Most teams have systems in place for storing raw data, such as network servers, EDC systems, RedCap, and locations to store published data. Unfortunately, many notes, observations, terms, and comments can be poorly documented. In LabArchives, your team can show the full story of your data that will help someone in the future review your work.
LabArchives is designed to help researchers collaborate and communicate with their teams. This can be as simple as documenting weekly meetings, writing reports, and sharing resources in a lab notebook.
Many dry labs use LabArchives to show their work as a “snapshot in time”. In this case, a page or folder in LabArchives would contain the full story of their data including all notes, observations, links, references, and even output datasets. These groups would develop a template with rich text entries and headings that direct the researcher to document the necessary information. On a regular basis (daily, weekly, quarterly, etc.) the researcher would fill out this template. This makes it easy for a colleague or supervisor to review the data and see how the project has updated or changed throughout the research life-cycle.
Talk to your Team About Data Policies
It is recommended that your team setup a data management plan. This is a formal document that describes the way your team will collect, analyze, store, and publish data. If you do not have a data management plan or policy in place, your institution, funding agency, or company may be able to provide guidance.
Raw Data and Working with Data Outside of LabArchives
In some cases, the raw data for a project may be very large, stored in secure systems, or the files may be used so frequently that uploading and downloading is inefficient. In these instances, the raw data is often stored outside of LabArchives. You may use tools like RedCap, a network drive, or an EDC system. For more information on linking to data stored outside of LabArchives Click Here.
Codes, Scripts, and Output Datasets
You may use software packages like SAS, SPSS, R, STATA, MATLAB, or Python to analyze data or draw conclusions from a dataset. If the output dataset, code, scripts, or output files can be exported, this information can be stored in LabArchives. If the project requires installation, you may want to include installation instructions, a README file, or a list of support commands. This information can be stored as a rich text entry, plain text entry, or as an attachment, like a .txt file.
Rich text entries can be used to format text that appears on the page. You can add code blocks into a div or you can add formatting to the code. For more information on rich text entries, Click Here.
The plain text entry type will display markdown and for more information on plain text entries Click Here.
Attachments can be uploaded in a variety of ways. To automatically upload data to LabArchives, you can use Folder Monitor. To learn more about Attachments Click Here
Software and Hardware
It is very important that you document the software and hardware that is used to generate your work. This can include software version, operating system, plugins, and any hardware requirements like CPU or GPU. As an example, if you use Python, be sure to include the Python version, operating system, environment, and any relevant packages that are used.
Include a Data Dictionary
A data dictionary contains descriptions, labels, identifiers, variables, abbreviations, and terms that are used as part of your work. It is good practice to define any terms that you use in a central place so that a colleague or supervisor can fully understand your work. If you introduce a new term or phrase you want to update your data dictionary.
Notes, Observations, comments, and Conclusions
In general, someone should be able to review your work and understand exactly what has been done without needing to speak with you. As you work, document your thoughts or observations in LabArchives. This can make it easy to search for information in the future and will help inform your colleagues why a certain decision was made. As an example, if you decide to remove a specific sample or data point, document why this information was excluded.