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Hantering av forskningsdata: Ordlista och vanliga frågor


access control

 Access control is the selective restriction or permission of access to a location (physical or virtual) or resource (research data).


 Direct identifiers are removed and/or decoded so that individuals cannot be identified.

 backup and recovery

 Procedures used in protecting research data against data loss and in reconstructing the research data after any kind of data loss.

copyright issues

Copyright may apply to research materials and data used in research and may play a role when creating, sharing and reusing data.

data interoperability

Data interoperability is the process that allows the sharing of data between different organisations or researchers.  Its purpose is to create a shared understanding of data. 

data quality control 

During data collection, processing and analysis, the researcher ensures that the data reflect  actual facts, responses, observations and events. 

data reuse

The use of data collected or produced prior to the current project. Existing data may have been collected for research or non-research purposes.

existing data 

Data collected prior to the current project, for research or non-research purposes by others or by the current researcher.

informing research participants

Process of informing research participants and getting permissions before conducting research on them.


Intellectual property rights (IPR) are the protection granted to the creators of IP, and include e.g  copyright and patents.



Describes the terms according which material or data can be reused, archived, re-distributed etc. Permissions are authorised or denied by the licensor, i.e. the owner or the producer of the data.

long-term preservation

Long-term preservation (LTP) requires a strategy which will ensure that your data can be found, read, opened and used in the future. In order to meet these requirements data needs to be actively curated while preserved for a long period (over 15 years) of time. 



Metadata describes the basic characteristics of the data, e.g.:
who created it,
what the data file contains
when, where, why and how the data were generated. Metadata elements usually include the origin, purpose, date, time coverage, geographic location, creator, access, and terms of use of the data. By using standardised metadata you ensure that the data can be fully understood and reused in the future.

metadata standards

Metadata standard refers to a standardised metadata schema which is approved by an official standardisation institution such as the International Organization for Standardization (ISO).

network drives

A shared online disk space where users can store and share files.

open standards

An open standard is a standard that is publicly available and can be implemented on a royalty-free basis. 

persistent identifiers (PID)

A persistent identifier is a reference to a digital object. A PID contains information about the object regardless of what happens to it even if its online location changes.

 cc by Casrai

personal or sensitive information

Any information that could potentially identify a specific individual. Sensitive personal information may include details e.g. regarding the medical, ethnic, cultural or socioeconomic background of a person.

cc by Casrai

privacy protection

The protection of the participants' identities, e.g. via anonymisation or by managing access control.

standard data collection methods

You need to know how the data were collected to be able to derive conclusions from it, i.e. you need to know the method(s) of data collection (e.g. census, sample survey, experiment, observational study). In the context of data collection, standardisation refers to the collection of data in a manner that enables easy comparisons.

third party data

Third party data is any information collected by an entity that does not have a direct relationship with the user the data is being collected on. Third party data is often collected for commercial purposes and is usually licenced or purchased.

version control

Management of changes in data. Ideally, each revision is associated with a timestamp and the person making the change. Revisions can be compared, restored, and with some types of files, merged.

Terms related to research data management, by Mari Elisa Kuusniemi, last modified by Jari Friman on 30.5.2017, the University of Helsinki.

Vanliga frågor

1. Hur ska jag börja min datahantering?

Följ de steg som anges i  Datahanteringsprocesser på Hanken. De erbjuder Hankens studerande respektive forskare en omfattande och grundlig guide över hela datahanteringsprocessen enkelt, tydligt och nära.

Forskare kan också börja med att skriva en datahanteringsplan (DMP). Datahantering börjar redan i forskningsplaneringsstadiet och forskare bör i detta skede skriva en datahanteringsplan och fylla i checklistan som täcker hela datahanteringsprocessen. Se DMP-guider och checklista.

2. Vad bör jag göra då det gäller datahantering?

Som Hanken-studerande och forskare är du ansvarig för att följa god datahanteringspraxis som inkluderar Hankens etiska forskningsriktlinjer för dataskydd, datasäkerhet och delning av data baserat på lagstiftning och forskningsintegritet. Du behöver:

  1. Fyll i beskrivningen av databehandlingen. Alla Hankens studerande och forskare bör fylla i Registrering av databehandlingsaktiviteter blankett (eBlanketten för BSc/MSc/eMBA studerande och blanketten för forskare och doktorander).
  2. Du bör fylla i begäran om etikprövning eBlankett och skicka in till Hankens forskningsetiska kommitté om din studie är en av de sex typerna som beskrivs i Etikprövning.
  3. Du bör informera dina forskningsdeltagare om du samlar in personuppgifter från dem eller om personuppgifterna tas emot från en annan källa än forskningsdeltagarna. Ändra Mallen för meddelande om samtycke för att informera dina forskningsdeltagare.
  4. Förvara och säkerhetskopiera data säkert under forskningens gång. Använd datalagringstjänster som tillhandahålls och underhålls på Hanken. Se datalagring och säkerhetskopiering.

Observera att det finns olika datahanteringsprocesser för BSc/MSc/eMBA studerande och för forskare och doktorander att följa respektive. Se dessa två processer som beskrivs i Datahanteringsprocesser på Hanken.