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Research data keyboard_double_arrow_right Dataset 2021Publisher:The University of Hong Kong Authors: Lishan Ran (9057026);This is the dataset for our research on assessing CO2 emissions from Chinese inland waters, including streams, rivers, lakes and reservoirs. The dataset includes three parts, including Part 1: Lakes and Reservoirs_1980s, Part 2: CO2 Dataset_2010s, and Part 3: Water chemistry records. Detailed information on these data can be found from the 'README' text file.
https://dx.doi.org/1... 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 33visibility views 33 download downloads 21 Powered bymore_vert https://dx.doi.org/1... 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 2023Embargo end date: 21 Nov 2023Publisher:Harvard Dataverse Authors: Odersky, Moritz; Löffler, Max;doi: 10.7910/dvn/puu3nf
Journal of Economic Inequality, accepted
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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=10.7910/dvn/puu3nf&type=result"></script>'); --> </script>
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Computer Network Information Center, Chinese Academy of Sciences Authors: lei zhang (10860255);Supplementary Information is available for this paper.
figshare 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 figshare 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 2024Publisher:Zenodo Alexander-Haw, Abigail; Dütschke, Elisabeth; Janßen, Hannah; Preuß, Sabine; Schleich, Joachim; Tröger, Josephine; Tschaut, Mareike;This dataset and codebook correspond to the second round of survey data gathered in Denmark in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained. This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort. New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023 NetherlandsPublisher:DANS Data Station Social Sciences and Humanities Authors: Gao, X.; De Hoge, I.E.; Fischer, A.R.H.;Fashion products made from repurposed materials (e.g., backpacks made from pineapple leaves) have become more prevalent nowadays, and their environmental sustainability is one of the core advantages. Yet, it is currently unclear how consumers respond to products made from repurposed materials. We conducted three experiments to examine the effects of three material features, namely function, sustainability, and distinguishability, on consumer preferences for fashion products made from repurposed materials. The results indicate that, when the function of repurposed materials is as good as that of conventional materials, consumers prefer a product made from repurposed materials over the same product made from conventional materials. Also, consumers in general prefer repurposed materials to be less visually distinguishable. Finally, when the sustainability of the repurposed products is emphasized, consumers appear more likely to choose products made from repurposed materials, even when these products have an inferior function. In conclusion, to promote fashion products made from repurposed materials, marketers may emphasize the function and sustainability of repurposed materials, and producers may manufacture repurposed materials that visually resemble conventional materials.
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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=10.17026/dans-x7c-pyv9&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Funded by:EC | REINVENTEC| REINVENTHansen, Teis; Keaney, Monica; Bulkeley, Harriet A.; Cooper, Mark; Mölter, Helena; Nielsen, Hjalti; Pietzner, Katja; Sonesson, Ludwig B.; Stripple, Johannes; S.I. Aan Den Toorn; Tziva, Maria; Tönjes, Annika; Vallentin, Daniel; Van-Veelen, Bregje;This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 13 Apr 2022Publisher:Dryad Gao, Guang; Beardall, John; Jin, Peng; Gao, Lin; Xie, Shuyu; Gao, Kunshan;The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.
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visibility 30visibility views 30 download downloads 17 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 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Rong, Xinyao;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.CAMS.CAMS-CSM1-0.ssp119' 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 CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) 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 Collection , Dataset 2020 NetherlandsPublisher:figshare Van Erven, Gijs; Kleijn, Anne; Patyshakuliyeva, Aleksandrina; Di Falco, Marcos; Tsang, Adrian; De Vries, Ronald P.; Van Berkel, Willem J.H.; Kabel, Mirjam A.;Abstract Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms. Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Science Data Bank Qi, Shu; Qiang, Wang; Zhenya, Song; Gui, Gao; Hailong, Liu; Shizhu, Wang; Yan, He; Rongrong, Pan; Fangli, Qiao;The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community. The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community.
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Research data keyboard_double_arrow_right Dataset 2021Publisher:The University of Hong Kong Authors: Lishan Ran (9057026);This is the dataset for our research on assessing CO2 emissions from Chinese inland waters, including streams, rivers, lakes and reservoirs. The dataset includes three parts, including Part 1: Lakes and Reservoirs_1980s, Part 2: CO2 Dataset_2010s, and Part 3: Water chemistry records. Detailed information on these data can be found from the 'README' text file.
https://dx.doi.org/1... 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 33visibility views 33 download downloads 21 Powered bymore_vert https://dx.doi.org/1... 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.
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 2023Embargo end date: 21 Nov 2023Publisher:Harvard Dataverse Authors: Odersky, Moritz; Löffler, Max;doi: 10.7910/dvn/puu3nf
Journal of Economic Inequality, accepted
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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=10.7910/dvn/puu3nf&type=result"></script>'); --> </script>
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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.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Computer Network Information Center, Chinese Academy of Sciences Authors: lei zhang (10860255);Supplementary Information is available for this paper.
figshare 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.
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 figshare 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 2024Publisher:Zenodo Alexander-Haw, Abigail; Dütschke, Elisabeth; Janßen, Hannah; Preuß, Sabine; Schleich, Joachim; Tröger, Josephine; Tschaut, Mareike;This dataset and codebook correspond to the second round of survey data gathered in Denmark in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained. This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort. New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.
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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.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023 NetherlandsPublisher:DANS Data Station Social Sciences and Humanities Authors: Gao, X.; De Hoge, I.E.; Fischer, A.R.H.;Fashion products made from repurposed materials (e.g., backpacks made from pineapple leaves) have become more prevalent nowadays, and their environmental sustainability is one of the core advantages. Yet, it is currently unclear how consumers respond to products made from repurposed materials. We conducted three experiments to examine the effects of three material features, namely function, sustainability, and distinguishability, on consumer preferences for fashion products made from repurposed materials. The results indicate that, when the function of repurposed materials is as good as that of conventional materials, consumers prefer a product made from repurposed materials over the same product made from conventional materials. Also, consumers in general prefer repurposed materials to be less visually distinguishable. Finally, when the sustainability of the repurposed products is emphasized, consumers appear more likely to choose products made from repurposed materials, even when these products have an inferior function. In conclusion, to promote fashion products made from repurposed materials, marketers may emphasize the function and sustainability of repurposed materials, and producers may manufacture repurposed materials that visually resemble conventional materials.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Funded by:EC | REINVENTEC| REINVENTHansen, Teis; Keaney, Monica; Bulkeley, Harriet A.; Cooper, Mark; Mölter, Helena; Nielsen, Hjalti; Pietzner, Katja; Sonesson, Ludwig B.; Stripple, Johannes; S.I. Aan Den Toorn; Tziva, Maria; Tönjes, Annika; Vallentin, Daniel; Van-Veelen, Bregje;This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.
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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 2022Embargo end date: 13 Apr 2022Publisher:Dryad Gao, Guang; Beardall, John; Jin, Peng; Gao, Lin; Xie, Shuyu; Gao, Kunshan;The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.
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visibility 30visibility views 30 download downloads 17 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 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Rong, Xinyao;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.CAMS.CAMS-CSM1-0.ssp119' 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 CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
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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.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Collection , Dataset 2020 NetherlandsPublisher:figshare Van Erven, Gijs; Kleijn, Anne; Patyshakuliyeva, Aleksandrina; Di Falco, Marcos; Tsang, Adrian; De Vries, Ronald P.; Van Berkel, Willem J.H.; Kabel, Mirjam A.;Abstract Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms. Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Science Data Bank Qi, Shu; Qiang, Wang; Zhenya, Song; Gui, Gao; Hailong, Liu; Shizhu, Wang; Yan, He; Rongrong, Pan; Fangli, Qiao;The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community. The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community.
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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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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=10.57760/sciencedb.16286&type=result"></script>'); --> </script>
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