Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.
askus Ask us

EOS 525 Research Data Management Library Seminar - Fall 2020

Selected Canadian Research Data Management Resources

Selected Canadian Research Data Management Resources

RDM Readings & Webinars - Earth & Ocean Sciences:

DataONE Webinar Series
Cutting-edge discussions in research data management. Monthly discussions on open science, the role of the data lifecycle, and achieving innovative science through shared data and ground-breaking tools.

  • What we wish we had learned in Graduate School - a data management training roadmap for graduate students - Webinar aired live on November 10-2020. View Recording.
    • Abstract: Data management training for graduate students is a very important but often undervalued area of graduate school education. Many graduate students will go on and become professionals who are using, producing, and/or managing data that have tremendous benefits for both the research community and society. However, our personal experiences as graduate students show that data lifecycle and data management training are not part of the core curriculum in graduate school. As Earth Science Information Partners (ESIP) Community Fellows, we understand that data management is a critical skill in earth science and we all wished we had an opportunity to integrate it from the beginning in our graduate school experience. To the issue of lack of formal data management training in graduate education, we convened a working session during the 2020 ESIP Summer Meeting called “What we wish we had learned in Graduate School?” The session was initially planned as a working session for early career professionals to share resources and lessons learned during our own graduate school experiences. The session has sparked broad interests from the Earth science data community and attracted participants across different career stages and with different levels of expertise. The outcome of the session has been summarized as a roadmap that follows the DataONE Data Lifecycle. This roadmap projects the data lifecycle into the traditional graduate school timeline and highlights the benefits and resources of data management training for each component in the data lifecycle. This roadmap for graduate data management training will be distributed via ESIP and be continued as part of the ESIP Community Program in the future to promote data management training for graduate students in Earth sciences and beyond.

Canada's PORTAGE NETWORK - Research Data Management Webinars

As the research data management landscape continues to evolve, it is crucial that researchers and data professionals have the information and training they need to stay up-to-date with the latest developments and best practices. The Training Expert Group (TEG) oversees a range of specific projects that collaboratively develop and deliver both training and training resources to support all aspects of RDM skill development for a variety of stakeholders across Canada. Current areas of interest include developing and implementing a national RDM training strategy, supporting initiatives such as webinars and “workshops in a box”, and working with Portage’s Expert and Working Groups to develop domain-specific, researcher-focused training.

Recommended Reading: 

  • Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance
    • Citation:Gil, Y., et al. (2016), Toward theGeoscience Paper of the Future: Bestpractices for documen ting and sharingresearch from data to software toprovenance, Earth and Space Science, 3,388–415, doi:10.1002/2015EA000136
    • Abstract Geoscientists now live in a world rich with digital data and methods, and their computationalresearch cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF)presents an approach to fully document, share, and cite all their research products including data, software,and computational provenance. This article proposes best practices for GPF authors to make data, software,and methods openly accessible, citable, and well documented. The publication of digital objects empowersscientists to manage their research products as valuable scientifi c assets in an open and transparent waythat enables broader access by other scientists, students, decision makers, and the public. Improvingdocumentation and dissemination of research will accelerate the pace of scientific discovery by improvingthe ability of others to build upon published work.
  • Ten Simple Rules for Creating a Good Data Management Plan
    • Citation: Michener WK (2015) Ten Simple Rules for Creating a Good Data Management Plan. PLoS Comput Biol 11(10): e1004525.
    • Introduction: Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). Fig 1 illustrates the relationship between hypothetical research and data life cycles and highlights the links to the rules presented in this paper. The DMP undergoes peer review and is used in part to evaluate a project’s merit. Plans also document the data management activities associated with funded projects and may be revisited during performance reviews.
  • NSF Atmospheric & Geospace Sciences Advice to PI’s on DMPs
    • Excerpt from PDF: The NSF Proposal and Award Policies and Procedures Guide articulates the agency's requirements for a supplementary document of no more than two pages that describes a plan for managing data (data is used here in very broad terms) produced by the project. This Data Management Plan (DMP) is required for all proposals. Proposals without a DMP will not be
      accepted by FastLane/ Reviewers are asked to consider the DMP as part of either
      the intellectual merit or the broader impacts review criterion. The scope of the proposal will
      determine which review criterion is appropriate.

  • Balancing Open Science and Data Privacy in the Water Sciences
    • Abstract: Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio‐hydrology), there is the potential for well‐meaning researchers to unintentionally violate the privacy and security of individuals or communities by sharing sensitive information. Here we identify the contexts in which privacy violations are most likely to occur, such as working with high‐resolution spatial data (e.g., from remote sensing), consumer data (e.g., from smart meters), and/or digital trace data (e.g., from social media). We also suggest practices for identifying and addressing privacy concerns at the individual, institutional, and disciplinary levels. We strongly advocate that the water science community continue moving toward open science and socio‐environmental research and that progress toward these goals be rooted in open and ethical data management.
  • Federation of Earth Science Information Partners - Data Management Short Course for Scientists
    • Excerpt: The 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. All courses are available under a Creative Commons Attribution 3.0 license that allows you to share and adapt the work as long as you cite the work according to the citation provided.

Data Journals

What is a Data Journal?  "Data journals consist of data articles that describe how, why and when a dataset was collected and any derived data product. Rather than presenting any analysis or conclusions, a data article may present arguments about the value of the data for future analysis. "Such publishing mechanism both give credit that is recognizable within the scientific ecosystem, and also ensure the quality of the published data and metadata through the peer review process"

Definition for data journals taken from "Whyte, A. (2015). ‘Where to keep research data: DCC checklist for evaluating data repositories’ v.1.1 Edinburgh: Digital Curation Centre. Available online:"

Examples of Data Journals:

Data Repositories & Discovery Tools

Lifecycle of Digital Research Data

While there are many ways of looking at the data life cycle, this particular image emphasizes the repurposing and re-use of data, which is a driving force behind the success of data intensive science and the reason why data management has been deemed so important.

Source: Humphrey, Charles. (2006). “e-Science and the life cycle of research.” Retrieved 23 January 2011 from

Why Research Data Management? A Video.

On older video, but still relevant: A data management snafu-story by Karen Hanson, Alisa Surkis and Karen Yacobucci.

Background Resources: The Changing Culture of Research Data Management - National & International Initiatives

Creative Commons License
This work by The University of Victoria Libraries is licensed under a Creative Commons Attribution 4.0 International License unless otherwise indicated when material has been used from other sources.