The Carbon Storage Open Database is a collection of spatial data obtained from publicly available sources published by several NATCARB Partnerships and other organizations. The carbon storage open database was collected from open-source data on ArcREST servers and websites in 2018, 2019, 2021, and 2022. The original database was published on the former GeoCube, which is now EDX Spatial, in July 2020, and has since been updated with additional data resources from the Energy Data eXchange (EDX) and external public data resources. The shapefile geodatabase is available in total, and has also been split up into multiple databases based on the maps produced for EDX spatial. These are topical map categories that describe the type of data, and sometimes the region for which the data relates. The data is separated in case there is only a specific area or data type that is of interest for download. In addition to the geodatabases, this submission contains: 1. A ReadMe file describing the processing steps completed to collect and curate the data. 2. A data catalog of all feature layers within the database. Additional published resources are available that describe the work done to produce the geodatabase: Morkner, P., Bauer, J., Creason, C., Sabbatino, M., Wingo, P., Greenburg, R., Walker, S., Yeates, D., Rose, K. 2022. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104945 Morkner, P., Bauer, J., Shay, J., Sabbatino, M., and Rose, K. An Updated Carbon Storage Open Database - Geospatial Data Aggregation to Support Scaling -Up Carbon Capture and Storage. United States: N. p., 2022. Web. https://www.osti.gov/biblio/1890730 Morkner, P., Rose, K., Bauer, J., Rowan, C., Barkhurst, A., Baker, D.V., Sabbatino, M., Bean, A., Creason, C.G., Wingo, P., and Greenburg, R. Tools for Data Collection, Curation, and Discovery to Support Carbon Sequestration Insights. United States: N. p., 2020. Web. https://www.osti.gov/biblio/1777195 Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Data provided by the Marine Institute, and may also incorporate data from other agencies and bodies.

This layer will help you identify some of the strategic opportunities in your area for positive change and work out plans with land managers to enhance carbon, whatever the current land use. Carbon abatement is the reduction in carbon dioxide presently being released to the atmosphere from the environment when land use and management are in conflict with the best carbon outcome. This layer compliments the carbon sequestration map and shows where different types of actions can be taken to maximise carbon storage and sequestration. The approach here has been to highlight the worst areas of the country for carbon loss and abatement potential. Where existing high quality semi natural habitat already exists there is likely to be opportunities to enhance carbon, but much of this is fine scale management decisions that a national data set cannot determine. So, a more precautionary approach has been applied that if a habitat presently exists on a site, land-use change has not been recommended. There will be places where an existing habitat could be enhanced or changed to another habitat to give carbon benefits. Many habitats have become degraded through management practices over time, such as drainage, which means it now sits in a lower carbon state & is losing carbon and as such has abatement potential such as conversion from a dry grassland habitat with drainage to much wetter fenland habitat. In the worse cases some habitats in our current priority habitat classification system are degraded versions of other habitats and have the potential to move between habitats and so abating carbon. This layer has made a broad assessment of which land use may be changed to an appropriate higher carbon variant. In the case of very productive grassland and pasture we have assumed only a change to high carbon management practices where clear abatement gains are present. This is because high value agricultural land is a key non-renewable resource which is needed for food security. In addition, such land generates a good economic revenue for agricultural goods.

This map was created differently to the others, by using a Python script to run the analysis. Firstly, a table was designed that looked at habitats and possible soil types they could develop upon. This was then used to create a logic table showing areas which were now not on suitable soil types, for example arable land on deep fen peat. Scoring was awarded from scientific review, using expert judgement by the team, and insights in developing the previous layers results, on the potential sequestration enhancement or land use of each type of land management action or change. All the technical information is available in the accompany technical report. The classes used in the abatement maps and models are shown in Table 1 with the Logic tables are found in -Appendix 4 Abatement logic rules in full technical report. Code/ Class / Notes 1/ Maintain - enhance existing habitats / Some of our existing habitats for example, blanket bog vegetation deep peat, are not in the best ecological condition they can be. This is particularly the case for peatlands and heather moorlands which have been drained to change blanket bog vegetation into heathland vegetation or to obtain a grazing value out of the blanket bog. 2/ Low/ On productive agricultural land (intensive grassland and arable) there are possibilities to enhance carbon by changing land management practice. 3/ Low/medium/ This was given where the habitat could be replaced with a more suitable habitats ; 4/ Medium/ This is allocated where changing land use could result in a fairly good enhancement of carbon sequestration. 6/ High /The highest benefits to abatement are restoring the deep peats which are currently under arable and intensive grazing. i.e. the fenlands area 7/ Urban / It was not in scope for this project to look at carbon values in urban areas as the data accuracy is to poor too make an informed decision. 8/ Water / It was not in scope for this project to look at carbon values within water bodies as the data accuracy is too poor to make an informed decision. NE PHI/ Ancient Woodland - OGL NE Living England - OGL NE Peat Map [2008] - Non- commercial licence NE SSSI data NFI-National Forest Inventory (NFI) Forest Research- OGL Soilscapes - Cranfield University/ HMSO- NE Bespoke Licence SRTM- NASA Shuttle Radar Topography- Open Topography

There were 628 projects in the commercial carbon capture and storage (CCS) facilities pipeline worldwide as of July 2024. 50 of these were operational, while almost 290 were in early development. The majority of projects in the CCS facilities pipeline are located in North America. CCS is a process of sequestration where carbon dioxide emitted from large power plants or heavy industries is captured and stored before reaching the atmosphere.

These spatial datasets consider the lands contribution to preventing and mitigating climate change, through storage of carbon in the Vegetation (above ground). This above Ground Carbon spatial datasets represent a strategic resource for England, that indicate the range of carbon storage values in tonnes of carbon per hectare (t C Ha-1 ). At a local scale (e.g. 1:50 000). They are presented as a series of raster datasets for use in GIS Systems at a resolution of 25m2. These maps will assist users to find out where the most important carbon stores in vegetation in their areas. They are not suitable for field scale carbon mitigation as this would require field scale carbon assessment.

Three data component layers were collated together to form a continuous habitat data layer for England using the best, freely available information on habitat types. these were: The National Forest Inventory (2016);The single layer priority habitat dataset (various dated); Living England habitat map from satellite imagery (2020). From the collation, each habitat type was scored in terms of the likely carbon they would store above ground (t carbon/ Ha). These data were taken form a very wide range of scientific studies but largely built on Carbon Storage and Sequestration by Habitat 2021 (NERR094). Where slopes are very steep (greater than 18o) then the habitat classes which are identified by their tree species were score slightly lower, this is because they tend to have thinner soils and support less growth of the tree above the ground. Where woodland is long established or manged for nature a slight enhancing of scoring was given, with locations taken from ancient woodland data (10% uplift) and the protected site data (10% uplift) including SSSI, SAV, LNR and NNR.
NE PHI - OGL NE Living England - OGL NE Peat Map [2008] - Non- commercial licence Soilscapes - Cranfield University- NE Bespoke Licence SRTM- NASA Shuttle Radar Topography- Open Topography

Open AccessYNP Chronosequence Carbon DataThis data file includes field data collected between 2004 and 2007 in and around Yellowstone National Park. It includes data from 77 lodgepole pine stands ranging in age from 12 years to 335 years old. Data include stand structural data as well as carbon data for all carbon pools in each stand. It is an MS Excel file with a tab describing each data column and a tab with the data themselves.

This dataset provides a single, seamless spatial database of carbon storage potentials for unmineable coal areas across the USA and parts of Canada compiled from regional datasets created by the RCSPs. Storage resource estimates are based on physically accessible CO2 storage pore volume within subsurface geologic formations, and on the assumption that the storage reservoirs are open systems in which in situ fluids will either be displaced from the injection zone or managed accordingly. Economic and regulatory constraints are not considered. These data are intended to be used as an initial assessment of potential geologic storage and are not a substitute for site-specific assessment, testing, and geologic investigation. This spatial data layer provides carbon capture and storage (CCS) project developers a starting point for further inquiry into CCS technologies aimed at reducing CO2 emissions and is intended for use by RCSPs, project developers, and governmental entities for regional- and national-scale assessments of potential CO2 storage resources in the United States and parts of Canada.

These spatial datasets consider the lands contribution to preventing and mitigating climate change, through storage of carbon in the Soils (below ground) & Vegetation (above ground). This total carbon spatial datasets represent a strategic resource for England, that indicate the range of carbon storage values in tonnes of carbon per hectare (t C Ha-1 ). At a local scale (e.g. 1:50 000). They are presented as a series of raster datasets for use in GIS Systems at a resolution of 25m2. These maps will assist users to find out where the most important carbon stores in soils in their areas. They are not suitable for field scale carbon mitigation as this would require field scale carbon assessment. It is often the case that where we have lower below ground carbon (mineral soils) the conditions are great for better tree and vegetation growth to get greater above ground carbon storage. Where below ground carbon storage is greater this is due to wetter conditions and soil waterlogging reducing organic matter decomposition. This leads to poorer growing conditions that result in lower above ground carbon storage. The addition of the above and below ground carbon layers seems to mean that carbon data is more homogenised and has much less carbon variation within areas. Although Total Carbon Storage is useful the data user will also need to refer back to input data to understand why the results occur across the landscape area.

Total Carbon Storage adds the two carbon storage layers together. As the above & below ground carbon storage layers indicate the range of carbon storage values in tonnes of carbon per hectare (t C Ha-1 ). At a local scale (e.g. 1:50 000). They are presented as a series of raster datasets for use in GIS Systems at a resolution of 25m2. Each 25m2 has a carbon total form the layers and these are added together spatially to create the total carbon layer in tonnes of carbon per hectare (t C Ha-1 ) NE PHI/ Ancient Woodland - OGL NE Living England - OGL NE Peat Map [2008] - Non- commercial licence NFI-National Forest Inventory (NFI) Forest Research- OGL Soilscapes - Cranfield University- NE Bespoke Licence SRTM- NASA Shuttle Radar Topography- Open Topography

This dataset provides a single, seamless spatial database of carbon storage potentials for oil and gas reservoirs across the USA and Canada compiled from regional datasets created by RCSPs and the KGS. Storage resource estimates are based on physically accessible CO2 storage pore volume within subsurface geologic formations, and on the assumption that the storage resources are open systems in which in situ fluids will either be displaced from the injection zone or managed accordingly. Economic and regulatory constraints are not considered. These data are intended to be used as an initial assessment of potential geologic storage and are not a substitute for site-specific assessment, testing, and geologic investigation. This spatial data layer provides carbon capture and storage (CCS) project developers a starting point for further inquiry into CCS technologies aimed at reducing CO2 emissions and is intended for use by RCSPs, project developers, and governmental entities for regional- and national-scale assessments of potential CO2 storage resources in the United States and parts of Canada.

This dataset provides a single, seamless spatial database of carbon storage potentials for saline formations across the USA and parts of Canada compiled from regional datasets created by the RCSPs and site characterization projects. Storage resource estimates are based on physically accessible CO2 storage pore volume within subsurface geologic formations, and on the assumption that the storage reservoirs are open systems in which in situ fluids will either be displaced from the injection zone or managed accordingly. Economic and regulatory constraints are not considered. These data are intended to be used as an initial assessment of potential geologic storage and are not a substitute for site-specific assessment, testing, and geologic investigation. This spatial data layer provides carbon capture and storage (CCS) project developers a starting point for further inquiry into CCS technologies aimed at reducing CO2 emissions and is intended for use by RCSPs, project developers, and governmental entities for regional- and national-scale assessments of potential CO2 storage resources in the United States and parts of Canada.

This study aims to estimate the carbon sequestration rates of soft landscapes in the University of British Columbia Vancouver campus and compare their carbon sequestration capacity. The significance of carbon sequestration rates in soft landscapes is discussed in the context of urban planning and the role of vegetation in mitigating climate change. LiDAR data and aerial photos estimate above-ground carbon sequestration and GIS and R are used for data analysis. The research objectives are to compare the attributes of different soft landscapes, calculate their carbon sequestration rates, identify which soft landscapes have the highest carbon sequestration capacity, and discuss the study's limitations and possible improvements for future research. The proposed methods include data pre-processing, developing a canopy height model, and estimating carbon sequestration capacity for each soft landscape area. The study suggests optimizing urban soft landscape services to increase carbon storage in UBC and Vancouver.

This data release documents the current version of the Coastal Carbon Data Library, an international database of soil carbon data from tidal wetlands synthesized by the Coastal Carbon Network. This database serves as an open source data tool designed to support soil Blue Carbon research and policy. This data can additionally be accessed through the Coastal Carbon Atlas, which is an interactive web application created to interface with the Data Library and allow for the exploration, filtering, and download of disaggregated soil core data.

This dataset contains the world Large Scale Carbon Capture Projects for the period 1972-2029. Data from Global CCS Institute. Follow datasource.kapsarc.org for timely data to advance energy economics research.Globally, there are 15 large-scale CCS projects in operation, with a further seven under construction. The 22 projects in operation or under construction represents a doubling since the start of this decade. The total CO2 capture capacity of these 22 projects is around 40 million tonnes per annum (Mtpa).There are another 10 large-scale CCS projects at the most advanced stage of development planning, the Concept Definition (or Define) stage, with a total CO2 capture capacity of around 14 Mtpa. A further 12 large-scale CCS projects are in earlier stages of development planning (the Evaluate and Identify stages) and have a total CO2 capture capacity of around 25 Mtpa.Two large-scale CCS projects became operational in 2015: The Quest project, located in Alberta, Canada (CO2 capture capacity of approximately 1 Mtpa) was launched in November 2015. The project, involving the manufacture of hydrogen for upgrading bitumen into synthetic crude oil, is North America’s first large-scale CCS project to store CO2 exclusively in a deep saline formation. The Uthmaniyah CO2-EOR Demonstration Project, located in the Kingdom of Saudi Arabia was launched in July 2015. The project is capable of capturing around 0.8 Mtpa of CO2 from the Hayiwah NGL (natural gas liquids) Recovery Plant.Two more industrial CCS projects are expected to become operational in early 2016: The Illinois Industrial CCS Project (CO2 capture capacity of 1 Mtpa) is located at the Archer Daniel Midlands corn-to-ethanol production facility in Decatur, Illinois (United States). The project, the world’s first bio-CCS project at large scale, will be the first integrated CCS project in the United States to inject CO2 into a deep saline formation at a scale of 1 Mtpa.The Abu Dhabi CCS Project (CO2 capture capacity of 0.8 Mtpa), the world’s first iron and steel project to apply CCS at large scale, will involve CO2capture from the direct reduced iron process used at the Emirates Steel plant in Abu Dhabi.Large-scale CCS projects in the power sector are now a reality, demonstrated by: The world’s first large-scale power sector CCS project – the Boundary Dam Carbon Capture and Storage Project in Canada (CO2 capture capacity of 1 Mtpa) – becoming operational in October 2014.Commissioning activities on a new-build 582 megawatt (MW) power plant beginning at the Kemper County Energy Facility in Mississippi (United States, CO2 capture capacity of 3 Mtpa) with CO2 capture expected to commence around the middle of 2016.The Petra Nova Carbon Capture Project at the W.A. Parish power plant near Houston, Texas (US, CO2 capture capacity of 1.4 Mtpa) entering construction in July 2014, with CO2 capture anticipated by the end of 2016.Definitions of what constitutes a large-scale integrated project and of the various stages in the lifecycle of a project are found at: http://www.globalccsinstitute.com/projects/large-scale-ccs-projects-definitions

URL: https://geoscience.data.qld.gov.au/dataset/ds000035

Funding provided by the Queensland Government through the Department of Employment, Economic Development and Innovation (DEEDI), has allowed Greenhouse Gas Storage Solutions (GGSS) to undertake a technical assessment of the CO2 geological storage prospectivity of the major sedimentary basins in Queensland; the basins were specified by DEEDI. Production of this Queensland Carbon Dioxide Geological Storage Atlas was contracted to GGSS in August 2008 and was completed in July 2009. This initiative by the Queensland Government places it at the forefront of the world in making such detailed precompetitive data sets available in the public domain that document the perceived geological storage prospectivity.

In this atlas, thirty six basins have been individually assessed for their CO2 geological storage prospectivity using structural traps or migration assisted storage mechanisms in deep, regional reservoir-seal intervals (i.e. ‘saline’ reservoirs and aquifers). Regional synopses are also provided on the potential for CO2 storage of depleted oil and gas fields (including CO2-enhanced oil recovery) and deep ‘unmineable’ coal seams (including enhanced coal-seam gas recovery). These assessments, in the form of this atlas and accompanying geographic information system (GIS), will be used to target basins or parts of basins for more detailed study to identify potential storage sites for CO2.

Contents:
-Queensland Carbon Dioxide Geological Storage Atlas in .pdf format
-A GIS in ArcGIS 9.2 containing digital copies of all files interpreted and used in the Atlas. All spatial data are also provided in MapInfo format
-Preliminary report on the potential for carbon geostorage in the Taroom Trough, Roma Shelf and the Surat, Eromanga and Galilee Basins in .pdf and .xls formats

Objective The purpose of this project is to develop the conversion coefficient from timber volume to biomass of 2 to 3 primary tree species for plantations and the model to estimate carbon pool of the plantations. Meanwhile, the soil carbon pool of the plantations will be calculated. The results of this project can be used to be as part of carbon sequestration database of forest resources and to assess the contributions of plantations on the storage of carbon.

Primary works The primary tree species for plantations are selected in the southern Taiwan to investigate and analyze the carbon storage of plants and soil of the ecosystems. The primary works are as following: 1. Study on spatial and time variation of carbon storage of trees: 2-3 tree species will be selected from the primary plantations in various zones in order to estimate the carbon storage of trees, understory vegetation, and litter layer, then to build its carbon sequestration model and to evaluate the carbon storage of the stand. The primary works for each species include plots establishment and inventory, sample-trees selection and harvest, biomass measurement of sample trees, regression model establishment, carbon content analysis, and carbon sequestration estimation. Thus the growth and carbon storage of plantations will be estimated. 2. Study on spatial and time variation of soil carbon storage: the primary works include model evaluation on soil storage or carbon pool, establishment soil carbon storage database for primary tree species, analysis soil carbon storage of plantations at different ages. Then soil carbon pools are calculated by using the database of different tree species and ages of plantations. 3. Study on soil CO2 evolution of the plantations: the soil CO2 evolution to be measured by open chamber method with continuous air flow. The works include measurements on daily and season variations of soil CO2 evolution in the forest stand and soil property survey, including soil temperature, soil water content, soil nitrogen form (NH4+-N, NO3--N, and total nitrogen), soil carbon content, and soil pH.

Expecting benefits 1. To develop the conversion coefficient from timber volume to biomass of 1 to 2 primary tree species for plantations and estimate the carbon storage of its plantations. 2. To develop the soil carbon storage database of different plantations. 3. To develop the standard soil sampling and analysis procedures for soil carbon. 4. To understand total amount soil CO2 evolution of different plantations and to evaluate the effect of soil characteristics on soil CO2 evolution.

Rangelands are often ignored in the discussion of using management to sequester carbon but demonstrating that restoration of vast degraded rangelands might pay for itself through carbon credit markets would be a significant conservation contribution. The additional amount and cost of carbon sequestered was quantified by simulating seeding perennial grass and shrub species in sagebrush shrublands dominated by non-native annual grass and forb species (NNAGF) compared to doing nothing in a 485,623 km2 Area Of Interest (AOI) centered around Nevada, USA. Using Sentinel-2 satellite imagery, NNAGF cover was mapped across the AOI to locate areas dominated by NNAGFs. Spatial state-and-transition simulation models with a carbon stock-and-flow sub-model simulated the seeding of perennial species in NNAGF-dominated sagebrush shrublands in the Columbian Plateau ecoregion (IL Ranch, Nevada), north-central Great Basin ecoregion outside the North American Monsoon (TS-Horseshoe Ranch, Nevada), and the southeastern Great Basin ecoregion within the North American monsoon (PVMH landscape, Utah). The net biome productivity (NBP) and cost per unit area of sagebrush shrublands was quantified by simulating restoration of NNAGF to perennial vegetation over a 25-year period. The unseeded PVMH landscape, IL Ranch, and TS-Horseshoe Ranch were sinks of carbon (i.e., positive NBP) at 84, 9, and 11 g C∙m-2∙yr-1, respectively. About 58% to 90% of NBP was stored in the soil. The IL Ranch required only small levels of seeding and was a small sink of C at 0.71 ± 0.65 g C∙m-2∙yr-1, whereas the additional NBP for the seeded PVMH landscape was 19.9 ± 10.6 g C∙m-2∙yr-1. When extrapolated to the AOI, the most and least carbon stored, respectively, was in Utah (136,132 metric Ton∙yr-1, cost: $287M) and the central Great Basin (3,196 metric Ton∙yr-1, cost: $23M). Positive NBP values reported here showed that carbon sequestration in sagebrush shrublands compares favorably with those of more productive systems in the USA and worldwide. Methods To quantity carbon sequestration and the cost of seeding, three products were required that formed this study’s three parts: (1) an NNAGF cover index map for the Area of Interest (AOI) that allowed the identification of areas dominated by NNAGFs that could be seeded; (2) estimated net amount of carbon stored per unit area of sagebrush shrublands through restoration of NNAGF to perennial vegetation, and (3) estimated cost of storing net carbon through restoration of sagebrush shrublands per unit area. Achieving the components of the methods required that we innovatively bring together past state-and-transition simulation models focused on management scenarios from different and widely distributed Intermountain West landscapes created by The Nature Conservancy and apply carbon stock-and-flow modeling within each of these simulation models. Overview of archived data: There are three folders in the archived data: (A) "AnnSpp_Estimation" is a raster map created in Google Earth Engine from several Sentinel 2 satellite imageries. This raster map (tiff format) and supporting GIS files were rendered in ESRI ARC PRO after download from Google earth Engine. (B) (i) "Carbon_IL&TSHS" folder primarily contains the simulation database "Newmont.ssim," which requires the freeware Syncrosim (Version 2.3.12 or later; downloaded from www.apexrms.com ) and packages ST-Sim and stsimsf (obtained remotely from within Syncrosim) and the input (Newmont.ssim.input) and output (Newmont.ssim.output) folders. The Syncrosim database and input and output folders are massive (>6 GB of memory compressed). Both input and output folders were created and managed by Syncrosim during simulations and should not be tampered with as all parameters and data to run the simulations are contained within. The Authors have never worked within these folders. The Newmont.ssim contains simulation scenarios for the distinct IL Ranch and TS-Horseshoe Ranch (i.e., the same simulation library contains both independent projects. (ii) In addition to the simulation database, two folders contain all geotiff rasters (no other GIS files) that were uploaded in the Syncrosim database (i.e., already uploaded). Geotiff rasters were provided for the IL Ranch project (GIS_IL Ranch) and the TS-Horseshoe Ranch (GIS_TSHS Ranch). These geotiff rasters might be needed for upload if the simulation is conducted on a server with different directory pathway organization than the Cloud server used by this project. (iii) A "Results" folder contains all the MS Excel files of results and there analyses that were exported from the Syncrosim database (i.e., Syncrosim-trained users can reproduce our results and never open these files).(C) (i) "Carbon_PV-MH+IP" folder primarily contains the simulation database "IP+PVMH.ssim," which requires the freeware Syncrosim (Version 2.3.12 or later; downloaded from www.apexrms.com ) and packages ST-Sim and stsimsf (obtained remotely from within Syncrosim) and the input (IP+PVMH.ssim.input) and output (IP+PVMH.ssim.output) folders. Both input and output folders were created and managed by Syncrosim during simulations and should not be tampered with as all parameters and data to run the simulations are contained within. Authors have never worked within these folders. The IP+PVMH.ssim contains simulation scenarios for the PVMH landscape, a single landscape. (ii) In addition to the simulation database, a GIS folder named "GIS" contains all geotiff rasters (no other GIS files therein) that were uploaded in the Syncrosim database (i.e., already uploaded). These geotiff rasters might be needed for upload if the simulation is conducted on a server with different directory pathway organization than the Cloud server used by this project. (iii) A "Results" folder contains all the MS Excel files of results and their analyses that were exported from the Syncrosim database (i.e., Syncrosim-trained users can reproduce our results and never open these files).

Siloed and disparate wellbore datasets from federal, state, and tribal entities have been acquired, processed, standardized, and integrated to produce a dynamic national wellbore database. The initial version (March 2023) served as a use case to test and implement the standardization and integration methodology. It was initially developed and titled CO2-Locate to inform injection site selection, permitting, and other stakeholder needs. Since its initial release, NETL has substantially enhanced this resource with expansions including 50+ more data sources and millions of additional well records. As an evolving resource, the next release is scheduled for Summer 2025 as the Wellbore Exploration and Location Logistic System (WELLS) Database. The WELLS Database will contain more than six million wellbore records, containing critical information to support a range of uses, including oil, gas, and critical mineral production. This is built to be a living database, which will eventually enable access through an application programming interface (API), but currently provides annual updates using a series of bespoke Python scripts that acquire, process, standardize, and integrate the resources automatically with minimal curation needed. Supplemental Information: In addition to the integrated wellbore data records, this resource includes supplemental information to support domestic energy advancements. Resources packaged within the database include spatial summary layers from proprietary wellbore resources, providing additional insights, including wellbore summaries queried by age, status, and depth. Moreover, this resource includes the previously published Global Oil and Gas Infrastructure (GOGI) Database, with updates on U.S. infrastructure. Metadata resources packaged with this database include a field dictionary with field (i.e., attribute) coverage across acquired public well resources; the resulting integrated public well layers are detailed in the supplemental information table, CO2_Locate_Field_Dictionary.pdf. Notes for consideration: This database will be updated as new data and information become available and are processed, reviewed, and approved for release. Additional state and federal entity data are planned to be integrated and included in future revisions. Summary layers provided in this application are derived from proprietary layers and may not always contain key features; therefore, these features might not be shown when data are queried.

Soil organic carbon (SOC) sequestration is increasingly emphasized as a climate mitigation solution, as scientists, policy makers, and land-managers prioritize enhancing belowground C storage. To identify key underlying drivers of total SOC distributions, we compiled a global dataset of soil C stocks held in three chemical forms, reflecting different mechanisms of organic C protection: free particulate organic C (fPOC), physically protected particulate organic C (oPOC), and mineral-protected soil organic C (mSOC). In our dataset, these three SOC pools were differentially sensitive to effects of climate, soil mineralogy, and ecosystem type, emphasizing the importance of distinguishing between physical and chemical C protection mechanisms. C stocks in all three pools varied among ecosystems: cropland soils stored the least amount in each pool, with forest and grassland soils both containing significantly more fPOC (40-60% greater in each ecosystem) than croplands. oPOC stocks did not significantly differ from zero in croplands, but were substantial in forest and grassland soils. Meanwhile, mSOC stocks were the greatest in grasslands and shrublands (90-100% greater than croplands). In cropland soils, there were no major effects of tillage on C storage in any of the three pools, while manure addition enhanced mSOC stocks, especially when added with inorganic N. Thus, the human land use intensity in croplands appears to reduce SOC storage in all major pools, depending upon management; retaining native vegetation should be emphasized to maintain current global SOC stocks. Methods We collected data from papers which utilized a density fractionation methodology to measure soil organic C (SOC) within the three fraction pools: free particulate organic fraction (fPOC), occluded particulate organic fraction (oPOC), and mineral soil organic carbon (mSOC). To filter through published studies reporting SOC stocks, we chose to extract data from papers which cited one of the following rigorous and influential fractionation methodologies: (Golchin et al., 1994; Shaymukhametov et al., 1984; Six et al., 1998; Sollins et al., 1984, 2006, 2009; Spycher et al., 1983; Steffens et al., 2009). On Google Scholar, there were 3915 papers citing one of the above papers through September 2020. We examined each publication and only extracted data from empirical studies, published in a peer-review journal in English, that measured SOC via density fractionation. When recording oPOC or mSOC data, we only collected data from papers which utilized sonication, sodium hexametaphosphate dispersion, or physical disruption (e.g., from glass beads) to separate oPOC from mSOC (Figure S1). Unless it was specified for a particular soil and reported in the respective study, we set a density cutoff of 1.85 g cm-3 for classifying mSOC. Since some studies isolated C pools along a density gradient, we summed SOC stored in fractions above or below the density cutoff. To make meaningful global comparisons, we chose to only include papers which reported enough information to calculate the C stock in units of g C m2. In cases where C concentrations but not stocks were reported, we used soil depth and bulk density (BD, g cm-3) to calculate the C stock in a given pool (fPOC, oPOC, mSOC). Data were recorded separately for different locations, ecosystem types, treatments, soil depths, and fractions. Because there was a wide range in soil depth increments used within studies, we summed C stocks from specific depth intervals (e.g. 0-10 cm, 10-30 cm) into two categories: ‘topsoil’ (0-30cm) or subsoil (31+cm). Soil volume associated with each observation was calculated assuming a 1x1 m sampling area and the sampling depth increment reported by the study authors (e.g. 0-10 cm, 15-30 cm).

This dataset represents the estimated carbon sequestration potential of wetlands throughout New Hampshire, based on data derived from the National Wetlands Inventory (NWI) Plus functional assessment framework. The layer is maintained by the New Hampshire Department of Environmental Services (NHDES) and is intended to support ecological assessments, land use planning, conservation prioritization, and climate resilience efforts.Using a combination of wetland type, geomorphic setting, hydrologic regime, and landscape position, the dataset evaluates key wetland functions, including the ability to store and sequester carbon. Carbon sequestration is a critical ecosystem service provided by wetlands, especially in the context of climate change mitigation and the maintenance of natural carbon sinks.Each feature in the dataset represents a mapped wetland polygon, classified using LLWW (Landscape, Landform, Water Flow Path, and Waterbody Type) codes. Functional attributes estimate carbon-related ecosystem services, along with other wetland functions such as water quality improvement, stormwater retention, and wildlife habitat support.Due to its origin in the NWI Plus framework, this dataset is best used at a landscape scale for planning, screening, and ecological prioritization. Site-specific determinations of carbon storage or functional performance should be confirmed with field investigations.Key Attributes:ATTRIBUTE: Unique wetland classification label.QAQC_CODE: Quality control designation for data accuracy or review status.WETLAND_TY: General type/classification of the wetland.ACRES: Area of the wetland polygon in acres.Landscape, Landscape2: Landscape position classifications.Landform, Landform2: Geomorphic setting of the wetland.WaterFlowP: Hydrologic flow path (e.g., surface flow, subsurface).LLWW_Code: LLWW classification code.OtherModif: Notes on additional modifications.Functional Attribute Fields (based on NWI Plus):BSS: Biogeochemical Storage and SequestrationCAR: Carbon Sequestration PotentialCSS: Carbon Sink StrengthFAIH: Flood Attenuation / Inundation HoldingNT, OWH, SM, SR, SWD, WBIRD, UWPC: Additional functional values related to nutrient transformation, open water habitat, sediment retention, stream recharge, and wildlife use.Shape_Leng, SHAPE_Length, SHAPE_Area: Geometric measurements of the wetland polygon.fid: Feature ID.

The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.