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(in Polish) Biodiversity Data – Collection, Management, and Analysis

General data

Course ID: 1400-BDMM
Erasmus code / ISCED: (unknown) / (unknown)
Course title: (unknown)
Name in Polish: Biodiversity Data – Collection, Management, and Analysis
Organizational unit: Faculty of Biology
Course groups: Elective subjects
ECTS credit allocation (and other scores): 9.00 Basic information on ECTS credits allocation principles:
  • the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS;
  • the student’s weekly hourly workload is 45 h;
  • 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes;
  • weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS;
  • work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language: English
Type of course:

elective courses
optional courses

Short description:

The aim of the classes is to acquire and develop skills in collecting, managing, and analyzing data on the occurrence of organisms in time and space in the context of climate data and the relationship between organisms. During the classes, students obtain data on biological diversity from various sources and then prepare a relational database schema for storing and managing this data. After organizing, cleaning, and filtering the data, students perform analyses and draw conclusions about the spatial structure of populations. In addition to modeling contemporary ranges, students reconstruct historical ranges and make predictions about future ranges, taking into account climate change. The classes take place mainly in a computer laboratory, but some data is collected directly in the field.

Full description:

Upon completion of the classes, the student is able to obtain the biodiversity data in the field and laboratory, or from literature/public repositories in a way that maximizes their scientific value and enables their scientific use in the future. During the classes, students use various applications and IT tools to obtain biodiversity data.

Students store the obtained biodiversity data in relational databases according to current standards, which enables easy use of the data both in their own research and sharing the data according to the FAIR (Findability, Accessibility, Interoperability, and Reuse of digital assets) principles. Students become familiar with IT tools for building such a database and managing large data sets, learn the basics of the terminology and standards used in databases on biological diversity, and the required scope of data and metadata necessary to maintain the high quality of the data set.

Students learn about various repositories of open biodiversity data, the principles of depositing data there, and prepare their own data set for sharing in these repositories. Controversies regarding open databases on biological diversity and appropriate remedies aimed at limiting the publication of sensitive content, such as precise information on the location of rare plant or fungi species, are discussed during the classes.

Students apply formal georeferencing protocols to determine the location based on verbal descriptions, e.g. information on a herbarium specimen label.

Students store spatial data in geographic information systems (GIS) and conduct basic analyses: natural inventory, spatial population structure – population size and density. Students visualize spatial data using appropriate cartographic projections: conformal, equal distance, and equal area.

Data on the occurrence of organisms in time and space, combined with climatic data, are used to model the ecological (climatic) niche of organisms. Students learn to model contemporary ranges of organisms and predict future changes in these ranges based on climate change forecasts. Based on contemporary ranges of organisms and their relationships, students reconstruct the history of the spread of selected taxa, e.g. using the DEC (Dispersal Extinction Cladogenesis) model.

Upon completion of the classes, the student can develop and write a data management plan required in applications for funding research projects.

Bibliography:

Recommended literature:

1. GBIF educational resources: https://www.gbif.org/training

2. R statistical software: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf

3. QGIS: https://docs.qgis.org/3.28/en/docs/gentle_gis_introduction/index.html

4. SQL: https://www.w3schools.com/sql/sql_intro.asp

5. OpenRefine: https://openrefine.org/docs

6. rgbif: https://www.gbif.org/tool/81747/rgbif

7. Maxent: https://www.gbif.org/tool/81279/maxent

8. Darwin Core: https://dwc.tdwg.org/terms/

Learning outcomes:

KNOWLEDGE:

K_W01 BI2, K_W04 BI2, K_W09 BI2, K_W11 BI2, K_W17 BI2, K_W18 BI2; K_W03 OS2, K_W06 OS2, K_W13 OS2, WG_03, WG_04, WK_03

Student:

● understands the complexity of processes and phenomena in nature, the understanding of which requires an interdisciplinary approach based on empirical data;

● understands the relationships between organisms and the environment, applying hypotheses concerning temporal and spatial determinants of biological diversity;

● knows specialized bioinformatic tools necessary for solving problems in the field of biological sciences;

● knows specialized methods and techniques for conducting field studies in the natural environment and ways of using them for environmental protection;

● understands the principles of copyright protection and intellectual property;

● recognizes the dynamic development of natural sciences and the emergence of new research disciplines, as well as identifies the most significant trends in the development of biological sciences in the field of his or her specialization;

● knows modern techniques used in the assessment of the state of the environment and the methods of environmental protection;

● knows the principles of research planning and modern data collection techniques;

● has in-depth knowledge of copyright law;

● knows and understands scientific research methodology within scientific disciplines in the field of exact and natural sciences;

● knows and understands the principles for dissemination of scientific results in the field of exact and natural sciences, including the open access mode;

● knows and understands the basic principles of knowledge transfer to the economic and social areas and commercialisation of the results of scientific activities within exact and natural sciences and of the know-how related to these results.

SKILLS:

K_U02 BI2, K_U03 BI2, K_U04 BI2, K_U06, K_U08 BI2, KU_11 BI2; K_U01 OS2, K_U02 OS2, K_U10 OS2, UW_02, UW_03, UK_02

Student:

● uses specialized methods and techniques applied in field work in the natural environment;

● uses scientific and popular science texts in Polish and English proficiently and communicates in English at the B2+ level;

● demonstrates the ability to critically analyze and select biological information, especially from electronic sources and media;

● applies appropriate statistical methods as well as algorithms and IT techniques to describe phenomena and analyze biological data;

● is able to critically develop a selected biological problem based on literature data and own research results, formulating their own opinions and conclusions;

● has the ability to prepare and deliver oral presentations in Polish and English in accordance with the requirements specified for the B2+ level;

● applies advanced statistical methods and tools to analyze empirical data and describe natural processes;

● predicts the direction of changes in the natural environment under the influence of various factors;

● is able to collect and interpret empirical data;

● is able to critically analysing and evaluating the results of scientific research in the field of exact and natural sciences, expert and other creative work and their contribution to the advancement of knowledge;

● is able to transferring the results of scientific activities to the economic and social areas;

● is able to disseminating the results of scientific activities in the field of exact and natural sciences, also with the use of popular forms.

SOCIAL COMPETENCES:

K_K01 BI2, K_K03 BI2, K_K04 BI2, K_K06 BI2; K_K09 OS2, KK_03, KO_01

Student:

● understands the need for lifelong learning, inspiring the development of this need in others;

● is able to convey knowledge about the latest achievements in natural sciences to society and explain the validity of conducting basic scientific research;

● is able to work in a team, carrying out their own research, and co-organizing the work of the entire team;

● applies research ethics principles, resolving dilemmas related to the profession;

● has a habit of using objective sources of scientific information;

● is ready to recognising the importance of knowledge in solving cognitive and practical problems within a specific discipline in the field of exact and natural sciences;

● is ready to fulfilling the social obligations of researchers and creators.

Assessment methods and assessment criteria:

Students work individually on projects throughout the semester divided into thematic blocks. The final grade is the average of all projects. Each of the projects must be passed with at least a satisfactory grade.

Classes in period "Summer semester 2023/24" (in progress)

Time span: 2024-02-19 - 2024-06-16
Selected timetable range:
Navigate to timetable
Type of class:
Classes, 90 hours more information
Coordinators: Łukasz Banasiak
Group instructors: Łukasz Banasiak, Piotr Tykarski, Mateusz Wilk
Students list: (inaccessible to you)
Examination: Grading
Notes:

The classes are mainly carried out in a computer laboratory, where short seminars and conversations also take place. Part of the data is collected during short field trips around Warsaw during regular class hours.

Course descriptions are protected by copyright.
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