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Research data keyboard_double_arrow_right Dataset 2023Embargo end date: 27 Mar 2023Publisher:Dryad Authors:Bouderbala, Ilhem;
Labadie, Guillemette; Béland, Jean-Michel; Boulanger, Yan; +4 AuthorsBouderbala, Ilhem
Bouderbala, Ilhem in OpenAIREBouderbala, Ilhem;
Labadie, Guillemette; Béland, Jean-Michel; Boulanger, Yan; Hébert, Christian; Desrosiers, Patrick; Allard, Antoine; Fortin, Daniel;Bouderbala, Ilhem
Bouderbala, Ilhem in OpenAIREAim Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We study how species community composition would change following anthropogenic and natural disturbances. We determine the main drivers of assemblage dissimilarity for bird and beetle communities. Location Côte-Nord, Québec, Canada. Methods We quantify two climate-induced pathways based on direct and indirect effects on species occurrence under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through habitat-based climate change. We develop empirical models to predict the distribution of more than 100 species over the next century. We analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into 'balanced variation in species occupancy and occurrence' and 'occupancy and occurrence gradient'. Results Both pathways increased dissimilarity in species assemblage. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Main conclusions The two pathways are complementary and alter biodiversity, mainly caused by species turnover. Yet, responses are much larger in magnitude under mixed climate effects. Therefore, the inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Moreover, we expect differences in occupancy between the two studied taxa. This could indicate the potential range of change in boreal species concerning novel environmental conditions.
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visibility 14visibility views 14 download downloads 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 United StatesPublisher:U.S. Geological Survey doi: 10.5066/p9c3n5vv
This data release contains the boundaries of assessment units and input data for the assessment of undiscovered oil and gas resources of strata older than the Torok Formation of the Western North Slope in the Northern Alaska province. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. Methodology of assessments is documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Cao, Jian; Wang, Bin;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.NUIST.NESM3.amip' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The NUIST ESM v3 climate model, released in 2016, includes the following components: atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa), land: JSBACH v3.1, ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4.1. The model was run by the Nanjing University of Information Science and Technology, Nanjing, 210044, China (NUIST) in native nominal resolutions: atmos: 250 km, land: 2.5 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2007Embargo end date: 03 Dec 2007Publisher:Harvard Dataverse Authors: P. Aldrich, Daniel;doi: 10.7910/dvn/ficlrd
The purpose of this study, Controversial Facilities in Japan, 1955 – 1995, is to understand the factors which lead decision-makers and authorities in Japan to select localities as host communities for often-unwanted and controversial facilities such as nuclear power plants, dams, and airports. Such projects regularly cause Not In My Back Yard, or NIMBY, responses from local residents around the world. <br /><br /> The dataset contains observations on approximately 500 Japanese cities, towns, and villages covering the period from 1955 through 1995. Data was collected through archival research, interviews with anti-facility activists and officials, and surveys of relevant government offices throughout Japan.<br /><br /> Variables assessed include the number of siting attempts and successes in the locality, the town’s location in Japan by prefecture and by political district code alongside batte ries of information on demographic, socioeconomic, and political factors. Demographic information includes sex ratios in the locality over time along with percentage of elderly in the population. Socioeconomic status was examined through measures of primary, secondary, and tertiary sector workforces over time along with variables on the coastal, mid-range, and deep sea fishing cooperatives (where applicable). Political variables include district magnitude, presence or absence of a prime minister from locally elected representatives, number of long-term Liberal Democratic Party (LDP) representatives, and the number of members of the town council and their political party. Additional political variables include the numbers and percentage of representatives from all major political parties in the national legislature, political party of the mayor, and measures of over-time support from the area for the long-dominant Liberal Democratic Party. The dataset contains publicly-available information on compensation provided to communities along with information on eminent domain use. Subject: STANDARD DEPOSIT TERMS 1.0 Type: DATAPASS:TERMS:STANDARD:1.0 Notes: This study was deposited under the of the Data-PASS standard deposit terms. A copy of the usage agreement is included in the file section of this study.;
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 03 Apr 2023Publisher:Dryad Authors:Dunn, Jessica;
Slattery, Margaret;Dunn, Jessica
Dunn, Jessica in OpenAIREKendall, Alissa;
Ambrose, Hanjiro; +1 AuthorsKendall, Alissa
Kendall, Alissa in OpenAIREDunn, Jessica;
Slattery, Margaret;Dunn, Jessica
Dunn, Jessica in OpenAIREKendall, Alissa;
Ambrose, Hanjiro; Shen, Shuhan;Kendall, Alissa
Kendall, Alissa in OpenAIREdoi: 10.25338/b82w7q
Batteries have the potential to significantly reduce greenhouse gas emissions from on-road transportation. However, environmental and social impacts of producing lithium-ion batteries, particularly cathode materials, and concerns over material criticality are frequently highlighted as barriers to widespread electric vehicle adoption. Circular economy strategies, like reuse and recycling, can reduce impacts and secure regional supplies. To understand the potential for circularity, we undertake a dynamic global material flow analysis of pack-level materials that includes scenario analysis for changing battery cathode chemistries and electric vehicle demand. Results are produced regionwise and through the year 2040 to estimate the potential global and regional circularity of lithium, cobalt, nickel, manganese, iron, aluminum, copper, and graphite, although the analysis is focused on the cathode materials. Under idealized conditions, retired batteries could supply 60% of cobalt, 53% of lithium, 57% of manganese, and 53% of nickel globally in 2040. If the current mix of cathode chemistries evolves to a market dominated by NMC 811, a low cobalt chemistry, there is potential for 85% global circularity of cobalt in 2040. If the market steers away from cathodes containing cobalt, to an LFP-dominated market, cobalt, manganese, and nickel become less relevant and reach circularity before 2040. For each market to benefit from the recovery of secondary materials, recycling and manufacturing infrastructure must be developed in each region. This data was collected through various sources, including from EV Volumes, International Energy Agency, Argonne National Lab, and published articles. A model was created with R to process the data. R is required to open the models.
ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BY NCData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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visibility 23visibility views 23 download downloads 104 Powered bymore_vert ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BY NCData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: He, Bian; Bao, Qing;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.GMMIP.CAS.FGOALS-f3-L' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Müller, Wolfgang; Ilyina, Tatiana;Li, Hongmei;
Li, Hongmei
Li, Hongmei in OpenAIRETimmreck, Claudia;
+48 AuthorsTimmreck, Claudia
Timmreck, Claudia in OpenAIREMüller, Wolfgang; Ilyina, Tatiana;Li, Hongmei;
Li, Hongmei
Li, Hongmei in OpenAIRETimmreck, Claudia;
Gayler, Veronika;Timmreck, Claudia
Timmreck, Claudia in OpenAIREWieners, Karl-Hermann;
Botzet, Michael;Wieners, Karl-Hermann
Wieners, Karl-Hermann in OpenAIREBrovkin, Victor;
Giorgetta, Marco;Brovkin, Victor
Brovkin, Victor in OpenAIREJungclaus, Johann;
Reick, Christian; Esch, Monika;Jungclaus, Johann
Jungclaus, Johann in OpenAIREBittner, Matthias;
Legutke, Stephanie;Bittner, Matthias
Bittner, Matthias in OpenAIRESchupfner, Martin;
Wachsmann, Fabian;Schupfner, Martin
Schupfner, Martin in OpenAIREHaak, Helmuth;
de Vrese, Philipp; Raddatz, Thomas;Haak, Helmuth
Haak, Helmuth in OpenAIREMauritsen, Thorsten;
Mauritsen, Thorsten
Mauritsen, Thorsten in OpenAIREvon Storch, Jin-Song;
Behrens, Jörg; Claussen, Martin; Crueger, Traute; Fast, Irina;von Storch, Jin-Song
von Storch, Jin-Song in OpenAIREFiedler, Stephanie;
Fiedler, Stephanie
Fiedler, Stephanie in OpenAIREHagemann, Stefan;
Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan;Hagemann, Stefan
Hagemann, Stefan in OpenAIRELasslop, Gitta;
Kornblueh, Luis;Lasslop, Gitta
Lasslop, Gitta in OpenAIREMarotzke, Jochem;
Marotzke, Jochem
Marotzke, Jochem in OpenAIREMatei, Daniela;
Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Nabel, Julia;Matei, Daniela
Matei, Daniela in OpenAIRENotz, Dirk;
Notz, Dirk
Notz, Dirk in OpenAIREPeters-von Gehlen, Karsten;
Peters-von Gehlen, Karsten
Peters-von Gehlen, Karsten in OpenAIREPincus, Robert;
Pincus, Robert
Pincus, Robert in OpenAIREPohlmann, Holger;
Pohlmann, Holger
Pohlmann, Holger in OpenAIREPongratz, Julia;
Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;Pongratz, Julia
Pongratz, Julia in OpenAIREStevens, Bjorn;
Voigt, Aiko; Roeckner, Erich;Stevens, Bjorn
Stevens, Bjorn in OpenAIREProject: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MPI-M.MPI-ESM1-2-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:SEANOE Authors:Salgueiro, Emília;
Salgueiro, Emília
Salgueiro, Emília in OpenAIREMagalhães, Vítor;
Magalhães, Vítor
Magalhães, Vítor in OpenAIRERebotim, Andreia;
Rebotim, Andreia
Rebotim, Andreia in OpenAIREMatos, Lélia;
+4 AuthorsMatos, Lélia
Matos, Lélia in OpenAIRESalgueiro, Emília;
Salgueiro, Emília
Salgueiro, Emília in OpenAIREMagalhães, Vítor;
Magalhães, Vítor
Magalhães, Vítor in OpenAIRERebotim, Andreia;
Rebotim, Andreia
Rebotim, Andreia in OpenAIREMatos, Lélia;
Matos, Lélia
Matos, Lélia in OpenAIRESchweizer, Magali;
Schweizer, Magali
Schweizer, Magali in OpenAIRESousa, Fátima;
Sousa, Fátima
Sousa, Fátima in OpenAIREGonzález Martín, Maria;
González Martín, Maria
González Martín, Maria in OpenAIREBatista, Luis Batista;
Batista, Luis Batista
Batista, Luis Batista in OpenAIREdoi: 10.17882/96495
The CARBO-ACID research cruise (EUROFLEETS+ SEA02_10) was carried out on the RV Ramón Margalef between August 2nd and August 11st, with departing from Vigo – Spain and ending in Lisbon – Portugal. The main objective of this cruise was to collect data and samples to study the potential effects of ocean acidification on carbonate marine organisms (coccolithophores, pteropods, planktonic and benthic foraminifera, and corals) along the Iberian margin. With this objective, oceanographic data and water samples, plankton, cold-water corals and sediment samples were collected during an upwelling season, along two transects coinciding with the two persistent upwelling filaments off the Iberia Margin: the Cape Finisterra and the Cape Roca. In this dataset is guiven all the acquired data recollected onboad. During the CARBO-ACID cruise we did a total of 7 stations, 4 stations along the Cape Finisterra transect (from W to E: CA3, CA2, CA7, CA8) and 3 stations at the Cape Roca (from W to E: CA6, CA5, CA4) transect (Fig). At each station we usually started with a multibeam survey, a CTD and Rosette cast. These initial operations allowed to identify the different water masses present in this area, characterize their physical properties and to recover seawater samples at specific depth levels. The seawater samples were onboard subsampled, preserved in cold conditions or with chemicals and/ or filtered for several further analysis in the shore-based laboratories: DNA, chlorophyll, fitoplankton, coccolithophores, pH, alkalinity, stable isotopic composition, trace elements concentration and Suspend Particulate Matter. Subsequently to these operations, at each station, two vertical tows with a plankton multinet (with 5 nets) were done on the top 700 m of the water column to sample the planktonic communities of the different water depths. After this, sediment samples were recovered with a box-corer to study the past oceanographic conditions, between the pre-industrial Era and the Present, with multi-proxies used in paleoceanography and sedimentology. A total of 10 box-cores were recollected and each of them was onboard sub-sampled for eDNA, enzymes and benthic foraminifera. Fifteen shipek grab samples were recollected at the Fontanelas seamount (Estremadura Spur), station CA6, to characterize the sedimentary cover and to evaluate the presence of deep cold-water corals. Preliminary results show that the stations CA7, CA8 and CA4, located close to the coast, as expected, are the most influenced by the coastal upwelling, exhibiting colder surface water, higher values of fluorescence, and more zooplankton content reflecting higher phyto-zooplankton concentrations, as typical of the upwelling waters. At station CA4 temperature was higher and fluorescence showed lower values, indicative of less phytoplankton, and interpreted as indicating a different upwelling source water from that upwelled further north. Based on the CTD data, the Cape Roca transect is more influenced by the subtropical East North Atlantic Central Water (ENACWst), while the Cape Finisterra transect is more under the influence of the subpolar branch (ENACWsp). Seafloor sediment samples showed significant differences between the stations. Along the northern transect (Cape Finisterra) the seafloor sediments show an increase in grain size from the offshore to the coast. The offshore stations CA3 and CA2 revealed finer grained sediments, CA8 were composed of coarser sand and the station CA7, the shallowest station 77 m, presented the sediment composed mainly of shell fragments and coarse grain sand. Along the southern transect (Cape Roca), the offshore station CA6 (Fontanelas seamount) has coarser sandy sediments with rock clasts and cold-water coral fragments, and the stations CA5 and CA4 with fine sand to muddy sediments. The detailed CA6 bathymetry allowed to verify the existence of small plateaus on the slope of the Fontanelas seamount, where the fossil cold-water corals fragments were found, suggesting that this area is a very interesting system deserving further study with a ROV, and to characterize the corals fields and verify if there are live corals. These recollected data and samples will allow not only to reconstruct the pH variability under different environmental conditions, but also to estimate the biogeochemical changes along the coastal ocean waters as the anthropogenic influence increases. These results will contribute to better understand and model the effects on the biota under the future expected oceans pH changes.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 SpainPublisher:UTM-CSIC Authors: Buessler, Ken; CSIC - Unidad de Tecnología Marina (UTM);handle: 10261/339577
EXport Processes in the Ocean from Remote Sensing (EXPORTS) is a large-scale NASA-led field campaign that will provide critical information for quantifying the export and fate of upper ocean net primary production (NPP) using satellite observations and state of the art ocean technologies. The goal of EXPORTS is to understand how the organic carbon makes it to the twilight zone and deep ocean interior, and how lon it stays there, which is vital to understanding present and future ocean ecosystems and global climate. Oceanographic data acquired during the EXPORTS Cruise (29SG20210503) on board the Research Vessel Sarmiento de Gamboa in 2021.
Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTADataset . 2021Data sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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more_vert Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTADataset . 2021Data sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:The Smithsonian Institution doi: 10.25573/data.10042406.v3 , 10.25573/data.10042406.v5 , 10.25573/data.10042406.v21 , 10.25573/data.10042406.v22 , 10.25573/data.10042406.v19 , 10.25573/data.10042406.v20 , 10.25573/data.10042406.v8 , 10.25573/data.10042406.v7 , 10.25573/data.10042406.v18 , 10.25573/data.10042406.v14 , 10.25573/data.10042406.v6 , 10.25573/data.10042406.v11 , 10.25573/data.10042406.v1 , 10.25573/data.10042406.v24 , 10.25573/data.10042406.v10 , 10.25573/data.10042406 , 10.25573/data.10042406.v2 , 10.25573/data.10042406.v16 , 10.25573/data.10042406.v15 , 10.25573/data.10042406.v23 , 10.25573/data.10042406.v4 , 10.25573/data.10042406.v17 , 10.25573/data.10042406.v9 , 10.25573/data.10042406.v13 , 10.25573/data.10042406.v12
doi: 10.25573/data.10042406.v3 , 10.25573/data.10042406.v5 , 10.25573/data.10042406.v21 , 10.25573/data.10042406.v22 , 10.25573/data.10042406.v19 , 10.25573/data.10042406.v20 , 10.25573/data.10042406.v8 , 10.25573/data.10042406.v7 , 10.25573/data.10042406.v18 , 10.25573/data.10042406.v14 , 10.25573/data.10042406.v6 , 10.25573/data.10042406.v11 , 10.25573/data.10042406.v1 , 10.25573/data.10042406.v24 , 10.25573/data.10042406.v10 , 10.25573/data.10042406 , 10.25573/data.10042406.v2 , 10.25573/data.10042406.v16 , 10.25573/data.10042406.v15 , 10.25573/data.10042406.v23 , 10.25573/data.10042406.v4 , 10.25573/data.10042406.v17 , 10.25573/data.10042406.v9 , 10.25573/data.10042406.v13 , 10.25573/data.10042406.v12
Barro Colorado Island (BCI), Lutz catchment tower 42m & 48mLocation 9°9'42.36"N, 79°50'15.67"WSolar Radiation, Pyranometer, interval max/min/avgLutz catchment is a 9.73ha protected watershed on BCIThe Lutz tower was built in 1972 and was originally 42m. In 2002 it was increased to 48mData from 42m and 48m can be considered to be one series since the change from 42m to 48m should not have affected the measurements
https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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