Aimaan Maredia

In the FlyTunes project on Zooniverse, I contributed to a citizen science initiative focused on understanding how road noise impacts insect soundscapes. The primary task involved listening to short audio clips recorded from various environments and identifying whether the clip contained insect sounds, road noise, both, or neither. By accurately labeling these clips, I helped researchers create a machine learning dataset that can distinguish between natural and human-made noises. This work supports the development of automated tools that analyze large volumes of sound data to monitor environmental health and biodiversity.

FlyTunes is part of a broader effort to study the ecological effects of anthropogenic noise pollution. Insects play critical roles in ecosystems through pollination, decomposition, and as a food source for other animals, but their communication and behavior can be disrupted by constant background noise from highways and urban areas. The project aims to identify patterns in how noise affects insect presence and activity, which has larger implications for ecosystem stability and conservation strategies. Participating in FlyTunes allowed me to directly contribute to real-world environmental research and gave me a greater appreciation for how small data contributions, when pooled together, can support impactful scientific discoveries in conservation and ecological monitoring.

ReData is one of the most recent Italian projects related to the recovery of historical meteorological data. Its goal is to recover and digitize meteorological data archived between 1879 and 1940 at the Royal Meteorological Office. The digitization of historical meteorological data is crucial for several reasons, each contributing to the advancement of climate science, weather prediction, and our understanding of environmental changes. For this reason, in recent years, this activity of digitization has being pursued by many countries.

Understanding global climate change requires a comprehensive view of weather patterns across the world, and this is only possible when countries contribute their historical data to the global dataset. By digitizing their records, countries enhance global climate models, enabling better predictions and more effective responses to climate change.

Disaster management and risk mitigation are also significant drivers of this work. Countries face unique climate-related risks, such as hurricanes, droughts, and floods, which have profound impacts on their economies and populations. Digitized historical data allow governments to analyze past events, improving early warning systems and disaster response strategies. This, in turn, supports the building of more resilient infrastructures and communities.

Then, from an economic and agricultural perspective, historical weather data is invaluable for planning and decision-making.

Finally, scientific research benefits greatly from digitized data, as it supports the advancement of climate science and innovation. This data is not only essential for current research but also serves as a valuable educational resource, helping to train future generations of scientists and meteorologists. Additionally, preserving historical meteorological records through digitization safeguards them as part of a country's cultural and scientific heritage, ensuring they are not lost over time.

In the Cloud Spotting on Mars project through Zooniverse, I participated in a citizen science initiative designed to help NASA scientists identify and classify clouds in Mars’ atmosphere. My main task was to review short video clips taken by the Mars Reconnaissance Orbiter and determine whether specific cloud formations were present in each clip. These clouds, often appearing as thin, wispy formations, are important indicators of atmospheric patterns and climate conditions on Mars. By classifying these images, I contributed to a growing database used to train artificial intelligence models that can more accurately detect and analyze Martian clouds on a larger scale.

This project plays a crucial role in understanding Martian weather and its potential implications for future exploration. Clouds on Mars can affect temperature, dust storm formation, and possibly even water vapor cycles—all of which are essential for predicting conditions that spacecraft and astronauts may encounter. Through my participation, I supported efforts to refine our understanding of Mars’ climate and improve the accuracy of automated detection systems. Cloud Spotting on Mars deepened my interest in space science and showed me how collaborative, crowdsourced efforts can drive forward cutting-edge research in planetary science and long-term space exploration planning.