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TRANSCRIPT
QUICK TIPS
Data Lifecycle
Many international organizations have created models of the stages that data moves through from the time a research project is first con-ceptualized to its completion and beyond when it may be archived and reused. These models vary by discipline, point of view, and emphasis on different stages of the lifecycle.
Looking across various models, common gen-eral stages of the data lifecycle include plan-ning, collecting, archiving, sharing, and reus-ing.
Some services and practices stretch across multiple stages of the lifecycle, and activities during one stage may affect what happens at later stages (e.g., documenting data during collection help it to be shared and reused).
Data Management Plans
Some funding agencies now require a plan detailing how data will be managed as part of the grant application process. These plans generally indicate how data will be described, stored, archived, and shared.
Even if it is not required by a funding agency, a detailed data management plan can be very helpful in making sure data is adequately de-scribed, organized, securely stored, backed-up, archived, and preserved.
It may be helpful to think of a data manage-ment plan consultation as similar to an in-depth reference interview.
Data Archiving and Preservation
To enable sharing and future use, data must be properly archived and preserved. Preserva-tion policies/actions ensure that accuracy of and access to data persist over time. Data should be archived in open file formats when possible.
There are a number of individual practices that can affect how easily data is preserved. Encouraging researchers to follow best prac-tices in terms of metadata standards and file formats will help with long-term preservation of their data.
Data Documentation (Metadata)
Documenting data is important for data shar-ing and reuse. Documentation includes any contextual information (e.g., instrument set-tings, environmental conditions, spatial loca-tions, etc.) needed for humans and machines to understand the data. It can also include descriptive information about the entire data set (e.g., title, creator, date, etc.) similar to the discovery metadata commonly used in libraries.
When possible, metadata should be created according to established standards and best practices. These standards are numerous and vary by discipline.
Data Sharing
Sharing of data is an essential part of the sci-entific process. Sharing data allows research to be verified and replicated. As many funding agencies now recognize, most notably the Na-tional Science Foundation, data sharing is also necessary to enable new forms of collabora-tive and interdisciplinary research.
By making data openly available (or available to qualified researchers in the case of sensi-tive data), duplicative research efforts can be reduced and data can be reused and repur-posed in ways not intended by the research-ers who first collected it.
There are a number of data repositories in many disciplines that facilitate widespread sharing of data.
Quick Tips, Resources, and Tools DataDay!
Data Citation
The ability to properly cite data is essential in order both to encourage data sharing and to enable reuse of data. Researchers using a da-ta set need to be able to provide an accurate reference to it in order for others to under-stand and replicate their work. In addition, citing data allows data creators to receive credit for their work thus rewarding the time and effort it takes to share data.
Standards and best practices around data ci-tation are still emerging. Some of the organi-zations working on this issue include the In-
on Data for Science and Technology (CODATA), the National Information Stand-ards Organization (NISO), and DataCite.
At minimum, data citations should include in-formation about the author(s), title, date, and location (URL).
RESOURCES
Data Lifecycle
Overview of many of the most prominent data lifecycle models written by Alex Ball:
(http://opus.bath.ac.uk/28587/1/redm1rep120110ab10.pdf)
Data Citation
Guide to data citations from the UK Digital Cura-tion Centre:
(http://www.dcc.ac.uk/resources/how-guides/cite-datasets)
Data Management Plans
Fairly comprehensive list of funding agency re-quirements from CU-Boulder Research Data Ser-
https://data.colorado.edu/funder-requirements
Data Documentation (Metadata)
Brief list of (some) metadata standards from vari-ous disciplines from CU-Boulder Research Data Services: https://data.colorado.edu/metadata
TOOLS
DMPTool (https://dmp.cdlib.org/)
Tool from the California Digital Library that helps generate data management plans based on the requirements of various funding agencies. Can be customized with links to campus resources.
Databib (http://databib.org/)
Definitive source for data repositories where many types of data from a wide range of disci-plines can be found and/or archived and shared.
DataUp (http://dataup.cdlib.org/)
Tool from the California Digital Library (available as web application or Excel plugin) that makes it easier to document, manage, archive, and share tabular data.
Research Data MANTRA (http://datalib.edina.ac.uk/mantra/)
Online training course from the University of Ed-inburgh on research data management.
DataONE Best Practices Database (http://www.dataone.org/best-practices)
Searchable database of best practices related to data management from DataONE.
DataCite (http://datacite.org/)
International organization that registers and pro-vides citations to data sets and develops tools and services for finding and citing data.
EZID (http://www.cdlib.org/services/uc3/ezid/)
Service from the California Digital Library that libraries and other institutions can use to assign Digital Object Identifiers (DOIs) to data sets.
Quick Tips, Resources, and Tools DataDay!
