Difference between revisions of "Data terminology"

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<span style="line-height:normal"><span style="height:35.6pt">Tool to help you manage the data for a specific research project. It can takes different forms depending on the stage of your project, for example a DMP to submit with a grant application will be different from the DMP required to publish your data. A DMP evolves with your project and it is useful to record your data provenance</span></span>
 
<span style="line-height:normal"><span style="height:35.6pt">Tool to help you manage the data for a specific research project. It can takes different forms depending on the stage of your project, for example a DMP to submit with a grant application will be different from the DMP required to publish your data. A DMP evolves with your project and it is useful to record your data provenance</span></span>
  
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| style="width: 99pt; text-align: center;" width="132" | Metadata
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| style="width:351.8pt" width="469" | Metadata is the information on data, examples are metadta files accompanying observations with details of instrumentations and location, the attributes of a NetCDF file. A metadata record or repositories will contain information on a dataset but not the data itself
 
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| style="width: 99pt; text-align: center;" width="132" | '''Open Access'''
 
| style="width: 99pt; text-align: center;" width="132" | '''Open Access'''

Revision as of 22:24, 13 July 2021

We are listing here some data management key concepts and frequently recurring terms and acronyms.

NB this is a work in progress so it is not yet an exhaustive list

 

Key concepts    

FAIR

The FAIR Data Principles:

  • Findable:  data should be easy to find and identify. 
  • Accessible: data should have open access whenever possible.
  • Interoperable: well formatted data that uses discipline conventions and vocabularies, for both the data itself and the metadata used to describe it.
  • Reusable: data should be accompanied by enough information on how it was collected or processed, as to guarantee its quality and hence make it usable by other

File Management

Methods for storing, organising, naming, discovering and retrieving files in a structured consistent manner. 

Data Storage

The location and/or system you use to store your data during a research project. This could include disk on personal computers, disk or tape on a shared server,  external storage devices such as hard drives or SD cards, and networked drives managed by your institution, commercial or research cloud storage.

Data Back Up

The process of saving your data to protect against data loss. This can be an automatic process, where the storage location automatically retains previous versions of your data, or a manual process, where you need to actively save the data in another location.

Data Archiving or Preservation

The process of putting your data in long term storage following the completion of a project or publication for a minimum of 5 years. This includes identifying who can access the data and how it can be accessed. Many Institutions have Repositories which can be used by staff and students.

Data Sharing

Making your data available for use by other researchers for their own research projects. This requires quality metadata to determine data source and changes made to allow for reuse. The best way to share data is to publish it then it will be more discoverable and will be assigned a persistent identifier (such as DOI) which helps other to cite the data.

Data Provenance

Data provenance describes the journey data goes through. It documents the evolution of a dataset from the original source including all the processes and methodology by which it was produced.

Data_Management_Plan (DMP)

Tool to help you manage the data for a specific research project. It can takes different forms depending on the stage of your project, for example a DMP to submit with a grant application will be different from the DMP required to publish your data. A DMP evolves with your project and it is useful to record your data provenance

Metadata Metadata is the information on data, examples are metadta files accompanying observations with details of instrumentations and location, the attributes of a NetCDF file. A metadata record or repositories will contain information on a dataset but not the data itself
Open Access A set of principles and a range of practices through which research outputs are distributed online, free of cost or other access barriers.

 

Other terms

attribution - is the act of recognising the author/s of a piece of work that you used in your research. It is a common requirement of licenses 

citation - is the way you attribute a piece of work, it should contain all the information necessary to locate the original work

copyright - is a form of intellectual property meant to protect the right of the author of a creative work to control how the work is used. More comprehensive but readale information on copyright is available here.

license - a copyright license is a legal document stating what someone else is allowed or not allowed to do with a research product

 

Acronyms

ARDC (ex ANDS) - Australian Research Data Commons is a NCRIS project aimed to enable the Australian research community and industry access to nationally significant, data intensive digital research infrastructure, platforms, skills and collections of high quality data.

CC - Creative Commons, a non-profit organisation that produces licenses to encourage sharing of knowledge, commonly used for data products

CF - Climate and Forecast conventions, conventions used to set metadata attributes in NetCDF files

DMP - Data Management Plan

FAIR - see definition in key concepts

 

RDA - Research Data Australia is the data discovery service of the Australian Research Data Commons (ARDC)

RDA - Research Data Alliance is a global community-driven initiative with the goal of building the social and technical infrastructure to enable open sharing and re-use of data.