Data is to science as to what keys are to a lock. Data allows scientists, those from any discipline who employ the scientific method, to answer questions. Since data can be a valuable tool, it is worthwhile to understand the background related to various types of data. For this example, I will describe climate data and its application to drought monitoring and ultimately famine prevention.
Generally, there are three main types of climate data: remotely sensed, historical reanalysis, and in-situ. Remotely sensed data is generated from sensors that measure information from a secondary source such as a camera or a satellite. This is ideal for collecting data in regions that are not easily accessible such as in the middle of the ocean or in polar environments. In situ data, also known as observational data, is information that is retrieved at a distinct location usually manually derived. Examples of this are weather balloons that can take measurements at a location at various altitudes, qualitative weather descriptions, or data observations such as surface temperature, rainfall, windspeed, etc. Ideally, climate scientists would like to have a continuous data record over an extended period of time (the longer the better) for all points on earth (and even at all vertical dimensions).
Unfortunately, as we know, it is not possible to have observations at all times from everywhere. For one, this would be very expensive and also it is unrealistic in open ocean areas or extreme conditions such as in remote high elevation locations. So, to counteract the course spatial resolution of data observations, some scientists have developed a data set collectively known as reanalysis data that combines both observations with remote sensing and statistically evolves with high skill what certain variables (temperature, wind speeds, humidities, etc.) would be on a globally continuous spatial scale. In a way this solves the issue of limited data, but as we know, directly measured globally continuous data would be a gold mine, albeit totally unrealistic for now…
Now that you have an understanding of the power of data and to some degree observational or in-situ data, we can then understand how valuable that data is. But, what does extra data help climate scientists do? A major project that uses data helps to develop improved climate diagnostics and forecasts related to famine prevention through drought monitoring. Examples of some of these programs are the Famine Early Warning Systems Network (FEWS www.fewsnet.net) and the FAO Global Information Early Warning System (GIEWS www.fao.org/giews/english/index.htm.). These groups both use rainfall, vegetation, and temperature information to research drought periods as well as issue forecasts for potentially unfavorable or famine related conditions.
Everybody agrees that direct or in-situ data has many benefits as long as it is taken in a standard format and methods are standardized throughout the record. A classic example of this is the Keeling Curve that famously depicts the rising CO2 records atop the Mauna Loa observatory in Hawaii providing the first real evidence of increasing greenhouse gas emissions. In that example, the data taken from direct measurements had enormous value years after the first record was taken. But, what happens to all of the observational data that is taken in poor regions where data storage and upkeep is too expensive? And, who uses the data that is hand recorded and how do you relay that data to scientists across the planet? There are numerous regions in the world that have data waiting to be used by climate scientists, but are in a format that is not easily transferred: hand written records spanning in some cases decades. Usually, much of this data is stored in less than ideal conditions; think opposite of a sterile supercomputing facility.
Luckily, there is a group called the International Environmental Data Rescue Organization (IEDRO www.iedro.org) that specializes in recovering and reformatting data from poor regions into a digital format that can then be easily distributed to any interested party for use in various research projects. Their motto is: Saving data, saving lives. Ultimately, this data can then be used by groups such as FEWS and GIEWS to improve their research and applications towards famine prevention and overall food security. This is just one of many uses for rescued data.
Do you suspect that the local river is becoming more polluted over time? If so, start collecting and recording data over time so that you may have the information to either confirm or reject your hypothesis….hmmm, sounds like SCIENCE! Data can be a very valuable tool to help answer some very important questions for yourself and others. Spread the love, spread the data!