Overview Acquiring Data

1. Overview Acquiring Data This section will start the introduction of a �Data Life Cycle�, give an overview of what the life cycle of data might involve. I will then focus on one of the initial areas, Acquiring data, whether it be internally collected or obtained from an external source.This section will start the introduction of a �Data Life Cycle�, give an overview of what the life cycle of data might involve. I will then focus on one of the initial areas, Acquiring data, whether it be internally collected or obtained from an external source.

2. Data Life Cycle A typical data life cycle looks something like this in flow form. I�ll break down the various steps in a moment. First let us look at another data life cycle model.A typical data life cycle looks something like this in flow form. I�ll break down the various steps in a moment. First let us look at another data life cycle model.

3. The BLM model for a data life cycle is very similar to the project workflow diagram and therefore gives one a good understanding of the connection between a projects workflow and the life of the data involved in that project. Note also that quality is linked at every step in the life cycle.The BLM model for a data life cycle is very similar to the project workflow diagram and therefore gives one a good understanding of the connection between a projects workflow and the life of the data involved in that project. Note also that quality is linked at every step in the life cycle.

4. Data Life Cycle Now I�ll break the various steps of a Data Life Cycle down into it�s parts and also make it easier to digest. 1- The first step of course is the field/lab data collection (this data is recorded on hardcopy field sheets the majority of the time and therefore needs to be digitally entered, this data is considered Raw data and is archived in it�s original form prior to further use (hard copy data sheets). Then we may be acquiring external data to incorporate (Wx, data loggers, etc�). 2- Next week add this into our working database and the data is verified (what�s on data sheet is correctly typed). 3- After all the data is in the working database and verified we may cycle through this till the field season is completed, at which point we validate (make sure values are plausible), the database will be certified at that point and metadata will be either updated (if this is an on-going project) or created for the first time. 4- Typically next we�ll develop information products from the data; analyze the data for trends, graph it, create maps, and write reports and peer reviewed articles). 5- We�ll archive the field season or short-term project and make sure we get the necessary data into identified repositories (curation, species data, WQ data, etc�). 6- If this is a long-term project then the data will be incorporated into the Master Database and again informational products will be created. 7- Lastly we want to be sure that the informational products also get disseminated into the appropriate formats and repositories for the sharing of the information and perhaps the data.Now I�ll break the various steps of a Data Life Cycle down into it�s parts and also make it easier to digest. 1- The first step of course is the field/lab data collection (this data is recorded on hardcopy field sheets the majority of the time and therefore needs to be digitally entered, this data is considered Raw data and is archived in it�s original form prior to further use (hard copy data sheets). Then we may be acquiring external data to incorporate (Wx, data loggers, etc�). 2- Next week add this into our working database and the data is verified (what�s on data sheet is correctly typed). 3- After all the data is in the working database and verified we may cycle through this till the field season is completed, at which point we validate (make sure values are plausible), the database will be certified at that point and metadata will be either updated (if this is an on-going project) or created for the first time. 4- Typically next we�ll develop information products from the data; analyze the data for trends, graph it, create maps, and write reports and peer reviewed articles). 5- We�ll archive the field season or short-term project and make sure we get the necessary data into identified repositories (curation, species data, WQ data, etc�). 6- If this is a long-term project then the data will be incorporated into the Master Database and again informational products will be created. 7- Lastly we want to be sure that the informational products also get disseminated into the appropriate formats and repositories for the sharing of the information and perhaps the data.

5. Data Life Cycle � Acquiring Data Acquiring Data � the first step in the life cycle of data. All subsequent steps depend upon the accuracy and quality of data from this step. Data acquisition can be obtained from a number of different sources; Field data collection is a common form, yet with today's� technological this doesn�t necessarily mean filling out a field data sheet. Field data can from data loggers and other electronic instrumentation that digitally records the desired attributes and parameters. Additional technology has added the ability to collect spatial and image information digitally instead of analog. There maybe laboratory data as well for a project. Along with these �Internal� data, we have other �External� data that maybe wanted for the project, these can be either interior (data collected by another team or project within the park or site location) and/or exterior data (collected on site or near, by other sources such as weather and climate data).Acquiring Data � the first step in the life cycle of data. All subsequent steps depend upon the accuracy and quality of data from this step. Data acquisition can be obtained from a number of different sources; Field data collection is a common form, yet with today's� technological this doesn�t necessarily mean filling out a field data sheet. Field data can from data loggers and other electronic instrumentation that digitally records the desired attributes and parameters. Additional technology has added the ability to collect spatial and image information digitally instead of analog. There maybe laboratory data as well for a project. Along with these �Internal� data, we have other �External� data that maybe wanted for the project, these can be either interior (data collected by another team or project within the park or site location) and/or exterior data (collected on site or near, by other sources such as weather and climate data).

6. Internal Data External Data Existing Data

7. Internal (Raw) Data Field Data Digital and/or Analog Tabular and/or Spatial (GPS) Images or photographs Lab and/or �Field Equipment� Data

8. External Data Interior Data Exterior Data

9. Existing Data Data Mining Legacy and/or Historical Data