Course description for 2024/25
Scientific Communication and Research Methods
BI300F
Course description for 2024/25

Scientific Communication and Research Methods

BI300F
This course offers practical training in basic scientific methods and communication skills for master students in aquaculture. It covers introductory experimental design and data analysis, and emphasizes development of skills for efficient retrival, critical review and management of academic information.
Admission to the course follows the admission requirements of the study program Master in Biosciences.

Knowledge The student should:

  • have practical knowledge of relevant scientific methods and communication
  • understand common statistical methods and the general assumptions underlying both parametric and non-parametric analyses
  • understand the ethical challenges involved in communicating research and world-wide dissemination of new scientific results

Skills The student should:

  • have the necessary skills for efficient retrieval, critical review and management of academic information
  • be able to use relevant reference tools, presentation techniques, and demonstrate scholarly writing skills
  • be able to participate in informed quantitative assessments of published results from aquaculture or marine ecology

General competence The student should:

  • be able to apply basic research methods and academic communication skills of relevance for the completion of his/her master project
  • be able to exchange views and experiences with others involved in aquaculture or marine ecology research and thereby contribute to the continued development of good research practices
No costs except semester registration fee and syllabus literature.

Compulsory:

Master in biosciences and Nordic Master in Sustainable Production and Utilization of Marine Bioresources

Web based lectures focusing on general aspects of scientific method, scientific writing and ethics. For stats analyses, a flipped classroom approach with web based lectures, followed by classroom sessions with discussion of curriculum and worked examples of stats analyses. A number of statistical analysis exercises are to be handed in throughout the course as part of the work requirement. Group sessions with student assistants are used to support the students' work with these hand-ins. Collective feedback is given on hand-ins.
Midterm evaluation (dialogue meeting between lecturer and students). Written, web-based final evaluation.

Course work:

1) Handing in of three exercises addressing R coding and data analysis, delivered before set deadline, and solved in accordance with specifications given for acceptable answers. Evaluation: Approved / not approved. Not approved answers are returned to students for re-delivery. All three exercises must be approved to pass the course.

2) Oral presentation of scientific article performed in groups. Evaluation: Approved / not approved.

Exam: Written school exam, 4 hours. 100/100 of total course grade. Grades: A-F

Pen, ruler and up to 2 bilingual dictionaries.

Generating an answer using ChatGPT or similar artificial intelligence and submitting it wholly or partially as one's own answer is considered cheating.