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47 results for “CCS”
Experimental CCS Values in PubChem
<p>The collection of experimental collision cross section (CCS) values from ion mobility experiments in PubChem, retrieved via the code developed <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/tree/master/annotations/CCS/CCS_retrieval">here</a>.</p> <ul> <li> "All_CCS_in_PubChem.csv" contains all CCS values extracted from PubChem (previous versions contained entries with no CIDs, current version has all entries mapped to CIDs).</li> <li>"All_CCS_in_PubChem_wInfo.csv" contains all CCS values, plus post-processing (splitting annotations, adducts and comments, adding chemical identifiers), but excludes all entries with no CID (and thus no structural information - not applicable in current version). </li> </ul> <p>Details how this file was produced are given <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/tree/master/annotations/CCS/CCS_retrieval">here</a>.</p>
S50 | CCSCOMPEND | The Unified Collision Cross Section (CCS) Compendium
<p>This is the collection associated with list S50 CCSCOMPEND on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S50 CCSCOMPEND <strong>The Unified Collision Cross Section (CCS) Compendium</strong></p> <p>>3800 experimental collision cross section values (drift tube MS), provided by Jackie Picache and John McLean, Vanderbilt. Further details available here: <a href="https://lab.vanderbilt.edu/mclean-group/collision-cross-section-database/">https://lab.vanderbilt.edu/mclean-group/collision-cross-section-database/</a></p> <p>v0.1.1: removed char errors in InChIKey file. v0.1.2 (17 July 2022): added SMILES and separate substance deposition file, updated InChIKeys. SMILES were added via InChIKey in the CCS records (PubChem ID Exchange) then filling in gaps using PubChem Search to find the preferred tautomer; the substance deposition created from unique CIDs (via webchem), then the InChIKey file was created from this. v0.1.3 (19 July 2022): mapped to parents; substance deposition, SMILES, CIDs and annotations now based on parent form (not original salt form).</p>
S79 | UACCSCEC | Collision Cross Section (CCS) Library from UAntwerp
<p>This is the collection associated with list S79 UACCSCEC Collision Cross Section (CCS) Library from UAntwerp on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A library containing the collision cross section (CCS) values of 311 adducts of 148 contaminants of emerging concern (CECs) and their metabolites measured with drift tube ion mobility high resolution mass spectrometry (in positive and negative ionization modes with N2 as drift gas) as described in Belova <em>et al.</em> (2021) DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.analchem.1c00142">10.1021/acs.analchem.1c00142</a>.</p> <p>Changes: 27/04/2021 added new CIDs from deposition. 10/5/2021: added transformations table. 30/8/2022: corrected [M+H]+ for BDCIPP (CID <a href="https://pubchem.ncbi.nlm.nih.gov/compound/188119#section=Collision-Cross-Section">188119</a>) to 157.35 A^2 (from 178.72) upon request of the authors (see Belova <em>et al</em>. (2022) DOI: <a href="https://doi.org/10.1016/j.aca.2022.340361">10.1016/j.aca.2022.340361</a>).</p>
Impact on pressure in above zone monitoring interval from unintentional CO2 release from a CCS reservoir
<p>The data set was created by running dynamic numerical simulations with the CMG-software on a static geologic model. It consists of simulations of 16 different CO2 release scenarios from a subsurface storage formation and describes the change in pressure in the above zone monitoring interval over time. The data are daily means of the pressure over a period of 1001 days and are defined on a 160x160 grid. It contains data from 4 release locations where for each location it is simulated 4 different release rates.</p>
PubChemLite for Exposomics + predicted CCS from CCSbase - 5 Sept. 2025
<p>PubChemLite is a subset of PubChem (<a href="https://pubchem.ncbi.nlm.nih.gov/">https://pubchem.ncbi.nlm.nih.gov/</a>) selected from major categories of the Table of Contents page at the PubChem Classification Browser (<a href="https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72">https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72</a>). This version of PubChemLite for Exposomics has predicted collision cross section (CCS) values for 11 adducts provided by Libin Xu and team at CCSbase (<a href="https://ccsbase.net/">https://ccsbase.net/</a>) calculated from the latest <a href="https://github.com/dylanhross/c3sdb/">c3sdb</a> code.</p> <p>PubChemLite <em>exposomics</em> is compiled from 11 categories: AgroChemInfo, BioPathway, DrugMedicInfo, FoodRelated, PharmacoInfo, SafetyInfo, ToxicityInfo, KnownUse, DisorderDisease, Identification, ChemClass.</p> <p>CCS adducts provided are: [M+H]+, [M-H]-, [M+Na]+, [M+K]+, [M+NH4]+, [M+H-H2O]+, [M+HCOO]-, [M+CH3COO]-, [M+Na-2H]-, [M]+, [M]-</p> <p>Details on the CCS prediction are given here: Ross <em>et al</em>. (2020) Analytical Chemistry, DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.analchem.9b05772">10.1021/acs.analchem.9b05772</a></p> <p>PubChemLite is described in Schymanski <em>et al. </em>(2021) J. Cheminformatics, DOI: <a href="https://doi.org/10.1186/s13321-021-00489-0" target="_blank" rel="noopener">10.1186/s13321-021-00489-0</a></p> <p>An article describing these joint efforts is available: Elapavalore <em>et al</em>. (2025) ES&T Letters, DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a></p> <p>PubChemCIDs have been collapsed by InChIKey first block, reporting the structure from the most annotated CID, plus related CIDs. Entries that will be ignored by MetFrag (salts, disconnected substances) or cause errors (e.g. transition metals) have been removed. The Patent and PubMed ID counts are extracted from files on the PubChem FTP site. The "AnnoTypeCount" term counts how many of the categories are represented, the subsequent column (named per category) counts the number of annotation categories available in the next sub-category of the TOC entry.</p> <p>These files can be used "as is" as localCSV for MetFrag Command Line (<a href="https://ipb-halle.github.io/MetFrag/">https://ipb-halle.github.io/MetFrag/</a>) - please do NOT upload these files directly to the web interface, they are too large and will be available in a drop-down menu.</p> <p>Further details are described in Schymanski <em>et al.</em> (2021) DOI:<a href="https://doi.org/10.1186/s13321-021-00489-0">10.1186/s13321-021-00489-0</a> and Elapavalore <em>et al</em>. (2025) DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a></p> <p><strong><em>NOTE: The latest PubChemLite for Exposomics version can be downloaded at DOI:</em></strong><em><strong><a href="https://doi.org/10.5281/zenodo.5995885">10.5281/zenodo.5995885</a> (currently updating monthly). This file will be updated shortly after. <br></strong></em></p> <p>Please cite this data source and Elapavalore <em>et al</em>. (2025) DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a> when using this dataset.</p>
OMP_CCS_Compendium
<p>This is a CCS compendium for environmental organic micropollutants. This compendium contains a total of 9407 experimental CCS values from 4170 organic contaminants, measured by both drift tube and traveling wave ion mobility spectrometry. After the consolidation of duplicate records, 7017 CCS values were retained.</p>
Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework
<p>Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"</p>
Data for research article "CCS investment – fiddling while the planet burns"
<p>This data set contains electricity systems and technology data for the UK, Poland, Texas, Wyoming, South Korea, and Indonesia. This data was used for modeling and analysis for the research article titled 'CCS investment – fiddling while the planet burns', authored by Yoga Wienda Pratama and Niall Mac Dowell from Imperial College London.</p> <p>In this work, we modeled and optimised the systems using Electricity Systems Optimisation framework (DOI: 10.5281/zenodo.1048943) that was developed General Algebraic Modeling System (GAMS). Data for this study is therefore provided in .gdx format that is suitable for GAMS.</p> <p>Procedure to reproduce this study is discussed in the article. Further questions can be addressed to Yoga Wienda Pratama (y.pratama18@imperial.ac.uk) or Niall Mac Dowell (niall@imperial.ac.uk).</p>
RFR-CCS: A monthly surface pCO2 product for the California Current Large Marine Ecosystem
<p><strong>Description</strong></p> <p>RFR-CCS is a monthly data product of surface pCO2 for the California Current Large Marine Ecosystem. It was built using pCO2 observations from the Surface Ocean CO2 Atlas, fit against a variety of driver variables (sources of which are listed below) using a Random Forest Regression, and provided on a 0.25 degree resolution grid from January 1998 to December 2020.</p> <p><strong>Citation</strong></p> <p>If using this product, please also cite the original manuscript:</p> <p>Sharp, J.D., Fassbender, A.J., Carter, B.C., Lavin, P.D., and Sutton, A.J. <a href="https://doi.org/10.5194/essd-14-2081-2022">A monthly surface pCO2 product for the California Current Large Marine Ecosystem</a>. Earth System Science Data, 14, 2081–2108, 2022. doi: 10.5194/essd-14-2081-2022.</p> <p><strong>Driver Variables (<a href="https://figshare.com/articles/dataset/RFR-CCS/15152013/2">figshare</a>)</strong></p> <p>1. Sea Surface Temperature: <a href="https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html">OISSTv2</a></p> <p>2. Sea Surface Salinity: <a href="https://www.ecco-group.org/products.htm">ECCO2</a></p> <p>3. Chlorophyll-a: <a href="http://sites.science.oregonstate.edu/ocean.productivity/1080.by.2160.monthly.inputData.php">Oregon State (via NASA)</a></p> <p>4. Wind Speed: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5">ERA5</a></p> <p>5. Atmospheric pCO2: <a href="https://gml.noaa.gov/ccgg/mbl/">NOAA MBL</a></p> <p>6. Mixed Layer Depth: <a href="https://www.hycom.org/hycom/">HYCOM</a></p> <p>7. Distance from Shore: Calculated from latitude and longitude (<a href="https://www.chadagreene.com/CDT/CDT_Contents.html">dist2coast, Climate Data Toolbox</a>)</p> <p>8. Year</p> <p>9. Month of Year</p>
Text-fig. 2. Stratigraphic column of the Govone section with a detail of the sampled intervals on the right. CCS – Cassano Spinola Conglomerates; Gm and Gg – sedimentary cycles, respectively marl-dominated or gypsum-dominated; MES – Messinian Erosional Surface; MSC – Messinian Salinity Crisis; PLG – Primary Lower Gypsum; RLG – Resedimented Lower Gypsum. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 2. Stratigraphic column of the Govone section with a detail of the sampled intervals on the right. CCS – Cassano Spinola Conglomerates; Gm and Gg – sedimentary cycles, respectively marl-dominated or gypsum-dominated; MES – Messinian Erosional Surface; MSC – Messinian Salinity Crisis; PLG – Primary Lower Gypsum; RLG – Resedimented Lower Gypsum.
S116 | REFCCS | Collision Cross Section (CCS) Values from Literature
<p>This is the collection associated with list S116 REFCCS Collision Cross Section Values from Literature on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This dataset is an entry point for users to contribute collision cross section values from the literature for addition to the NORMAN SLE, SusDat and the PubChem Collision Cross Section annotations.</p>
KTH-dESA/OSeMBE: CCS technologies
<p>v1.0.0</p> <p>This release adds <strong>CCS</strong> technologies to OSeMBE. To capture the characteristics of CCS also the OSeMOSYS code has been slightly modified to allow negative emissions. The following aspects have been modified:</p> <ul> <li>Added CCS technologies <ul> <li>Allowed to be installed after 2020</li> </ul> </li> <li>Fixed a bug related ESBMST</li> </ul>
Large CCS dataset (~90,000,000 molecules) predicted by SigmaCCS
<p>We developed a model named SigmaCCS which can be used to predict CCS of compounds,and a dataset including CCS values of ~90,000,000 molecules from Pubchem was formed.For each molecules,there are "Pubchem ID","SMILES","InChi","Inchikey","Molecular Weight","Exact Mass","Formula"and predicted CCS values of three adduct ion type ([M+H]<sup>+</sup>,[M-H]<sup>-</sup>,[M+Na]<sup>+</sup>).</p> <p>We believe this dataset can unleash the true potential of IMS in molecule identification.</p>
S61 | UJICCSLIB | Collision Cross Section (CCS) Library from UJI
<p>This is the collection associated with list S61 UJICCSLIB on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of 970 collision cross section values from 556 compounds (both positive and negative ionization modes, and N2 as a drift gas using TWIMS-QTOF instrument) provided by Alberto Celma et al. (submitted)</p> <p>Update 10 April 2020: added charge information in an extra column to the xlsx and csv files, for PubChem integration. InChIKey and DTXSID files did not change. 30 Jul 2020: fixed adduct/charge mismatches in CSV.</p>
CCS-B30 - Vídeo Ciència Ciutadana (subt.) (trad. castellano)
<p><strong><span lang="ES">TRADUCCIÓN - CASTELLANO </span></strong></p> <p><strong><span lang="ES">La ciencia ciudadana</span></strong></p> <div> </div> <p><strong><span lang="ES">Introducción a la ciencia ciudadana</span></strong><span lang="ES"><br><em>00:00:00 – 00:00:11</em><br>Hola, soy Sofía Mojica de la Universidad Autónoma de Barcelona. En esta píldora formativa os hablaré sobre la ciencia ciudadana.<br>¿Qué es la ciencia ciudadana?</span></p> <div> </div> <p><strong><span lang="ES">Definición y objetivo de la ciencia ciudadana</span></strong><span lang="ES"><br><em>00:00:13 – 00:00:23</em><br>La ciencia ciudadana se orienta a la generación de nuevo conocimiento con la participación activa e imprescindible de la ciudadanía en alguna etapa del proceso de investigación científica.</span></p> <div> </div> <p><strong><span lang="ES">Beneficios generales de la ciencia ciudadana</span></strong><span lang="ES"><br><em>00:00:28 – 00:00:45</em><br>Los posibles beneficios de la ciencia ciudadana son:</span></p> <ul> <li><span lang="ES">La mejora de la salud y el bienestar.</span></li> <li><span lang="ES">El aumento del conocimiento sobre métodos científicos.</span></li> <li><span lang="ES">El aumento de los sentimientos de responsabilidad y éxito.</span></li> <li><span lang="ES">El aumento de la percepción de empoderamiento cognitivo, político, social y económico.</span></li> </ul> <div> </div> <p><strong><span lang="ES">Beneficios de la ciencia ciudadana escolar</span></strong><span lang="ES"><br><em>00:00:46 – 00:01:02</em><br>En el contexto escolar, los beneficios incluyen:</span></p> <ul> <li><span lang="ES">El enriquecimiento de las situaciones de aprendizaje y la participación activa.</span></li> <li><span lang="ES">La mejora del rendimiento y la retención en el aula.</span></li> <li><span lang="ES">El aumento del interés en diferentes ramas del conocimiento.</span></li> </ul> <div> </div> <p><strong><span lang="ES">Competencias y actitudes promovidas</span></strong><span lang="ES"><br><em>00:01:03 – 00:01:32</em><br>Algunas de las competencias que se pueden alcanzar con la ciencia ciudadana son:</span></p> <ul> <li><span lang="ES">Competencias científicas.</span></li> <li><span lang="ES">Competencias comunicativas.</span></li> <li><span lang="ES">Competencias relacionadas con la ética.<br>También se promueve la actitud participativa y la motivación.<br>Desde hace muchos años, científicos y científicas ciudadanas contribuyen al impacto de muchos proyectos científicos en todo el mundo.<br>A continuación, os presentaré algunos proyectos sociales de ciencia ciudadana escolar con la participación del personal investigador de la UAB.</span></li> </ul> <div> </div> <p><strong><span lang="ES">Proyectos de ciencia ciudadana en la UAB</span></strong><span lang="ES"><br><em>00:01:35 – 00:02:15</em></span></p> <ul> <li><strong><span lang="ES">Proyecto Atención:</span></strong><span lang="ES"><br>Involucrar al alumnado, comunidad docente y científica en un proyecto de ciencia ciudadana para avanzar la ciencia profesional de una manera abierta y socialmente responsable. También busca alfabetizar y empoderar a la comunidad educativa, especialmente al alumnado, en la toma de decisiones y actuación respecto a la problemática de la contaminación atmosférica.</span></li> <li><strong><span lang="ES">Proyecto Ritmo Natura:</span></strong><span lang="ES"><br>Busca ciudadanos amantes de la naturaleza que quieran aprender a hacer observaciones fenológicas y ayudar a los científicos a estudiar los impactos del cambio climático sobre las plantas y los animales.</span></li> <li><strong><span lang="ES">Proyecto Alerta Forestal:</span></strong><span lang="ES"><br>Analiza el estado de salud actual de los bosques de Cataluña y prevé, mediante modelos de simulación, cómo será el estado de salud de esos bosques en el futuro, teniendo en cuenta el cambio climático y otros cambios ambientales que están ocurriendo a escala global y local.</span></li> </ul> <div> </div> <p><strong><span lang="ES">Referencias y recursos para profundizar</span></strong><span lang="ES"><br><em>00:02:32 – 00:02:48</em><br>Algunas referencias para quienes quieran profundizar más en la temática son:</span></p> <ul> <li><span lang="ES">La guía “Ciencia ciudadana y naturaleza. 10 recomendaciones para integrar la ciencia ciudadana en el aula de Ciencias”.</span></li> <li><span lang="ES">El análisis del impacto de la introducción de la ciencia ciudadana en las escuelas.</span></li> <li><span lang="ES">Algunos materiales didácticos.</span></li> </ul> <div> </div> <p><strong><span lang="ES">Cierre</span></strong><span lang="ES"><br><em>00:02:50 – 00:02:53</em><br>Muchas gracias por vuestra atención.</span></p> <p> </p> <p>Referències:</p> <ol> <li>Escoda, V. Garcia, J. Sala, M. Xifra, C. (2022) Ciència ciutadana i natura: un repte per a l’educació per a la sostenibilitat. Servei d’Educació Ambiental, Departament d’Acció Climàtica, Alimentació i Agenda Rural. <a href="https://mediambient.gencat.cat/web/.content/home/ambits_dactuacio/educacio_i_sostenibilitat/educacio_per_a_la_sostenibilitat/ciencia-ciutadana-natura/natura-a-laula/Guia-Ciencia-Ciutadana.pdf">https://mediambient.gencat.cat/web/.content/home/ambits_dactuacio/educacio_i_sostenibilitat/educacio_per_a_la_sostenibilitat/ciencia-ciutadana-natura/natura-a-laula/Guia-Ciencia-Ciutadana.pdf</a> </li> <li>Solé, C. Couso, D. Hernández, M. I. (2024) Participem en una iniciativa de ciència ciutadana? 10 recomanacions per integrar la ciència ciutadana a l’aula de ciències. Revista Ciències No 48. <a href="https://doi.org/10.5565/rev/ciencies.494">https://doi.org/10.5565/rev/ciencies.494</a></li> </ol>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
CCS-RTM-GBDT-BO
<p>Carbon Capture Storage (CCS) relevant Reactive Transport Modelling (RTM) of microfractures in basaltic rock; emulated using Gradient Boosted Decision Trees (GBDT) and subsequently optimised using Bayesian Optimisation (BO) framework. This project's code is hosted on Github at https://github.com/ThomasDodd97/CCS-RTM-GBDT-BO. This upload on Zenodo contains the dataset used to train four XGBoost GBDT surrogate models, whose model files are also uploaded here.</p>
CVoiceFake-Full ("SafeEar: Content Privacy-Preserving Audio Deepfake Detection", ACM CCS 2024)
<h1><strong>Introduction:</strong></h1> <p>CVoiceFake (Full) encompasses <strong>five common languages (English, Chinese, German, French, and Italian)</strong> and utilizes <strong>multi-advanced and classical voice cloning techniques</strong> (Parallel WaveGAN, Multi-band MelGAN, Style MelGAN, Griffin-Lim, WORLD, and DiffWave) to produce audio samples that bear a high resemblance to authentic audio.</p> <ol> <li><strong>Parallel WaveGAN</strong>: As a non-autoregressive vocoder-based model, Parallel WaveGAN produces high-fidelity audio rapidly, ideal for efficient and quality deepfake generation.</li> <li><strong>Multi-band MelGAN</strong>: Multi-band MelGAN is a variant of MelGAN that divides the frequency spectrum into sub-bands for faster and more stable multi-lingual vocoder training, enhancing the robustness and scalability of the dataset.</li> <li><strong>Style MelGAN</strong>: Style MelGAN is designed to capture fine prosodic and stylistic nuances of speech, making it particularly compelling for deepfake applications that require high levels of expressivity and variation in speech synthesis.</li> <li><strong>Griffin-Lim</strong>: This algorithm reconstructs waveforms from spectrograms using an iterative phase estimation method. Though less high-fidelity than neural vocoders, it serves as a traditional baseline for comparing deepfake generation.</li> <li><strong>WORLD</strong>: WORLD is a statistical parameter-based voice synthesis system that offers fine control over the spectral and prosodic features of the synthesized audio. Its fine manipulation is useful for crafting the nuanced variations needed in deepfake datasets.</li> <li>We have also built the SOTA diffusion-based deepfake audio (DiffWave); please contact the author at <code>xinfengli@zju.edu.cn</code> if you are interested in the dataset, particularly the DiffWave portion. Furthermore, any additional discussions are welcomed.<br><strong>DiffWave</strong>: DiffWave is a diffusion probability model for waveform generation. It converts the white noise signal into structured waveform through a Markov chain, capable of both conditional and unconditional generation tasks. DiffWave represents the advanced synthesis method for its fast synthesis speed and high synthesis quality.</li> </ol> <h1><strong>🔥 News:</strong></h1> <p>Please note that we recently released our DiffWave subset in Version 2 in comparison to Version 1. You can download the file named CVoiceFake_Large_diffwave_update.tar.gz.xx, and after unzipping it, you will find it retains the same file structure as before.<br> <strong>| CVoiceFake_Large_diffwave_update.tar.gz.00 |<br> | CVoiceFake_Large_diffwave_update.tar.gz.01 |</strong></p> <p> </p> <h1><strong>Full Dataset & Project Page:</strong></h1> <p>The sampled small dataset is available on <a href="11124319" target="_blank" rel="noopener">CVoiceFake Small</a> as well. Please kindly also refer to the project page: <a title="SafeEar Website" href="https://safeearweb.github.io/Project/" target="_blank" rel="noopener">SafeEar Website</a>.</p> <p> </p> <h1><strong>Citation:</strong></h1> <p>If you find our paper/code/dataset helpful, please kindly consider citing this work with the following reference:</p> <pre>@inproceedings{li2024safeear,<br> author = {Li, Xinfeng and Li, Kai and Zheng, Yifan and Yan, Chen and Ji, Xiaoyu, and Xu, Wenyuan},<br> title = {{SafeEar: Content Privacy-Preserving Audio Deepfake Detection}},<br> booktitle = {Proceedings of the 2024 {ACM} {SIGSAC} Conference on Computer and Communications Security (CCS)}<br> year = {2024},<br>}</pre>
Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework
<p>Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"</p>
CCS analysis results and scripts of de novo methyltransferases research in Tetrahymena
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