All Learning Resources
RDMRose Learning Materials
RDMRose is a JISC funded project to produce taught and continuing professional development (CPD) learning materials in Research Data Management (RDM) tailored for Information professionals.
Do-It-Yourself Research Data Management Training Kit for Librarians
During autumn and winter 2012-13, data librarians at the University of Edinburgh (Robin Rice and Anne Donnelly) led a pilot course for four University academic service librarians on Research Data Management (RDM) covering five topics involving reading assigments from the MANTRA course, reflective writing, and 2-hour face-to-face training sessions, including group exercises from the UK Data Archive (UKDA). The course was deemed successful by participants and Information Services managers, and was delivered to all the University's academic service librarians.
Here we share our training for small groups of librarians anywhere who wish to gain confidence and understanding of research data management. The DIY Training Kit is designed to contain everything needed to complete a similar training course on your own (in small groups) and is based on open educational materials.
The materials have recently been enhanced with Data Curation Profiles and reflective questions based on the experience of academic librarians who have taken the course.
Users are welcome to apply their own creativity to reshape the course as they wish. For example, there are an abundance of group exercises available from the UKDA training resources pack, many of which are not included here. However, we acknowledge that the time commitment required for professional development activity such as this is significant for busy professionals, and many will appreciate the 'out-of-the-box' readiness of the training as we provide in the kit.
Digital Curation 101 Materials
Digital Curation 101 employs the curation lifecycle model sections as a means of presenting content to students by means of the curricula materials on this website. The model enables granular functionality to be mapped against it: to define roles and responsibilities and build a framework of standards and technologies to implement. The model describes digital curation in the following stages: Conceptualisation, Create and or Receive, Appraise and Select, Ingest, Preservation Action, Store, Access and Reuse.
It can be used to help identify additional steps that may be required – or actions not required by certain situations or disciplines – and to ensure that processes and policies are adequately documented.
The DCC is keen to support the reuse of our generic training materials as the basis of more specific training aimed at different disciplines and/or institutions. Our generic materials are accessible for review and tailoring.
We kindly request that you cite these materials in any derivatives that you develop and encourage you to share your tailored materials with us so that we can disseminate them to a wider audience. Archived versions of this curriculum are available from the main website.
DataONE Data Management Module 02: Data Sharing
When first sharing research data, researchers often raise questions about the value, benefits, and mechanisms for sharing. Many stakeholders and interested parties, such as funding agencies, communities, other researchers, or members of the public may be interested in research, results and related data. This 30-40 minute lesson addresses data sharing in the context of the data life cycle, the value of sharing data, concerns about sharing data, and methods and best practices for sharing data and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 01: Why Data Management
As rapidly changing technology enables researchers to collect large, complex datasets with relative ease, the need to effectively manage these data increases in kind. This is the first lesson in a series of education modules intended to provide a broad overview of various topics related to research data management. This 30-40 minute module covers trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data life cycle and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
ETD+ Toolkit: Training Students to manage ETD+ research outputs
The ETD+ Toolkit is a Google Drive Open Curriculum package that is an approach to improving student and faculty research output management. Focusing on the Electronic Thesis and Dissertation (ETD) as a mile-marker in a student’s research trajectory, it provides in-time advice to students and faculty about avoiding common digital loss scenarios for the ETD and all of its affiliated files.
The ETD+ Toolkit provides free introductory training resources on crucial data curation and digital longevity techniques. It has been designed as a training series to help students and faculty identify and offset risks and threats to their digital research footprints.
What it is:
An open set of six modules and evaluation instruments that prepare students to create, store, and maintain their research outputs on durable devices and in durable formats. Each is designed to stand alone; they may also be used as a series.
What each module includes:
Each module includes Learning Objectives, a one-page Handout, a Guidance Brief, a Slideshow with full presenter notes, and an evaluation Survey. Each module is released under a CC-BY license and all elements are openly editable to make reuse as easy as possible.
Support for Data Management
Research Data Services is a collaboration between the University of Iowa Libraries, the Office of the Vice President of Research and Economic Development, Information Technology Services, and other campus offices, to support researchers' data management needs. This guide is summary of the services and resources available on this campus as well as external tools, websites, and repositories that may be useful.
Guidelines for Effective Data Management Plans
Data Management Plans
Federal funding agencies are increasingly recommending or requiring formal data management plans with all grant applications. To help researchers meet those requirements, ICPSR offers these guidelines. Based on our Data Management Plan Web site, this document contains a framework, example data management plans, links to other resources, and a bibliography of related publications. ICPSR also hosts a blog on data management plans, and a recent webinar on the subject can be viewed on our Web site. We hope you find this information helpful as you craft a data management plan. Please contact us at firstname.lastname@example.org with any comments or suggestions.
Introduction to R
In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. Topics include: an introduction to basics, vectors, matrices, factors, lists and data forms. Approximately 62 exercises are included.
Intro to Python for Data Science
Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses. Topics covered include: Python basics, Python lists, functions and packages, and NumPy, an array package for Python.
Using, learning, teaching, and programming with the Paleobiology Database
The Paleobiology Database is a public database of paleontological data that anyone can use, maintained by an international non-governmental group of paleontologists. You can explore the data online in the Navigator, which lets you filter fossil occurrences by time, space, and taxonomy, and displays their modern and paleogeographic locations; or you can download the data to your own computer to do your own analyses. The educational resources offered by the Paleobiology include:
- Presentations including lectures and slide shows to introduce you to the PBDB
- Web apps that provide a variety of online interfaces for exploring PBDB data via the API
- Mobile apps that provide applications for iOS and Android providing new views of the PBDB's data via the API
- Lesson plans and teaching activities using the Paleobiology Database
- Tutorials on how to get and use data from the website, and on how to contribute data to the database, viewable on Youtube
- Libraries and functions for interacting with PBDB data via R
- Documentation, code examples, and issue reporting for the PBDB API
- Other Paleobiology Database related external resources including a link to the Paleobiology Github repository
For more information about the Paleobiology Database, see: https://paleobiodb.org/#/faq .
The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in the R programming language, data structure manipulations and times and dates. This R tutorial will allow you to learn R and take the next step in advancing your overall knowledge and capabilities while programming in R.