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.

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.

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.

Collection of data from all pancreatic islet isolations.

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.

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.

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

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

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.

• 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).

United States CPI W: sa: FB: Alcoholic Beverages (AB) data was reported at 252.838 1982-1984=100 in Jun 2018. This records an increase from the previous number of 251.743 1982-1984=100 for May 2018. United States CPI W: sa: FB: Alcoholic Beverages (AB) data is updated monthly, averaging 147.900 1982-1984=100 from Jan 1967 (Median) to Jun 2018, with 618 observations. The data reached an all-time high of 252.838 1982-1984=100 in Jun 2018 and a record low of 45.300 1982-1984=100 in Feb 1967. United States CPI W: sa: 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.I013: Consumer Price Index: Urban Wage and Clerical Workers: sa.

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.

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

The database contains information about individual water well drilling reports, chemical analysis reports up to the end of 1986, springs, flowing shot holes, test holes, and pump tests conducted on the wells. There is approximately 500,000 records within the database with about 5,000 new records being added each year.

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.

Top five significant results from BLASTP search of EBP(Ec) homology.

Ab-initio phonon calculation for Tl(BH)6 / Fm-3 (202) Phonon band structure, phonon DOS, thermal properties at constant volume, and phonon raw data are presented. The initial crystal structure used to perform phonon calculation is obtained from the Materials Project using pymatgen MPRester. The phonon band structure paths are determined using SeeK-path.