All Learning Resources

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

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

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

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

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

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

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

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

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

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

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

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

  • Research Data Management Hands on Workshop

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

  • 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 [email protected] 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)

  • 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. 

  • 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 [email protected].

    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. 

  • Digital Curation 101 Materials

    Digital Curation 101 employs the curation lifecycle model sections as a means of presenting content to students by means of the curricula materials on this website.  The model enables granular functionality to be mapped against it: to define roles and responsibilities and build a framework of standards and technologies to implement.  The model describes digital curation in the following stages:  Conceptualisation, Create and or Receive, Appraise and Select, Ingest, Preservation Action, Store, Access and Reuse.

    It can be used to help identify additional steps that may be required – or actions not required by certain situations or disciplines – and to ensure that processes and policies are adequately documented. 

    The DCC is keen to support the reuse of our generic training materials as the basis of more specific training aimed at different disciplines and/or institutions. Our generic materials are accessible for review and tailoring.

    We kindly request that you cite these materials in any derivatives that you develop and encourage you to share your tailored materials with us so that we can disseminate them to a wider audience.  Archived versions of this curriculum are available from the main website.

  • DataONE Data Management Module 02: Data Sharing

    When first sharing research data, researchers often raise questions about the value, benefits, and mechanisms for sharing. Many stakeholders and interested parties, such as funding agencies, communities, other researchers, or members of the public may be interested in research, results and related data. This 30-40 minute lesson addresses data sharing in the context of the data life cycle, the value of sharing data, concerns about sharing data, and methods and best practices for sharing data and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.

  • DataONE Data Management Module 01: Why Data Management

    As rapidly changing technology enables researchers to collect large, complex datasets with relative ease, the need to effectively manage these data increases in kind. This is the first lesson in a series of education modules intended to provide a broad overview of various topics related to research data management. This 30-40 minute module covers trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data life cycle and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.

  • Introduction to R

    In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis.  Topics include:  an introduction to basics, vectors, matrices, factors, lists and data forms.  Approximately 62 exercises are included.

  • Intro to Python for Data Science

    Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses.  Topics covered include:  Python basics, Python lists, functions and packages, and NumPy, an array package for Python.

  • Using, learning, teaching, and programming with the Paleobiology Database

    The Paleobiology Database is a public database of paleontological data that anyone can use, maintained by an international non-governmental group of paleontologists. You can explore the data online in the Navigator, which lets you filter fossil occurrences by time, space, and taxonomy, and displays their modern and paleogeographic locations; or you can download the data to your own computer to do your own analyses.  The educational resources offered by the Paleobiology include:
    - Presentations including lectures and slide shows to introduce you to the PBDB
    - Web apps that provide a variety of online interfaces for exploring PBDB data via the API
    - Mobile apps that provide applications for iOS and Android providing new views of the PBDB's data via the API
    - Lesson plans and teaching activities using the Paleobiology Database
    - Tutorials on how to get and use data from the website, and on how to contribute data to the database, viewable on Youtube
    - Libraries and functions for interacting with PBDB data via R   
    - Documentation, code examples, and issue reporting for the PBDB API
    - Other Paleobiology Database related external resources including a link to the Paleobiology Github repository
    For more information about the Paleobiology Database, see:  https://paleobiodb.org/#/faq .

  • Intermediate R

    The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in the R programming language, data structure manipulations and times and dates. This R tutorial will allow you to learn R and take the next step in advancing your overall knowledge and capabilities while programming in R.

  • Hivebench Electronic Lab Notebook

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

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

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

  • Introduction to SAGA GIS Software

    A quick introduction to the System for Automated Geographic Analysis (SAGA) GIS software which is an open source Geographic Information System software package. SAGA GIS has been designed for an easy and effective implementation of spatial algorithms and offers a comprehensive, crowing set of geoscientific methods. A data management module is included in the software.

  • ESRI Academy: Data Management

    ESRI, the creator of ArcMap and other Geographic Information Systems (GIS) software product, provides a large number of training courses on topics that include Data Management as well as other skills such as the use of GIS, Python Programming, and other GIS skills.  The types of training materials include tutorials, videos, web courses, instructor-led courses, training seminars, learning plans (including one that leads to 6 courses on the Fundamentals of Data Management) and story maps.  Some training materials are available online while others are on location;  some are free, and some have an associated fee.  Each course provides a certificate once it is completed.  

  • Data Carpentry Geospatial Workshop

    This workshop is designed to teach both general geospatial concepts, but also build capacity related to the use of the "R" programming language for data management skills.  The learner will find out how to use "R" with geospatial data, particularly geospatial raster and vector data.  The workshop lessons include:
    - Introduction to Geospatial Concepts to help the learner understand data structures and common storage and transfor formats for spatial data. The goal of this lesson is to provide an introduction to core geospatial data concepts. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience working with geospatial data.

    - Introduction to R for Geospatial Data to help the learner import data into $, cacluate summary statistics, and create publication-quality graphics by providing an introduction to the R programming language.

    - Introduction to Geospatial Raster and Vector Data with R in which the learner will open, work with, and plot vector and raster-format spatial data in R.   This lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.  It assumes that learners are already familiar with both geospatial data concepts and the core concepts of R.

  • 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.

  • 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 

  • ETD+ Toolkit: Training Students to manage ETD+ research outputs

    The ETD+ Toolkit is a Google Drive Open Curriculum package that is an approach to improving student and faculty research output management. Focusing on the Electronic Thesis and Dissertation (ETD) as a mile-marker in a student’s research trajectory, it provides in-time advice to students and faculty about avoiding common digital loss scenarios for the ETD and all of its affiliated files.

    The ETD+ Toolkit provides free introductory training resources on crucial data curation and digital longevity techniques. It has been designed as a training series to help students and faculty identify and offset risks and threats to their digital research footprints.

    What it is:
    An open set of six modules and evaluation instruments that prepare students to create, store, and maintain their research outputs on durable devices and in durable formats. Each is designed to stand alone; they may also be used as a series.

    What each module includes:
    Each module includes Learning Objectives, a one-page Handout, a Guidance Brief, a Slideshow with full presenter notes, and an evaluation Survey. Each module is released under a CC-BY license and all elements are openly editable to make reuse as easy as possible.

  • Open Access to Publications in Horizon 2020 (May 2017)

    This webinar is part of the OpenAIRE Spring Webinars 2017.
    It dealt with the Open Access mandate in H2020, what is expected of projects with regards to the OA policies in H2020 and how OpenAIRE can help. 

    Webinar led by Eloy Rodrigues and Pedro Príncipe (UMinho)

    Webinar presentation: https://www.slideshare.net/OpenAIRE_eu/openaire-webinar-open-access-to-publications-in-horizon-2020-may-2017
    Webinar recordings: https://webinars.eifl.net/2017-05-29_OpenAIRE_H2020_OAtopublications/index.html

    Last updated on 30 December 2017.

  • DMP Bingo - the good, the bad, the ugly (v.2)

    Updated to v2 on 2016-11-10

    An activity for teaching research data management and data management plans. The bingo cards have both "good" and "bad" DMP attributes which can be used for discussion.
    Set contains:
    1. Instructions
    2. A set of 20 different bingo cards in 2 formats ("ready to print" PDF file /  editable Excel file). 
    3. A zip file containing 7 DMPs
    All DMPs included in this file-set have had identifying information deleted or changed. Institutions may be identifiable but no individuals.

  • Smithsonian Libraries: Describing Your Project : Citation Metadata

    Smithsonian Libraries Metadata Guide.  

    The overall description for your project could be referred to as project metadata, citation metadata, a data record, a metadata record, or a dataset record. The information supplied in the project description should be sufficient to enable you and others to find and properly cite your data.

    A metadata record gives the basic who, what, where, and when of the data. It is a high level description that others can use to cite your data. It may be submitted with a dataset as a separate file when deposited in a repository, or displayed in the repository with data entered into a form.

  • Metacat Administrator's Guide

    Metacat is a repository for data and metadata (documentation about data) that helps scientists find, understand and effectively use data sets they manage or that have been created by others. Thousands of data sets are currently documented in a standardized way and stored in Metacat systems, providing the scientific community with a broad range of science data that–because the data are well and consistently described–can be easily searched, compared, merged, or used in other ways.  

    This Metacat Administrator's Guide includes instruction on the following topics:
    Chapter 1:  Introduction
    Chapter 2:  Contributors
    Chapter 3:  License
    Chapter 4:  Downloading and installing Metacat
    Chapter 5:  Configuring Metacat
    Chapter 6:  DataONE Member Node Support
    Chapter 7:  Accessing and submitting Metadata and data
    Chapter 8:  Metacat indexing
    Chapter 9:  Modifying and creating themes
    Chapter 10:  Metacat authentication mechanism
    Chapter 11:  Metacat's use of Geoserver
    Chapter 12:  Replication
    Chapter 13:  Harvester and harvest list editor
    Chapter 14:  OAI protocol for metadata harvesting
    Chapter 15:  Event logging
    Chapter 16:  Enabling web searches:  sitemaps
    Chapter 17:  Appendix:  Metacat properties
    Chapter 18:  Appendix:  Development issues

  • QGIS - for Absolute Beginners

    This video is a complete rundown of the basics in QGIS, a free GIS software package designed as an alternative to ArcMap. 
    QGIS is a user friendly Open Source Geographic Information System (GIS) licensed under the GNU General Public License. QGIS is an official project of the Open Source Geospatial Foundation (OSGeo). It runs on Linux, Unix, Mac OSX, Windows and Android and supports numerous vector, raster, and database formats and functionalities.

     

  • QGIS Training Manual

    A training manual written by the QGIS Development Team. It includes instruction on the basic use of the QGIS interface, applied applications,  and other basic operations.   Topics include:  general tools, QGIS GUI, working with projections, raster and vector data, managing data sources and integration with GRASS GIS.  Examples are given of working with GPS and OGC data.  A list of plugins is also included.

    QGIS aims to be a user-friendly GIS, providing common functions and features. The initial goal of the project was to provide a GIS data viewer. QGIS has reached the point in its evolution where it is being used by many for their daily GIS data-viewing needs. QGIS supports a number of raster and vector data formats, with new format support easily added using the plugin architecture.

  • Training Materials for Data Management in Reclamation

    This document (downloadable from this landing page) provides supplementary educational materials focused upon US Bureau of Reclamation (USBR) approaches to data management that use and expand upon a number of USGS training modules on data management.  The USBR supplementary materials include:

    • A discussion of the Reclamation data lifecycle
    • A Reclamation data management plan template
    • Examples of Reclamation data management best practice
    • Lessons learned from various USBR data management efforts. 
  • 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