Difference between revisions of "Institution data requirements"

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{{Template:Working_on}} 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 publish the data via their portal, but this might not have capacity for a large dataset, like a model output. The key here is to remember that all these policies and services are here to make sure you satisfy the same principles. The ARC, your own university and CLEX want your data to be [[FAIR]]. They want you to:  
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{{Template:Working_on}} 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 publish the data via their portal, but this might not have capacity for a large dataset, like a model output. The key here is to remember that all these policies and services are here to make sure you satisfy the same principles. The ARC, your own university and CLEX want your data to be [[FAIR|FAIR]]. They want you to:
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*share and publish your data and code whenever possible;  
 
*share and publish your data and code whenever possible;  
 
*to do this ethically (see [[Australian_Code_for_the_Responsible_Conduct_of_Research|Research Code of Conduct]]);  
 
*to do this ethically (see [[Australian_Code_for_the_Responsible_Conduct_of_Research|Research Code of Conduct]]);  
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'''<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Australian Research Council data policy</span></span>'''
 
'''<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Australian Research Council data policy</span></span>'''
  
The ARC&nbsp;
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The ARC &nbsp;doesn not have yet an Open Access&nbsp;policy for data. Their [https://www.arc.gov.au/policies-strategies/policy/arc-open-access-policy Open Access policy] explicitly does not cover data.
&nbsp; &nbsp; &nbsp; &nbsp;  
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However, the ARC has a [https://www.arc.gov.au/policies-strategies/strategy/research-data-management Data Management Recommendations] page,&nbsp;clearly stating&nbsp;that&nbsp;
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<blockquote>
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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.
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</blockquote>
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In particular:
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*From 2020, the ARC requires&nbsp;researchers to provide a Data Management&nbsp;Plan&nbsp;(DMP)&nbsp;as part of the agreement for funding under the&nbsp;[https://www.arc.gov.au/grants/national-competitive-grants-program National Competitive Grants Program].
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*Detailed&nbsp;DMPs should be in place before the start of the project, while a not detailed version is sufficient when submitting a grant.
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*The ARC requires researcher to follow the&nbsp;[[Australian_Code_for_the_Responsible_Conduct_of_Research|''Australian Code for the Responsible Conduct of Research 2018'']], which also cover data sharing and proper data management.&nbsp;  
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&nbsp; '''<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Institutional data policies&nbsp;and services</span></span>'''
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*[[ANU_data_requirements_and_tools|<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Australian National University</span></span>]]
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*<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">[[Melbourne_University_data_requirements_and_tools|Melbourne University]]</span></span>
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*[[Monash_data_requirements_and_tools|<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">Monash University</span></span>]]
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*[[UNSW_data_requirements_and_tools|<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">University of New South Wales</span></span>]]
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*[[UTAS_data_requirements_and_tools|<span style="font-size:medium;"><span style="font-family:Arial,Helvetica,sans-serif;">University of Tasmania</span></span>]]
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[[Category:Data induction]]
 
[[Category:Data induction]]

Revision as of 23:11, 27 May 2021

Template:Working on New page under construction

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 publish the data via their portal, but this might not have capacity for a large dataset, like a model output. The key here is to remember that all these policies and services are here to make sure you satisfy the same principles. 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  doesn not have yet an Open Access policy for data. Their Open Access policy explicitly does not cover data.

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