Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “Emission Factors”
Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
Open the record for dataset details and reuse information.
Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales
Open the record for dataset details and reuse information.
DatabaseNH3 : EOM ammonia emission factor measured with INRAE Caract'Air device (under controlled conditions)
<p>This dataset includes ammonia volatilization measurements led by ECOSYS INRAE with Caract’Air device. Ammonia measurements, based on the principle of a mass balance in dynamic chambers are performed under thoroughly controlled and replicative conditions. Caract’Air was designed to be as close as possible to field conditions (in situ soil cores) while optimizing ambient conditions (temperature, air humidity and soil water content) and exchange conditions (flow rate, head volume, air circulation conditions) (Génermont et al., 2021; Décuq et al., 2023). A variety of EOMs are represented ranging from historic livestock effluents and manure to emerging biowastes produced by human urban and agro-industrial activities, all these biowastes having undergone a variety of treatments: raw, separated, composted, anaerobically stored slurries, farm yard manure, sewage sludges, municipal and domestic wastes; various digestats from mechanization; urine based fertilizers; etc. The types and origins of the EOM are reported. The physico-chemical properties of the EOM as well as soils on which the EOM were applied are detailed, leading to 20 and 30 parameters accompanied by information on analytical methods. Measurement conditions are also described including the application dose, the experimental set duration, the ambient conditions, and also the reproductibily conditions, etc. Finally, ammonia volatilization data are compiled, in terms of total cumulative loss (kg N/ha) or volatilization rates (% N and % N-NH4 applied).</p>
Phenolic compounds and Aromatic acids emission factors
<p>Primary and secondary emission data of phenolic compounds and Aromatic acids from different fuels combustion.</p>
Immuno Positron Emission Tomography Study of GSK2849330 in Subjects With Human Epidermal Growth Factor Receptor 3-Positive Solid Tumors
ClinicalTrials.gov study NCT02345174. IPD Sharing: YES. Countries: 1. Publications: 1.
Terpenoid emission factors for OBEIC
<p><span>To use this dataset, please cite our publication and the original publication of the data.</span><span> The original publication information can be found in this dataset.</span></p> <p><strong>OUR PUBLICATIONS:</strong></p> <p>Underestimated contribution of open biomass burning to terpenoid emissions revealed by a novel hourly dynamic inventory,<br>Science of The Total Environment,<br>2024,<br>172764,<br>ISSN 0048-9697,<br>https://doi.org/10.1016/j.scitotenv.2024.172764.<br>(https://www.sciencedirect.com/science/article/pii/S0048969724029115)</p> <p>More data updates will be posted here in the future. For special orders, please contact lijiangyong1105@foxmail.com</p> <p><strong>Data Update Announcement:</strong> None</p>
Data from: Driving factors on greenhouse gas emissions in permafrost region of Daxing'an Mountains, Northeast China
<p>Permafrost regions are an important source of greenhouse gases. However, the effects of different permafrost wetland types on greenhouse gas emissions and the driving factors are still unclear in the permafrost region. Here, we selected three typical permafrost wetlands from the Daxing'an Mountains to investigate the effects of permafrost wetland types on greenhouse gas emissions. <span class="fontstyle71"><span>The cumulative </span></span>N<sub>2</sub>O, CO<sub>2</sub>, and CH<sub>4</sub> emissions were 84–122, 657,942–1,446,121, and 173–16,924 kg km<sup>−2</sup>, respectively. The linear mixed effects model indicated that N<sub>2</sub>O emissions were significantly affected by the NO<sub>3</sub><sup>−</sup>-N content, whereas CO<sub>2</sub> emissions were mainly driven by soil temperature, water table level, and NO<sub>3</sub><sup>−</sup>-N content. CH<sub>4</sub> emissions were affected by soil temperatue and water table level. Permafrost wetland types significantly affected the average and cumulative N<sub>2</sub>O, CO<sub>2</sub>, and CH<sub>4</sub> emissions. The cumulative N<sub>2</sub>O emissions were highest in the <i>Larix gmelinii - Carex</i> <i>appendiculata </i>(<i>LC</i>) wetland and lowest in the <em>Betula fruticosa Pall. </em>(<em>B</em>) wetland<span class="fontstyle71"><span>, driven by </span></span>NO<sub>3</sub><sup>−</sup>-N content. The cumulative CO<sub>2</sub> emissions were highest in the (<em>B</em>) wetland and lowest in the <em>L. gmelinii</em> - Ledum palustre var. dilatatum (<em>LL</em>) wetland. The cumulative CH<sub>4</sub> emissions from <span class="fontstyle71"><span><i>B</i></span></span><span class="fontstyle71"><span> wetland were significantly higher than those from </span></span><i>LL</i> and <i>LC</i> wetlands. The differences in cumulative CO<sub>2</sub> and CH<sub>4 </sub>emissions were driven by the water table level. Our findings indicate that NO<sub>3</sub><sup>−</sup>-N content affect the spatial-temporal variation of N<sub>2</sub>O emissions, whereas water table level influence the spatial-temporal variation of CO<sub>2</sub> and CH<sub>4</sub> emissions in the permafrost region of the Daxing'an Mountains.</p>
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>
Unit emission data for Tikka et al. (2024) Displacement factors for aerosol emissions from alternative forest biomass use
<p>This dataset contains compiled unit emission data used in displacement factor calculations in Tikka et al. (2024) Displacement factors for aerosol emissions from alternative forest biomass use. The original sources are provided in the file.</p>
Data from: Driving factors on greenhouse gas emissions in permafrost region of Daxing’an Mountains, Northeast China
Open the record for dataset details and reuse information.
LBA-ECO TG-10 Fire Emission Factors in Mato Grosso, Para, and Amazonas, Brazil: 2004
This data set provides derived emission factors (EFs), reported in grams of compound emitted per kilogram of dry fuel (g/kg), for PM10 (particulate matter up to 10 micrometers in size), O3, CO2, CO, NO, NO2, HONO, HCN, NH3, OCS, DMS, CH4, and up to 48 non-methane organic compounds (NMOC) from the Tropical Forest and Fire Emissions Experiment (TROFFEE). TROFFEE used laboratory measurements followed by airborne and ground based field campaigns in Mato Grosso, Para, and Amazonas, Brazil during the 2004 Amazon dry season to quantify the emissions from pristine tropical forest and several plantations as well as the emissions, fuel consumption, and fire ecology of tropical deforestation fires. EFs were determined for 19 tropical deforestation fires in August and September, 2004. The combined output of these fires created a massive megaplume more than 500-km wide and covered a large area in Brazil, Bolivia, and Paraguay for about one month. For the megaplume, the EFs (reported in grams of compound emitted per kilogram of dry fuel (g/kg)) represented the effective emissions factor measured downwind from the source.There are two comma-delimited data files (.csv) and one text file (.txt) with this data set. The text file contains information regarding the fuel/fire sources, latitude and longitudes (also provided in the data files).
Characterization Factors for Microplastic Emissions in Life Cycle Assessment Considering Multimedia Fate Modelling
<p>These data provide supporting information for a forthcoming publication proposing new <strong>Regionalized Characterization Factors for Microplastic Emissions in Life Cycle Assessment</strong>. </p> <p>The Characterization Factors (CFs) are developed thanks to a fate model based on SimpleBox4Plastics: https://doi.org/10.5281/zenodo.5743268 and adapted to meet the USEtox methodology. </p> <p>They are derived from the "Regionalized_CFs_for_MP_emissions_in_LCIA.ipynb" script available on GitHub at https://github.com/Julouve/Regionalized_Characterization_Factors_for_Microplastic_Emissions_in_LCIA.git</p> <p>The CFs are developed for 14 polymers (EPS, PP, LDPE, HDPE, PS, PAN, PHA, PA, PLA, strach blend, PBAT, PET, PVC, TRWP) at 5 sizes (1, 10, 100, 1000, 5000 μm) for 9 different regions (North America, Latin America, Europe, Africa & Middle East, Central Asia, Southeast Asia, Northern regions, Oceania, World) for 9 environmental compartments (air, lake water, river water, sea water, their sediment, natural soil, agricultural soil) on two scales (continental & global) and for 3 ecosystems (marine, freshwater and terrestrial). </p> <p>CFs are computed thanks to 2 approaches: a surface approach (in PDF*m2*year/kg) and a species approach (in PDF*year/kg or species*year/kg). </p> <p> </p>
Multimetabolic 18F-Fluorodeoxyglucose (FDG) and 18F-Fluorocholine (FCH) Positron Emission Tomography (PET) as an Early Predictive Factor of Overall Survival in Patients With Advanced Hepatocellular Ca
ClinicalTrials.gov study NCT02847468. IPD Sharing: NO. Countries: 1. Publications: 0.
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.