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Research data keyboard_double_arrow_right Dataset 2022Publisher:SEANOE Salgueiro, EmÃlia; Magalhães, VÃtor; Rebotim, Andreia; Matos, Lélia; Schweizer, Magali; Sousa, Fátima; González MartÃn, Maria; Batista, Luis Batista;doi: 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 TecnologÃa 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.
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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 Authors: Paton, Steve;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.
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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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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 United StatesPublisher:U.S. Geological Survey Authors: Merrill, Matthew D; Enomoto, Catherine B;doi: 10.5066/p9w8fatw
This data release contains the boundaries of assessment units and input data for the assessment of undiscovered Deep Tuscaloosa Group sandstones conventional gas resources in the U.S. Gulf Coast in Louisiana and Mississippi. 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. In addition to the shapefile, for U.S. assessments, allocation tables are provided that enumerate percentages assigned to various land categories. Machine-readable tables are also provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are 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.eu1 citations 1 popularity Top 10% influence Top 10% impulse Average Powered by BIP!
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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 Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong;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.ScenarioMIP.NOAA-GFDL.GFDL-CM4.ssp585' 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 GFDL-CM4 climate model, released in 2018, includes the following components: aerosol: interactive, atmos: GFDL-AM4.0.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa), atmosChem: fast chemistry, aerosol only, land: GFDL-LM4.0.1 (1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 20 levels; bottom level 10m); land-Veg:unnamed (dynamic vegetation, dynamic land use); land-Hydro:unnamed (soil water and ice, multi-layer snow, rivers and lakes), landIce: GFDL-LM4.0.1, ocean: GFDL-OM4p25 (GFDL-MOM6, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-BLINGv2, seaIce: GFDL-SIM4p25 (GFDL-SIS2.0, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 5 layers; 5 thickness categories). The model was run by the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA (NOAA-GFDL) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 25 km, ocnBgchem: 25 km, seaIce: 25 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 Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Mackallah, Chloe; Druken, Kelsey; Ridzwan, Syazwan Mohamed;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.C4MIP.CSIRO.ACCESS-ESM1-5' 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 Australian Community Climate and Earth System Simulator Earth System Model Version 1.5 climate model, released in 2019, includes the following components: aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: WOMBAT (same grid as ocean), seaIce: CICE4.1 (same grid as ocean). The model was run by the Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia (CSIRO) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 03 Oct 2022Publisher:Dryad Authors: Gallagher, Brian; Geargeoura, Sarah; Fraser, Dylan;Salmonids are of immense socio-economic importance in much of the world but are threatened by climate change. This has generated a substantial literature documenting effects of climate variation on salmonid productivity in freshwater ecosystems, but there has been no global quantitative synthesis across studies. We conducted a systematic review and meta-analysis to gain quantitative insight into key factors shaping the effects of climate on salmonid productivity, ultimately collecting 1,321 correlations from 156 studies, representing 23 species across 24 countries. Fisher’s Z was used as the standardized effect size, and a series of weighted mixed-effects models were compared to identify covariates that best explained variation in effects. Patterns in climate effects were complex, and were driven by spatial (latitude, elevation), temporal (time-period, age-class), and biological (range, habitat type, anadromy) variation within and among study populations. These trends were often consistent with predictions based on salmonid thermal tolerances. Namely, warming and decreased precipitation tended to reduce productivity when high temperatures challenged upper thermal limits, while opposite patterns were common when cold temperatures limited productivity. Overall, variable climate impacts on salmonids suggest that future declines in some locations may be counterbalanced by gains in others. In particular, we suggest that future warming should (1) increase salmonid productivity at high latitudes and elevations (especially >60° and >1,500m), (2) reduce productivity in populations experiencing hotter and dryer growing season conditions, (3) favor non-native over native salmonids, and (4) impact lentic populations less negatively than lotic ones. These patterns should help conservation and management organizations identify populations most vulnerable to climate change, which can then be prioritized for protective measures. Our framework enables broad inferences about future productivity that can inform decision-making under climate change for salmonids and other taxa, but more widespread, standardized, and hypothesis-driven research is needed to expand current knowledge. See README document and R code. See README document.
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visibility 3visibility views 3 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 2022 United StatesPublisher:U.S. Geological Survey Authors: Birdwell, Justin E; Oliver, Thomas A; Warden, Augusta;doi: 10.5066/p9q5voqb
The Eocene Green River Formation in the Piceance Basin of Colorado contains some of the richest oil shale deposits in the world (Johnson and others, 2009; Birdwell and others, 2013). The datasets compiled here were collected with the purpose of refining our understanding of geochemical variability through the depositional history of Eocene Lake Uinta in Piceance Basin. The effects of salinity, water column stratification, and redox conditions were of particular interest. The inclusion of basin center (cores) and basin margin (outcrop) samples through the full stratigraphic interval of the Green River Formation facilitated temporal and spatial assessment of trends within the basin. Geochemical and sedimentologic features of the Green [...]
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Funded by:EC | CORALASSISTEC| CORALASSISTAuthors: Lachs, Liam; Humanes, Adriana; Martinez, Helios;Image dataset used for a colour analysis of coral branches throughout a long-term marine heatwave emulation experiment using machine learning. Article: "Within population variability in coral heat tolerance indicates climate adaptation potential" by Humanes and Lachs et al. Code to analyse the dataset is found at 10.5281/zenodo.6256164. LL received funding from Natural Environment Research Council (NERC) ONE Planet Doctoral Training Partnership (NE/S007512/1).
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visibility 75visibility views 75 download downloads 8 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.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 MalaysiaPublisher:MDPI AG S. Nithyapriya; Sundaram Lalitha; R. Z. Sayyed; M. S. Reddy; Daniel Joe Dailin; Hesham A. El Enshasy; Ni Luh Suriani; Susila Herlambang;doi: 10.3390/su13105394
Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.
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For further information contact us at helpdesk@openaire.euAccess Routesgold 110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
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Research data keyboard_double_arrow_right Dataset 2022Publisher:SEANOE Salgueiro, EmÃlia; Magalhães, VÃtor; Rebotim, Andreia; Matos, Lélia; Schweizer, Magali; Sousa, Fátima; González MartÃn, Maria; Batista, Luis Batista;doi: 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 TecnologÃa 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.
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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 Authors: Paton, Steve;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.
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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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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 United StatesPublisher:U.S. Geological Survey Authors: Merrill, Matthew D; Enomoto, Catherine B;doi: 10.5066/p9w8fatw
This data release contains the boundaries of assessment units and input data for the assessment of undiscovered Deep Tuscaloosa Group sandstones conventional gas resources in the U.S. Gulf Coast in Louisiana and Mississippi. 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. In addition to the shapefile, for U.S. assessments, allocation tables are provided that enumerate percentages assigned to various land categories. Machine-readable tables are also provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are 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.eu1 citations 1 popularity Top 10% influence Top 10% impulse Average Powered by BIP!
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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 Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong;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.ScenarioMIP.NOAA-GFDL.GFDL-CM4.ssp585' 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 GFDL-CM4 climate model, released in 2018, includes the following components: aerosol: interactive, atmos: GFDL-AM4.0.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa), atmosChem: fast chemistry, aerosol only, land: GFDL-LM4.0.1 (1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 20 levels; bottom level 10m); land-Veg:unnamed (dynamic vegetation, dynamic land use); land-Hydro:unnamed (soil water and ice, multi-layer snow, rivers and lakes), landIce: GFDL-LM4.0.1, ocean: GFDL-OM4p25 (GFDL-MOM6, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-BLINGv2, seaIce: GFDL-SIM4p25 (GFDL-SIS2.0, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 5 layers; 5 thickness categories). The model was run by the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA (NOAA-GFDL) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 25 km, ocnBgchem: 25 km, seaIce: 25 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 Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Mackallah, Chloe; Druken, Kelsey; Ridzwan, Syazwan Mohamed;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.C4MIP.CSIRO.ACCESS-ESM1-5' 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 Australian Community Climate and Earth System Simulator Earth System Model Version 1.5 climate model, released in 2019, includes the following components: aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: WOMBAT (same grid as ocean), seaIce: CICE4.1 (same grid as ocean). The model was run by the Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia (CSIRO) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6cmicsae&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert 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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6cmicsae&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 03 Oct 2022Publisher:Dryad Authors: Gallagher, Brian; Geargeoura, Sarah; Fraser, Dylan;Salmonids are of immense socio-economic importance in much of the world but are threatened by climate change. This has generated a substantial literature documenting effects of climate variation on salmonid productivity in freshwater ecosystems, but there has been no global quantitative synthesis across studies. We conducted a systematic review and meta-analysis to gain quantitative insight into key factors shaping the effects of climate on salmonid productivity, ultimately collecting 1,321 correlations from 156 studies, representing 23 species across 24 countries. Fisher’s Z was used as the standardized effect size, and a series of weighted mixed-effects models were compared to identify covariates that best explained variation in effects. Patterns in climate effects were complex, and were driven by spatial (latitude, elevation), temporal (time-period, age-class), and biological (range, habitat type, anadromy) variation within and among study populations. These trends were often consistent with predictions based on salmonid thermal tolerances. Namely, warming and decreased precipitation tended to reduce productivity when high temperatures challenged upper thermal limits, while opposite patterns were common when cold temperatures limited productivity. Overall, variable climate impacts on salmonids suggest that future declines in some locations may be counterbalanced by gains in others. In particular, we suggest that future warming should (1) increase salmonid productivity at high latitudes and elevations (especially >60° and >1,500m), (2) reduce productivity in populations experiencing hotter and dryer growing season conditions, (3) favor non-native over native salmonids, and (4) impact lentic populations less negatively than lotic ones. These patterns should help conservation and management organizations identify populations most vulnerable to climate change, which can then be prioritized for protective measures. Our framework enables broad inferences about future productivity that can inform decision-making under climate change for salmonids and other taxa, but more widespread, standardized, and hypothesis-driven research is needed to expand current knowledge. See README document and R code. See README document.
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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.t76hdr83z&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 3visibility views 3 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.
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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.t76hdr83z&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 United StatesPublisher:U.S. Geological Survey Authors: Birdwell, Justin E; Oliver, Thomas A; Warden, Augusta;doi: 10.5066/p9q5voqb
The Eocene Green River Formation in the Piceance Basin of Colorado contains some of the richest oil shale deposits in the world (Johnson and others, 2009; Birdwell and others, 2013). The datasets compiled here were collected with the purpose of refining our understanding of geochemical variability through the depositional history of Eocene Lake Uinta in Piceance Basin. The effects of salinity, water column stratification, and redox conditions were of particular interest. The inclusion of basin center (cores) and basin margin (outcrop) samples through the full stratigraphic interval of the Green River Formation facilitated temporal and spatial assessment of trends within the basin. Geochemical and sedimentologic features of the Green [...]
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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5066/p9q5voqb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert 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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5066/p9q5voqb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Funded by:EC | CORALASSISTEC| CORALASSISTAuthors: Lachs, Liam; Humanes, Adriana; Martinez, Helios;Image dataset used for a colour analysis of coral branches throughout a long-term marine heatwave emulation experiment using machine learning. Article: "Within population variability in coral heat tolerance indicates climate adaptation potential" by Humanes and Lachs et al. Code to analyse the dataset is found at 10.5281/zenodo.6256164. LL received funding from Natural Environment Research Council (NERC) ONE Planet Doctoral Training Partnership (NE/S007512/1).
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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6256189&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 75visibility views 75 download downloads 8 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.
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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6256189&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 MalaysiaPublisher:MDPI AG S. Nithyapriya; Sundaram Lalitha; R. Z. Sayyed; M. S. Reddy; Daniel Joe Dailin; Hesham A. El Enshasy; Ni Luh Suriani; Susila Herlambang;doi: 10.3390/su13105394
Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.
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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su13105394&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert 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.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su13105394&type=result"></script>'); --> </script>
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