate that the development of a deeper active layer under climate change could enhance the soil’s sink capacity and mitigate net VOC emissions from permafrost thaw.Detailed methods and interpretations of the results can be found in the associated publication.

The package includes the data and scripts for generating the figures for the paper entitled "High temperature sensitivity of Arctic isoprene emissions explained by sedges".

The package includes the data and scripts for generating the figures for the paper entitled "High temperature sensitivity of Arctic isoprene emissions explained by sedges".

Data for Nature manuscript titled “Environmental drivers of increased ecosystem respiration in a warming tundra”Corresponding author Dr. Sybryn Maes – [email protected] Github contains all R scripts on https://github.com/mjalava/tundrafluxPart A. Meta-analysisThe bold names refer to scripts (see the Github repository https://github.com/mjalava/tundraflux) and names in italics refer to files in this repositorydf_0-Study design Figure 1 and Extended Fig. 1 from main textdf_1a-Effect size calculations of response (ER)-Links to df_1.csv file with raw flux and environmental data-Only the experiments that state ‘Open Access’ in the excel file Authors_Datasets (sheet 2). For experiments stating ‘Available Upon Request’, you need to contact the authors for the -raw flux data.df_1b-Effect size calculations of environmental drivers-Links to df_1.csv file with raw flux data data (see above) and Dataset_ID.csv (this file includes all dataset IDs to merge the drivers into one dataframe)df_2a-f-Meta-analysis (2a) and meta-regression models (2b-f) (ER, N=136)-Links to df_2.csv file with effect size data and context-dependencies and Forestplot_horiz_weights_fig.csv (this file includes the mean pooled Hedges SMD as well as the individual dataset Hedges SMD to plot figure 2)-Contains code for Figs. 2-4 and Extended Figs 2-3df_3-Meta-regression for experimental warming duration-Contains code for Fig. 5df_4a            -Effect size calculations of autotrophic-heterotrophic respiration partitioning (Ra, Rh, N=9)-Links to df_3.csv file with raw partitioning data of subset experiments (output file df_4.csv)df_4b            -Sub-meta-analysis models (ER, Ra, Rh)-Links to df_4.csv (input file)NOTES·       All additional input files for the meta-analysis R-scripts are included within the folders. ·       ER, Ra, Rh = ecosystem, autotrophic, and heterotrophic respiration·       N = sample size (number of datasets) Part B. Upscaling resultsFor upscaling, the input data is described in the code files (see the Github repository) and the accompanying Readme.txt.percentageChangeResp_tundraAlpine.tif: modelled change in respirationbaseResp_tundraAlpine.tif: baseline respiration (calculated from the data from literature)modResp_tundraAlpine.tif: modelled respiration after warming (our calculations: (percentageChangeResp_tundraAlpine+1) * baseResp_tundraAlpine)changeResp_tundraAlpine.tif: modResp-baseRespstandError_tundraAlpine.tif: standard error of modelled respiration (standError_tundraAlpine_onlyDataUncertainty.tif: standard error of modelled respiration where only data uncertainty is taken into account

Data for Nature manuscript titled “Environmental drivers of increased ecosystem respiration in a warming tundra”Corresponding author Dr. Sybryn Maes – [email protected] Github contains all R scripts on https://github.com/mjalava/tundrafluxPart A. Meta-analysisThe bold names refer to scripts (see the Github repository https://github.com/mjalava/tundraflux) and names in italics refer to files in this repositorydf_0-Study design Figure 1 and Extended Fig. 1 from main textdf_1a-Effect size calculations of response (ER)-Links to df_1.csv file with raw flux and environmental data-Only the experiments that state ‘Open Access’ in the excel file Authors_Datasets (sheet 2). For experiments stating ‘Available Upon Request’, you need to contact the authors for the -raw flux data.df_1b-Effect size calculations of environmental drivers-Links to df_1.csv file with raw flux data data (see above) and Dataset_ID.csv (this file includes all dataset IDs to merge the drivers into one dataframe)df_2a-f-Meta-analysis (2a) and meta-regression models (2b-f) (ER, N=136)-Links to df_2.csv file with effect size data and context-dependencies and Forestplot_horiz_weights_fig.csv (this file includes the mean pooled Hedges SMD as well as the individual dataset Hedges SMD to plot figure 2)-Contains code for Figs. 2-4 and Extended Figs 2-3df_3-Meta-regression for experimental warming duration-Contains code for Fig. 5df_4a            -Effect size calculations of autotrophic-heterotrophic respiration partitioning (Ra, Rh, N=9)-Links to df_3.csv file with raw partitioning data of subset experiments (output file df_4.csv)df_4b            -Sub-meta-analysis models (ER, Ra, Rh)-Links to df_4.csv (input file)NOTES·       All additional input files for the meta-analysis R-scripts are included within the folders. ·       ER, Ra, Rh = ecosystem, autotrophic, and heterotrophic respiration·       N = sample size (number of datasets) Part B. Upscaling resultsFor upscaling, the input data is described in the code files (see the Github repository) and the accompanying Readme.txt.percentageChangeResp_tundraAlpine.tif: modelled change in respirationbaseResp_tundraAlpine.tif: baseline respiration (calculated from the data from literature)modResp_tundraAlpine.tif: modelled respiration after warming (our calculations: (percentageChangeResp_tundraAlpine+1) * baseResp_tundraAlpine)changeResp_tundraAlpine.tif: modResp-baseRespstandError_tundraAlpine.tif: standard error of modelled respiration (standError_tundraAlpine_onlyDataUncertainty.tif: standard error of modelled respiration where only data uncertainty is taken into account

The package includes the data and scripts for generating the figures for the paper entitled "High temperature sensitivity of Arctic isoprene emissions explained by sedges".

Data deposited in association with above named article to be published in the journal: Biogeosciences. Biogenic Volatile Organic Compound (BVOC) data covers full processing from retention times, comparison to standards and quantification of relevant compounds. Ecosystem respiration data has already been converted to flux (linear increase in concentration of carbon dioxide (CO2) over time over sampling period). Plant Root Simulator (PRS) probe, moisture, and temperature data listed for each site and treatment. Vegetation community described for each community based on percent cover of each species.

Data deposited in association with above named article to be published in the journal: Biogeosciences. Biogenic Volatile Organic Compound (BVOC) data covers full processing from retention times, comparison to standards and quantification of relevant compounds. Ecosystem respiration data has already been converted to flux (linear increase in concentration of carbon dioxide (CO2) over time over sampling period). Plant Root Simulator (PRS) probe, moisture, and temperature data listed for each site and treatment. Vegetation community described for each community based on percent cover of each species.

The Antarctic tundra, dominated by non-vascular photoautotrophs (NVP) like mosses and lichens, serves as a vital habitat for sea animals, which contribute organic matter and oceanic sulfur to the land, potentially influencing sulfur transformations. Here, we measured OCS fluxes from the Antarctic tundra and linked them to soil biochemical properties.This dataset therefore is collected from these experiments. It includes the figure source data associated with a peer-reviewed publication that is currently under review. Once the manuscript is published, the URL and DOI number will be provided here and this description will be updated accordingly.Results revealed that the NVP-dominated upland tundra acted as an OCS sink (-1.0 ± 0.6 pmol m-2 s-1), driven by NVP and OCS-metabolizing enzymes from soil microbes (e.g., Acidobacteria, Verrucomicrobia, and Chloroflexi). In contrast, tundra within sea animal colonies exhibited OCS emissions (1.4 ± 0.4 pmol m-2 s-1), resulting from the introduction of organosulfur compounds that stimulated concurrent OCS production. Furthermore, sea animal colonization likely influenced OCS-metabolizing microbial communities and further promoted OCS production. Overall, this study highlighted the role of sea animal activities in shaping soil-atmospheric exchange of OCS through interacting with soil chemical properties and microbial compositions.