All Learning Resources

  • Best Practices in Data Collection and Management Workshop

    Ever need to help a researcher share and archive their research data? Would you know how to advise them on managing their data so it can be easily shared and re-used? This workshop will cover best practices for collecting and organizing research data related to the goal of data preservation and sharing. We will focus on best practices and tips for collecting data, including file naming, documentation/metadata, quality control, and versioning, as well as access and control/security, backup and storage, and licensing. We will discuss the library’s role in data management, and the opportunities and challenges around supporting data sharing efforts. Through case studies we will explore a typical research data scenario and propose solutions and services by the library and institutional partners. Finally, we discuss methods to stay up to date with data management related topics.

    This workshop was presented at NN/LM MAR Research Data Management Symposium: Doing It Your Way: Approaches to Research Data Management for Libraries.  Powerpoint slides are available for download.  files include a biophysics case study.

    Terms of Access:  There is 1 restricted file in this dataset which may be used;  however, you are asked not to share the Mock lab notebook. It is completely fictitious. Users may request access to files.

  • Pyunicorn Tutorials

    pyunicorn (Unified Complex Network and RecurreNce analysis toolbox) is a fully object-oriented Python package for the advanced analysis and modeling of complex networks. Above the standard measures of complex network theory such as degree, betweenness and clustering coefficient it provides some uncommon but interesting statistics like Newman’s random walk betweenness. pyunicorn features novel node-weighted (node splitting invariant) network statistics as well as measures designed for analyzing networks of interacting/interdependent networks.

    Moreover, pyunicorn allows to easily construct networks from uni- and multivariate time series data (functional (climate) networks and recurrence networks). This involves linear and nonlinear measures of time series analysis for constructing functional networks from multivariate data as well as modern techniques of nonlinear analysis of single time series like recurrence quantification analysis (RQA) and recurrence network analysis. Other introductory information about pyunicorn can be found at: .

    Tutorials for pyunicorn are designed to be self-explanatory.  Besides being online, the tutorials are also available as ipython notebooks.  For further details about the used classes and methods please refer to the API at:

  • The Agriculture Open Data Package

    he third GODAN Capacity Development Working Group webinar, supported by GODAN Action, focused on the Agriculture Open Data Package (AgPack).
    In 2016 GODAN, ODI, the Open Data Charter and OD4D developed the Agricultural Open Data Package (AgPack) to help governments to realize impact with open data in the agriculture sector and food security. 
    During the webinar the speakers outlined examples and use cases of governments using open data in support of their agricultural sector and food security. Also, the different roles a government can pick up to facilitate such a development, how open data can support government policy objectives on agriculture and food security. 

  • E-Infrastructures and Data Management Toolkit

    This online toolkit provides training and educational resources for data discovery, management, and curation across the globe, in support of an international collaborative effort to enable open access to scientific data.  Tools within the toolkit include:
    - DDOMP Researcher Guide which has resources and tips for creating a successful DDOMP (data management plan)
    - Data Management Training including webinars, courses, certifications, and literature on data management topics
    - Best Practices & Standards which provide guidelines for effective data management.
    Video tutorials about each of these tools are available at: 
    Other capacity building tools include a Data Skills Curricula Framework to enhance information management skills for data-intensive science which was developed by the Belmont Forum’s e-Infrastructures and Data Management (e-I&DM) Project to improve data literacy, security and sharing in data-intensive, transdisciplinary global change research.  More information about the curricula framework including a full report and an outline of courses important for researchers doing data-intensive research can be found at: .

  • Introduction to Research Data Management - half-day course (Oxford)

    Teaching resources for a half-day course for researchers (including postgraduate research students), giving a general overview of some major research data management topics. Included are a slideshow with presenter's notes, a key resources hand-out, and two other hand-outs for use in a practical data management planning exercise. These course materials are part of a set of resources created by the JISC Managing Research Data programme-funded DaMaRO Project at the University of Oxford. The original version of the course includes some Oxford-specific material, so delocalized versions (which omit this) of the slideshow and the key resources hand-out are also provided

  • Introduction to Humanities Research Data Management

    Reusable, machine-readable data are one pillar of Open Science (Open Scholarship). Serving this data
    reuse aspect requires from researchers to carefully document their methods and to take good care of
    their research data. Due to this paradigm shift, for Humanities and Heritage researchers, activities and
    issues around planning, organizing, storing, and sharing data and other research results and products
    play an increasing role. Therefore, during two workshop sessions, participants will dive
    into a number of topics, technologies, and methods that are connected with
    Humanities Research Data Management. The participants will acquire knowledge and skills that will
    enable them to draft their own executable research data management plan that will support the
    production of reusable, machine-readable data, a key prerequisite for conducting effective and
    sustainable projects. Topics that will be covered are theoretical reflections on the role of data within
    humanities research and cultural heritage studies, opportunities and challenges of eHumanities and
    eResearch, implementing the FAIR principles and relevant standards, and basics of Data Management
    Learning outcomes: Participants of this workshop will gain an overview about issues related to
    Humanities Research Data Management and learn about relevant tools and information resources.
    Through a hands-on session, the participants will be especially equipped and skilled to draft the nucleus
    of their own Research Data Management Plan.

  • Research data management training modules in Social Anthropology (Cambridge)

    Looking after digital data is central to good research. We all know of horror stories of people losing or deleting their entire dissertation just weeks prior to a deadline. Even before this happens, good practice in looking after research data from the beginning to the end of a project makes work and life a lot less stressful. Defined in the widest sense, digital data includes all files created or manipulated on a computer (text, images, spreadsheets, databases, etc). With publishing and archiving of research increasingly being online, we all have a responsibility to ensure the long-term preservation of our research data, while at same time being aware of issues of sensitive data, intellectual property rights, open access, and freedom of information. The DataTrain teaching materials have been designed to familiarise post-graduate students in good practice in looking after their research data. A central tenet is the importance of thinking about this in conjunction with the projected outputs and publication of research projects. This teaching package is focussed on data management for Social Anthropology.
    For each of three modules of the course, notes and powerpoint presentations are available as well a a survey model, a list of useful sofwrae, and a list os references and web-based resources as handouts.  Topics include the process of fieldwork, the kinds of data collected, and the methods for their collection.  Other topics relate to the organisation of data including basic information on file management, some practical demonstration of software tools and back-up techniques.  
    Course materials are available in a downloadable zip file.

  • Library Carpentry OpenRefine

    This Library Carpentry lesson introduces librarians to working with data in OpenRefine. At the conclusion of the lesson you will understand what the OpenRefine software does and how to use the OpenRefine software to work with data files.  This lesson is a supplement to

  • RDM Training (Herts) Module 3: Safeguarding Data

    Discussing the benefits and risks of storage media, back up systems, sharing data across the internet, and general security, this training module encourages secure storage and sharing of data whilst in the working stage of a research project. Slides and training notes are included in this pack in one collection, but can be divided into four sections; storage solutions, keep it safe - back up, sharing, and security. A lesson plan is included in the zip package of module files.
    This module is 3 of 4.  The topics of the other modules are:
    Module 1:  Project Planning: 
    Module 2:  Getting Started:
    Module 4:  Finishing Touches:

  • RDM Training (Herts) Module 2: Getting Started

    This module highlights research data management issues that should be addressed when starting a project: choosing file structures and naming conventions, file versioning, metadata and documentation, software choices, and the best practice for programming. Considering these details before data collection ensures that the data are well managed and organised, and require fewer transformations when preparing them for publication. Slides and training notes are included in this pack in one collection, but can be divided into five sections: filing systems, metadata, software, documentation, and coding.
    This module is 2 of 4.  The topics of the other modules are:
    Module 1:  Project Planning:
    Module 3:  Safeguarding Data:
    Module 4:  Finishing Touches:

  • RDM Training (Herts) Module 4: Finishing Touches

    At the end of a research project, the science is published and the data should be preserved. This final RDM module includes advice on where to publish the science outcomes and the supporting data as well as how to select the data, anonymise it, and choose the right archive for your data. Slides and training notes are included in this pack in one collection, but can be divided into four sections: publication, preserving data, anonymisation, and archiving data.
    This module is 4 of 4.  The topics of the other modules are:
    Module 1:  Project Planning:
    Module 2:  Getting Started:
    Module 3:  Safeguarding Data:

  • Data Management and Data Management Plans

    Modern research requires special tooling, software and processes that allow researchers to link, transform, visualise and interpret the data. Lack of proper data management practices can lead in extreme cases to irreversible loss of data. As a consequence, reproducibility of scientific experiments can be questioned. This in turn reduces trust in scientific findings and undermines reputation of researchers and their institutions. For this reason, excellent data management skills are nowadays an essential asset to successful researchers. This talk will introduce participants to Data Management Plans that help to plan how data is handled during experiments so that no data is lost, can easily be found, correctly interpreted using provided metadata, and properly licensed. Participants will learn about practical aspects of data management. They will also get familiar with research funder requirements for Data Management Plans that are becoming an obligatory project deliverable around the world.
    Topics covered:
    • Why good data management is important?
    • What are the Data Management Plans?
    • How to create a Data Management Plan?
    • What are the best data management practices?
    • How to make research data FAIR?
    • What are the funder requirements for Data Management Plans?
    • What does the future look like concerning data management?

  • RDM Training (Herts) Module 1: Project Planning

    This module introduces data management plans and the considerations that should be made during the planning stage of a project. After an introduction to funding body requirements and the benefits of sharing data to research, the DMPonline tool is promoted as the prefered method of completing a data management plan, as required by funding bodies. Finally, a brief breakdown of the content of data management plans and advice on completing them is included. Slides and training notes are included in this pack in one collection, but can be divided into three sections: an introduction to RDM, data management plans, and the project lifecycle, to be used separately. A lesson plan is included in the zip package of module files.

    This module is 1 of 4.  The topics of the other modules are:
    Module 2:  Getting Started:
    Module 3:  Safeguarding Data:
    Module 4:  Finishing Touches:

  • Python Intro for Libraries

    This lesson is an introduction to programming in Python for librarians with little or no previous programming experience. It uses examples that are relevant to a wide range of library use cases, and is designed to be used as a prerequisite lesson for other Python based lessons that will be developed in the future, e.g. using the Pandas for data analysis.

    This lesson references the Spyder IDE, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.  More information about this lesson can be found at: 


  • Introduction to Data Management Planning

    This slide presentation is part of a workshop offered at Riga Technical University, Riga, to research support staff as an introduction to research data managment.  The slide presentation introduces data management plans, which are often submitted as part of grant applications, but are useful whenever researchers are creating data.  See below for instructions on downloading the slides.  The presentation covers the following topics:

    • What is a data management plan (DMP)?
    • Reasons for developing a DMP
    • Horizon 2020 EU Research and Innovation program templates
    • DMP deliverables 
    • Key decisions in DMP development
    • Resources about the DMP review process
    • Example DMPs

    This PowerPoint slide presentation can be downloaded from the provided web page by clicking on "Introduction to Data Management Planning" (11:45) on the agenda. 

  • Demonstration of DMPOnline (Data Management Planning Tool)

    Slide presentation demonstration of DMPOnline is part of a workshop offered at Riga Technical University, Riga giving an introduction to research data management for research support staff.  The slide presentation is designed to help research support staff help researchers create, review, and share data management plans that meet institutional and funder requirements.  The slides can be downloaded by going to item 12:15 on the agenda.  More information can be found about the DMPonline tool at:

  • How to Customise DMPonline

    This downloadable slide presentation is part of a workshop offered at the Stratford Library and Learning Centre in 2016, and discusses what to consider when customising the Data Management Planning Tool (DMPTool) which is used to create, review, and share data management plans that meet institutional and funder requirements. See instructions for downloading the slides below.  The presentation covers:

    • The concept of guidance by theme
    • An overview of options and follow-along demo
    • Adding templates
    • Adding guidance
    • Customising funder templates

    This PowerPoint slide presentation can be downloaded from the provided web page by clicking on "How to customise DMPonline" (10:00) on the agenda. More information about the DMPonline tool can be found at:

  • Demonstration of Customising DMPonline

    This slide presentation is part of a workshop offered at the Stratford Library and Learning Centre, and provides a practical lab exercise for using the administrative interface to customise the DMPonline tool.  More information about the DMPonline tool can be found at:

  • Introduction to Research Data Management

    This slide presentation is part of a workshop presented at the Library of Birmingham, Birmingham U.K., and provides an introduction to the research data management landscape, data sharing, and data management planning.
    This PowerPoint slide presentation can be downloaded from the provided web page by clicking on "Introduction to Research Data Management" (10:10) on the agenda.

  • 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.

  • RDM for Librarians

    This is an introductory research data management (RDM) presentation for librarians. PowerPoint slides are available for download at the provided URL. The course covers:

    • Research data and RDM
    • Data management planning
    • Data sharing
    • Skills
    • RDM at University of Northampton

    An RDM for librarians handbook is also available at the provided URL.

  • "I'm leaving you... my data!" -- Practical Research Data Sharing Within Your Institution and the Wider Community

    This slide presentation discusses recent developments in research data management (RDM) practices in response to *Horizon 2020, United Kingdom's Engineering and Physical Science Research Council (EPSRC), Research Councils UK (RCUK), and institutional University of Southampton policy.
    Topics include:

    • Research, data, and repositories
    • European, national, and institutional policy
    • Research Data Alliance - workflows for data publishing
    • Identifiers and data citation
    • Force 11 data citation principles
    • DataCite and digital object identifiers (DOIs)
    • Linking data and publications
    • Scenarios exploring data management concepts and processes
    • How to get researchers' attention
    • Research costing
    • Active data sharing
    • Timeline for implementing institutional data management
    • Biomedical research software as a service (BRISSkit) overview

    *About Horizon 2020 (from​):By coupling research and innovation, Horizon 2020 is helping to achieve this with its emphasis on excellent science, industrial leadership and tackling societal challenges. The goal is to ensure Europe produces world-class science, removes barriers to innovation and makes it easier for the public and private sectors to work together in delivering innovation.

  • Analyzing DMPs to Inform Research Data Services

    Presentation about lessons learned from the DART project, which developed an analytic rubric to standardize the review of data management plans as a means to inform targeted expansion or development of research data services at academic libraries. 

  • Research Data Management and Integrating an Electronic Lab Notebook (ELN) with a University Research Infrastructure

    Two slide presentations:
    1. An overview of University of Edinburgh research data management policy and implementation
    2. Integrating an Electronic Lab Notebook (ELN) with a University Research Infrastructure: Case Study with Rspace at the University of Edinburgh, which includes:

    • Where demand for ELNs is coming from
    • RSpace - origins and overview
    • RSpace at Edinburgh - linking to files and depositing content in Edinburgh DataStore and archiving in Edinburgh DataVault
    • Platform for integration with other research data management infrastructures


  • Project TIER - Demo Project

    Project TIER (Teaching Integrity in Empirical Research) promotes the integration of principles and practices related to transparency and replicability in the research training of social scientists. 

    The demo project available below consists of a "hypothetical" research paper, accompanied by complete replication documentation that meets the standards of the TIER Protocol Specifications.  The paper is "hypothetical" in the sense that it was prepared to provide a brief and user-friendly example of TIER replication documentation, rather than to report on substantive research.  Nonetheless, the results presented in the paper were generated from real data, and the documentation can be used to replicate the data processing and analysis that produced the results.

    We suggest you explore this demo project in tandem with the TIER Protocol Specifications (located at: the Specifications give general descriptions of all the components that should be included in the replication documentation for a paper; the demo project provides concrete examples of these components.

  • University of Oxford - Research Data Management Training Materials

    The Data Management Rollout at Oxford (DaMaRO) Project created a research data management policy for the University and the infrastructure to enable researchers to comply with it.

    In spring and summer 2013, the DaMaRO Project ran a series of face-to-face training events, aimed chiefly at postgraduate research students and early career researchers. The final versions of the teaching materials from these events are available at this resources website. In addition, there are key resources handouts, case studies in the humanities, social sciences and physical sciences, a list of 20 questions to ask regarding research data management, and the top ten things researchers need to know about research data management.  Slides are available for the training events.    

  • LYRASIS Online Classes

    This webpage lists the courses, both free and with a fee, on various subjects related to the treatment and management of data. Examples of courses include Dublin Core Metadata, Instructional Design for Librarians, Introduction to Copyright for Digitization, and many others.  The topics vary over time.  

  • DataONE Data Management Module 04: Data Entry and Manipulation

    When entering data, common goals include: creating data sets that are valid, have gone through an established process to ensure quality, are organized, and reusable. This lesson outlines best practices for creating data files. It will detail options for data entry and integration, and provide examples of processes used for data cleaning, organization and manipulation and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise, handout, and supporting data files.

  • School of Data - Data Fundamentals

    The key skills to understand, manage and work with data. This webpage contains links to modules on Data Fundementals, Data Cleaning, Exporing Data, Extracting Data, Mapping, Collecting Data, and Presenting Data. Each topic area contains a number of modules within it.  Modules may contain links to video presentations, tasks of various lengths, quizzes and evaluation opportunities.

    School of Data is a global network committed to advancing data literacy in civil society. Information that directly impact people’s lives is increasingly accessible but civil society is falling behind in making effective use of it. Through our global network of data literacy practitioners and trainers, School of Data seeks to address this data skills gaps in order to amplify the messages of civil society through the use of data. We level the playing field by ensuring that civil society organisations and newsrooms have the knowledge, resources and tools they need to participate fully in the information age.

    School of Data is a network of data literacy practitioners composed of organisations and individuals. Together, we implement an array of data literacy programmes in our respective countries and regions. Members of School of Data network work to support civil society organizations (CSOs), journalists, and citizens to engage with and use data in their efforts to create better, more equitable and more sustainable societies. Over the past several years, School of Data has succeeded in developing and sustaining a thriving network of data literacy practitioners across Europe, Latin America, Asia and Africa.  Find out more information about School of Data at: .

  • Finding stories in data

    When effectively analysed and presented in a clear and compelling way, data has the potential to create impact. At the heart of driving change is the skill of finding and telling stories using, where relevant, compelling visualisations.

    No field is more experienced at finding and telling stories than journalism, and no field better at using data than data science. This set of modules looks at what these fields can learn from each other, in order to find and tell compelling stories with data.  Modules include mini quizzes, videos many visual aids, and opportunities to monitor your progress in completing the modules, and providing feedback.   Data module topics are:
    1. Introduction to data storytelling
    2. The four step process
    3. Understanding your rights to use data
    4. Gathering data
    5. Organising data
    6. Cleaning data
    7. Filtering and pivot tables
    8. Data visualisation formats
    9. Data visualisation best practice
    10. Visual deception
    11. Narrating your story

  • Open data essentials

    Welcome to the Open Data Institute's e-Learning programme developed for the European Commission. This programme has been designed to enable you to discover what open data is and how it is changing the lives of everyone on our planet. There are 13 lessons for you to explore covering the essentials of open data, how to plan and measure success and how to implement an open data programme technically. This programme is free and should take a maximum of 2-3 hours to complete. 

    Modules include mini quizzes, videos many visual aids, and opportunities to monitor your progress in completing the modules, and providing feedback. Module topics include:
    - What is open data?
    - Unlocking value from open data
    - Open data:  agent of change
    - Why do we need to license?
    - What makes quality open data
    - Measuring success for open data
    - Why should we worry about sustainability?
    - Gettting to grips with platforms
    - Choosing the right format for open data
    - How useful is my data
    - How to clean your data
    - finding hidden data on the Web
    - Linking up the web of data


  • Author Skills

    The Author Skills modules engage you in a series of readings, hands-on activities and/or group discussions along with assignments designed to help you address many issues in the scholarly publishing process.  How to read, write and publish research outcomes are some of the skills this training will help you develop. Join us to become more effective writers… and better researchers. The completion of the course will take 5-7 hours of instruction depending on the number of exercises finished. Topics include:

    How to Read a Scientific Paper
    An overview of the key activities needed to properly read and understand a paper. It discusses the types of scientific papers, organization of a paper, actions to take – to properly read a paper – and difficulties in reading scientific papers.

    How to Write a Scientific Paper
    Discusses key elements of publishing (ethical issues, style and language, structure and components of a paper including structured abstracts, the article submission process including peer review and author vs. reader priorities. It also includes an Appendix that summarizes the publishing process from a low-income country author’s perspective. The extensive exercises include assignments to write a structured abstract, designate keywords and, from abstracts of articles, decide which journal an article should be submitted to.

    Intellectual Property, Copyright, and Plagiarism 
    Reviews the definition of intellectual property and how it links to copyright and plagiarism. Discusses copyright and plagiarism giving an overview of the concepts providing guidelines and resources. Finally the exercises review the material covered and provide questions to identify plagiarism in several documents.

    Strategies for Effective Writing 
    Discusses the integrated topics of using concrete words and building forceful sentences and reviews the writing processes of editing & proofreading. It includes examples on how to write more effectively.

    Web Bibliography
    An annotated list of links to WWW based, full-text information on how to conduct ethical research, read and write a scientific paper, write a structured abstract, prepare manuscripts for submission and write footnotes and bibliographies. These links give the participants valuable resources that are available on the Internet.

    Reference management tools 
    Reference management tools help scholars to create and manage their lists of references for research projects. Most tools are designed to organize citations into specific formats. Our trainng will teach you how to use two of the most used reference management tools: Mendeley and Zotero.

    Research4Life is the collective name for five programmes – Hinari, AGORA, OARE, ARDI and GOALI – that provide developing countries with free or low-cost access to academic and professional peer-reviewed content online.  Find out more about Research4Life partners and programs at: .

  • Data Tree

    Data Tree is a free online course with all you need to know for research data management, along with ways to engage and share data with business, policymakers, media and the wider public.

    The self-paced training course will take 15 to 20 hours to complete in eight structured modules. The course is packed with video, quizzes and real-life examples of data management, along with valuable tips from experts in data management, data sharing and science communication.

    The training course materials will be available for structured learning, but also to dip into for immediate problem solving.

    The course is for researchers, scientists and anyone working with data.

    The course is especially aimed at postgraduates, PhD students and early career researchers who want to learn research data management skills, but it is for anyone who wants to get the right data habits now, including thinking of end-users of your data.

    Modules include:
    1. Data Management:  Context
    2. Data Management:  Practicalities
    3. Data Management: NERC
    4. Data Application:  Analysis
    5. Data Application:  Visualisation
    6. Data & Research:  Working with Policy
    7. Data & Research:  Working with Business
    8. Data & Research:  Working with the Media & Public

  • United Nations Online Access to Research in Environment (UN OARE) Training Materials

    Here you can find training modules on information management training topics that help you learn not only how to open journals and download full-text articles from the OARE website, but also how to use OARE’s search databases to find articles about specific topics in thousands of scientific journals from major publishers around the world.  Topics include:  searching strategies for finding scientific research using environmental issues, accessing full-text articles, e-journals, e-books, and other internat resources such as indexes for searching EBSCO, SCOPUS (Elsevier), environmental gateways and other portals.  Downloadable powerpoint slides are available for each topic along with a workbook for most of the modules.  

  • Catmandu - a (meta)data toolkit

    Catmandu provides a suite of software modules to ease the import, storage, retrieval, export and transformation of (meta)data records. After a short introduction to Catmandu and its features, we will present the command line interface (CLI) and the domain specific language (DSL). Participants will be guided to get data from different sources via APIs, to transform data records to a common data model, to store/index it in Elasticsearch or MongoDB, to query data from stores and to export it to different formats. The intended audience is Systems librarians, Metadata librarians, and Data managers. Participants should be familiar with command line interfaces (CLI). Programming experience is not required.  Exercises in using the tool and functions are included in the PDF version of the slides presented.
    These materials were presented at the Code4Lib conference (code4lib), Philadelphia, 7-10 March 2016.

  • Digital Preservation for Researchers - teaching modules (Cambridge)

    The JISC Managing Research Data Programme-funded PrePARe project ran from 1 Nov 2011 to 31 July 2012.  It aimed to encourage researchers to take an interest and responsibility in digital preservation of their research outputs. Training materials in digital preservation have been designed to slot into existing training courses on related areas, such as Information Literacy and reference management. Five short modules provide an introduction to why digital preservation matters, and cover the issues of safe storage of digital materials, documentation and metadata, data sharing and re-use and data management planning, to think about these issues early in the research process. The resources consist of a series of slides for each module, and a guide on how the resources can be used, including suggested discussion questions and script for use with the slides.

  • Dataverse for the Canadian Research Community: Developing reusable and scalable tools for data deposit, curation, and sharing

    Presentation on Dataverse at the Research Data Management Workshop 2019 (RDM Workshop 2019), Ottawa, Canada, January 23, 2019. 
    Topics Include:

    • What is Dataverse
    • Scholars Portal Dataverse
    • Platform Features
    • CANARIE Project Goals and Deliverables
    • Roadmap
  • Research Data Management: Simple Ways to Make your Research Life Easier

    This presentation provides concepts related to Research Data Management  with context and examples from the life sciences.  The presentation has a number of versions as it has been offered as a part of the CalTech Data Analysis in the Biological Sciences BE/Bi 103 course. This link goes to v1.4 presented in October 2018.  

  • First steps in Open Science (15 min mini training)

    This presentation "First steps in Open Science" is the result of our group activity at the Foster Open Science Trainer Bootcamp, 19.04.2018, Barcelona.
    It is a “15 min minitraining" including a participatory activity. Your are welcome to use this activity in your training activities. Please read the slide notes for context and instructions.

  • FSCI-AM7: DataViz in R with ggplot2

    This is an introduction to creating figures in R using the package ggplot2. We will show how to read data into R using a few sources, including the data repository Dataverse. We then will introduce ggplot2, which we think provides an elegant and lego-like way to provide an introduction to dataviz. It implements a grammar of graphics that lets users layer graphical elements in a modular and adaptable way.
    This material was taught at the  FORCE11 Scholarly Communication Institute (FSCI), La Jolla, CA, 31 July - 04 August (Session AM7).  The material is a Sspplement to