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Data Storage and Reporting

Data Storage

No matter your data objective, nor the kinds of data you collect, data storage should be considered prior to collecting your data. Where will you store your data, how will you share your data, and how will you ensure the data are available for future use are all considerations for proper data storage. Relatively small data sets can be easily managed in excel spreadsheets, larger ones may require databases to manage multiple years and data types. The Colorado Data Sharing Network stores all kinds of data no matter what parameter and can accommodate thousands and thousands of records form an organization. Whatever your storage need is, it is of great importance that you consider future data users. Proper internal documentation of naming conventions, data storage locations, storage strategy are all contained in procedure documents such as Information Management Plans (IMAP) or Quality Assurance Project Plan (QAPP).

Data Reporting

Finally!!! You have identified data objectives, collected data, ensured process for QA/QC and data management and are now ready to report your findings. Prior to writing up your data and turning it into useful information, revisit your data objectives (hotlink this to the objectives page) and the audience that you wanted to reach in the first place. With this audience in mind, create your reports that speak to that audience. Reports intending to affect regulation should be written to a technical level sufficient to meet the standards of the agency with which you are working. Watershed groups may create reports to influence fundraisers that translate the scientific analysis into common terms and include multiple photographs to influence their giving. Consider the amount of data to share; it is easy to overwhelm with pages of graphs and tables, be selective of how you report the information so that your message comes across clean and easy to read. Lastly, honesty is the best policy. Should your data be inconclusive, or your QA program identified issues in your data set, be clear and open with your reader. Outliers, problem data sets and incomplete data sets should be retained, but appropriately qualified with a rationale of why the data are suspect. Suspect data may still have value and should not be deleted. Always keep in mind that others may use your findings and data for their own work, therefore provide all the information (good and bad) associated with your data set.