Data secondary

Analysis of Data. A veteran practitioner of chemical market research likened this step to the assembly of a jigsaw puzzle. There are many pieces of unequal size and importance that must be put together to make a picture understandable to everyone. Call reports, secondary data inputs, experience, and judgment are the tools used by the market researcher to analyze the data, reach conclusions, make recommendations, and write the report. 

Decisions affecting the future direction of the organization and its products and services are made from information gleaned through market research. Should this information be grossly inaccurate, over optimistic or pessimistic the result may well be the loss of many customers to the competition. It is therefore vital that objective data is used to make these decisions. The data can be primary data (data collected for the first time during a market research study) or secondary data (previously collected data). However, you need to be cautious with secondary data, as it could be obsolete or have been collected on a different basis than needed for the present study. 

A source of secondary data can be automotive trade press reports and independent reviews. Reading the comments about other vehicles can give you some insight into the needs and expectations of potential customers. 

The gear mesh should be in a plane opposing the preload creating the primary data-measurement point on each shaft. A secondary data-measurement point should be located at 90° to the primary point. 

The quality of data entering the LCA study is to be determined in view of temporal, spatial, technological, data sources (it must be determined whether primary data required or secondary data can be used), their accuracy etc. It concerns the determination of all requirements for the input data [5]. 

In terms of informed policy decision, it would be desirable to relate information to both the input and to the output. However, working with secondary data implies that many studies do not provide complete information. In most cases, though, data on an environmental indicator is only available on the input, the per unit of land area basis. Although in scientific terms it is deplorable that most information is not available on a per unit of output basis, this is less problematic for today s practical EU policy. Food surpluses are more of a problem in the current political environment than food scarcity and there seems to be a broad consensus to keep the amount of farmland relatively stable. Therefore, in the EU, in most cases, the policy relevant way is to apply the data on environmental indicators to the input on a the per unit of land area term. 

Another direction of development of the data set is to strengthen the in vitro-in vivo correlations and develop multivariate models to predict in vivo endpoints, such as therapeutic effects and adverse events. In this respect, it will be interesting to examine which data (among in silico descriptors, in vitro primary and secondary data, in vitro functional data, etc.) are most appropriate to derive robust and predictive models. 

Problem of Verification. Much of the data used in this study were gathered by interviews with R D personnel from the sample firms. These individuals provided both subjective and objective information about their companies and the manner in which environmental protection regulations impact their R D activities. Given the size and complexity of these sample firms, this data were difficult to verify. However, to help substantiate the validity of the data provided, the researchers analyzed the responses for consistencies or possible contradictions. Comparisons were made between individual responses and data gathered from trade journals, annual reports, and other secondary data sources. Further, in selected instances, the researchers made plant tours to personally observe the manner in which the companies had been affected. 

The MDL is one of the secondary data quality indicators. The EPA provides the definition of the MDL as the minimum concentration that can be measured and reported with 99 percent confidence that the analyte concentration is greater than zero (EPA, 1984a). 

The terms estimated quantitation limit (EQL) and practical quantitation limit describe the limit of quantitation, another secondary data quality indicator. These terms are used interchangeably. In fact, the common term used by the laboratories is the PQL. The EPA, however, prefers to use the term EQL and defines it as follows The estimated quantitation limit EQL) is the lowest concentration that can be reliably achieved within specified limits of precision and accuracy during routine laboratory operating conditions (EPA, 1996a). The PQL is defined similarly (EPA, 1985). 

A secondary data source, such as a hospital or HMO database, can be a useful resource. Secondary data sources are particularly useful when gathering economic data because there is usually a direct correlation with how much an insurance company is charged and how much the product or service actually costs to provide. A researcher or clinician can also examine the data on a large pool of patients to see if an intervention actually affects a population of patients. A disadvantage of data such as these is that they are completely anonymous. There are no patient identifiers, and the data cannot be linked to individual patients. As a result, if one sample of patients patronizing one pharmacy is provided an intervention and another sample of patients is not, there is no way to detect economic change through the secondary data source. 

Cynthia is using a secondary data source to conduct her cost-benefit analysis. After she has identified her variables of interest, she asks the HMO to provide her with baseline information on all heart failure patients prior to implementing her service. The HMO is able to stratify the data by diagnosis, so Cynthiaasks for annual numbers and costs of hospitalizations, emergency room visits, and medications for their heart failure patients (see Table 27-3). 

As with economic data, clinical information can be collected from primary and secondary data sources. Primary data can be collected at the point of care, when the pharmacist sees the patient. Primary data collection is the most reliable method of collecting clinical information because it provides the most control over the data collection process. The pharmacist can ask patients questions, perform necessary laboratory tests, conduct physical assessments, and record information either electronically or on paper. 

Index. A secondary data field generated from one or more primary data fields, to enhance the searching and retrieval of the primary data. An index in a chemical database may be a characteristic of the database, such as Oracle indexes, or it may be a chemistiy-specific index such as a tree index for substructure searching. Indexes require extra space, and they typically must be created and maintained by some administrative process in the database. 

A solution is the definition of dependencies between metadata, which has consequences on the input of metadata if an object is created or uploaded. If a value is selected from the pick list for any metadata defined as primary data, available values for the secondary metadata are modified — usually reduced — according to the definition of dependencies. If only a single value remains to be valid for secondary data, the corresponding value is automatically selected. 

The task of data mining in a chemical context is to evaluate chemical data sets in search of patterns and common features to find information that is somehow inherent to the data set but not obvious. One of the differences between data mining and conventional database queries is that the characterization of chemicals is performed with the help of secondary data that are able to categorize data in a more general way and helps in finding patterns and relationships. It would be an unsuccessful approach to try to keep all potentially useful information about a chemical substance in a structure database. Thus, the extraction of relevant information from multiple data sources and the production of reliable secondary information are important for data mining. 

The effective access of raw data that has been saved in previous experiments is important for repetition of experiments and to avoid unnecessary calculations. Information that was proved to be useful can be saved and retrieved for later use with other data sets. It is usually recommended to store raw data (input data), descriptors, and query data in separate database entities. By separation of primary structure data, secondary data, and query data, a simple retrieval of a previous experiment as well as the use of already calculated descriptors is possible. 

Estimation of long term exposure of farm workers to agrichemicals based on secondary data from industry and expert opinions... 

The per-unit impedance Zpu can be simply derived from the ohmic impedance values and knowing either the primary rated current or the kVA rating of the transformer. It will, however, be seen that the per-unit impedance Zpu is the same whether it is calculated from the primary or the secondary data. 

In this article we are concerned with the use of computer techniques. There has been speculation that in due course the whole of the primary literature will become available in machine-readable form. Although some experiments are being done the difficulties of achieving this, particularly for structural, graphical and tabular data, has meant that progress has been slow. So although computerized information retrieval direct from the primary literature may come in the future it is probably some years away. Current activity has been concerned with computerisation of the document retrieval function, using machine-readable secondary data bases. 

We have already seen that numeric data banks exist primarily for retrospective searching. What is therefore required in those where numeric data consitute the basic file, is to record the most reliable values in the system. In other words a critical evaluation is really required, prior to input to the system. The promotion of such evaluations is an area where Codata is particularly active. Furthermore, particular values may be determined repetitively and one wishes to record only the best value. This may mean replacing a value with a later, more accurate one, so the facility to do this must be built into the system. Finally, primary data gathered from the literature may be transformed in some way or used to calculate secondary data which can also be stored. Examples are the Information Centre for Mineral Thermodynamics in Grenoble 22), which uses primary thermodynamic data to calculate other thermodynamic functions, and the Online Data Bank on Atomic and Molecular Physics at Belfast which is working on the automatic transformation and development of relations between different data sets. This last points the way towards exciting future developments in dynamic, as opposed to static, data banks. 

Much of the work in this area has arisen from the conversion to machine processing of formerly manual operations by the publishers of secondary literature. Taking Chemical Abstracts Service as a prime example, they began to computerise their operations in the early 1950 s, primarily to increase their efficiency in coping with the ever-increasing flood of primary literature. As a result, their secondary data bases have b un to appear in computer-readable form, in essentially natural language, beginning with Chemical Titles in 1962. These data bases can then form the basis of computerised retrieval services such as those offered by UKCIS 42). 

The authors acknowledge the financial and technical support for this project provided by the Ministry of Science, Technology and Innovation and Universiti Putra Malaysia under the Science Fund Project no. 01-01-04-SF0733. The authors wish to thank,the Department of Environment, and Department of Irrigation and Drainage, Ministry of Natural Resources and Environment of Malaysia, Institute for Development and Environment (LESTARI), Universiti Kebangsaan Malaysia, Universiti Malaya Consultancy Unit (UPUM) and Chemistry Department of Universiti Malaya, who have provided us with secondary data and valuable advice. 

Data requirements Production and packaging of the Garment and Socks were based on primary data. Distribution, use and end of life were modelled based on secondary data and scenarios for the US and EU. 

Upstream processes were modelled using secondary data firom the GaBi Databases 2013 (PE, 2013). 

---