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33 results for “ipcc”
Collection of documents of the IPCC DDC at WDCC
<p>Materials about the plans of the IPCC DDC at WDCC/DKRZ for AR6 circled among the participants of the First IPCC AR6 Data Workshop (19/20 September 2017) at DKRZ in Hamburg, Germany, prior to the meeting. It includes a talk given at the IPCC Expert Meeting on the future of TGICA (01/2016 in Geneva, Switzerland) and a draft list of variables for the CMIP6 data pool compiled from the DICAD project partners' data requests and from statistics of the AR5 variable usage (status: 06/2017).</p>
Global nitrous oxide emissions from livestock manure during 1890−2020: An IPCC Tier 2 inventory
Open the record for dataset details and reuse information.
Relative sea-level projections for Norway based on IPCC AR6
<p>This dataset contains IPCC AR6 relative sea-level projections tailored to Norway and published in the Norwegian Centre for Climate Services report by Simpson et al. (2024).</p> <p>The projections has been produced by taking the IPCC AR6 relative sea-level projections without the background vertical land motion (VLM) component (Kopp, 2021), which have then been combined with the semi-empirical NKG2016LU VLM model (Vestøl et al., 2019). See Simpson et al. (2024) for more details.</p> <p><strong>References</strong></p> <p>Kopp, R. E. (2021). IPCC AR6 Relative Sea Level Projections without Background Component (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5967269</p> <p>Vestøl, O., Ågren, J., Steffen, H., Kierulf, H., & Tarasov, L. (2019). NKG2016LU: A new land uplift model for Fennoscandia and the Baltic Region. <em>Journal of Geodesy</em>, <em>93</em>(9), 1759–1779. <a href="https://doi.org/10.1007/s00190-019-01280-8">https://doi.org/10.1007/s00190-019-01280-8</a></p> <p>Simpson, M.J.R., Bonaduce, A., Borck, H.S., Breili, K., Breivik, Ø., Ravndal, O.R., Richter, K., 2024. Sea-Level Rise and Extremes in Norway: Observations and Projections Based on IPCC AR6. Norwegian Centre for Climate Services report 1/2024, ISSN 2704-1018, Oslo, Norway.</p>
Analysis of references in the IPCC AR6 WG2 Report of 2022
<p>This repository contains data on 17,419 DOIs cited in the <a href="https://www.ipcc.ch/report/ar6/wg2/">IPCC Working Group 2 contribution to the Sixth Assessment Report</a>, and the code to link them to the dataset built at the Curtin Open Knowledge Initiative (COKI).</p> <p>References were extracted from the report's PDFs (downloaded 2022-03-01) via <a href="https://www.scholarcy.com/">Scholarcy</a> and exported as RIS and BibTeX files. DOI strings were identified from RIS files by pattern matching and saved as CSV file. The list of DOIs for each chapter and cross chapter paper was processed using a custom Python script to generate a pandas DataFrame which was saved as CSV file and uploaded to Google Big Query.</p> <p>We used the main object table of the Academic Observatory, which combines information from Crossref, Unpaywall, Microsoft Academic, Open Citations, the Research Organization Registry and Geonames to enrich the DOIs with bibliographic information, affiliations, and open access status. A custom query was used to join and format the data and the resulting table was visualised in a Google DataStudio dashboard.<br> <br> This version of the repository also includes the set of DOIs from references in the <a href="https://www.ipcc.ch/report/ar6/wg1/">IPCC Working Group 1 contribution to the Sixth Assessment Report</a> as extracted by Alexis-Michel Mugabushaka and shared on Zenodo: <a href="https://doi.org/10.5281/zenodo.5475442">https://doi.org/10.5281/zenodo.5475442</a> (CC-BY)</p> <p>A brief descriptive analysis was provided as a <a href="https://openknowledge.community/tracking-climate-change-openaccess/">blogpost on the COKI website</a>.</p> <p><strong>The repository contains the following content:</strong></p> <p>Data:</p> <ul> <li><strong>data/scholarcy/RIS/</strong> - extracted references as RIS files</li> <li><strong>data/scholarcy/BibTeX/</strong> - extracted references as BibTeX files</li> <li><strong>IPCC_AR6_WGII_dois.csv</strong> - list of DOIs</li> <li><strong>data/10.5281_zenodo.5475442/</strong> - references from IPCC AR6 WG1 report</li> </ul> <p>Processing:</p> <ul> <li><strong>preprocessing.R</strong> - preprocessing steps for identifying and cleaning DOIs</li> <li><strong>process.py</strong> - Python script for transforming data and linking to COKI data through Google Big Query</li> </ul> <p>Outcomes:</p> <ul> <li><a href="https://console.cloud.google.com/bigquery?project=utrecht-university&ws=!1m23!1m3!8m2!1s145441926252!2sd59dfac7972a45f8a2f5ee4ac866c34d!1m4!4m3!1sacademic-observatory!2sobservatory!3sdoi20220226!1m4!4m3!1sutrecht-university!2sipcc_ar6!3sdoi_table!1m3!3m2!1sutrecht-university!2sipcc_ar6!1m4!4m3!1sutrecht-university!2sipcc_ar6!3sipcc_ar6_dois&d=ipcc_ar6&p=utrecht-university&page=table&t=doi_table&pli=1&authuser=1">Dataset on BigQuery</a> - requires a google account for access and bigquery account for querying</li> <li><a href="https://datastudio.google.com/s/vZN2zLr9wS4">Data Studio Dashboard</a> - interactive analysis of the generated data</li> <li><a href="https://www.zotero.org/groups/4614109">Zotero library</a> of references extracted via Scholarcy</li> <li><strong>PDF version of blogpost</strong></li> </ul> <p><strong>Note on licenses:</strong> <br> Data are made available under <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> (with the exception of WG1 reference data, which have been shared under <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY 4.0</a>)<br> Code is made available under <a href="http://www.apache.org/licenses/">Apache License 2.0</a></p>
Classification of Global Forests for IPCC Aboveground Biomass Tier 1 Estimates, 2020
This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Forest classes include primary, young secondary (<=20 years), and old secondary forests (>20 years). Classification was based on a Boolean combination of a suite of existing Earth Observation (EO) products of forest tree cover, height, age, and land use classification layers representing years 2000 to 2020. This forest status/condition classification prioritizes the reduction of potential errors of commission in the delineations by minimizing the inclusion of ambiguous pixels. Hence, it provides a conservative estimate of global forest area, identifying approximately 3.26 billion ha of forests worldwide. The classification was created on the collaborative open-science cloud-computing system, the ESA-NASA Multi-mission Analysis and Algorithm Platform (MAAP). The data are provided in cloud-optimized GeoTIFF format.
IPCC Special Report on Emissions Scenarios (SRES) Emissions Scenarios Dataset Version 1.1
The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) Emissions Scenarios Dataset Version 1.1 consists of 40 global and regional greenhouse gases (GHGs) and sulfur emissions scenarios projected every 10 years beginning in 1990 through 2100. The scenarios are based on extensive assessment of driving forces and emissions in the scenario literature, alternative modeling approaches, and an open process that solicited wide participation and feedback. The scenarios provide the basis for future assessments of climate change and possible response strategies. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
IPCC Fourth Assessment Report (AR4) Observed Climate Change Impacts Database
The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a "change consistent with warming" or a "change not consistent with warming", based on information from the underlying studies.
IPCC Fifth Assessment Report (AR5) Observed Climate Change Impacts Database, Version 2.01
The Intergovernmental Panel on Climate Change Fifth Assessment Report (AR5) Observed Climate Change Impacts Database, Version 2.01 contains observed responses to climate change across a wide range of systems as well as regions. These responses include systems for which climate change has played a major role in observed changes, regional-scale impacts where climate change has played a minor role, and sub-regional impacts. Impacts on physical, biological, and human systems were differentiated, and the area impacted can vary from specific locations to broad areas such as a major river basin.
IPCC IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1
The Intergovernmental Panel on Climate Change (IPCC) IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1 consists of six global and regional greenhouse gases (GHGs) emissions scenarios projected from 1990 through 2100. The six alternative IPCC scenarios (IS92 A to F) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumptions affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adopted. The data set was originally produced by IPCC in 1992, and the digital version was re-edited in 2005 to resolve the discrepancies among versions of the data over years. The definitive version of this data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
IPCC Special Report on Emissions Scenarios (SRES) 1x1 Degree Gridded Emissions Dataset
The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) 1x1 Degree Gridded Emissions Dataset consists of global gridded emissions for greenhouse gases (GHGs) projected every 10 years beginning in 1990 through 2100. The grids are produced for reactive gases Methane (CH4), Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Non-Methane Volatile Organic Compounds (NMVOC), along with Sulfur Dioxide (SO2), based on the IPCC SRES Emissions Scenarios Data Set Version 1.1. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
IPCC Socio-Economic Baseline Dataset
The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset consists of population, human development, economic, water resources, land cover, land use, agriculture, food, energy and biodiversity data . This dataset was collated by IPCC from a variety of sources such as The World Bank, United Nations Environment Programme (UNEP), and Food and Agriculture Organization of the United Nations (FAO), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
IPCC Special Report on Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset
The Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset consists of global and regional emissions of Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), Sulfur Hexafluoride (SF6), Cholorfluorocarbons (CFCs) and Hydrochlorofluorocarbons (HCFCs) projected every 10 years beginning in 1990 through 2100. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Infiller database for silicone: IPCC AR6 WGIII version
<p><strong>Download and license information</strong></p> <p>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/ar6/#/downloads">AR6 Scenario Explorer</a>.</p> <p>Details about the be found in the <a href="https://data.ece.iiasa.ac.at/ar6/#/license">license section</a> of the AR6 Scenario Explorer .</p> <p><strong>About the data set</strong></p> <p>To fill in emissions not reported for scenarios in their submission to the database, we use a large set of harmonized AR6 global emissions pathways for inferring pathways based on the relationships between concurrent species development over time observed in the larger set.</p> <p>Infilling ensures that all relevant anthropogenic emissions are included in each climate run for each scenario. This makes the climate assessment of alternative scenarios more comparable and reduces the risk of a biased climate assessment, because not all climatically active emission species are reported by all IAMs. The infilling methods used are from an open-source Python software package called ‘<a href="https://github.com/GranthamImperial/silicone">silicone</a>’ (Lamboll et al. 2020)</p> <p>This file is the harmonized emissions database that was used as "infiller database" for the IPCC AR6 WGIII report on the Mitigation of Climate Change, using data from the chapter on <em>Mitigation Pathways Compatible with Long-Term Goals </em>(Riahi and Schaeffer et al. 2022)<em> </em>as available in the AR6 Scenarios Database (Byers et al. 2022).</p> <p><strong>References</strong></p> <p>Edward Byers, Volker Krey, Elmar Kriegler, Keywan Riahi, Roberto Schaeffer, Jarmo Kikstra, Robin Lamboll, Zebedee Nicholls, Marit Sanstad, Chris Smith, Kaj-Ivar van der Wijst, Franck Lecocq, Joana Portugal-Pereira, Yamina Saheb, Anders Strømann, Harald Winkler, Cornelia Auer, Elina Brutschin, Claire Lepault, Eduardo Müller-Casseres, Matthew Gidden, Daniel Huppmann, Peter Kolp, Giacomo Marangoni, Michaela Werning, Katherine Calvin, Celine Guivarch, Tomoko Hasegawa, Glen Peters, Julia Steinberger, Massimo Tavoni, Detlef von Vuuren, Piers Forster, Jared Lewis, Malte Meinshausen, Joeri Rogelj, Bjorn Samset, Ragnhild Skeie, Alaa Al Khourdajie.<br> <em>AR6 Scenarios Database hosted by IIASA</em><br> International Institute for Applied Systems Analysis, 2022.<br> doi: <a href="https://doi.org/10.5281/zenodo.5886912">10.5281/zenodo.5886912</a> | url: <a href="https://data.ene.iiasa.ac.at/ar6">data.ene.iiasa.ac.at/ar6/</a></p> <p>Keywan Riahi, Roberto Schaeffer, et al.<br> <em>Mitigation Pathways Compatible with Long-Term Goals</em>, in "Mitigation of Climate Change".<br> Intergovernmental Panel on Climate Change, Geneva, 2022.<br> url: <a href="https://www.ipcc.ch/report/sixth-assessment-report-working-group-3/">Sixth Assessment Report Working Group III</a></p> <p>Lamboll, R.D., Nicholls, Z.R., Kikstra, J.S., Meinshausen, M. and<br> Rogelj, J., 2020. Silicone v1. 0.0: an open-source Python package for<br> inferring missing emissions data for climate change research. <em>Geoscientific Model Development</em>, <em>13</em>(11), pp.5259-5275.</p>
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)
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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.