From the project website: https://netl.doe.gov/carbon-management/carbon-storage/worldwide-ccs-database

Welcome to the National Energy Technology Laboratory’s (NETL) Carbon Capture and Storage (CCS) Database, which includes information on active, proposed, and terminated CCS projects worldwide.

Publicly available information has been aggregated to provide a one-stop interactive tool that contains valuable data, including, but not limited to:

Technologies being developed for capture. Evaluation of sites for carbon dioxide (CO2) storage. An estimation of project costs. Project description and current status. Amount of CO2 captured/stored. NETL’s CCS Database provides the public with information regarding efforts by various industries, public groups, and governments that are being made towards development and eventual deployment of CCS technology. As of April 2018, the database contained 305 total CCS projects worldwide, with 299 sites identified. The 299 site-located projects include 76 capture, 76 storage, and 147 for capture and storage in more than 30 countries across 6 continents. While several of the projects are still in the planning and development stage, and many have been completed, 37 are actively capturing and/or injecting CO2.

The CCS Database is presented using a Tableau Dashboard which is entirely interactive. Hovering, clicking, and/or dragging any of the icons will customize your view. Adjusting the toolbar on the left allows you to narrow your selection, pan, or zoom in/out on the map.

NETL's Carbon Capture and Storage Database includes active, proposed, canceled, and terminated CCS projects worldwide. Information in the database regarding technologies being developed for capture, evaluation of sites for carbon dioxide (CO2) storage, estimation of project costs, and anticipated dates of completion is sourced from publicly available information. The CCUS Database provides the public with information regarding efforts by various industries, public groups, and governments towards development and eventual deployment of CCUS technology. This is an active database that will be updated as information regarding these or new projects are released to the public.

The American College Catalog Study Database (CCS) contains academic data on 286 four-year colleges and universities in the United States. CCS is one of two databases produced by the Colleges and Universities 2000 project based at the University of California-Riverside. The CCS database comprises a sampled subset of institutions from the related Institutional Data Archive (IDA) on American Higher Education (ICPSR 34874). Coding for CCS was based on college catalogs obtained from College Source, Inc. The data are organized in a panel design, with measurements taken at five-year intervals: academic years 1975-76, 1980-81, 1985-86, 1990-91, 1995-96, 2000-01, 2005-06, and 2010-11. The database is based on information reported in each institution's college catalog, and includes data regarding changes in major academic units (schools and colleges), departments, interdisciplinary programs, and general education requirements. For schools and departments, changes in structure were coded, including new units, name changes, splits in units, units moved to new schools, reconstituted units, consolidated units, departments reduced to program status, and eliminated units.

Why am I doing this? Because I liked this dataset: https://www.kaggle.com/datasets/alistairking/co2-sequestration-2016-2022 But it only had 13 rows of data, I went and found more and have shared. (The author of the linked dataset is listed as a contributor below.)

The National Energy Technology Laboratory’s (NETL) Carbon Capture and Storage (CCS) Database includes information on active, proposed, and terminated CCS projects worldwide. Publicly available information has been aggregated to provide a one-stop interactive tool that contains valuable data, including, but not limited to:

• Technologies being developed for capture.
• Evaluation of sites for carbon dioxide (CO2) storage.
• An estimation of project costs.
• Project description and current status.
• Amount of CO2 captured/stored.

NETL’s CCS Database provides the public with information regarding efforts by various industries, public groups, and governments that are being made towards development and eventual deployment of CCS technology. While several of the projects are still in the planning and development stage, and many have been completed, 37 are actively capturing and/or injecting CO2.

The CCS Database is presented using a Tableau Dashboard which is entirely interactive (located here https://netl.doe.gov/carbon-management/carbon-storage/worldwide-ccs-database ).

Disclaimer of Liability: The CCS Database is made available by an agency of the United States Government. Neither the United States Government, the Department of Energy, the National Energy Technology Laboratory, nor any of their employees, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information or data disclosed, or represents that its use would not infringe privately owned rights.

The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility-mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites. In this work, we first experimentally measured CCS values (ΩN2) of ∼400 metabolites in nitrogen buffer gas and used these values as training data to optimize the prediction method. The high prediction precision of this method was externally validated using an independent set of metabolites with a median relative error (MRE) of ∼3%, better than conventional theoretical calculation. Using the SVR based prediction method, a large-scale predicted CCS database was generated for 35 203 metabolites in the Human Metabolome Database (HMDB). For each metabolite, five different ion adducts in positive and negative modes were predicted, accounting for 176 015 CCS values in total. Finally, improved metabolite identification accuracy was demonstrated using real biological samples. Conclusively, our results proved that the SVR based prediction method can accurately predict nitrogen CCS values (ΩN2) of metabolites from molecular descriptors and effectively improve identification accuracy and efficiency in untargeted metabolomics. The predicted CCS database, namely, MetCCS, is freely available on the Internet.

Pichache-McLean CCS Database

3800 CCS Values for Compound Characterization

By [source]

About this dataset

This dataset contains 3800 experimental collision cross section values, allowing us to get an unprecedented glimpse into the fundamentals of particle mobility. Collected by researchers Jackie Picache and John McLean of Vanderbilt University, these values were generated using drift tube MS and represent the CCS of different compounds. With this info we can uncover data on molecular properties that up until now remain hidden. From the adduct type and charge to the kingdom and superclass, many factors are at play when it comes to collisions between atoms and molecules- now we can study them in detail! Here you will find all relevant information such as: molecular formula, CAS number, InChIKey version 3 & 4, InChI string v1.02 & 1.04, PubChem CID SMILES string for both canonical & isomeric forms as well as XLogP values- not to mention an abundance of other metrics like m/z ratios, peak numbers & collision cross sections! All this makes it possible to study particle interactions on a level never seen before-- discover it today!

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How to use the dataset

Guide: How to use the Picache-McLean CCS Database

This dataset contains 3800 experimental collision cross section (CCS) values collected by researchers Jackie Picache and John McLean of Vanderbilt University. These experimental values enable users to better understand the structure and properties of various compounds, and create more effective models for use in collisions studies where CCS is used as a measure of fragmentation efficiency.

In order to make optimal use of this dataset, it is important to understand the various columns present in it. This guide provides an overview of these columns and gives examples on how they can be used to get meaningful insights from the data.

The first column, MolecularFormula gives us an idea about the general structure of a compound represented by its empirical formula (e.g., C6H12O6). The second column CanonicalSMILES is an internationally accepted string notation developed for representing molecules; it allows computational models to accurately store chemical information as well as retrieve it quickly. The IsomericSMILES column stores data related specifically to stereoisomers; that is, any molecule which is composed of atoms connected in such a way that there are multiple ways in which they can be organized spatially but with same connectivity pattern have similar SMILES strings but different Isomeric SMILES strings due their different orientations (e.g., left handed vs right handed forms). The InChI and InChIKey columns contain International Chemical Identifier (InChI) system codes generated by the software application known as Chemdraw along with separate keys generated from them respectively; these two codes allow us uniquely identify a particular chemical substance from other chemicals having completely different structures but same elements so if any chemical compound has same InChI key then both will correspondrom identical molecule representation in either 2D or 3D space also IUPACName helps user for easily understanding name of polymer without confusing its structural aspects or interpretation through advanced scholarly sources such as book literature preferably when no canonical representation like molecular formula exists for respective polymer for distinction (e.g., caffeine). XLogP entries provide us information about hydrophobicity associated with particular pharmacological agents whereas ExactMass determinates how much mass his held by single atom/molecule represented through each entry giving scientists accurate calculation enabling them designing more precise experiment whose results between difference amounting 0-1 error might range changes vastly than significant undeterminant increase/decrease expected when masses estimated manually depending upon experience good

Research Ideas

• Using the Complete Collision Cross Sections (CCS) to predict physical properties of compounds such as boiling point, melting point and partition coefficient.
• Utilizing the SMILES strings to create an AI/ML algorithm that can accurately generate CCS data for each compound quickly and easily.
• Developing a web-based interface utilizing the InChIKeys to allow researchers to search and discover related compounds with similar collision cross sections

Acknowledgements

If you use this dataset in your research, please credit the original authors. [Data Source](https://zenodo.org/record/...

This worldwide database tracks Carbon Capture, Utilisation, and Storage (CCUS) projects that are either commissioned or in various planning stages. Developed by the International Energy Agency (IEA), it serves as a key resource for monitoring global CCUS advancements.The dataset includes projects commissioned since the 1970s with a clear emissions reduction scope, focusing on large-scale CO2 capture (over 100,000 tonnes/year) and Direct Air Capture (over 1,000 tonnes/year). It specifically excludes CO2 capture for low-climate-benefit uses (e.g., food/beverages), conventional industrial processes, and naturally occurring CO2 for enhanced oil recovery.

The NATCARB Viewer allows users to browse and query data under Regional Carbon Sequestration Partnership (RCSP), Atlas V, Worldwide CCS Database, Brine Well Samples, and other tabs. The number of stationary CO2 sources, CO2 emissions, and CO2 storage resource estimates reported in Atlas V is based on information gathered by the National Carbon Sequestration Database and Geographic Information System (NATCARB). NATCARB is a relational database and geographic information system (GIS) that integrates CCS data from the RCSPs and other sources. NATCARB provides a national view of the carbon storage potential; data from NATCARB is uploaded into Energy Data eXchange (EDX).

Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion’s gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8–2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.

Ion mobility-mass spectrometry (IM-MS) can provide orthogonal information, i.e., m/z and collision cross section (CCS), for the identification of drugs and drug metabolites. However, only a small number of CCS values are available for drugs, which limits the use of CCS as an identification parameter and the assessment of structure–function relationships of drugs using IM-MS. Here, we report the development of a rapid workflow for the measurement of CCS values of a large number of drug or drug-like molecules in nitrogen on the widely available traveling wave IM-MS (TWIM-MS) platform. Using a combination of small molecule and polypeptide CCS calibrants, we successfully determined the nitrogen CCS values of 1425 drug or drug-like molecules in the MicroSource Discovery Systems’ Spectrum Collection using flow injection analysis of 384-well plates. Software was developed to streamline data extraction, processing, and calibration. We found that the overall drug collection covers a wide CCS range for the same mass, suggesting a large structural diversity of these drugs. However, individual drug classes appear to occupy a narrow and unique space in the CCS–mass 2D spectrum, suggesting a tight structure–function relationship for each class of drugs with a specific target. We observed bimodal distributions for several antibiotic species due to multiple protomers, including the known fluoroquinolone protomers and the new finding of cephalosporin protomers. Lastly, we demonstrated the utility of the high-throughput method and drug CCS database by quickly and confidently confirming the active component in a pharmaceutical product.

Collision cross section (CCS) databases based on single-laboratory measurements must be cross-validated to extend their use in peak annotation. This work addresses the validation of the first comprehensive TWCCSN2 database for steroids. First, its long-term robustness was evaluated (i.e., a year and a half after database generation; Synapt G2-S instrument; bias within ±1.0% for 157 ions, 95.7% of the total ions). It was further cross-validated by three external laboratories, including two different TWIMS platforms (i.e., Synapt G2-Si and two Vion IMS QToF; bias within the threshold of ±2.0% for 98.8, 79.9, and 94.0% of the total ions detected by each instrument, respectively). Finally, a cross-laboratory TWCCSN2 database was built for 87 steroids (142 ions). The cross-laboratory database consists of average TWCCSN2 values obtained by the four TWIMS instruments in triplicate measurements. In general, lower deviations were observed between TWCCSN2 measurements and reference values when the cross-laboratory database was applied as a reference instead of the single-laboratory database. Relative standard deviations below 1.5% were observed for interlaboratory measurements (

The development of ion mobility–mass spectrometry (IM–MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM–MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM–MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

This is the collection associated with list S50 CCSCOMPEND on the NORMAN Suspect List Exchange.

https://www.norman-network.com/nds/SLE/

S50 CCSCOMPEND The Unified Collision Cross Section (CCS) Compendium

>3800 experimental collision cross section values (drift tube MS), provided by Jackie Picache and John McLean, Vanderbilt. Further details available here: https://lab.vanderbilt.edu/mclean-group/collision-cross-section-database/

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).

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

This feature class has been created to show the footprint location of all the playgrounds within the Charles Sturt Council area. Each feature has been drawn as a polygon of the soft fall area surrounding the playground equipment. The polygon was drawn based on aerial photography.

These datasets contain summary data about the Annual California Children’s Services (CCS) Whole Child Model (WCM) program. These summary files are intended to accompany the CCS Power BI Dashboard which is posted on the DHCS internet. The CCS and WCM Programs provide diagnostic and treatment services, case management, and physical and occupational therapy services to children under age 21 with CCS-eligible medical conditions. Examples of CCS-eligible conditions include, but are not limited to, chronic medical conditions such as cystic fibrosis, hemophilia, cerebral palsy, heart disease, cancer, traumatic injuries, and infectious diseases producing major sequelae.

Forage species such as Pacific sardine, northern anchovy, and market squid are critical ecological links between the planktonic food web and higher trophic levels in the California Current System, as well as supporting valuable fisheries. Environmental variability drives large fluctuations in their abundance and distribution. This dataset includes the outputs of Species Distribution Models (SDMs) for 6 key forage species, combining multiple survey datasets with environmental fields from a high-resolution Regional Ocean Modeling System (ROMS) developed at the University of California - Santa Cruz, and the Copernicus-Globcolour interpolated surface chlorophyll product (https://doi.org/10.48670/moi-00100). The sardine and anchovy SDMs also included predictors indexing stock biomass (MacCall et al. 2016; Kuriyama et al. 2020). As temporally continuous salinity fields are not available from the UCSC ROMS, we included a measure of the distance to the nearest major river (mean > 50,000 CFS discharge) in the herring SDM, to capture to association of this species with estuaries. The 6 species represented are Pacific sardine (Sardinops sagax), northern anchovy (Engraulis mordax), market squid (Doryteuthis opalescens), Pacific herring (Clupea pallasii), Pacific (chub) mackerel (Scomber japonicus), and Jack mackerel (Trachurus symmetricus). Results from two different SDMs are shown: Generalized Additive Models and Boosted Regression Trees, and both models predict the probability of occurrence of each species. We used data from the NOAA Southwest Fisheries Science Center Coastal Pelagic Species and Columbia River Predator (Emmett et al. 2006) trawl surveys to train all SDMs, with the exception of market squid, where juvenile salmon survey data were used instead (see Chasco et al. 2022 for description of these data). More details on preliminary versions of the sardine and anchovy SDMs are available in Muhling et al. (2019). An additional manuscript in preparation will include description of up-to-date data, methods, and model structure. Until this is published, we strongly recommend contacting Barbara Muhling (Barbara.Muhling@noaa.gov) before working with this dataset, to ensure complete understanding of the details and caveats. Funding for this work was provided by NOAA Office of Sustainable Fisheries, and the NOAA Climate and Fisheries Adaptation program. References Chasco, B. E., Hunsicker, M. E., Jacobson, K. C., Welch, O. T., Morgan, C. A., Muhling, B. A., & Harding, J. A. (2022). Evidence of Temperature-Driven Shifts in Market Squid Doryteuthis opalescens Densities and Distribution in the California Current Ecosystem. Marine and Coastal Fisheries, 14(1), e10190. Emmett, R. L., Krutzikowsky, G. K., & Bentley, P. (2006). Abundance and distribution of pelagic piscivorous fishes in the Columbia River plume during spring/early summer 1998-2003: relationship to oceanographic conditions, forage fishes, and juvenile salmonids. Progress in Oceanography, 68(1), 1-26. Kuriyama, P. T., Zwolinski, J. P., Hill, K. T., & Crone, P. R. (2020). Assessment of the Pacific sardine resource in 2020 for US management in 2020-2021. NOAA Technical Memorandum NOAA-TM-NMFS-SWFSC-628 MacCall, A. D., Sydeman, W. J., Davison, P. C., & Thayer, J. A. (2016). Recent collapse of northern anchovy biomass off California. Fisheries Research, 175, 87-94. Muhling, B., Brodie, S., Snodgrass, O., Tommasi, D., Dewar, H., Childers, J., Jacox, M. Edwards, C. A., Xu, Y. & Snyder, S. (2019). Dynamic habitat use of albacore and their primary prey species in the California Current System. CalCOFI Reports 60: 1-15.

Comprehensive dataset containing 10 verified CCS locations in Brazil with complete contact information, ratings, reviews, and location data.

High-throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and environmental samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent technological advances, metabolomics studies still result in the detection of a disproportionate number of features that cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics, the reliance on reference libraries constructed by analysis of authentic reference materials with limited commercial availability. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e., conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs first-principles simulation, distinguished by the use of molecular dynamics, quantum chemistry, and ion mobility calculations, to generate structures and chemical property libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over 2 orders of magnitude. Calculated CCS values were validated against 1983 experimentally measured CCS values and compared to previously reported CCS calculation approaches. Average calculated CCS error for the validation set is 3.2% using standard parameters, outperforming other density functional theory (DFT)-based methods and machine learning methods (e.g., MetCCS). An online database is introduced for sharing both calculated and experimental CCS values (metabolomics.pnnl.gov), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including providing evidence for the presence of an environmental degradation product, the separation of molecular isomers, and an initial characterization of complex blinded mixtures of exposure chemicals. This work represents a method to address the limitations of small molecule identification and offers an alternative to generating chemical identification libraries experimentally by analyzing authentic reference materials. All code is available at github.com/pnnl.

CCS is the process of separating CO2 from industrial emissions and injecting it in a storage site to prevent its re-entry into the atmosphere. The site is continuously monitored over its lifetime to detect potential risks, which makes this process foolproof in permanently putting away CO2. CCS is a complex process that is realized by the fusion of organic chemistry, geology, and advanced engineering. It primarily involves the isolation of carbon dioxide from other gases in the emission stream of an industrial facility. Oil majors, which are some of the largest emitters of CO2, are the leading adopters of CCS technology. Read More