Difference between revisions of "Data Management Plan"

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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">A Data management plan is …</span></span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">The definition and actual form of a data management plan can vary depending on the use the plan is created for.</span></span></span></span>
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&nbsp;
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">A DMP can be required:</span></span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">to apply for a grant</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">by your institution for PhD and/or major projects</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">when you want to publish your data in a repository</span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">and it is also useful:</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">to manage storage and computing resources at CLEX level</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">whenever you share data, even if you do not intend to publish yet</span></span></span>
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&nbsp;
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=== '''<span style="font-size:large;"><span style="font-family:Arial,Helvetica,sans-serif;">Creating a Data Management Plan</span></span>''' ===
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&nbsp;
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">CLEX encourage you to create a DMP as early as you can in your project.&nbsp;We distinguish between three data management levels: personal, group and public.</span></span></span></span>
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&nbsp;
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==== <span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">'''Personal'''</span></span></span></span> ====
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">&nbsp;This applies to any project exploring new ideas and/or procedures. A single researcher mostly conducts the work and hence there are no specific sharing requirements.</span></span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">At this level a formal data management plan is not necessary, however one&nbsp;can still&nbsp;be useful to start collecting information in case the project becomes bigger and to help plan how the research will be conducted.</span></span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">At&nbsp;this stage a plan can help design&nbsp;the experiment:</span></span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">which data will be used, if it is locally available (see Data Access page) and if can be used (se e licenses)</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">basic workflow description</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">bulk estimate of data volume and compute resources</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">software and support (including training) necessary to run experiment and/or post-process data</span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">It is also never too early to start to work on your project [[Provenance|provenance]]:</span></span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">applying where possible</span> <span style="color:black">relevant CF metadata conventions to any data produced, it is easier when done from the first analysis steps rather than having to update the metadata subsequently.</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">adopting a version control for your code</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">building references to data/code sources</span></span></span>
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&nbsp;
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==== <span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">'''Group'''</span></span></span></span> ====
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<span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">&nbsp;<span style="font-size:medium;">This applies to any project where data will be shared. Hence this applies to any group activity but also to single researcher projects whose output could be of interest to other researchers (this is often true of model experiments).</span></span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">When you share data with others it is important for the data to be accompanied by its provenance (link). This is so others can verify the validity of the data and also use the data in the correct way.</span></span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">A DMP should also include:</span></span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">list of researchers responsible for the data and all the interested parties</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">all information previously entered in the DMP should be reviewed to make sure it is still applicable</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">directory structure and filenames should be consistent and follows conventions wherever possible, CF standard should be adopted if not already in use.</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">data reduction plan: does all model data need to be saved after analysis?</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">data retention plan: when can data be deleted?</span></span></span>
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">If research is part of a collaboration project:</span></span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">agreements with collaborators on how data will be shared/published should be in place from the start</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">any data policy and/or standard adopted by the project should be followed</span></span></span>
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==== &nbsp; ====
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==== <span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">'''Public'''</span></span></span></span> ====
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="caret-color:#000000"><span style="color:#000000">&nbsp;This applies to the final output of the project when data is ready for publication. In addition to the requirements of the group level:</span></span></span></span>
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&nbsp;
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">a descriptive title and acronym</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">a version, including a versioning&nbsp;strategy for future updates</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">a plain English abstract</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">links to relevant documentation and code</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">license</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">keywords</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">long-term storage strategy (this could be the same across the CoE as a first step and adapted if necessary)</span></span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><span style="color:black">any other specific requirements that the chosen repository might have, must be addressed.</span></span></span>
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=== <span style="font-family:Arial,Helvetica,sans-serif;"><span style="font-size:medium;">'''Tips'''</span></span> ===
  
Some tips about research papers that can be applied to DMP too:
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;"><font color="#000000"><span style="caret-color: rgb(0, 0, 0);">As a DMP is documenting and describing your data output as a paper documents and describes your research output, s</span></font>ome tips about research papers that can be applied to DMP, too. We collect a few below.</span></span>
  
 
...<span style="background-color: #fbfbf9; color: #04060f; font-family: Corsiva; font-size: 12pt; vertical-align: baseline;">Pitfalls include using complicated jargon, including unnecessary details, and writing for your highly specialized colleagues instead of a wider audience. ...</span>
 
...<span style="background-color: #fbfbf9; color: #04060f; font-family: Corsiva; font-size: 12pt; vertical-align: baseline;">Pitfalls include using complicated jargon, including unnecessary details, and writing for your highly specialized colleagues instead of a wider audience. ...</span>

Revision as of 22:48, 15 June 2021

Template:Consider Delete This information is especially outdated, and we're considering deleting it

A Data management plan is …

The definition and actual form of a data management plan can vary depending on the use the plan is created for.

 

A DMP can be required:

  • to apply for a grant
  • by your institution for PhD and/or major projects
  • when you want to publish your data in a repository

and it is also useful:

  • to manage storage and computing resources at CLEX level
  • whenever you share data, even if you do not intend to publish yet

 

Creating a Data Management Plan

 

CLEX encourage you to create a DMP as early as you can in your project. We distinguish between three data management levels: personal, group and public.

 

Personal

 This applies to any project exploring new ideas and/or procedures. A single researcher mostly conducts the work and hence there are no specific sharing requirements.

At this level a formal data management plan is not necessary, however one can still be useful to start collecting information in case the project becomes bigger and to help plan how the research will be conducted.

At this stage a plan can help design the experiment:

  • which data will be used, if it is locally available (see Data Access page) and if can be used (se e licenses)
  • basic workflow description
  • bulk estimate of data volume and compute resources
  • software and support (including training) necessary to run experiment and/or post-process data

It is also never too early to start to work on your project provenance:

  • applying where possible relevant CF metadata conventions to any data produced, it is easier when done from the first analysis steps rather than having to update the metadata subsequently.
  • adopting a version control for your code
  • building references to data/code sources

 

Group

 This applies to any project where data will be shared. Hence this applies to any group activity but also to single researcher projects whose output could be of interest to other researchers (this is often true of model experiments).

When you share data with others it is important for the data to be accompanied by its provenance (link). This is so others can verify the validity of the data and also use the data in the correct way.

A DMP should also include:

  • list of researchers responsible for the data and all the interested parties
  • all information previously entered in the DMP should be reviewed to make sure it is still applicable
  • directory structure and filenames should be consistent and follows conventions wherever possible, CF standard should be adopted if not already in use.
  • data reduction plan: does all model data need to be saved after analysis?
  • data retention plan: when can data be deleted?

If research is part of a collaboration project:

  • agreements with collaborators on how data will be shared/published should be in place from the start
  • any data policy and/or standard adopted by the project should be followed

 

Public

 This applies to the final output of the project when data is ready for publication. In addition to the requirements of the group level:

 

  • a descriptive title and acronym
  • a version, including a versioning strategy for future updates
  • a plain English abstract
  • links to relevant documentation and code
  • license
  • keywords
  • long-term storage strategy (this could be the same across the CoE as a first step and adapted if necessary)
  • any other specific requirements that the chosen repository might have, must be addressed.

Tips

As a DMP is documenting and describing your data output as a paper documents and describes your research output, some tips about research papers that can be applied to DMP, too. We collect a few below.

...Pitfalls include using complicated jargon, including unnecessary details, and writing for your highly specialized colleagues instead of a wider audience. ...

Only abbreviations firmly established in the field are eligible, … avoiding those which are not broadly used ...

… when looking for keywords, avoid words with a broad meaning and words already included in the title.

… you need to include detailed information so a knowledgeable reader can reproduce the experiment. However, do not repeat the details of established methods; use References and Supporting Materials to indicate the previously published procedures.

… indicate uses and extensions if appropriate. Moreover, you can suggest future experiments and point out those that are underway ...

Effective research articles are interesting and useful to a broad audience, including scientists in other fields.

use standard systems … conventions