Schedule, syllabus and examination date

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Course content

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.

Learning outcome

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 project discussion, participants will gain the ability to critically evaluate the whole process of distribution modelling.


Students attending BIO9115 will have a significantly larger syllabus compared to students at BIO4115, and are expected to develop a deeper understanding of distribution modelling both in terms of theoretical basis and practical applications.

Admission

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.

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

Prerequisites

Recommended previous knowledge

BIO1000 - Elementary Biology (discontinued), BIO2100 - General Ecology, BIO2150A - Biostatistics, and BIO4021 - Methods of Gradient Analysis.

The course does not provide technical training in geographical information systems (GIS). Knowledge of GIS and statistical software (R) will be an advantage.

Overlapping courses

Teaching

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 (80 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 sessions 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.

Students attending BIO9115 will have to read approx. 10 scientific papers with relevant literature. Compared to students at BIO4115, they have to discuss more issues in their project report and during the practical modelling tasks.

 

Examination

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

Students attending BIO9115 are assumed show a deeper understanding of distribution modelling both in terms of theoretical basis and practical applications.

Grading scale

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

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Evaluation

The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Facts about this course

Credits

10

Level

PhD

Teaching

Every other autumn starting 2017

Examination

Every other autumn starting 2017

Teaching language

Norwegian (English on request)