Enumeration Results Section
Due at the beginning of Lab 3.
Your assignment will be to show how your enumeration data addresses one of your investigative goals: How many microorganisms are in a gram of soil?. To do this you will construct a results section in scientific paper format. It should include a narrative data analysis and data processed into a table or composite figure (table + images).
You should begin by designing a table and or composite figure (table + image(s)) that effectively uses your processed enumeration data to help a reader (who is unfamiliar with your project or your experiments) see clearly and easily the main point or answer to the question addressed. It is important that your reader understand that you used two different methods, a colony count and a cell count, to answer this question. The former is a culture dependent method and the later is culture independent.
The goal of the narrative that accompanies the table/figure is to explain the goal of the experiments you describe, the experimental design, the most important findings, and the conclusion(s). Be careful not to include excessive methods detail when you briefly summarize the essentials of the experimental design. You should briefly describe the measuring tools not the procedures. The reader does not need to know exactly how to perform these tests.
Begin this narrative with: "In order to find out________ (your goal). After you provide the context for the experiments by giving a clear statement of goal, summarize the experimental design (NOT METHODS!). Follow with pointing to the most important parts of the data in your table/figure and explaining what the data mean in terms of answering our question, "How many microorganisms there are in a gram of the soil you are studying"? If your answer is not the approximately the same when you use different measuring tools to address the same question, you may not be able to answer this question from these data at this point. That does not mean that you can make NO conclusions from these data. Stress the conclusions that you can make from your data. Are you astonished by the number of microbes in ONE GRAM of soil?!!!! You should be; therefore, don't fail to include in the narrative as part of your conclusions a stress on the mind-boggling abundance of microorganisms that ALL your data clearly shows. That important conclusion does not depend on getting a reproducible count from your two measuring tools. The enormous numbers are actually the main point in gathering evidence investigating abundance of microbes in soil communities. When the numbers are this high, an order of magnitude or two between measurements can seem like splitting hairs. Realize that all experimental tools 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 experimental design has limitations or is 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 findings is sometimes useful, ie. "it appears likely that______; ________ seems to indicate that____...
DO NOT include "sources of error" that trash your experimental techniques. However, if you think that one of your measuring tools is better than the other, you should explain why you have more confidence in a subset of your data. Is one of your tools "better" than the other? Explain why you think so. If there is another microbial enumeration technique that you think you should add to give you a clearer answer and to address inconsistencies between those you used, include that at the end of your narrative. Be aware that your data may not be a perfect fit with published estimates of numbers of total soil bacteria, but that does not mean that you didn't get the "right" answer for your soil community. 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 accurate, within the limitations of the enumeration methods used. DO NOT trash your data or your experimental design in your narrative, but DO (BRIEFLY) make your reader aware of limitations in the methods used when you make conclusions.
TABLE FORMATTING: Insert your table at the end of the paragraph where the table is first introduced and mentioned by table or figure number. The table should have a table number and a title that summarizes the table. The table number and title are found ABOVE the table. If the data collection methods or processing is not obvious (for these data it is NOT obvious), there should be an explanatory legend that is found BELOW the table, separated from the table with parallel lines below and above the information it includes. You should look at published journal article tables that model this structure. The legend explains the essentials of how the data were collected and processed into the form shown (for example: you will need to explain what was counted and to give the formulas for converting counts to # of microbes/gram of dry soil, which means you will also have to explain how you determined the conversion factor from wet to dry).
Your 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 lab's Sakai site Reference folder. In this folder you will find valuable information on how to format figures/ tables with proper legends and the basics of how to write about data.
Results Section: Data Analysis with Figure(s)/Table(s) Rubric- 20 points
| || At or Above Standard || Below Standard || Possible|
| Table(s) and/or Figure(s) well designed to illustrate conclusions. Included all crucial information that allows the figure or table to make the main points visually and to “stand alone”: novice reader does not need to read the narrative or the legend to see the data’s main meaning. All data adequately identified; correct units included; labeling appropriate.
|| Figure(s) or table(s) not well designed to illustrate main points visually, clearly, or in most direct and simple way or missing essential information needed for understanding.
|| Figure legend is below figure & includes a number. Table # & title is above table, legend info below. Tables numbered sequentially, independent of figure numbers. All legends include all essential information and no unnecessary detail about how data shown was generated. Figure or table title gives the main point of the figure or table. Body of legend does not summarize main conclusions or include other material more appropriate for the narrative data analysis. All data adequately identified and parameters, ambiguous symbols or terms defined.
|| Missing figure or table#, title, or legend. Legend (or title) is in wrong place or does not include appropriate numbering. Missing information about how data was generated. Missing part or all of key to symbols/ colors or other ambiguous information. Missing part or all crucial information that helps the figure or table to “stand alone”. Legend includes unimportant detail or includes a summary of the findings that is more appropriate for the narrative portion of the data analysis.
| Data Analysis
|| Narrative is structured appropriately: begins with a concise description of topic, experimental goals and experimental design. Narrative references figures & tables directly and describes key findings accurately, concisely and clearly & includes only relevant information. Data analysis is thorough and leads incrementally & clearly to appropriate conclusions to experimental question and addresses topic’s goals. Conclusions, where possible, are clearly stated. Analysis is understandable to an audience unfamiliar with topic and principles used in the experimental design.
|| Narrative doesn't begin with an appropriately concise description of the experimental goals and experimental design. Narrative omits key findings, describes the data inaccurately or unclearly, includes irrelevant information, or is repetitive. Narrative fails to give appropriate conclusions to the experimental questions or fails to show how the experimental data allow the conclusions. Specific figure and table numbers for data that support conclusions is not cited in the narrative. Data analysis requires background knowledge that general audience may not have. Conclusions missing.