Research data management: Management, Planning, and Dissemination

Research data concerns the data collected, used or generated during the research project.

Definition

“Events, observations or experiences on the basis of which an argument or theory is constructed or tested. Data can be numerical, descriptive, auditory or visual; raw, summarized or analysed; experimental or observational”
Source: LEARN Project 

Classification and examples

• Content: numerical, tabular, textual, audiovisual, sound, multimedia, computational or genomic sequencing data, among others.
• Format: spreadsheets, images, maps, surveys, audio files, sound and audiovisual recordings or structured text, among others.
• Method: 
  • Observational: non-repeatable data captured in real time.
  • Simulations: data generated by predictive models.
  • Experimental: collected when/by doing experiments.
  • Derived or reference: data derived from other data can be raw or processed data.
• Nature: digital (created digitally) or non-digital (paper surveys, data collected on field sheets, etc.).
• Resource:
  • Primary data: collected for the first time and therefore original in nature.
  • Secondary data: data already collected by another person, that has already undergone some kind of processing, usually statistical, and that someone else has created for a specific purpose.
• Processing:
  • Raw data: unprocessed, primary data.
  • Dades processades: any modification of raw data, such as data cleaning, modification or calculations.

Source: CSUC

Research data management (RDM) includes the collection, organization,  documentation, storage and the preservation of the data used when a research project is being carried out.

Download the illustration of the research data management circuit. Source: CSUC

Why publish data in open access?

Publishing data in open source allows ensuring free and universal access to such data, as well as its due preservation, exploitation, reproduction, dissemination, visibility and impact. It also enables meeting the requirements of the funding agencies.

This open data must follow the FAIR principles (Findable, Accessible, Interoperable, Reusable), which involve facilitating the reproduction, replication and reuse of data, and apply to different repositories, such as CORA.RDR (research data repository) or Zenodo, among others.
 

Download the document What is meant by FAIR data?

Personal data directly or indirectly identifies or can identify specific individuals. Examples: a name, an identification number, geographic data, an online identifier or one or more elements of physical, physiological, genetic, psychological, economic and cultural identity, etc. 

• Please also check information published by UPF on the protection of personal data on the Personal data protection intranet.