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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| FIELD CROPS (MASTER) (WITH THESIS) | |
| Code | TB591 |
| Name | Data Visualization in Field Crop Research |
| Term | 2026-2027 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi MUZAFFER BARUT |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
By the end of this course, students will be able to select appropriate visualization techniques for quantitative and qualitative data obtained in field crop research, organize and interpret such data in accordance with scientific principles, and present research findings effectively.
Course Content
This course includes information on the fundamental principles of data visualization; data structures in field crops research; data cleaning and organization; visualization of descriptive statistics; bar charts, scatter plots, and line graphs; correlation visualizations; heat maps; multivariate data visualization; reporting experimental results through graphs; the presentation of error bars and variation; and figure design for scientific publications, theses, and presentations.
Course Precondition
No prerequisities.
Resources
The course notes are sent to the e-mails of the students.
Notes
Wickham, H. (2016). Elegant graphics for data analysis. Fundamentals of Data Visualization – Claus O. Wilke Wickham, H., & Grolemund, G. (2017). R for data science (Vol. 2). Sebastopol: O'Reilly. O'Donoghue, S. I., Gavin, A. C., Gehlenborg, N., Goodsell, D. S., Hériché, J. K., Nielsen, C. B., ... & Wong, B. (2010). Visualizing biological data—now and in the future. Nature methods, 7(Suppl 3), S2-S4.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains the fundamental concepts and principles of data visualization. |
| LO02 | Distinguishes among the types of data used in field crop research. |
| LO03 | Selects the appropriate graph type according to the research question and data structure. |
| LO04 | Organizes, cleans, and transforms raw data prior to visualization. |
| LO05 | Presents descriptive statistics using appropriate tables and graphs. |
| LO06 | Interprets the effects of cultivar, location, year, and treatment through comparative graphs. |
| LO07 | Demonstrates correlation relationships using appropriate visualization techniques. |
| LO08 | Presents multivariate agricultural data in a clear, readable, and scientifically accurate manner. |
| LO09 | Designs high-quality figures for scientific articles, theses, posters, and oral presentations. |
| LO10 | Applies correct design principles in the use of scale, axes, color, labels, and legends in visualization. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Graduates become a specialist on the field crops area by improving their skills. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | They comprehend interdisciplinary interaction in his specialization area. | 3 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | They can follow the latest developments in field crops area and get access to the knowledge source, gather this knowledge, reach new knowledge and synthesize by evaluating available studies and follow the innovations in his field. | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | They can synthesize and interpret interdisciplinary knowledge by using theoretical and practical skills at a specialist level in field crops area. | |
| PLO05 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | They find solutions for problems related to field crops by using research methods and establish cause effect relationships. | |
| PLO06 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | They incorporate their knowledge on his special area by combining their knowledge with those from the other scientific areas and produce new knowledge and also solve the problems by using scientific research methods. | |
| PLO07 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | They determine a problem independently in field crops subject, provide solutions, evaluate the results and implement whenever required. | |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | They are qualified to solve a problem in their field. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | They are equipped with analytical and critical thinking ability to direct their learning and to conduct advanced studies in their fields independently. | |
| PLO10 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | They transfer current developments and their own studies in the field crops area systematically to the his working group and to different groups from other study fields orally or visually by supporting them with qualitative and quantitive data. | 4 |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | They study and improve the social relationships and standards leading these relationship by a critical point of view and take action when needed. | |
| PLO12 | Yetkinlikler - Alana Özgü Yetkinlik | They are able to understand and translate an article written in a foreign language. | |
| PLO13 | Yetkinlikler - Alana Özgü Yetkinlik | They develop policy, strategy and experimental plans related to field crops area and evaluate obtained results within the framework of quality processes. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Importance of Data Visualization in Field Crops Research | Review of relevant literature | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Data Types: Numerical, Categorical, Time-Series, and Multi-Environment Trial Data | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Principles of Good Graph Design: Simplicity, Accuracy, Readability, and Comparability | Review of sample articles | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Data Organization, Data Cleaning, and Visualization of Outliers and Missing Observations | Preliminary review of the dataset | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Basic Graphs I: Bar Charts, Histograms, Box Plots, and Violin Plots | Review of article and data examples | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Basic Graphs II: Scatter Plots, Line Graphs, Group Comparisons, and Error Bars | Dataset preparation | Öğretim Yöntemleri: Anlatım, Tartışma |
| 7 | Visualization of Mean Comparisons in Field Experiments: Variety and Treatment Effects | Review of statistical outputs | Öğretim Yöntemleri: Anlatım, Tartışma |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Correlation Plots and Correlation Matrices | Review of sources and datasets | Öğretim Yöntemleri: Anlatım, Tartışma |
| 10 | Heatmaps | Review of sample studies | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Visualization of Multi-Location and Multi-Year Trial Results | Data preparation | Öğretim Yöntemleri: Anlatım, Tartışma |
| 12 | Combined Visualization of Yield and Quality Parameters | Review of figure examples | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Multivariate Visualization: PCAbiplots | Review of figure examples | Öğretim Yöntemleri: Anlatım, Tartışma |
| 14 | Visualization of Cluster Analyses | Review of figure examples | Öğretim Yöntemleri: Anlatım, Tartışma |
| 15 | Figure Design for Theses, Articles, Posters, and Presentations: Color, Font, Caption, and Legend Selection | Review of figure examples | Öğretim Yöntemleri: Anlatım, Tartışma |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||