finish first pass

datamgmt_plan.qmd

@@ -10,18 +10,18 @@ Once your lab data are well managed, it becomes easier to archive and share rele
 
 Before writing your plan, we recommend you get familiar with the [FAIR](https://www.go-fair.org/fair-principles/) and [CARE](https://www.gida-global.org/care) principles to guide your process.
 
-![source: <https://www.gida-global.org/care>](img/datamgmt_be-FAIR-and-CARE.png){width="80%" fig-align="left"}
+![source: <https://www.gida-global.org/care>](img/datamgmt_be-FAIR-and-CARE.png){width="70%" fig-align="center"}
 
 Those two principles should be the overarching guidelines that will guide the development of your data management plan.
 
 
 ## Developing your Lab Data Management Plan (DMP)
 
-You might already have experience with writing project specific data management plans as they are often required in proposals. The process to develop your lab's data management plan is very similar, but will be more focused on developing general guidelines for your lab that can be adatped for project specific needs. We recommend using the FAIR & CARE principles as guidance to maximize the reusability of your data by you, your collaborators, other researchers, and futureself.  Your plan should ensure that detailed documentation adopting existing standards is developed during the entire duration of your project (don't wait until the very end!!) and that this documentation is archived along with your data and code in a publicly accessible data repository will set you up for success.
+You might already have experience with writing project specific data management plans as they are often required in proposals. The process to develop your lab's data management plan is very similar, but will be more focused on developing general guidelines for your lab that can be adapted for project specific needs. We recommend using the FAIR & CARE principles as guidance to maximize the reusability of your data by you, your collaborators, other researchers, and futureself.  Your plan should ensure that detailed documentation adopting existing standards is developed during the entire duration of your project (don't wait until the very end!!) and that this documentation is archived along with your data and code in a publicly accessible data repository will set you up for success.
 
-![source: <https://www.library.ucsb.edu/sites/default/files/dls-n04-2021-fair-navy.pdf>](img/datamgmt_fair-principles.png){width="80%" fig-align="left"}
+![source: <https://www.library.ucsb.edu/sites/default/files/dls-n04-2021-fair-navy.pdf>](img/datamgmt_fair-principles.png){width="70%" fig-align="center"}
 
-Below is a set of questions that will help your team think about the data and resources you will need along your projects' data lifecycle.
+Below is a set of questions that will help your team to develop your guidelines about the data and resources you will need along projects' data lifecycle in your research lab.
 
 1.  **Describing the research data generated by your lab**: Provide a description of the data the group will collect or re-use, including the file types, data set size, the number of expected files or sets, content, and source of the data (creator and method of collection).
 
@@ -36,36 +36,21 @@ Below is a set of questions that will help your team think about the data and re
 
 3.  **Metadata**: Metadata is documentation that helps make data sets reusable. Think about what details someone would need in order to be able to understand and use these files. For example, perhaps a `readme.txt` file is necessary to explain variables, the structure of the files, etc. In addition, it is recommended to leverage metadata disciplinary standards, including ontologies and vocabularies. Here is a [good resource](https://rdamsc.bath.ac.uk/subject/Environmental%20sciences) for metadata standards in environmental sciences.  When applicable, also describe other scientific products - models, scripts, and/or workflows - your group will be producing using README files and documenting your code.
 
-4.  **Data sharing and access** The data may have significant value for other researchers beyond this project, and sharing this data is part of the group's responsibility as members of the scientific community. Specify the extent to which data can be reused, including any access limitations. List any proprietary software that might be needed to read the files. If some data is not shareable due to confidentiality, non-disclosure agreements (NDA), or disclosure risk, state such limitations and the rationale behind them.
+4.  **Intellectual property and re-use**: If data were collected from the client organization, does the group have the right to redistribute it? If so, are there any restrictions on redistribution? If the group created its data files, would it assign a Creative Commons license to its data?
 
-5.  **Intellectual property and re-use**: If data were collected from the client organization, does the group have the right to redistribute it? If so, are there any restrictions on redistribution? If the group created its data files, would it assign a Creative Commons license to its data?
-
-6.  **Data archiving and preservation**: Throughout the project, the group may produce a large number of files. At the end of the project, groups must submit data produced by the project (except data protected by non-disclosure agreements) and when relevant raw data as well. Not all data needs to be saved. Here are some questions to ask yourselves:
+5.  **Data sharing and preservation**: The data may have significant value to other researchers beyond this project, and sharing this data can be a valuable contribution to your field. Specify the extent to which data can be reused, including any access limitations. List any proprietary software that might be needed to read the files. If some data is not shareable due to confidentiality, non-disclosure agreements (NDA), or disclosure risk, state such limitations and the rationale behind them. Note that not being allow to share your data does not mean you can not document!! Not all data needs to be saved. Here are some questions to ask yourselves:
 
     i)  If another researcher wanted to replicate the group's work or re-use the group's data, what data and documentation would be required for them to do so?\
     ii) Where will the data and metadata be stored after the project is completed?
     iii) Is there a subject-specific and/or open-access repository that is appropriate for the data?
 
 One advantage to depositing your data in a data repository is that you can get a [DOI](https://www.doi.org/the-identifier/what-is-a-doi/) that lets you easily share and cite your data. Most of the data repositories also track views, downloads, and citations for your data archive, which can be used as a metric or a proxy for research impact.
 
-## Data Management Plan Tool
-
-There is a tool that you can use to guide your process: **the DMP Tool**. It is a little bit like an online form on steroids. Note that you do not have to use this tool for your project, but from our experience, it provides good guidance for this process.
-
--   (Almost) everything in one page: <https://www.library.ucsb.edu/sites/default/files/dls-n05-2022-dmptool-navy_0.pdf>
-
-<iframe width="100%" height="1000" src="https://www.library.ucsb.edu/sites/default/files/dls-n05-2022-dmptool-navy_0.pdf">
-
-</iframe>
-
--   Guidelines on developing your project data management plan using the DMP Tool: *Renata G Curty. (2023). DMP Recommendations (DCC Template). Zenodo.* <https://doi.org/10.5281/zenodo.7566971>
-
--   Get started with the tool: <http://dmptool.org/>\
-    *Make sure to create an account using your UCSB email!*
 
 ## Using your Data Management Plan
 
-Ok, you have a plan, now what!? **A data management plan should be seen as a living document** that you update as your project develops and data needs evolve. We thus recommend sharing this plan with all your team members and external partners when relevant. The DMP Tool has the capacity to share plans directly from the tool. If you do not choose to use it, we recommend choosing a file format that can be collectively edited and provide some versioning/track changes feature, such as Google Docs or other cloud-based storage and documents.
+Ok, you have a plan, now what!? **A data management plan should be seen as a living document** that you update as your projects develop and data needs evolve. We thus recommend sharing this plan with all your team members and external partners when relevant. You can also encourage contribution from your lab members by choosing a file format that can be collectively edited and provide some versioning/track changes feature, such as Google Docs or other cloud-based storage and documents.
+
 
 ## Further Reading Recommendations