About Digital Curation
- Data Lifecycle-U.S. Geological SurveyPlan
Create a data management plan and learn about important planning activities. Prior to starting a project, it is important to plan how data will be managed throughout the lifecycle.
Acquire
Acquiring data for a project involves collecting or generating new data or obtaining existing data.
Process
Processing data involves various activities associated with the preparation of new or previously collected data inputs...
Analyze
Data analysis involves various activities associated with exploring and interpreting processed data.
Preserve
Preservation involves actions and procedures used to ensure long-term viability and accessibility of data.
Publish/Share
Publishing and sharing data is an important and required stage in the research process, just like publishing traditional peer-reviewed journal articles.
How and why?
- Benefits of data managementTo make your research as time-efficient, reproducible and safe as possible, it is important that your data management is well thought through, structured, and documented. A good data management strategy takes into account technical, organisational, structural, legal, ethical and sustainability aspects. The time invested in setting up a good data management strategy pays off when the time comes to reproduce your analysis and results. You will be able to easily find and understand your data, increase your data's reuse potential and comply with funder mandates at the same time.
- Data Management Short Course for ScientistsThe ESIP Federation, in cooperation with NOAA and the Data Conservancy, seeks to share the community's knowledge with scientists who increasingly need to be better data managers, as well as to support workforce development for new data management professionals. Over the next several years, the ESIP Federation expects to evolve training courses which seeks to improve the understanding of scientific data management among scientists, emerging scientists, and data professionals of all sorts.
- How and why you should manage your research data: a guide for researchersThis guide provides an introduction to engaging with research data management (RDM) processes. The guide is of interest to university researchers and research data management professional support staff. This includes any staff who give advice to researchers on the storage, management, publication and archiving of their research data. Research data is any information that has been collected, observed, generated or created to validate original research findings. Although usually digital, research data also includes non-digital formats such as laboratory notebooks and sketchbooks.
- Research data managementTo understand data management, one must first understand data.† On the simplest level, data can be defined as “facts and statistics collected together for reference and analysis” [1]. From an information science perspective, data can be defined more contextually in the scope of research to mean that it “is collected, observed, or created, for purposes of analysis to produce original research results” [2]. It is important to recognize that data goes beyond spreadsheets of numbers. Data can take many forms: biospecimens, video recordings, images, software programs, algorithms, paper lab notebooks. It is perhaps most useful to think of data as everything that would be needed to reproduce a given scientific output.
Government Websites
- Data Management Life Cycle-U.S. Fish and Wildlife Service
The data life cycle organizes and illustrates the elements of data management. Our data are corporate assets with value beyond our immediate need and should be managed throughout the entire data life cycle. - Data Lifecycle-U.S. Geological SurveyData are corporate assets with value beyond USGS's immediate need and should be manage throughout the entire data lifecycle. Questions of documentation, storage, quality assurance, and ownership need to be answered for each stage of the lifecycle.
- Data Lifecycle and Scalable Workflows GroupThe Data Lifecycle & Scalable Workflows Group of the Advanced Technologies Section enables data stewardship and enriched scalable data access capabilities. Develops end-to-end scientific workflow technologies to user programs.
Simple Data Curation Process
Data Curation Process
The Curation Lifecycle Model helps you to define research data management workflows and associated roles and responsibilities within your organisation. The data-centric model supports a holistic approach to RDM infrastructure development and optimisation and can be used to help organisations map research data management activities and support across functional and operational units.