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Data from: 30 Years Brings Changes to the Arthropod Community of Kibale National Park, Uganda by Opito, E.A., T. Alanko, U. Kalbitzer, M. Nummelin, P. Omeja, A. Valtonen, and Colin A. Chapman. 2023, Biotropica, Article DOI: 10.1111/btp.13206

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.DKRZ.MPI-ESM1-2-HR.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 MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

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.DAMIP.MPI-M.MPI-ESM1-2-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped. {"references": ["Nickel S, Schr\u00f6der W 2017. Fuzzy modelling and mapping soil moisture for observed and future periods. An alternative for dynamic modelling at the national and regional scale? Annals of Forest Science 74:71"]}

Pour obtenir un aperçu spécifique des rôles que les micro-organismes pourraient jouer dans la stéatose hépatique non alcoolique (NAFLD), certaines bactéries intestinales et lactiques et une levure (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) ont été caractérisées par une chromatographie liquide haute performance pour la production d'éthanol lorsqu'elles sont cultivées sur différents glucides : hexoses (glucose et fructose), pentoses (arabinose et ribose), disaccharides (lactose et lactulose) et inuline. Les quantités les plus élevées d'éthanol ont été produites par S. cerevisiae, L. fermentum et W. confusa sur le glucose et par S. cerevisiae et W. confusa sur le fructose. En raison de la mannitol-déshydrogénase exprimée dans L. fermentum, la production d'éthanol sur le fructose a été significativement réduite (P < 0,05). Le pyruvate et le citrate, deux accepteurs d'électrons potentiels pour la régénération du NAD+/NADP+, ont considérablement réduit la production d'éthanol avec de l'acétate produit à la place dans L. fermentum cultivé sur glucose et W. confusa cultivé sur glucose et fructose, respectivement. Dans les boues fécales préparées à partir des matières fécales de quatre volontaires en surpoids, on a constaté que l'éthanol était produit lors de l'ajout de fructose. L'ajout d'A. caccae, L. acidophilus, L. fermentum, ainsi que de citrate et de pyruvate, respectivement, a aboli la production d'éthanol. Cependant, l'ajout de W. confusa a entraîné une augmentation significative (P < 0,05) de la production d'éthanol. Ces résultats indiquent que des micro-organismes comme W. confusa, une bactérie lactique hétéro-fermentaire, négative à la mannitol-déshydrogénase, peuvent favoriser la NAFLD par l'éthanol produit à partir de la fermentation du sucre, tandis que d'autres bactéries intestinales et des bactéries lactiques homo- et hétéro-fermentaires mais positives à la mannitol-déshydrogénase peuvent ne pas favoriser la NAFLD. En outre, nos études indiquent que les facteurs alimentaires interférant avec le microbiote gastro-intestinal et le métabolisme microbien peuvent être importants dans la prévention ou la promotion de la NAFLD. Para obtener información específica sobre los roles que podrían desempeñar los microorganismos en la enfermedad del hígado graso no alcohólico (NAFLD, por sus siglas en inglés), algunas bacterias intestinales y del ácido láctico y una levadura (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) se caracterizaron por cromatografía líquida de alto rendimiento para la producción de etanol cuando se cultivaron en diferentes carbohidratos: hexosas (glucosa y fructosa), pentosas (arabinosa y ribosa), disacáridos (lactosa y lactulosa) e inulina. Las cantidades más altas de etanol fueron producidas por S. cerevisiae, L. fermentum y W. confusa en glucosa y por S. cerevisiae y W. confusa en fructosa. Debido a la manitol-deshidrogenasa expresada en L. fermentum, la producción de etanol en fructosa se redujo significativamente (P < 0.05). El piruvato y el citrato, dos aceptores de electrones potenciales para la regeneración de NAD+/NADP+, redujeron drásticamente la producción de etanol con acetato producido en su lugar en L. fermentum cultivado en glucosa y W. confusa cultivado en glucosa y fructosa, respectivamente. En suspensiones fecales preparadas a partir de heces de cuatro voluntarios con sobrepeso, se encontró que el etanol se producía tras la adición de fructosa. La adición de A. caccae, L. acidophilus, L. fermentum, así como citrato y piruvato, respectivamente, abolió la producción de etanol. Sin embargo, la adición de W. confusa resultó en un aumento significativo (P < 0.05) de la producción de etanol. Estos resultados indican que microorganismos como W. confusa, una bacteria de ácido láctico hetero-fermentativa, negativa para manitol-deshidrogenasa, pueden promover NAFLD a través del etanol producido a partir de la fermentación de azúcar, mientras que otras bacterias intestinales y bacterias de ácido láctico homo- y hetero-fermentativas pero positivas para manitol-deshidrogenasa pueden no promover NAFLD. Además, nuestros estudios indican que los factores dietéticos que interfieren con la microbiota gastrointestinal y el metabolismo microbiano pueden ser importantes para prevenir o promover la EHGNA. To gain some specific insight into the roles microorganisms might play in non-alcoholic fatty liver disease (NAFLD), some intestinal and lactic acid bacteria and one yeast (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) were characterized by high performance liquid chromatography for production of ethanol when grown on different carbohydrates: hexoses (glucose and fructose), pentoses (arabinose and ribose), disaccharides (lactose and lactulose), and inulin. Highest amounts of ethanol were produced by S. cerevisiae, L. fermentum and W. confusa on glucose and by S. cerevisiae and W. confusa on fructose. Due to mannitol-dehydrogenase expressed in L. fermentum, ethanol production on fructose was significantly (P < 0.05) reduced. Pyruvate and citrate, two potential electron acceptors for regeneration of NAD+/NADP+, drastically reduced ethanol production with acetate produced instead in L. fermentum grown on glucose and W. confusa grown on glucose and fructose, respectively. In fecal slurries prepared from feces of four overweight volunteers, ethanol was found to be produced upon addition of fructose. Addition of A. caccae, L. acidophilus, L. fermentum, as well as citrate and pyruvate, respectively, abolished ethanol production. However, addition of W. confusa resulted in significantly (P < 0.05) increased production of ethanol. These results indicate that microorganisms like W. confusa, a hetero-fermentative, mannitol-dehydrogenase negative lactic acid bacterium, may promote NAFLD through ethanol produced from sugar fermentation, while other intestinal bacteria and homo- and hetero-fermentative but mannitol-dehydrogenase positive lactic acid bacteria may not promote NAFLD. Also, our studies indicate that dietary factors interfering with gastrointestinal microbiota and microbial metabolism may be important in preventing or promoting NAFLD. لاكتساب بعض الأفكار المحددة حول الأدوار التي قد تلعبها الكائنات الحية الدقيقة في مرض الكبد الدهني غير الكحولي (NAFLD)، تميزت بعض بكتيريا حمض الأمعاء واللاكتيك وخميرة واحدة (Anaerostipes caccae، Bacteroides thetaiotaomicron، Bifidobacterium longum، Enterococcus fecalis، Escherichia coli، Lactobacillus acidophilus، Lactobacillus fermentum، Lactobacillus plantarum، Weissella confusa، Saccharomyces cerevisiae) بتصوير سائل عالي الأداء لإنتاج الإيثانول عند زراعته على كربوهيدرات مختلفة: hexoses (الجلوكوز والفركتوز)، pentoses (الأرابينوز والريبوز)، disaccharides (اللاكتوز واللاكتولوز)، و inulin. تم إنتاج أعلى كميات من الإيثانول بواسطة S. cerevisiae و L. fermentum و W. confusa على الجلوكوز و S. cerevisiae و W. confusa على الفركتوز. بسبب نازعة هيدروجين المانيتول المعبر عنها في L. fermentum، انخفض إنتاج الإيثانول على الفركتوز بشكل كبير (P < 0.05). قلل البيروفات والسيترات، وهما مستقبلان محتملان للإلكترون لتجديد NAD +/NADP+، بشكل كبير من إنتاج الإيثانول مع الأسيتات المنتجة بدلاً من ذلك في L. fermentum المزروع على الجلوكوز و W. confusa المزروع على الجلوكوز والفركتوز، على التوالي. في الملاط البرازي الذي تم تحضيره من براز أربعة متطوعين يعانون من زيادة الوزن، وجد أن الإيثانول يتم إنتاجه عند إضافة الفركتوز. إضافة A. caccae، L. acidophilus، L. fermentum، وكذلك السترات والبيروفات، على التوالي، ألغت إنتاج الإيثانول. ومع ذلك، أدت إضافة W. confusa إلى زيادة كبيرة في إنتاج الإيثانول (P < 0.05). تشير هذه النتائج إلى أن الكائنات الحية الدقيقة مثل W. confusa، وهي بكتيريا حمض اللاكتيك السلبية غير المتجانسة، قد تعزز NAFLD من خلال الإيثانول المنتج من تخمير السكر، في حين أن البكتيريا المعوية الأخرى وبكتيريا حمض اللاكتيك الإيجابية غير المتجانسة ولكن غير المتجانسة قد لا تعزز NAFLD. أيضًا، تشير دراساتنا إلى أن العوامل الغذائية التي تتداخل مع الكائنات الحية الدقيقة في الجهاز الهضمي والتمثيل الغذائي الميكروبي قد تكون مهمة في منع أو تعزيز NAFLD.