BIO4115 - Distribution modelling
Schedule, syllabus and examination date
This course provides an introduction to distribution modelling and to essential data processing and analytical tools used in this field. through lectures and practical exercises the course gives an introduction to the theory of distribution modeling, critical evaluation of strengths, weaknesses, errors and uncertainties throughout the distribution modelling process, overview of data, methodology, software and useful tools, preparation for analyses and practical modelling exercises. The students shall write a mandatory project report and discuss the subject during a mandatory plenary session.
The course will provide a sound theoretical understanding of the principles of distribution modelling and practical experience with some of the most important data analysis tools used in this field. Through the project report and the practical distribution modelling work and the accompanying discussion, participants will gain the ability to critically evaluate the whole process of distribution modelling.
Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.
PhD-students have to register to BIO9115 - Distribution modelling.
The courses BIO4115 and BIO9115 have common admission. Applicants are ranked by the following criteria:
1. PhD students and master students at the MN faculty who have the course as part of the approved curriculum.
2. Other PhD students and visiting PhD students.
3. Students with admission to single courses on master’s level and exchange students
4. Applicants are ranked by credits in each group; all applicants within 1st rank before applicants in 2nd etc. If admission is limited to a fixed number of participants, admission will be decided by drawing lots for students who are ranked equally
Recommended previous knowledge
The course does not provide technical training in geographical information systems (GIS). Knowledge of GIS and statistical software (R) will be an advantage.
Instruction will be provided in the form of lectures (30 hrs) and computer laboratory/ practical data laboratory exercises (30 hrs). The course includes a mandatory project report (60 hrs) providing the basis for the presentation and discussion at a mandatory final plenary presentation lasting 1-2 days (40 hrs). Attendance for all lectures and data laboratory exercises is required; although a 20% absence for special circumstances is permitted. Attendance to the first lecture and the final plenary session is mandatory and non-negotiable.
The course will be given during week 46-50 (November and December). Lectures and data laboratory exercises will be given in a concentrated block in the first half. The mandatory project report must be submitted and will be evaluated (approved/not approved) before the plenary session. Approved project reports will be made available to all participants, presented and discussed on the final plenary sessions in December.
To pass the course you have to:
•get approved mandatory participation on the lectures and the data laboratory exercises
•get approved the mandatory project report
•get approved mandatory presentation and discussion at the final plenary session
Grades are awarded on a pass/fail scale. Read more about the grading system.
Explanations and appeals
Resit an examination
This course offers both postponed and resit of examination. Read more:
Withdrawal from an examination
If you wish to withdraw from the exam you must do so in Studentweb at least two weeks prior to the deadline. Failure to do so will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.