Before this class, I had a very limited knowledge of the world of sensor journalism. I thought sensors were strictly used for gathering data for scientific experiments and had no room in the world of journalism. However, after listening to Patrick Herron and Lily Bulli’s presentations and participating in our in-class water conductivity workshop, I’ve discovered just how useful sensors can be when it comes to reporting. There are huge benefits to using data to tell stories. Mainly, in comparison to interviewing people, data can be a much more reliable source of information. Asking someone about whether they think the Mystic River is polluted is not the same as actually testing a sample and gathering concrete, numeric evidence that proves it is. Data adds a level of authenticity to your story that simple word of mouth can’t match. The other benefit of using sensors specifically to tell stories is that it allows you to call attention to such important matters as the pollution of the Mystic River. While anyone can write a story about how the Mystic River is polluted and needs to be cleaned up, it’s hard to get people to gather around the project unless you have evidence. Sensors allow journalists to gather the data they need from water samples and to write multiple, well researched stories from their findings that call attention to the issue. Patrick Herron’s presentation about his work with the Mystic River was extremely eye opening to just how beneficial sensors are when it comes to reporting. When he showed us the picture of the sign that read “No Swimming” in front of beach full of people swimming, it made it clear how imperative the issue of pollution was. It was also enlightening to see the large sensor that he and his team were using to collect data and the see the work that we had done in our in-class workshop applied in the real world on a larger scale. Having said this, there are drawbacks to using sensors to tell data driven stories. Patrick told us that in his efforts to gather data from the river, he had handed our sensors to volunteers to and had found that most of the data collected was inconsistent. This showed that human error can play a huge part in data collection and create a margin of error. From my own experience testing a sample of water from the Charles, I also noticed a few inconsistencies that could negatively affect the data. Number one, everyone in the class who collected Charles River samples extracted the sample from different locations, the samples yielded different levels of conductivity. In order to get accurate data, we would have to come up with a way to get one single level of conductivity that someone represented most of the Charles. There’s also the question of how you gather numeric data from the frequency admitted from the sensor. While it’s easy to judge which sample is the most conductive given by the noise admitted by the sensor, it’s hard to translate those finding into words. Some sort of pitch reader would be needed to read the frequency and translate it into numbers, but this process could muddle the data slightly and give us slightly inaccurate readings. I think that for future journalists to be able to tell stories with sensors, the technology would have to be more readily available. The sensor that Patrick showed us seemed to be an extremely effective way to gather data, but he also mentioned that the sensor was $10,000 and that’s not something a lot of journalists can afford just to gather data for potentially a single story. If smaller, more affordable sensors were more accessible to journalists, we could gather data and write stories more easily and frequently. Even having access to the basic sensors we used in class would be effective and affordable for journalists. I think sensor journalism is an extremely effective way to tell stories and it’s something that should increase as the world of journalism shifts more into the world of technology.