You have been asked to document a local race. The proposed track is below. Where would you position yourself to document the race? Why? What would you see from that position? What information about the race could you collect from your vantage point?

A typical science paper is organized into sections: Introduction, Methods, Results, Discussion, Conclusions. Draw a diagram that relates the content in the abstract to the content in these typical sections. This doesn’t mean place the abstract in order (the abstract comes first). It means connect the information that is presented in these typical sections to the information that is presented in the abstract.

Collecting and analyzing existing data might be a project in itself, or it might support a project that includes new data collection. In either case, it is worth practising how to find, prepare, and use existing environmental data. To start a data search, think about the research area or topic that you are interested in. It helps to pose a question.

A good scientific question will motivate a good research project. The process of asking questions and gaining knowledge is iterative. Asking a question directs an action: an investigation into what has been done in this field before and what is already known about this problem. Sometimes the answer to your question can be found in the published scientific literature. If that is the case, you can refine your question. Answering a question generates new knowledge, which in turn generates new questions, and so on. So your questions will become clearer and more useful as you gain information, resources, and experience in your field. Be prepared to review your research questions regularly. They may need to change over time.

In Chapter 8, the Mauna Loa CO2 data from 1958 to 2017 was decomposed to isolate the annual signal from the long-term trend. That exercise demonstrated that two non-interacting cycles, existing in the very same data set, can be considered separately. Once isolated, the amplitude of each cycle can be compared to identify the relative dominance of the different signals. Taking this to heart, a conceptual understanding of time series analysis can be developed.

The natural (or physical) sciences usually work with quantitative data to gain insight. The social sciences commonly use qualitative data, particularly about questions relating to human behaviour. Environmental science is interdisciplinary and sometimes bridges these two approaches. Depending on your research question, you might want to use both quantitative and qualitative data. The main focus of this book is on quantitative data.

We have used the Mauna Loa monthly CO2 data set to isolate and characterize changes in the anthropogenic and biological contributions to atmospheric CO2 concentrations between 1960 and 2017. We have also studied variability in the seasonal cycle, driven by changes in photosynthesis and respiration from year to year. But is the CO2 record from Mauna Loa, a mountain in Hawaii, representative of the global variability in atmospheric CO2?

An abstract is a summary or precis with a particular focus to communicate new scientific findings in context. Reading the abstract is not a substitute for reading the full paper, but the abstract should communicate the keys points that will be further elaborated in the paper.

Models are simplified representations of reality. We use models in many different ways, for many different purposes: physical models are used to study urban planning, vehicle impacts, new inventions, and molecular structures; rats are used as model organisms to test new pharmaceuticals for side effects; conceptual models are used to simplify and understand concepts, ideas, and relationships; mathematical models are used to understand, quantify, and predict the behaviour of the natural world. All models are intended to capture some key feature of reality (shape, behaviour, relationships, etc.); the specific focus of a model will depend on the purpose for which the model is intended to be used.

In Chapter 8, the Mauna Loa CO2 data from 1958 to 2017 was decomposed to isolate the annual signal from the long-term trend. That exercise demonstrated that two non-interacting cycles, existing in the very same data set, can be considered separately. Once isolated, the amplitude of each cycle can be compared to identify the relative dominance of the different signals. Taking this to heart, a conceptual understanding of time series analysis can be developed.

This book is intended to help students shift from being passive consumers of scientific content to active participants in the process of science. This transition, from the student in the classroom to the effective practitioner, can be frustrating at first. A genuine scientific question can be asked and answered in a variety of ways. There is no one correct way to tackle a problem and no one correct approach to answer a question. Starting to actually do real science can be intimidating. What constitutes a good question? How do you know what data you need to answer it? How do you convince others that what you are doing is worthwhile?

After completing the steps in Chapter 7, I now have a well-prepared data set of monthly averaged atmospheric CO2. The placeholders have been removed and the gaps have been filled. I can now proceed to answer my original question.

Collecting and analyzing existing data might be a project in itself, or it might support a project that includes new data collection. In either case, it is worth practising how to find, prepare, and use existing environmental data. To start a data search, think about the research area or topic that you are interested in. It helps to pose a question.

Environmental science is the study of the biological, chemical, and physical processes of the Earth, our environment. The interactions of human society and the environment are place-based and embedded within the processes of the Earth itself. The Earth is orbiting the Sun. The Earth rotates on its axis once a day (every 24 hours) and orbits around the Sun once a year (every 365.25 days). These timescales (one day, one year), and more, are inextricably linked with environmental processes.

Environmental data is more than a bunch of numbers. Meaningful information about the natural world is embedded in those numbers as patterns, cycles, trends, changes, and events. Arranging your data in different ways can highlight different features of the phenomena captured by your data set. This dual aspect of a data set is its power: data is quantifiable information. You can describe the key features of your data set using words, but you can also quantify, or characterize, critical information using numbers, mathematical equations, or statistical concepts.

Writing a proposal is the first step to getting a project approved and funded. In many cases, a call for proposals is like a competition where the most persuasive proposal will get approved and others will not. In science, persuasive writing is not hyperbolic or purposefully evasive. Taking a narrow view of a topic to elevate its importance is not an effective way to write a persuasive science proposal. A persuasive science proposal clearly and accurately articulates the motivating problem and outlines the methodology chosen to address the problem in a logical and systematic way. Scientists use references as supporting documents to authenticate statements. Effective referencing increases the quality of the proposal.