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2,837 results for “Climate Data”
Data of the paper No City Left Behind: Building Climate Policy Bridges between the North and South
<p>Cities are progressively heightening their climate aspirations to curtail urban carbon emis- sions and establish a future where economies and communities can flourish within the Earth’s eco- logical limits. Consequently, numerous climate initiatives are being launched to control urban car- bon emissions, targeting various sectors, including transport, residential, agricultural, and energy. However, recent scientific literature underscores the disproportionate distribution of climate poli- cies. While cities in the Global North have witnessed several initiatives to combat climate change, cities in the Global South remain uncovered and highly vulnerable to climate hazards. To address this disparity, we employed the Balanced Iterative Reducing and Clustering using the Hierarchies (BRICH) algorithm to cluster cities from diverse geographical areas that exhibit comparable socio- economic profiles. This clustering strives to foster enhanced cooperation and collaboration among cities globally, with the goal of addressing climate change in a comprehensive manner. In summary, we identified similarities, paerns, and clusters among peer cities, enabling mutual and generaliza- ble learning among worldwide peer-cities regarding urban climate policy exchange. This exchange occurs through three approaches: (i) inner-mutual learning, (ii) cross-mutual learning, and (iii) outer-mutual learning. Our findings mark a pivotal stride towards aaining worldwide climate ob- jectives through a shared responsibility approach. Furthermore, they provide preliminary insights into the implementation of “urban climate policy exchange” among peer cities on a global scale.</p>
Supporting data case study, ensemble climate-impact modelling
<p>Data supporting the case study in 'Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions', publication under review.</p>
Monthly and annual climate data averaged from 2011 to 2013 for 79 research plots on the southern slopes of Mt. Kilimanjaro - V 1.0
<p>Annual and monthly means of air temperature, air humidity and precipitation averaged between 2011 and 2013 and based on in situ observations, spatial interpolation and/or statistical modelling for 79 research plots along the southern slopes of Mt. Kilimanjaro.</p> <p>The data set is based on the work of sub-project 1 (Climate Dynamics of the Kilimanjaro Region and Remote Sensing) as part of the DFG research unit 1246 (Kilimanjaro ecosystems under global change). In addition, multi-year rainfall observations from A. Hemp are included (for a detailed description and references, please see the NOTES data file of the data set).</p> <p>For more information on the sub-project, please visit <br /> http://environmentalinformatics-marburg.de/projects/climate-dynamics-of-the-kilimanjaro-region/<br /> <br /> Feel free to contact us via ZENODO or send an e-mail to zenodo@environmentalinformatics-marburg.de</p>
Monthly and annual climate data averaged from 2011 to 2013 for 79 research plots on the southern slopes of Mt. Kilimanjaro - V 1.1
<p>Annual and monthly means of air temperature, air humidity and precipitation averaged between 2011 and 2013 and based on in situ observations, spatial interpolation and/or statistical modelling for 79 research plots along the southern slopes of Mt. Kilimanjaro.</p> <p>The data set is based on the work of sub-project 1 (Climate Dynamics of the Kilimanjaro Region and Remote Sensing) as part of the DFG research unit 1246 (Kilimanjaro ecosystems under global change). In addition, multi-year rainfall observations from A. Hemp are included (for a detailed description and references, please see the NOTES data file of the data set).</p> <p>For more information on the sub-project, please visit <br /> http://environmentalinformatics-marburg.de/projects/climate-dynamics-of-the-kilimanjaro-region/<br /> <br /> Feel free to contact us via ZENODO or send an e-mail to zenodo@environmentalinformatics-marburg.de</p>
Data for: Unraveling the influence of essential climatic factors on the number of tones through an extensive database of languages in China
Open the record for dataset details and reuse information.
Data supporting the findings of "Projecting trends of arabica coffee yield under climate change: A process-based modelling study at continental scale"
Open the record for dataset details and reuse information.
Open Data sets from Cold Climate Wind Farms in Finland, Pori
<p>This dataset includes 6 years of meteorological mast data and operational data from one turbine located in Pori, Western Finland. Dataset also includes simultaneous and longer-term monthly icing time series from WIceAtlas database for reference. Site can be described as an easy site both in terms of icing (IEA Ice Class 2) and terrain complexity. </p> <p>The work was funded by an EU IRPWIND project.</p>
Open Data sets from Cold Climate Wind Farms in Finland, Olostunturi
<p>This dataset includes 6 years of open access meteorological mast data and operational data of multiple turbines from Olos wind farm in Finland, and simultaneous and longer-term monthly icing time series from WIceAtlas database for reference. Olos is a complex terrain site with severe icing conditions during winter (IEA Ice Class 4).</p> <p>The work was funded by an EU IRPWIND project.</p>
NGFS Climate Scenarios Data Set
<h1><strong>Notice to users of NGFS long-term climate scenarios</strong></h1> <p>The NGFS informs users that the academic paper underpinning the physical risk estimates in Phase V of its long-term scenarios, Kotz et al. (2024), has received critiques in a post-publication review at <em>Nature</em>. The authors have revised their analysis, with limited impacts on results. The updated paper still has to undergo peer-review.</p> <p>The NGFS closely monitors the academic process and will incorporate any necessary updates in future iterations of its long-term scenarios.</p> <p>Users are reminded that neither the NGFS, nor its member institutions, nor any person acting on their behalf, is responsible or liable for any reliance on, or for any use of the NGFS scenarios and/or supplementary documentation. This also applies to the use of the data produced under the scenarios – see section 5 in <a title="https://protect.checkpoint.com/v2/r02/___https://data.ene.iiasa.ac.at/ngfs/%23/license___.YzJlOmlpYXNhOmM6bzpkYjlkNzM0YWJiNzc3OTZkOWFhNjVkMzJlOGU5OWMxMDo3OmRlNWI6MDc1NDk3ZWUxNDE1MGNiM2I3ZThmOTQxZjAxNTY5MzUxNmQ1ZTUzMThiNWEzYzk4NGQ3NWEzNzlmM2IyMDhjNjpoOkY6Tg" href="https://protect.checkpoint.com/v2/r02/___https://data.ene.iiasa.ac.at/ngfs/%23/license___.YzJlOmlpYXNhOmM6bzpkYjlkNzM0YWJiNzc3OTZkOWFhNjVkMzJlOGU5OWMxMDo3OmRlNWI6MDc1NDk3ZWUxNDE1MGNiM2I3ZThmOTQxZjAxNTY5MzUxNmQ1ZTUzMThiNWEzYzk4NGQ3NWEzNzlmM2IyMDhjNjpoOkY6Tg" target="_blank" rel="noopener noreferrer">https://data.ene.iiasa.ac.at/ngfs/#/license</a>. Thus, while the NGFS climate scenarios are certainly a helpful tool, they do not alleviate the responsibility of users, including banks and other (financial) organisations, to design and implement their own risk management frameworks.</p> <h1><strong>Download information</strong></h1> <h2><strong>Please do not request a data download here.</strong></h2> <p>Rather, the data is available for download at the <a href="https://data.ece.iiasa.ac.at/ngfs">NGFS Scenario Explorer</a> under this <strong>download link:</strong> <a href="https://data.ece.iiasa.ac.at/ngfs/#/downloads">https://data.ece.iiasa.ac.at/ngfs/#/downloads</a>. In order to download click on <em>Guest login. </em>You will be forwarded to the downloads page. At the <strong>bottom</strong> of the <strong>downloads</strong> page you can download the data.</p> <p>The license permits use of the scenario ensemble for scientific research and commercial use, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ece.iiasa.ac.at/ngfs/#/license">legal code</a> for more information.</p> <h2><strong>About NGFS</strong></h2> <p>The Network for Greening the Financial System (NGFS) is a group of 127 central banks and supervisors and 20 observers committed to sharing best practices, contributing to the development of climate– and environment–related risk management in the financial sector and mobilising mainstream finance to support the transition toward a sustainable economy.</p> <p>This Scenario Explorer is a web-based user interface for NGFS Scenarios. This provides intuitive visualizations & display of time series data and download of the data in multiple formats.</p> <p>NGFS scenarios were produced by NGFS Workstream on Scenarios Design and Analysis in partnership with an academic consortium from the Potsdam Institute for Climate Impact Research (PIK), International Institute for Applied Systems Analysis (IIASA), University of Maryland (UMD), Climate Analytics (CA), and the National Institute of Economic and Social Research (NIESR). This work was made possible by grants from Bloomberg Philanthropies and ClimateWorks Foundation.</p> <p>The bespoke scenarios developed in Phase 4 of this project are generated by state-of-the-art well-established integrated assessment models (IAMs), namely GCAM, MESSAGE-GLOBIOM and REMIND-MAgPIE, as well as the NiGEM macroeconomic model.</p>
Data sets of fish growth, population viability and climate across Europe.
<p>Data sets of fish growth, population viability and climate across Europe.</p>
Complementary Data of Groundwater model for the Publication: "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate"
<p>This repository provides the resources related to the publication "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate." It includes the groundwater model data sets.</p>
Data publication for the paper entitled Impact of Ocean Data Assimilation on Climate Predictions with ICON-ESM
<p>The files contain the source code of ICON-ESM-V1.0, primary data and scripts used in the analyses and for producing the figures for the paper "Impact of Ocean Data Assimilation on Climate Predictions with ICON-ESM" by Pohlmann, H., Brune, S., Fröhlich, K., Jungclaus, J. H., Sgoff, C., and Baehr, J. (2022, JAMES).</p>
Data for: Extreme shifts in habitat suitability under contemporary climate change for a high-Arctic herbivore
<p>Data and code associated with MaxEnt analyses to quantify shifts in habitat suitability of muskoxen in the Northeast Greenland National Park. Details on how to use the files are provided in the README.docx file</p>
Complementary Data and Model Repository for the Publication: "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate"
<p>This repository provides the resources related to the publication "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate." It includes the data sets used in the study, the SWAT model and python script for evaluation of the different methods compared in this study.</p>
Climatic data of the region of Beja from 23/03/2017 to 30/06/2017
<p>Tm: minimum temperature</p> <p>TM: maximum temperature</p> <p>H: Humidity</p> <p>PP: precepitations</p>
Data from: Climate change promotes allopatric divergence and ecological adaptation in a tropical montane bird. SNP data used to analyse the demographic history of Amblyornis papuensis.
<p>Environmental heterogeneity and Pleistocene glaciations have largely contributed to speciation and adaptation processes that have promoted genetic diversity of montane birds in temperate regions, but how these processes have influenced montane species in tropical regions is less known. We study these processes using a chromosome-level genome and comparative population genomics in Archbold's bowerbird (<i>Amblyornis papuensis</i>), a rare and poorly known montane bird in New Guinea. The analyses showed a deep divergence between the populations isolated in the eastern and western parts of the New Guinea Highlands, respectively. Using demographic model inference, we estimated that the two populations were part of the same population until ca. 140,000 years ago. They then diverged as the warmer climate in the Eemian interglacial forced them to retreat to higher elevations, which also led to decreasing population sizes. Subsequently, the two isolated populations evolved considerable phenotypic differences, particularly in body size, and in accordance with this, we observed strong divergent selection in genes related to body development. The phenotypic and genomic differences are likely a response to heterogenic environmental conditions in their respective habitats.</p>
Arctic Sea Ice Seasonal Change and Melt/Freeze Climate Indicators from Satellite Data, Version 1
The product contains melt-season indicators that can be used to delineate various stages in the summer melt and freeze-up period of sea ice. The data were primarily derived using Sea Ice Concentration (SIC) observations from the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration and brightness temperature observations from the DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures; both input data sets are archived at NSIDC. The main parameters for this data set include the dates of melt onset, early melt onset, and continuous melt onset; dates of early and continuous freeze onset; day of opening (last day SIC is above 80%); day of retreat (last day SIC drops below 15%); day of advance (first day SIC increases above 15%); day of closing (first day SIC increases above 80%); total outer ice-free period; total inner ice-free period; seasonal loss-of-ice period; seasonal gain-of-ice period; and the seasonal ice zone.These data are available for 1979 through 2017. They are gridded on the NSIDC northern hemisphere polar stereographic grid at 25 km.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.