EXTENSION
DATA LITERACY
The Data Literacy Program develops projects that help the UF family and friends, increase the adoption of data technologies, and increase the efficiency of scientific research and extension activities.
Reproducible Research (workshop)
After a two hour hands-on workshop, 80% of participants will be aware of academic fraud and able to differentiate between Replicability and Reproducibility, as measured by pre/post self reports. Similarly, 80% of participants will be able to generate reproducible reports in R, including text, code, and figures, as measured by the report generated during the workshop. This workshop is ideal for anyone that creates reports based on data. For example, graduate students, data scientists, and data analysts. If you have to continuously create reports, and can benefit from an automation of the process, this workshop is ,for you!
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ggplot2 (workshop)
After a two hour hands-on workshop, 80% of participants will be able to produce good-looking graphics that convey information for analysis, using the grammar of graphics in ggplot2, as measured by pre/post self-reports by participants. The audience of this workshop is anyone that needs to present the results of data analysis, to diverse audiences, this includes students, and people in industry that need to communicate data-based findings to decision makers. If you can benefit from figures that convey data so you can influence others and help them make smart decisions, you will love this workshop.
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Syngenta crop-challenge
This challenge in analytics is organized by Syngenta and sponsored by the Analytics Society of INFORMS (Institute for Operations Research and Management Sciences). This annual competition started in 2017, it introduces an analytical problem with real data sets to solve problems related to world hunger. Winners receive prizes between $5000 to $1000. For more information write me at [email protected] with subject: "Syngenta Crop-Challenge"
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DATA RICH APPLICATIONS
,By integrating our expertise with state of the art data technologies and infrastructure, we create data-intensive web applications with significant scientific and/or social value. These applications are designed with a philosophy of simplicity and added value to the user.
- Machine Learning for Cattle Retention Payoff: This application allow ranchers to provide basic information about their cattle (age, milk production, etc) using an excel sheet. Our servers analyze the information live and use machine learning techniques to predict the retention pay of per each cow in the herd. (Involved Personnel: Jorge Barrera, Albert De Vries).
- Big Data for Florida Water Quality Analytics: In Florida alone, the National Water Quality Monitoring Council collects data from more than 30K monitoring Stations (EPA and USGS). In UF we have mirrored, cleaned, organized and processed this data, to provide Analytics regarding water quality for the years 2007 through 2015. This data includes chemical characteristics such as Alkalinity, Aluminium concentration, Arsenic, Bromide, Calcium, Chloride, Dissolved Oxygen, Lead, Oxygen, pH, conductance, dissolve oxygen, zinc, among others. The sites of collection have also been classified into wells, Rivers/Stream/Canals, Lake/Reservoir/Impoundment, Estuary, Spring, Ocean, Wetland_Palustrine/Estuarine. With this rich data set, we can study the changes in water quality for areas with different type of developments throughout those years. For example, an overview of the data shows an increase in the levels of Aluminum in the Saint Jones river basin, between the years 2007 through 2010, which goes back to former levels in 2013. Similar patterns are observed for different years and chemical characteristics of the water.
- Precision Agriculture, online Multi and Hyper Spectral Analysis (Under development): In collaboration with the UF UASRP[1], farmers can schedule an aerial inspection to their land for a hyper spectral imaging of their land. This information can be uploaded to a cloud service for diagnosis of the land. This can provide early detection of anomalies such as nutrient imbalance, and equipment malfunctioning. (Tentative Involved personnel: Jorge Barrera, Aditya Singh, Jim Fletcher)
[1] http://uas.ifas.ufl.edu/