Difference between revisions of "Institution data requirements"

 
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<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Each institution has their own data policy and data services they have to provide to publish and/or archive data. This can create confusion as a researcher might have to follow, for the same dataset, a journal publishing requirements, their own institution policy, the funder policy, usually the Australian Research Council,&nbsp;and on top of that we, as CLEX, are also asking them to follow our own guidelines.</span></span><span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Sometimes an institution might require you to do something which is not possible, as publishing model&nbsp;data on their repository&nbsp;when this&nbsp;does not have capacity for a large dataset.</span></span>
 
<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Each institution has their own data policy and data services they have to provide to publish and/or archive data. This can create confusion as a researcher might have to follow, for the same dataset, a journal publishing requirements, their own institution policy, the funder policy, usually the Australian Research Council,&nbsp;and on top of that we, as CLEX, are also asking them to follow our own guidelines.</span></span><span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Sometimes an institution might require you to do something which is not possible, as publishing model&nbsp;data on their repository&nbsp;when this&nbsp;does not have capacity for a large dataset.</span></span>
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">to use a repository, standards and conventions relevant to your&nbsp;discipline;&nbsp;</span></span>  
 
*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">to use a repository, standards and conventions relevant to your&nbsp;discipline;&nbsp;</span></span>  
 
*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">and to archive for 5 years essential data underpinning your PhD thesis and journal publications.</span></span>  
 
*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">and to archive for 5 years essential data underpinning your PhD thesis and journal publications.</span></span>  
 
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=== <span style="font-size:large;">'''<span style="font-family:Arial,Helvetica,sans-serif;">Australian Research Council data policy</span>'''</span> ===
 
=== <span style="font-size:large;">'''<span style="font-family:Arial,Helvetica,sans-serif;">Australian Research Council data policy</span>'''</span> ===

Latest revision as of 21:46, 28 June 2021

Each institution has their own data policy and data services they have to provide to publish and/or archive data. This can create confusion as a researcher might have to follow, for the same dataset, a journal publishing requirements, their own institution policy, the funder policy, usually the Australian Research Council, and on top of that we, as CLEX, are also asking them to follow our own guidelines.Sometimes an institution might require you to do something which is not possible, as publishing model data on their repository when this does not have capacity for a large dataset.

The key to navigate  all these policies and services is to remember that all these reccomendation and rules have a common aim. The ARC, your own university and CLEX want your data to be FAIR. They want you to:

  • share and publish your data and code whenever possible;
  • to do this ethically (see Research Code of Conduct);
  • to use a repository, standards and conventions relevant to your discipline; 
  • and to archive for 5 years essential data underpinning your PhD thesis and journal publications.

Australian Research Council data policy

The ARC  does not yet have an Open Access policy for data, their Open Access policy applies to research outputs with the exclusion of data and code.

However, the ARC has a Data Management Recommendations page, clearly stating that 

Effective data management is an important part of ensuring open access to publicly funded research data. Data management planning from the beginning of a research project helps to outline how data will be collected, formatted, described, stored and shared throughout, and beyond, the project lifecycle.

In particular:

  Institutional data policies and services