Module Details

Module Code: AGRI S9Z09
Full Title GIS, Data Management and Statistics
Valid From: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits: 7.5
Module Owner: Enda Clinton
Departments: Unknown
Module Description: The aim of this module is to equip students with the knowledge and skills to conduct applied research in agricultural biotechnology and to interpret and synthesise existing research to inform their Project. This course will increase student awareness of research design and the main ethical issues associated with modern research. This course will provide an in depth analysis of data handling techniques, providing information on appropriate methods for sorting, storing and using data, particularly in the manipulation of large complex datasets. Students will become proficient in applying scientific data to appropriate statistical tests and how to import data to data-handling programs, manipulate and graph them. This module aims to provide students with an appreciation of ICT in Agriculture production and processing level. It will also enable them to assess current novel precision (smart) techniques including the area of advanced biotechnology specific to Agri-Food and Environment.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Interpret and appraise various forms of technology data acquisition within precision agriculture (biosensors, remote sensing, yield monitoring)
MLO2 Demonstrate an appropriate use of GIS and GPS software in agricultural practices (eg crop and/or livestock management)
MLO3 Evaluate the importance and relevance of intellectual property (IP) within a scientific context and to critically analyse the main ethical issues associated with current and future developments in the biotechnology field.
MLO4 Critically synthesise the importance of different types of data and assess the rationale for different methods of data collection, sorting, storage, visualisation, presentation and analyses, including quantitative and qualitative approaches.
MLO5 Select appropriate qualitative and quantitative data analysis techniques, report and critically interpret the outcomes of these analyses.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named DkIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
 
Module Indicative Content
Research Design
Evaluation of reliability and validity of experimental design. Rationale behind random sampling, and issues such as sample selection bias and endogeneity will also be raised. Choosing between qualitative and quantitative approaches. Understanding the limitations of data and how to avoid drawing tentative conclusions. Critically evaluate the ethical and political dimension to research and its implications for the researcher. Introduction will be given to DOE software (e.g. Stat-Ease).
Ethical Framework
The application of ethical and regulatory frameworks to experimental design, the application of national and international legislation, agreements, conventions and guidelines, decision making and whistle blowing.
Intellectual Property (IP)
Differences between artistic (copyright) and industrial property (including trademarks, patents, etc.). Overview of the importance and application of industrial property in the area of Biotechnology.
Data Collection, Analysis and Interpretation
Defining data, data collection, ensuring reliability and validity of collected data. Identifying appropriate statistical analyses, introduction to statistical software, selecting and extracting raw data in appropriate formats for interpretation of specific statistical analyses using a range of statistical software, dealing with outliers and incomplete or missing data. Examining the relationship between design and analysis, describing and illustrating quantitative data, content analysis, understanding variation, probability and inferential statistics.
Information/communication and Location Technology;
Information/communication and Location Technology; Geographical Information Systems: capture, store, manipulate, analyze, manage, and present all types of geographical data. Global Positioning Systems, Differential Global Positioning Systems, coordinate formats. Information/communication and Geographical Information Systems: capture, store, manipulate, analyze, manage, and present all types of geographical data. Global Positioning Systems, Differential Global Positioning Systems, coordinate formats.
Percision agriculture and data management
Overview of current (incl historic timeline) ICT in crop and livestock management, emerging technologies, future farming, cloud infrastructure. Data base management and analytics. Data logging, yield monitors, remote sensing. Importance of historical and real time data on farm business metrics such as livestock/crop production performance. Typical web interfaces, algorithm development, data interpretation and scenario predictions.
Module Assessment
Assessment Breakdown%
Course Work30.00%
Project50.00%
Practical20.00%
Module Special Regulation
 

Assessments

Full Time

Course Work
Assessment Type Class Test % of Total Mark 30
Marks Out Of 0 Pass Mark 40
Timing n/a Learning Outcome 3,4,5
Duration in minutes 0
Assessment Description
The students carry out a variety of practical exercises using suitable software relating to the sorting, storage, visualisation, and analyses of complex datasets while addressing ethical concerns related to data collection and handling.
Project
Assessment Type Project % of Total Mark 50
Marks Out Of 0 Pass Mark 40
Timing n/a Learning Outcome 2,3,4,5
Duration in minutes 0
Assessment Description
Project Proposal: The student will submit a proposal for a research project, which will include a work plan, project objectives and a timeline as a Gantt chart, for their research project. Where feasible projects which include links to on-going funded research projects, to industry and to local stakeholders, including links to communities will be encouraged. The plan should include reference to the methods to be employed in the practical component and any ethical or IP issues which may arise.
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Marks Out Of 0 Pass Mark 40
Timing n/a Learning Outcome 1,2
Duration in minutes 0
Assessment Description
Practicals such as GIS and Data logging / gathering.
No Final Examination
Reassessment Requirement
No repeat examination
Reassessment of this module will be offered solely on the basis of coursework and a repeat examination will not be offered.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Weekly lectures will be delivered which will place an emphasis on deep learing and student- centered approaches. All lecture material will made available to the students through DkITs virtual learning environment (Moodle). Every Week 1.00 1
Practical Contact Weekly 2 hour computer laboratory sessions will enable application and use of data handling techniques, statistics and experimental design software Every Week 2.00 2
Independent Study Non Contact No Description Every Week 7.50 7.5
Directed Reading Non Contact No Description Every Week 3.00 3
Online Contact Contact Online Discussion Forum Every Week 0.50 0.5
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 3.50
This module has no Part Time workload.
 
Module Resources
Recommended Book Resources
  • Field, Andy. (2012), Discovering Statistics Using R, 1st. SAGE Publications Ltd., London, [ISBN: 978-1-4462-00].
  • Zuur, Alain, Ieno, Elena N., Meesters, Erik,. (2009), A Beginner's Guide to R, Springer, [ISBN: 978-0-387-938].
  • Goddard, W. and Melville, S.. (2007), Research Methodology: An introdution, 2nd. McGraw-Hill.
  • Dawson, C.. (2009), Introduction to research methods: A practical guide for anyone undertaking a research project, 4th. Oxford: how to Books Ltd., [ISBN: 1845283678].
  • Box, G.E.P., Hunter, W.G. and Hunter, J.S.. (2005), Statistics for experimenters, John Wiley & Sons, [ISBN: 0471093157].
This module does not have any article/paper resources
Other Resources
 
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