All Learning Resources

  • Photogrammetry Workshop UNM GEM Lab

    This course provides an introduction to photgrammetry with a full set of data to utilize in building a Digital Elevation Model using Agisoft Photoscan.  The course uses a gitHub repository to grow the workshop into a full featured course on the applications of modern remote sensing and photogrammetry techniques in and for the environmental and geosciences.

     

  • Open Access Post-Graduate Teaching Materials in Managing Research Data in Archaeology

    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! But 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 online we all have a responsibility to ensure the long-term preservation of archaeological 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. The eight presentations, followed by group discussion and written exercises, follow the lifecycle of digital data from pre-project planning, data creation, data management, publication, long-term preservation and lastly to issues of the re-use of digital data. At the same time the course follows the career path of researchers from post-graduate research students, through post-doctoral research projects, to larger collaborative and inter-disciplinary projects.

    The teaching material is targeted at co-ordinators of Core Research Skills courses for first year post-graduate research students in archaeology. The material is open access and you are invited to re-use and amend the content as best suits the requirements of your university department. The complete course is designed to run either as a four hour half-day workshop, or 2 x 2 hour classes. Alternatively, individual modules can be slotted into existing data management and core research skills teaching.

  • DATUM for Health: Research data management training for health studies

    The DATUM for Health training programme covers both generic and discipline-specific issues, focusing on the management of qualitative, unstructured data, and is suitable for students at any stage of their PhD. It aims to provide students with the knowledge to manage their research data at every stage in the data lifecycle, from creation to final storage or destruction. They learn how to use their data more effectively and efficiently, how to store and destroy it securely, and how to make it available to a wider audience to increase its use, value and impact.

    The programme comprises:

    Overview: programme aims and scope, design, outline content and materials, recommendations 
    Session 1: Introduction to research data management (URL
    Session 2: Data curation lifecycle
    Session 3: Problems and practical strategies and solutions

    For each session the materials comprise PPT slides, notes for tutors and handouts.

  • Datatree - Data Training Engaging End-users

    *Requires sigining up for a free account*

    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 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 plenty of additional background information.

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

  • Data Management Expert Guide

    This guide is written for social science researchers who are in an early stage of practising research data management. With this guide, CESSDA wants to contribute to professionalism in data management and increase the value of research data.

    If you follow the guide, you will travel through the research data lifecycle from planning, organising, documenting, processing, storing and protecting your data to sharing and publishing them. Taking the whole roundtrip will take you approximately 15 hours, however you can also hop on and off at any time.

  • Diversity Workbench (DWB) in 15 Steps

    Introduction and demonstration of the Diversity Workbench (DWB), ​a "virtual research environment for multiple scientific purposes with regard to management and analysis of life and environmental sciences data. ​The framework is appropriate to store different kinds of bio- and geodiversity data, taxonomies, terminologies, and facilitates the processing of ecological, molecular biological, observational, collection and taxonomic data" (DWB).
    For detailed information about DWB, go to ​https://diversityworkbench.net/Portal/Diversity_Workbench.

  • CESSDA Expert Tour Guide on Data Management

    Target audience and mission:
    This tour guide was written for social science researchers who are in an early stage of practising research data management. With this tour guide, CESSDA wants to contribute to increased professionalism in data management and to improving the value of research data.
    Overview:
    If you follow the guide, you will travel through the research data lifecycle from planning, organising, documenting, processing, storing and protecting your data to sharing and publishing them. Taking the whole roundtrip will take you approximately 15 hours. You can also just hop on and off.
    During your travels, you will come across the following recurring topics:
    Adapt Your DMP
    European Diversity
    Expert Tips
    Tour Operators
    Current chapters include the following topics:  Plan; Organise & Document; Process; Store; Protect;  Archive & Publish.  Other chapters may be added over time.

  • 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.

  • Essentials 4 Data Support

    Essentials 4 Data Support is an introductory course for those people who (want to) support researchers in storing, managing, archiving and sharing their research data.

    Essentials 4 Data Support is a product of Research Data Netherlands.

  • 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. 

  • DMP Bingo - the good, the bad, the ugly

  • Introduction to Data Management Plans

    Video presentation and slides introducing the concept of Data Management Plans given by Dr. Andrew Stephonson  at the Research Resource Forum at Northwestern University in 2016.  Dr. Stephenson is Distinguished Professor of Biology and Associate Dean for Research and Graduate Education in the Eberly College of Science at Penn State.  As an active researcher, he has generated and collected data for many years and served on many a panel reviewing grant proposals. From his perspective, data management plans make good sense. In the following video, he describes the elements of a DMP and why they are important.  The video presentation is available at:  https://www.youtube.com/watch?v=uHyDzt6E3qU 
    This presentation is part of a Data Management Plan Tutorial prepared by the Penn State University Libraries and contains the following modules:

    • Introduction to Data Management Plans
      • Why Do You Need a Data Management Plan?
      • Components of a Typical Plan
      • Tools and Other Resources for Data Management Planning
      • Summary
    • Part 1: Data and Data Collection
    • Part 2: Documenting the Data
    • Part 3: Policies for Data Sharing and Access
    • Part 4: Reuse and Redistribution of Data
    • Part 5: Long-Term Preservation and Archiving of Data
    • Next Steps to Take


    The entire Data Management Plan tutorial can be found at:  https://www.e-education.psu.edu/dmpt

  • The BD2K Guide to the Fundamentals of Data Science Series

    The Big Data to Knowledge (BD2K) Initiative presents this virtual lecture series on the data science underlying modern biomedical research. Since its beginning in September 2016, the webinar series consists of presentations from experts across the country covering the basics of data management, representation, computation, statistical inference, data modeling, and other topics relevant to “big data” in biomedicine. The webinar series provides essential training suitable for individuals at an introductory overview level. All video presentations from the seminar series are streamed for live viewing, recorded, and posted online for future viewing and reference. These videos are also indexed as part of TCC’s Educational Resource Discovery Index (ERuDIte). This webinar series is a collaboration between the TCC, the NIH Office of the Associate Director for Data Science, and BD2K Centers Coordination Center (BD2KCCC).

    View all archived videos on our YouTube channel: 
    https://www.youtube.com/channel/UCKIDQOa0JcUd3K9C1TS7FLQ 

  • 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.

  • Hands-on Intro to Data Cleaning with OpenRefine

    It's extremely difficult to keep all errors out of your datasets. It's even harder if multiple people have contributed to the compilation of that dataset. In addition, some software is picky about the precise format of data, the presence of spaces, etc.
    OpenRefine is an Interactive Data Transformation tool (IDT) that allows you to easily perform data assessment and cleaning without having to write customized scripts to perform all of these tasks. OpenRefine offers many useful features, including:

    • All steps of your data processing are captured and can be shared with publishers and funders to more fully document your research
    • All steps are easily reversed
    • All work is saved to a new file -- your original dataset is never altered
    • Data cleaning is much easier and more efficient
    • Complex concepts like clustering algorithms are simplified and made easier to use
    • Data cleaning tasks are easy to repeat on multiple files

    This workshop will teach the basics of working using OpenRefine for cleaning up messy data. This workshop is based on content from the book Using OpenRefine, listed in the resources at the bottom of the page.

  • 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.