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FIGURE 14. A-G in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 14. A-G. Amamriabatis heni nov gen. nov. sp. A. anterior tooth KEB 1-218, A1. Occlusal view, A2. Basal view, A3. Lingual view, B. antero-lateral tooth KEB 1-219 (Holotype), B1. Occlusal view, B2. Lingual view, B3. Basal view, C. juvenile tooth KEB 1-220, occlusal view, D. antero-lateral tooth KEB 1-221, D1. Occlusal view, D2. Basal view, D3. Labial view, E. lateral tooth KEB 1-222, E1. Occlusal view, E2. Lingual view, E3. Basal view, F. antero-lateral tooth KEB 1-223, F1. Occlusal view, F2. Basal view, F3. Labial view, G. lateral tooth KEB 1-224, G1. Occlusal view, G2. Basal view, G3. Lingual view; H. Archaeomanta sp. KEB 1-225, H1. Lateral view, H2. Labial view.
FIGURE 12. A-C in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 12. A-C. Mecotrygon asperodentulus nov. gen nov. sp. A. anterior tooth KEB 1-188, A1. occlusal view, A2. Profile, A3. Basal view; B. lateral tooth KEB 1-189, B1. occlusal view, B2. labial view, B3. Profile; C. lateral tooth KEB 1-190, HOLOTYPE, C1. Occlusal view, C2. Lingual view, C3. Basal view, C4. Profile; D-M. Himantura souarfortuna nov. sp. D.?posterior tooth KEB 1-191, D1. Occlusal view, D2. Profile, D3. Basal view; E. antero-lateral tooth KEB 1- 192, occlusal view; F. anterior tooth KEB 1-193, F1. Occlusal view, F2. Labial view; G. antero-lateral tooth KEB 1-194, G1. Occlusal view, G2. Profile; H. anterior tooth KEB 1-195, occlusal view; I. lateral tooth KEB 1-196, occlusal view; J. A. anterior tooth KEB 1-197, occlusal view; K. lateral tooth KEB 1-198, K1. Occlusal view, K2. Basal view; L. A. anterior tooth KEB 1-199, occlusal view; M. lateral tooth KEB 1-200, occlusal view. N-O. Dasyatoid indet. N. antero-lateral tooth KEB 1-201, N1. Occlusal view, N2. Basal view; O. A. antero-lateral tooth KEB 1-202, occlusal view; P-Q. Arechia sp. P. lateral tooth KEB 1-203, occlusal view; Q. A. anterior tooth KEB 1-204, Q1. Occlusal view, Q2. Lingual view.
FIGURE 10. A-D in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 10. A-D: Propristis cf. schweinfurti. A. Rostral denticle KEB 1-172, A1. Profile, A2. dorsal view; B. Rostral denticle KEB 1-173, Profile; C. Rostral denticle KEB 1-174, C1. Profile, C2. Dorsal view, C3. basal view; D. Rostral denticle KEB 1-175, D1. profile. D2. dorsal view; E: Pristis sp. Rostral denticle, KEB 1-165, dorsal view; F-G. Rhynchobatus cf. vincenti. F. anterior tooth KEB 1-176, F1. Occlusal view, F2. Basal view; G. anterior tooth KEB 1-177, occlusal view; H-I.?Torpedo sp. H. lateral tooth KEB 1-178, occlusal view, I. lateral tooth KEB 1-179, occlusal view.
FIGURE 9. A-F in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 9. A-F: Propristis cf. schweinfurti, A. Anterior oral tooth KEB 1-166, A1. Lingual view, A2. Occlusal view, A3. profile; B. Antero-lateral oral tooth KEB 1-167, B1. Occlusal view, B2. Lingual view, B3. Labial view; C. Anterior oral tooth KEB 1-168, C1. Lingual view, C2. Basal view, C3. Profile; D. lateral tooth KEB 1-169, D1. Lingual view, D2. Occlusal view, D3. Basal view, D4. Magnificence of crown-root boundary of D3; E.?male lateral tooth KEB 1-170, E1. Occlusal tooth, E2. Basal view; F.?male anterior tooth KEB 1-171, F1. Lingual view, F2. Occlusal view; G-L: Pristis sp. G. porterior tooth KEB 1-158, G1. Occlusal view, G2. Basal view; H. anterior tooth KEB 1-159, occlusal view; I. lateral tooth KEB 1-160, I1 occlusal view, I2., lingual view. J lateral tooth KEB 1-161, occlusal view; K. anterior tooth KEB 1-162, occlusal view; L. lateral tooth of?juvenile KEB 1-163, L1. Occlusal view, L2. Basal view; M. lateral tooth of juvenile KEB 1-164, M1. Occlusal view, M2. Profile.
FIGURE 1 in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 1. Paleotemperatures (ice-free deep-ocean T°/ tropical sea surface T°) and Thermic events during the "doubthouse" conditions of Eocene period (from Cramwinckel et al., 2018 modified with events dating from Hollis et al., 2019). Stratigraphically and geographical locations of the main deposits with Elasmobranch associations along the southwestern Tethys. Abbreviations: DAK: Dakhla (Adnet et al., 2010), GEN: Genam (Zouhri et al., 2017, in press); AZ: Aznag (Tabuce et al., 2005) PM: Phosphate ores (see Noubhani and Cappetta, 1997), Morocco; GAF: Gafsa basin (see Arambourg, 1952); KEBAR: Kébar (this work and Adnet et al., 2019); MBK: Mabrouk (see Sweydan et al., 2019), Tunisia; EG: ElGedida (see Strougo et al., 2007); KM: KM11 (see Adnet et al., 2011) MT: Minqar Tabaghbagh (see Zalmout et al., 2012); BQ: Birquet Qarun QS: Quar et Sa; GE: Genahamm Fm.; MI: Midawara FM. from Wadi al Hitan, see Underwood et al., 2011), Egypt; QD: Qa Faydat al Dahikya, Jordania, see Mustafat and Zalmout, 2002).
Manipulation of netCDF data with R for climate change research: Multi-model analysis for CMIP5 models.
<p>Geoscientists now live in a world with an exponential growth in digital data and methods.<br> Climate change studies usually describe computational methods informally. Climate scientists seek to<br> share their information, the justification of reproducible research has received increasing attention in<br> geosciences. To have it in an open-source format makes it easier to interchange not only with fellow<br> scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source<br> computer language powerful and highly extensible that can promotes reproductive science techniques in a<br> easier way. R is highly accessible for non-computational scientists when coupled with packages like<br> ‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out<br> complex data analysis. In this paper we have assessed the power of R language for manipulating climate<br> data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we<br> have proposed an example of best practices to handle model ensembles. This is the first study to our<br> knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster<br> package capabilities provides efficient access to the multi-model, with crucial applications in climate<br> change research. In recent years more than 100 peer-reviewed scientific publications have used the<br> CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools<br> to author papers and disseminate information about the process and products of their research.</p>
Raw data sets from Jones et al. 2018 QSR publication: A multi-proxy approach to understanding complex responses of saltlake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain
<p>Attached are the raw data sets containing the pollen data, DXR, Grain size and C14 ages from the recent publication: Jones et al. 2018 QSR publication: A multi-proxy approach to understanding complex responses of saltlake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain.</p> <p>Note that these data sets do contain hiatuses and a major age-reversal due to erosian which have likely been caused by increased seasonal wetness at the onset of the Holocene. A full explanation is provided in our 2018 publication. If you do wish to use the data, it is essential that you read the publication inorder to interpret the results correctly. We also require that when using this data that you correctly cite it (Bibliographic reference and the doi number of the data set). There were some problems uploading the XRF (geochemical) data sets, so I haven't included these yet, but hopefully will do eventually. </p> <p>Below I have also included the abstract from our publication, which provides an overview of the purpose of our work and a brief summary of the main findings.</p> <p>Abstract of Jones et al. 2018:</p> <p>The article focuses on a former salt lake in the upper Vinalopo Valley in south-eastern Spain. The study spans the Late Pleistocene through to the Late Holocene, although with particular focus on the period between 11 ka cal BP and 3000 ka cal BP (which spans the Mesolithic and part of the Bronze Age). High resolution multi-proxy analysis (including pollen, non pollen palynomorphs, grain size, X-ray fluorescence, and X-ray diffraction) was undertaken on the lake sediments. The results show strong sensitivity to<br> both long term and small changes in the evaporation/precipitation ratio, affecting the surrounding vegetation composition, lake-biota and sediment geochemistry. To summarise the key findings the main general trends identified include: 1) Hyper-saline conditions<br> and low lake levels at the end of the Late Glacial 2) Increasing wetness and temperatures which witnessed an expansion of mesophilic woodland taxa, lake infilling and the establishment of a more perennial lake system at the onset of the Holocene 3) An increase in solar insolation after 9 ka cal BP which saw the re-establishment of pine forests 4) A continued trend towards increasing dryness (climatic optimum) at 7 ka cal BP but with continued freshwater input 5) An increase in sclerophyllous open woody vegetation (anthropogenic?), and increasing wetness (climatic?) is represented in the lake record between 5.9 and 3 ka cal BP 6) The Holocene was also punctuated by several aridity pulses, the most prominent corresponding to the 8.2 ka cal BP event. These events, despite a paucity of well dated archaeological sites in the surrounding area, likely altered the carrying capacity of this area both regionally and locally, particularly during the Mesolithic-Neolithic transition, in terms of fresh water supply for human/animal consumption, wild plant food reserves and suitable land for crop growth.</p>
Climatic dissimilarity data for North America at 1 km resolution.
<p>The raster data layers contained in this archive represent climatic dissimilarity as the distance in multivariate climate space (represented by the 1st and 2nd axes of a principal components analysis based on 11 bioclimatic variables) between current (1981-2010) and future (either 2041-2070 ("2055") or 2071-2100 ("2085")) time periods. Ensemble data are based on mean projections from 15 CMIP5 models (CanESM2, ACCESS1.0, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, CCSM4, HadGEM2-ES, CNRM-CM5, CSIRO Mk 3.6, GFDL-CM3, INM-CM4, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, GISS-E2R) that were chosen to represent all major clusters of similar AOGCMs. Projections from 8 of these 15 GCMs were also used to generate dissimilarity values based on individual GCM projections. Input bioclimatic data was developed by statistical downscaling using the ClimateNA software developed by T. Wang. Additional description of the data can be found at https://adaptwest.databasin.org/pages/climatic-dissimilarity.</p>
Downscaled ERA-Interim gridded historical climate data over China (1980-2010)
<p><strong>Gridded historical climate data over China, spanning 1981 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied. Data created as part of the Met Office Climate Science for Service Partnership China (<a href="https://www.metoffice.gov.uk/research/collaboration/cssp-china">CSSP China</a>), work package 1 output, supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) <a href="https://www.gov.uk/government/publications/newton-fund-building-science-and-innovation-capacity-in-developing-countries/newton-fund-building-science-and-innovation-capacity-in-developing-countries">UK-China Research Innovation Partnership Fund</a>.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled 20CRv2c data set: <a href="https://zenodo.org/record/2558135#.XJj2uaD7RWE">Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)</a> doi: 1</em>0.5281/zenodo.2558135</p>
Data from: Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios
<p>Datasets for manuscript "Andres, K. J., Chien, H., and Knouft, J. H. Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios. Science of the Total Environment. <a href="https://doi.org/10.1016/j.scitotenv.2019.03.292">https://doi.org/10.1016/j.scitotenv.2019.03.292</a>"</p> <p>landmarks.zip: landmarks digitized on images of 1081 specimens using TpsDig2 software.</p> <p>streamflow_estimates.csv: Contemporary (1980-2009) and future (2070-2099) streamflow estimates [avg: average annual streamflow discharge (m3 s-1); cv: coefficient of variation of annual discharge] in sub-basins containing populations of 6 minnow species in IL, USA</p>
Data Storage Report. RODBreak - Wave run-up, overtopping and damage in rubble-mound breakwaters under oblique extreme wave conditions due to climate change scenarios
<p>Wave breaking / run-up / overtopping and their impact on the stability of rubble-mound breakwaters (both at trunk and roundhead) are not adequately characterized yet for climate change scenarios. The same happens with the influence of high-incidence angles on such phenomena.</p> <p>To study these phenomena a stretch of a rubble-mound breakwater (head and part of the adjoining trunk, with a slope of 1(V):2(H)) was built in the wave basin of the LUH, The trunk of the breakwater was 7.5 m long and the head had the same cross section as the exposed part of breakwater. The model was 9.0 m long, 0.82 m high and 3.0 m wide. The angle between the longitudinal axis of the breakwater and the tank wall was 70º. Two types of armour elements (rock and Antifer cubes) were tested.</p> <p>60 tests were carried out in this experiment to assess, under extreme wave conditions (wave steepness of 0.055) with different incidence wave angles (from 40º to 90º), the structure behaviour in what concerns wave run-up, wave overtopping and damage progression of the armour layer.</p> <p>The report describes the data collected in those tests as well as how such data is stored.</p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
Data for recreating figures of scientific paper by Hermanson et al on volcanic impacts on climate
<p>This data is the data necessary to recreate the figures that appear in a draft manuscript submitted to the AGU journal Journal of Geophysical Research - Atmospheres for peer review. When / if the manuscript is accepted then the article will be linked from here. The data is in netcdf files. The data was created by processing (mainly averaging) data from five different institutions. It was given to the authors for the purpose of scientific research only.</p>
Modelling environmental suitability of sorghum, wheat and maize in Europe under climate change (Code & data)
<p><span>Wheat and maize play an important role as crops for human consumption and animal feed in Europe. To guarantee food security and the stability of the agricultural sector in Europe, it is crucial to determine how climate change will impact the environmental suitability and thus the potential geographic distribution of these crops. Sorghum, a crop that originates in Africa, has seen a recent increase in cultivation in Europe. Due to its tolerance to more extreme climate conditions and its versatility of use, it might inherit a high potential as an alternative crop. </span></p> <p><span>Occurrence data of sorghum, wheat and maize as well as several environmental variables were used as input data for an ensemble modelling approach that averages machine learning models for species distribution modelling (SDM). CHELSA served as a source for present bioclimatic conditions and future climate scenarios, namely SSP126 and SSP370 for the period 2041-2070, and HSWD supplied soil variables, since both climate and soil influence crop development. A set of models was evaluated to select the best performing models for the ensemble modelling. The ensemble models were extrapolated to the future scenarios to predict geographic shifts of suitable cultivation areas due to climate change and analyze sorghum’s potential as an alternative crop. </span></p> <p><span>Under the climate scenarios, the three crops saw a shift of suitability in Europe with losses in Southern Europe and expansions of suitable environmental conditions in the northeast of Europe. Sorghum was the crop with the highest potential to replace maize and wheat in Southern Europe in areas where they lose suitability under climate change. Therefore, sorghum confirmed its function as an alternative crop. It also was the crop that benefits consistently from climate change, growing its total suitable area in Europe under both climate scenarios. Maize loses total suitable area in Europe in both climate scenarios but kept the highest amount of total suitable area in Europe in all projected time periods. </span></p> <p><span>The outcome of this study is of high importance for European farmers and policy makers as it enables them to apply effective adaptation and mitigation strategies that will support crop production under future climate conditions. <br></span></p>
Data and analysis and plotting scripts for Swaminathan et al., "Regional Impacts Poorly Constrained by Climate Sensitivity"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Regional Impacts Poorly Constrained by Climate Sensitivity", by Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Colin Jones, Richard A. Betts, Chantelle Burton, Chris D. Jones, Matthias Mengel, Christopher Reyer, Andrew G. Turner & Katja Weigel, submitted for publication in Earth's Futures. Scripts used for plotting and analysis are also included.</p>
Data and analysis code for Repo et al., "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes"
<p>This repository contains analysis code and pre-processed data for the study "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes" by Repo et al.<br>Data processing and analysis mainly done by Aapo Jantunen, Katharina Albrich<br>Due to respository space limitations, the original model outputs are archived in the Finnish "Allas" data storage service. For access, contact katharina.albrich@luke.fi<br>The code used to process the raw data is included here for reproducibility.</p> <p>If you are interested in using iLand, visit https://iland-model.org/ and https://iland-model.org/iland-book/ for information on using the model and a guide to setting up a landscape.</p> <p><span>This work was supported by the Ministry of Agriculture and Forestry by funding project Future multifunctional forests and their disturbance risk in the changing climate (Foster) through the “Catch the Carbon” initiative (<span>project number VN/28654/2020)</span>. A.R. has been supported by the grant [TRACY Trade-offs and synergies in land-based climate change mitigation and biodiversity conservation decision 322066 by the Academy of Finland.], J. H by the grant [CASCADE - Changing Disturbance Regimes and Forest Landscapes of Fennoscandia 342569 by the Academy of Finland]. </span></p> <p> </p>
Data from: The comparative biogeography of Philippine geckos challenges predictions from a paradigm of climate-driven vicariant diversification across an island archipelago
A primary goal of biogeography is to understand how large-scale environmental processes, like climate change, affect diversification. One often-invoked but seldom tested process is the so-called ''species-pump'' model, in which repeated bouts of co-speciation is driven by oscillating climate-induced habitat connectivity cycles. For example, over the past three million years, the landscape of the Philippine Islands has repeatedly coalesced and fragmented due to sea-level changes associated with the glacial cycles. This repeated climate-driven vicariance has been proposed as a model of speciation across evolutionary lineages codistributed throughout the islands. This model predicts speciation times that are temporally clustered around the times when interglacial rises in sea level fragmented the islands. Given the significance and conceptual impact the model has shown, surprisingly few tests of this prediction have been provided. We collected comparative genomic data from 16 pairs of insular gecko populations to test the prediction of temporally clustered divergences. Specifically, we analyze these data in a full-likelihood, Bayesian model-choice framework to test for shared divergence times among the pairs. Our results provide support against the species-pump model prediction in favor of an alternative interpretation, namely that each pair of gecko populations diverged independently. These results suggest the repeated bouts of climate-driven landscape fragmentation has not been an important mechanism of speciation for gekkonid lizards on the Philippine Islands. Interpretations of shared mechanisms of diversification historically have been pervasive in biogeography, often advanced on the basis of taxonomy-based depictions of species distributions. Our results call for possible re-evaluation of other, classic co-diversification studies in a variety of geographic systems.
Data from ATTRICI 1.1 - counterfactual climate for impact attribution
<p>Data as produced and presented in the publication</p> <pre><strong>ATTRICI v1.1 - counterfactual climate for impact attribution</strong></pre> <p>in Geoscientific Model Development.</p> <p>Abstract:</p> <p>Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC WGII a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system’s behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause". Impact attribution following this definition remains a challenge because the counterfactual baseline cannot be observed. Process-based and empirical impact models can fill this gap as they allow to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution set-up of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.</p>
Data from: Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass
<ul> <li>Global change forecasts in ecosystems require knowledge of within species diversity, particularly of dominant species within communities. We assessed site-level diversity and capacity for adaptation of the dominant species of the shortgrass steppe biome of the Central US, Bouteloua gracilis.</li> <li>We quantified genetic diversity from 17 sites across regional scales, north-south from New Mexico to South Dakota, and local scales in Northern Colorado. We also quantified phenotype and plasticity within and among sites and determined the extent to which phenotypic diversity in B. gracilis was related to climate.</li> <li>Genome sequencing indicated pronounced population structure at the regional scale, and local differences indicated gene flow and/or dispersal may also be limited. Within a common environment, we found evidence for genetic divergence in biomass-related phenotypes, plasticity, and phenotypic variance, indicating functional divergence and different adaptive potential. Phenotypes differentiated according to climate, chiefly median Palmer Hydrological Drought Index and other aridity metrics.</li> <li>Our results indicate conclusive differences in genetic variation, phenotype, and plasticity in this species and suggest a mechanism explaining variation in shortgrass steppe community responses to global change. This analysis of B. gracilis intraspecific diversity across spatial scales will improve conservation and management of the shortgrass steppe ecosystem moving forward.</li> </ul>
Climate Watch Historical Country Greenhouse Gas Emissions Data (1990-2018)
<p>Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. See <a href="http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf">http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf</a> for details regarding data source and methodology.</p> <p> </p> <p>Climate Watch Historical GHG Emissions. 2021. Washington, DC: World Resources Institute. Available online at: <a href="https://www.climatewatchdata.org/ghg-emissions">https://www.climatewatchdata.org/ghg-emissions</a></p>
ScienceDex guides
Understand access before you commit
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.