All Learning Resources

  • Coffee and Code: Basics of Programming with Python

    This collection of materials was developed for the University of New Mexico Libraries' Code & Coffee workshop series to provide a high-level introduction to programming concepts illustrated with the Python programming language. The workshop content is contained in a collection of Jupyter Notebooks:

    Conceptual Overview: Programming Concepts.ipynb
    Surname analysis example: Name_Data.ipynb
    Library shelf space analysis example: Space Analysis.ipynb
    IR Keywords Versus IR "Aboutness" example [no longer functional due to decommissioning of UNM DSpace instance]: IR Keywords Versus IR "Aboutness".ipynb

    Why learn the basic principles of programming?¶

    Thinking algorithmically (a key element in the process used in developing programming solutions) is a powerful problem solving skill that is reinforeced with practice. Practicing programming is great practice.

    • Defining a problem with sufficient specificity that a solution can be effectively developed
    • Defining what the end-product of the process should be
    • Breaking a problem down into smaller components that interact with each other
    • Identifying the objects/data and actions that are needed to meet the requirements of each component
    • Linking components together to solve the defined problem
    • Identifying potential expansion points to reuse the developed capacity for solving related problems
    • Capabilities to streamline and automate routine processes through scripting are ubiquitous
    • Query languages built into existing tools (e.g. Excel, ArcGIS, Word)
    • Specialized languages for specific tasks (e.g. R, Pandoc template language, PHP)
    • General purpose languages for solving many problems (e.g. Bash shell, Perl, Python, C#)
    • Repeatabilty with documentation
    • Scalability
    • Portability
  • Mozilla Science Lab Open Data Instructor Guides

    This site is a resource for train-the-trainer type materials on Open Data. It's meant to provide a series of approachable, fun, collaborative workshops where each of the modules is interactive and customizable to meet a variety of audiences.

  • Data Management: Using Metadata to Find, Interpret & Share Your Data

    Ever struggle to find that file you tucked away last semester (or last week)? Having trouble remembering details in order to re-use your own data? Need others to understand & use your data? This workshop will introduce you to the power of metadata: what it is, why it’s so important, and how to get started with it. Stop wasting time in finding, interpreting or sharing your data. Whether you are new to thinking about metadata or you’re looking to build off some basic knowledge, this workshop is for you!

  • Data Management: Strategies for Data Sharing and Storage

    Not sure how to publish and share your data? Unclear on the best formats and information to include for optimal data reuse? This workshop will review existing options for long-term storage and strategies for sharing data with other researchers. Topics will include: data publication and citation, persistent identifiers, versioning, data formats and metadata for reuse, repositories, cost models and management strategies.

  • Learning programming on Khan Academy

    In this course, we'll be teaching the concepts of the JavaScript programming language and the cool functions you can use with it in the ProcessingJSlibrary. Before you dig in, here's a brief tour of how we teach programming here on Khan Academy, and how we think you can learn the most.

    Normally, we teach on Khan Academy using videos, but here in programming land, we teach with something we call "talk-throughs". A talk-through is like a video, but it's actually interactive- you can pause at any time if you want to play with the code yourself, and you can spin-off if you want to make your own version of what we made.  An animated GIF of a talk-through is included.

    See Terms of Service at:  https://www.khanacademy.org/about/tos 

  • 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.  The Essentials 4 Data Support course aims to contribute to professionalization of data supporters and coordination between them. Data supporters are people who support researchers in storing, managing, archiving and sharing their research data.  Course may be taken online-only (no fee) with or without registration, or online plus face to face meetings as a full course with certificate (for a fee).  

  • Unidata Data Management Resource Center

    In this online resource center, Unidata provides information about evolving data management requirements, techniques, and tools. They walk through common requirements of funding agencies to make it easier for researchers to fulfill them. In addition, they explain how to use some common tools to build a scientific data managment workflow that makes the life of an investigator easier, and enhances access to their work. The resource center provides information about: 1) Agency Requirements, 2) Best Practices, 3) Tools for Managing Data, 4) Data Management Plan Resources, 5) Data Archives, and 6) Scenarios and Use Case.

  • Introduction to code versioning and collaboration with Git and GitHub: An EDI VTC Tutorial.

    This tutorial is an introduction to code versioning and collaboration with Git and GitHub.  Tutorial goals are to help you:  

    • Understand basic Git concepts and terminology.
    • Apply concepts as Git commands to track versioning of a developing file.
    • Create a GitHub repository and push local content to it.
    • Clone a GitHub repository to the local workspace to begin developing.
    • Inspire you to incorporate Git and GitHub into your workflow.


    There are a number of exercises within the tutorial to help you apply the concepts learned.  
    Follow up questions can be directed via email to:  o Colin Smith  ([email protected]) AND Susanne Grossman-Clarke ([email protected]).

  • Transform and visualize data in R using the packages tidyr, dplyr and ggplot2: An EDI VTC Tutorial.

    The two tutorials, presented by Susanne Grossman-Clarke, demonstrate how to tidy data in R with the package “tidyr” and transform data using the package “dplyr”. The goal of those data transformations is to support data visualization with the package “ggplot2” for data analysis and scientific publications of which examples were shown.

  • Principles of Database Management

    There are 14 videos included in this web lecture series of Prof. dr. Bart Baesens: Introduction to Database Management Systems. Prof. dr. Bart Baesens holds a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications. For more information, visit http://www.dataminingapps.com.   In this lecture series, the fundamental concepts behind databases, database technology, database management systems and data models are explained. Discussed topics entail: applications, definitions, file based vs. databased data management approaches, the elements of database systems and the advantages of database design.  Separate URLs are provided for each lecture in this series, found on the YouTube lecture series page.  

     

  • CMU Intro to Database Systems Course

    These courses are focused on the design and implementation of database management systems. Topics include data models (relational, document, key/value), storage models (n-ary, decomposition), query languages (SQL, stored procedures), storage architectures (heaps, log-structured), indexing (order preserving trees, hash tables), transaction processing (ACID, concurrency control), recovery (logging, checkpoints), query processing (joins, sorting, aggregation, optimization), and parallel architectures (multi-core, distributed). Case studies on open-source and commercial database systems will be used to illustrate these techniques and trade-offs. The course is appropriate for students with strong systems programming skills.  There are 26 videos associated with this course which was originally offered in Fall 2018 as Course 15 445/645 at Carnegie Mellon University.  

  • Research Data Management: Basics and Best Practices

    Big data, data management, and data life cycle are all buzzwords being discussed among librarians, researchers, and campus administrators across the country and around the world. Learn the basics of these terms and what services an academic library system might be expected to offer patrons, while identifying personal opportunities for improving how you work with your own data. You will have the opportunity to explore DMPTool during this session.

  • Linux for Biologists

    This workshop is designed to prepare a biologist to work in an interactive Linux environment of our BioHPC Lab workstations.  Slides are available for each of the sessions.  Basics are covered of the Linux operating system that are needed to operate workstations. In particular the topics will include: 

    Navigating a Linux workstation:
    Logging in and out of a Linux machine, directory structure, basic commands for dealing with files and directories
    Working with text files
    Transfer of files to and from a Linux workstation
    Basics of running applications on Linux
    Using multiple CPUs/cores: parallel applications
    Basics of shell scripting

  • 2018 NOAA Environmental Data Management Workshop (EDMW)

    The EDMW 2018 theme is "Improving Discovery, Access, Usability, and Archiving of NOAA Data for Societal Benefit." The workshop builds on past work by providing training, highlighting progress, identifying issues, fostering discussions, and determining where new technologies can be applied for management of environmental data and information at NOAA. All NOAA employees and contractors are welcome, including data producers, data managers, metadata developers, archivists, researchers, grant issuers, policy developers, program managers, and others.  Links to recordings of the sessions plus presentation slides are available.
    Some key topic areas include:

    • Big Earth Data Initiative (BEDI)
    • Data Governance
    • NCEI's Emerging Data Management System
    • Data Visualization
    • Data Lifecycle Highlights
    • Data Archiving
    • Data Integration
    • Metadata Authoring, Management & Evolution
    • NOAA Institutional Repository
    • Video Data Managment & Access Solutions
    • Unified Access Format (UAF), ERDDAP & netCDF
    • Improving Data Discovery & Access to Data 
    • Arctic & Antarctic Data Access
  • Creating a Data Management Plan

    Video presentation and slides introducing tips for creating a data management plan (DMP) provided by Clara S. Fowler, a Research Services and Assessment Manager at the Research Medical library of the University of Texas MD Anderson Cancer Center, including discussion of what a data management plan is, what is required for a National Institutes of Health (NIH)  DMP, what changes can be expected for a NIH DMP and the tools for creating data management plans. Topics include:
    Data management guide
    Sample data management plan for the National Science Foundation (NSF)
    Sample data sharing plan from the NIH
    NIH data sharing policy and implementation guide

  • Creating Effective Graphs

    Sunita Patterson, a scientific editor in Scientific Publications at the University of Texas MD Anderson Cancer Center, goes over the fundamentals of good graph design and data presentation including a discussion of when to use graphs, general principles for effective graphs and introducing different types of graph. Effective graphs “improve understanding of data”. They do not confuse or mislead; one graph is more effective than another if its quantitative information can be decoded more quickly or more easily by most observers.  Examples of graphs are given realted to Medicine and Health Services.
    Objectives of Short Course:
    - Know more effective ways to present data and know where to find more information on these graph forms.
    - Learn general principles for creating clear, accurate graphs.
    - Understand that different audiences have varying needs and the presentation should be appropriate for the audience.
    Summary of Course:
    - Limitations of some common graph forms
    - Human perception and our ability to decode graphs
    - Newer and more effective graph forms
    - General principles for creating effective tables and graphs

  • New Self-Guided Curriculum for Digitization

    Through the Public Library Partnerships Project (PLPP), DPLA has been working with existing DPLA Service Hubs to provide digital skills training for public librarians and connect them sustainably with state and regional resources for digitizing, describing, and exhibiting their cultural heritage content.

    During the project, DPLA collaborated with trainers at Digital Commonwealth, Digital Library of Georgia, Minnesota Digital Library, Montana Memory Project, and Mountain West Digital Library to write and iterate a workshop curriculum based on documented best practices. Through the project workshops, we used this curriculum to introduce 150 public librarians to the digitization process.

    Now at the end of the project, we’ve made this curriculum available in a self-guided version intended for digitization beginners from a variety of cultural heritage institutions. Each module includes a video presentation, slides with notes in Powerpoint, and slides in PDF. Please feel free to share, reuse, and adapt these materials.  Topics (with separate URLs for each) include:
    Planning for Digitization
    Selecting Content for a Digitization Project
    Understanding Copyright
    Using Metadata to Describe Digital Content
    Digital Reformatting and File Management
    Promoting Use of Your Digital Content
     

  • Introduction to Data Management

    This short course on data management is designed for graduate students in the engineering disciplines who seek to prepare themselves as “data information literate" scientists in the digital research environment. Detailed videos and writing activities will help you prepare for the specific and long-term needs of managing your research data. Experts in digital curation will describe current sharing expectations of federal funding agencies (like NSF, NIH) and give advice on how toethically share and preserve research data for long-term access and reuse.

    Students will get out of this course:

    • Seven web-based lessons that you can watch anytime online or download to your device.
    • A Data Management Plan (DMP) template with tips on how to complete each section. Your completed DMP can be used in grant applications or put into practice as a protocol for handling data individually or within your research group or lab. 
    •  Feedback and consultation on your completed DMP by research data curators in your field. 

    Module topics include: 
    1. Introduction to Data Management
    2. Data to be Managed
    3. Organization and Documentation
    4. Data Access and Ownership
    5. Data Sharing and Re-use
    6. Preservation Techniques
    7. Complete Your DMP.

  • Managing Your Research Code

    Do you write software? Have you been required by funders or publishers to share your code, or do you want to make it accessible to others to use? Documenting, sharing and archiving your research software can make your research more transparent and reproducible, and can help you get credit for your work. This workshop reviews reasons to share your software, best practices and considerations for documenting your software and making it citable, and options for archiving and publishing research software, including software papers and managing software with associated data sets, and some best practices for citing and documenting all of the software that you use.

  • Data Management: File Organization

    Do you struggle with organizing your research data? This workshop teaches practical techniques for organizing your data files. Topics include: file and folder organizational structures, file naming, and versioning.

  • Management Challenges in Research Infrastructures

    This module will look at some of the key management issues that arise within research infrastructures with a clarity and urgency they don’t often have below the infrastructural scale.  It will also look at key trends and developments in these areas, and how exemplar projects are applying them.  In particular, this module will cover: User Engagement, Communications and Audiences, Sustainability, and the Macro-level issues, including managing the political environment.

    This training module is targeted to the Intermediate Level student who wants to learn about digital humanities research infrastructures and includes approaches some of the major challenges in building and maintaining research infrastructures.  These materials are somewhat more dense than the beginning level module.  Students would benefit from a more comprehensive grounding in digital humanities and the management of research projects.

    PARTHENOS training provides modules and resources in digital humanities and research infrastructures with the goal of strengthening the cohesion of research in the broad sector of Linguistic Studies, Humanities, Cultural Heritage, History, Archaeology and related fields.  Activities designed to meet this goal will address and provide common solutions to the definition and implementation of joint policies and solutions for the humanities and linguistic data lifecycle, taking into account the specific needs of the sector including the provision of joint training activities and modules on topics related to understanding research infrastructures and mangaging, improving and openin up research and data for both learners and trainers. 

    More information about the PARTHENOS project can be found at:  http://www.parthenos-project.eu/about-the-project-2.  Other training modules created by PARTHENOS can be found at:  http://training.parthenos-project.eu/training-modules/.

  • Introduction to Research Infrastructures

    This training module provides an introduction to research infrastructures targeted to the Beginner Level.  Beginner Level assumes only a moderate level of experience with digital humanities, and none with research infrastructures.  Units, videos and lectures are all kept to short, manageable chunks on topics that may be of general interest, but which are presented with an infrastructural twist..  By the end of this module, you should be able to…

    • Understand the elements of common definitions of research infrastructures
    • Be able to discuss the importance of issues such as sustainability and interoperability
    • Understand how research infrastructure supports methods and communities
    • Be aware of some common critiques of digital research infrastructures in the Humanities.

    PARTHENOS training provides modules and resources in digital humanities and research infrastructures with the goal of strengthening the cohesion of research in the broad sector of Linguistic Studies, Humanities, Cultural Heritage, History, Archaeology and related fields.  Activities designed to meet this goal will address and provide common solutions to the definition and implementation of joint policies and solutions for the humanities and linguistic data lifecycle, taking into account the specific needs of the sector including the provision of joint training activities and modules on topics related to understanding research infrastructures and mangaging, improving and openin up research and data for both learners and trainers.

    More information about the PARTHENOS project can be found at:  http://www.parthenos-project.eu/about-the-project-2.  Other training modules created by PARTHENOS can be found at:  http://training.parthenos-project.eu/training-modules/.

  • Manage, Improve and Open Up your Research and Data

    This module will look at emerging trends and best practice in data management, quality assessment and IPR issues.

    We will look at policies regarding data management and their implementation, particularly in the framework of a Research Infrastructure.
    By the end of this module, you should be able to:

    • Understand and describe the FAIR Principles and what they are used for
    • Understand and describe what a Data Management Plan is, and how they are used
    • Understand and explain what Open Data, Open Access and Open Science means for researchers
    • Describe best practices around data management
    • Understand and explain how Research Infrastructures interact with and inform policy on issues around data management

    PARTHENOS training provides modules and resources in digital humanities and research infrastructures with the goal of strengthening the cohesion of research in the broad sector of Linguistic Studies, Humanities, Cultural Heritage, History, Archaeology and related fields.  Activities designed to meet this goal will address and provide common solutions to the definition and implementation of joint policies and solutions for the humanities and linguistic data lifecycle, taking into account the specific needs of the sector including the provision of joint training activities and modules on topics related to understanding research infrastructures and mangaging, improving and openin up research and data for both learners and trainers.

    More information about the PARTHENOS project can be found at:  http://www.parthenos-project.eu/about-the-project-2.  Other training modules created by PARTHENOS can be found at:  http://training.parthenos-project.eu/training-modules/.

  • Introduction to Collaboration in Research Infrastructures

    Is humanities research collaborative?  Some would say that with our traditions of independent research and single authorship, it is not. This is not really true for any humanist, however, as collaboration does occur within classrooms, on-line communities, and within disciplinary networks.  For the digital humanities, this is even more the case, as the hybridity of our methods require us to work together.  Very few digital humanists can master entirely on their own the domain, information and software challenges their approach presents, and so we tend to work together.

    This training module provides an introduction to research infrastructures targeted to the Advanced Level, and as such, presents some of the exciting new research directions coming out of the PARTHENOS Cluster.  These modules approach some of the theoretical issues that shape the design, delivery and indeed the success of research infrastructure developments, challenging us to think about how we develop and support humanities at scale in the interaction with technology.

    By the end of this module, you should be able to….

    • Understand what is meant by collaboration in humanities research
    • Be aware of how this model impacts upon the development of digital humanities, and digital humanities research infrastructures

    PARTHENOS training provides modules and resources in digital humanities and research infrastructures with the goal of strengthening the cohesion of research in the broad sector of Linguistic Studies, Humanities, Cultural Heritage, History, Archaeology and related fields.  Activities designed to meet this goal will address and provide common solutions to the definition and implementation of joint policies and solutions for the humanities and linguistic data lifecycle, taking into account the specific needs of the sector including the provision of joint training activities and modules on topics related to understanding research infrastructures and mangaging, improving and openin up research and data for both learners and trainers

    More information about the PARTHENOS project can be found at:  http://www.parthenos-project.eu/about-the-project-2.  Other training modules created by PARTHENOS can be found at:  http://training.parthenos-project.eu/training-modules/.

  • LEARN Toolkit of Best Practice for Research Data Management

    The LEARN Project's Toolkit of Best Practice for Research Data Management expands on the issues outlines in the  LERU Roadmap for Research Data (2013).  It is freely downloadable, and is a deliverable for the European Commission.  It includes:

    • 23 Best-Practice Case Studies from institutions around the world, drawn from issues in the original LERU Roadmap;
    • 8 Main Sections, on topics such as Policy and Leadership, Open Data, Advocacy and Costs;
    • One Model RDM Policy, produced by the University of Vienna and accompanied by guidance and an overview of 20 RDMpolicies across Europe;
    • An Executive Briefing in six languages, aimed at senior institutional decision makers.


    The Executive Briefing of the LEARN Toolkit is available in English, Spanish, German, Portuguese, French and Italian translations.

  • Introduction, FAIR Principles and Management Plans

    This presentation introducing the FAIR (Findable Accessible Interoperable Re-usable) data principles and management plans is one of 9 webinars on topics related to FAIR Data and Software that was offered at a Carpentries-based Workshop in Hannover, Germany, Jul 9-13 2018.  Presentation slides are also available in addition to the recorded presentation.

    Other topics included in the series include:
    - Findability of Research Data and Software through PIDs and FAIR
    - Accessibility through Git, Python Funcations and Their Documentation
    - Interoperability through Python Modules, Unit-Testing and Continuous Integration
    - Reusability through Community Standards, Tidy Data Formats and R Functions, their Documentation, Packaging, and Unit-Testing
    - Reusability:  Data Licensing
    - Reusability:  Software Licensing
    - Reusability:  Software Publication
    - FAIR Data and Software - Summary

    URL locations for the other modules in the webinar can be found at the URL above.
     

  • Introduction to Research Data Management

    This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2017-02-15. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.  Various data policies are referenced that are pertinent to the UK including the Research Councils of the UK's common Principles on Data Policy and the EPSRC Policy Framework on Research Data.  Research data skills guide and tools are referenced.  Text of the slides are also available.  

  • Preparing Your Research Material for the Future

    This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2018-06-08. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.

  • Science Data Resources: From Astronomy to Bioinformatics

    In this one-hour workshop, Michelle Hudson, Kayleigh Bohemier and Kristin Bogdan gives an overview of the types, formats, sources and general refernce resources of scientific data in various disciplines: geology, astronomy, physics, and physical samples. Rolando Garcia Milian, who recently joined the Cushing/Whitney Medical Library as Biomedical Sciences Research Support librarian, gives an overview of bioinformatics as a discipline, and the kinds of questions he answers in the course of his work including tools for data retrieval and data mining.

  • ISO Online Metadata Training

    Course Description: This course presents the concept, principles and value of metadata utilizing the International Organization for Standardization (ISO) metadata in seven online sessions. It provides the content and structure of the IS0 191** series metadata in detail, along with methods for writing quality metadata. Each session will last approximately one hour. URL brings you to an index (FTP) page for the course which includes downloadable resources for the course including:  agenda, exercises, handouts, presentation slides, recorded sessions, sample metadata, schemas, templates, transforms and workbooks.  Please contact [email protected] with any questions. 

    Other online courses from the parent directory at:  ftp://ftp.ncddc.noaa.gov/pub/Metadata/Online_ISO_Training/ include introductions to CSDGM, and to Geospatial Metadata.

  • Research Data Management: Can Librarians Really Help?

    This presentation provides information on how librarians can help with research data managment from the point of view of assisting researchers in the research lifecycle.  The presentation was made at the GL 20, the Twentieth International Conference on Grey Literature.   

  • C++ Programming Tutorial Series

    This is everything you need to know to get started as a C++ Programming Software developer / Software engineer. We start off with the super basics and work our way to intermediate topics.  Videos are available as an "All-in-One Tutorial Series" of 10 hours or 101 shorter videos that range from introductory concepts to various functions such as swap functions and function overloading, and creating makefiles and namespaces to name a few.

  • JavaScript Tutorial

    This playlist is an introductory course to the concepts behind JavaScript!  This series of 101 short videos will better help you understand how JavaScript works behind the scenes. The series covers everything you need to know to start building applications in JavaScript.  Fundamentals are covered first but eventually cover topics including object-oriented programming, scoping, hoisting, closures, ES6 classes, factory and constructor functions and more.

  • Data Management for the Humanities

    The guidelines available from this web page cover a number of topics related to Data Management. Many of the resources and information found in this guide have been adapted from the UK Data Archive and the DH Curation Guide. The guidelines are targeted to researchers wishing to submit data to the social science research data, and would be useful to new data curators and data librarians in the Arts & Humanities as well.  Each section has useful references for further study, if desired.

    What You Will Find in This Guide:
    -How to Document and Format your Data
    -Examples of Data Management Plans (DMP) and Data Curation Profiles (DCP)
    -Tools to Help You Create DMPs and DCPs
    -California Digital Library Data Repositories
    -Where to Get Help on Campus
    -A list of Federal Funding Agencies and Their Data Management Requirements
    -A Description of Data Curation for the Humanities and What Makes Humanities Data Unique
    -Information on Data Representation
    -Resources on Data Description Standards

  • Introduction to Python GIS- CSC Training 2018

    Introduction to Python GIS is 6 lessons organized by CSC Finland – IT Center for Science. During the course you will learn how to do different GIS-related tasks in Python programming language. Each lesson is a tutorial with specific topic(s) + Exercises where the aim is to learn how to solve common GIS-related problems and tasks using Python tools. Lecturer of the course is Henrikki Tenkanen who is a geo-data scientist and postdoctoral researcher at the Digital Geography Lab, University of Helsinki. These lessons are for  those who know the basics of Python programming.  If Python is not familiar to you, we recommend to start with a course from us focusing on the basics of Python from geo-python.github.io.

    The majority of this course will be spent in front of a computer learning to program in the Python language and working on exercises. The provided exercises will focus on developing basic programming skills using the Python language and applying those skills to various GIS related problems.

    Learning objectives

    At the end of the course you should have a basic idea how to conduct following GIS tasks in Python:

    Read / write spatial data from/to different file formats
    Deal with different projections
    Conduct different geometric operations and spatial queries
    Convert addresses to points (+ vice versa) i.e. do geocoding
    Reclassify your data based on different criteria
    Know how to fetch data from OpenStreetMap easily with Python
    Know the basics of raster processing in Python
    Visualize data and create (interactive) maps

    Course information

    Lesson 1:GIS with Python; Spatial data model; Geometric Objects; Shapely
    Lesson 2:Working with Geo Data Frames; Managing projections
    Lesson 3: Geocoding; Table join; Working with Open Street Map data
    Lesson 4: Geometric operations; Spatial queries
    Lesson 5: Visualization, making static and interactive maps
    Lesson 6:Raster processing in Python

  • Data Management Plan Templates

    Do you need a template to draft a data management plan?  Not everyone wants to use the DMPTool, and we understand. Maybe you would like to have a template that you can use in a classroom setting so your students can practice writing a plan.  Perhaps you would simply like to see what the requirements are for a given funder, so you can get a head start on your next grant proposal.

    The NSF and NEH templates are identical to the ones in the DMPTool.  The NIH and DOE templates were created in response to these funders changing landscapes. Some funders have specific requirements for a program, and those guidance documents are also available here. All templates are in Word format, and Rich Text format are available by request.

  • Data Management Plan Template

    This link takes you to a MS Word based, downloadable Data Management Plan (DMP) template with tips on how to complete each section. Your completed DMP can be used in grant applications or put into practice as a protocol for handling data individually or within your research group or lab. This template provides a basic method of organizing your research data managment information as you begin a new project. The template was created and made available as part of a workshop series on data management in Winter 2015.  
     

  • Research Data Management (RDM) Open Training Materials

    Openly accessible, curated online training materials which can be shared and repurposed for RDM training. All contributions in any language are welcome.  Resources are stored in the Zenodo platform.  Formats, licenses and terms for use vary.

  • 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.  Presentation slides and video recording of this event is available at the link given.

  • Data Services: Data Management Classes

    This Libguide 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. Presentation slides are also available as well as references more specifically tailored to the University of Tennessee, Knoxville.