Property Numeric Databases

Beilstein and Gmelin are the world s largest factual databases in chemistiy. Beil-stein contains facts and structures relating to organic chemistry, whereas Gmelin provides information on inorganic, coordination, and organomctallic compounds. 

Both database sources contain evaluated data on millions of compounds, and allow the retrieval of bibliographic information and of structures. 

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Numeric databases primarily contain numeric data on chemical compounds, such as physicochemical values and the results of series of measurements. Therefore, the files correspond to printed tables of numeric property data. Since the attributes of numeric data are different from those of text data, the search has to be managed... 

The protein sequence database is also a text-numeric database with bibliographic links. It is the largest public domain protein sequence database. The current PIR-PSD release 75.04 (March, 2003) contains more than 280 000 entries of partial or complete protein sequences with information on functionalities of the protein, taxonomy (description of the biological source of the protein), sequence properties, experimental analyses, and bibliographic references. Queries can be started as a text-based search or a sequence similarity search. PIR-PSD contains annotated protein sequences with a superfamily/family classification. 

OtherD t b ses. Available from different vendors (Table 8). For example, the researcher can obtain physical properties by usiag the Merck Index Online or the Dictionary of Organic Compounds available by Chapman and Hall Chemical Database. In DIALOG, numeric databases are collected under the name of CHEMPROP. 

Numeric. Researchers routinely use reported nunierie measurements and data in their work. Numeric databases include the Beilstein Handbook of Organic Chemistry, ihc Gmelin Handbook of Inorganic and Orgunomelallic Chemistry, properly daia networks the Materials Property Data Network Inc. (MPDt and Chemical Property Data Network (CPDNtl. and TDS NUMERICA. 

To predict the solid solubihty, in addition to model-based property models, databases and numerical solvers are necessary. To better illustrate each step of the solid solubility calculation, the necessary workflow and dataflow are highhghted in Figure 10.1, starting with the necessary pure component properties and ending with the phase diagram generation. It is important to note that when the experimental values of the... 

The descriptors of the 17 property types (Chap. 2.1.1) included in the numerical database are stored in the ELBT13APropTypes.txt file (Fig. 3.7) on the same drive as the program. 

ELDAR is distributed as part of DECHEMA s numerical database for theimophysical property data DETHERM [16, 17], To access ELDAR, one can therefore use several options. 

The Center for Energy Resources Engineering (CERE) of the Technical University of Denmark (DTU) is operating a data bank for electrolyte solutions [18]. It is a compilation of experimental data for (mainly) aqueous solutions of electrolytes and/or nonelectrolytes. The database is a mixture between a literature reference database and a numerical database. Currently references to more than 3,000 papers are stored in the database together with around 150,000 experimental data. The main properties are activity and osmotic coefficients, enthalpies, heat capacities, gas solubilities, and phase equihhria like VLE, LLE, and SLE. The access to the htera-ture reference database is free of charge. The numerical values must be ordered at CERE. 

The mechanical, thermal, and electrical properties of numerous ceramic materials have been presented in this chapter. Neither the number of materials nor the properties cited more than scratch the surface of data that are available, either in the literature or from numerous databases that are accessible through the Internet. Later chapters of this handbook, and publications referred to in the text, should be consulted if in-depth coverage is needed. It is hoped that enough data have been presented in this chapter to provide a guide for deeper investigations as needed. 

TRCTHERMO is a numeric database containing the evaluated data published in hardcopy form in the TRC Thermodynamic Tables - Hydrocarbons and TRC Thermodynamic Tables - Non-Hydrocarbons. It does not contain references to the data used in the evaluations, nor references to additional experimental and evaluated data from other sources. The database features the most frequently used thermodynamic data, including, when available, boiling point, critical compressibility, critical constants, density, dynamic viscosity, enthalpy, and other physical properties. 

Heats of reaction generally will not be found in databases under chemical safety . They can either be found in special databases on chemical reactions and reaction conditions or they can be calculated using the heat of formation of the substances involved. Heats of formation usually are found in numerical databases covering physical properties of pure substances and mixtures. 

The different information types that are present in chemical databases require different techniques for the indexing and retrieval of the various information units. All these techniques for information retrieval demand a good knowledge of both the database indexing policy and the specific functions of the information retrieval system. Most databa.ses contain text information and it is therefore necessary for all users of databases to understand the principles of text indexing and retrieval. Numerical databases are based on either properties. 

Numerical databases -> factual databases with a large variety of single fields with numeric data, e.g. physico-chemical properties, which can be searched by numeric operators. Sometimes calculations are possible as well. 

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Each plastic in this database is first characterized by descriptive data such as its trade name, manufacturer, product group, form of supply, or additives. Then follows complex technical information on each material, with details of fields of application, recommended processing techniques, and special features. The central element of this material database is the numerical values it gives on a wide range of mechanical, thermal, electrical, optical, and other properties. All these items can... 

POLYMAT light POLYMAT light Materials Data for Plastics is a manufacturer independent, materials database for plastics and contains properties of thermoplastics, thermoplastic elastomers and blends. In total, data from approximately 13,000 commercial products of 170 manufacturers are available products and data can be retrieved via searching in 35 different numerical properties and 15 text fields. 

Large databases on aqueous solubility exist, such as AQUASOL dATAbASE (http //www.pharmacy.arizona.edu/outreach/aquasol/), which contains almost 20,000 solubility records for almost 6,000 compounds, or the already mentioned PhysProp. However, not all situations are covered and the ability to predict this property is still useful. This remark has favoured the development of numerous mathematical models and much prediction software [46]. 

Risk assessments for anionic surfactants are obtained by comparing environmental exposure concentrations to effect levels (the quotient method). A protection factor that reflects the environmental safety of the material is calculated by dividing the exposure level by the effect concentration. If the protection factor is greater than 1, the material is deemed safe. Although this approach to assessing risk yields a numerical value that could be interpreted as the relative safety of a compound, comparisons of protection factors for different compounds should be avoided. The risk assessment for each material must be considered separately because of differences in chemical properties and differences in the database used to obtain the protection factor. In addition, the degree of uncertainty in the exposure and effect... 

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