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Data modeling

Coordinate atomic coordinate data MODEL, AEOM, SIC ATOM... [Pg.115]

The primal advantage of hierarchical databases is that the relationship between the data at the different levels is easy. The simplicity and efficiency of the data model is a great advantage of the hierarchical DBS. Large data sets (scries of measurements where the data values are dependent on different parameters such as boiling point, temperature, or pressure) could be implemented with an acceptable response time. [Pg.233]

After intakes have been estimated, they arc organized by population, as appropriate. Then, tlie sources of uncertainty (e.g., variability in analytical data, modeling results, parameter assumptions) and their effect on tlie exposure estimates are evaluated and sunuiumzed. Tliis information on uncertainty is important to site decision-makers who must evaluate tlie results of the e.xposure... [Pg.356]

Data — Model — Prediction — Further Data — Adjustment of the... [Pg.251]

Lombardo F, Gifford E and Shalaeva MY. In silico ADME prediction data, models, facts and myths. Mini Rev Med Chem 2003 3 861-75. [Pg.508]

Such conformational dependence presents challenges and an opportunity. The challenges he in properly accounting for its consequences. In many cases, exact conformational energetics and populations in a sample may be unknown, and the nature of the sample inlet may sometimes also mean that a Boltzmann distribution cannot be assumed. Introducing this uncertainty into the data modeling process produces some corresponding uncertainty in the theoretical interpretation of data... [Pg.319]

Models have been formulated to enable the simulation of the concentration vs. radial distance profile as it develops with time, from which the time-dependent concentration vs. distance, d, profile, observed at the probe, can be extracted for comparison with experimental data. Models based on Eqs. (29) and (30) give similar results for conditions encountered practically. [Pg.350]

Jouzel J, G. Hoffmann G, Koster RD, Masson V (2000) Water isotopes in precipitation data/model comparison for present-day and past chmates. Quat Sci Rev 19 363-379 Kagan E, Agnon A, Bar-Matthews M, Ayalon A (2002) Cave deposits as recorders of paleoseismicity A record from two caves located 60 km west of the Dead Sea Transform (Jerusalem, Israel). In Environmental Catastrophes and Recoveries in the Holocene (onhne abstract http //atlas-conferences.com/cgi-bin/abstract/caiq-38)... [Pg.456]

The Clinical Data Interchange Standards Consortium (CDISC) is a non-profit group that defines clinical data standards for the pharmaceutical industry. CDISC has developed numerous data models that you should familiarize yourself with. Four of these models are of particular importance to you ... [Pg.5]

Operational Data Model (ODM). The ODM is a powerful XML-based data model that allows for XMF-based transmission of any data involved in the conduct of clinical trials. SAS has provided support for importing and exporting ODM files via the CDISC procedure and the XML LIBNAME engine. [Pg.5]

You will be exporting, importing, and creating data for these models, so it is important that you learn about them. The FDA has begun to formally endorse the use of these data models in their guidance. Eventually the FDA will probably require data to be formatted to the CDISC model standards for regulatory submissions. [Pg.5]

Operational Data Model (ODM) for clinical data interchange... [Pg.74]

Denormalization of data is needed when a statistical procedure requires that the information to be analyzed must be on the same observation. Procedures in SAS that perform data modeling are often the ones that require denormalized data, as they require that the dependent variable be present on the same observation as the independent variables. For example, imagine that you are trying to determine a mathematical model that predicts under what conditions a therapy is successful. That model might look like this ... [Pg.95]

Filters are designed to remove unwanted information, but do not address the fact that processes involve few events monitored by many measurements. Many chemical processes are well instrumented and are capable of producing many process measurements. However, there are far fewer independent physical phenomena occurring than there are measured variables. This means that many of the process variables must be highly correlated because they are reflections of a limited number of physical events. Eliminating this redundancy in the measured variables decreases the contribution of noise and reduces the dimensionality of the data. Model robustness and predictive performance also require that the dimensionality of the data be reduced. [Pg.24]

Swanson, L. W. (1998). Brain Maps Structure of the Rat Brain a Laboratory Guide with Printed and Electronic Templates for Data, Models, and Schematics. New York, NY Elsevier. [Pg.107]

In the independence approach the planning functions are stripped from the old-fashioned ERP system and a modem complete APS system is added as a separate server system with an independent persistent data model and integrated by an interface (Fig. 12.2). Some users of the ERP system also use this separate APS system. This approach is supported, e.g., by the software products SAP APO resp. [Pg.264]

In the embedding approach the ERP system is enhanced with subordinate planning systems that are integrated into the user interface of the ERP system and create a local temporary data storage (LiveCache). All data is still held persistently only in the ERP system (Fig. 12.3). This allows for the LiveCache to use a projection of the ERP data model that is more suitable for APS purposes. This approach is used, e.g., by the software product OR Soft SCHEDULE++. [Pg.265]

The independence approach allows for a good encapsulation of the APS issue but requires major changes in business processes. It requires the creation and maintenance of an independent data model with its own data structure and the definition of new business processes. Introduction of the APS system must be done in a big bang. Integration is not fully guaranteed, its quality depends on the throughput and the error tolerance of the integration interface. [Pg.265]

The embedding approach may require an improvement of modeling in the ERP system (i.e., to maintain additional detailed information for APS purposes) but it can utilize all established business processes, data models and infrastructures. Introduction of the subordinate planning system can be done step-by-step with minimum impact on established business processes. Integration is fully guaranteed... [Pg.265]


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A New Data Model for Chemical Reactions and Properties

Alignment Building the Data Model

Analytical methods method comparison data model

Animal models experimental data

Applying the Stockpiling Model to Empirical Data

CAPE-SAFE Data Model

Calibration data modeling

Cauchy data models

Characteristics of Data Processing for Industrial Process Modeling

Chemical Kinetic Data for Stratospheric Modeling

Classification Modelling of Data Structures

Clinical Relevance of Data Derived from Experimental Models

Cluster model, spectroscopic data

Combining the Velocity Data Model with Other Physical Models

Commercial Computer Programs for Modelling of Impedance Data

Comparing micromechanical models with experimental data

Comparison Data Model

Comparison of model and experimental data

Comparison of the Modified Campbell-Dontula Model with Experimental Data

Compositional data, modeling

Compositional data, modeling summarizing

Computational modeling crystallographic data

Computer modeling input data problem with

Conceptual modelling data analysis

Curve crossing model experimental data

DCC Model Lattice Parameter and Lns-Mossbauer Data Analysis

Data Analysis Quantitative modeling

Data Analysis by Modelling

Data Basis for Model Development

Data Modeling and Analysis

Data Treatment and Modeling

Data Used for Model Parameterization and Validation

Data analysis specific models

Data flow diagrams model

Data for combustion modelling

Data generation models

Data interpretation model-based methods

Data matrix used for modelling

Data model

Data model consequences

Data model organism

Data model using

Data needs for modelling

Data of the Truck Model

Data preparation, model specification and residual checking

Data processing and information management models

Data sources for modelling

Data vs Models of System

Data-driven intonation models

Data-driven modelling

Description of Models and Data

Dose-response data modeling

Electronic data interchange models

Evolving factor analysis data modeling

Experimental Data and Mathematical Models

Experimental data empirical models from

Experimental data modeling

Experimental data modeling alternating least squares

Experimental data modeling chromatography

Experimental data modeling neural networks

Experimental data modeling principal component analysis

Experimental data, model

Exponential model kinetic data

Fitting Dynamic Models to Experimental Data

Fitting Model to Experimental Data

Fitting data Carreau-Yasuda model

Fitting models to data

Fitting the Model to Experimental Data

Fluid Sorption Data and Modeling

Fuzzy modeling data clustering

General input data for the MOREHyS model

Global analysis data modeling

Impedance data modeling

Impedance data modeling Equivalent circuit models

Impedance data modeling Physicochemical models

Impeller Modeling with Velocity Data

Interpretation of Heterogeneous Kinetic Rate Data Via Hougen-Watson Models

Interpretation of Response Data by the Dispersion Model

Is the Data Set Suitable for Modeling

Kinetic Data Analysis and Evaluation of Model Parameters for Uniform (Ideal) Surfaces

Kinetic model of the photoinitiated polymerization and its comparison with experimental data

Mathematical modeling, data mining

Measurement method comparison data model

Meta-Regression Models for Historical Data

Meta-Regression Models for Survival Data

Meta-data model

Mixing-cell data, model fitting

Model Based on the Rate Equation and Experimental Data

Model Building using Crystallographic Data

Model analytical data errors

Model building data interpretation

Model data for

Model data, predictions

Model data-driven

Model development reference data

Model for the Data

Model predictions and experimental data

Model representation, data mining methods

Model thermodynamic data errors

Model validation data splitting

Modeling Supersaturated Dissolution Data

Modeling applications with thermodynamic data

Modeling data assimilation

Modeling data-driven

Modeling of Bitumen Oxidation and Cracking Kinetics Using Data from Alberta Oil Sands

Modeling of data

Modeling of experimental data

Modelling from Noisy Step Response Data Using Laguerre Functions

Modelling, of experimental data

Models Elongational viscosity data

Models Shear viscosity data

Models ammonia synthesis single crystal data

Models for multivariate dependent and independent data

Models kinetic, from surface science data

Multiple Feature Tree Models Applications in HTS Data Analysis

Operational Data Model

PHYSICAL DATA MODEL OF CAPE-SAFE

Parameter Estimation from Experimental Data and Finer Scale Models

Parameter kinetic models, data storage

Pharmaceutical data, modeling

Pharmacokinetic-pharmacodynamic model validation data

Phylogenetic data model

Physical Data Model Specifications

Predictions from Model Ecosystem (Microcosm and Mesocosm) Data

Predictive Models from Pharmacological Data

Probit Analysis Models Used for Fitting Response Data

Process Modeling with Multiresponse Data

Process Modeling with Single-Response Data

Retention data modeling

Safety Historical Data Modeling

Selection of Kinetic Data for Modeling

Single program, multiple data model

Some specific GCE models and related observational data

Sorption data, matrix model

Statistics method comparison data model

Stirred Tank Modeling Using Experimental Data

Stirred tank, crystallization model data analysis, example

Study Data Tabulation Model

Submission data tabulation model

Surface complexation models microscopic data

Survival data proportional hazards model

THE NCBI DATA MODEL

Treatment of Rheological Data Using Models

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