Atlas.ti Data Curation Primer

Key Info
URL - the landing page for the learning resource: 
https://conservancy.umn.edu/handle/11299/210211
Description - a brief synopsis, abstract or summary of what the learning resource is about: 
Altas.ti is a software application that allows researchers to analyze qualitative data in a systematic and transparent way, increasing the validity of results (Friese 2019). ATLAS.ti handles different types of data that are kept in a project. The project files can contain text documents, images, audio recordings, videos, pdf files, geodata, Twitter data, citations from Evernote and reference managers, and survey data. The purpose of this primer is to guide a data curator through the curation process for Altas.ti files.
Key questions for curation review
-What ATLAS.ti version was used?
-Can other researchers open the project without the ATLAS.ti?
-Does the project include metadata/documentation/codebook?
-Are there consent forms/participation agreements? Is there sensitive information that can compromise human subjects’ rights?
-Are there associated data that has been exported (i.e. result reports, codebook) outside the project?

This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers

Authoring Person(s) Name: 
Margarita Corral
Access Cost: 
No fee
Citation - format of the preferred citation for the learning resource: 
Corral, Margarita. (2020). Atlas.ti Data Curation Primer. Data Curation Network. Retrieved from the University of Minnesota Digital Conservancy, http://hdl.handle.net/11299/210211.
Primary language(s) in which the learning resource was originally published or made available: 
English
More info about
Keywords - short phrases describing what the learning resource is about: 
Data analysis
Data curation
Data visualization
Humanities data
Metadata standards
Qualitative data analysis
Software management
Software preservation
Published / Broadcast: 
Friday, January 3, 2020
Created: 
Wednesday, May 29, 2019
ID - identifier that provides the means to locate the learning resource or its citation: 
http://hdl.handle.net/11299/210211
Type - namespace prefix for the citable locator, if any: 
Handle
Publisher - organization credited with publishing or broadcasting the learning resource: 
Data Curation Network
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Text - an explanation of a concept or a story using human readable characters formed into words, usually distinguished from graphical images.
Contributor Name: 
Name: 
Hannah Hadley
Type: 
Collaborator
Name: 
Dave Fearon
Type: 
Mentoring
Contributor Organization(s): 
Name: 
Institute of Museum and Library Services (IMLS)
Type: 
Funding and sponsorship
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Citizen scientist
Data professional
Early-career research scientist
Educator
Graduate student
Librarian
Mid-career research scientist
Research faculty
Research scientist
Software engineer
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
Up to 1 hour
Framework - A community-based organization plan or set of steps for education or training: 
FAIR Data Principles