It is evident that Big Data is becoming a new and explicit facet of academic research, directly shaping the way in which research is conceived and conducted. As a result, it is essential for those within the academic community to not only develop a conceptual understanding of Big Data, but also fortify methods for responding to the volume, variety, veracity, and velocity that are inherent in Big Data.
Please join the University of Wisconsin-Madison College of Agricultural and Life Sciences to explore “Big Data and Ecoinformatics in Agricultural Research” at a day-long symposium on Thursday, April 27, 2017 at Union South.
While our expected audience for a symposium consists primarily of the broad academic community at the University of Wisconsin-Madison, the symposium will emphasize the role of Big Data within the agricultural sector. In addition to a focus on agricultural applications, we have chosen to highlight Big Data in relation to education, historical and scientific perspectives, as well as incorporating the role of policy and national research entities. It is our goal that not only will the symposium increase knowledge on the subject of Big Data, but help to increase awareness for current research incorporating Big Data, and potentially lead to future collaborations.
- Skills required to become a data scientist in a field inundated with Big Data
- Discussion on cloud computing, shared data platforms, and other methods used for Big Data analysis as well as techniques for working with unstructured data
- Differentiating between data scientists, statisticians, computer scientists, etc.
- Identifying growth in the field as well as job opportunities associated with such growth
- Determining steps for University and educational institutions when it comes to incorporating Big Data initiatives and degree programs
- Improved production through advances in breeding and precision agriculture
- Big Data as applied to the fields of entomology, agronomy, dairy science, etc.
- Development of digital tools, applications and processes that can be used to collect and analyze information at the farm-level
- Explanation of the incorporation of farm-level data into Big Data analysis-the collaboration between individual farmers and large agro-businesses
Historical and Broad Perspectives:
- Provide a broader viewpoint by discussing the parameters used to define Big Data
- Critical analysis of Big Data, featuring issues of privacy and inequalities stemming from access issues
- Description of key benefits of the use of Big Data, including present and possible future advances (ex: predicting spread of disease, specialized medical treatments, etc.)
- Past and present methods of Big Data analysis and how this transition occurred
- Disciplines that have previously transitioned into the Big Data era, and what can be learned from such experiences (ex: bioinformatics, molecular physics)
National Policy and Research Responses:
- Discuss the role federal agencies play in Big Data applications, specifically with a focus on policy
- Explanation of the changing role of the scientific method in response to Big Data
- The response of the scientific community to the emphasis on correlation, rather than causation, that is apparent in Big Data
- Discussion of present initiatives