BISC209/S11: Assignment 209 Lab7

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Wellesley College-BISC 209 Microbiology -Spring 2011

Partial Results Section

Due at the beginning of Lab 8.

Your assignment will be to show, in figures or tables with proper legends and a data analysis in the form of a narrative, how some of your data addresses our topic, diversity and abundance of bacteria in a soil community: co-operation and competition. This data analysis should use experimental evidence from your work completed so far this semester to answer one or more of our experimental questions: "How many microbes are in your soil community? Who are they? What is the phylogenetic and functional diversity of the bacteria in this soil community? How might the microbes in this soil community co-operate and compete to exploit the resources available and to fulfill the metabolic needs of the microbial community and the ecosystem?". You may not yet have the evidence you need to address all of our experimental questions. For example, the identification of who is there and the phylogenetic diversity evidence will come, largely, from our 16s rRNA gene sequencing. You won't be able to analyze this evidence until you learn how to use Ribosome Database analysis tools in Lab 9. In this assignment, you should focus on the quantitative work we've completed that addresses enumerating the culturable vs. direct count number of microbes in your soil community and the community level analysis of metabolic diversity in carbon source utilization as well as the functional capacities that you estimated from community exo-enzyme testing. What evidence do you have from that community testing or from your cultured isolates (as examples of bacteria in your community) that show the functional capacity to effectively share or recycle resources and/or provide for their own or for other members' metabolic needs?

You will write in the form of a Results section in a scientific paper. Scientific writing uses data from experimentation to answer questions and shed some light on broader topics. You did the experiment and you figured out the answer from comparing your expectations (which are based on more than a hundred years of other investigators combined wisdom) to your results. Now you need to present your evidence and your reasoning to an audience that doesn't know much about bacteria or soil. In science, this part is called data analysis or results. There are two equally important parts to a data analysis: figures (graphs, drawings, or photos) and/or tables, WITH a thorough explanatory narrative that begins with the overall topic, the experiment goal(s) and a brief summary of the methods---"In order to find out_________, we did__________."

Before you begin to write, you should objectively analyze the data collected by your team. Your data is likely to be far from a perfect fit with any of published estimates of numbers of culturable or total soil bacteria, but that does not mean that you didn't get the "right" answer. In science it is rare to be able to know if your findings or quantitative estimates are "accurate". Unlike a lot of your lab experiments in other courses, your instructor doesn't have the right answer. No one does. There is no reason to think that your answer, however different from previously published estimates, isn't as good as those published by other investigators using similar methods. Students have trouble having confidence in their findings. If you did your experiments carefully, there is no reason that you can't make conclusions and answer your experimental question with confidence. DO NOT trash your data or your experiment in your narrative!!! Do not have a "sources of error" section where you stress all the difficulties or the things you did wrong. This is not a lab report. We don't write lab reports in BISC courses; rather, think of this as part of a research report that might, eventually, be sent to a journal for publication. No one wants to publish (or read) a report of a lousy set of experiments that couldn't conclude anything. Realize that all experiments have their problems and no data are perfect. Our goal in science is NOT to "prove" a hypothesis but to objectively see whether or not our experiments and our data from them allow us to form some answers to the questions that drove the investigation. If we don't have perfect confidence in our conclusions because our experiments or our data collection was imperfect, that's pretty much the universal situation in science. We tell the truth as best we can figure it out from our data using "hedge words" that reflect our confidence in our conclusions, ie. "it appears likely that______; ________ seems to indicate that; it is possible that_____,although______; it appears as though____, but_____; etc."

Visual information is crucial in a Results section. In science, you must have both visual presentations of, usually, processed rather than raw data, AND a narrative that refers to that visual data found in figures or tables. The narrative must include direct references to those figures or tables. The narrative points out the most salient information before giving the conclusions gleaned. Remember that your reader doesn’t necessarily know about the great plate count anomaly, unless they read your introduction (and they might not start reading at the beginning of the paper). Therefore, you will need to both show AND explain (figures/tables and text), not only giving the quantitative results, but also include a brief explanation of why you wanted to find out these things (give context).

Since you must write as though the reader and evaluator of this data analysis is NOT your lab instructor and is NOT another student in this class who has access to this wiki or knows much about bacteria or soil, you will have to include, BRIEFLY, an explanation of the experiments. Be careful not to include too much methods detail. The reader does not need to know how to perform these tests, but generally, the essentials of what you did to get your data to make conclusions. This data analysis/results section must be written completely in your own words (not the wiki's) and it should include only what is necessary for a new reader to follow your data analysis from question to conclusion. You may consult with your teammates or others to discuss your data and its meaning, but all writing must be your own.

There are many ways to write a good Results analysis. To get a feel for how a data analysis is written as a Results section in a scientific paper, take a look at the results section in a variety of published science research reports such as those provided in your Reference folder. The Wellesley library has electronic subscriptions to many other journals that model this concept well. Also refer to the “How to Write a Scientific Paper” section in the Resources section of this wiki. There you will find valuable information on how to format figures/ tables with proper legends and the basics of how to write about data.

Remember that the Resources section of the wiki has a lot of valuable information on every section of a research report. You should read carefully the information there on the Results section, including on effective figure and table design and how to construct a proper legend.

Good luck!