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Research data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | HELIXEC| HELIXThiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; Gudmundsson, Lukas; Seneviratne, Sonia I.; Andrijevic, Marina; Frieler, Katja; Emanuel, Kerry; Geiger, Tobias; Bresch, David N.; Zhao, Fang; Willner, Sven N.; Büchner, Matthias; Volkholz, Jan; Bauer, Nico; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; François, Louis; Grillakis, Manolis; Gosling, Simon N.; Hanasaki, Naota; Hickler, Thomas; Huber, Veronika; Ito, Akihiko; Jägermeyr, Jonas; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Lutz, Wolfgang; Mengel, Matthias; Müller, Christoph; Ostberg, Sebastian; Reyer, Christopher P. O.; Stacke, Tobias; Wada, Yoshihide;This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:SCAR - Microbial Antarctic Resource System Barret, Maialen; Thalasso, Frederic; Gandois, Laure; Cruz, Klara Martinez; Jaureguy, Armando Sepulveda; Lavergne, Céline; Teisserenc, Roman; Polette Aguilar; Gerardo-Nieto, Oscar; Etchebehere, Claudia; Martins, Bruna; Fochesatto, Javier; Tananaev, Nikita; Svenning, Mette; Seppey, Christophe; Tveit, Alexander; Chamy, Rolando; Astorga-España, Maria Soledad; Mansilla, Andres; Van De Putte, Anton; Sweetlove, Maxime; Murray, Alison; Cabrol, Léa;doi: 10.15468/hhkhz2
Methane emissions from aquatic and terrestrial ecosystems play a crucial role in global warming, which is particularly affecting high-latitude ecosystems. As major contributors to methane emissions in natural environments, the microbial communities involved in methane production and oxidation deserve a special attention. Microbial diversity and activity are expected to be strongly affected by the already observed (and further predicted) temperature increase in high-latitude ecosystems, eventually resulting in disrupted feedback methane emissions. The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments. We report here a small subunit ribosomal RNA (16S rRNA) analysis of lake, peatland and mineral soil ecosystems.
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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: Narayanasetti, Sandeep; Panickal, Swapna; Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; +2 AuthorsNarayanasetti, Sandeep; Panickal, Swapna; Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; Singh, Manmeet; Raghavan, Krishnan;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.CCCR-IITM.IITM-ESM.ssp126' 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 IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) 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 2021Publisher:Zenodo Funded by:EC | MPC-. GTEC| MPC-. GTBastero, Josué Borrajo; Figueroa, Iago Cupeiro; Picard, Damien; Helsen, Lieve; Laverge, Jelle;The dataset consists of the building energy simulation data for three case-study buildings (Infrax office building (BE); Ter Potterie elderly home (BE) and Haus M multi-family building (CH)). For these buildings, HVAC-concepts were simulated: the hybridGEOTABS-MPC concept (not for Haus M), hybridGEOTABS-RBC baseline concept and nonGEOTABS-RBC concept. The dataset includes the hourly time series for a one year simulation for the major outputs of the BES-model. The results are reported in hybridGEOTABS deliverable D4.9. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723649
ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: 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 ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: 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 2023Publisher:Science Data Bank Authors: Tanmay Sinha;Gas chromatography spectral data Gas chromatography spectral data
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Wimalasiri, Eranga M.; Ashfold, Matthew; Jahanshiri, Ebrahim; Karunaratne, Asha S.;Proso millet (Panicum miliaceum L.) is a drought tolerant underutilised crop cultivated in rainfed subsistence agricultural systems. Proso millet yields were simulated using a calibrated Agricultural Production Systems Simulator (APSIM) model for 95 locations in Sri Lanka. The yield maps were generated according to the Inverse Distance Weighting (IDW) model using ArcMap 10.7.1. The database contains Proso millet yield maps for current climate and yield change under 5 hypothetical climate change scenarios; 1oC, 1.5 oC and 2 oC temperature increments, 25% rainfall increment, and 25% rainfall reduction compared to the baseline (1980-2009) climate.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | C123EC| C123Authors: Motte, Jordy; Alvarenga, Rodrigo A. F.; Thybaut, Joris W.; Dewulf, Jo;Supplementary material for the associated publication.
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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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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024Publisher:EDP Sciences Authors: Aman Mittal; G. Karuna;The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV A. Yerrayya; A. Nikunj; P. Francis Prashanth; S.R. Chakravarthy; Upendra Natarajan; R. Vinu;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.eu21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
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Research data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | HELIXEC| HELIXThiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; Gudmundsson, Lukas; Seneviratne, Sonia I.; Andrijevic, Marina; Frieler, Katja; Emanuel, Kerry; Geiger, Tobias; Bresch, David N.; Zhao, Fang; Willner, Sven N.; Büchner, Matthias; Volkholz, Jan; Bauer, Nico; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; François, Louis; Grillakis, Manolis; Gosling, Simon N.; Hanasaki, Naota; Hickler, Thomas; Huber, Veronika; Ito, Akihiko; Jägermeyr, Jonas; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Lutz, Wolfgang; Mengel, Matthias; Müller, Christoph; Ostberg, Sebastian; Reyer, Christopher P. O.; Stacke, Tobias; Wada, Yoshihide;This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.
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visibility 317visibility views 317 download downloads 197 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 2022Publisher:SCAR - Microbial Antarctic Resource System Barret, Maialen; Thalasso, Frederic; Gandois, Laure; Cruz, Klara Martinez; Jaureguy, Armando Sepulveda; Lavergne, Céline; Teisserenc, Roman; Polette Aguilar; Gerardo-Nieto, Oscar; Etchebehere, Claudia; Martins, Bruna; Fochesatto, Javier; Tananaev, Nikita; Svenning, Mette; Seppey, Christophe; Tveit, Alexander; Chamy, Rolando; Astorga-España, Maria Soledad; Mansilla, Andres; Van De Putte, Anton; Sweetlove, Maxime; Murray, Alison; Cabrol, Léa;doi: 10.15468/hhkhz2
Methane emissions from aquatic and terrestrial ecosystems play a crucial role in global warming, which is particularly affecting high-latitude ecosystems. As major contributors to methane emissions in natural environments, the microbial communities involved in methane production and oxidation deserve a special attention. Microbial diversity and activity are expected to be strongly affected by the already observed (and further predicted) temperature increase in high-latitude ecosystems, eventually resulting in disrupted feedback methane emissions. The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments. We report here a small subunit ribosomal RNA (16S rRNA) analysis of lake, peatland and mineral soil ecosystems.
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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: Narayanasetti, Sandeep; Panickal, Swapna; Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; +2 AuthorsNarayanasetti, Sandeep; Panickal, Swapna; Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; Singh, Manmeet; Raghavan, Krishnan;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.CCCR-IITM.IITM-ESM.ssp126' 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 IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) 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 2021Publisher:Zenodo Funded by:EC | MPC-. GTEC| MPC-. GTBastero, Josué Borrajo; Figueroa, Iago Cupeiro; Picard, Damien; Helsen, Lieve; Laverge, Jelle;The dataset consists of the building energy simulation data for three case-study buildings (Infrax office building (BE); Ter Potterie elderly home (BE) and Haus M multi-family building (CH)). For these buildings, HVAC-concepts were simulated: the hybridGEOTABS-MPC concept (not for Haus M), hybridGEOTABS-RBC baseline concept and nonGEOTABS-RBC concept. The dataset includes the hourly time series for a one year simulation for the major outputs of the BES-model. The results are reported in hybridGEOTABS deliverable D4.9. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723649
ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: 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 ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: 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 2023Publisher:Science Data Bank Authors: Tanmay Sinha;Gas chromatography spectral data Gas chromatography spectral data
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Wimalasiri, Eranga M.; Ashfold, Matthew; Jahanshiri, Ebrahim; Karunaratne, Asha S.;Proso millet (Panicum miliaceum L.) is a drought tolerant underutilised crop cultivated in rainfed subsistence agricultural systems. Proso millet yields were simulated using a calibrated Agricultural Production Systems Simulator (APSIM) model for 95 locations in Sri Lanka. The yield maps were generated according to the Inverse Distance Weighting (IDW) model using ArcMap 10.7.1. The database contains Proso millet yield maps for current climate and yield change under 5 hypothetical climate change scenarios; 1oC, 1.5 oC and 2 oC temperature increments, 25% rainfall increment, and 25% rainfall reduction compared to the baseline (1980-2009) climate.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | C123EC| C123Authors: Motte, Jordy; Alvarenga, Rodrigo A. F.; Thybaut, Joris W.; Dewulf, Jo;Supplementary material for the associated publication.
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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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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024Publisher:EDP Sciences Authors: Aman Mittal; G. Karuna;The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV A. Yerrayya; A. Nikunj; P. Francis Prashanth; S.R. Chakravarthy; Upendra Natarajan; R. Vinu;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.eu21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
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