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68 results for “oil and gas”
Oil and Gas Infrastructure Mapping (OGIM) database
<p>The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular dataset of oil and gas infrastructure. It is developed by Environmental Defense Fund (EDF) (<a href="https://www.edf.org/">www.edf.org</a>) and MethaneSAT, LLC (<a href="https://www.methanesat.org/">www.methanesat.org</a>), a wholly owned subsidiary of EDF. The OGIM database helps fill a crucial geospatial data need, by supporting the quantification and source characterization of oil and gas methane emissions. The database is developed via acquisition, analysis, curation, integration, and quality-assurance (performed at EDF) of publicly available geospatial data sources. These oil and gas facility datasets are reported by governments, industry, academics, and other non-government entities.</p> <p>OGIM is a collection of data tables within a GeoPackage. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including: oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. OGIM v2.7 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines.</p> <p>Please see the PDF document in the “Files” section of this page for more information about this version, including attribute column definitions, key changes since the previous version, and more. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite this paper when using any version of the database:</p> <p><span>Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., </span><a href="https://doi.org/10.5194/essd-15-3761-2023"><span>https://doi.org/10.5194/essd-15-3761-2023</span></a><span>, 2023.</span></p> <p>Important note: While the results section of this manuscript is specific to v1 of the OGIM, the methods described therein are the same methods used to develop and update v2.7. Additionally, while we describe our data sources in detail in the manuscript above, and include maps of all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these locations when appropriate permissions to make them publicly accessible are obtained. </p> <p>OGIM v2.7 is based on public-domain datasets reported in February 2025 or prior. Each record in OGIM indicates a date (SRC_DATE) when the original source of the record was published or last updated. Some records may contain out-of-date information, for example, if a facility’s status has changed since we last visited a data source. We anticipate updating the OGIM database on a regular cadence and are continually including new public domain datasets as they become available.</p> <p>---</p> <p>Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Madeleine O’Brien (maobrien@methanesat.org) and Mark Omara (momara@edf.org).</p>
Darwin Core Archive: Santa Barbara Channel fish surveys at shallow regions of oil and gas platforms (SCUBA)
This dataset included fish counts that were surveyed in the shallow sections (0 – 40 meters) of the oil and gas platforms using Scuba. The oil and gas platforms are located in the Santa Barbara Channel, California, USA. Each of the eleven platforms (GILDA, GINA, HOLLY, IRENE, HERMOSA, HIDALGO, GAIL, GRACE, HARVEST, C, and HENRY) were surveyed multiple times a year from 1995 to 2000. This scuba project was conducted and reported under a cooperative agreement (Agreement 1445-CA09-95-0836) between the U. S. Geological Survey (Biological Resources Division) and the University of California, Santa Barbara. This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. Citation of report: Love, M. S., D. M. Schroeder, and M. M. Nishimoto. 2003. The ecological role of oil and gas production platforms and natural outcrops on fishes in southern and central California: a synthesis of information. U. S. Department of the Interior, U. S. Geological Survey, Biological Resources Division, Seattle, Washington, 98104, OCS Study MMS 2003-032. http://www.lovelab.id.ucsb.edu/Report.pdf These fish surveys at platforms were conducted within a few days of surveys at rock outcrops, which can be viewed at: https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=112 The deeper sections of the oil platform were surveyed using the research submarine Delta; the data can be viewed at: https://portal. edirepository.edu/nis/mapbrowse?scope=edi&identifier=111
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
High-resolution oil and gas methane emission inventory for the Permian Basin
<p>This dataset consists of a high-resolution (0.01<sup>o</sup> × 0.01<sup>o</sup>) oil and gas methane emission inventory for the Permian Basin, developed at Environmental Defense Fund (<a href="http://www.edf.org">www.edf.org</a>). The Permian Basin in western Texas and southern New Mexico is the largest oil producing basin in the U.S., accounting for more than 40% of national oil production in 2021. It is also the nation's largest methane emitting basin, with recent measurement-based estimates of more than three million metric tons per year. Here, we develop an improved inventory of oil and gas methane emissions for the Permian Basin, based on recent facility-scale measurements and updated oil and gas activity data for the year 2021.</p> <p>Full details for the oil and gas methane emission inventory development and key results can be found in the following journal paper, which is under review at Earth System Science Data journal.</p> <p>Please cite the paper when using the methane inventory dataset:</p> <p>Omara, M., Gautam, R., O'Brien, M.A., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D.R., Chulakadaba, A., Miller, C.C., Franklin, J., Wofsy, S., and Hamburg, S.P. Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution. <em>In review</em>, Earth System Science Data journal (2023).</p> <p>Points of Contact at Environmental Defense Fund: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).</p>
Characterising underwater noise and changes in harbour porpoise behaviour during the decommissioning of an oil and gas platform
<p>Many man-made marine structures (MMS) will have to be decommissioned in the coming decades. While studies on the impacts of the construction of MMS on marine mammals exist, no research has been done on the effects of their decommissioning. The complete removal of an oil and gas platform in Scotland in 2021 provided an opportunity to investigate the response of harbour porpoises to decommissioning. Arrays of broadband noise recorders and echolocation detectors were used to describe noise characteristics produced by decommissioning activities and assess porpoise behaviour. During decommissioning, sound pressure levels in the frequency range 100 Hz to 10 kHz were 30-40 dB higher than baseline, with the presence of vessels being the main source of noise. The study detected small-scale (< 2 km) and short-term levels of porpoise displacement during decommissioning, with porpoise occurrence increasing immediately after this. These findings can inform the consenting process of future decommissioning projects.</p>
CH4 isotopic signatures of emissions from oil and gas extraction sites in Romania
<p>Dataset linked to the manuscript "CH4 isotopic signatures of emissions from oil and gas extraction sites in Romania", submitted to Elementa: Science of the Anthropocene.</p> <p>Abstract: Methane (CH4) emissions to the atmosphere from the oil and gas sector in Romania remain highly uncertain, despite their relevance for the European Union’s goals to reduce greenhouse gas emissions. Measurements of the isotopic composition of CH4 can be used for source attribution, which is important in top-down studies of emissions from extended areas. We performed isotope measurements of CH4 in atmospheric air samples collected from an aircraft (24 locations) and ground vehicles (83 locations), around oil and gas production sites in Romania, with focus on the Romanian Plain. Ethane to methane ratios (C2:C1) were derived at 412 locations of the same fossil fuel activity clusters. The resulting isotopic signals (δ13C and δ2H in CH4) covered a wide range of values, indicating mainly thermogenic gas sources (associated with oil production) in the Romanian Plain, mostly in Prahova county (δ13C from -67.8 to -22.4 per mille V-PDB; δ2H from -255 to -138 per mille V-SMOW) but also the presence of some natural gas reservoirs of microbial origin in Dolj, Ialomia, Prahova and likely Teleorman counties. The classification based on C2:C1 ratios was generally in agreement with the one based on CH4 isotopic composition, and confirmed the characterisation of the gas origin. In several cases, the CH4 enhancements sampled from the aircraft could directly be linked to the underlying production clusters using wind data. The combination of δ13C and δ2H signals determined on these samples confirm that the oil and gas production sector is the main source of CH4 emissions in the target areas.</p>
Tables and data for "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data"
<p>These are tables and data files for the paper "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data" submitted to the Journal of Atmospheric Research: Atmospheres</p>
Dataset: First Trust Nasdaq Oil & Gas ETF (FTXN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Tables and Data for "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado"
<p>These are data sets and tables used in the paper "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado" to be submitted to Earth and Space Science. The monitor site 2016 and 2017 counts files have gridded HYSPLIT back trajectory counts for the 4 highest ozone concentration days at each site, as described in the manuscript.</p>
Locating undocumented orphaned oil and gas wells with smartphones
<p>Majority of the estimated 3 million abandoned oil and gas wells in the U.S. have missing documents and lack surface equipment making them difficult to locate. However, most of them have casings made of iron alloys which are magnetic and can be sensed by magnetometers. Here we utilize an iPhone 12 mini smartphone as a magnetometer to locate two abandoned wells. We designed a simple unmanned aerial vehicle (UAV) survey setup where the iPhone 12 mini was hung from an inexpensive small drone. We surveyed the two sites by flying the drone at altitudes, 10 m, 15 m, and 20 m above ground level. Our results show that at altitude of 10 magl the smartphone magnetometer could pick the magnetic anomaly of either of the wells at intensities ≥ 52 μT; sufficient to accurately locate the wells. At altitude of 15 magl the smartphone could locate the wells within ~5 m radius of the actual wells’ location, and it was unable to detect any magnetic anomalies at 20 magl. Simplicity of the setup, minimal required scientific knowledge and low cost of the setup makes this setup an ideal tool for locating orphaned wells by citizen scientists.</p>
Low Emission Oil and Gas Open (LEOGO) platform specification
<p>This dataset describes the integrated energy system of a hypothetical but realistic offshore oil and gas platform referred to as the LEOGO reference platform. It reflects what would be considered a typical installation on the Norwegian Continental Shelf.</p> <p>Included with the dataset is</p> <ul> <li>input data for analysis with the open-source Oogeso (https://github.com/oogeso/oogeso) operational optimisation/simulation tool, including simplified links between electricity and heat demand and the gas/oil/water flow rates through the processing system.</li> <li>A detailed electrical model implemented in the Digsilent PowerFactory tool</li> </ul> <p> </p>
Middle East oil and gas methane emissions signature captured at a remote site using light hydrocarbon tracers
<p>Datasets of measured species mixing ratios during the Cape Greco winter campaign in 2021-2022 associated publication of the same name: "Middle East oil and gas methane emissions signature captured at a remote site using light hydrocarbon tracers". While CO2 values are given in ppm, other compounds units are ppb.</p>
Characterising underwater noise and changes in harbour porpoise behaviour during the decommissioning of an oil and gas platform
Open the record for dataset details and reuse information.
Wyoming Oil and Gas Development Spatial Datasets
<p>This file contains a file geodatabase with spatial datasets that accompany the scientific paper titled: "Recent Greater Sage Grouse (Centrocercus urophasianus)<br /> Population Dynamics in Wyoming Are Primarily Driven by<br /> Climate, not Oil and Gas Development" (Ramey, Thorley, Ivey 2015).</p>
Characterizing ambient air quality and oil and gas air pollution emissions in Broomfield County, CO
<p>Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production.</p>
Datasets for airborne in-situ quantification of methane emissions from oil and gas production in Romania
<p>This dataset includes airborne in-situ measurements taken around target clusters and regions of oil and gas production sites in Romania, as well as two models outputs interpolated to the flight tracks during the ROMEO (ROmanian Methane Emissions from Oil and gas) campaign that took place in Romania in 2019. The dataset is used for the evaluation presented in the manuscript titled: "Airborne in-situ quantification of methane emissions from oil and gas production in Romania."</p>
Quantification of volatile organic compound emissions from unconventional oil and gas development
<p>Oil and gas (O&G) development in the U.S. has accelerated in the past two decades, aided by unconventional extraction techniques including hydraulic fracturing and horizontal drilling. Potential environmental and health impacts of volatile organic compounds (VOCs) originating from O&G activities in populated regions have raised concerns. In Broomfield, Colorado, six new O&G well pads were approved for development in 2017 and an air monitoring program was established in October 2018 to collect weekly and plume-triggered air samples. This study addresses the limited existing knowledge of activity-specific VOC emission rates from unconventional O&G development (UOGD), utilizing these observations and dispersion model simulations through emission inversion methods. Emissions are characterized from well drilling, hydraulic fracturing, coiled tubing/millout, flowback, and production operations.</p> <p>Substantial variations in average VOC emission rates, determined using weekly canister observations, are observed across different UOGD phases. Drilling and coiled tubing/millout operations exhibit the highest VOC emission rates, attributed to hydrocarbon release from shale formations and drilling mud. In contrast, hydraulic fracturing gives lower emission rates, consistent with injection of fluids into the well, minimizing the probability of subsurface hydrocarbon emissions. Diesel-powered engines are identified as the primary ethyne sources during hydraulic fracturing. Production was characterized by lower VOC emission rates than pre-production phases but remains an important emission category due to its long duration (decades). Internal variations of emission rates within each phase highlight the complexity of factors and activities influencing emission rates, including, for example, vertical vs. horizontal drilling and periodic maintenance activities. VOC emission rates associated with drilling mud volatilization and hydraulic fracturing suggest that previously published emission estimates (EPA (2022), and Hecobian et al. (2019)) underestimate VOC emission rates during these activities. Significantly lower emission rates during flowback compared to previous work (Hecobian et al., 2019) reveal how improved management practices, including tankless, closed-loop fluid handling systems have effectively reduced what used to be a dominant source of pre-production VOC emissions. Plume-triggered samples, capturing transient high-concentration plumes, reveal short-term VOC emission rates approximately ten times higher for drilling and flowback than determined from weekly samples. In the case of flowback, short-term emission pulses have been linked to periodic emptying of sand canisters used to trap fracking sand emerging from previously fracked wells.</p>
Soil greenhouse gas fluxes and associated parameters from forest and oil palm in the SAFE landscape
<b>Description: </b><p>Greenhouse gas fluxes measured by the static chamber method including associated environmental parameters</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><b>Characterising soil microbial communities and measuring associated biogeochemical fluxes</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258117">here</a></p><p><b>Files: </b>This consists of 1 file: 3_GHG_jdrewer.xlsx</p><p><b>3_GHG_jdrewer.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>data one off field</b> (described in worksheet Data_one_off)</p><p>Description: Soil and litter parameters</p><p>Number of fields: 14</p><p>Number of data rows: 56</p><p>Fields: </p><ul><li><b>Location</b>: Location measurement was taken (Field type: Location)</li><li><b>site</b>: Location measurement was taken (Field type: ID)</li><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>landuse</b>: Land use of location (Field type: Categorical)</li><li><b>pH</b>: Soil pH (Field type: Numeric)</li><li><b>bulk_density</b>: dry weight of soil (Field type: Numeric)</li><li><b>soil_N%</b>: Percentage of soil N (Field type: Numeric)</li><li><b>soil_C%</b>: Percentage of soil C (Field type: Numeric)</li><li><b>litter_N%</b>: Percentage of leaf Nitrogen (Field type: Numeric)</li><li><b>litter_C%</b>: Percentage of leaf Carbon (Field type: Numeric)</li><li><b>C/N_soil</b>: Ratio of soil Carbon: Nitrogen (Field type: Numeric)</li><li><b>Latitude</b>: Latitude of sampling point (Field type: Latitude)</li><li><b>Longitude</b>: Longitude of sampling point (Field type: Longitude)</li><li><b>Elevation</b>: Elevation of sampling point (Field type: Numeric)</li></ul></li><li><p><b>data of repeated measures</b> (described in worksheet Data_repeated_measures)</p><p>Description: Soil greenhouse gas flux data and associated variables</p><p>Number of fields: 14</p><p>Number of data rows: 672</p><p>Fields: </p><ul><li><b>Location</b>: Location measurement was taken (Field type: Location)</li><li><b>site</b>: Location measurement was taken (Field type: ID)</li><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>landuse</b>: Land use of location (Field type: Categorical)</li><li><b>date</b>: Date the measurement was taken (Field type: Date)</li><li><b>time</b>: Time the measurement was taken (Field type: Time)</li><li><b>flux_CH4</b>: Soil CH4 flux (Field type: Numeric)</li><li><b>flux_CO2-C</b>: Soil CO2 flux (Field type: Numeric)</li><li><b>flux_N2O-N</b>: Soil N2O flux (Field type: Numeric)</li><li><b>NH4-N</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>air_temp</b>: Air temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_temp</b>: Soil temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_moisture</b>: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2015-01-01 to 2016-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Soil greenhouse gas fluxes along transects from oil palm to riparian forests in the SAFE landscape
<b>Description: </b><p>Riparian greenhouse gas fluxes measured by the static chamber method including associated environmental parameters and river water </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><b>Characterising soil microbial communities and measuring associated biogeochemical fluxes</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258079">here</a></p><p><b>Files: </b>This consists of 1 file: 1_HJ_river_water_riparian.xlsx</p><p><b>1_HJ_river_water_riparian.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>river_water</b> (described in worksheet river_water)</p><p>Description: river water measurments</p><p>Number of fields: 17</p><p>Number of data rows: 63</p><p>Fields: </p><ul><li><b>site</b>: location sample was taken (Field type: Location)</li><li><b>location</b>: habitat (Field type: Categorical)</li><li><b>replicate</b>: water sample replicate number (Field type: Replicate)</li><li><b>sampling_occasion</b>: date of sample collection (Field type: Date)</li><li><b>date</b>: date of sample analysis (Field type: Date)</li><li><b>TDS</b>: Total Desolved Solids (Field type: Numeric)</li><li><b>pH</b>: water pH (Field type: Numeric)</li><li><b>conductivity</b>: water conductivity (Field type: Numeric)</li><li><b>Temp</b>: tempreture of river water (Field type: Numeric)</li><li><b>air_CH4</b>: air concentration of CH4 (Field type: Numeric)</li><li><b>water_CH4</b>: water concentration of CH4 (Field type: Numeric)</li><li><b>air_N2O</b>: air concentration of N2O (Field type: Numeric)</li><li><b>water_N2O</b>: water concentration of N2O (Field type: Numeric)</li><li><b>air_CO2</b>: air concentration of CO2 (Field type: Numeric)</li><li><b>water_CO2</b>: water concentration of CO2 (Field type: Numeric)</li><li><b>NH4-N</b>: concentration of NH4-N in water (Field type: Numeric)</li><li><b>NO3-N</b>: concentration of NO3-N in water (Field type: Numeric)</li></ul></li><li><p><b>data_one_off_field</b> (described in worksheet data_one_off_field)</p><p>Description: soil and littter property measurements</p><p>Number of fields: 12</p><p>Number of data rows: 48</p><p>Fields: </p><ul><li><b>Location</b>: location of chamber (Field type: Location)</li><li><b>chamber_id</b>: chamber ID (Field type: ID)</li><li><b>site</b>: Site ID (Field type: ID)</li><li><b>landuse</b>: land use type (Field type: Categorical)</li><li><b>pH</b>: soil pH (Field type: Numeric)</li><li><b>soil_N</b>: soil nitrogen content (Field type: Numeric)</li><li><b>soil_C</b>: soil carbon content (Field type: Numeric)</li><li><b>litter_N</b>: litter nitrogen content (Field type: Numeric)</li><li><b>litter_C</b>: litter carbon content (Field type: Numeric)</li><li><b>C_N</b>: soil C:N ratio (Field type: Numeric)</li><li><b>Latitude</b>: GPS co-ordinate that the sample was taken (Field type: Latitude)</li><li><b>Longitude</b>: GPS co-ordinate that the sample was taken (Field type: Longitude)</li></ul></li><li><p><b>data_repeated_measures</b> (described in worksheet data_repeated_measures)</p><p>Description: repeated soil measures</p><p>Number of fields: 16</p><p>Number of data rows: 336</p><p>Fields: </p><ul><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>site</b>: Site ID (Field type: ID)</li><li><b>landuse</b>: land use type (Field type: Categorical)</li><li><b>sampling_occasion</b>: date of sample collection (Field type: Date)</li><li><b>date</b>: date of sample analysis (Field type: Date)</li><li><b>time</b>: Time the measurement was taken (Field type: Time)</li><li><b>flux_CH4-C</b>: Soil CH4 flux (Field type: Numeric)</li><li><b>flux_CO2-C</b>: Soil CO2 flux (Field type: Numeric)</li><li><b>flux_N2O-N</b>: Soil N2O flux (Field type: Numeric)</li><li><b>NH4-N_H2O</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N_H2O</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>NH4-N_KCl</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N_KCl</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>air_temp</b>: Air temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_temp</b>: Soil temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_moisture</b>: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2016-11-01 to 2017-11-30</p><p><b>Latitudinal extent: </b>4.3960 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Qatar Peninsula' High Vulnerability to Oil Spills and its Implication for the Potential Disruption in Global Gas Supply (Datasets)
<p>Outputs of oil spill dispersal simulations performed backward in time using OpenDrift for the following study:</p> <p>Anselain, T., Heggy, E., Dobbelaere, T., & Hanert, E. (2022). Qatar Peninsula’ High Vulnerability to Oil Spills and its Implication for the Potential Disruption in Global Gas Supply. <em>Nature Sustainability</em>. In press.</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.