All Learning Resources

  • Code of Best Practices and Other Legal Tools for Software Preservation: 2019 Webinar Series

    Since 2015, the Software Preservation Network (SPN) has worked to create a space where organizations from industry, academia, government, cultural heritage, and the public sphere can contribute their myriad skills and capabilities toward collaborative solutions that will ensure persistent access to all software and all software-dependent objects. The organization's goal is to make it easier to deposit, discover, and reuse software.
    A key activity of the SPN is to provide webinar series on topics related to software preservation.  The 2019 series include:
    Episode 1: The Code of Best Practices for Fair Use in Software Preservation, Why and How?
    Episode 2:  Beginning the Preservation Workflow
    Episode 3:  Making Software Available Within Institutions and Networks
    Episode 4:  Working with Source Code and Software Licenses
    Episode 5:  Understanding the Anti-circumvention Rules and the Preservation Exemptions
    Episode 6:  Making the Code Part of Software Preservation Culture
    Episode 7:  International Implications
    See information about each episode separately.
     

     

  • HarvardX Biomedical Data Science Open Online Training - Data Analysis for the Life Sciences Series

    HarvardX Biomedical Data Science Open Online Training

    In 2014 funding was received from the NIH BD2K initiative to develop MOOCs for biomedical data science. The courses are divided into the Data Analysis for the Life Sciences series, the Genomics Data Analysis series, and the Using Python for Research course.

    This page includes links to the course material for the three courses:

    Data Analysis for the Life Sciences:  Genomics Data Analysis Using Python for Research

    Video lectures are included with, when available, an R markdown document to follow along, and the course itself. Note that you must be logged in to EdX to access the course. Registration is free. Links to the course pages are also included.

    This site inclues a link to two other course sets: Genomics Data Analysis, and Using Python for Research.

  • Creating Documentation and Metadata: Creating a Citation for Your Data

    This training module is part of the Federation of Earth Science Information Partners (or ESIP Federation's) Data Management for Scientists Short Course. The subject of this module is "Creating a Citation for Your Data." This module was authored by Robert Cook from the Oak Ridge National Laboratory. Besides the ESIP Federation, sponsors of this Data Management for Scientists Short Course are the Data Conservancy and the United States National Oceanic and Atmospheric Administration (NOAA).  This module is available in both presentation slide and video formats.

  • Responsible Data Use: Data Restrictions

    This training module is part of the Federation of Earth Science Information Partners (or ESIP Federation's) Data Management for Scientists Short Course.  The subject of this module is "Data Restrictions".  The module was authored by Robert R. Downs from the NASA Socioeconomic Data and Applications Center which is operated by CIESIN – the Center for International Earth Science Information Network at Columbia University.  Besides the ESIP Federation, sponsors of this Data Management for Scientists Short Course are the Data Conservancy and the United States National Oceanic and Atmospheric Administration (NOAA).  This module is available in both presentation slide and video formats.  

  • How to Make a Data Dictionary

    A data dictionary is critical to making your research more reproducible because it allows others to understand your data. The purpose of a data dictionary is to explain what all the variable names and values in your spreadsheet really mean. This OSF Best Practice Guide gives examples and instruction on how to asemble a data dictionary.

  • R Program (Data Analysis)--Full Course

    A full basic course in R software for data analysis, produced by Simply Statistics. This 42 part video course provides basic instruction on the use of R, where to get help with programming questions, and a number of real world examples.  Links to all the videos are available from the YouTube landing page and include topics such as:  Getting Help, What is Data, Representing Data, etc.  The course is also offered via Coursera (See https://simplystatistics.org/courses).  The lecture slides for Coursera's Data Analysis class are available on github at:  https://github.com/jtleek/dataanalysis.

  • MIT Open Courseware: 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. Course presented as taught in Fall 2016.
    Course features include:
    Video lectures
    Captions/transcript 
    Interactive assessments
    Lecture notes
    Assignments: problem sets (no solutions)
    Assignments: programming with examples

    MITx offers a free version of this subject on edX. Please register to get started:

    6.00.1x Introduction to Computer Science and Programming Using Python (Started January 22, 2019)  [help icon]

    6.00.2x Introduction to Computational Thinking and Data Science (Started March 26, 2019)

  • Why Cite Data?

    This video explains what data citation is and why it's important. It also discusses what digital object identifiers (DOIs) are and how they are used.

  • Data Management Resources (University of Arizona Research Data Management Services)

    The information on this website is intending to provide information on developing data management plans now being required by some federal agencies and to support researchers in the various stages of the research cycle.  Topics covered include:
    - Research Data Lifecycle
    - Data Management Plans with funding requirements from many agencies
    - Sharing Data
    - Managing Data
    Workshops and tutorials are available as recordings, slides, and exercises on topics such as:  Data Literacy for Postdocs, Increasing Openness and Reproducibility using the OSF, and Research Data Life Cycle.

  • 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.  Topics include:
    Week 1:  Introduction
    Week 2:  Ethics
    Week 3:   Data Governance
    Week 4:  Data Architecture & Data Modeling and Design
    Week 5:  Data Storage & Operations - Data Security
    Week 6:  Data Storage & Operations - Data Security
    Week 7: Data Integration & Operability, Metadata

  • Coffee and Code: The Command Line - An Introduction

    Graphical user interfaces are fast, often more than fast enough to suit our needs. GUIs are feature rich, can be intuitive, and often filter out a lot of stuff we don't need to know about and aren't interested in. Nearly everything we need to do can be done simply and quickly using a GUI.

    The command line is a great resource for speeding up and automating routine activities without using a lot of processing power. In some cases, it can be better for:

    • Searching for files
    • Searching within files
    • Reading and writing files and data
    • Network activities

    Some file and data recovery processes can only be executed from the command line.

    Plus:

    • The command line is old fashioned
    • Potential efficiency gains take time to manifest
    • Even Neal Stephenson says it's obsolete
  • Coffee and Code: TaskJuggler

    What is TaskJuggler?

    TaskJuggler is an open source project (written in Ruby) planning and management application that provides a comprehensive set of tools for project planning, management, and reporting. Versions of TaskJuggler are available for Linux, the Mac OS, and Windows and multiple docker containers have been created that encapsulate TaskJuggler for ease of execution without having to directly install it within the host computer operating systemn.

    Some key characteristics of TaskJuggler include:

    • Text-based configuration files
    • A command-line tool that is run to perform scheduling and report generation
    • An optional local server process that can be run and with which a client tool can interact to more rapidly generate reports for projects that have been loaded into the server
    • Email-based workflows for large-scale project tracking
    • Support for web-based, CSV, and iCal reports enabling delivery of plan products through web browsers, further analysis and visualization of scheduling data outside of TaskJuggler, and sharing of project plan for integration into calendar systems.
    • Scenario support for comparing alternative project paths.
    • Accounting capabilities for estimating and tracking costs and revenue through the life of a project.
  • Coffee and Code: Database Basics

    Why Use a Database to Organize Your Data

    • Consisten structure - defined by you
    • Enforced data types
    • Can scale from single tables to sophisticated relational data models
    • Can be a personal file-based or shared server-based solution, depending on your needs
    • Standard language for interacting with your data
    • "Virtual Tables" can be created on the fly based on database queries 
    • Data can be accessed by many analysis tools
  • Coffee and Code: R & RStudio

    What is R?
    R is an [Open Source](https://opensource.org) programming language that is specifically designed for data analysis and visualization. It consists of the [core R system](https://cran.r-project.org) and a collection of (currently) over [13,000 packages](http://cran.cnr.berkeley.edu) that provide specialized data manipulation, analysis, and visualization capabilities. R is an implementation of the *S* statistical language developed in the mid-1970s at Bell Labs, with the start of development in the early 1990s and a stable beta version available by 2000. R has been under continuous development for over 25 years and has hit major development [milestones](https://en.wikipedia.org/wiki/R_\(programming_language\)#Milestones) over that time.
    R syntax is relatively straighforward and is based on a core principle of providing reasonable default values for many functions, and allowing a lot of flexibility and power through the use of optional parameters.

  • 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).  

  • The Geoscience Paper of the Future: OntoSoft Training

    This presentation was developed to train scientists on best practices for digital scholarship, reproducibility, and data and software sharing.  It was developed as part of the NSF EarthCube Initiative and funded under the OntoSoft project.  More details about the project can be found at http://www.ontosoft.org/gpf.
    A powerpoint version of the slides is available upon request from [email protected].
    These OntoSoft GPF training materials were developed and edited by Yolanda Gil (USC), with contributions from the OntoSoft team including Chris Duffy (PSU), Chris Mattmann (JPL), Scott Pechkam (CU), Ji-Hyun Oh (USC), Varun Ratnakar (USC), Erin Robinson (ESIP).  They were significantly improved through input from GPF pioneers Cedric David (JPL), Ibrahim Demir (UI), Bakinam Essawy (UV), Robinson W. Fulweiler (BU), Jon Goodall (UV), Leif Karlstrom (UO), Kyo Lee (JPL), Heath Mills (UH), Suzanne Pierce (UT), Allen Pope (CU), Mimi Tzeng (DISL), Karan Venayagamoorthy (CSU), Sandra Villamizar (UC), and Xuan Yu (UD).  Others contributed with feedback on best practices, including Ruth Duerr (NSIDC), James Howison (UT), Matt Jones (UCSB), Lisa Kempler (Matworks), Kerstin Lehnert (LDEO), Matt Meyernick (NCAR), and Greg Wilson (Software Carpentry).  These materials were also improved thanks to the many scientists and colleagues that have taken the training and asked hard questions about GPFs.

  • OntoSoft Tutorial: A distributed semantic registry for scientific software

    An overview of the OntoSoft project, an intelligent system to assist scientists in making their software more discoverable and reusable.
    For more information on the OntoSoft project, go to ​http://imcr.ontosoft.org.

  • 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

  • Rethinking Research Data | Kristin Briney | TEDxUWMilwaukee

    The United States spends billions of dollars every year to publicly support research that has resulted in critical innovations and new technologies. Unfortunately, the outcome of this work, published articles, only provides the story of the research and not the actual research itself. This often results in the publication of irreproducible studies or even falsified findings, and it requires significant resources to discern the good research from the bad. There is way to improve this process, however, and that is to publish both the article and the data supporting the research. Shared data helps researchers identify irreproducible results. Additionally, shared data can be reused in new ways to generate new innovations and technologies. We need researchers to “React Differently” with respect to their data to make the research process more efficient, transparent, and accountable to the public that funds them.
    Kristin Briney is a Data Services Librarian at the University of Wisconsin-Milwaukee. She has a PhD in physical chemistry, a Masters in library and information studies, and currently works to help researchers manage their data better. She is the author of “Data Management for Researchers” and regular blogs about data best practices at dataabinitio.com.
    This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

  • Data Carpentry Ecology Workshop

    Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. This workshop uses a tabular ecology dataset and teaches data cleaning, management, analysis and visualization.
    The workshop can be taught using R or Python as the base language.
    Overview of the lessons:
    Data organization in spreadsheets
    Data cleaning with OpenRefine
    Introduction to R or python
    Data analysis and visualization in R or python
    SQL for data management

  • 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/.