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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Metsaranta, Juha; Mamet, Steven; Maillet, Jay; Barr, Alan;

    These datasets are associated with the following paper: Metsaranta, J.M., Mamet, S.D., Maillett, J., Barr, A.G. (2021). Comparison of tree-ring and eddy covariance derived annual ecosystem production estimates for jack pine and trembling aspen forests in Saskatchewan, Canada. Agricultural and Forest Meteorology. There are two files: (1) CBMOutput.zip. This contains the hybrid biometric modelled ecosystem C stock and flux estimates. (2) StandReconstructionData.zip. This contains the field measurement data and the tree level biomass and wood volume data for the Stand Reconstruction plots used to develop the hybrid biometric modelled estimates. The data are formatted as .csv files, and an associated Microsoft Excel spreadsheet explains the data columns and provides information on the associated units of measure.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Stewart, Frances; Micheletti, Tatiane; McIntire, Eliot; Chubaty, Alex;

    Most research on boreal populations of Woodland caribou (Rangifer tarandus caribou) has been conducted in areas of high anthropogenic disturbance. However, a large portion of the species’ range overlaps relatively pristine areas primarily disturbed by natural disturbances, such as wildfire. Climate-driven habitat change is a key concern for the conservation of boreal-dependent species, where management decisions have yet to consider knowledge from multiple ecological domains integrated into a cohesive and spatially explicit forecast of species-specific habitat and demography. We used a novel ecological forecasting framework to provide climate-sensitive projections of habitat and demography for five boreal caribou monitoring areas within the Northwest Territories (NWT), Canada, over 90 years. Importantly, we quantify uncertainty around forecasted mean values. Our results suggest habitat suitability may increase in central and southwest regions of the NWT’s Taiga Plains ecozone but decrease in southern and northwestern regions driven by conversion of coniferous to deciduous forests. We do not project boreal caribou population growth rates to change despite forecasted changes to habitat suitability. Our results emphasize the importance of efforts to protect and restore northern boreal caribou habitat despite climate uncertainty while highlighting expected spatial variations that are important considerations for local people who rely on them. An ability to reproduce previous work, and critical thought when incorporating sources of uncertainty, will be important to refine forecasts, derive management decisions, and improve conservation efficacy for northern species at risk. Please see the README document ("README.md") and the accompanying published article: Stewart, Micheletti et al. 2023. Climatepinformed forecasts reveal dramatic local habitat shifts and population uncertainty for nothern boreal caribou. Ecological Applications.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    Borealis
    Dataset . 2023
    Data sources: Datacite
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      Borealis
      Dataset . 2023
      Data sources: Datacite
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kravchinsky, Vadim A.; Zhang, Rui; Borowiecki, Ryan; Tarasov, Pavel E.; +4 Authors

    A lack of adequate high resolution climate proxy records for the Last Glacial Maximum (LGM) has prevented the extrapolation of climate–solar linkages on centennial time scales prior of the Holocene. Therefore, it is still unknown whether centennial climate variations of the last ten thousand years convey a universal climate change or merely represent a characteristic of the Holocene. Recently published high resolution climate proxy records for the LGM allowed us to extrapolate climate–solar linkages on centennial time scales ahead of the Holocene. Here we present the analysis of a high resolution pollen concentration record from Lake Kotokel in southern Siberia, Russia, during the LGM. The record reflects the dynamics of vegetation zones and temperature change with a resolution of ~ 40 years in the continental climate of north-eastern Asia. We demonstrate that our pollen concentration record, the oxygen isotope δ18O record from the Greenland ice core project NGRIP (NorthGRIP), the dust-fall contributions in Lake Qinghai, China, grain size in the Gulang and Jingyuan loess deposits, China, and the composite oxygen isotope δ18O record from the Alpine cave system 7H reveal cooler to warmer climate fluctuations between ~ 20.6 and 26 ka. Such fluctuations correspond to the ~ 1000-yr, 500-600-yr and 210-250-yr cycles possibly linked to the solar activity variations and recognized in high resolution Holocene proxies all over the world. We further show that climate fluctuations in the LGM and Holocene are spectrally similar suggesting that linkages between climate proxies and solar activity at the centennial time scale in the Holocene can be extended to the LGM. {"references": ["Vadim A. Kravchinsky, Rui Zhang, Ryan Borowiecki, Pavel E. Tarasov, Mirko van der Baan, Taslima Anwar, Avto Goguitchaichvili, Stefanie M\u00fcller, 2021. Centennial scale climate oscillations from southern Siberia in the Last Glacial Maximum. Quaternary Science Reviews, in press."]}

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Opito, Emmanuel A.; Alanko, Timo; Kalbitzer, Urs; Nummelin, Matti; +3 Authors

    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

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://dx.doi.org/1...arrow_drop_down
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    https://dx.doi.org/10.17617/3....
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
    B2FIND
    Dataset . 2023
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://dx.doi.org/1...arrow_drop_down
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      https://dx.doi.org/10.17617/3....
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
      B2FIND
      Dataset . 2023
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; +33 Authors

    This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Stolar, Jessica; Stralberg, Diana; Naujokaitis-Lewis, Ilona; Nielsen, Scott E.; +1 Authors

    Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Narayanasetti, Sandeep; Panickal, Swapna; Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; +2 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    In an era of climate change, supply chain issues and the necessary transitions, green chemistry, green engineering and inherent safety offer possibilities for a more safe and resilient industry. A literature study with application to a pilot Organosolv lignocellulosic feedstock bioreactor should show possibilities and ways to strengthen sustainable and safer production. It highlights challenges in practical implementation like solvent selection, solvent recovery, intrinsically safe equipment and process intensification like membrane processes for saving energy. Process safety techniques should guide the way to and should help to find possible restrictions and opportunities for more resilient processes and a more resilient future. Keywords: process safety; green chemistry; organosolv; biorefinery; sustainability; solvent selection;

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    Mendeley Data
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      Mendeley Data
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      Mendeley Data
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: O'Reilly, Ryan; Cohen, Jed; Reichl, Johannes;

    Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
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    Authors: Anhaus, Philipp; Schiller, Martin; Planat, Noémie; Katlein, Christian; +1 Authors

    Transmitted solar irradiance was measured using an ACC (Advanced-Cosine-Collector) RAMSES hyper-spectral radiometer (TriOS) mounted on the ROV during the ARTofMELT2023 expedition in May and June 2023 and normalized by the incident solar irradiance as measured using an ACC RAMSES hyper-spectral radiometer (TriOS) installed on-board the ship. All times are given in Universal Coordinated Time (UTC).

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    PANGAEA
    Dataset . 2024
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 2024
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2024
      Data sources: PANGAEA
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      PANGAEA
      Dataset . 2024
      Data sources: PANGAEA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Metsaranta, Juha; Mamet, Steven; Maillet, Jay; Barr, Alan;

    These datasets are associated with the following paper: Metsaranta, J.M., Mamet, S.D., Maillett, J., Barr, A.G. (2021). Comparison of tree-ring and eddy covariance derived annual ecosystem production estimates for jack pine and trembling aspen forests in Saskatchewan, Canada. Agricultural and Forest Meteorology. There are two files: (1) CBMOutput.zip. This contains the hybrid biometric modelled ecosystem C stock and flux estimates. (2) StandReconstructionData.zip. This contains the field measurement data and the tree level biomass and wood volume data for the Stand Reconstruction plots used to develop the hybrid biometric modelled estimates. The data are formatted as .csv files, and an associated Microsoft Excel spreadsheet explains the data columns and provides information on the associated units of measure.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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    Authors: Stewart, Frances; Micheletti, Tatiane; McIntire, Eliot; Chubaty, Alex;

    Most research on boreal populations of Woodland caribou (Rangifer tarandus caribou) has been conducted in areas of high anthropogenic disturbance. However, a large portion of the species’ range overlaps relatively pristine areas primarily disturbed by natural disturbances, such as wildfire. Climate-driven habitat change is a key concern for the conservation of boreal-dependent species, where management decisions have yet to consider knowledge from multiple ecological domains integrated into a cohesive and spatially explicit forecast of species-specific habitat and demography. We used a novel ecological forecasting framework to provide climate-sensitive projections of habitat and demography for five boreal caribou monitoring areas within the Northwest Territories (NWT), Canada, over 90 years. Importantly, we quantify uncertainty around forecasted mean values. Our results suggest habitat suitability may increase in central and southwest regions of the NWT’s Taiga Plains ecozone but decrease in southern and northwestern regions driven by conversion of coniferous to deciduous forests. We do not project boreal caribou population growth rates to change despite forecasted changes to habitat suitability. Our results emphasize the importance of efforts to protect and restore northern boreal caribou habitat despite climate uncertainty while highlighting expected spatial variations that are important considerations for local people who rely on them. An ability to reproduce previous work, and critical thought when incorporating sources of uncertainty, will be important to refine forecasts, derive management decisions, and improve conservation efficacy for northern species at risk. Please see the README document ("README.md") and the accompanying published article: Stewart, Micheletti et al. 2023. Climatepinformed forecasts reveal dramatic local habitat shifts and population uncertainty for nothern boreal caribou. Ecological Applications.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    Borealis
    Dataset . 2023
    Data sources: Datacite
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      Borealis
      Dataset . 2023
      Data sources: Datacite
      DRYAD
      Dataset . 2023
      License: CC 0
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    Authors: Kravchinsky, Vadim A.; Zhang, Rui; Borowiecki, Ryan; Tarasov, Pavel E.; +4 Authors

    A lack of adequate high resolution climate proxy records for the Last Glacial Maximum (LGM) has prevented the extrapolation of climate–solar linkages on centennial time scales prior of the Holocene. Therefore, it is still unknown whether centennial climate variations of the last ten thousand years convey a universal climate change or merely represent a characteristic of the Holocene. Recently published high resolution climate proxy records for the LGM allowed us to extrapolate climate–solar linkages on centennial time scales ahead of the Holocene. Here we present the analysis of a high resolution pollen concentration record from Lake Kotokel in southern Siberia, Russia, during the LGM. The record reflects the dynamics of vegetation zones and temperature change with a resolution of ~ 40 years in the continental climate of north-eastern Asia. We demonstrate that our pollen concentration record, the oxygen isotope δ18O record from the Greenland ice core project NGRIP (NorthGRIP), the dust-fall contributions in Lake Qinghai, China, grain size in the Gulang and Jingyuan loess deposits, China, and the composite oxygen isotope δ18O record from the Alpine cave system 7H reveal cooler to warmer climate fluctuations between ~ 20.6 and 26 ka. Such fluctuations correspond to the ~ 1000-yr, 500-600-yr and 210-250-yr cycles possibly linked to the solar activity variations and recognized in high resolution Holocene proxies all over the world. We further show that climate fluctuations in the LGM and Holocene are spectrally similar suggesting that linkages between climate proxies and solar activity at the centennial time scale in the Holocene can be extended to the LGM. {"references": ["Vadim A. Kravchinsky, Rui Zhang, Ryan Borowiecki, Pavel E. Tarasov, Mirko van der Baan, Taslima Anwar, Avto Goguitchaichvili, Stefanie M\u00fcller, 2021. Centennial scale climate oscillations from southern Siberia in the Last Glacial Maximum. Quaternary Science Reviews, in press."]}

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Opito, Emmanuel A.; Alanko, Timo; Kalbitzer, Urs; Nummelin, Matti; +3 Authors

    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

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    https://dx.doi.org/10.17617/3....
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
    B2FIND
    Dataset . 2023
    Data sources: B2FIND
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      https://dx.doi.org/10.17617/3....
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
      B2FIND
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Thiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; +33 Authors

    This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
      License: CC BY
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      ZENODO
      Dataset . 2021
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    Authors: Stolar, Jessica; Stralberg, Diana; Naujokaitis-Lewis, Ilona; Nielsen, Scott E.; +1 Authors

    Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.

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    ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
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    Authors: Narayanasetti, Sandeep; Panickal, Swapna; Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; +2 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
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    In an era of climate change, supply chain issues and the necessary transitions, green chemistry, green engineering and inherent safety offer possibilities for a more safe and resilient industry. A literature study with application to a pilot Organosolv lignocellulosic feedstock bioreactor should show possibilities and ways to strengthen sustainable and safer production. It highlights challenges in practical implementation like solvent selection, solvent recovery, intrinsically safe equipment and process intensification like membrane processes for saving energy. Process safety techniques should guide the way to and should help to find possible restrictions and opportunities for more resilient processes and a more resilient future. Keywords: process safety; green chemistry; organosolv; biorefinery; sustainability; solvent selection;

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    Mendeley Data
    Dataset . 2023
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    Mendeley Data
    Dataset . 2023
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      Mendeley Data
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      Mendeley Data
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    Authors: O'Reilly, Ryan; Cohen, Jed; Reichl, Johannes;

    Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

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    ZENODO
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      ZENODO
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    Authors: Anhaus, Philipp; Schiller, Martin; Planat, Noémie; Katlein, Christian; +1 Authors

    Transmitted solar irradiance was measured using an ACC (Advanced-Cosine-Collector) RAMSES hyper-spectral radiometer (TriOS) mounted on the ROV during the ARTofMELT2023 expedition in May and June 2023 and normalized by the incident solar irradiance as measured using an ACC RAMSES hyper-spectral radiometer (TriOS) installed on-board the ship. All times are given in Universal Coordinated Time (UTC).

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    PANGAEA
    Dataset . 2024
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 2024
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      PANGAEA
      Dataset . 2024
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