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69 results for “carbon 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(unpublished)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>
Forest carbon removal factor variance by climate domain
<p>Uncertainty (variance) in removal factor (annual sequestration rate) for forest carbon in new and existing forests by climate domain (tropical, subtropical, temperate, boreal). Uncertainty analysis is from Harris et al. 2021 Nature Climate Change. Units are aboveground carbon Mg^2/ha^2/year^2. New and existing forest are distinguished by the presence or absence of Hansen et al. 2013 tree cover gain pixels. </p> <p>Note: Uncertainty for existing temperate forest removal factors is so high because the IPCC national greenhouse gas inventory guidelines have a very high uncertainty for these forests (2019 refinement of guidelines). </p> <p>Note: Uncertainty analysis is for published version of the model (v1.2.0).</p> <p>https://github.com/wri/carbon-budget</p>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (CiPEHR and DryPEHR): Weekly 13C Keeling Plot Signatures of Ecosystem Respiration from CiPEHR, DryPEHR and vegetation removal plots, and auxilliary data, 2015
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. How does warming and water table change impact the phenology of dominant plant species? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming and OTC air warming on CIPEHR plots began in 2008; OTCs and drying on DryPEHR in 2011, though the soil warming effect had legacy since 2008. Vegetation removal was done outside the CiPEHR footprint, in July 2012. All vegetation was clipped at the surface and plots were trenched to 30cm, regrowth was prevented by frequent weeding and by 2015 very little new growth was observed in the plots. Vegetation removal plots were paired with undisturbed, vegetated plots. The data presented here specifically addresses the questions, 1) What is the seasonal signal of ecosystem respiration 13C during the growing season, from snow melt to snow fall, 2) How
The Carbon Dioxide Removal Gap dataset
<p>Data files and R code for the publication: The Carbon Dioxide Removal Gap</p>
Data from: Mechanisms underpinning the net removal rates of dissolved organic carbon in the global ocean
<p>With almost 700 Pg of carbon, marine dissolved organic carbon (DOC) stores more carbon than all living biomass on Earth combined. However, the environmental controls behind the persistence and the spatial patterns of DOC concentrations on basin scale remain largely unknown, precluding quantitative assessments of the fate of this large carbon pool in a changing climate. We present the first global dynamic DOC model in agreement with more than 40,000 DOC observations, in which a feedback between DOC and picoheterotrophs is explicitly included (model of MICrobial-DOC interactions, MICDOC). This dataset contains model output and related information for a global model simulation in which a colimitation of macronutrients and organic carbon on microbial DOC uptake is implemented and explains >70% of the global variation of observed DOC concentrations. It provides the model output, source code and meta data for the simulations performed for the publication (doi: 10.1029/2023GB007912) "Mechanisms Underpinning the Net Removal Rates of<br>Dissolved Organic Carbon in the Global Ocean" by Lennartz et al.</p>
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?
<p>Dataset supporting publication. High resolution images, and full data set as produced in manuscript figures.</p> <p><strong>Preprint</strong>: <a href="https://doi.org/10.26434/chemrxiv.10320752.v1">https://doi.org/10.26434/chemrxiv.10320752.v1 </a></p> <p><strong>Published article:</strong> <a href="https://doi.org/10.1016/j.cherd.2020.03.017">https://doi.org/10.1016/j.cherd.2020.03.017 </a></p> <p><strong>Abstract:</strong> Removal of persistent organic compounds from aqueous solutions is generally achieved using adsorbent like activated carbon (AC) but it suffers from limited adsorption capacity due to low surface area. This paper describes a pioneering work on the adsorption of an organic pollutant, 2-chlorophenol (2-CP) by two MOFs with high surface area and water stability; MIL-101 and its amino-derivative, MIL-101-NH<sub>2</sub>. Although MOFs have higher surface area than AC, the latter was proven better having the highest equilibrium 2-CP uptake (345 mg.g<sup>-1</sup>), followed by MIL-101 (121 mg.g<sup>-1</sup>) and MIL-101-NH<sub>2</sub> (84 mg.g<sup>-1</sup>). Used MIL-101 could be easily regenerated multiple times by washing with ethanol and even showed improved adsorption capacity after each washing cycle. These results can open the doors to meticulous adsorbent selection for treating 2-CP-contaminated water. </p>
MAR2PROTECT Efficient Removal of Perfluorooctanoic acid (PFOA) using Fluorinated Ionic Liquids and Granular Activated Carbon DATASET
<p>Perfluoroalkylated and polyfluoroalkylated substances (PFAS) are persistent and bioaccumulative compounds in the environment and the human body. Their presence in high concentrations brings devastating consequences to health. Nowadays, there is an urgent need to develop efficient and sustainable processes that mitigate the damage caused by PFAS. This leads to improving the life quality of individuals. In the present work, for the first time, the extraction properties of PFOA with fluorinated ionic liquids (FILs) in aqueous media were evaluated. The most promising FIL is [P44414] [C4F9SO3] obtaining adsorption capacities up to 280 mg·g-1 in 72 h. Additionally, adsorption processes were carried out with granular activated carbon (GAC1240W) obtaining adsorption capacities up to 666 mg·g-1 best fitted with the pseudo-second order kinetic model with an equilibrium time of 48 h. Equilibrium assays showed maximum uptake capacities around 500 mg.g-1, and a multilayer adsorption due to the best fit to Freundlich model. Both FILs and GAC1240W can open new paths in PFAS adsorption processes from aqueous solutions. Taking advantage of the properties of each one of them, new, more efficient, and sustainable processes can be optimized in the future.</p>
Heavy metal removal from coal fly ash for low carbon footprint cement
<p>Source data for the publication "Heavy metal removal from coal fly ash for low carbon footprint cement"</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>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR) Extended sites: winter ecosystem respiration chamber measurements using snow removal method. Oct-Nov 2009, Oct-Dec 2011, Oct-Nov; March-April 2012, Feb-May 2013.
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This dataset contains point measurements of winter ecosystem respiration fluxes using the snow removal method and the soil temperature, air temperature, and snow depth associated with each flux.
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.
Woody debris removal modifies carbon stocks and soil properties in a fragmented tropical rainforest
<p>We examined whether and how woody debris removal for domestic fuel affects carbon storage and soil properties in an Indian rainforest. Fuelwood removal reduced aboveground carbon stocks, increased soil bulk density, and possibly reduced soil phosphorus stocks. Equitably balancing this subtle trade-off between climate-regulating and vital, widely-utilized provisioning functions, is a challenge for tropical forest research and management.</p>
Model output from historical and future scenarios related to 'Carbon Dioxide Removal: Tradeoffs and Lags'
Open the record for dataset details and reuse information.
Supporting data for "Electrochemical Removal of HF from Carbonate-based LiPF6-containing Li-ion Battery Electrolytes"
<p>This is the dataset of electrochemical experiments and BET analysis for our publication "Electrochemical Removal of HF from Carbonate-based LiPF6-containing Li-ion Battery Electrolytes" (DOI: <span><a href="https://doi.org/10.1149/1945-7111/ad30d3"><span>https://doi.org/10.1149/1945-7111/ad30d3</span></a></span>). This archive contains the raw data and Python Jupyter Notebook computer code to process, analyze, and generate the plots in this manuscript and its Supporting Information.</p> <p> </p> <p><span>Abstract for the manuscript:</span></p> <p> </p> <p><span>Due to the hydrolytic instability of LiPF6 in carbonate-based solvents, HF is a typical impurity in Li-ion battery electrolytes. HF significantly influences the performance of Li-ion batteries, for example by impacting the formation of the solid electrolyte interphase at the anode and by affecting transition metal dissolution at the cathode. Additionally, HF complicates studying fundamental interfacial electrochemistry of Li-ion battery electrolytes, such as direct anion reduction, because it is electrocatalytically relatively unstable, resulting in a LiF passivation layer. Methods to selectively remove ppm levels of HF from LiPF6-containing carbonate-based electrolytes are limited. We introduce and benchmark a simple yet efficient electrochemical method to selectively remove ppm amounts of HF from LiPF6-containing carbonate-based electrolytes. The basic idea is the application of a suitable potential to a high surface-area metallic electrode upon which only HF reacts (electrocatalytically) while all other electrolyte components are unaffected under the respective conditions.</span></p> <p> </p>
Wastewater alkalinity addition enhancement for carbon emission reduction and marine CO2 removal
<p>The ROMS_RCA model settings of 2010 runs. The reference date of the 'TIME' variable is 1983-01-01.</p> <p>The files with 'Y2010_' in their names contain the boundary data and initial fileds for the model run. The file "ROMSeutro_1strun.inp" lists all the model parameters, while the files with 'CPB_WWTP_ps' in the names are the settings of the discharges from each WWTP outlet. </p> <p>The data used to generate the figures are provided in the MAT file.</p>
Removal of Crystal Violet (CV) in aqueous solution by Activated Carbon- Equilibrium, kinetic and Thermodynamic
<p>The quantitative kinetic and equilibrium adsorption parameters for Crystal Violet (CV) removed by commercial activated carbon (AC) were studied by UV visible absorption spectroscopy. (AC) with a high specific surface area (1250.320m2 /g) was characterized by the Brunauer-Emmett-Teller (BET) method and the zero charge point (pzc).The adsorptive properties of (AC) with (CV) was conducted at variable stirring speed , adsorbent dose, solution pH, initial (CV) concentrations, contact time and temperature using batch mode operation to find the optimal conditions for a maximum adsorption. The adsorption mechanism of CV onto (AC) was studied using the first pseudo order, second pseudo order and Elovich kinetic models. The kinetic was found to follow a pseudo-second order kinetic model. The equilibrium adsorption data for CV on AC were analyzed by the Langmuir, Freundlich, Elovich and Temkin models. The results indicate that the Langmuir model provides the best correlation (qmax = 35.71, 90.91 mg/g at 25 °C and 40 °C respectively).The adsorption isotherms at different temperatures have been used for the determination of thermodynamic parameters i.e. the free energy (ΔGo = -2.30 to -5.34 kJ/mol), enthalpy (ΔHo = 36.966 kJ/mol), entropy (ΔSo = 0.131 kJ/mol K) and activation energy (Ea) 40.208 kJ/mol of adsorption. The negative ΔGo and positive ΔHo values indicate that the overall adsorption is spontaneous and endothermic in nature</p>
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>
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?
<p>A more complete version of this dataset, along with publication details, is available from <a href="https://zenodo.org/record/2586955">https://zenodo.org/record/2586955</a></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>
ScienceDex guides
Understand access before you commit
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.