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238 results for “carbon fluxes”
Carbon, water and energy fluxes at the subtropical forest in Kaziranga National Park in India
<p>This dataset contains measured and modeled records of gross primary productivity (GPP), sensible heat flux and latent heat flux from 2016 to 2018 at the Kaziranga National Park, India. The measured fluxes are obtained from eddy covariance technique at the flux tower established by the Indian Institute of Tropical Meteorology (IITM) Pune as part of the MetFlux India network funded by the Ministry of Earth Sciences (MoES), the Government of India, whereas the Integrated Science Assessment Model (ISAM) at the Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Illinois, USA is used to simulate these fluxes. Additionally, the leaf area index (LAI) and meteorological measurements used as the model input are also included in this dataset. </p>
Code and tables for main figures of Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020
<p>Code and tables to reproduce the main figures (2a,2b,3a,3b,4a,5a) of Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020.</p> <p> </p>
Spatial data of Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020
<p>Spatial data used in the main figures of the manuscript Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020. </p> <p>To reproduce Figure 4b sum the maps of Figure 2c+2e and Figure 4c sum the maps of Figure 2d+2e. </p> <p>To reproduce Figure 5b sum the maps of Figure 3c+3d. </p> <p>The shapefiles with the Brazilian Amazon and biogeographical Amazon are also provided. </p>
UFLUX European ensemble 0.25deg daily carbon, water, and energy fluxes from 2000 - 2020
<h3>UFLUX Ensemble Europe025ddaily (European 0.25° Daily)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European daily fluxes at 0.25° spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based vegetation proxies </strong>— including MODIS NIRv, GOME-2 SIF, and OCO-2 SIF — with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The dataset includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX global ensemble 0.25deg monthly carbon, water, and energy fluxes from 2001 - 2021
<p> </p> <h3>UFLUX Ensemble Globe025dmonthly (Global 0.25° Monthly, 13 Members)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> provides <strong>global monthly fluxes at 0.25° spatial resolution</strong>, incorporating <strong>13 ensemble members</strong> derived from different combinations of satellite-based vegetation proxies and climate reanalysis data. The dataset includes five key ecosystem flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Ensemble Members:</strong><br>Each member combines unique satellite vegetation indices with climate datasets:</p> <ol> <li> <p>MODIS-NIRv-CFSV2</p> </li> <li> <p>MODIS-NIRv-ERA5</p> </li> <li> <p>OCO-2-CSIF-ERA5</p> </li> <li> <p>GOME-2-SIF-ERA5</p> </li> <li> <p>GOSAT-755-SIF-ERA5</p> </li> <li> <p>GOSAT-772-SIF-ERA5</p> </li> <li> <p>MODIS-NDVI-ERA5</p> </li> <li> <p>MODIS-EVI2-ERA5</p> </li> <li> <p>AVHRR-NIRv-ERA5</p> </li> <li> <p>AVHRR-NDVI-ERA5</p> </li> <li> <p>AVHRR-EVI2-ERA5</p> </li> <li> <p>MODIS-NIRv-ERA5-WY</p> </li> <li> <p>MODIS-NIRv-ERA5-NT</p> </li> </ol> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
A drained nutrient-poor peatland forest in boreal Sweden constitutes a net carbon sink after integrating terrestrial and aquatic fluxes
<div>In this study, we estimated the net ecosystem carbon balance (NECB) from a nutrient‐poor drained peatland forest and an adjacent natural mire in northern Sweden by integrating terrestrial carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) fluxes with aquatic losses of dissolved organic C (DOC) and inorganic C based on eddy covariance and stream discharge measurements, respectively, over two hydrological years. Each variable presented was measured during each experimental period in sites.</div>
Dataset Mayen et al_Temporal variations of water carbon and atmospheric carbon dioxide fluxes in a temperate salt marsh and influence of aquatic metabolism
Open the record for dataset details and reuse information.
Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO2 Retrievals
<p>TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO<sub>2</sub> dry air mole fraction (XCO<sub>2</sub>). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO<sub>2</sub> retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO<sub>2</sub>. Evaluations against independent CO<sub>2</sub> observations and comparison with previous estimates indicate that the inverted land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, however, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. </p> <p>There are 4 monthly mean variables in this dataset, including prior NEE, posterior NEE, prior ocean flux, and posterior ocean flux, which are all in a spatial resolution of 5 deg by 4 deg. </p>
Re-assessment of the climatic controls on the carbon and water fluxes of a boreal aspen forest over 1996-2016: changing sensitivity to long-term climatic conditions
<p>Recent evidence suggests that the relationships between climate and boreal tree growth are generally non-stationary; however, it remains uncertain whether the relationships between climate and carbon (C) fluxes of boreal forests are stationary or have changed over recent decades. In this study, we used continuous eddy-covariance and microclimate data over 21 years (1996-2016) from a 100-year-old trembling aspen stand in central Saskatchewan, Canada to assess the relationships between climate and ecosystem C and water fluxes. Over the study period, the most striking climatic event was a severe, 3-year drought (2001-2003). Gross ecosystem production (GEP) showed larger interannual variability than ecosystem respiration (<em>R</em><sub>e</sub>) over 1996-2016, but <em>R</em><sub>e</sub> was the dominant component contributing to the interannual variation in net ecosystem production (NEP) during post-drought years. The inter-annual variations in evapotranspiration (ET) and C fluxes were primarily driven by temperature and secondarily by water availability. Two-factor linear models combining precipitation and temperature performed well in explaining the inter-annual variation in C and water fluxes (<em>R</em><sup>2</sup>>0.5). The temperature dependence of all three C fluxes (NEP, GEP and <em>R</em><sub>e</sub>) declined over 1996-2015 (<em>p</em><0.05), and as a result, the phenological controls on annual NEP weakened. The decreasing temperature sensitivity of the C fluxes over 1996-2015 may reflect changes in forest structure, related to the over-maturity of the aspen stand at 100-years of age and exacerbated by high tree mortality following the severe 2001-2003 drought. These results may provide an early warning signal of driver shift or even an abrupt status shift of aspen forest dynamics. They may also imply a universal weakening in the relationship between temperature and GEP as forests become over-mature, associated with the structural and compositional changes that accompany forest ageing.</p>
Graminoids vary in functional traits, carbon dioxide and methane fluxes in a restored peatland: implications for modeling carbon storage
<p>1. One metric of peatland restoration success is the re-establishment of a carbon sink, yet considerable uncertainty remains around the timescale of carbon sink trajectories. Conditions post-restoration may promote the establishment of vascular plants such as graminoids, often at greater density than would be found in undisturbed peatlands, with consequences for carbon storage. Although graminoid species are often considered as a single plant functional type (PFT) in land-atmosphere models, our understanding of functional variation among graminoid species is limited, particularly in a restoration context.</p> <p>2. We used a traits-based approach to evaluate graminoid functional variation and to assess whether different graminoid species should be considered a single PFT or multiple types. We tested hypotheses that greenhouse gas fluxes (CO<sub>2</sub>, CH<sub>4</sub>) would vary due to differences in plant traits among five graminoid species in a restored peatland in central Alberta, Canada. We further hypothesized that species would form two functionally distinct groupings based on taxonomy (grass, sedge).</p> <p>3. Differences in gas fluxes among species were primarily driven by variation in leaf physiology related to photosynthetic efficiency and resource-use, and secondarily by plant size. Multivariate analyses did not reveal distinct functional groupings based on taxonomy or environmental preferences. Rather, we identified functional groups defined by plant traits and carbon fluxes that are consistent with ecological strategies related to differences in growth rate, resource-acquisition, and leaf economics, representing plants with either a strategy to grow quickly and invest in resource capture or to prioritize structural investment and resource conservation. These functional groups displayed larger average carbon fluxes compared to graminoid PFTs currently used in modeling.</p> <p>4. Existing PFT designations in peatland models may be more appropriate for pristine or high-latitude systems than those under restoration. Although replacing PFTs with plant traits remains a challenge in peatlands, traits related to leaf physiology and growth rate strategies offer a promising avenue for future applications.</p>
Estimating net carbon balances and greenhouse gas radiative balances of potato and pea crops on a conventional farm in western Canada (Flux and meteorological data)
<p>Data accompanying the paper titled as "Estimating net carbon and greenhouse gas balances of potato and pea crops on a conventional farm in western Canada". Data includes measurements from eddy covariance, chamber, and meteorological sensors. Measurements were mainly conducted in 2018 and 2019, please refer to the paper for the detailed information.</p>
Temporal dynamics of canopy properties and carbon and water fluxes in a temperate evergreen angiosperm forest
<p>Dataset and code for the Manuscript "<span>Temporal dynamics of canopy properties and carbon and water fluxes in a temperate evergreen angiosperm forest"</span></p>
Seasonal carbon dioxide concentrations and fluxes throughout Denmark's stream network
<h2>About</h2> <p>Repository containing code and data for the study:</p> <p><strong>Title</strong> Seasonal carbon dioxide concentrations and fluxes throughout Denmark’s stream network</p> <p><strong>Authors</strong> Kenneth Thorø Martinsen<sup>1</sup>, Kaj Sand-Jensen<sup>1*</sup>, Victor Bergmann<sup>1</sup>, Tobias Skjærlund<sup>1</sup>, Johan Emil Kjær<sup>1</sup>, Julian Koch<sup>2</sup></p> <p><strong>Affiliations</strong> <sup>1</sup>Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark <sup>2</sup>Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen, Denmark</p> <p><strong>Corresponding author</strong> *Correspondence: <a href="mailto:ksandjensen@bio.ku.dk">ksandjensen@bio.ku.dk</a></p> <p><strong>GitHub repository</strong> <a href="https://github.com/KennethTM/denmark_stream_co2">https://github.com/KennethTM/denmark_stream_co2</a></p> <div> <h2>Contents</h2> <a href="https://github.com/KennethTM/denmark_stream_co2#contents"></a></div> <p>The repository contains R and Python scripts for the analysis and figures.</p> <p>The <code>data/insitu_flux</code> directory contains the <em>in-situ</em> CO<sub>2</sub> flux measurements in Excel format.</p> <p>The <code>data/modeling</code> directory contains artifacts from modeling.</p> <p>The <code>data/products</code> directory contains products resulting from the analysis (predicted CO<sub>2</sub> concentrations and estimated CO<sub>2</sub> fluxes throughout Denmark) in csv format.</p>
Carbon Dioxide and Methane Flux Meta Analysis, Schaerer et al: Permafrost microbes unleashed: thaw reactors provide timely insights into greenhouse gas feedbacks for climate stewardship
<p>Meta-analysis results and workflow: <strong>Meta-Analysis-Report-V1.pdf</strong> </p> <p>raw data tables for input into meta-analysis:</p> <p><strong>co2_flux_by_layer_temp.csv</strong></p> <p><strong>co2_flux_by_layer_time.csv</strong></p> <p><strong>ch4_flux_by_layer_temp.csv</strong></p> <p><strong>ch4_flux_by_layer_time.csv</strong></p> <p><strong>co2_flux_by_headspace_temp.csv</strong></p> <p>(Data included in these tables was digitized using the R package metaDigitize)</p> <p>****</p> <p>We also attempted to summarize the raw data from 12 studies which is summarized in the <strong><em>Flux_Summary_Report </em></strong>document. we converted all units into mg C / g Soil * d (calculations are included in the <strong><em>co2_meta_analysis</em></strong> spreadsheet). For studies not reporting raw data or data tables (7/12 studies), we estimated the values from the figures manually. This typically resulted in an estimate of the mean flux of several replicates (all studies had 3-10 replicates). We filled in metadata as well as we could based on the information available in the papers, although there were many gaps. This information is summarized in the <strong><em>flux_data_compilation</em> </strong>spreadsheet.</p> <p>Studies in the raw data comparison include: Mackelprang 2011, Waldrop 2010 & 2021, Barbato 2022, Dang 2022, Muller 2018, Monteaux 2020, Dutta 2006, Lee 2012, O'Donnell 2009, Roy Chowdhury 2014, Trubl 2021.</p>
Daily cycles in soil carbon flux
<p><strong>Description: </strong></p> <p>Measurements of 24 hour cycles in soil CO2 flux taken from soil collars in the Belian Carbon plot at Maliau. Measurements were taken from 12 subplots over four days at 5-hourly intervals, ensuring good coverage of the complete 24 hour cycle. Air and soil temperatures, soil moisture content and CO2 flux were taken from each plot at each visit. 9 subplots only have a single total soil respiration collar, but 3 subplots also have soil flux partitioning treatments to separate contributions to total respiration from soil organic matter, mycorrhizae and roots.<br> <br> This data was collected by the 2019 cohort of the Tropical Forest Ecology MRes at Imperial College London.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152"><strong>MRes Tropical Forest Ecology Field Course</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247592">here</a></p> <p><strong>Files: </strong>This dataset consists of 2 files: Carbon_corrected_slopes.xlsx, raw_egm.zip</p> <p><strong>Carbon_corrected_slopes.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>24 hour observations of CO2 flux patterns</strong> (described in worksheet Carbon_flux_data)</p> <p>Description: Estimated CO2 flux values, soil moisture and air and soil temperatures from Carbon subplots</p> <p>Number of fields: 14</p> <p>Number of data rows: 296</p> <p>Fields:</p> <ul> <li><strong>record_no</strong>: EGM 'Plot' value - record number on EGM machine for this collar (Field type: ID)</li> <li><strong>plot</strong>: Carbon subplot number (Field type: Location)</li> <li><strong>date</strong>: Calendar date that measurements taken (Field type: Date)</li> <li><strong>time</strong>: Time that measurements taken (Field type: Time)</li> <li><strong>soil_wmc</strong>: Soil water moisture content (Field type: Numeric)</li> <li><strong>soil_temp</strong>: Soil temperature (Field type: Numeric)</li> <li><strong>air_temp</strong>: Air temperature (Field type: Numeric)</li> <li><strong>treatment</strong>: Exclusion treatments for partitioning soil respiration components (Field type: Categorical)</li> <li><strong>field_flux</strong>: CO2 flux reported in the field by EGM (Field type: Numeric)</li> <li><strong>Source</strong>: EGM dat file of source data used for corrected fluxes where available (Field type: File)</li> <li><strong>corrected_flux</strong>: Corrected flux measurements using by eye exclusion of raw flux data (Field type: Numeric)</li> <li><strong>n_points</strong>: Number of points in EGM record (Field type: Numeric)</li> <li><strong>n_used</strong>: Number of points used for corrected slope estimation (Field type: Numeric)</li> <li><strong>flux</strong>: Final flux values, using corrected values where available (Field type: Numeric)</li> </ul> </li> <li> <p><strong>EGM raw data</strong> (described in worksheet EGM_raw_data)</p> <p>Description: Duplicates key information from raw EGM files and indicates points excluded in calculation of corrected flux values</p> <p>Number of fields: 7</p> <p>Number of data rows: 7114</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: EGM recorder 'plot' code, actually just the record sequence number. (Field type: ID)</li> <li><strong>RecNo</strong>: EGM record number - time points of gas measurement at a single plot (Field type: ID)</li> <li><strong>Datetime</strong>: Time of gas concentration measurement (Field type: Datetime)</li> <li><strong>CO2.Ref</strong>: Measured CO2 (Field type: Numeric)</li> <li><strong>Input.E</strong>: EGM internal variable used in slope estimation (Field type: Numeric)</li> <li><strong>Source</strong>: Original EGM dat file containing the flux data (Field type: File)</li> <li><strong>ignore</strong>: Indicates where points from raw data excluded from corrected slope calculations (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>raw_egm.zip</strong></p> <p>Description: Zipfile of raw EGM dat files</p> <p><strong>Date range: </strong>2019-02-18 to 2019-02-21</p> <p><strong>Latitudinal extent: </strong>4.7467 to 4.7480</p> <p><strong>Longitudinal extent: </strong>116.9693 to 116.9704</p>
Data from: Desiccation and rehydration of mosses greatly increases resource fluxes that alter soil carbon and nitrogen cycling
1. Mosses often have positive effects on soil carbon and nitrogen cycling, but we know little about how environmentally determined cycles of desiccation and rehydration in mosses influence these processes. 2. In this context, we compared carbon and nitrogen in throughfall after precipitation passed through eight moss species that were either hydrated continuously or desiccated and rehydrated. Also, the throughfall of four moss species was added to soil and used to determine the net effect of carbon and nitrogen added in moss throughfall on soil CO2 and N2O efflux. 3. Depending on the species, desiccated-rehydrated (rehydrated) mosses lost 2-31 times more carbon in throughfall than mosses that were continuously hydrated (hydrated). Hydrated mosses lost little to no detectable nitrogen; whereas most rehydrated mosses lost some nitrogen in throughfall. Throughfall from both hydrated and rehydrated mosses generated higher CO2 and N2O efflux than water treated soils, but rehydrated moss throughfall promoted larger N2O efflux than hydrated moss throughfall. Throughfall from hydrated mosses caused net negative changes in soil carbon and had very little effect on soil nitrogen, whereas throughfall from rehydrated mosses generated positive changes in soil carbon and nitrogen. 4. Synthesis. Our results indicate that resources lost from desiccated mosses during rehydration influence soil carbon and nitrogen transformations and may be important drivers of carbon and nitrogen cycling and storage in ecosystems.
Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"
<p>Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"</p>
Data and Code for Atmospheric oxygen abundance, marine nutrient availability, and organic carbon fluxes to the seafloor
<p>Code and Data for manuscript "<strong>Atmospheric oxygen abundance, marine nutrient availability, and organic carbon fluxes to the seafloor"</strong></p>
Maps of predicted carbon dioxide and methane fluxes from waterbodies in the Yukon-Kuskokwim Delta, Alaska
<p>In the Arctic, waterbodies are abundant, and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO<sub>2</sub> and CH<sub>4</sub> fluxes across the Yukon-Kuskokwim (YK) Delta of south-west AK. These datasets include carbon dioxide and methane dissolved concentrations and diffusive fluxes from a research watershed in the central YK Delta. </p>
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
<p>The datasets contain country-level net and gross CO2 flux data for land use, land-use change and forestry (LULUCF) from various approaches as used in the paper "Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry" (<a href="https://doi.org/10.5194/essd-16-605-2024">Obermeier et al., 2024, <em>Earth System Science Data</em></a>).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.