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3 results for “Scope 3”
KM2112 SCOPE-PARAGON cruise, Seaglider 626, Mission 3
<p>The 2021 SCOPE-PARAGON research expedition is a coordinated effort to characterize particle dynamics and remineralization in the upper 500 m of the North Pacific Subtropical Gyre. Despite the importance of particles as key sites of biological activity in an otherwise dilute seawater medium, there remain major gaps in our understanding of the specific processes and organisms controlling particle transformations. The 2021 SCOPE-PARAGON cruise will be directed towards understanding the processes and rates of biological particulate matter transformations (e.g., production, decomposition, sinking) as well as the ecological interactions that occur in association with suspended and sinking particles. These targeted studies of particle dynamics at Station ALOHA will leverage ongoing ecological and biogeochemical modeling efforts in SCOPE.</p>
Fig. 3 in The scope of flavin-dependent reactions and processes in the model plant Arabidopsis thaliana
Fig. 3. Oxidation of succinate to fumarate catalyzed by succinate dehydrogenase (complex II). The reduced FAD donates the electrons to a pair of iron-sulfur clusters from where they are finally delivered to a cytochrome leading to the reduction of ubiquinone (CoQ) in the inner mitochondrial membrane.
ExioML: Emission Factor Database for Scope 3 Emission Estimation Machine Learning Benchmarks
<h1>🙋‍♂️ Introduction</h1> <p>ExioML is the first ML-ready benchmark dataset in eco-economic research, designed for global sectoral sustainability analysis. It addresses significant research gaps by leveraging the high-quality, open-source EE-MRIO dataset ExioBase 3.8.2. ExioML covers 163 sectors across 49 regions from 1995 to 2022, overcoming data inaccessibility issues. The dataset includes both factor accounting in tabular format and footprint networks in graph structure.</p> <p>We demonstrate a GHG emission regression task using a factor accounting table, comparing the performance of shallow and deep models. The results show a low Mean Squared Error (MSE), quantifying sectoral GHG emissions in terms of value-added, employment, and energy consumption, validating the dataset's usability. The footprint network in ExioML, inherent in the multi-dimensional MRIO framework, enables tracking resource flow between international sectors.</p> <p>ExioML offers promising research opportunities, such as predicting embodied emissions through international trade, estimating regional sustainability transitions, and analyzing the topological changes in global trading networks over time. It reduces barriers and intensive data pre-processing for ML researchers, facilitates the integration of ML and eco-economic research, and provides new perspectives for sound climate policy and global sustainable development.</p> <h1>📊 Dataset</h1> <p>ExioML supports graph and tabular structure learning algorithms through the Footprint Network and Factor Accounting table. The dataset includes the following factors in PxP and IxI:</p> <p>- Region (Categorical feature)<br>- Sector (Categorical feature)<br>- Value Added [M.EUR] (Numerical feature)<br>- Employment [1000 p.] (Numerical feature)<br>- GHG emissions [kg CO2 eq.] (Numerical feature)<br>- Energy Carrier Net Total [TJ] (Numerical feature)<br>- Year (Numerical feature)</p> <h2>☁️ Factor Accounting</h2> <p>The Factor Accounting table shares common features with the Footprint Network and summarizes the total heterogeneous characteristics of various sectors.</p> <h2>🚞 Footprint Network</h2> <p>The Footprint Network models the high-dimensional global trading network, capturing its economic, social, and environmental impacts. This network is structured as a directed graph, where directionality represents sectoral input-output relationships, delineating sectors by their roles as sources (exporting) and targets (importing). The basic element in the ExioML Footprint Network is international trade across different sectors with features such as value-added, emission amount, and energy input. The Footprint Network helps identify critical sectors and paths for sustainability management and optimization. The Footprint Network is hosted on Zenodo.</p> <h1>🔗 Code and Data Availability</h1> <p>The ExioML development toolkit in Python and the regression model used for validation are available on the GitHub repository: (https://github.com/YVNMINC/ExioML). The complete ExioML dataset is hosted by Zenodo: (https://zenodo.org/records/10604610).</p> <h1>💡 Additional Information</h1> <p>More details about the dataset are available in our paper: *ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability*, accepted by the ICLR 2024 Climate Change AI workshop: (https://arxiv.org/abs/2406.09046).</p> <h1>📄 Citation</h1> <pre>@article{guo2024exioml, title={ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability}, author={Guo, Yanming and Guan, Charles and Ma, Jin}, journal={arXiv preprint arXiv:2406.09046}, year={2024} }</pre> <h1>🌟 Reference</h1> <p>Stadler, Konstantin, et al. "EXIOBASE 3." Zenodo. Retrieved March 22 (2021): 2023.</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.