Indicate whether specialized tools are needed to access or manipulate shared scientific data to support replication or reuse, and name(s) of the needed tool(s) and software. If applicable, specify how needed tools can be accessed.
Adapted from: Writing a Data Management & Sharing Plan
Why is this being asked?
In order to ensure your data can be used in the future, either by you or another researcher, it is important to excplicitly list any and all research tools, whether widely available or custom-built, that were used during data collection and analysis. In an ideal scenario, everything should be listed in this section that would allow a user to take your data and reproduce your results following the same general workflow. Obviously this is not always feasible, but there should be an attempt made to make your data analysis as reproducable as possible.
What to Include
- Indicate any tools or software necessary to access or manipulate data. This should include the following information, if applicable:
- Which statistical package or program was used to manipulate the data, along with the version of the software that was used and any packages, scripts, or settings that were used or developed during the course of the study, as well as how users can access the software
- Whether there were any custom workflows or pipelines developed as part of the study necessary to analyze or process the data, and how
- Whether there were any executable programs or macros written as part of the study necessary to analyze or process the data, as well as how users can access the code
- All analysis of tabular data will be carried out using R, version 4.2.1, available free of charge. The complete code used to transform and analyze the data, along with all custom and pre-existing packages, will be made available as a text file. Image data will be processed using ImageJ, available free of charge. Any macros or scripts developed or used throughout the project will be made available as a text file. The G-code used to generate the physical specimens will be made available as a text file. NCPlot will be used to edit the G-code, but any text editor can be used to view and manipulate the code.
- All statistical analyses will be done using SAS 9.4. The complete SAS script will be made available as a text file. SAS 9.4 is commercial software and might not be available to everyone, so the dataset will also be made available as a CSV file with a summary of the steps taken so that a user can import and run a similar analysis in their program of choice.
- All clinical data will be captured via data entry into a custom REDCap database. The data entry form will be made available alongside the data as a text file. The data will be cleaned and processed using Microsoft Excel, version 2022. The steps done will be recorded and made available as a text file. Custom macros have been developed for statistical analysis and will also be made available as a text file so that they can be run inside of Excel. While there is a free, web-based version of Excel, a licensed desktop version is required to run the macros. Most universities or workplaces have Microsoft licenses.