Good record keeping is essential for ensuring the integrity of research data. It is therefore important for researchers who deal with data to form good habits of note taking. There are a number of online resources that provide guidelines and samples for note taking in research. For example, the “Experimental Biosciences Resources” page on Rice University has extensive guidelines on the contents and organization of lab notebooks; the website also shares examples of lab notebook entries. Keep in mind that the contents, organization, and even styles of lab notebook will vary by disciplines, thus an important part of research training is learning about and following the preferred format of note taking in your own discipline. In general, entries to a lab notebook should be thorough, consistent, and communicative.
Figure 4 A Biologist’s Field Notes.
(Smithsonian Institution / Public Domain)
In some disciplines of natural and social sciences, instructors often urge students to “write down everything you can” when they are conducting field research. In fact, it is hardly an exaggeration to ask the same from researchers in the lab. As we explain in Definitions of Data, what counts as data often depends on the context and scope of research. When researchers are “in the wild” running experiments, it is difficult to make accurate judgments about what should be documented as relevant data (especially for less experienced researchers). Therefore, we recommend comprehensively documenting what is happening in the lab, such as the procedures being operated, the configuration of equipment, values of samples, and any anomalies. A good experimentalist should be busy all the time while she is in the lab. Thorough note taking also contributes to the reproducibility of research, which is deemed an important epistemic value by the research community. Comprehensive documentation of the procedures, conditions, and parameters of experiments would enhance the chance of their successful repetition by your own as well as other research groups. Because science and engineering research emphasizes verifiable and scalable results, a thorough note taker also makes a contribution to knowledge production in the entire discipline.
Besides conforming to well-established epistemic norms and improving the integrity of data, active note taking also provides a good medium for academic thinking. An idea is good only when it is communicated in a clear and tangible form. Writing lab notes thus provides an opportunity for the researcher to summarize, evaluate, and refine her research ideas. Keep on capturing your thoughts in words and sketched graphics on a lab notebook, and you might be rewarded with increased productivity and better communication skills. In addition, many researchers have found that comprehensive documentation is very helpful in determining the origin of ideas and in assigning credits to researchers. Researchers frequently share ideas and information with their colleagues, and such exchanges sometimes make it difficult to track down the original person from whom an idea has been generated. In such cases, a notebook that documents the first appearance of an idea (with the precise date) would be invaluable in assigning credits and even settling disputes about authorship and ownership of intellectual properties.
Does More Data Mean More Knowledge?
Many researchers are driven by intellectual curiosity, or the eagerness to “know more.” Depending on how one interprets “knowing,” to know more could mean the possession of more information, and accordingly, more data. Therefore, we can understand why some researchers are very excited about big data. For people who equate knowledge with information, big data enables them to know more by acquiring and processing enormous volume of information. According to this view, then, big data leads to more thorough, precise, and reliable knowledge. For example, in pharmaceutical research, data about how a chemical performs in human bodies from a larger sample over a longer period of time will arguably improve the rate of success for drug development. However, we should also note that human beings are capable of multiple forms of knowing, some of which focus not on the quantity of data but on how we make sense of it.
Figure 5 Seven Traditional “Liberal Arts” Represent Different Forms of Knowing. (Public Domain)
Because a lab notebook communicates important information about the procedures and results of experiments not only to the data collector but also to other researchers, entries to a lab notebook should follow a consistent style, and the contents should be clearly organized. Familiarize yourself with the tradition of note taking in your discipline. Read several examples and choose a style that’s appropriate for your research and your habit of writing, then stick with it. It is also a good idea to discuss note taking during research group meetings, for such discussions can inform new members. Ideally a group of experimentalists who work on the same project should agree on a common standard for note taking. This is especially important when multiple people are entering information to the same notebook.
As we explained earlier, the purpose of following a consistent style of note taking is to ensure the communicability of the lab notes. Many of us have had the experience of not able to read our own writing in the past. The loss could be significant if this were to happen with the lab notes. In general, consistent format, clear handwriting, and use of full sentences would increase the communicability of lab notes. A good editorial style can also be eminently helpful, such as numbering the pages of the notebook, entering the name and contact information of the note taker as well as the date for each new entry, creating a table of contents and summary for a completed notebook.
It is debatable whether one should keep several copies of a lab notebook. The main reasons against doing so might be the increase of workload and storage space. On the other hand, there are compelling reasons for backing up lab notes. Safety is an important one. There have been numerous cases of alleged research misconduct that included “lost lab notebooks.” Sometimes the loss of lab notes is used as an excuse for not presenting the original data. Another reason for making copies relates to the ownership of lab notebooks. A lab notebook usually belongs to the PI, her institution, or sponsors of the research, but sometimes the owner might permit a researcher (e.g., a graduate student) to make a personal copy when she leaves the lab. The owner’s permission is crucial in such cases, and the permission is sometimes contingent on shared authorship or acknowledgements of the owner. If you are used to jotting down your thoughts on the lab notebook, it might be advisable to keep a separate notebook that records both the experimental data and your research ideas, so that you could enter the “clean version” of experimental data into the lab notebook later on. If you choose to do so, be sure to notify the PI of the existence of a personal notebook and acquire their permission.
The availability and ease of digitalizing technologies add strong support to backing up lab notebooks. Smartphone applications like iScan can swiftly make digital copies of handwritten notes. However, digital copies raise concern about the protection of confidential data. We discuss this issue in Unit 4.
Following are some additional best practices for good record keeping published by Columbia University:
Recording should be done as soon as possible after data are collected and specific note should be made as to whether it represents the date of the recording or the date of collection, if the two are not the same. Modifications should be clearly identified and dated.
A second loose-leaf notebook should be kept for data, such as photographs, machine printouts, questionnaires, chart recordings, and autoradiograms that cannot fit into the primary record book.
Supervisors should review and sign off on notebooks to signify their completeness and accuracy. Queries should be addressed as soon as possible and changes signed by both. Some data may need to be witnessed by a colleague. (Witnessing of data becomes important in commercial research laboratories.)
Methodology used in an experiment should be written down or a reference to how an experiment deviated from a standard laboratory technique should be explained.
Lot numbers should be recorded and special attention should be given to the hazardous-substance use.
Equipment calibrations need to be recorded.
Data should be noted directly into notebooks without putting it on scraps of paper or relying on memory beforehand.
All raw data should be included. Be honest.
Errors should be identified by crossing out the mistakes without obscuring the initial data.
Material should be logged chronologically.
Data interpretation should be carefully written.
Areas in a notebook left blank intentionally should be indicated.
Correspondence and note conversations related to experiments should be kept.
Consent forms should be kept with raw data.
2.4 Collective review of data
Because PIs and senior research staff usually take the ultimate responsibility for the accuracy and integrity of published research data, the above “best practices” from Columbia University recommends that “Supervisors should review and sign off on notebooks to signify their completeness and accuracy.” Besides verifying lab notebooks by supervisors, Pascal (2006) also suggests collective review of data by the research group as a way of educating members about data collection and analysis. According to Pascal (2006), “[o]ne of the best ways to do this is to have weekly or bi-weekly meetings with the research staff, where research trainees can give presentations and discuss their progress for ongoing research, including discussion of the data, identifying any problems that may have developed, determining whether the data supports the hypothesis, and other data issues that may arise.” Another good opportunity for collective review and discussion of data is when the group works on publications or grant applications (Pascal, 2006). In principle, all the co-authors of a publication should participate in such discussions to understand how data supports the research findings as well as the limitations of the research.
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