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514 results for “Waste”
A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images
<p>CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
The official dataset of the papper " Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics"
<p>This is the official repository of the paper "Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics" (https://doi.org/10.1080/15538362.2024.2322743)</p> <p>DISCLAIMER</p> <p>The repository contains experimental data and is published for the sole purpose of giving additional background details on the respective publication "Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics" (https://doi.org/10.1080/15538362.2024.2322743).<strong> </strong>See the README.txt file for more details.</p>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
SCALIBUR video 1: From household food waste to bioplastics and biopesticides
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows:</p> <p>Each of us throws away a whopping 200 kilograms of food and organic waste each year. More and more cities separately collect this bio-waste. But what to do with it all? The SCALIBUR project is developing innovative technologies to convert household organic waste into valuable products. Where we see waste, SCALIBUR partners see a resource. One approach uses novel biochemical conversion, combining enzymatic hydrolysis and fermentation processes, to transform organic waste into sustainable bio-based products... Like bio-pesticides for more ecological agriculture, or biodegradable and compostable biopolymers for sustainable bioplastics. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bioeconomy in Europe.</p>
SCALIBUR video 2: From retail food waste to protein, lipids, and chitin
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows: </p> <p>Eyes bigger than your stomach? Hotels and restaurants make a big contribution to the 100 million tonnes of organic waste produced each year in the EU. The SCALIBUR project is developing innovative technologies to convert waste from the food service industry into valuable products. Where we see waste SCALIBUR partners see a resource. Insects like black soldier flies love leftovers, efficiently converting food scraps into a rich biomass. New processes are being developed to extract the valuable materials like proteins, lipids and chitin: raw materials for bioplastics, and food and feed products. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bio-economy in Europe.</p>
Underlying Data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products"
<p>The embodied files include the raw data and initial calculations used to generate the extended data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products". The files include calculations for Phosphorus Recovery from Hydrochar, as well as Heavy Metals Fractions retrieved by the hydrochar (solid product of Hydrothermal Carbonization).</p>
Bio-oil production from biogenic wastes, the hydrothermal conversion step - Data
<p>Food wastes are an abundant resource that can be effectively valorised by hydrothermal liquefaction to produce bio-fuels. The objective of the European project Waste2Road is to demonstrate the complete value chain from waste collection to engine tests. The principle of hydrothermal liquefaction is well known but there are still many factors that make the science very empirical. Most experiments in the literature are performed on batch reactors. Comparison of results from batch reactors with experiments with continuous reactors are rare in the literature.</p> <p>This dataset presents fully documented experiments, performed in this project, on food wastes, with different compositions, conditions and solvents. The data set is extended with data from the literature. This data set also includes bio-oil and aqueous phase analysis by gas chromatography coupled with mass spectrometry.</p>
Australian waste account 2007-2021
<p>This dataset updates the previous release from 2019 to 2021, in line with the <a href="https://www.dcceew.gov.au/environment/protection/waste/national-waste-reports/2022">National Waste Database</a> update. The previous release is available here: https://zenodo.org/records/5646740. This release also incorporates LGA waste generation data where it is available (<a href="https://www.epa.nsw.gov.au/-/media/epa/corporate-site/resources/wastestrategy/23p4660-lg-warr-report-2021-22.pdf">NSW </a>and <a href="https://www.vic.gov.au/victorian-local-government-waste-data-dashboard">Victoria</a>).</p> <p>Updated notes: </p> <p>The National Waste Database (https://www.awe.gov.au/environment/protection/waste/national-waste-reports/2020) is a repository for Australia's solid waste data. This collection of waste data is useful however has some issues: The timeseries is not complete, as some years are missing. Allocation to industries is very coarse, there are only 3 waste generating entities: construction and demolition, commercial and industrial, and municipal (households). Further, not all reporting regions (States and Territories) provide data at the same resolution of material type.</p> <p>We have created an open source dataset in an attempt to solve some of these issues. Missing years are filled using linear interpolation. The regional resolution is disaggregated to SA2 regions using the ABS Business Register (https://www.abs.gov.au/Ausstats/abs@.nsf/0/49658AFA6CC395CECA2583A700121A41). Municipal (households) waste is split into SA2s from state totals using population. The ABS Waste Account is used to establish a relationship between waste types and generating sectors (https://www.abs.gov.au/statistics/environment/environmental-management/waste-account-australia-experimental-estimates/latest-release).</p> <p>The data is published as labelled flat files (.csv). The dataset dimensions are:</p> <p>- years: 2007 - 2021,</p> <p>- regions: 2310 SA2 (2016) ASGS regions,</p> <p>- entities: 116 generating entities; 115 SUPG (supply-use product group) industries + 1 households,</p> <p>- waste_types: 69 waste material types,</p> <p>- treatments: 5 waste treatment methods</p> <p>This dataset is made available under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/</p>
RealDirt: waste detection dataset
<p>RealDirt is a waste detection dataset collected on a real waste recycling plant.</p> <p>It includes 2000 images and following classes:</p> <ol> <li>Plastic bottle</li> <li>Plastic bag</li> <li>Carton </li> </ol> <p>We hope that our dataset will provide researchers and machine learners in the sphere of waste detection with insightful and helpful dataset.</p> <p>Dataset was collected with IDS UI-5250 CP Rev.2 camera and 9mm Azure lens. </p>
Gummern - Mining Waste Deposits 5.73cm DEM UAV-derived
<h2>Abstract</h2> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits 5.73cm DEM UAV-derived</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Mining Waste Deposits, DEM, DTM, DSM, UAV, Drone</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>See "Mining Waste Deposits Multispectral Drone Imagery" <a href="https://doi.org/10.5281/zenodo.13622458">https://doi.org/10.5281/zenodo.13622458</a></p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Hydrography</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>3.10.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>3.10.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>See "Mining Waste Deposits Multispectral Drone Imagery"</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.0573m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.05m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4258</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Gummern - Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo
<h2>Abstract</h2> <p>Mining Waste Deposits 0.305m Digital Elevation Model derived from 2023-10-02 Panchromatic TriStereo Pleiades Neo Dataset.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits 0.305m Digital Elevation Model derived from 2023-10-02 Panchromatic TriStereo Pleiades Neo Dataset</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>DEM, DSM, DTM, Pleiades Neo, Minning Waste Deposits</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Compare with "Mining Waste Deposits 5.73cm DEM UAV-derived" and "Mining Waste Deposits 0.495m DEM (2024-04-12) from World-View2"</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>20.06.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>20.06.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.30495m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 25833</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Gummern - Mining Waste Deposits 0.495m DEM (2024-04-12) from WorldView2
<h2>Abstract</h2> <p>Mining Waste Deposits 0.495m resolution Digital Elevation Model derived from 2024-04-12 Panchromatic Stereo WorldView2 Dataset.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits 0.495m DEM (2024-04-12) from WorldView2</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits 0.495m resolution Digital Elevation Model derived from 2024-04-12 Panchromatic Stereo WorldView2 Dataset</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>DEM, DSM, DTM, Pleiades Neo, WorldView2, Minning Waste Deposits</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Compare with "Mining Waste Deposits 5.73cm DEM UAV-derived" and "Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo"</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>12.04.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>12.04.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.495m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 25833</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Dataset of a multiphase flow and reactive transport benchmark for radioactive waste disposal
<p>The files include the full dataset (tables and figures) of the comparion the results of a multiphase flow and reactive transport<br>benchmark for radioactive waste disposal. The codes INVERSE-FADES-CORE V2, DuMuX , TOUGHREACT and<br>iCP were benchmarked with 6 test cases of increasing complexity, starting with conservative tracer transport under variably<br>unsaturated conditions and ending with water flow, gas diffusion, minerals and cation exchange.</p>
Over 6 billion liters of Canadian milk wasted since 2012
<p>This file provides data used to approximate the annual Canadian milk yield based on herd statistics and estimated on-farm use. Using a mass balance approach, subtracting farm gate sales data, the balance is assumed to be wasted before leaving the farm gate. Data sources are provided with supporting literature where relevant.</p> <p>This recomended citation for this record is:</p> <p>Elliot, T., Goldstein, B. P., and Charlebois, S. (2025). Over 6 billion liters of Canadian milk wasted since 2012. <em>Ecological Economics, 227</em>, 108413. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolecon.2024.108413" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ecolecon.2024.108413</span></span></a></p>
Supplementary data to Challenges and Opportunities for the Recovery of Critical Raw Materials from Electronic Waste
<p>Content of the excell file:</p> <p>Table S1: Relationship between UNU-keys and MINCOTUR codes</p> <p>Table S2: UNU-Key composition and alpha and beta values for Weibull distributions</p> <p>Table S3: Metal prices used in this study.</p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
The LOTUS Initiative for Open Natural Products Research: waste to recycle
<p>Dataset not uploaded to Wikidata. </p> <p>Generated in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a></p> <p>Shared for further curation.</p>
Environmental Sustainability Assessment of Hydrogen from Waste Polymers
<p>Dataset associated with the publication "Environmental Sustainability Assessment of Hydrogen from Waste Polymers" by Cecilia Salah, Selene Cobo, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1021/acssuschemeng.2c05729">https://doi.org/10.1021/acssuschemeng.2c05729</a>. The dataset includes the numeric data used both in figures and tables of the main text and the supporting information, in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>01_LCI</strong>: numerical values associated with the life cycle inventories of the two gasification processes presented in this work (wPG and wPG+CCS). These values are displayed as in Tables S1 and S2 in the Supporting Information.</li> <li><strong>02_GWI</strong>: numerical values associated with the global warming impact of the 13 technologies assessed in this study. These values are used to generate Figure 3 of the main text.</li> <li><strong>03_PB-LCIA</strong>: numerical values associated with the results from the planetary boundaries life cycle impact assessment of the 13 technologies assessed in this study. This sheet contains data on all three PB-LCIA assessments carried out in this work: transgression level (TL) of the technologies relative to SOS<sub>H2,wP,b</sub> following a utilitarian downscaling, the TL of the technologies relative to SOS<sub>GLO</sub>, and the TL of the technologies relative to SOS<sub>H2,wP,b</sub> downscaled following the grand-fathering downscaling. These values are used to generate respectively Figure 4 of the main text, and Figures S1 and S2 in the Supporting Information.</li> <li><strong>04_LCOH</strong>: numerical values associated with the levelized cost of hydrogen (LCOH) of the 13 technologies assessed in this work. The sheet contains the values used to generate Figure 5 of the main text, and the values as displayed in Tables S3, S4, and S5 in the Supporting Information, which are used to calculate the LCOH of PEM-BECCS, PEM-hydro and PEM-2018 grid mix.</li> <li><strong>05_TEA</strong>: numerical values associated with the cost breakdown of the wPG and wPG+CCS technologies. These values are used to generate Figure S3 in the Supporting Information, and are displayed as in Tables S6, S7 and S8 in the Supporting Information.</li> <li><strong>06_endpoints</strong>: numerical values associated with the total endpoint environmental impacts of the different technologies per impact category, broken down by process component. These values are used to generate Figure S4, and are displayed as in Table S9 in the Supporting Information.</li> <li><strong>07_TCH</strong>: numerical values associated with the total cost of hydrogen (TCH) of the 13 technologies assessed in this work. The sheet contains the values used to calculate the TCH of the different technologies, and the data used to generate Figure S5 in the Supporting Information.</li> <li><strong>08_LP</strong>: numerical values associated with the results of the optimization that minimizes the cost of meeting the global H<sub>2</sub> demand within planetary boundaries. These values are used to generate Figure 6 of the main text and Figure S6 in the Supporting Information.</li> </ul>
Characterization of dairy processing waste in the north of France
<p>Dataset containing the characterization of dairy processing waste from 10 different dairy factories in the north of France.</p>
Dataset for publication Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718
<p>Dataset consisting of G-code for 3D mortar specimen's printing path and particle size distribution (Origin file) of materials used in the study Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718. <a href="https://doi.org/10.1016/j.jobe.2021.102718">https://doi.org/10.1016/j.jobe.2021.102718</a></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.