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289 results for “Regional assessment”
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Lightning Assimilation in the Weather Research and Forecasting (WRF) Model: Technique Updates and Assessment of the Applications from Regional to Hemispheric Scales
<p>Figure 1. The data is proprietary, but it can be purchased from Vaisala Inc. (https:// <a href="http://www.vaisala.com/en/products/systems/lightning-detection">www.vaisala.com/en/products/systems/lightning-detection</a>), and the WWLLN raw data are also available for purchase at <a href="http://wwlln.net">http://wwlln.net</a>.</p> <p>Figure 2. Maps, data is not applicable.</p> <p>Figure 3. Data file: NLDN_WWLLN_Prism_Rainfall_Analysis.xlsx</p> <p>Figure 4. Data file: NLDN_WWLLN_METVARS_T2_Jul_2016.xlsx</p> <p>Figure 5. Data file: CONUSall_METOBS_q_Jul_2016.xlsx</p> <p>Figure 6. Data file: CONUSall_METOBS_ws_Jul_2016.xlsx</p> <p>Figure 7. Created using the R script: Hemi_Rain_ModelOnlyWGPM.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds, AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds, and GPM_WRF_Paired_rain2Hemispheric_July2016.rds.</p> <p>Figure 8. Created using the R script: Hemi_Rain_Aanlysis.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds and AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds.</p> <p>Figure 9. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 10. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 11. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 12. Created using the R script: CreateCPCdataforUSdomain_vs_Prism.R based on the R oject files: Prism_CFC_WRF*.rds</p> <p>Figure 13. Data file: Hemi_lta_METOBS_T2_Jul_2016.xlsx</p> <p>Figure 14. Data file: Hemi_lta_METOBS_q_Jul_2016.xlsx</p> <p> </p>
Supporting information for Parametrized regionalization of paper recycling life-cycle assessment
<p>This supporting information provides the numerical results for (1) the process parameters' regionalization (S4); (2) the regionalized LCA climate change results for three different paper grades (S7); (3) the destinations of the mixed paper bales exiting Quebec's sorting centers (S8); (4) the LCA results for the scenarios for Quebec's case study (S9) and (5) the sensitivity analysis results, performed on the most uncertain parameters from Quebec's case study (S10).</p>
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
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.
Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany).
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3.
Supplementary material 1 from: Pontoppidan M, Nachman G (2013) Spatial Amphibian Impact Assessment – a management tool for assessment of road effects on regional populations of Moor frogs (Rana arvalis). Nature Conservation 5: 29-52. https://doi.org/10.3897/natureconservation.5.4612
Full model description following the protocol suggested by Grimm et al. (2006, 2010) and model parameterisation. (doi: 10.3897/natureconservation.5.4612.app). File format: Adobe PDF document (pdf).:
FIGURE 2 in Using community phylogenetics to assess phylogenetic structure in the Fitzcarrald region of Western Amazonia
FIGURE 2 | Ultrametric cladogram of the Fitzcarrald region fish fauna (modified from S2) showing distributions of species in habitats and river basins. Red boxes indicate species presences in: Rivers, Stream, Lakes, Purus, Yuruá, Urubamba and Las Piedras. Note phylogenetic clustering in the Urubamba basin and the stream habitat.
Assessing digital infrastructure in internet use: A comparative study of South East Asia and the Balkan Region
<p>Data used for assessing digital infrastructure in internet use - comparison of South East Asia and Balkan Region. Data on mobile cellular, fixed broadband, GDP, Key global ICT indicators . Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381)</p>
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>
Model fields supporting the publication "Integrated Assessment of the Risks to Ocean Acidification in the Northern High Latitudes: Regional Comparison of Exposure, Sensitivity and Adaptive Capacity of Pelagic Calcifiers"
<p>These are the model outputs supporting the described manuscript. They include monthly averaged output of aragonite saturation state for each year during the 10-year hindcast. Also included is the particle tracking output, for both the Bering Sea and the Gulf of Alaska, as described in the manuscript.</p>
Database of US Regional Sea-level Rise Assessment Reports (Current for 2021)
<p>Database of regional sea-level rise assessment reports in the U.S. The data set includes nearly 400 projections from 31 reports for 54 locations in the U.S. and Puerto Rico, and accompanies the publication "Evaluating Knowledge Gaps in Sea-level Rise Assessments from the United States", Garner et al., <em>Earth's Future</em>. The data set is comprised of the most recent published assessment reports for each location (deadline of December 31<sup>st</sup>, 2021). Fields included in the database are listed below. </p> <p>Though substantial effort was made to ensure that all available and relevant assessment reports were included in the database, it is perhaps inevitable that a small number of reports were overlooked and may not be included here. </p> <p>1) Title of the Assessment Report</p> <p>2) Region of focus for the projection</p> <p>3) Broader geographical region for the projection (U.S. Northeast, U.S. South, or U.S. West)</p> <p>4) Latitude of the projection</p> <p>5) Longitude of the projection</p> <p>6) Lead Author of the report</p> <p>7) Sectors with which the authors are affiliated</p> <p>8) Third-party report flag (Yes = not locally produced, No = locally produced)</p> <p>9) Year the report was published</p> <p>10) Year the previous iteration of the report was published, if applicable</p> <p>11) Methodology of the projection</p> <p>12) Emission scenario used for the project</p> <p>13) Baseline year for the projection</p> <p>14) End year for the projection</p> <p>15) Lower estimate of sea-level rise</p> <p>16) Definition of the lower estimate of sea-level rise</p> <p>17) Central estimate of sea-level rise</p> <p>18) Definition of the central estimate of sea-level rise</p> <p>19) Upper estimate of sea-level rise</p> <p>20) Definition of the upper estimate of sea-level rise</p> <p>21) Vertical Land Motion (Yes = included, No = excluded)</p> <p>22) Land Water Storage (Yes = included, No = excluded)</p> <p>23) Greenland Ice Sheet (Yes = included, No = excluded)</p> <p>24) Antarctic Ice Sheet (Yes = included, No = excluded)</p> <p>25) Glaciers (Yes = included, No = excluded)</p> <p>26) Thermal Expansion (Yes = included, No = excluded)</p> <p>27) Ocean Dynamics (Yes = included, No = excluded)</p> <p>28) Link to the report containing the projection</p> <p>29) Notes relevant to the projection's database entry</p>
Tables and Data for "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado"
<p>These are data sets and tables used in the paper "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado" to be submitted to Earth and Space Science. The monitor site 2016 and 2017 counts files have gridded HYSPLIT back trajectory counts for the 4 highest ozone concentration days at each site, as described in the manuscript.</p>
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
<p>This is the preprocessed remote sensing dataset used in the paper<strong> "Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation"</strong>. It contains the preprocessed NOAA data along with the additional files necessary for the TSMP simulation.</p>
Assessment of acetochlor use areas in the Sahel region of Western Africa using geospatial methods
Open the record for dataset details and reuse information.
Data from: Assessment of conservation status of Ferula huber-morathii: Association with population genetic structure and regional climate
Open the record for dataset details and reuse information.
Pond diatom communities from the McMurdo Sound region of Antarctica, assessed from modern samples (2012-2016) and from historical samples collected during Shackleton’s Nimrod Expedition (1908-1909)
We performed a field survey to characterize diatom communities living in the benthic microbial mats of ponds across the McMurdo Sound region of Antarctica. Samples were collected during the Austral summers of 2012-13, 2013-14, and 2015-16 from the McMurdo Dry Valleys, Cape Royds, and from Hut Ridge near McMurdo Station. We also characterized pond diatom communities in samples collected on various dates from 1908-1909 during Ernest Shackleton’s Nimrod Expedition to compare against modern samples. Historical samples were taken from Cape Royds and the Stranded Moraines, and were analyzed from slides stored at the Natural History Museum, London. This data package includes relative abundance data for diatom species found in these modern and historical samples.
Figure 2. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 2. - Time line of the tasks in UNICORN-project
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.