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Notebook Entry #3

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Alexandra Lalor edited this page Sep 27, 2022 · 3 revisions

Alexandra Lalor

2022-09-28

This week we learned about project management. Using some of the topics we discussed, I created a project management plan for my Masters research:

data management plan.docx

Data Management Plan

  1. Data and Information to be Produced

Data will include weekly measurements of seedlings to track the progression of plant death. Measurements include percent brown (visually estimating percent brown needles, photos (to capture color change and validate percent brown estimates), weight (in grams, of total plant and pot weight), and conductance (in [mmol/(m²·s)], measuring stomatal conductance of plants.

Data will be collected weekly. Measurements of percent brown, weight, and conductance will be taken in an excel data sheet (and converted to .csv format), and all photos will be labeled with the sample ID and placed in a folder with the date that photos were taken. Additionally, conductance data is stored on the physical handheld porometer device and can be transferred to any computer directly. All data will be uploaded to a project folder on the lab’s University of Arizona Box account. Box is a cloud-based storage center and version control system. Updated excel sheets will store as a new version so that data can be preserved for record keeping and quality control. Data will also be stored on a backup external hard drive as a third layer of protection against data loss.

Data manipulations will be performed with R code, which will be well-annotated and replicable. Raw data will be manipulated to allow R to easily run analysis on data using the “data_clean” scripts. Quality control will be conducted with “data_QAQC” scripts. Analysis will be performed using the “data_analysis” scripts to clearly describe the analysis process. All code changes will be tracked using GitHub. When publishing, R version will be reported.

  1. Data, Metadata, and Code Standards

All data, metadata, and code will be named consistently, concisely, and informatively. Names will be described in a “readme” file. Naming conventions should indicate the project phase (1 or 2, as ambient or hotter temps) if appropriate for differentiating datasets. Metadata will include information each individual plant, including baseline temperature settings, treatment (drought or watered, with/without heatwave), and height/diameter/biomass/age data. Data analysis and visualizations will be produced in R and accessible in GitHub. Data sheets will be shared in a .csv format. Photos will be shared in a .tif format. Written descriptions of work will be saved in a .txt format (Michener and Jones, 2012).

  1. Data and Code Access, Sharing and Security

Data will be licensed under Creative Commons 0. Creative Commons is a standardized tool to give permission for others to share and use data that is collected. It is an easy-to-use copyright license. Creative Commons 0 is a license type with allows creators to give up their copyright for public domain. This option puts zero conditions of data access. https://creativecommons.org/about/cclicenses/ Code will be licensed under the BSD 0-clause license. A Berkeley Source Distribution (BSD) license is used for open source software and does not have requirements for redistribution. The BSD 0-clause license does not contain any clauses or requirements for using code and is a public domain license. https://opensource.org/licenses/0BSD Code will be available through Git on GitHub to facilitate access and sharing. https://github.com/

  1. Data and Code Re-use and Re-distribution

Data will be licensed under Creative Commons 0 and code will be licensed under BSD 0-clause license. These will be available for reuse and redistribution under the rules and regulations of each license. Code will also be available through Git on GitHub to facilitate access and sharing. Peer-reviewed published research findings will be submitted to ESA. Peer-reviewed papers will receive a Digital Object Identifier (DOI) and will also include clickable DOI links to data and source code (Mislan et al., 2016)

  1. Data and Code Stewardship, Archiving and Preservation

Data and code will be archived in Zenodo and the UArizona Data Repository. Zenodo is an open-access repository for researchers to deposit papers, data, code, reports, and other digital information for archiving. It is a way to fully document your research process and allow complete data access and sharing. All published materials receive a DOI for citation and access purposes. https://about.zenodo.org/

The UArizona Data Repository is an institutional repository to make data and associated papers, code, grants, and all research materials publically available. All published materials receive a DOI for citation and access purposes. https://data.library.arizona.edu/data-management/services/research-data-repository-redata

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