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33 results for “IAM”
Data to calculate metal demand from IAM energy projections and add flexibility to the technological mix
<p>In the "Data" folder, you’ll find all necessary data to run the <strong>"IAM_metal_optimisation.py"</strong> script, available on GitHub (<a href="https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master" target="_new" rel="noopener">https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master</a>).</p> <p>The <strong>"IAM_metal_optimisation.py"</strong> script is a tool designed to:</p> <ol> <li>Quantify metal supply and demand by sector through 2050, using energy projections from various IAMs and SSP-RCP scenarios.</li> <li>Optimize the IAM technological mix, minimizing adjustments needed to prevent metal demand from exceeding supply constraints.</li> </ol> <p>This tool supports all IAMs with power projections for SSP-RCP scenarios (except SSP3) provided in the IPCC’s Sixth Assessment Report (AR6), Working Group III, and available in the IIASA database.</p> <p>Data for RCP 2.6, SSPs 1, 2, 4, 5, of the SSP marker IAM models of the IPCC, are organized in the "Capacity Factor IAM," "GDP IAM," and "Power Capacity IAM" folders. See the GitHub README for more details.</p>
IAM_COMPACT_Study_7_Behavioural_changes
<p>This dataset contains the underling raw data of IAM COMPACT "Study 7 - Behavioural changes".</p> <p>The study addresses the impact of behavioural change on diets, buildings, and mobility <span>(Frilingou et al., 2024)</span>. This study aims to understand the economic ramifications arising from shifts in behaviour and social innovation within the context of the energy transition as conceptualized in Task 5.5, emphasizing the relevance of behavioural change as an added social value resulting from policy implementation, rather than solely focusing on cost or effectiveness evaluation. </p> <p>Initially, study 7 carries out a contextualisation of behavioural change by a literature review and an analysis of the behaviour-related policy representation in IAM COMPACT models (based on D4.1 methodology application). Next, it collects and processes stakeholders’ questions and propositions on behavioural changes and classifies them according to sectors and models which can respond to them. Finally, four scenarios are created from applying different policies, yielding outputs in the fields of energy, climate and land.</p> <p>Results of the study have been documented in D5.6 - Behaviour, social and disruptive innovation (DOI <a href="../doi/10.5281/zenodo.12751959">10.5281/zenodo.12751959</a>)</p>
Results and plotting scripts for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'
<p><br>This archives the results for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'</p> <p>For the model version used to create these results, please see: https://doi.org/10.5281/zenodo.13981520</p> <p>Files to reproduce the figures, in R language:<br>SuCCESs validation.R - Reads GDX files and produces plots for energy and emissions.<br>SuCCESs validation MC.R - The same, but with the Monte Carlo GDXs.</p> <p>External data sources:</p> <p>***<br>GHG emissions are from IGCC and PRIMAP</p> <p>IGCC: https://climatechangetracker.org/igcc (CC-BY license)</p> <p>PRIMAP:<br>Gütschow, Johannes; Jeffery, M. Louise; Gieseke, Robert; Gebel, Ronja; Stevens, David; Krapp, Mario; Rocha, Marcia (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, https://doi.org/10.5194/essd-8-571-2016<br>Gütschow, Johannes ; Busch, Daniel ; Pflüger, Mika (2024): The PRIMAP-hist national historical emissions time series (1750-2023) v2.6. Zenodo. https://doi.org/10.5281/zenodo.13752654<br>https://primap.org/primap-hist/ (CC-BY-4.0 license)</p> <p>***<br>Historical energy production and use data are from IEA Energy Statistics Data Browser (CC BY 4.0 licence).<br>https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySource</p> <p>***<br>IAM results are from the SSP database: https://tntcat.iiasa.ac.at/SspDb </p> <p>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlík, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni.<br>The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017,<br>DOI:110.1016/j.gloenvcha.2016.05.009</p> <p>Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D.P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik, P., Humpenöder, F., Stehfest, E., Tavoni, M., Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8, 2018, 325-332.<br>DOI:10.1038/s41558-018-0091-3</p>
Effect of IAM With an I-gel® on Ventilation Parameters in Simulated Pediatric OHCA
ClinicalTrials.gov study NCT05498402. IPD Sharing: YES. Countries: 1. Publications: 1.
The Effect of USM-IAM-based Counselling
ClinicalTrials.gov study NCT05125185. IPD Sharing: Not stated. Countries: 1. Publications: 1.
IAM Strikethrough Database
<p>This database contains synthetic strikethrough that has been applied to images from the IAM database. In order to not infringe on the copyright of the original database, this repository does not contain the modified images but only the diffs, which have to be merged with the original images. Code for merging is included in this repository.</p> <p> </p> <p>Update 2024-01-27: train.zip previously did not contain any training files. The update fixes this.</p>
IAM_words
Open the record for dataset details and reuse information.
Dataset: Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms
<div>Dataset for publication</div>
Dataset from publication Modeling of small molecule's affinity to phospholipids using IAM-HPLC andQSRR approach enhanced by similarity-based machine algorithms
Open the record for dataset details and reuse information.
Skin IaM: An Exploratory Clinical Trial to Evaluate Changes in Skin Appearance, Colour, and/or Texture Following the Induction of a Local Inflammatory Skin Response
ClinicalTrials.gov study NCT03192280. IPD Sharing: NO. Countries: 1. Publications: 0.
Alternating Systemic Chemotherapy and Intra-Arterial Melphalan (IAM) Chemotherapy in Children With Intra-Ocular Retinoblastoma
ClinicalTrials.gov study NCT02116959. IPD Sharing: NO. Countries: 1. Publications: 0.
RNA sequencing of iAM-1 cells at different stages during one cycle of differentiation and subsequent dedifferentiation
GEO Series GSE164763. Rattus norvegicus. 36 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing in iAM-1 differentiated cells and RnSbk2 knockdown
GEO Series GSE186626. Rattus norvegicus. 12 samples. Type: Expression profiling by high throughput sequencing.
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.