All Learning Resources

  • Plan, a chapter of the CESSDA Expert Tour Guide on Data Management

    This introductory chapter features a brief introduction to research data management and data management planning.
    Before we get you started on making your own Data Management Plan (DMP), we will guide you through the concepts which provide the basic knowledge for the rest of your travels. Research data, social science data and FAIR data are some of the concepts you will pass by.
    After completing your travels through this chapter you should be:
    Familiar with concepts such as (sensitive) personal data and FAIR principles;
    Aware of what data management and a data management plan (DMP) is and why it is important;
    Familiar with the content elements that make up a DMP;
    Able to answer the DMP questions which are listed at the end of this chapter and adapt your own DMP.

  • Organise & Document, a chapter of the CESSDA Expert Tour Guide on Data Management

    In this chapter, we provide you with tips and tricks on how to properly organise and document your data and metadata.
    We begin with discussing good practices in designing an appropriate data file structure, file naming and organising your data within suitable folder structures. You will find out how the way you organise your data facilitates orientation in the data file, contributes to understanding the information contained and helps to prevent errors and misinterpretations.
    In addition, we will focus on an appropriate documentation of your data. Development of rich metadata is required by FAIR data principles and any other current standards promoting data sharing.
    After completing your travels through this chapter on organising and documenting your data you should:
    Be aware of the elements which are important in setting up an appropriate structure and organisation of your data for intended research work and data sharing;
    Have an overview of best practices in file naming and organising your data files in a well-structured and unambiguous folder structure;
    Understand how comprehensive data documentation and metadata increases the chance your data are correctly understood and discovered;
    Be aware of common metadata standards and their value;
    Be able to answer the DMP questions which are listed at the end of this chapter and adapt your own DMP.

  • Process, a chapter of the CESSDA Expert Tour on Data Management

    In this chapter we focus on data operations needed to prepare your data files for analysis and data sharing. Throughout the different phases of your project, your data files will be edited numerous times. During this process it is crucial to maintain the authenticity of research information contained in the data and prevent it from loss or deterioration.
    However, we will start with the topics of data entry and coding as the first steps of your work with your data files. Finally, you will learn about the importance of a comprehensive approach to data quality.
    After completing your travels through this chapter you should:
    Be familiar with strategies to minimise errors during the processes of data entry and data coding;
    Understand why the choice of file format should be planned carefully;
    Be able to manage the integrity and authenticity of your data during the research process;
    Understand the importance of a systematic approach to data quality;
    Able to answer the DMP questions which are listed at the end of this chapter and adapt your own DMP.

  • Store, a chapter of the CESSDA Expert Tour on Data Management

    The data that you collect, organise, prepare, and analyse to answer your research questions, and the documentation describing it are the lifeblood of your research. Put bluntly: without data, there is no research. It is therefore essential that you take adequate measures to protect your data against accidental loss and against unauthorised manipulation.
    Particularly when collecting (sensitive) personal data it is necessary to ensure that these data can only be accessed by those authorized to do so. In this chapter, you will learn more about measures to help you address these threats.
    After completing your travels through this chapter you should:
    Be familiar with strategies to minimise errors during the processes of data entry and data coding;
    Understand why the choice of file format should be planned carefully;
    Be able to manage the integrity and authenticity of your data during the research process;
    Understand the importance of a systematic approach to data quality;
    Able to answer the DMP questions which are listed at the end of this chapter and adapt your own DMP.

  • Protect, a chapter of the CESSDA Expert Tour on Data Management

    This part of the tour guide focuses on key legal and ethical considerations in creating shareable data.
    We begin with clarifying the different legal requirements of Member States, and the impact of the upcoming General Data Protection Regulation (GDPR) on research data management. Subsequently, we will show you how sharing personal data can often be accomplished by using a combination of obtaining informed consent, data anonymisation and regulating data access. Also, the supporting role of ethical review in managing your legal and ethical obligations is highlighted.
    After completing your trips around this chapter you should:
    Be aware of your legal and ethical obligations towards participants and be informed of the different legal requirements of Member States;
    Understand how well-protecting your data, protects you against violating laws and promises made to participants;
    Understand the impact of the upcoming General Data Protection Regulation (GDPR; European Union, 2016);
    Understand how a combination of informed consent, anonymisation and access controls allows you to create shareable personal data;
    Be able to define what elements should be integrated into a consent form;
    Be able to apply anonymisation techniques to your data;
    Be able to answer the DMP questions which are listed at the end of this chapter and adapt your own DMP.

  • Archive & Publish, a chapter of the CESSDA Expert Tour on Data Management

    High-quality data have the potential to be reused in many ways. Archiving and publishing your data properly will enable both your future self as well as future others to get the most out of your data.
    In this chapter, we venture into the landscape of research data archiving and publication. We will guide you in making an informed decision on where to archive and publish your data in such a way that others can properly access, understand, use and cite them.
    Understand the difference between data archiving and data publishing;
    Be aware of the benefits of data publishing;
    Be able to differentiate between different data publication services (data journal, self-archiving, a data repository);
    Be able to select a data repository which fits your research data's needs;
    Be aware of ways to promote your research data publication;
    Be able to answer the DMP questions which are listed at the end of this chapter and adapt your own DMP.

  • Research Data Management Hands on Workshop

    Description: This project includes material designed for teaching a 1.5 hour research data management workshop. It involves a case study that requires workshop participants to navigate messy data to identify the data that corresponds with the data represented in a figure from an article. Workshop attendees are then required to modify the messy data to follow research data management best practices.

  • Data Services: Data Management Classes

    This guide provides information on managing data and obtaining secondary data for research. This site includes videos on writing a data management plan, data management best practices, and links to tool and data sources. 

  • Database Administration Courses

    If your job involves database administration, monitoring, maintenance, security, upgrading, configuration or installation, you've come to the right place! Our demo-rich database administration courses give you get practical guidance on the whole set of admin activities, straight from our experts. Best of all, because our courses are free and available on demand, you can get the database administration training you need on your schedule.

  • Penn State Online: Introduction to GIS modeling and Python

    This unit is Lesson 1 of the online course, GEOG 485: GIS Programming and Software Development at PennState University's College of Earth and Mineral Sciences.
    As with GEOG 483 and GEOG 484, the lessons in this course are project-based with key concepts embedded within. However, because of the nature of computer programming, there is no way this course can follow the step-by-step instruction design of the previous courses. You will probably find the course to be more challenging than the others.

  • Ag Data Commons Monthly Webinar Series

    Each month the Ag Data Commons offers a webinar with topics ranging from introduction for new users to topics with a data management or curation focus. We also leave time for organized question and answer periods. To join us for any of the upcoming webinars, you can email NAL-ADC-Curator@ars.usda.gov and we will mail the join information to you for upcoming webinars. You can also check the news section for the next webinar's connect information. Upcoming webinars are listed on the Ag Data Commons News Page at https://data.nal.usda.gov/news, complete with details about the webinar subject and connect information. Please note each meeting number will be different.
    Topics include: 
    Making Data Machine Readable
    Creating a Data Management Plan
    Data Dictionaries
    Data-Literature Linking in the Ag Data Commons
    Data Science & Agriculture
    Introduction to GeoData

  • University of California Libraries: Research Data Matters

    What is research data and why is managing your data important? Where can you get help with research data management? In this introductory video, three University of California researchers address these questions from their own experience and explain the impact of good data management practices.  Researchers interviewed include Professor Christine Borgman, Professor Rick Prelinger, and Professor Marjorie Katz.  

     
  • Introduction to Python GIS for Data Science

    Module on Python and GIS part-time data science course was offered by General Assembly during Summer 2015. The module provides a quick introduction to Python and how it relates to GIS.  

  • Research Data Management: Practical Data Management

    A series of modules and video tutorials describing research data management best practices. 
    Module 1: Where to start - data planning

    1.1 ​Data Life Cycle & Searching for Data (5:59 minutes)
    1.3 File Naming (3:39 minutes)
    1.4 ReadMe Files, Library Support, Checklist (4:29 minutes)

    Module 2: Description, storage, archiving

    2.1 Data Description (2:16 minutes)
    2.2 Workflow Documentation & Metadata Standards (4:36 minutes)
    2.3 Storage & Backups (2:48 minutes)
    2.4 Archiving: How (2:50 minutes)
    2.5 Archiving: Where (3:57 minutes)

    Module 3: Publishing, sharing, visibility 

    3.1 What is Data Publishing? (4:50)
    3.2 What and Where to Publish? (1:47)
    3.3 Data Licenses (1:51)
    3.4 Citing and DOI's (1:09)
    3.5 ORCID (2:04)
    3.6 Altmetrics (2:15)

  • Data Management Lifecycle and Software Lifecycle Management in the Context of Conducting Science

    This paper examines the potential for comparisons of digital science data curation lifecycles to software lifecycle development to provide insight into promoting sustainable science software. The goal of this paper is to start a dialog examining the commonalities, connections, and potential complementarities between the data lifecycle and the software lifecycle in support of sustainable software. We argue, based on this initial survey, delving more deeply into the connections between data lifecycle approaches and software development lifecycles will enhance both in support of science.

  • Research Rigor & Reproducibility: Understanding the Data Lifecycle for Research Success

    This course provides recommended practices for facilitating the discoverability, access, integrity, and reuse value of your research data.  The modules have been selected from a larger Canvas course "Best Practices for Biomedical Research Data Management (https://www.canvas.net/browse/harvard-medical/courses/biomed-research-da... ).

    Biomedical research today is not only rigorous, innovative and insightful, it also has to be organized and reproducible. With more capacity to create and store data, there is the challenge of making data discoverable, understandable, and reusable. Many funding agencies and journal publishers are requiring publication of relevant data to promote open science and reproducibility of research.

    In this course, students will learn how to identify and address current workflow challenges throughout the research life cycle. By understanding best practices for managing your data throughout a project, you will succeed in making your research ready to publish, share, interpret, and be used by others.  Course materials include video lectures, presentation slides, readings and resources, research case studies, interactive activities and concept quizzes.  

  • Best Practices for Biomedical Research Data Management

    This course presents approximately 20 hours of content aimed at a broad audience on recommended practices facilitating the discoverability, access, integrity, reuse value, privacy, security, and long-term preservation of biomedical research data.

    Each of the nine modules is dedicated to a specific component of data management best practices and includes video lectures, presentation slides, readings & resources, research teaching cases, interactive activities, and concept quizzes.

    Background Statement:
    Biomedical research today is not only rigorous, innovative and insightful, it also has to be organized and reproducible. With more capacity to create and store data, there is the challenge of making data discoverable, understandable, and reusable. Many funding agencies and journal publishers are requiring publication of relevant data to promote open science and reproducibility of research.

    In order to meet to these requirements and evolving trends, researchers and information professionals will need the data management and curation knowledge and skills to support the access, reuse and preservation of data.

    This course is designed to address present and future data management needs.

    Best Practices for Biomedical Research Data Management serves as an introductory course for information professionals and scientific researchers to the field of scientific data management.  The course is also offered by Canvas Instruction, at:  https://www.canvas.net/browse/harvard-medical/courses/biomed-research-da... .

    In this course, learners will explore relationships between libraries and stakeholders seeking support for managing their research data. 

  • Data Management Plans - EUDAT best practices and case study

    Science and more specifically projects using HPC is facing a digital data explosion. Instruments and simulations are producing more and more volume; data can be shared, mined, cited, preserved… They are a great asset, but they are facing risks: we can miss storage, we can lose them, they can be misused… To start this session, we reviewed why it is important to manage research data and how to do this by maintaining a Data Management Plan. This was based on the best practices from EUDAT H2020 project and European Commission recommendation. During the second part we interactively drafted a DMP for a given use case.

  • Research Data Management (RDM) Open Training Materials

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

  • EUDAT Research Data Management

    This site provides several videos on research data management, including why its important, metadata, archives, and other topics. 

    The EUDAT training programme is delivered through a multiple channel approach and includes:
    eTraining components delivered via the EUDAT website: a selection of presentations, documents and informative video tutorials clustered by topic and level of required skills targeting all EUDAT stakeholders.

    Ad-hoc workshops organised together with research communities and infrastructures to illustrate how to integrate EUDAT services in their research data management infrastructure. Mainly designed for research communities, infrastructures and data centres, they usually include pragmatic hands-on sessions.  Interested in a EUDAT workshop for your research community? Contact us at info@eudat.eu.

    One hour webinars delivered via the EUDAT website focusing on different research data management components and how EUDAT contributes to solving research data management challenges. 

  • Rocky Mountain Data Management Training for Certification

    This free training for the Data Management Association's Certified Data Management Professional® exam is brought to you by DAMA's Rocky Mountain Chapter. If you're studying for the CDMP exam, get your discounted copy of the DMBOK V2.

    Data Management Association International – Rocky Mountain Chapter (DAMA-RMC) is a not-for-profit, vendor-independent, professional organization dedicated to advancing the concepts and practices of enterprise information and data resource management (IRM/DRM).

    DAMA-RMC’s primary purpose is to promote the understanding, development and practice of managing information and data as key enterprise assets.

  • Introduction to Computer Science and Programming in Python

    6.0001 Introduction to Computer Science and Programming in Python
     is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

  • MIT Open Courseware: Data Management

    The MIT Libraries Data Management Group hosts a set of workshops during IAP and throughout the year to assist MIT faculty and researchers with data set control, maintenance, and sharing. This resource contains a selection of presentations from those workshops. Topics include an introduction to data management, details on data sharing and storage, data management using the DMPTool, file organization, version control, and an overview of the open data requirements of various funding sources.

  • Spatial Database Management and Advanced Geographic Information Systems

    This class offers a very in-depth set of materials on spatial database management, including materials on the tools needed to work in spatial database management, and the applications of that data to real-life problem solving.  Exercises and tools for working with SQL, as well as sample database sets, are provided.  A real-life final project is presented in the projects section.

    This semester long subject (11.521) is divided into two halves. The first half focuses on learning spatial database management techniques and methods and the second half focuses on using these skills to address a 'real world,' client-oriented planning problem. 

  • MANTRA Research Data Management Training

    MANTRA is a free online course for those who manage digital data as part of their research project.

  • Python: Working with Multidimensional Scientific Data

    The availability and scale of scientific data is increasing exponentially. Fortunately, ArcGIS provides functionality for reading, managing, analyzing and visualizing scientific data stored in three formats widely used in the scientific community – netCDF, HDF and GRIB. Using satellite and model derived earth science data, this session will present examples of data management, and global scale spatial and temporal analyses in ArcGIS. Finally, the session will discuss and demonstrate how to extend the data management and analytical capabilities of multidimensional data in ArcGIS using python packages.

  • 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.

  • 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.

  • 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 .

  • Intermediate R

    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.

  • Hivebench Electronic Lab Notebook

    The time it takes to prepare, analyze and share experimental results can seem prohibitive, especially in the current, highly competitive world of biological research. However, not only is data sharing mandated by certain funding and governmental bodies, it also has distinct advantages for research quality and impact. Good laboratory practices recommend that all researchers use electronic lab notebooks (ELN) to save their results. This resource includes numerous short video demonstrations of Hivebench:

    • Start using Hivebench, the full demo
    • Creating a Hivebench account
    • Managing protocols & methods
    • Storing experimental findings in a notebook
    • Managing research data
    • Doing research on iPhone and iPad
    • Editing experiments
    • Collaborating with colleagues
    • Searching for results
    • Staying up to date with the newsfeed
    • Planning experiments with the calendar
    • Using open science protocols
    • Mendeley Data Export
    • Managing inventory of reagents
    • Signing and counter signing experiments
    • Archiving notebooks
    • How to keep data alive when researchers move on? Organizing data, methods, and protocols.
  • Introduction to Data Management for Undergraduate Students: Data Management Overview

    This library guide covers the basics and best practices for data management for individuals who are new to the research and data-collecting process.  Topics included in this guide are:
    - Data Management Overview
    - Data Documentation
    - Data Preservation
    - Filenaming Conventions
    - Data Backup