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Below you'll find a series of example scenarios and best practices for addressing each scenario.
A year after you complete your summer project and publish your results, you get an email from a student working on a new phase of the project with a question about your study design. You are having difficulty remembering because it has been a year since you last thought about the project.
What to record in your electronic or paper notebook depends on the type of research project you are working on.
For example, a field notebook for an archeologist will look different from a lab notebook for a chemist working at the bench. It is good practice to check with an adviser, Principal Investigator (PI), or colleague for recommendations on the minimal information necessary to document your methods and the data you collect and analyze for your project. A good rule of thumb is to be as detailed as possible so that someone else can understand what you did and replicate your procedures.
You would like to use digital data from a colleague at another university who has collected data in a separate study. When it comes time to combine your respective datasets, you notice that you used different terminology and data structure. It will take some time to reconcile the differences.
You are submitting a manuscript to a journal that requires you to deposit the data underlying your reported results in a specific online database, or "repository." The repository has required fields for details about your samples and the settings of the instrument at the time the samples were analyzed that you did not record. Now you have to spend several hours rerunning the experiments to collect the missing metadata.
Use Reporting Standards. Various research communities have come together to agree on the minimal information necessary to describe an experiment or method of data collection in their field. These “minimal information standards” help to ensure that enough context is provided that can help other researchers evaluate the data as well as potentially reuse or repurpose the data in another project.
Some lab instruments generate metadata describing the setting and analysis as separate machine-readable files, so it is good practice to ensure these are being output to a folder in case you need to reference them.
A colleague has published their study in an area that you also actively research. You ask the colleague to send you a copy of their data so you can learn more about the study and its findings, as well as a copy of the research software they developed so you can use it on a future study. Your colleague sends you the final data in a spreadsheet, but you have no idea what the variable abbreviations in the column headings mean, what standards of measurement were used, or what steps were taken to come up with the results. Similarly, the software copy is just the scripts without any documentation on how to install and run.
You read an article describing a study published in a journal, and you want to know more about the authors’ methods and data. Unfortunately, the authors did not cite their data or state where they had made the files available online. You email one of the authors, but weeks go by without a response.
Deposit and cite data in a repository. Historically, researchers have communicated by publishing their findings in a journal. Yet, data and code are essential parts of understanding and evaluating a study. Over the last decade, researchers have shifted to depositing their data, code, and documentation in online repositories for others to access and citing the location of these files in their articles. Other researchers can access the data in the repository to use the data or code, recreate some or all of the study, and cite the original study in their own published findings.
You find some files of data deposited in a repository by colleagues, and you decide that you would like to use some of their data in an upcoming project. You email them for permission but hear nothing back. You do not want to use their data without their permission. Eventually you give up and have to leave out data that would have potentially improved the project.
Assign a license. Planning for potential reuse of your work by others requires you and your co-creators to provide information along with your files so that others know who holds the rights to the data and whom to contact for permission to reuse the data and code. The best practice is to assign or provide a license that explains any permissions you automatically grant to others so they know the acceptable terms for reusing your data and code.
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