Hey everyone!
We’re officially into the second week of the Fabric Community DataViz Contest for May 2025 - and the energy is flying high! Whether you're still deciding how to approach your visual, or already in DAX formulas, this post is for you.
And if you’ve opened the official dataset we provided, you’ll know it’s absolutely packed with columns waiting to be charted, sliced, and explored in Power BI. So today, we’re going to do a proper walkthrough of the dataset, column by column. Let’s get into it!
First Things First - What’s In the Dataset?
There are 41 columns in this dataset, and each one tells a piece of the bird migration story. Let’s break them down so you can get a better feel for what’s available.
Bird_ID is the unique identifier assigned to each bird in the study, kind of like a passport number for avian travelers. It ensures every record is traceable and prevents duplication, making it essential for maintaining data integrity.
Species column identifies the species of the bird, such as Warbler, Hawk, or Crane. It plays a huge role in biological analysis, migration behavior, and habitat preference exploration.
Region tells us the broad geographic area the bird originates from or is typically associated with - think North America, Europe, or South America. This is incredibly useful for visual mapping, trend segmentation, and comparing environmental influences.
Habitat describes the primary type of environment the bird lives in - such as Urban, Grassland, or Mountain areas. It gives us a sense of the bird’s lifestyle and vulnerability.
Weather_Condition captures the prevailing weather during the migration event like Stormy or Windy. It’s key for understanding external pressures on bird journeys and success rates.
Migration_Reason explains why the bird migrated (for instance, Feeding, Breeding, Climate Change, or Avoiding Predators). It connects behavior with necessity and provides context for route choices and timing.
Start_Latitude is the geographical latitude where the bird's migration began. This value gives you a north-south position on the globe, crucial for mapping and route analysis. You can pair it with Start_Longitude to create migration origin points on custom visuals or maps in Power BI.
Start_Longitude represents the east-west position where the bird’s journey started. Combined with Start_Latitude, it provides precise geolocation of the bird's origin. You can use these coordinates to trace paths on a map, showcasing migratory corridors from specific entry points.
End_Latitude tells you the latitude of the migration destination, where the bird ended its journey. It allows us to visualize how far birds move vertically across the globe, which is especially insightful for seasonal migration analysis.
End_Longitude completes the geospatial puzzle by locating the bird’s destination on the east-west axis. It helps define the full travel route and can be visualized on map layers or route overlays.
Flight_Distance_km tells you how far the bird flew during its migration, measured in kilometers. It gives immediate insight into the scale of the journey and the physical demands on the bird.
Flight_Duration_hours is where you get the total time, in hours, it took for a bird to complete its migration. Combined with distance, it allows you to calculate real-time performance metrics like average speed.
Average_Speed_kmph tells you how fast the bird flew on average and it’s a fascinating metric for comparing efficiency. You can use this to highlight high-performance birds or analyze how speed is affected by wind, weather, or altitude.
Min_Altitude_m is the lowest recorded flying altitude during migration. When paired with max altitude, it helps you understand altitude range and variability across flights.
Max_Altitude_m shows the highest altitude (in meters) a bird reached during its flight. It offers insight into how certain species handle extreme conditions like low oxygen or freezing temperatures.
Temperature_C shows the ambient temperature during the bird’s journey, measured in Celsius. It’s useful for analyzing how birds respond to cold vs. warm conditions, and how this may impact success or behavior.
Wind_Speed_kmph tracks how fast the wind was blowing while the bird was in flight. Wind can assist or hinder migration depending on its direction and speed so this is great for analysis.
Humidity_% shows how moist the air was during the flight, expressed as a percentage. High humidity might make flying more challenging or signal stormy conditions. It’s useful when comparing environmental factors across different habitats or months.
Pressure_hPa indicates atmospheric pressure in hectopascals, which can affect altitude performance and weather conditions. Lower pressure often signals stormy or high-altitude conditions, especially relevant for birds flying over mountains.
Visibility_km shows how far a bird could see during flight, based on prevailing conditions. Poor visibility often correlates with delays, rest stops, or even failed migrations. You can analyze this alongside interruption reasons like “Storm” or “Lost Signal.”
Nesting_Success is a post-migration outcome indicating whether the bird was able to nest successfully. It helps tie migration behavior to reproductive success (which is the whole point of many migrations!).
Tag_Battery_Level_% this tracks the remaining battery life on the bird’s tracking tag. You can use this to filter out poor-quality records or compare tag performance across researchers. Great for tooltips or reliability indicators in visuals.
Signal_Strength_dB measured in decibels, indicates how strong the tracking signal was. Low signal might lead to missing data or incorrect journey paths. You can build visuals showing which birds had good coverage and which had gaps.
Migration_Start_Month / Migration_End_Month help you understand seasonality and migration timing. You can create timelines, bar charts, and animated visualizations showing month-by-month activity.
Rest_Stops shows how many times the bird stopped during its journey. More rest stops might suggest stress, poor weather, or energy depletion. You can correlate this with flight duration, success, or weather variables.
Predator_Sightings tracks how many predators were encountered during the flight. It adds an element of danger and helps you explore survival odds. You can use this in visuals to show regions or routes with high risk.
Tag_Type tells you what kind of device was used to track the bird (e.g., GPS, Radio). It’s relevant for evaluating data quality and signal strength. You can use it to group visuals by tag type or check which tags perform better.
Migrated_in_Flock is a Yes/No field showing whether the bird migrated alone or in a flock. Social birds often migrate more successfully and this field helps explore that hypothesis. It’s great for comparative analysis and storytelling.
Flock_Size tells you how big the flock was. Larger flocks might deter predators or improve navigation. You can analyze flock size vs. success or speed. Also good for exploring if bigger is always better.
Food_Supply_Level indicates the food availability at the bird’s starting location (High, Medium, or Low). This directly impacts energy reserves and readiness. It’s a fantastic input for success prediction models.
Tracking_Quality Shows the overall quality of the tracking (Excellent, Good, Fair, or Poor.) This tells your audience how reliable each data row is. Use this for filtering, conditional formatting, or tooltip flags. It helps maintain credibility in your insights.
Migration_Interrupted is a Yes/No field showing if the bird’s journey was disrupted. This is key for survival analysis and root cause exploration. Use it in summary KPIs or filter down to just successful journeys.
Interrupted_Reason tells you why (Storm, Injury, Lost Signal, etc.) as it’s ideal for a grouped bar chart. You can analyze what the biggest threats are and if they vary by region or species.
Tagged_By is the researcher who attached the tag to the bird. It’s helpful for performance comparisons across individuals or teams. You can analyze if tagging quality varies or if any bias exists.
Tag_Weight_g is the weight of the tracking tag in grams. Heavier tags might affect flight making this a critical factor for fair analysis. You can explore correlations with success or rest stops. Use it in histograms or scatter plots as it adds an engineering dimension to the data.
Migration_Success indicates whether the bird completed its journey successfully. This is the core outcome field and your north star for most analysis. Use this in all kinds of visuals as it’s your success rate tracker.
Recovery_Location_Known tells whether the recovery site was known post-migration. Useful for validating journey data or estimating ground-truth reliability. Use it as a filtering tool or metadata tag as it helps determine which records are complete.
Recovery_Time_days shows how many days it took to recover. Longer times might mean remote routes or poor signal. This can be visualized over time or grouped by region. Also good for tag performance evaluation. Adds a logistical aspect to the story.
Observation_Counts is how many times the bird was spotted throughout the journey. High counts often mean better tracking or social behavior. Use this for line charts, frequency maps, or ranking visuals.
Observation_Quality is a qualitative rating of how good those observations were (High, Moderate, Low.) This supports trust and filtering of your visuals. Use it to add conditional color codes or tooltip warnings.
Now, Let Your Data Take Flight
This contest isn’t about perfection. It’s about curiosity. It’s about experimenting, learning, and putting your own creative twist on the data. Whether you're a Power BI beginner or a visualization pro, there's something in here for everyone to discover, analyze, and share.
So pick your path. Maybe it's the fastest bird. The most dangerous journey. The surprising success against all odds. Whatever direction you take, trust your instinct and let the data lead.
Submissions close May 21, 2025, and I genuinely can’t wait to see what you create.
Happy building!
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