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33 results for “IPCC”
IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)
<p><strong>Description</strong></p> <p>These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p>Resolution: 0.5 arc degree</p> <p>CRS: lon/lat WGS 84</p> <p><strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.</p> <p> </p> <p><strong>Methods</strong></p> <p>The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html"><em>lubridate</em></a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><em>magrittr</em></a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html"><em>terra</em></a> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made:</p> <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul> <p> </p> <p><strong>Known discrepancies</strong></p> <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul> <p> </p> <p><strong>Disclaimer</strong></p> <p><strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p> </p> <p><strong>File description</strong></p> <ul> <li><em>README.html</em> - ~this description file.</li> <li><em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li><em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li><em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map.</li> <li><em>ipcc_climate_zones_2019.R</em> - the script used to produce these data.</li> <li><em>session_info.txt</em> - the R session info.</li> </ul>
Dataset regarding the « Reasons for concern » about climate change from figures in IPCC and related publications
<p>This data corresponds to the 'burning ember' diagrams from IPCC reports and related publications (IPCC TAR, Smith et al. 2009 for AR4-related embers, AR5 and SR15). It was used to build figure 3 of Zommers et al. 2020 (<em>Burning Embers: Towards more transparent and robust climate change risk assessments</em>. Accepted for publication in Nature Reviews Earth & Environment). The data provided here is the result of extraction of information from the original figures, as presented in the related technical document <a href="https://doi.org/10.5281/zenodo.3992856">10.5281/zenodo.3992856</a>. As explained in the Supplementary Information of Zommers et al. 2020 and the technical document, this is not data from the IPCC. The provided values are approximations of the global mean temperature increase corresponding to each change in risk in the original diagrams. The rigour of the preparation process and the limitations of the dataset are explained in the technical document.</p>
Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations
<p>Photos and metadadata pannels of a "Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" produced to support the "A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" presentation given at EGU 2024.</p> <p>------</p> <p>With ever growing abilities to process greater volumes of data the abiiity to sustain the citability and tracability of the underluing source data within outputs such as publications is becoming increasingly challenging. With a range of use-cases, work on how to handle complex citations from the perspective of those producing outputs, journals and those handling the knowledge graph and associated services, is exmaning a how to handle these situations in a sustainable and manageable fashion.<br><br>At the European Geophysical Union (EGU) General Assembly in Vienna, 2024, a pragmatic solution using Zenodo to store 'reliquary' objects was presented. The poster presentation demonstrated the use of existing strucutres within a Zenodo object to address the complex citation use-case around figure, the related data and the source datasets related to the IPCC's AR5 figure data. I.e. how to utulise the existing constructs of a Zenodo item and the range of available metadata fields to give an off-the-shelf solution to allow tracability to the specific datasets used (via their Handle identifiers) and citability of the higher level, DOI-ed dataset collections within which the specific Handle-ed datasets were selected from. Additionally, the connectivity between these two levels of PID objects was also captured within the stored files around which the rich metata was captured.<br><br>The concept of a complex citation 'reliquary' as a metadtata rich object, acting as a referencable nexus in the knowledge graph has been put forth as a solution to the complex citation challenge. It borrows the concept from its historical use, denoting a container or shrine, often richly embellished, for sacred relics (e.g. saints bones, artefacts etc). In the same way here we have both the rich metadata 'container' around the specific details (the 'bones in the box', with their preserved connectivity).<br><br>However, the term 'reliquary' is often a hard one to convey, being somewhat of an obscure term (likewise the term 'nexus' may also be one lacking wider recogniton). Thus, to aid the discussions around the presentation by Pascoe et al. (2024) at the EGU 2023 General Assembly, a physical representation of a metadata reliquary object was produced.<br><br>The purpose of this object was two fold:<br><br> - The first was to show how the reliquary container itself is metadata rich, detailing through the use of ORCIDS, RORs and a DOI, references to external items, complemented by further metadata concerning the specifics of the reliquary's own metadata (its title and the credit for the artist that created it). Futher more, the relationship between the reliquary and those referenced parties/objects was also captured. The contents were also used to demonstrate the importance of making the contents useful for onward users (in this case contact details on business cards). <br> - The second, and for the funder of this piece, arguably the most important aspect was a degree of outreach this provided, both to engage the audience of Pascoe et al (2024), and directly to the artist to demonstrate the importance of this work to the international research data management community and overall to aid engagemeng with the funder's work.<br><br>This resource is provided here as a repository of images of the reliquary itself and in context at the EGU 2024 event as a potential resource others may use to aid further discussions around the use of reliquaries with regards to complex citations. The slides provided of the reliquary box labels are also provided with some annotation to further expand on the metadata aspects of their content.</p>
Demonstration record for discoverable IPCC WGIII data
<p>This is a record used to demonstrate the concept of discoverable data for IPCC AR7 WGIII.</p>
Twiter Dataset on climate change discussions: COP27, IPCC, climate refugees and Doñana - Clint project
<p><strong>CLINT Data</strong></p> <p>This repository contains the date used in the project CLINT and the paper "<a href="https://arxiv.org/abs/2410.21187">A cross-platform analysis of polarization and echo chambers in climate change discussions</a>" </p> <p><strong>Open Twitter Data</strong></p> <p>We used the Twitter’s search to gather historical tweets and the streaming API to follow specified accounts and also collect in real-time tweets that mention specific keywords. To comply with <a href="https://developer.twitter.com/en/developer-terms/agreement-and-policy">Twitter’s Terms of Service</a>, we are only publicly releasing the tweet IDs of the collected tweets. The data is released for non-commercial research use. </p> <p><strong>With Twitter's changes to its Academic API policies, it’s no longer possible to collect or rehydrate tweets </strong><strong>as we usually did, however we open data in case at some point it will become feasible to do it.</strong></p> <table> <tbody> <tr> <td> </td> <td><strong>IPCC</strong></td> <td><strong>Doñana</strong></td> <td><strong>Climate Refugees</strong></td> <td><strong>COP27</strong></td> </tr> <tr> <td><strong>Number of tweets</strong></td> <td>352,723 </td> <td>1,487,425</td> <td>1,938,932</td> <td>6,225,508 </td> </tr> <tr> <td><strong>Number of authors</strong></td> <td>157,056</td> <td>290,782</td> <td>841,454 </td> <td>1,351,903 </td> </tr> <tr> <td><strong>First tweet date</strong></td> <td>2023-03-18</td> <td>2019-01-01</td> <td>2008-03-10 </td> <td>2022-09-01 </td> </tr> <tr> <td><strong>Last tweet date</strong></td> <td>2023-03-26</td> <td>2023-04-30</td> <td>2022-12-31 </td> <td>2022-11-27</td> </tr> </tbody> </table> <p> </p>
An open dataset of IPCC reports 'references (6th Assessment Cycle) – Version 1
<p>We provide a first version of an open dataset of publications cited by the IPCC reports of the 6<sup>th</sup> Assessment Cycle (<a href="https://www.ipcc.ch/reports/">Reports — IPCC</a>)</p> <p>The lists were extracted from the reference sections of three special reports and the first assessment report. [see Figure 1]. The data are presented in two formats: one on hand in text files with a list of references for each section of the reports (generally each chapter) and one other hand a structured format (json) with identifiers for the documents and the sections, the reference in string format as well as the extracted digital object identifiers (dois). In this first version, the dois extracted are mainly those which are provided in the references. The table 1 show the number of references and doi for each report.</p> <p>We plan, for subsequent releases, following enhancements:</p> <ul> <li>Further quality assurance of dois. We note that some entries are provided in references of the IPCC reports without dois although they are indexed in Crossref. The table 2 shows substantial differences in doi coverage among reference sections. Spot checks of the dataset suggest that those differences are mainly due to referencing behaviour of the section’s authors rather than on type of documents cited. We aim in the next version to complete those missing dois and systematically verify the dois included in the references.</li> <li>Expand the references lists to reports from past assessment cycles</li> </ul> <p>In addition, we plan a more detailed documentation of our dataflow & extraction process and to demonstrate how it can be used to create open, community curated, datasets of references of others non-scholarly documents.</p> <p>The json files have each two keys (1) schema with the structure of the table and (2) data: with the records.</p> <p>A simple way to read them into table is via a pandas dataframe</p> <p><em>import pandas as pd</em></p> <p><em>df = pd.read_json(file_name.json, orient = ‘table’)</em></p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>We thank Valentin Hancu (EC/DG ECFIN) - for fruitful discussions on the data extraction process.</p> <p><strong>Disclaimer: </strong></p> <p>The views expressed in this paper are the author’s. They do not reflect the views or official positions of the European Commission.</p>
IPCC Working Group 1 (WG1) Sixth Assessment Report (AR6) Annex III Extended Data
<p>Extended data relating to atmospheric abundences and effective radiative forcing from historical and future projections. Data is presented in abridged form in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) Working Group 1 (WG1) Annex 3. </p> <p>In this dataset, data is provided for all years, and includes additional scenarios not included in the published tables.</p> <p><strong>Contents</strong></p> <ul> <li>table A3.1: historical observed greenhouse gas (GHG) abundances. All subtables a-f in the printed report are combined into one CSV file.</li> <li>table A3.2: future projections (2020-2500) of GHG abundances for nine SSP scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0-lowNTCF, SSP4-3.4, SSP4-6.0, SSP5-3.4-over, SSP5-8.5). Orignal data is from Meinshausen et al. (2020): https://doi.org/10.5194/gmd-2019-222 </li> <li>table A3.3: historical effective radiative forcing (ERF) for 1750-2019 (unit is W m<sup>-2</sup>) <ul> <li>best estimate</li> <li>5th percentile</li> <li>95th percentile</li> <li>100000 member Monte Carlo ensemble (HDF file)</li> </ul> </li> <li>table A3.4: future projections of ERF from 1750-2500 (including historical to 2014, projections starting from 2015). Unit is W m<sup>-2</sup>. <ul> <li>table A3.4a: SSP1-1.9 (best estimate, 5th and 95th percentile)</li> <li>table A3.4b: SSP1-2.6 (best estimate, 5th and 95th percentile)</li> <li>table A3.4c: SSP2-4.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4d: SSP3-7.0 (best estimate, 5th and 95th percentile)</li> <li>table A3.4e: SSP5-8.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4f: breakdown of minor greenhouse gases, and aggregated categories, for the five Tier 1 SSP scenarios in tables A3.4a to A3.4e (best estimate)</li> <li>tables A3.4x: tables A3.4a to A3.4f for Tier 2 SSP scenarios: <ul> <li>SSP3-7.0-lowNTCF</li> <li>SSP3-7.0-lowNTCFCH4</li> <li>SSP4-3.4</li> <li>SSP4-6.0</li> <li>SSP5-3.4-over</li> </ul> </li> </ul> </li> <li>table A3.5: projections of ERF from 1750-2500 from RCP2.6, RCP4.5, RCP6.0 and RCP8.5 using AR6 assessment (best estimate, 5th and 95th percentile, breakdown of minor gases; unit is W m<sup>-2</sup>)</li> </ul> <p><strong>Citation</strong></p> <p>IPCC, 2021: Annex III: Tables of historical and projected well-mixed greenhouse gas mixing ratios and effective radiative forcing of all climate forcers [Dentener F.J., B. Hall, C. Smith (eds.)]. In <em>Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</em> [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press.</p>
IPCC AR6 Relative Sea Level Projection P-Boxes
<p><strong>Description</strong></p> <p>This data set contains detailed elements of the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections for all of the p-boxes described in AR6 WG1 9.6.3 (under ar6-regional-pboxes.zip), as well as a variant excluding the AR6 estimates of background sea level change (under ar6-regional_novlm-pboxes.zip).</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p> <p> </p>
IPCC AR6 Relative Sea Level Projection Distributions
<p><strong>Description</strong></p> <p>This data set contains detailed elements the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projection distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as distributions for the components contributing to relative sea level change.</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p> <p> </p>
IPCC AR6 Relative Sea Level Projections without Background Component
<p><strong>Description</strong></p> <p>This data set contains detailed elements the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as p-boxes derived from these distributions.</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
IPCC AR6 Sea Level Projections
<p><strong>Description</strong></p> <p>This data set contains the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It contains the full set of samples for the global projections (under ar6.zip), as well as summary relative sea level projections (under ar6-regional-confidence.zip and, without the AR6 estimate of background sea level process rates, ar6-regional_novlm-confidence.zip). Most users will want to focus on the confidence_output_files, which correspond most directly to the figures and tables in the report. For the global projections, samples from the individual probability distributions described in AR6 WG1 9.6.3 are in the full_sample* directories.</p> <p>Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at <a href="https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool">https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool</a>.</p> <p>See <a href="../communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p> <p>See <a href="https://github.com/Rutgers-ESSP/IPCC-AR6-Sea-Level-Projections">https://github.com/Rutgers-ESSP/IPCC-AR6-Sea-Level-Projections</a> for a guide to available resources.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea Level Change Team for developing and hosting the IPCC AR6 Sea Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
Location List for IPCC AR6 Sea Level Projections
<p>This data set contains the location list file for the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It can be used to cross-reference location IDs with names of the locations.</p> <p>Column 1 – Location name (string with spaces having been replaced with underscores)<br> Column 2 – Location ID (integer value)<br> Column 3 – Latitude (-90 to 90 degrees)<br> Column 4 – Longitude (-180 to 180 degrees)</p> <p>See <a href="https://zenodo.org/communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p>
Figure 7.2 of IPCC-IPBES report - The effects of actions to mitigate climate changes on action to mitigate biodiversity and of actions to mitigate biodiversity loss on actions to mitigate climate change
<p>Here are the underlying data and codes for Figure 7.2 of the IPCC-IPBES report (https://doi.org/10.5281/zenodo.4659158)<br> </p> <p>We decide to represent only the recognized links (positive and negative) in Figure 7.2. The table1 file represents these relationships. If you decide to represent the non-recognized interactions (gray), you should use the table2 file and re-divide it in the code.</p> <p>The code produces the Sankey diagram, which was used to produce Figure 7.2. The order of the nodes was organized manually to represent better the Chapter 7 discussion. After that, you can export the figure and work in an external program.</p> <p>The final figure was produced using PowerPoint and with labels and icons inserted manually. </p>
Dataset corresponding to the « Reasons for concern » about climate change impacts from all IPCC reports (TAR to AR6)
<p>This data corresponds to all the "burning embers" diagrams for the "Reasons for Concern" published in IPCC reports (and the related paper Smith et al. 2009 for AR4) until AR6 (thus including TAR, AR4, AR5, SR1.5 and AR6). For TAR to SR1.5, the data is the result of extracting information from the original figures, as presented in the related technical document <a href="https://doi.org/10.5281/zenodo.3992856">10.5281/zenodo.3992856</a>. As also explained in the Supplementary Information of Zommers et al. (2020), the data does not come directly from the IPCC, although it is based on the assessment provided in the IPCC reports listed in the references. For IPCC AR6, the source is the supplementary material of chapter 16. Details regarding specific values provided in the dataset are explained alongside the values in the main file: "RFCs-ALL-2023_05_12.xlsx". </p> <p>The main file includes the parameters needed to produce a diagram that supplements figure 3 from Zommers et al. (2020) with AR6 data and the confidence levels from previous reports when available. The Excel files in RFCs-2023-UsageExamples.zip contain the same data with different parameters, so that uploading these files to the Ember Factory (<a href="https://climrisk.org/emberfactory">https://climrisk.org/emberfactory</a>) produces different figures - including a comparison between AR5 and AR6 (as in IPCC AR6 Synthesis Report, but with AR5 confidence levels included). The resulting diagrams are also provided.</p>
R10 region mask based on IPCC AR6 WG3 and ISIMIP
<p>region_classification.tsv: Tab-separated value file of ISO3 code, country name and the R10 mapping used.</p> <p>r10masks_fractional.nc: 0.5° grid of fractions of grid cell that is part of one of 10 world regions as defined by IPCC Working Group 3.</p> <p>The "region" variable is a dimension (11, 360, 720) variable. The first axis is the fraction (0-1 scale) of the grid cell at latitude and longitude (defined by second and third axis) falling into each world region.</p> <p>Values of first axis correspond to following regions defined in the country mappings file:</p> <ul> <li>0=South-East Asia and developing Pacific</li> <li>1=Eurasia</li> <li>2=Asia-Pacific</li> <li>3=Africa</li> <li>4=Middle East</li> <li>5=Latin America and Caribbean</li> <li>6=North America</li> <li>7=Eastern Asia</li> <li>8=Southern Asia</li> <li>9=Europe</li> <li>10=World (all land, excluding Antarctica, and the sum of fractions in 0-9).</li> </ul> <p>Oceans and inland lakes are not counted within countries/regions.</p> <p>The starting point for this data is the 0.5° country mask from Perrette (2023).</p> <p>Country mappings in the TSV file follow the prescription of IPCC Working Group 3 Annex II (Al Khourdajie et al. 2022) where possible. Some ambiguities exist with regards to post-colonial, geographically detached, and disputed territories which are not explicitly defined in Annex II. These have been grouped by geographic rather than political region (example: French Guiana is classified as Latin America & Caribbean, which it is most definitely geographically part of, rather than Europe, which it is politically part of).</p> <p> </p> <p>Perette, 2023: ISI-MIP/isipedia-countries (v2.6). GitHub repository. <a href="https://github.com/ISI-MIP/isipedia-countries/releases/tag/v2.6">https://github.com/ISI-MIP/isipedia-countries</a><a href="https://github.com/ISI-MIP/isipedia-countries/releases/tag/v2.6">/releases/tag/v2.6</a></p> <p>Al Khourdajie et al., 2022: Annex II: Definitions, Units and Conventions [Al Khourdajie, A., R. van Diemen, W.F. Lamb, M. Pathak, A. Reisinger, S. de la Rue du Can, J. Skea, R. Slade, S. Some, L. Steg (eds)]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.021</p>
Code and Dataset used for assessment of key variables in manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report".
<p>This repository contains the code used for extraction of data from the IPCC scenarios database for key variables that are assessed in the manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report". It also contains data for key variables for scenario categories C1, C2, C3, and C4</p>
Global temperature time series from IPCC AR6
<p>Annual global mean temperature time series used in IPCC Sixth Assessment Report (AR6). Includes both consolidated mulit-dataset mean and individual component data sets. Further documentation is available in AR6 (Working Group I, section 2.3.1). </p>
IPCC AR6 Sea Level Milestones
<p><strong>Description</strong></p> <p>This data set contains elements of the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains files indicating the likelihood of when sea level milestones are crossed over time. For global mean sea level, it includes probability distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as p-boxes derived from these distributions; for regional projections, with and without vertical land motion, it includes the p-boxes associated corresponding to those shown in the milestone excedance timing figures.</p> <p>Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
Climate change risks illustrated by the IPCC "burning embers": dataset
<p>This dataset contains numerical data and descriptive information on all 'burning ember' diagrams presented in the reports of the Intergovernmental Panel on Climate Change (IPCC), from the first appearance of these diagrams in 2001 to the 6th Assessment Report, published in 2022. The aim of this dataset is to bring together the data and metadata needed to reconstruct the burning embers diagrams and acquire essential information on the risks assessed and their evolution, within a single, homogeneous framework. The file presented here has been extracted from the database at the indicated date: it is a versioned archive of the database (excluding internal development fields, which are not publicly available). Analyses and figures based on this dataset are presented in Marbaix et al., 2024 [1], which provides information about the data. The data are provided in a text file in JSON format, the structure of which is described in the file itself and in the Supplement to Marbaix et al. 2024 [1].</p> <p>The IPCC secretariat has confirmed that these data can be distributed under the CC-BY licence as indicated here. When using this dataset, we ask you to provide the reference to each IPCC report which is the source of the data (and additional sources listed in the references to this dataset when relevant), as well as to the dataset, adding the related paper [1] as soon as it is available.</p> <div> <div>[1] Marbaix, P., Magnan, A. K., Muccione, V, Thorne, P. W., and Zommers, Z: Climate change risks illustrated by the IPCC "burning embers", submitted.</div> </div>
IPCC-AR6-SPM material + info on reference regions
<p>### Contents</p> <p>Included material is from sources indicated below.</p> <p>- `spm/` from **[1]**<br> - `reference-regions/` from **[2]** except for `hexagon_grid_locations.csv`<br> - `reference-regions/hexagon_grid_locations.csv` from **[3]**</p> <p>### [1] source of the data in `spm/`</p> <p>```<br> IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.<br> ```</p> <p>```<br> Gillett, N.P.; Malinina, E.; Kaufman, D.; Neukom, R. (2021): Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20210809). NERC EDS Centre for Environmental Data Analysis, 09 August 2021. doi:10.5285/76cad0b4f6f141ada1c44a4ce9e7d4bd. http://dx.doi.org/10.5285/76cad0b4f6f141ada1c44a4ce9e7d4bd<br> ```</p> <p>### [2] source of the data in csv files</p> <p>```<br> Iturbide, Maialen, Fernández, Jesús, Gutiérrez, José Manuel, Bedia, Joaquín, Cimadevilla, Ezequiel, Díez-Sierra, Javier, Manzanas, Rodrigo, Casanueva, Ana, Baño-Medina, Jorge, Milovac, Josipa, Herrera, Sixto, Cofiño, Antonio S., San Martín, Daniel, García-Díez, Markel, Hauser, Mathias, Huard, David, & Yelekci, Özge. (2021). Repository supporting the implementation of FAIR principles in the IPCC-WGI Atlas (v2.0-final). Zenodo. https://doi.org/10.5281/zenodo.5171760<br> ```</p> <p>### [3] source of the data in csv files</p> <p>```<br> Gael Forget, Lauren Milechin, & Philippe Roy. (2021). JuliaClimate/GlobalOceanNotebooks: new webpage, docker+sysimage+Pluto, add IPCC+other notebooks, cleanup (v0.3.5). Zenodo. https://doi.org/10.5281/zenodo.5537709<br> ```</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.