{"data":{"id":"10.17026/ls/hmoc5m","type":"dois","attributes":{"doi":"10.17026/ls/hmoc5m","prefix":"10.17026","suffix":"ls/hmoc5m","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"R.C.H. Aben","nameType":"Personal","affiliation":["Radboud University Nijmegen"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6182-4789","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Replication Data for: A mid-cost automated floating chamber system for high-frequency CO2 and CH4 flux measurements: design, calibration, and comparison with infrequent measurement strategies"}],"publisher":"DANS Data Station Life Sciences","container":{},"publicationYear":2026,"subjects":[{"subject":"Medicine, Health and Life Sciences"},{"subject":"Peatland ditches"},{"subject":"Vaisala"},{"subject":"GMP343"},{"subject":"LGD Compact-A"},{"subject":"NDIR"},{"subject":"Laser spectroscopy"},{"subject":"Greenhouse gas"},{"subject":"Temporal variability"},{"subject":"Peatlands"},{"subject":"Freshwater ecosystems"},{"subject":"High-frequency measurements"},{"subject":"Diurnal variation"},{"subject":"Calibration"},{"subject":"TDLS"},{"subject":"Axetris"}],"contributors":[{"name":"Aben, R.C.H.","nameType":"Personal","givenName":"R.C.H.","familyName":"Aben","affiliation":["Radboud University"],"contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2025-12-18","dateType":"Submitted"},{"date":"2026-07-10","dateType":"Available"},{"date":"2026-10-13","dateType":"Updated"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.5194/egusphere-2026-5600","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["180927065"],"formats":["application/octet-stream","text/tab-separated-values","text/comma-separated-values","text/csv","type/x-r-syntax","text/plain","application/json","application/gzip"],"version":"1.1","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"lang":"en","rights":"Creative Commons Attribution Non Commercial Share Alike 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-sa-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Small inland waters are important greenhouse gas (GHG) sources; however, most field campaigns rely on daytime-only measurements collected at multi-week intervals, potentially biasing flux estimates. We present the Buoyant Emission Recorder for Temporal High-Frequency Analyses (BERTHA), a mid-cost automated floating chamber system for high-frequency measurements of CO2 and CH4 fluxes. We describe the system’s design and show how empirically derived pressure and water vapour correction factors for the mid-cost CO2 and CH4 sensors increase the accuracy of reported concentrations and derived flux estimates. Additionally, we calculated the mean CO2 and CH4 emission using high-frequency measurements during 97 days and simulated alternative measurement scenarios based on the full dataset and evaluated how well these ‘reference means’ could be approached by 1) single, daytime-only chamber measurements at daily to monthly intervals, and 2) short-term (2–3 days) automated chamber deployments every 2–3 weeks combined with machine learning-based gap-filling. Results show that daytime-only measurements consistently and strongly underestimated the reference CO2 mean. CH4 emissions matched the reference mean well for daily, but not for monthly measurements (\u0026gt;90% and ~23% of simulations deviated \u0026lt;20% of the reference mean, respectively). Short-term automated chamber campaigns, gap-filled using machine learning closely reproduced the CO2 reference mean, with 89% and 48% of means being within ±20% of the reference mean, for campaign intervals of 2 and 3 weeks, respectively. We conclude that high-frequency GHG measurements provide reliable emission estimates, a robust benchmark for evaluating reduced measurements strategies and gap-filling approaches, and improve process-based understanding of GHG fluxes.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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