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286 results for “global assessment”
IPBES Data Management Tutorials - Session 6.2: Literature review from the Global Assessment chapter 4
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The chapter on<em> Examples of implementing the IPBES data management Policy</em> contains examples of how certain data management tasks and workflows were implemented within IPBES so that they follow the data management policy. <strong>Currently, this chapter contains legacy videos and the most recent examples can be found within the IPBES technical guidelines here:</strong> <a href="https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines">https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines</a></p> <p>This session <em>Literature review from the Global Assessment chapter 4 </em>walks you through each step of the data management of the systematic literature review from the Chapter 4 of the Global Assessment. </p>
Global Human Settlement Layer per zoom-level 18 Quadtree tile for selected countries as Spatialite database with OpenStreetMap building completeness assessment
<p>This Spatialite database contains the built-up area of the Global Human Settlement Layer (GHSL) per zoom-level 18 Quadtree tile. Additionally, it provides a comparison of the GHSL with buildings in OpenStreetMap: For each tile the built-up ratio between the building footprints and the GHSL is given and a binary completeness assessment (buildings complete, not complete) is provided for easy use. This dataset was created using the obmgapanalysis tool: https://git.gfz-potsdam.de/dynamicexposure/openbuildingmap/obmgapanalysis</p>
Integrated global assessment of the natural forest carbon potential (tifs)
<p>Since we have large data files for the maps used in the paper 'Integrated global assessment of the natural forest carbon potential', we have uploaded the maps separately here. After downloading the maps, you can place them in the path: Data/BiomassMergedMaps. Then, the code for making figures will be replicable.</p> <p>This is version 1.1, which includes the addition of the 'readMe.txt' and the soil carbon potential map, and corrections to each model's maps in TGB for both full and net potential.</p>
Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers
<p>Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.</p> <p>Publication <a href="https://www.nature.com/articles/s41597-020-0479-6">https://www.nature.com/articles/s41597-020-0479-6</a></p>
Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers
<p>Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.</p> <p>Publication <a href="https://www.nature.com/articles/s41597-020-0479-6">https://www.nature.com/articles/s41597-020-0479-6</a></p>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
Bibliography of the Systematic Literature Review of the IPBES Global Assessment, Chapter 4
<p>Bibliographies from the systematic literature review in Chapter 4 of the IPBES Global Assessment</p> <p>DOI: 10.5281/zenodo.3553579</p>
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
Data for the publication "Assessing the potential for simplification in global climate model cloud microphysics"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Martin Staab, and Ulrike Lohmann<br> Titel: Assessing the potential for simplification in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.5506588)</p>
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021.
Supplement code and data for "A global multi-indicator assessment of the environmental impact of livestock products"
<p>This data and code supplements the publication "A global multi-indicator assessment of the environmental impact of livestock products" by Giorgio A. Bidoglio, Florian Schwarzmueller, Thomas Kastner available here: https://doi.org/10.1016/j.gloenvcha.2024.102853<br><br></p> <p>Version 1.1 add the code to the visualization tool available online at https://livestockimpactassessment.shinyapps.io/multi-indicator_impact_assessment_of_livestock_products/</p>
Data for: Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use
<p>This dataset contains the code and the data files needed to create the figures shown in the paper titled "Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use".</p>
Fig. 1 in Global systematic diversity, range distributions, conservation and taxonomic assessments of graylings (Teleostei: Salmonidae; Thymallus spp.)
Fig. 1 Map showing the global distribution range of Thymallus species. Information on sampling sites and species is given in Table S1. Numbers in the map refer to known contact zones of the following species: 1 = T. arcticus s.l. and T. baicalensis in the lower Enisei River; 2 = T. arcticus s.l. and T. baicalolenensis in the lower Lena River; 3 = T. nikolskyi and T. baicalensis in tributaries of the upper Ob River; 4 = T. baicalolenensis and T. baicalensis in tributaries of Lake Baikal; 5 = T. grubii, T. tugarinae
Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget: Supplementary Material
<p>Detailed calculations supporting the results in the published paper, <em>Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget</em>, <a href="https://link.springer.com/journal/11027">Mitigation and Adaptation Strategies for Global Change</a>, DOI: <a href="https://doi.org/10.1007/s11027-019-09881-6">10.1007/s11027-019-09881-6</a>.</p> <ul> <li><strong>IE-CO2-Quota-2015.ods</strong>: Spreadsheet/workbook in <a href="http://opendocumentformat.org/">Open Document</a> format. Includes table and charts as presented in the paper. Prepared using <a href="http://www.libreoffice.org">LibreOffice</a> (v 5.0+). Should also be accessible also in Microsoft Excel, but some formatting or functionality may be lost.</li> <li><strong>IE-CO2-Quota-2015.ipynb</strong>: Mathematical background and cross-check of detailed calculations in interactive <a href="https://jupyter.org/">Jupyter notebook</a> format (coding in <a href="https://www.python.org/">python</a>).</li> <li><strong>IE-CO2-Quota-2015-ipynb.html</strong>: Static HTML version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> <li><strong>IE-CO2-Quota-2015-ipynb.pdf</strong>: Static version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> </ul>
IMPACT World+ / a globally regionalized method for life cycle impact assessment
<p><strong>IMPACT World+</strong> is a life cycle impact assessment method which characterizes thousands of substances spanning across various compartments and sub-compartments of the environment. It differentiates 19 impact categories at midpoint level and 34 impact categories at damage level. For more information on IW+, refer to our <a href="https://www.impactworldplus.org/">website</a> and <a href="https://doi.org/10.1007/s11367-019-01583-0">scientific article</a>. For information on the updates of IMPACT World+, you can register to the <a href="http://eepurl.com/dEeeJL">newsletter</a> of CIRAIG.</p> <p>The v2.1 update is the biggest update of the IMPACT World+ method in many years as it introduces new impact categories and updates many models with the latest available research.</p> <p>IMPACT World+ comes in three interpretation levels: <em><strong>midpoint</strong></em>, <em><strong>expert</strong></em> and <em><strong>footprint</strong></em>. You can find explanations for these three interpretation levels <a href="https://www.impactworldplus.org/version-2-0-1/">here</a>.</p> <p>The <em><strong>expert</strong></em> and <em><strong>midpoint</strong></em> versions of IMPACT World+ also come with two different implementations regarding how to account for <strong>biogenic carbon</strong>. One with the traditional biogenic <em>carbon neutrality approach</em> (e.g., where biogenic carbon dioxide is set at 0 and biogenic methane is set at 27kgCO2eq for GWP100) and one including the uptake of biogenic carbon dioxide, where the release of biogenic carbon is therefore set at the same CFs as fossil carbon, but the uptake is with a negative sign, i.e., a <em>-1/+1 approach</em> for biogenic carbon (look for the files marked “(incl. CO2 uptake)”).</p> <p>While we provide the -<em>/+1 approach</em>, we must make it clear to the users that this approach is heavily dependent on the quality of the inventory you are using, and that there are still <strong>issues </strong>currently with the LCI databases (even in ecoinvent 3.10). Furthermore, if you are using this approach, you either <strong>MUST </strong>adopt a <em>cradle-to-grave </em>approach to both account for the uptake and release of biogenic carbon (otherwise you will only account for the uptake of the carbon and have skewed results) or if you adopt a <em>cradle-to-gate</em> approach because you need to provide results to someone downstream of your supply chain you <strong>MUST </strong>communicate with that downstream user to tell them that they should account for the release of biogenic carbon in a <em>-1/+1 approach</em> also, otherwise, you and your downstream user will <strong>double count the benefits</strong> of using biogenic products, which is incorrect. This is especially true is the case of food products where the carbon emissions post consumption are typically not included in the inventories, which could result in a substantial under estimation of the impacts of this product over its life cycle.</p> <h3>Description of the files</h3> <p>- The dev file is a file useful for developers and maintainers of databases/datasets who wish to link IW+ 2.1 to their databases/datasets. It regroups all existing characterization factors of the IW+ LCIA method in an Excel format, using the terminology of IW+.</p> <p>- The "ecoinvent" files are Excel files matching with "pure" ecoinvent and its flow name terminology (as in unaltered by various software) in an Excel table. This is useful if you are using ecoinvent outside of LCA software.</p> <p>- The exiobase file links IW+ to the <a href="https://doi.org/10.5281/zenodo.5589597">Exiobase GMRIO database</a>. Once the file is read through pandas (pandas.read_excel()), the resulting matrix can directly be multiplied to the environmental extensions of exiobase (S, F or F_Y if using the pymrio package).</p> <p>- The openLCA file can be directly imported in the openLCA software as a JSON-LD file.</p> <p>- The SimaPro file can be directly imported in the SimaPro software as a CSV file.</p> <p>- The brightway2 files are ecoinvent-version dependent, so you need to select the correct file to work with the correct version of ecoinvent. Else, some of the ecoinvent flows which name did change in between versions of ecoinvent would not be characterized, leading to underestimated results. To import a file, you need to pass through brightway2 itself (it cannot be done through the activity-browser for now). The function to import a .bw2package file is bw2.BW2Package.import_file().</p> <p>- Finally, the source file regroups all the native information used by IW+ to derive the characterization factors. This file is primarily useful for the IW+ internal team. It is provided for transparency, as well as for curious users or users who wish to generate all these files themselves through the <a href="https://github.com/CIRAIG/IWP_Reborn">open-access code of IW+</a>.</p> <h3>New indicators</h3> <p>- <em>Plastic physical effect on biota</em></p> <p>This new indicator measures the effect of plastic resins emitted in the water environments (both freshwater and marine) on biota, in PDF.m2.yr. It also comes with a midpoint indicator in CTUe. This indicator is the result of the work of the <a href="https://marilca.org/characterization-factors/">MariLCA working group</a>.</p> <p>- <em>Fisheries impact</em></p> <p>This new indicator measures the impact on biodiversity of fisheries activities. It is only assessed at the ecosystem quality damage level (in PDF.m2.yr). This is based on the work of <a href="https://doi.org/10.3390/su16093870">Stanford-Clark et al.</a></p> <p>- <em>Marine ecotoxicity</em></p> <p>In the v2.1 we decided to finally integrate these two ecotoxicity indicators, at the damage level only. These are based on an old version of Usetox (v2.02). As the v3 of Usetox is on the verge of being released, all ecotoxicity and toxicity categories will be updated in the next version of IW+.</p> <p>- <em>Terrestrial ecotoxicity</em></p> <p>In the v2.1 we decided to finally integrate these two ecotoxicity indicators, at the damage level only. These are based on an old version of Usetox (v2.02). As the v3 of Usetox is on the verge of being released, all ecotoxicity and toxicity categories will be updated in the next version of IW+.</p> <p>- <em>Photochemical ozone formation</em></p> <p>For this impact category, IW+ adopts what the ReCiPe methodology recommends. In their <a href="https://doi.org/10.1007/s11367-016-1246-y">2016 update</a>, ReCiPe renamed the indicator "Photochemical oxidant formation" to "Photochemical ozone formation". In addition, they calculated the impact of this category on ecosystem quality. There are thus two corresponding impact categories at damage level: "Photochemical ozone formation, human health" and "Photochemical ozone formation, ecosystem quality"</p> <h3>Updated indicators</h3> <p>- <em>All climate change indicators</em></p> <p>IMPACT World+ v2.1 proposes the carbon neutrality approach (i.e., CO2-bio = 0) as well as the -1/+1 approach (CO2-bio uptake = -1 / CO2-bio release = +1). However, the latter is only available in the expert and midpoint versions. In the footprint version, the carbon neutrality assumption is still being used.</p> <p>Furthermore, we added CFs for temporary storage of biogenic carbon that can be used (e.g., Correction for delayed emissions, carbon dioxide, biogenic).</p> <p>- <em>Climate change, human health</em></p> <p>In the v2.0.1, we updated the GWP100 and GTP100 indicators following the recommendations of the AR6 from the IPCC2021. Now in the v2.1, we are also updating our damage indicators for climate change to follow the AR6 recommendations. Notably, the cumulative AGTP500 used in the derivation of these CFs was recalculated with updated equations (which we obtained thanks to Yue He and Thomas Gasser from the International Institute for Applied Systems Analysis - IIASA). In addition, the effect factors were also updated. Previously it was based on data from the World Health Organization from 2003, it is now based on the WHO 2014 report as well as the <a href="https://backend.orbit.dtu.dk/ws/portalfiles/portal/329521472/PhD_Thesis_Lea_Rupcic.pdf">work of L</a><a href="https://backend.orbit.dtu.dk/ws/portalfiles/portal/329521472/PhD_Thesis_Lea_Rupcic.pdf">. </a><a href="https://backend.orbit.dtu.dk/ws/portalfiles/portal/329521472/PhD_Thesis_Lea_Rupcic.pdf">Rupcic</a>.</p> <p>- <em>Climate change, ecosystem quality</em></p> <p>Similarly to the human health indicator the cumulative AGTP500 were recalculated. However, the effect factor was not updated yet for this impact category.</p> <p>- <em>Particulate matter formation</em></p> <p>Those CFs were updated to the latest model from Fantke, et al. This is composed of a series of articles on updates to <a href="https://doi.org/10.1021/acs.est.7b02589">fate</a> and <a href="https://doi.org/10.1021/acs.est.9b01800">effect</a> factors</p> <p>This model now provides regionalized characterization factors per town of more than 100,000 inhabitants. The CFs at the town-level are available in the source file, but in the dev file and in the different software versions, we only provide national/regional (e.g., RER) as well as global values, aggregated from the town-level factors.</p> <p>- <em>Water availability, human health</em></p> <p>Those CFs were updated to the latest model of L. Debarre (2024) [<em>publication in review, link will be added once published</em>]. This model includes a harmonization of methodology between the domestic and agriculture water use, updates the exposition factors using the latest Gross National Income data and updates the EF. Overall, the values of the characterization factors of this category have dramatically decreased, by a minimum of 65%.</p> <p>- <em>Water availability, terrestrial ecosystems</em></p> <p>While the original value of the characterization was not updated (e.g., 0.21 PDF.m2.yr in Netherlands), the regionalization was updated based on an estimation of depths of groundwater, based on <a href="https://doi.org/10.1126/science.abc2755">Jasechko (2021)</a>.</p> <p>- <em>Water scarcity</em></p> <p>Those CFs were updated to the latest update of the AWARE model Seitfudem (2024) [<em>publication in review, link will be added once published</em>].</p> <p>- <em>Fossil and nuclear energy use</em></p> <p>The HHV values were updated to match the updated HHVs in ecoinvent.</p> <p>- <em>Ozone layer depletion</em></p> <p>Those CFs were adapted to match the latest data from the <a href="https://ozone.unep.org/sites/default/files/2023-02/Scientific-Assessment-of-Ozone-Depletion-2022.pdf">World Meteorological Organization (2022)</a>. In addition, the time horizon has now been extended to the infinite instead of limiting it to 500 years.</p> <h3>Methodology</h3> <p>For more detail on the methodology behind each impact category, refer to our <a href="https://github.com/CIRAIG/IWP_Reborn/tree/master/Methodology">Github</a>, in the future it will be available directly on our website.</p> <h3>Corrections</h3> <p>In this section we only provide information on the major corrections that were made. For a full report of all the changes please refer to out <a href="https://github.com/CIRAIG/IWP_Reborn/tree/master/Report_changes">Github</a>.</p> <p>- There are challenges associated with using IMPACT World+ files across databases or software for which they were not specifically designed. For instance, this is why we now provide files adapted to particular ecoinvent versions in brightway2. Similarly, both SimaPro and openLCA periodically update the names of their elementary flows. When an impact assessment method has previously been imported into one of these tools, an embedded procedure in their update processes is supposed to adjust the characterization factor names in line with the new flow names, ensuring compatibility with the updated list. However, we lack detailed knowledge of this procedure, meaning we cannot guarantee that the updated versions of IW+ in these tools would align with our specific modeling choices. Likewise, the IW+ versions we provide for a given release of SimaPro or openLCA might be incompatible with previous or subsequent versions due to discrepancies in flow names and characterization factors. Consequently, users of these software should verify which flows are characterized and make adjustments if necessary.</p> <p>- Harmonization of regionalized flows</p> <p>Regionalized impact model do not operate at the same geographical granularity. In the previous version, some flows were characterized in one impact category but not in the other. For instance, the flow "Water, lake, US-TRE" was characterized for the "water scarcity" indicator but not for "Water availability, human health". All regionalized flows are now characterized for all the impact categories they affect. This is also true for the newest regionalized impact category (Particulate matter formation) where SO<sub>2</sub> for example is regionalized at a much more granular level than in the freshwater acidification impact category.</p> <p>- Fossil and nuclear energy use</p> <p>For the SimaPro version of the v2.0.1, some flows that only exist in SimaPro were not characterized, such as "Oil, crude, 43.4 MJ per kg". Now they are properly characterized using the energy content value specified in the name. Results obtained with SimaPro will thus differ from results obtained with brightway2 and openLCA, since the latter do not use such flows and only use flow such as "Oil, crude".</p> <p>- Thermally polluted water</p> <p>In the v2.0.1, the flows "Water, turbine use, unspecified natural origin" were not linked to the correct proxy, which meant they did not impact the Thermally polluted water category, which underestimated the impact of this category.</p> <p>- The problem of "Nitrogen"</p> <p>"Nitrogen" can mean two different things. It can literally mean the "N" element, but it can also mean the "N2" molecule. The issue is that it is not clear in the LCI databases, which meaning does "Nitrogen" have. Previously, our understanding was that "Nitrogen" meant "N" but since then, ecoinvent notably, added formulas to the elementary flows they provide and associated the formula "N2" to their "Nitrogen" flows, indicating that they understand it as "N2" and not "N". Furthermore, in SimaPro and openLCA, the associated CAS number is generally "7727-37-9" which again corresponds to "N2". Thus, we now consider that “Nitrogen” represents dinitrogen, and thus does not impact the "Marine eutrophication" impact category anymore. We also corrected a previous mistake: N<sub>2</sub>O is not characterized anymore for this impact category.</p>
Data from: Nocturnal basking in freshwater turtles: a global assessment
<p>Diurnal basking ("sunning") is common in many ectotherms and is generally thought to be a behavioural mechanism for thermoregulation. Recent studies have reported the occurrence of nocturnal basking in a few distantly-related species of freshwater turtles, but the true extent of this behaviour is unknown, and it may be underreported due to sampling biases (e.g., not surveying for turtles at night). Therefore, we initiated a global, collaborative effort to systematically document and quantify basking activity (diurnal and nocturnal) across a wide range of freshwater turtle species and locations. We conducted camera trap or manual surveys in North America, the Caribbean, Europe, Asia, Africa, the Seychelles, and Australia. We collected 873,111 trail camera photographs (25,273 hrs of search effort) and obtained data on 29 freshwater turtle species from seven families. Nocturnal basking was documented in 13 species, representing six families (Chelidae, Emydidae, Geoemydidae, Kinosternidae, Pelomedusidae, and Trionychidae), including representatives in Central America, Trinidad and Tobago, Africa, the Seychelles, Asia, and Australia. Nocturnal basking was restricted to tropical and sub-tropical locations, suggesting that environmental temperature plays a role in this behaviour. However, the primary factors driving nocturnal basking are yet to be determined and may vary geographically and by species. The frequency and duration of nocturnal basking varied among species and seasons, but nocturnal basking events were often substantially longer than diurnal events. This is the first study to document a widespread occurrence of nocturnal basking, and our results suggest that nocturnal basking may be a common, although overlooked, aspect of many species' ecology.</p>
Dataset for article Rodriguez et al "Assessing global urban CO2 removal"
<p>This is a complementary dataset associated with the following publication: Rodriguez et al. "Assessing global urban CO2 removal" <em>Nature Cities.</em></p>
A Global Study to Assess the Drug Dynamics, Efficacy, and Safety of Venglustat (GZ/SAR402671) in Parkinson's Disease Patients Carrying a Glucocerebrosidase (GBA) Gene Mutation
ClinicalTrials.gov study NCT02906020. IPD Sharing: YES. Countries: 16. Publications: 3.
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.