Keeping the data

If you work for any length of time in the laboratory you will rapidly acquire a large number of spectra. It is important that you keep these safe and in proper order, with an unambiguous cross-referencing system so that you (and anyone else) can locate the spectra or measurements which apply to the product of a particular experiment (see Chapter 3 for detailed advice on this). Spectra are best kept in clearly labelled folders or binders of some description, preferably ones which allow for removable attachment of the spectra. The other data should be recorded in the laboratory notebook along with the experimental write up. If data sheets are used then all the data should be recorded on these as they are measured. 

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Here the Any Event counts are placed before the body system and preferred term counts so that they appear first in the summary. At this point, the percentages are calculated by using n0, nl, and nt as denominators. The columns (coll-col3) are created and formatted as XXX (XXX%). Finally, the system and term variable is created as an index to keep the data grouped by preferred term in the output. 

The instrument for measurement of radon with weather parameters and water use was designed, calibrated and used in the field for 1.5 years with excellent results. The power surge and power interruptions were the only limitations on the use of the instrument. The system software was written mostly during the design phase of the project. However, modifications were made in the software to permit better data collection during power interruptions. Battery backup was needed to keep the data during interruptions of our one week studies. 

Explain the meaning of the quotation at the beginning of this chapter Get the right data. Get the data right. Keep the data right. ... 

Homonuclear contour plots are symmetric with respect to both spectral widths, and data point resolutions are almost always identical. Linear prediction can help greatly in making DRi equal to DR2 by keeping the data accumulation time within acceptable limits. 

Keep a historical record of data To maintain a historical record, an ELN prevents users from deleting data physically. Instead, it marks the data as deleted and keeps the data in history. 

Output mode. The server mode will keep the data on the server for further usage without displaying it. By default, sequences are not displayed to minimize data transfer through the web browser, but if the connection is fast enough, the link can be clicked to see the sequence. The display mode will display the sequences in the results page. The email mode will prepare the sequences on the server, and send an email when the task is finished. This can be useful for large queries, but it is usually not necessary. 

The database management must be responsible for this concurrent usage and offer concurrency control to keep the data consistent. Concurrency control is achieved by serializing multiple transactions through use of some mechanism such as locking or timestamp. 

With this model, the complete information chain of a laboratory is made visible starting from the suppliers to the customers and valuable insights are provided for handling of documents that contain the essential data and information. For the proper treatment of the documents during creation, modification, distribution, and archiving, it is necessary to determine in which data formats they are available. Hence, it can be decided in what manner it should be possible to keep the data accessible, recoverable, and legible during the retention period. 

The information gathered prior to closure must be recorded as well as those from the institutional phase. It is particularly relevant to keep the data from corrective or maintenance actions. 

LCAs attempt to provide comprehensive analyses in two dimensions. First, the full set of manufacturing, transportation, and solid-waste management processes required to support the manufacture, use, and disposal of a product is considered to be potentially within the scope of the study. In practice, however, boundary rules must be adopted to keep the data requirements within feasible limits (ISO, 1998). Second, the full set of relevant flows to and from the environment is potentially within the scope of the study. Rules are adopted to make the task manageable. 

The figure also illustrates the speed with which data can be transferred from the memory associated with other processors. This can require thousands or tens of thousands of processor cycles. The trick to developing scalable algorithms is to keep the data close to the processor(s) that need it. This is, of course, easier said than done. 

To keep the data in Tables 13 and 14 in proper perspective, we should keep in mind that the presence of carbon steel is a severe test of PAA stability this material was selected for these studies for that reason. Another important factor to keep in mind is that in the field solutions will not be in contact with carbon steel for 2 days, unless unexpected equipment problems cause a delay. These studies, then, were designed to evaluate various stabilizers under severe conditions to discriminate more easily between them. 

Share only the data that truly provide value. The value of data depeuds on where one sits in the supply chain. A retailer finds point-of-sale data to be quite valuable in measuring the performance of its stores. However, a manufacturer selling to a distributor that, in turn, sells to retailers does not need all the point-of-sale detail. The manufacturer finds aggregate demand data to be quite valuable, with marginally more value coming from detailed point-of-sale data. Keeping the data shared to what is truly required decreases investment in IT and improves the chances of successful collaboration. 

In this section, we characterize size distributions and their properties by using examples based on a specific set of particle size data. The result of a careful size analysis might be a list of 1000 particle sizes. In some situations, keeping the data in this form may be desirable— for example, if the list is stored in a computer. In most situations, however, we would like to have a picture of how the particles are distributed among the various sizes and to be able to calculate several different kinds of statistics that describe the properties of the aerosol. For that purpose, a list of 1000 numbers is an awkward format, so it is necessary to resort to descriptive statistics to summarize the information. 

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