You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Science principles along with practices for open data management and open data sharing.

Since research increasingly relies on software which is used to model and simulate, and to deal with the ever growing volume of research data, the course will also introduce FAIR software practices.

You'll learn to establish links between publications, data, software and methods, how to attach a persistent identifier and metadata to your results, and methods for clarifying usage rights. You will also discover ways to apply these principles to your daily research and adapt existing routines. Finally, you'll uncover potential barriers to sharing research and discuss possible solutions.

This course will help you grasp the key principles of Open Science, with answers to questions like:

How can researchers effectively store, manage, and share research data?
What kinds of open access publishing are most effective?
How can researchers increase the visibility and impact of their research?
How can the use of social media contribute to the visibility and impact of research?
How can researchers be acknowledged for the research software they write?
You will apply the topics of the course to a variety of case studies on Open Science adoption, which you will then discuss among fellow students. You will also be presented with a hands-on guide to publishing your research with open access. This will help you to apply Open Science principles in your daily work. It will enable you to implement and benefit from the Open Science policies that are currently being developed by governments and research institutions.

This course is aimed at professionals. Those who will see the most benefit include academic researchers at different levels: PhD students, postdoctoral researchers, and professors; researchers working for governments; researchers working for commercial enterprises; MSc and BSc students interested to learn about the principles of Open Science.

The development of this course is supported by the VRE4EIC project with funding from the European Union's Horizon 2020 Research and Innovation Programme.

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.

The Research Data Curation and Management Bibliography includes over 800 selected English-language articles and books that are useful in understanding the curation of digital research data in academic and other research institutions. It covers topics such as research data creation, acquisition, metadata, provenance, repositories, management, policies, support services, funding agency requirements, open access, peer review, publication, citation, sharing, reuse, and preservation. Most sources have been published from January 2009 through December 2019; however, a limited number of earlier key sources are also included. The bibliography has links to included works. Abstracts are included in this bibliography if a work is under certain Creative Commons Attribution licenses. It is available as a 250-page PDF or a website.

The purpose of this resource is explain Research Data Management (RDM) in a data intensive research landscape.

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 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

Simmons University and academic health sciences libraries across the USA are partnering to offer a post-master’s certificate program in the area of Inter-Professional Informationist (IPI), for the purpose of bridging the gap between traditional and emergent skills in health sciences librarianship and increasing the diversity in the workforce. A small cohort of librarians in the program will complete seven IPI courses, and partner institutions will connect them with researchers and clinical leaders who will mentor their capstone. This project was made possible in part by the Institute of Museum and Library Services with the Laura Bush 21st Century Librarian Program Grant [RE-17-19-0032-19]. Simmons University, School of Library and Information Science, College of Organizational, Computational and Information Science provides cost-share of the project.

One of the courses included in the IPI program is “Scientific Research Data Management” was taught Fall 2020 by Elaine Martin and Julie Goldman. This course had been an elective in the Simmons School of Library and Information Science curriculum for many years, but underwent a redesign to include and address many of the newer emerging areas related to data services in libraries. For example, the course included “Special Topics” that included Data Curation, Data Skills, Reproducibility, and Informationists. While basic understanding of data management is critical for librarians to work with researchers, there are these emerging areas where librarians can provide even more specialized help to their communities. It is one of the IPI’s project’s goals to bridge the gap between traditional and emergent skills in health sciences librarianship.

This Open Science Framework project site includes curriculum materials for Simmons IPI LIS-532U-OL Scientific Research Data Management (course offered Fall 2020). This course serves as an introduction to the field of scientific data management, and aims to help prepare information professionals and information students for engaging with scientists.

This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have been written on questions of data sharing, data provenance, research reproducibility, licensing, attribution, privacy, and more—but our goal here is not to review that literature. Instead, we present a short guide intended for researchers who want to know why it is important to “care for and feed” data, with some practical advice on how to do that. The final section at the close of this work (Links to Useful Resources) offers links to the types of services referred to throughout the text.

A class covering the basics of writing a successful data management plan for federal funding agencies such as the NEH, NSF, NIH, NASA, and others.

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