Analog and Digital RepresentationsLayers of RepresentationCuratorial RequirementsFormat Information
Ex 1: Data Management Plan - Topic Modeling for Humanities ResearchEx 2: Data Management Plan - The Visual PageEx 3: Data Curation Profile - Architectural History / EpigraphyEx 4: Data Curation Profile - LinguisticsEx 5: Data Curation Profile - Sociology/Demographics
DMP Tool - California Digital LibraryData Curation Profiles Toolkit - Purdue UniversityUK Data ArchiveThe Digital Curation Centre (DCC - UK)
UC3 MerritteScholarshipEZID
OverviewStudy-LevelData-Level
File Formats and SoftwareOrganizing Data
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Data Management for the Humanities   Tags: data management  

Last Updated: Jul 17, 2014 URL: http://guides.library.ucla.edu/data-management-humanities Print Guide RSS Updates

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What Is Data Curation?

There are a number of competing terms used to describe the activity of managing digital materials for research: digital curation, digital stewardship, data curation, digital archiving. As a simple definition, data curation is "the active and ongoing management of data throughout its entire lifecycle of interest and usefulness to scholarship."

 

Data Management Checklist

This checklist, from the UK Data Archive, can help you identify what to put in place for good data practices, and which actions to take to optimize data sharing.

  • Are you using standardized and consistent procedures to collect, process, check, validate and verify data?
  • Are your structured data self-explanatory in terms of variable names, codes and abbreviations used?
  • Which descriptions and contextual documentation can explain what your data mean, how they were collected and the methods used to create them?
  • How will you label and organize data, records and files?
  • Will you apply consistency in how data are catalogued, transcribed and organized, e.g. standard templates or input forms?
  • Which data formats will you use? Do formats and software enable sharing and long-term validity of data, such as non-proprietary software and software based on open standards?
  • When converting data across formats, do you check that no data or internal metadata have been lost or changed?
  • Are your digital and non-digital data, and any copies, held in a safe and secure location?
  • Do you need to securely store personal or sensitive data?
  • If data are collected with mobile devices, how will you transfer and store the data?
  • If data are held in various places, how will you keep track of versions?
  • Are your files backed up sufficiently and regularly and are back-ups stored safely?
  • Do you know what the master version of your data files is?
  • Do your data contain confidential or sensitive information? If so, have you discussed data sharing with the respondents from whom you collected the data?
  • Are you gaining (written) consent from respondents to share data beyond your research?
  • Do you need to anonymous data, e.g. to remove identifying information or personal data, during research or in preparation for sharing?
  • Have you established who owns the copyright of your data? Might there be joint copyright?
  • Who has access to which data during and after research? Are various access regulations needed?
  • Who is responsible for which part of data management?
  • Do you need extra resources to manage data, such as people, time or hardware?
 

Thank You to...

A special thank you to the UK Data Archive and the DH Curation Guide for providing a multitude of valuable information. Many of the resources and information found in this guide have been adapted from the UK Data Archive and the DH Curation Guide.

 

How to Develop a Data Management and Sharing Plan

 

Why Share Data?

Sharing research data:

  • encourages scientific enquiry and debate
  • promotes innovation and potential new data uses
  • leads to new collaborations between data users and data creators
  • maximizes transparency and accountability
  • enables scrutiny of research findings
  • encourages the improvement and validation of research methods
  • reduces the cost of duplicating data collection
  • increases the impact and visibility of research
  • provides credit to the researcher as a research output in its own right
  • provides great resources for education and training
 

How to Share Data

Your data can be shared by:

  • deposit in a specialist data center or archive
  • submitting to a journal to support a publication
  • deposit in a self-archiving system or an institutional repository
  • dissemination via a project or institutional website
  • informal peer-to-peer exchange