Data+Logging


 * Data Logging - **

" //The process of using a computer to collect data through sensors, analyze the data and save and output the results of the collection and analysis. Data logging is commonly used in scientific experiments and in monitoring systems where there is the need to collect information faster than a human can possibly collect the information and in cases where accuracy is essential//. " - [|Webopedia]

Examples of the types of data that can be collected using sensors can include:

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 * Temperatures
 * Humidity
 * Wind Speed
 * Precipitation
 * Solar radiation
 * Sound frequencies
 * Vibrations
 * Light intensities
 * Electrical currents
 * Pressure
 * Magnetic Fields

Components that every data logger system must have include:


 * An analogue sensor - Hardware


 * A analogue-digital converter (ADC) - Software
 * An area for data storage such as a PC - Hardware
 * Data-logging software for data acquisition, analysis, and presentation - Software

Advantages: -

= [|Datalogger manufacturer achieves environmental standard] = >
 * Measurements are always taken at the right time. Unlike a human the computer will not forget to take a reading or take a reading too late or too early.
 * Mistakes are not made in reading the results. Humans can make errors.
 * Data logging devices can be sent to places that humans can not easily get to. e.g. to the planet Mars, into the bottom of a volcano, or onto a roof of a tall building to get to a weather station.
 * Graphs and tables of results can be produced automatically by the data logging software.
 * Safer in the short term as well as long term -[|improved Tinytag wireless data logging system]
 * Efficient and eco-friendly -[|////Growing Awareness of Data Logging to improve temperature management and energy efficiency.////],

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Disadvantages:-


 * Initial cost of purchasing the equipment - [|MicroDA//Q//]

media type="custom" key="9246402"  Reliability - It miight stop working which would risk the intent
 * Initial cost of implementing the equipment
 * Cost of maintenance
 * You need to train the people on how to use the data and analyze the graphs
 * In some cases it makes jobs redundant