Research Data Management - Readings & Resources [From Fall 2020 EOS 525 Seminar] - EOS 525 Library Seminar - Fall 2022 - Sept 28 - LibGuides at University of Victoria Libraries

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.

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. https://doi.org/10.1371/journal.pcbi.1004525
- 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/Grants.gov. 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: https://www.dcc.ac.uk/guidance/how-guides/where-keep-research-data"

Examples of Data Journals:

Scienctific Data (Nature)
Scientific Data is a peer-reviewed, open-access journal for descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data.
Geoscience Data Journal (Wiley)
Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered.
Earth System Science Data
Earth System Science Data (ESSD) is an international, interdisciplinary journal for the publication of articles on original research data (sets), furthering the reuse of high-quality data of benefit to Earth system sciences. The editors encourage submissions on original data or data collections which are of sufficient quality and have potential to contribute to these aims. The journal maintains sections for regular-length articles, brief communications (e.g. on additions to data sets) and commentaries, as well as review articles and special issues.
AGU - Data for Publication [Guidleines to suppoer author compliance with open data standards]
The data that supports your research and the visualizations in your paper must be deposited in a trusted repository that supports the FAIR principles. When identifying the most appropriate repositories for your data, please consider the following prioritization. We recommend a repository that specializes in the data for your scientific domain as this will maximize the probability that the deposited data will be interoperable and reusable. If that is not available for your data type, next is your institutional repository, your computing center, and finally a general repository. Please note that the repository you select must offer a translation to English in order to comply. Provides links to Journal-Specific Data Guidance
AGU Data Policy
For the purposes of this policy, data include, but are not limited to, the following: 1) Data used to generate, or be displayed in, figures, graphs, plots, videos, animations, or tables in a paper. 2) New protocols or methods used to generate the data in a paper. 3) New code/computer software used to generate results or analyses reported in the paper.4) Derived data products reported or described in a paper.
Data in Brief [Elsevier]
Data in Brief provides a way for researchers to easily share and reuse each other's datasets by Publishing data articles that: 1) Thoroughly describe your data, facilitating reproducibility. 2) Make your data, which is often buried in supplementary material, easier to find. 3) Increase traffic towards associated research articles and data, leading to more citations. 4) Open up doors for new collaborations.
Biodiversity Data Journal
Biodiversity Data Journal (BDJ) is a community peer-reviewed, open access, comprehensive online platform, designed to accelerate publishing, dissemination and sharing of biodiversity-related data of any kind. All structural elements of the articles – text, morphological descriptions, occurrences, data tables etc. – will be treated and stored as data, in accordance with the Data Publishing Policies and Guidelines of Pensoft Publishers.
Data Publishing [Wikipedia]
Data publishing (also data publication) is the act of releasing research data in published form for use by others. It is a practice consisting in preparing certain data or data set(s) for public use thus to make them available to everyone to use as they wish. This practice is an integral part of the open science movement. There is a large and multidisciplinary consensus on the benefits resulting from this practice.
Journal of open archaeology data
The Journal of Open Archaeology Data (JOAD) features peer reviewed data papers describing archaeology datasets with high reuse potential. We work with a number of specialist and institutional data repositories to ensure that the associated data are professionally archived, preserved, and openly available. Equally importantly, the data and the papers are citable, and reuse is tracked.
Data journals: A survey [2015]
Data occupy a key role in our information society. However, although the amount of published data continues to grow and terms such as data deluge and big data today characterize numerous (research) initiatives, much work is still needed in the direction of publishing data in order to make them effectively discoverable, available, and reusable by others. Several barriers hinder data publishing, from lack of attribution and rewards, vague citation practices, and quality issues to a rather general lack of a data‐sharing culture. Lately, data journals have overcome some of these barriers. In this study of more than 100 currently existing data journals, we describe the approaches they promote for data set description, availability, citation, quality, and open access. We close by identifying ways to expand and strengthen the data journals approach as a means to promote data set access and exploitation.
A Growing List of Data Journals by Katherine Akers (Posted May 9, 2014)
In one of my recent projects, I’ve been investigating the development and current state of data journals and other scholarly journals that publish data papers. Rather than forming arguments or drawing conclusions from data, data papers call attention to and describe particular research datasets, which may increase the likelihood that the datasets could be re-used or re-purposed by other researchers in the future. What I particularly like about data papers is that they are peer-reviewed, can be listed on CVs, and can accumulate citations just like traditional journal articles, thus providing an incentive for researchers to put time and effort into preparing their datasets for public access.

Data Repositories & Discovery Tools

UVic Libraries Dataverse
The University of Victoria Dataverse is a research data repository for our faculty, students, and staff. Files are held in a secure environment on Canadian servers. Researchers can choose to make content available to the public, to specific individuals, or to keep it locked.
Federated Research Data Repository [FRDR] - Canadian
Search FRDR to find research datasets originating from researchers affiliated with Canadian institutions. Data deposited to other repositories across Canada can also be found by searching in FRDR. Portage, Compute Canada (CC) and the Canadian Association of Research Libraries (CARL) are collaborating to provide a scalable federated platform for digital research data management (RDM) and discovery. They are pleased to announce that the Federated Research Data Repository (FRDR) service has finished Beta and is now in Limited Production.
DataONE
DataONE is a community driven program providing access to data across multiple member repositories, supporting enhanced search and discovery of Earth and environmental data. DataONE promotes best practices in data management through responsive educational resources and materials. We envision researchers, educators, and the public using DataONE to better understand and conserve life on earth and the environment that sustains it.
DataONE - Find Data Portal
DataONE launches metrics visualizations for datasets through the search and discovery platform, https://search.dataone.org. These standardized metrics include live counts of citations, downloads, and views for each dataset in the network.
PANGEA - Data Publisher for Earth & Environmental Science
The information system PANGAEA is operated as an Open Access library aimed at archiving, publishing and distributing georeferenced data from earth system research. The system guarantees long-term availability of its content through a commitment of the hosting institutions.
Google Dataset Search
Dataset Search enables users to find datasets stored across the Web through a simple keyword search. The tool surfaces information about datasets hosted in thousands of repositories across the Web, making these datasets universally accessible and useful.
Data Citation Index - Web of Science
Discover research data sets and data studies from a wide range of international data repositories in the sciences, social sciences, and arts and humanities. Discover research data connected to articles published in journals, books, and conference proceedings. Link directly to data repositories for easy access to the deposited data sets.
Zenodo
Built and developed by researchers, to ensure that everyone can join in Open Science. The OpenAIRE project, in the vanguard of the open access and open data movements in Europe was commissioned by the EC to support their nascent Open Data policy by providing a catch-all repository for EC funded research. CERN, an OpenAIRE partner and pioneer in open source, open access and open data, provided this capability and Zenodo was launched in May 2013. In support of its research programme CERN has developed tools for Big Data management and extended Digital Library capabilities for Open Data. Through Zenodo these Big Science tools could be effectively shared with the long-tail of research.
DRYAD
The Dryad Digital Repository is a curated resource that makes research data discoverable, freely reusable, and citable. Dryad provides a general-purpose home for a wide diversity of data types. Dryad originated from an initiative among a group of leading journals and scientific societies to adopt a joint data archiving policy (JDAP) for their publications, and the recognition that open, easy-to-use, not-for-profit, community-governed data infrastructure was needed to support such a policy. These remain our guiding principles.Dryad’s vision is to promote a world where research data is openly available, integrated with the scholarly literature, and routinely re-used to create knowledge. Our mission is to provide the infrastructure for, and promote the re-use of, data underlying the scholarly literature.
figshare
figshare is a repository where users can make all of their research outputs available in a citable, shareable and discoverable manner
re3data.org [Registry of Research Data Repositories ]
Since its launch in 2012, re3data has become the resource of information about research data repositories. It indexes and provides extensive information about more than 2450 research data repositories.