Optimizing Data Access – Know your Hardware

The Library of Congress has a lot of information — hundreds of millions of pages of books and manuscripts. But no one has ever suggested that we store all of that information in a single, billion-page book. Instead, individual books are stored on shelves in stacks in rooms according to an organized system. Managing large datasets is just the same:  data should exist in manageable sized files stored in hierarchically organized directories. Unfortunately, many people working with large datasets try to do just the opposite. This post describes how converting thirty 200Gb files into three million 200Kb files reduced data access times from several hours to under a second.

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Data Management Questionnaire

Sometimes merely filling out a questionnaire can cause you to think about problems in a new way.  When asked to answer a question that has never occurred to you before, you may find yourself reevaluating some of your core assumptions — assumptions you may not have known you had.  That is the power of asking questions. Our data management questionnaire poses questions in 12 categories that will help you figure out what you need, what you want, and perhaps give you a hint of how to get there.

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Standard Country Names

What’s in a name?  That which we call a rose
By any other name would smell as sweet.

Ahhh love.  Juliet speaks lovely poetry but we learn, as the story unfolds, that names and the identification they impart are in fact extremely important.  This is no less true in data management where country names are anything but standardized.

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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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Qualitative Display of Air Quality Data

Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.

Edward Tufte, The Visual Display of Quantitative Information

This post briefly summarizes our thoughts on best practices for designing public-facing data graphics for air quality data. Focus will be on the types of charts we feel are appropriate to use with data (e.g. from low-cost sensors) that may not be as accurate as data collected by monitors using Federal Regulatory or Federal Equivalent Methods (see FRMs/FEMs and Sensors). Visualization types discussed will include:

  • maps
  • time-series charts
  • calendars
  • status and forecast tables
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Cross-origin requests with beakr

beakr is a lightweight and flexible web framework that allows you to incorporate R code as the Middleware responsible for handling web requests. At Mazama Science, we developed beakr to simplify the process of creating R-based web services that we use to deliver a variety of products: data files, images, rendered Rmarkdown documents, etc.

In this article, we discuss using beakr to set a CORS header and create an example beakr instance that can respond to cross-origin javascript requests.

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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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Web Frameworks for R – A Brief Overview

Having recently announced the beakr web framework for R, we have received several questions about context and why we choose beakr over other options for some of our web services. This post will attempt to answer some of those questions by providing a few opinions on beakr and other web frameworks for R.

The comparison will by no means be exhaustive but will attempt to briefly summarize some of the key features each web framework has to offer. While there are some differences in the approach each package takes to developing web services, they all share similar basic functionality. In the end, the choice of a particular framework will come down largely to personal preference.

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