Significant Digits

Everyone who has taken a first year chemistry class has learned that significant digits (aka “significant figures” or “sig figs”) indicate the precision of a measurement. The basic rule is that you save all measurement digits you are certain about plus one more that you estimate. Unfortunately, computers don’t know anything about significant digits. Developers creating data systems for scientific measurements should always include a rounding step as part of any data output. Not embracing significant digits can have … uhm … “significant” consequences.

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Systematic error messages

Anyone writing code for use in data processing systems needs to have a well thought-out protocol for generating error messages and logs. When a complex pipeline breaks, good logs and recognizable error messages are key to debugging the problem. This post describes improvements to the MazamaCoreUtils package that help you create systematic error messages that can be better handled by calling functions.

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Comparing Air Quality Sites

Air quality continues to be in the news with New York Times articles like these:

A quick review of web based air quality resources shows a range of sites featuring maps, time series plots and relevant information.

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Methow Valley Air Quality

Mazama Science has released a new set of tutorials demonstrating the use of air quality R packages to investigate data from regulatory monitors and low-cost sensors. This post is just a short summary of what the tutorials cover. We invite anyone interested in wildfire smoke and air quality to run through the tutorials and provide feedback.

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MazamaSpatialUtils R package

Version 0.7 of the MazamaSpatialUtils is now available on CRAN and includes an expanded suite of spatial datasets with even greater cleanup and harmonization than in previous versions. If your work involves environmental monitoring of any kind, this package may be of use. Here is the description:

A suite of conversion functions to create internally standardized spatial polygons dataframes. Utility functions use these data sets to return values such as country, state, timezone, watershed, etc. associated with a set of longitude/latitude pairs. (They also make cool maps.)

In this post we discuss the reasons for creating this package and describe its main features.

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Data producers vs. data consumers

In the marketplace, the needs of producers and consumers are often at odds:  producers want higher prices, consumers lower ones; producers want easy assembly, consumers easy dis-assembly; producers want flexibility and rapid prototyping, consumers reliability and long-term support.

The same competing needs exist in the world of scientific data management where producers of data and consumers of data often operate in very different worlds with very different sets of tools.

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When is a number not a number?

Have you ever asked yourself whether your telephone number is really a number?  It’s got numbers in it but does it measure anything?

How about your credit card number?  PO Box?  Social Security Number?  Zip code? What would happen if you subtracted one of these from another?

As it turns out, many of the “numbers” we deal with every day are actually identifiers and not a measure of something.  Sadly, too many data managers do not distinguish between the two even though making this distinction is quite simple.

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Standard Latitudes and Longitudes

What?  Where?  When?

These are key questions that every scientist or other collector of environmental data must answer.

  • What is the value of the thing we are measuring?
  • Where are we taking the measurement?
  • When are we taking the measurement?

In a previous post we discussed how to standardize “when”.  But what about “where”?

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