The Beasts of Mesozoic
Analisando todo o período Mesozóico, geologia, museus, formações, e muito mais. Devo continuar o desenvolvimento? Comente o que vc achou do relatório. #dinosaurs #mesozoic #paleontology eyJrIjoiYTExOWM1ZWUtMGZlNi00MTU2LTlmMDktMjEzZjU5MWRhYjNkIiwidCI6ImNmNzJlMmJkLTdhMmItNDc4My1iZGViLTM5ZDU3YjA3Zjc2ZiIsImMiOjR9EducaTV Portifólio
Portifólio de vídeos EducaTV Youtube Recomendamos visualizar pelo FireFox Visite nossos outros relatórios: https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Um-relat%C3%B3rio-de-testes-de-possibilidades/td-p/4832617 https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/DINOSAURS-REBOOT/m-p/4148138 https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/The-Beasts-of-Mesozoic/td-p/5128222 https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Global-Deaths-dual-language/td-p/4839470 eyJrIjoiYTU5YzZhZGYtZWJlYS00YzQ5LWI5YzMtMWYyNzE4ZmQ4OTcyIiwidCI6ImYxYjUwNGEzLWM3MDctNGMyOC05YmJmLWQ0ZTExOTQ0MDY4NyJ9&pageName=a8a9a3650029effc4c8dDINOSAURS! REBOOT
Just a visual reboot for the incredible DINOSAUR! report from Greg Deckler. Thanks again master Greg! I´ll do another one, ffrom zero, with the latest database from PBDB... More about it: https://www.behance.net/gallery/207791635/DINOSAURS-REBOOT eyJrIjoiOGZiNzE1NmQtODMxZC00ZDQwLTg2ZmQtOWQ4ZGVhMDE5Mzg2IiwidCI6ImNmNzJlMmJkLTdhMmItNDc4My1iZGViLTM5ZDU3YjA3Zjc2ZiIsImMiOjR9Global Deaths - dual language
30 Anos de Histórias, 202 Países, 1 Relatório: Acabei de finalizar meu novo projeto de Power BI sobre as Causas de Morte Globais (1990-2019), agora bilíngue! Este relatório não é apenas um dashboard; é uma análise profunda de 6.160 linhas de dados, construído para ser a ferramenta mais dinâmica e intuitiva possível para explorar a mortalidade mundial. O que você vai encontrar: ✅Visão de Longo Prazo: Explore a evolução das mortes e o crescimento populacional ao longo de 30 anos (1990-2019). ✅Métricas Chave: Analise o Total de Mortes, Taxa de Mortalidade, População, Crescimento Anual (YoY) e Densidade Demográfica por país e continente. ✅Análise Profunda: Dados categorizados em 31 causas e 7 grupos principais (DCNT, Lesões, Infecciosas, etc.). ✅Modelo Multi-Idioma: Built-in com a função USERCULTURE(), permitindo que o relatório se ajuste automaticamente ao formato de números e datas do usuário (pt-BR, en-US, etc.). Este projeto também foi um exercício em otimização de dados: usei Power Query para transformar e o DAX para criar as medidas dinâmicas, garantindo um arquivo Power BI o mais enxuto possível. Interessado em ver como a saúde global evoluiu nas últimas três décadas? #PowerBI #DataAnalysis #GlobalHealth #CausesOfDeath #DataViz #DAX #BusinessIntelligence eyJrIjoiYTAwOGE3MjUtYTI5OC00NTE3LWE4YTUtYzM1N2Y3NzkxMjhiIiwidCI6ImNmNzJlMmJkLTdhMmItNDc4My1iZGViLTM5ZDU3YjA3Zjc2ZiIsImMiOjR9Power BI Template for Inventory Analysis
Inventory problems can quietly eat away at your profits. Stockouts, delayed orders, and poor availability don’t just cause frustration, they cost your business real money and unhappy customers. Most teams only notice these problems after it’s too late. That’s where the Out-of-Stock Report Dashboard (Power BI Template) comes in. This ready-to-use tool gives you clear, real-time visibility into your inventory so you can spot issues early, take action fast, and keep your shelves stocked. Why This Dashboard Matters With simple visuals and smart insights, this template makes it easy for supply chain teams, warehouse managers, and operations heads to: Track stockouts in real time and know exactly which products are affected. Measure revenue loss caused by poor stock availability. See service levels and fill rates to understand how well you’re meeting demand. Monitor backorders and identify delayed fulfillment risks. Review inventory turnover to avoid constant understocking. Check stockout frequency and duration across items, categories, and time. Who Should Use It? This template is perfect for anyone managing stock and supply chains: Inventory Managers Supply Chain Analysts Warehouse Teams Operations Leaders eyJrIjoiZTcxMTBhYTktNTAwYy00OGU3LWI0Y2MtMDExZTJkZDBiMzQzIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9
Global Development and Macroeconomic Insights 1960-2018
Overview: Global Development and Macroeconomic Insights The global economy is a complex, ever-shifting system where the paths to human development vary wildly across borders. This Power BI dashboard transforms a massive historical matrix into an interactive, multi-page data story. By mapping 58 years of development metrics, the project explores how changes in wealth, healthcare, and digital infrastructure reshape the modern world, while utilizing advanced DAX modeling to eliminate statistical outlier bias. Data Description This comprehensive dataset tracks macro-level human and economic development indicators across 211 unique nations over a continuous 58-year timeline. Originally compiled from global administrative reporting bodies, this curated analytics-ready dataset was sourced directly from Maven Analytics. Dataset Profile Source: Maven Analytics (originally curated from the World Bank Development Indicators repository and the International Labour Organization database). Temporal Scale: Annual records spanning 1 January 1960 to 31 December 2018. Scope: 211 unique countries and territories categorized by geographic macro-regions and official income tier groups. Core Data Domains The transaction layer monitors annual indicators across four fundamental socio-economic pillars: Category Monitored Indicators Economic Status GDP per Capita (Current USD), Regional Classifications, and Income Group Tiers. Health & Longevity Life Expectancy at Birth (Years) and Infant Mortality Rates (per 1,000 live births). Infrastructure & Energy Internet Penetration Rates (% of population) and Electric Power Consumption (kWh per capita). Labor Market Dynamics Total Unemployment Rates (% of the active labor force). Key Analytical Insights & Dashboard Features The Preston Curve Dynamics: A dynamic scatter plot modeling the non-linear relationship between wealth and longevity, proving that economic growth yields sharp health returns early on but flattens significantly past higher-income thresholds. The Digitization Effect: An investigation into structural energy decoupling, highlighting how explosive global internet adoption from 1999 to 2018 flattened per capita electricity demand due to modern infrastructure efficiencies. Robust Statistical Design: Built entirely using single-direction star schema architecture. To prevent global economic giants from heavily skewing regional baselines, custom DAX measures were engineered to compute dynamic regional medians instead of simple arithmetic means. Data Source & References Primary Data Provider: Sourced via the Maven Analytics Data Playground repository. Original Administrative Sources: Global Data compiled by the World Bank Group (World Development Indicators) and the International Labour Organization (ILO). eyJrIjoiMGIxODZiNjctNTI2Ny00Y2ViLWJlNDgtNDFlM2MzNmRjNDFkIiwidCI6ImY1MTg1YWY3LTQ2YmUtNDRhNS05MDkyLWM4ZWMwZmQ4ZDBhNyJ9Roadmap to Safety: Analyzing NYC Traffic Collision Trends & the 2020 Lockdown Anomaly
Overview This interactive Power BI dashboard transforms raw city incident logs into a comprehensive operational data story analyzing 75,000 traffic collisions across New York City. The primary objective of this report is to identify critical peak hours, high-risk geographic bottlenecks, and primary contributing factors to help urban planners and logistics managers optimize road safety initiatives. Key Business Insights Discovered: The Lockdown Anomaly: The report features a time-series analysis highlighting the dramatic drop in daily accidents precisely when the COVID-19 lockdown was enacted, providing clear historical context to the data. Peak Risk Windows: Utilizing a dual-axis matrix heatmap, the analysis isolates Friday afternoons (16:00 - 18:00) as the highest-density window for collisions across the city. Vulnerable Road Users: Dedicated analytics isolate the impact on cyclists and pedestrians, breaking down injury and fatality rates to pinpoint exactly which boroughs require immediate infrastructure intervention. Behavioral Drivers: The data clearly identifies "Driver Inattention/Distraction" and "Unsafe Speed" as the overwhelming leading causes of both total collisions and fatalities. Technical Features Built Into This Report: Advanced UX/UI Design: A seamless, custom-built left navigation panel that mirrors a premium SaaS application for intuitive user exploration. Dynamic Filtering: Integrated timeline slicers and borough cross-filtering to allow end-users to drill down from high-level KPIs to granular, street-level data. Data Density Control: Strategic use of conditional formatting matrixes and custom tooltips to display dense multi-dimensional data without cluttering the canvas. Data Source & References Primary Data Provider: Sourced via the Maven Analytics Data Playground repository. eyJrIjoiZGRkOTM2NjMtMGEwMS00OTc0LWIzNjctOTI3OWRkMzNiNDFkIiwidCI6ImY1MTg1YWY3LTQ2YmUtNDRhNS05MDkyLWM4ZWMwZmQ4ZDBhNyJ9
Custom Visual for cliamate Warming strips for multi location comparisaon
Can 3D charts make Sense for Data Viz? If you do not want to do Classical Finance and controlling reporting- absolutely YES! In my setup, I played around with the data behind the famous, inspiring Climate Warning stripes. My idea was to show multiple Locations at once. I wanted to display how things changed over time and also by location. My Technical Setup: -I used Claude Design and a picture of the original stripes to create an HTML version - Fable 5 and Opus 5 -Than i created a git repo and connected it with Claude Code Fable in High Effort mode as default -I loadad every thing need for the creation of a custom Power BI visual -I got a CSV with the temperature and the location -Upload to Power BI Service 🙂 Thanks to Ed Hawkins for the great inspiration and your effort to rise the awarness for Global Warming. eyJrIjoiNDBjNTYyNjktNDdiZC00ZTQ3LTllNjItYjRmMjk4OGNmMTIwIiwidCI6IjI0ZWIxMGYwLTBmY2QtNGU5Ny1hMGQ0LTg0NjhhODRhOWU5MCIsImMiOjh9Global AI Adoption & Workforce Impact
Artificial Intelligence is transforming organizations faster than workforce adaptation can keep pace. This analysis examines how AI adoption influenced productivity, employment outcomes, and reskilling efforts across 30 countries, 25 industries, and 8 skill categories between 2021 and 2024. The findings show that while AI adoption accelerated and supported productivity gains, workforce outcomes remained negative overall, resulting in a net loss of approximately 290K jobs across the study period. At the same time, significant differences emerged across industries, regions, and skill categories, highlighting areas where disruption is most concentrated. This report explores three key questions: How has AI adoption reshaped workforce outcomes? Where is AI delivering measurable value? Where should governments and employers prioritize reskilling investment? 🔗 Dataset : July 2026 DataDNA – Global AI Adoption Workforce Displacement Index Analytics Challenge eyJrIjoiYmEwOTgxYzctMGNkMC00NzMwLTlkMjctMDgwMmUwM2I5NDMwIiwidCI6IjUwOWU3OGY0LTQ0MTctNDkyNi05MzFjLTQ3YzVjNWQwMThlZCIsImMiOjF9
