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.

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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

• 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).

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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.

• 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

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

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.

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

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

This is a database of ~280,000 MOFs which have been either experimentally characterized or computationally generated, spanning all publicly available MOF databases. DFT-derived REPEAT charges, adsorption data, and various descriptors are available for all MOFs.

all_structures_1.tar.gz and all_structures_2.tar.gz – these are the cif files that were considered to compose the “entire known design space” of MOFs, with any bad structures removed (split into two separate tarballs since it is a lot of data).

ARCMOF_20220610.tar.gz – these are all of the cif files with REPEAT charges composing ARC-MOF.

flig-clusters.csv, func-clusters.csv, geo-clusters.csv, mc-clusters.csv – Each file indicates for each MOF which cluster it belongs to, and whether the MOF is present in ARC-MOF. This is done for each "type" of MOF chemistry and for the geometric properties. Clusters with a negative value indicate the MOF does not belong to any cluster (i.e., it is assumed to be "unique").

all_topology_lists.csv – a csv file containing the topology reported by the filename of applicable structures, and the topology reported by CrystalNets.jl

ML_test_set.tar.gz – these are the cif files (with REPEAT charges) of the MOFs in the diverse-mc subset, but missing from ARC-MOF (for the purposes of a ML test set for the prediction of metal charges).

geometric_properties.csv – a csv file containing geometric descriptors computed for this study for all MOFs. The csv file also indicates which MOFs are present in ARC-MOF, and the order in which they were chosen for the farthest point sampling (up to 100K MOFs).

RACs.csv – See geometric_properties.csv description. Same type of file, but with the RAC descriptors.

RDFs.csv – The RDFs for each MOF, using several atomic properties. Some atomic properties are not available for all elements. In the cases where the atomic property is not available for a particular structure, no value is assigned.

methane.csv, methane_purification-CH4.csv, methane_purification_CO2.csv, post_comb_vsa-CO2.csv, post_comb_vsa-N2.csv, pre_comb_4040-CO2.csv, pre_comb_4040-H2.csv, landfill-CH4.csv, landfill-CO2.csv – these are csv files of the raw uptake data and various temperature, pressure conditions (with standard deviations) for each gas separation process specified in the file overall_process.csv.

overall_process.csv – This is a csv file of the adsorption properties of the MOFs. Particularly, the csv files contain the working capacity (mmol/g_working_capacity) and selectivity of each MOF for each of the five process conditions.

mc-diverse-set.csv, func-diverse-set.csv – csv files containing which MOFs are present in each diverse set (from farthest point sampling of the MOFs based on either their functional group chemistry or metal chemistry). The file indicates which MOFs are present in ARC-MOF and which are not.

Version history of repository:

v2 -- added file: "all_topology_lists.csv"

v3 -- added file: "ML_test_set.tar.gz"

v4 -- replaced file: "ML_test_set.tar.gz". Originally incorrect repository of cifs

v5 -- A slightly updated version of ARC-MOF has been provided. Some MOFs were removed from ARC-MOF due to structural errors. Some MOFs in ARC-MOF containing Sm were updated, as they had incorrectly assigned charges. Additional MOFs from all_structures containing Sm were added to ARC-MOF.

v6 -- Updated version of ARC-MOF. Removed of all m29 structures from the Boyd-Woo database, since the inorganic SBU is not known to exist.

• 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 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.

A B Collections Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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.

Multi-Agency Ground Plot (MAGPlot) database (DB) is a pan-Canadian forest ground-plot data repository. The database synthesize forest ground plot data from various agencies, including the National Forest Inventory (NFI) and 12 Canadian jurisdictions: Alberta (AB), British Columbia (BC), Manitoba (MB), New Brunswick (NB), Newfoundland and Labrador (NL), Nova Scotia (NS), Northwest Territories (NT), Ontario (ON), Prince Edward Island (PE), Quebec (QC), Saskatchewan (SK), and Yukon Territory (YT), contributed in their original format. These datasets underwent data cleaning and quality assessment using the set of rules and standards set by the contributors and associated documentations, and were standardized, harmonized, and integrated into a single, centralized, and analysis-ready database. The primary objective of the MAGPlot project is to collate and harmonize forest ground plot data and to present the data in a findable, accessible, interoperable, and reusable (FAIR) format for pan-Canadian forest research. The current version includes both historical and contemporary forest ground plot data provided by data contributors. The standardized and harmonized dataset includes eight data tables (five site related and three tree measurement tables) in a relational database schema. Site-related tables contain information on geographical locations, treatments (e.g. stand tending, regeneration, and cutting), and disturbances caused by abiotic factors (e.g., weather, wildfires) or biotic factors (e.g., disease, insects, animals). Tree-related tables, on the other hand, focus on measured tree attributes, including biophysical and growth parameters (e.g., DBH, height, crown class), species, status, stem conditions (e.g., broken or dead tops), and health conditions. While most contributors provided large and small tree plot measurements, only NFI, AB, MB, and SK contributed datasets reported at regeneration plot level (e.g., stem count, regeneration species). Future versions are expected to include updated and/or new measurement records as well as additional tables and measured and compiled (e.g., tree volume and biomass) attributes. MAGPlot is hosted through Canada’s National Forest Information System (https://nfi.nfis.org/en/maps). --------------------------------------------------- LATEST SITE TREATMENTS LAYER: --------------------------------------------------- Shows the most recently applied treatment class for each MAGPlot site. These treatment classes are broad categories, with more specific treatment details available in the full dataset. ----------- NOTES: ----------- The MAGPlot release (v1.0 and v1.1) does not include NL and SK datasets due to pending Data Sharing Agreements, ongoing data processing, or restrictions on third-party sharing. These datasets will be included in future releases. While certain jurisdictions permit open or public data sharing, given that requestor signs and adheres the Data Use agreement, there are some jurisdictions that require a jurisdiction-specific request form to be signed in addition to the Data Use Agreement form. For the MAGPlot Data Dictionary, other metadata, datasets available for open sharing (with approximate locations), data requests (for other datasets or exact coordinates), and available data visualization products, please check all the folders in the “Data and Resources” section below. Coordinates in web services have been randomized within 5km of true location to preserve site integrity Access the WMS (Web Map Service) layers from the “Data and Resources” section below. A data request must be submitted to access historical datasets, datasets restricted by data-use agreements, or exact plot coordinates using the link below. NFI Data Request Form: https://nfi.nfis.org/en/datarequestform --------------------------------- ACKNOWLEDGEMENT: --------------------------------- We acknowledge and recognize the following agencies that have contributed data to the MAGPlot database: Government of Alberta - Ministry of Agriculture, Forestry, and Rural Economic Development - Forest Stewardship and Trade Branch Government of British Columbia - Ministry of Forests - Forest Analysis and Inventory Branch Government of Manitoba - Ministry of Economic, Development, Investment, Trade, and Natural Resources - Forestry and Peatlands Branch Government of New Brunswick - Ministry of Natural Resources and Energy Development - Forestry Division, Forest Planning and Stewardship Branch Government of Newfoundland & Labrador - Department of Fisheries, Forestry and Agriculture - Forestry Branch Government of Nova Scotia - Ministry of Natural Resources and Renewables - Department of Natural Resources and Renewables Government of Northwest Territories - Department of Environment & Climate Change - Forest Management Division Government of Ontario - Ministry of Natural Resources and Forestry - Science and Research Branch, Forest Resources Inventory Unit Government of Prince Edward Island - Department of Environment, Energy, and Climate Action - Forests, Fish, and Wildlife Division Government of Quebec - Ministry of Natural Resources and Forests - Forestry Sector Government of Saskatchewan - Ministry of Environment - Forest Service Branch Government of Yukon - Ministry of Energy, Mines, and Resources - Forest Management Branch Government of Canada - Natural Resources Canada - Canadian Forest Service - National Forest Inventory Projects Office

11177 Global export shipment records of A B Switches with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Agricultural Regions of Alberta Soil Inventory Database (AGRASID) is a spatial database of soils for Alberta’s Agricultural area. This version, Version 1, of the database is outdated and has been replaced by online versions of the data. See "detailed information" for more usage considerations, and "related" for a link to the online version.

558 Global import shipment records of Ab Circle with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.