Electronic transport in materials is governed by a series of tensorial properties such as conductivity, Seebeck coefficient, and effective mass. These quantities are paramount to the understanding of materials in many fields from thermoelectrics to electronics and photovoltaics. Transport properties can be calculated from a material’s band structure using the Boltzmann transport theory framework. We present here the largest computational database of electronic transport properties based on a large set of 48,000 materials originating from the Materials Project database. Our results were obtained through the interpolation approach developed in the BoltzTraP software, assuming a constant relaxation time. We present the workflow to generate the data, the data validation procedure, and the database structure. Our aim is to target the large community of scientists developing materials selection strategies and performing studies involving transport properties.

• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467). * Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312) * Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries). * Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex- ploration and design of amorphous materials is hampered by the absence of a com- prehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from sys- tematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductiv- ity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in uni- versal machine learning potentials, impacting design beyond that of non-crystalline materials.

The NIST Computational Chemistry Comparison and Benchmark Database is a collection of experimental and ab initio thermochemical properties for a selected set of gas-phase molecules. The goals are to provide a benchmark set of experimental data for the evaluation of ab initio computational methods and allow the comparison between different ab initio computational methods for the prediction of gas-phase thermochemical properties. The data files linked to this record are a subset of the experimental data present in the CCCBDB.

To meet the challenge of antibiotic resistance worldwide, a new generation of antimicrobials must be developed. This communication demonstrates ab initio design of potent peptides against methicillin-resistant Staphylococcus aureus (MRSA). Our idea is that the peptide is very likely to be active when the most probable parameters are utilized in each step of the design. We derived the most probable parameters (e.g., amino acid composition, peptide hydrophobic content, and net charge) from the antimicrobial peptide database by developing a database filtering technology (DFT). Different from classic cationic antimicrobial peptides usually with high cationicity, DFTamP1, the first anti-MRSA peptide designed using this technology, is a short peptide with high hydrophobicity but low cationicity. Such a molecular design made the peptide highly potent. Indeed, the peptide caused bacterial surface damage and killed community-associated MRSA USA300 in 60 min. Structural determination of DFTamP1 by NMR spectroscopy revealed a broad hydrophobic surface, providing a basis for its potency against MRSA known to deploy positively charged moieties on the surface as a mechanism for resistance. Our ab initio design combined with database screening led to yet another peptide with enhanced potency. Because of the simple composition, short length, stability to proteases, and membrane targeting, the designed peptides are attractive leads for developing novel anti-MRSA therapeutics. Our database-derived design concept can be applied to the design of peptide mimicries to combat MRSA as well.

Nordreg AB Whois Database, discover comprehensive ownership details, registration dates, and more for Nordreg AB with Whois Data Center.

AB RIKTAD Whois Database, discover comprehensive ownership details, registration dates, and more for AB RIKTAD with Whois Data Center.

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• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467).
• Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312)
• Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries).
• Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).

This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

United States CPI U: FB: Alcoholic Beverages (AB) data was reported at 248.844 1982-1984=100 in Jun 2018. This records an increase from the previous number of 248.126 1982-1984=100 for May 2018. United States CPI U: FB: Alcoholic Beverages (AB) data is updated monthly, averaging 129.900 1982-1984=100 from Dec 1952 (Median) to Jun 2018, with 671 observations. The data reached an all-time high of 248.844 1982-1984=100 in Jun 2018 and a record low of 39.300 1982-1984=100 in Mar 1953. United States CPI U: FB: Alcoholic Beverages (AB) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I002: Consumer Price Index: Urban.

United States US LT Sec: Agency Bonds (AB) data was reported at 1,041.053 USD bn in Aug 2018. This records an increase from the previous number of 1,033.523 USD bn for Jul 2018. United States US LT Sec: Agency Bonds (AB) data is updated monthly, averaging 947.077 USD bn from Dec 2011 (Median) to Aug 2018, with 81 observations. The data reached an all-time high of 1,057.924 USD bn in Feb 2012 and a record low of 819.757 USD bn in Jul 2014. United States US LT Sec: Agency Bonds (AB) data remains active status in CEIC and is reported by US Department of Treasury. The data is categorized under Global Database’s United States – Table US.Z048: US Long Term Securities by Foreign Holders.

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Molecular mechanics (MM) force field models have been demonstrated to have difficulty reproducing certain potential energy surfaces of π-stacked complexes. Here, we examine the performance of the AMBER and CHARMM models relative to high-quality ab initio data across systematic helical parameter scans and typical B-DNA geometries for π-stacking energies of nucleobase dimers. These force fields perform best for typical B-DNA geometries (mean absolute error < 1 kcal mol–1), whereas errors typically approach ∼2 kcal mol–1 for broader potential scans, with maximum errors > 10 kcal mol–1 relative to high-quality ab initio reference interaction energies. The adequate performance of MM models near minimum energy structures is accomplished through cancellation of errors in various energy terms, whereas large errors at short intermolecular distances are caused by large MM electrostatics errors due to a lack of explicit terms modeling charge penetration effects.

• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467). * Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312) * Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries). * Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

United States CPI U: AW: FB: Alcoholic Beverages (AB) data was reported at 0.945 % in 2017. This records a decrease from the previous number of 0.952 % for 2016. United States CPI U: AW: FB: Alcoholic Beverages (AB) data is updated yearly, averaging 1.013 % from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 1.127 % in 2008 and a record low of 0.945 % in 2017. United States CPI U: AW: FB: Alcoholic Beverages (AB) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I011: Consumer Price Index: Urban: Weights (Annual).

Ab-initio Data Repository for Physics-Informed Data-Driven Model

This repository saved the precise Density Functional Theory (DFT) calculations and Vienna Ab initio Simulation Package (VASP) codes to provide a comprehensive dataset for physics-informed models. It specifically considers the steelmaking process by focusing on different types of non-metallic inclusions (NMIs) within the steel melt.

Data Sets Included:

1. Hamaker Constants values
2. Coagulation coefficients and Entrapment Probabilities
3. Kinematic Viscosity of SAE 1055 steel
4. Dynamic Viscosity of SAE 1055 steel
5. Dielectric function of oxides
6. Lattice parameters for molten and bulk Iron
7. Surface Tension of molten Fe.

Purpose and Application: This repository is designed to support advanced physics-informed modeling approaches, such as those using machine learning algorithms to predict clogging and inclusion behaviors in steelmaking processes.

The datasets includes following types of NMIs with detailed characteristics in the size range of 1-10 µm:

• Al2O3
• CaO
• MgO
• MgAl2O4
• CaS
• ZrO2

Contains measurement observations and supplementary data of antineoplastic drug contamination at nine cancer care sites in Alberta and Minnesota for the years 2018 and 2019. Data was collected in support of a study investigating exposure of healthcare workers to antineoplastic drugs, which can present an occupational health hazard.

REGCHEM (Regional Exploration Geochemistry) is a database of geochemical analyses compiled from open-file mining company reports. The database contains elemental analyses of stream sediment, pan concentrate, BCL (bulk cyanide leach), BLEG (bulk leach extraction gold), soil, and rock chip samples. Entries contain information on the sample type, analytical laboratory, analytical method and detection limits, and analyses for elements in ppm (unless otherwise stated)

New data is not being added to this database because of a lack of resourcing.

The Regional Exploration Geochemistry (REGCHEM) database was developed and managed by GNS Science ca. 1994 to 2016. It is a comprehensive repository of geochemical analyses derived from open-file mining company exploration reports that are held by New Zealand Petroleum and Minerals (www.nzpam.govt.nz). The database includes elemental analyses of diverse geological sample types, such as stream sediments, pan concentrates, soil, and rock chip samples. It provides detailed metadata for each entry, including sample type, laboratory, analytical method, detection limits, and elemental concentrations. Initially developed to investigate geochemical trends of stream sediment samples in selected regions of New Zealand, REGCHEM was expanded to cover the entire country and several sample types. The database encompasses over 284,000 analyses from approximately 53,000 sites. Elements analysed include gold, silver, arsenic, copper, lead, zinc, and others relevant for identifying mineralisation. This database is now archived due to a lack of resourcing for maintenance and updates. The database served multiple purposes: • Identifying natural background levels and geochemical anomalies, both from mineral deposits and anthropogenic activities such as old mine workings. • Supporting mineral prospecting studies by mapping areas with economic mineralisation potential and anomalous pathfinder element concentrations. • Establishing baseline data for monitoring heavy metal pollution. • Assisting in developing genetic models for mineralisation. REGCHEM data can be visualized using a geographic information system (GIS) tool such as ArcGIS or QGIS, and when supplemented with geochemical statistical analyses, enable the spatial representation of anomalies. The database, although no longer updated due to resource limitations, may remain a tool for regional geochemical assessments and mineral exploration. The database is archived as series of CSV text format files and a logical entity relationship diagram. It is also provided in a ‘flat file’ format for quick access and analysis. These files can be downloaded from a link in the ‘Distribution’ tab below. References: Warnes PN & Christie AB. 1995. Regional stream sediment geochemistry database (REGCHEM) for selected regions of New Zealand. In: Mauk JL, St George JD, Australasian Institute of Mining and Metallurgy: p. 611-616.

Christie AB, Sheppard DS, Goff JR, Carver R. 2006. Stream sediment geochemical surveys: a pilot multi-element project near Thames, Hauraki Goldfield, and further developments of the REGCHEM database. In. Australasian Institute of Mining and Metallurgy, 39th New Zealand Branch annual conference: p. 179-189.

Files, folders, tabular data and some raw data used in the publication: AB-SR reconstructs polyclonal antibody Fv domains after bottom-up proteomic de-novo sequencing (N. Maillet & B. Saunier). The AB-SR software reconstructs the sequences of most pairs of heavy and light chain variable regions from (in silico) pools containing up to 500 immunoglobulins in just a few minutes. For each Figure, the data before and after AB-SR software are available (see README.md for detailed explanations). Data presented here are used to benchmark AB-SR. More precisely, each experiment consists in IgGs coming from public databases being in silico digested using RPG software. Resulting peptides are then fed to AB-SR that reconstructs most initial IgGs.