(in Polish) Advanced techniques in environmental data analysis 2600-ATCH-GC-1-S2

Lectures (dr hab. Marcin Koprowski, prof. UMK) - 7 h

Lectures (dr hab. Agnieszka Kalwasińska, prof. UMK) - 9 h
1. Introduction to environmental data analysis (1h)
2. Basic statistics (1h)
3. Research questions and statistical test requirements (1h)
3. Rarefraction curves, taxonomy visualisation, Venn diagrams (1h)
4. Hypothesis testing (1.5h)
5. Correlations. Heatmaps. Linear regression (1.5h)
6. Diversity. Alpha diversity metrics (1)
7. Beta diversity. Distance, similarity, dissimilarity, cluster analysis (1h)
8. Ordination techniques (1h)

Ćwiczenia (dr hab. Marcin Koprowski, prof. UMK) - 12 h

Ćwiczenia (dr hab. Agnieszka Kalwasińska, prof. UMK) - 18 h
1. Assessment review (2h)
2. Basic statistics (2h)
3. Research questions and statistical test requirements (2h)
4. Hypothesis testing (2h)
5. Correlations. Heatmaps. Linear regression (2h)
6. Biodiversity. Rarefaction curves. Alpha diversity metrics (2)
7. Beta diversity. Distance, similarity, dissimilarity, cluster analysis (2h)
8. Ordination techniques for biodiversity analysis (2h)
9. Students' own project. Final assignment (2h)

Total student workload

60 h including: • work with teacher: 30 h • individual student consultation: 1 h • individual work: 25 hours • preparations for assessment: 4 hours

Learning outcomes - knowledge

K_W02 To explain the biological concepts and relationships between natural phenomena and processes, as well as relationships between structure and function K_W06 To supply an understanding of the impact of organisms on their environment K_W07 To provide in-depth knowledge of the impact of the environment on human health K_W08 To supply an in-depth knowledge of statistics and specialized IT tools appropriate for describing and forecasting the course of natural phenomena K_W09 Together with the methodologies required for qualitative and quantitative investigation of the biological sciences

Learning outcomes - skills

K_U01 A thorough knowledge of the use of statistics for describing biological phenomena K_U03 And facility with advanced measurement and analytical techniques used in biological research K_U04 Computer literacy is necessary to retrieve information, communicate, organize and analyse data, prepare reports and present results K_U05 An ability to correctly assess threats to human health and life K_U07 An ability to develop scientific hypotheses based on logical reasoning K_U08 Based on measurements aimed at interpreting observations: production of results upon which to arrive at conclusions. K_U09 Using English source information, and ability to perform analyses, summarise and critically assess data, allowing formulation of correct conclusions K_U14 An ability to present the results orally in English, as well as in the writing of scientific reports K_U15 Use of scientific language to a standard that enables the documentation and development of research results

Learning outcomes - social competencies

K_K01 An understanding of the need to constantly expand knowledge with the use of scientific and popular science magazines K_K02 An ability to keep abreast of professional developments in the field of natural sciences together with an ability to inspire and organize the learning processes in others K_K03 Develop a rational and critical approach to information obtained from scientific literature, the Internet, and other mass media, as well as popular beliefs relating to biological sciences K_K04 Remain aware of the responsibility for the reliability of analyzes and expert opinions K_K05 And aware of the need to follow the rules of ethics K_K08 Remain aware of the need to use mathematical, statistical and IT methods to develop and present the results and analyses K_K11 Capable of teamwork K_K12 And aware of the importance of taking the initiative

Teaching methods

Theoretical and practical (computer lab) classes using the Moodle platform.

Expository teaching methods

- informative (conventional) lecture

Exploratory teaching methods

- project work
- classic problem-solving
- seminar
- practical

Online teaching methods

- cooperation-based methods
- exchange and discussion methods
- methods developing reflexive thinking
- content-presentation-oriented methods

Type of course

compulsory course

Prerequisites

Basic knowledge in molecular biology and statistics. Ability to use the R. English language proficiency both written and spoken.

Course coordinators

Agnieszka Kalwasińska

Assessment criteria

Students perform tasks and send solutions for checking.
At the end of the course, students prepare a final report in the form of a PowerPoint presentation. They receive raw data for the project, which they have to analyze. The final report includes the purpose of the research, the methodology used, results (figures, tables), and conclusions from the conducted analyses.
Grading criteria:
92% 5,0
83% 4,5
74% 4,0
65% 3,5
55% 3,0
<55% 2,0

Practical placement

Not applicable

Bibliography

Chao A and Chiu CH, 2016. Species richness: estimation and comparison. Wiley StatsRef: Statistics Reference Online. 1-26.
Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJM., 2001. Counting the uncountable: statistical approaches to estimating microbial diversity. AEM.67.10.4399–4406.
GUide to STatistical Analysis in Microbial Ecology (GUSTAME) https://mb3is.megx.net/gustame/dissimilarity-based-methods
Friedman J, Alm EJ (2012) Inferring correlation networks from genomic survey data. PLoS Comput Biol 8:e1002687.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2600-ATCH-GC-1-S2 in USOSweb