The Portage Network offers a range of training materials – everything from one-page guides to online training modules and videos – that span the research data life cycle.

With the assistance of the Portage National Training Expert Group, the Portage Network of Experts continues to develop new bilingual training aids and online modules to support a community of practice for research data management in Canada.

These materials are intended for researchers, library data specialists, research data managers, and discipline and functional experts across the research data landscape. All training resources created by Portage are licensed under CC BY-NC 4.0 and are free to share and adapt for your own needs.

If you have questions about developing RDM training at your institution or would like assistance with creating in-person or online training resources or opportunities, please contact RDM-GDR@alliancecan.ca.

How researchers structure their data varies by disciplines and research questions. Still, there are general guidelines for structuring data that make it more likely to be usable in the future. The following questions should be considered for any project that gathers data. These questions should be considered first at the planning stage, again as data is being gathered and stored, and once more prior to final deposit into a digital archive or repository.

1. What are the data organization standards for your field? For example, there are often standards for labeling data fields that will make your data machinereadable. There may also be specific variables and coding guidelines that you can use that will make your work interoperable with other datasets. Lastly, there may be accepted hierarchies and directory structures in your discipline that you can build upon.
2. What are the data export options in the software you are using? If using proprietary and/or highly specialized software to analyze large data sets, export the data in a format that is likely to be supported in the future, and that will be accessible from other software programs. This usually means choosing an open format that is not proprietary. Remember that you may not have access to the same software in the future, and not all software upgrades can read old file types.
3. What forms of the data will be needed for future access? Consider the various forms the data may take, and the scale of the data involved. You may need to preserve not only the underlying raw data, but also the resulting analyses you have created from it.

The soup-to-nuts exercises take students through the entire process of research with statistical data, from the very beginning when they first access the original data, through cleaning and processing the data to prepare them for analysis, to the very end when they generate the results that they present in a written report. Throughout each exercise, there will be an emphasis on adopting a transparent workflow and constructing replication documentation that ensures all the work done for the exercise can be independently reproduced.

This course was developed and taught by Ben Marwick, Professor of Archaeology at University of Washington. It is a requirement for the UW Master of Science in Data Science, introduces students to the principles and tools for computational reproducibility in data science using R. Topics covered include acquiring, cleaning and manipulating data in a reproducible workflow using the tidyverse. Students will use literate programming tools, and explore best practices for organizing data analyses. Students will learn to write documents using R markdown, compile R markdown documents using knitr and related tools, and publish reproducible documents to various common formats. Students will learn strategies and tools for packaging research compendia, dependency management, and containerising projects to provide computational isolation.

RQM is a research methods course that focuses on modernizing the post-data collection portion of the scientific workflow. The course takes an approach that produces both conventional research products and trains students to make their work more efficient and reproducible. This handbook provides a framework for professors who would like to teach a 14-week class on reproducible quantitative methods, presuming an understanding of open workflows for publication, some intermediate R or (other command-line based data analysis software) skills, and basic GitHub operations and use.

The Research Data Curation Bibliography includes over 750 selected English-language articles, books, and technical reports that are useful in understanding the curation of digital research data in academic and other research institutions.

MANTRA is a free, online non-assessed course with guidelines to help you understand and reflect on how to manage the digital data you collect throughout your research. It has been crafted for the use of post-graduate students, early career researchers, and also information professionals. It is freely available on the web for anyone to explore on their own.

Through a series of interactive online units you will learn about terminology, key concepts, and best practice in research data management.

There are eight online units in this course and one set of offline (downloadable) data handling tutorials that will help you:

1. Understand the nature of research data in a variety of disciplinary settings
2. Create a data management plan and apply it from the start to the finish of your research project
3. Name, organise, and version your data files effectively
4. Gain familiarity with different kinds of data formats and know how and when to transform your data
5. Document your data well for yourself and others, learn about metadata standards and cite data properly
6. Know how to store and transport your data safely and securely (backup and encryption)
7. Understand legal and ethical requirements for managing data about human subjects; manage intellectual property rights
8. Understand the benefits of sharing, preserving and licensing data for re-use
9. Improve your data handling skills in one of four software environments: R, SPSS, NVivo, or ArcGIS

Each unit takes up to one hour, plus time for further reading and carrying out the data handling exercises. In the units you will find explanations, descriptions, examples, exercises, and video clips in which academics, PhD students and others talk about the challenges of managing research data. The data handling tutorials assume some experience with each software environment and provide exercises in PDF along with open datasets to download and work through using your own installed software.

MANTRA modules and data handling exercises are available for download via Zenodo: https://doi.org/10.5281/zenodo.1035218

The Research Data Management Academy (RDMLA) is a global, free online professional development program for librarians, information professionals, or other professionals who work in a research-intensive environment. The curriculum focuses on the knowledge and skills needed to collaborate with researchers and other stakeholders on data management. RDMLA features a unique partnership between a library and information science academic program, academic health sciences and research libraries, and industry publisher. All of the content is hosted on Canvas Network, freely available, and open for reuse under a CC-BY-NC-SA license.

This webliography is intended for librarians seeking to enhance their own knowledge and assist peers in improving their data management awareness. The webliography is organized by content type, first with more foundational materials such as established data management curricula and then with current awareness and community materials such as social media.

This version of this teaching module was published in Teaching Issues and Experiments in Ecology:

Jim McNeil and Megan A. Jones. April 2018, posting date. Data Management using National Ecological Observatory Network’s (NEON) Small Mammal Data with Accompanying Lesson on Mark Recapture Analysis. Teaching Issues and Experiments in Ecology, Vol. 13: Practice #9 [online]. http://tiee.esa.org/vol/v13/issues/data_sets/mcneil/abstract.html

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Undergraduate STEM students are graduating into professions that require them to manage and work with data at many points of a data management life cycle. Within ecology, students are presented not only with many opportunities to collect data themselves, but increasingly to access and use public data collected by others. This activity introduces the basic concept of data management from the field through to data analysis. The accompanying presentation materials mention the importance of considering long-term data storage and data analysis using public data.

This data set is a subset of small mammal trapping data from the National Ecological Observatory Network (NEON). The accompanying lesson introduces students to proper data management practices including how data moves from collection to analysis. Students perform basic spreadsheet tasks to complete a Lincoln-Peterson mark-recapture calculation to estimate population size for a species of small mammal. Pairs of students will work on different sections of the datasets allowing for comparison between seasons or, if instructors download additional data, between sites and years. Data from six months at NEON’s Smithsonian Conservation Biology Institute (SCBI) field site are included in the materials download. Data from other years or locations can be downloaded directly from the NEON data portal to tailor the activity to a specific location or ecological topic.

In this activity, students will:

- discuss data management practices with the faculty. Presentation slides are provided to guide this discussion.
- view field collection data sheets to understand how organized data sheets can be constructed.
- design a spreadsheet data table for transcription of field collected data using good data management practices.
- view NEON small mammal trapping data to a) see a standardized spreadsheet data table and b) see what data are collected during NEON small mammal trapping.
- use Microsoft Excel or Google Sheets to conduct a simple Lincoln-Peterson Mark-Recapture analysis to estimate plot level species population abundance.

Please note that this lesson was developed while the NEON project was still in construction. There may be future changes to the format of collected and downloaded data. If using data directly from the NEON Data Portal instead of using the data sets accompanying this lesson, we recommend testing out the data each year prior to implementing this lesson in the classroom.

This module was originally taught starting with a field component where students accompanied NEON technicians during the small mammal trapping. As this is not a possibility for most courses, the initial part of the lesson has been modified to include optional videos that instructors can use to show how small mammal trapping is conducted. Instructors are also encouraged to bring small mammal traps and small mammal specimens into the classroom where available.

The Data Sets

The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.

The following datasets are posted for educational purposes only. Data for research purposes should be obtained directly from the National Ecological Observatory Network (www.neonscience.org).

Data Citation: National Ecological Observatory Network. 2017. Data Product: NEON.DP1.10072.001. Provisional data downloaded from http://data.neonscience.org. Battelle, Boulder, CO, USA

Notes
Version 2.1: Includes correct Lincoln-Peterson Index formula in PPT, faculty, and student notes.

Version 2.0: This version of the teaching module was published in Teaching Issues and Experiments in Ecology. McNeil and Jones 2018. This version reflects updates based on comments from reviewers.

Version 1.0: This version of the teaching module was prepared as part of the 2017 DIG FMN. It was submitted for publication as part of the DIG Special Issue of TIEE.

Cite this work
Researchers should cite this work as follows:

Jim McNeil, Megan A. Jones (2018). Data Management using National Ecological Observatory Network's (NEON) Small Mammal Data with Accompanying Lesson on Mark Recapture Analysis. NEON - National Ecological Observatory Network, (Version 2.1). QUBES Educational Resources. doi:10.25334/Q4M121

The laboratory notebook is the cornerstone of any laboratory course. Students develop critical thinking, documentation, and communication skills while mastering scientific concepts. Furthermore, the use of electronic lab notebooks (ELNs) for documentation has become the standard for data management in industry and academic labs.

Replacing paper with an interactive research notebook provides students with authentic data management skills. It also offers a medium for easily incorporating quantitative reasoning into the curriculum to address real. Instructors using digital notebooks in their courses reported a significant increase in student engagement and assessment scores.

In this workshop, we will explore the current ELNs landscape and best practices for moving from paper to digital.

Cite this work
Researchers should cite this work as follows:

Stringer, N. (2019). Electronic Lab Notebooks: Options for Building Data Management and Quantitative Reasoning Skills. Evolution of Data in the Classroom: From Data to Data Science (SW 2019), QUBES Educational Resources. doi:10.25334/B1NP-B249

Description
This lesson introduces students to working with metadata, which can be broadly thought of as the data ABOUT existing data. Data isn’t complete without metadata, and this lesson will help students understand both how to work with metadata and how to create their own.

Data used: NEON aquatic macroinvertebrate datasets from multiple stations. It could be adapted to use any data sets or taxonomic groups though.

Activities: The lesson involves three major activities. 1) Querying and downloading datasets and corresponding products from NEON. 2) Reading and answering comprehension questions about metadata files that correspond with data files 3) Combining two datasets based off understanding the metadata in exercise 2 (e.g. understanding which columns indicate sampling dates and in which formats will allow them to appropriately combine multiple data sets).

Programs: No specific programming skills or language is required for this lesson. This lesson is designed to be done entirely in common office/student software programs (e.g. Microsoft Word and Microsoft Office) and could be done using online programs (e.g. my university has student licenses for Google Spreadsheets and Google Docs).

Learning objectives:

1 – Students will be able to define ‘metadata’ and understand how metadata is critical for reproducible research.

2 – Students will be able to correctly answer comprehension questions about a metadata file.

3 – Students will be able to apply their understanding of the metadata file to create a new data file from two data sets.

4 – Students will understand the importance of creating and understanding metadata to go along with datasets.

Timing: This lesson was designed to take place in two – 75 minute class periods that are in a workshop format. This lesson could easily be part of a longer lab, homework, or a remote / online / asynchronous assignment.

Notes
This version is current as of Spring 2019 and was classroom taught. I encourage folks to adapt, modify, and make new versions.

Cite this work
Researchers should cite this work as follows:

Whitney, K. S. (2019). Introduction to Data Management and Metadata using NEON aquatic macroinvertebrate data. NEON Faculty Mentoring Network, QUBES Educational Resources. doi:10.25334/SJX1-F373

The School of Data aims to make your learning experience as tailored as possible through independent learning modules. Learning modules are all stand-alone and can be taken in any order. To make your learning experience easier, we curated modules into a series of courses - with a focus on data basics as well as specific skills. When you identified the course you're interested in click on "Show Modules" to see all modules you might want to take.

Open Research Action Plan, Data Sharing, Open Access, Open Code & Software, Open Resarch Awards, Preprints, Preregistration & Registered Reports

This Github repository contains curriculum and materials for courses and workshops taught through the University of Miami Libraries.

If you are not looking for the repository, but simply the curriculum materials, please see the hosted version: https://umiamilibraries.github.io/courses-and-workshops/.

The repository was started through the Data Curation Initiative (http://library.miami.edu/datacuration) at the University of Miami Libraries (http://library.miami.edu).

The repository was created by Tim Norris with help and inspiriation from many others including Elizabeth Fish, Angela Clark, and all the students, faculty, and staff who have participated in the seminar.

These six interactive modules help researchers, data stewards, managers and the public gain an understanding of the value of data management in science and provide best practices to perform good data management within their organization.

Acknowledgments
The USGS Data Management Training modules were funded by the USGS Community for Data Integration and the USGS Office of Organizational and Employee Development's Technology Enabled Learning Program in collaboration with Bureau of Land Management, California Digital Library, and Oak Ridge National Laboratory. Special thanks to Jeffrey Morisette, Dept. of the Interior North Central Climate Science Center; Janice Gordon, USGS Science Analytics and Synthesis; National Indian Programs Training Center; and Keith Kirk, USGS Office of Science Quality Information.

Cite: U.S. Geological Survey, 2021, USGS Data Management Website: U.S. Geological Survey, https://doi.org/10.5066/F7MW2G15.

The Data Management and Sharing Plan (DMSP) Tool, or DMPTool, is a free resource for anyone to use that helps researchers create data management sharing plans as they write their funding proposal.

By the end of this tutorial, you will be able to:
- Log in to the DMPTool as an institutional affiliate.
- Access and use existing data management plans and templates.
- Identify project details for your plan that meet funder and institutional guidelines.
- Identify research outputs needed to meet funder and institutional guidelines.
- Request expert feedback for your plan.

Recall the steps to save, download, and submit your plan to your Research Administrator and submit updates as your project progresses.

Openly accessible online training materials which can be shared and repurposed for RDM training. All contributions in any language are welcome.

Curated by: LauraMolloy

Curation policy: We accept submissions of openly available online RDM training materials which can be re-used by others either in a class environment or for self-teaching. We do not accept irrelevant material, material that is not specifically a learning resource, or material that is licensed in such a way that inhibits reuse without fee. Submissions should clearly specify authoring information if CC-BY is used, and should clearly indicate topic areas, language and any other information that will help users to find appropriate learning resources.

Created: August 14, 2015

Data Carpentry for Biologists is a set of teaching materials for teaching biologists how to work with data through programming, database management and computing more generally.

This repository contains the complete teaching materials (excluding exams and answers to assignments) and website for a university style and self-guided course teaching computational data skills to biologists. The course is designed to work primarily as a flipped classroom, with students reading and viewing videos before coming to class and then spending the bulk of class time working on exercises with the teacher answering questions and demoing the concepts.

More information can be found on the project's GitHub page: https://github.com/datacarpentry/semester-biology/tree/v4.1.0

DMP Bingo was developed as a hands-on activity for an introductory level data management workshop
for graduate students, faculty, and staff. The activity was designed as a way to include a wide variety of
participants at different stages of their career and with different data and grant proposal experience
levels. The activity is usually preceded by a slideshow/discussion that covers the basics of data
management planning and the purpose of a Data Management Plan (DMP) and followed by a short
discussion.