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2020/21 Undergraduate Module Catalogue

BLGY1125 Biology Practicals and Data Analysis

20 creditsClass Size: 200

Module manager: Professor Jurgen Denecke
Email: j.denecke@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2020/21

Pre-requisite qualifications

meet entry requirements for BSc

This module is not approved as a discovery module

Module summary

This module offers a thorough introduction to laboratory experiments addressing processes at the molecular-, cellular-, whole organism and population level within the broader remit of biology. The selection of online practicals and weekly on-line seminars and formative work-packages should lead to a theoretical familiarisation with a broad range of standard laboratory practices, methods to study bio-molecular and genetic processes in vivo and in vitro, systematics, taxonomy, evolution, physiology, and morphology. The course focuses on safe working practices in a laboratory environment, teaches how to carry out routine calculations and how to keep accurate records that can be understood by third parties. In the data analysis part of the module, students will develop understanding and comfort with curating and handling data in spreadsheets, choosing and using appropriate statistical tests and accompanying graphs, and how to present these in the style used by scientific papers.Students will acquire an understanding of scientific experimentation design and process, to the extent that they appreciate the value of suitable controls and understand orders of magnitude and appropriate levels of accuracy required, as well as critical thinking and thorough statistical analysis to test reproducibility and significance of observations.

Objectives

The module provides an opportunity to gain understanding of routine laboratory techniques and offers a chance to appreciate basic experimental routines associated with the study of Biology at all levels. This includes a range of analytical methods to monitor biochemical reactions in a quantitative or qualitative manner, observe single cells and tissues as well as subcellular structures, and carry out research on whole organisms and populations. Students are to be familiarised with scientific principles such as the difference between experimental science and pure observation, data recording, the difference between accuracy and precision as well as distinguishing technical replicates from biological replicates. One of the key objectives is to allow students to become aware of the importance of critical analysis presentational skills as well as the formulation of models and testing a hypothesis. During the online sessions, there will be opportunities to meet University academics who will specifically guide you to complete regular exercises and familiarise students with basic equipment, analytical techniques and common work practices in order to help them to acquire basic conceptual understanding of scientific practices as well as adequate recording of experimental setups and obtained research data. This course will prepare students for the subsequent use of basic pieces of equipment and includes risk assessments for selected key activities. Students will be made aware of how to apply and interpret a range of statistical tests to scientific data and be taught how to import data into data-handling programs (i.e. Excel, SPSS), manipulate and graph them, and perform a number of parametric and non- parametric statistical tests. On completion of the course students should show an understanding of scientific experimentation, to the extent that they can begin to design experiments, appreciate the value of suitable controls and understand appropriate levels of accuracy required.

Learning outcomes
A basic appreciation of good experimental design principles is one of the key outcomes, as well as the definition of a working model and the principle that science cannot absolutely "prove" models, although a single type of experiment can disprove or refute a model, requiring a modification of the working model so that the previous and the new data can be explained. It is important that students understand that models are valuable until they are disproven. A second key outcome is to appreciate the complexity of day to day data/sample collection, the use of adequate records not only to have clear and transparent documentation of experimental conditions, but also to keep intermediate samples save and accessible for future research by labelling and constant cross-reference to laboratory books.
On completion of this module, students should have acquired basic conceptual understanding of laboratory practices for Cell Biology and Biochemistry, as well as whole organism and population studies. They should be able to carry out quick and reliable routine calculations in the laboratory including percentages, proportionalities, molarity, concentration and dilution factors, and appreciate a range of rational steps to streamline routine laboratory work, i.e. via the use of concentrated stock solutions and sample collections of all kind. Students should understand how to manage and interpret data (numbers but also images and sounds) and apply statistical tests to address issues of significance and reproducibility. This course teaches literacy in statistics and the use of Excel, both of which are skills that are highly sought after by employers.Professional development opportunities include personal time management, good record keeping, common sense and action planning for multi-task procedures, as well as standard presentational skills (spreadsheets, powerpoint presentations and word documents with embedded figures and legends).

Skills outcomes
Students will be expected to work following health & safety guidelines for manual handling, and apply reliable recording, critical thinking, trouble-shooting, mathematics and presentational skills.


Syllabus

This module is built around two types of on-line Lectures/Seminars:
1) Data analysis lectures on basic mathematics and in particular aiming for a thorough understanding of the various statistical methods to analyse different types of often highly variable biological data will be delivered weekly. A linked set of dedicated in silico practicals and computer classes will allow ample opportunity to practice specific IT skills required for statistical analysis. Support will be given through on line sessions that allow for real-time discussion and through the use of a dedicated discussion board. Feedback on these sessions will be weekly and both lectures and feedback will include screen-captured analysis.
2) Seminars on practical skills to cover the full range of scientific skills required in the biological sciences will be delivered weekly, starting from common laboratory numeracy, a thorough understanding of the terms molarity, molecular weight/mass, percentages, and basic units of volumes and weight, concentration, dilution as well as solving simple equations with single unknowns (respecting the units). Students will then explore/develop competence in preparing stock solutions and accurate dilutions/pipetting, using various forms of data analysis/display methods and record keeping. The basic principles behind a variety of biochemical, genetic and microscopy techniques will be explored in conjunction with an introduction to standard laboratory equipment (balances, spectrophotometers/plate readers, centrifuges, incubators and microscopes, basic bench practices and sterile handling techniques). A thorough understanding of the diversity of biological life forms, classification systems and the relationship between organisms and their environment will allow students to understand the complexity of life on earth. A solid appreciation of good experimental design principles remains one of the key outcomes for all aspects of the practical skills envisaged.

In addition to the seminars, the weekly 3 hour slots normally allocated to practical classes will be used for specific online learning classes in which students work in groups to solve problems, prepare notes, develop presentations and asked questions. These sessions will be supervised by an academic as well as a selected group of post-graduates in the faculty.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Computer Class101.0010.00
Lecture111.0011.00
Practical113.0033.00
Seminar111.0011.00
Private study hours135.00
Total Contact hours65.00
Total hours (100hr per 10 credits)200.00

Private study

Private study should be restricted to specific exercises in mathematics and statistics (50 hours), as well as the preparation of personal notes and private exam revision (50 hours), supplemented by any curiosity-driven extra reading on the subjects covered in the module (35 hours).

Recommend text:
Dytham, C. 2010, Choosing and Using Statistics (3rd edition Wiley-Blackwell)

Opportunities for Formative Feedback

Weekly formative in-course assessments will be held in weeks 1-4 and 6-10. Specific feedback will be provided on-line for each assessment to highlight class weaknesses and strengths.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
In-course MCQformative weekly assessments (week 1, 2, 3, 4, 6, 7, 8, 9))0.00
Computer Exerciseformative weekly data analysis assessments (week 1, 2, 3, 4, 6, 7, 8, 9)0.00
In-course MCQOnline data analysis MCQ (1 hour test) (mid-term exam)25.00
In-course MCQOnline practical MCQ (1 hour test) (mid-term exam)25.00
In-course MCQOnline data analysis MCQ (1 hour test) (end of term)25.00
In-course MCQOnline practice MCQ (1 hour test) (end of term)25.00
Total percentage (Assessment Coursework)100.00

Resits will be assessed by a single 3 hour MCQ exam covering the entire course, either online or under standard exam conditions depending on the COVID-19 developments

Reading list

The reading list is available from the Library website

Last updated: 13/11/2020 08:18:44

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