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30 results for “Carbon dioxide removal”
Tweets on carbon dioxide removal
<div> <h1><span>Growing online attention and positive sentiments towards</span><br><span>carbon dioxide removal</span></h1> <p>Tim Repke, Finn Müller-Hansen, Emily Cox, Jan Minx</p> <p><em><span>(unpublished)</span></em></p> <p><span>Scaling up CO2 removal is crucial to achieve net-zero targets and limit global warming. </span><span>Understanding public perception of large-scale carbon dioxide removal (CDR) is vital to </span><span>avoid opposition that could slow down development, investments, and deployment. <br></span><span>Using Twitter data from 2010 to 2022, we analysed attention and sentiments towards ten </span><span>CDR methods. Our study provides up-to-date time series evidence complementing survey </span><span>studies, capturing the opinions of users with knowledge or awareness of emerging CDR </span><span>methods. </span><span>Attention towards CDR has grown exponentially, particularly in recent years. <br></span><span>Overall, </span><span>the discourse on CDR has become more positive, except for BECCS. Conventional CDR </span><span>methods are the most discussed and receive more positive sentiments. We examined three </span><span>user types, each with varying levels of involvement in the discourse.</span> <span>Infrequent users (as</span><span>sumed less familiar) pay more attention to methods with biological sinks, while frequent </span><span>users (assumed more familiar) focus more on novel CDR methods.</span></p> <h3><span>Dataset description</span></h3> <p><strong><span>export.csv<br></span></strong><span>This dataset contains all Twitter IDs of tweets retrieved for this study. We also include all technology annotations, which technology-specific subquery this relates to, sentiment classification, and user type. Note, that this dataset contains more tweets than used in the study.</span></p> <p><strong>UserPanels.csv<br></strong>This is an overview of all users, their categorisation, and the number of positive/negative/neutral tweets. This could also be derived from the export.csv.</p> </div> <div> <div> </div> <div> </div> </div>
A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal
<p>This repository is linked to the following article:</p> <ul> <li>Prütz, R., Fuss, S., Lück, S. Stephan, L. & Rogelj, J., A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal. <em>Commun Earth Environ</em> <strong>5</strong>, 197 (2024). <a href="https://doi.org/10.1038/s43247-024-01365-z">https://doi.org/10.1038/s43247-024-01365-z</a></li> </ul> <p>This repository includes: </p> <ul> <li>The literature-based data set that was compiled and used to develop the taxonomy of carbon dioxide removal side effects</li> <li>Code to run the machine learning classifier and to process and visualise the data</li> <li>Training data and an abbreviation list which is required to run the provided code</li> </ul>
The Carbon Dioxide Removal Gap dataset
<p>Data files and R code for the publication: The Carbon Dioxide Removal Gap</p>
Direct evidence for atmospheric carbon dioxide removal via enhanced weathering in cropland soil: Supporting data
<p>Datasets (climate, alkalinity, moisture sensors) associated with the manuscript "Direct evidence for atmospheric carbon dioxide removal via enhanced weathering in cropland soil."</p>
Hubbard Brook Experimental Forest: Soil-atmosphere fluxes of carbon dioxide, nitrous oxide and methane on snow removal plots
Soil atmosphere fluxes of the trace gases; carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) have been measured at several locations at the Hubbard Brook Experimental Forest (HBEF) including 1) the “freeze” study reference plots that provide contrast between stands dominated (80%) by sugar maple versus yellow birch and low and high elevation areas, 2) the Bear Brook Watershed where trace gas sampling is coordinated with long-term monitoring of microbial biomass and activity and 3) watershed 1 where trace gas sampling locations were co-located with long-term microbial biomass and activity monitoring sites that are located near a subset of the lysimeter sites established for the calcium addition study on this watershed. This dataset contains the Freeze study data. Watershed 1 and Bear Brook trace gas data can be found in: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=116. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. These data have been published in: Groffman, P. M., Hardy, J. P., Driscoll, C. T., & Fahey, T. J. (2006). Snow depth, soil freezing, and fluxes of carbon dioxide, nitrous oxide and methane in a northern hardwood forest. Global Change Biology, 12, 1748–1760.
Model output from historical and future scenarios related to 'Carbon Dioxide Removal: Tradeoffs and Lags'
Open the record for dataset details and reuse information.
Feasibility of CO2 Pipeline Construction to Enable Gigaton-Scale Carbon Dioxide Removals: Evidence from historical precedent
<p>Various quantitative and qualitative data on the history, present status, and future projections of CO2 pipelines, as well as historical data and historical timelines of oil and gas pipelines to inform the potential future upscaling of CO2 pipelines.</p>
Data for 'Evaluating the role of enhanced weathering in marine carbon dioxide removal under high emission pathway'
<p><span>1. ALK.mat </span></p> <p><a name="OLE_LINK86"></a><a name="OLE_LINK87"></a><span>Description: This file includes three structs, ALK_CTL, ALK_OWE, ALK_NUT, representing alkalinity data under control run, OWE simulation, NUT simulation. Each struct includes 5 parameters, lat: latitude, lon: longitude, mean_sur: variation of average alkalinity for the upper 100 m, mean_total: variation of average alkalinity for the whole water column, para_a10: 10-years average alkalinity for the upper 100 m. </span></p> <p><span>Units: meq/m<sup>3</sup></span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1a, b and Fig.2 </span></p> <p><span> </span></p> <p><span>2. pH.mat</span></p> <p><span>Description: This file includes three structs, pH_CTL, pH_OWE, pH_NUT, representing surface pH data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pH, para_a10: 10-years average of surface pH. </span></p> <p><span>Units: unitless</span></p> <p><a name="OLE_LINK92"></a><a name="OLE_LINK93"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1c and Fig.3 </span></p> <p><span> </span></p> <p><span>3. total_DIC_ALK.mat</span></p> <p><a name="OLE_LINK88"></a><a name="OLE_LINK89"></a><span>Description: This file includes six structs, CTLALKt, CTLDICt, OWEALKt, OWEDICt, NUTALKt, NUTDICt, representing integrated alkalinity and DIC data in control run, OWE simulation, NUT simulation. Each struct includes two parameters, para_a: decadal average of DIC inventory, para_int: the sum of global DIC inventory.</span></p> <p><span>Units: mmol (DIC)/ meq (ALK) (we plot the figure with the unit Tmol in Fig. 4 and Pmol in Fig. 1d)</span></p> <p><span>Data in this file is used in Fig. 1d and Fig. 4.</span></p> <p><span> </span></p> <p><span>4. remapped_DIC_CTL.nc, remapped_DIC_OWE.nc and DIC_remapped_NUT.nc</span></p> <p><span>Description: The three .nc files include remapped standard-grid (360*180) DIC inventory under control run, OWE simulation and NUT simulation. Each .nc file has 4 variables, lon: longitude, lat: latitude, z_t: depth, DIC: remapped DIC. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><span>Coordinates: DIC: longitude * latitude* depth* time</span></p> <p><span>Data in this file is used in Fig. 5</span></p> <p><span> </span></p> <p><span>5. pCO2.mat</span></p> <p><span>Description: This file includes three structs, pCO2_CTL, pCO2_OWE, pCO2_NUT, representing surface pCO2 data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pCO2, para_a10: 10-years average of surface pCO2.</span></p> <p><span>Unit: ppmv</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig.6</span></p> <p><span> </span></p> <p><span>6. FG_CO2.mat</span></p> <p><a name="OLE_LINK94"></a><a name="OLE_LINK95"></a><span>Description: This file includes three structs, </span><a name="OLE_LINK90"></a><a name="OLE_LINK91"></a><span>FG_</span><span>CO2_CTL, FG_CO2_OWE, FG_CO2_NUT, representing DIC surface gas flux data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of FG_CO2, para_a10: 10-years average of FG_CO2.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK96"></a><a name="OLE_LINK97"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 7</span></p> <p><span> </span></p> <p><span>7. NPP.mat</span></p> <p><a name="OLE_LINK98"></a><a name="OLE_LINK99"></a><span>Description: This file includes three structs, NPP_CTL, NPP_OWE, NPP_NUT, representing total C fixation vertical integral data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of NPP, para_a10: 10-years average of NPP.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK104"></a><a name="OLE_LINK105"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 8</span></p> <p><span> </span></p> <p><span>8. spC.mat, diatC.mat, diazC.mat</span></p> <p><span>Description: The three files include phytoplankton biomass of small phytoplankton (spC_CTL, spC_OWE, spC_NUT), diatom (<a name="OLE_LINK100"></a><a name="OLE_LINK101"></a>diatC_CTL, diatC_OWE, diatC_NUT), and diazotroph (diazC_CTL, diazC_OWE, diazC_NUT). All phytoplankton biomass is measured in the unit of carbon. We store data on each phytoplankton species in the form of a struct. Each struct has five parameters, lat: latitude, lon: longitude, mean_sur: variation of <a name="OLE_LINK102"></a><a name="OLE_LINK103"></a>average of upper 100 m phytoplankton biomass; mean_total: variation of average phytoplankton biomass in the upper ocean, para_a10: 10-years average of average phytoplankton biomass in the upper ocean. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><a name="OLE_LINK108"></a><a name="OLE_LINK109"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK110"></a><a name="OLE_LINK111"></a><span>Data in these files are used in Fig. 9</span></p> <p><span> </span></p> <p><span>9. <a name="OLE_LINK106"></a><a name="OLE_LINK107"></a>calcToSed.mat</span></p> <p><span>Description: This file includes three structs, calcToSed_CTL, calcToSed_OWE, calcToSed_NUT, representing CaCO3 flux to sediments flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of CaCO3 flux, para_a10: 10-years average of CaCO3 flux. </span></p> <p><span>Unit: nmol/cm<sup>2</sup>/s (need to convert unit to mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK112"></a><a name="OLE_LINK113"></a><span>Data in these files are used in Fig. 10</span></p> <p><span> </span></p> <p><span>10. POC.mat</span></p> <p><span>Description: This file includes three structs, POC_CTL, POC_OWE, POC_NUT, representing 100 m POC flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of flux, para_a10: 10-years average of flux.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in these files are used in Fig. 11</span></p> <p><span> </span></p> <p><span>11. atmoCO2.mat</span></p> <p><span>Description: This file includes three structs, CTLATM, OWEATM, NUTATM. In each struct, atm_co2_trend represent the atmospheric CO<sub>2</sub> variation with the unit ppm. </span></p> <p><span>Data in this file is used in Fig.1f</span></p> <p><span> </span></p> <p><span> </span></p> <p><span> </span></p>
Carbon dioxide removal technologies are not born equal
<p>Update of datasets generated and analysed in this study and R script for producing figures from the main text and the SI. This update does not change any results, it is only to match the scenario names and variable categories as they have been uploaded to the AR6 database.</p> <p>We also provide additional data.</p>
Contrasting carbon dioxide removal potential and nutrient feedbacks of simulated ocean alkalinity enhancement and macroalgae afforestation
<p>PISCES model outputs supporting the associated publication.</p> <p>Files are for the following simulations: historical control (CTL), OAE without nutrient addition (OAE), OAE with nutrient addition (OAE_Fe_Si), macroalgae afforestation without nutrient feedbacks (MACRO) and macroalgae afforestation with nutrient feedbacks (MACRO_N_P). The following outputs are provided at monthly resolution: air-sea carbon flux (Cflx), export flux at 100m (EPC100) and depth integrated net primary production of phytoplankton (INTPP). In addition masks of the regions of OAE (mask_OAE) and macroalgae afforestation (mask_MACRO) are provided. All files have been regridded from the original eORCA025 grid to a regular 360x180 degree grid.</p>
Code and data in support of: Uncertainty in determining carbon dioxide removal potential of biochar
Open the record for dataset details and reuse information.
Large global variations in the carbon dioxide removal potential of seaweed farming due to biophysical constraints
Open the record for dataset details and reuse information.
Provincial-Level Assessment of Carbon Dioxide Removal to Meet China's 2060 Carbon Neutrality Goal
<p>20240827_Provincial-Level Assessment of Carbon Dioxide Removal to Meet China's 2060 Carbon Neutrality Goal manuscript scenario Input xmls, output data, data processing code, Figures, figure generation code.</p> <p> </p> <p>Fig2 revised version</p>
Bering10K BEST_NPZ ROMS model: Carbon dioxide removal simulation output
<p>This dataset accompanies our manuscript:</p> <p>Wang H, Pilcher DJ, Kearney KA, Cross JN, Shugart OM, Eisaman MD, Carter BR. Simulated impact of ocean alkalinity enhancement on atmospheric CO2 removal in the Bering Sea. Submitted to Earth's Future, in review</p> <p>The dataset includes selected output variables (temperature, salinity, and carbonate system variables) from two 10-year simulations of the Bering10K ROMS application with BEST_NPZ biogeochemistry. The simulations represent a control run and a point source alkalinity enhancement; see paper for further details. The source code used to run this this simulation is available on Github (https://github.com/beringnpz/roms-bering-sea) and archived at DOI: 10.5281/zenodo.7062782.</p>
Carbon Dioxide Removal Company Announcements
<p>Dataset of publicly announced ambitions for carbon dioxide removal from CDR companies from 2024 - 2050. Data was collected and cleaned from September 2023 - December 2023. </p> <p> </p> <p>For more details on the dataset, refer to README page of Excel sheet.</p>
The policy implications of an uncertain carbon dioxide removal potential: supporting data
<p>Supporting data for manuscript 'The policy implications of an uncertain carbon dioxide removal potential ', published in Joule. The full manuscript is accessible at https://doi.org/10.1016/j.joule.2021.09.004</p>
Diverse carbon dioxide removal approaches could reduce energy-water-land impacts (output data)
<p>GCAM scenario output data</p>
Low-flow Extracorporeal Carbon Dioxide Removal in COVID-19-associated Acute Respiratory Distress Syndrome
ClinicalTrials.gov study NCT04351906. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Protective Ventilation With Carbon Dioxide (CO2) -Removal Technique in Patients With Adult Respiratory Distress Syndrome (ARDS)
ClinicalTrials.gov study NCT00465309. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Retrospective Cohort Study of Low-Flow Extracorporeal Carbon Dioxide Removal (ECCO2R) System: Evaluating ECCO2R's Efficacy and Safety in Participants With Respiratory Failure
ClinicalTrials.gov study NCT07161271. IPD Sharing: NO. Countries: 1. Publications: 6.
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.