All Learning Resources

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)

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. 

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.

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.

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 .

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.

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.

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.

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.

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.

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. 

To prepare data for archival it must be organized in well-formatted, described, and documented datasets. Benefits of good data management include:

Short-term
Spend less time doing data management and more time doing research
Easier to prepare and use data for yourself
Collaborators can readily understand and use data files

Long-term (data publication)
Scientists outside your project can find, understand, and use your data to address broad questions
You get credit for archived data products and their use in other papers
Sponsors protect their investment

This page provides an overview of data management planning and preparation. It offers practical methods to successfully share and archive your data at the ORNL DAAC.  Topics include:  Best Practices for Data Management, Writing Data Management Plans including examples of data management plans, How-to's amd Resources.  

The Top 10 FAIR Data & Software Global Sprint was held online over the course of two-days (29-30 November 2018), where participants from around the world were invited to develop brief guides (stand alone, self paced training materials), called "Things", that can be used by the research community to understand FAIR in different contexts but also as starting points for conversations around FAIR. The idea for "Top 10 Data Things" stems from initial work done at the Australian Research Data Commons or ARDC (formerly known as the Australian National Data Service).

The Global Sprint was organised by Library Carpentry, Australian Research Data Commons and the Research Data Alliance Libraries for Research Data Interest Group in collaboration with FOSTER Open Science, OpenAire, RDA Europe, Data Management Training Clearinghouse, California Digital Library, Dryad, AARNet, Center for Digital Scholarship at the Leiden University, and DANS. Anyone could join the Sprint and roughly 25 groups/individuals participated from The Netherlands, Germany, Australia, United States, Hungary, Norway, Italy, and Belgium. See the full list of registered Sprinters.

Sprinters worked off of a primer that was provided in advance together with an online ARDC webinar introducing FAIR and the Sprint titled, "Ready, Set, Go! Join the Top 10 FAIR Data Things Global Sprint." Groups/individuals developed their Things in Google docs which could be accessed and edited by all participants. The Sprinters also used a Zoom channel provided by ARDC, for online calls and coordination, and a Gitter channel, provided by Library Carpentry, to chat with each other throughout the two-days. In addition, participants used the Twitter hashtag #Top10FAIR to communicate with the broader community, sometimes including images of the day.

Participants greeted each other throughout the Sprint and created an overall welcoming environment. As the Sprint shifted to different timezones, it was a chance for participants to catch up. The Zoom and Gitter channels were a way for many to connect over FAIR but also discuss other topics. A number of participants did not know what to expect from a Library Carpentry/Carpentries-like event but found a welcoming environment where everyone could participate.

The ReaDI Program has created several tutorials and templates to aid in the management of data during the collection phase of research and preparing for publication.  Tutorial topics include:   Good Laboratory Notebook Practices, Laboratory Notebook Checklist, Best Practices for Data Management When Using Instrumentation, and Guidelines on the Organization of Samples in a Laboratory.   Downloadable templates are available on related topics, such as data to figure map templates.  

The Research and Data Integrity (ReaDI) program is designed to enhance data management and research integrity at Columbia University. The ReaDI program provides resources, outreach and consultation services to researchers at all stages in their careers. Many of the resources are applicable to researchers at any institution.

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.

Research Data Services is a collaboration between the University of Iowa Libraries, the Office of the Vice President of Research and Economic Development, Information Technology Services, and other campus offices, to support researchers' data management needs. The  guides that are part of these Services include answers to key questions, but may also include short videos on the following topics:
- Data Management Plans
- Data Organization and Documentation
- Data Repositories
- Datasets
- Other University of Iowa services and resources available as well as external tools, websites, and repositories that may be useful.

This workshop provides an introduction to reproducibility and communication of research using notebooks based on RStudio and Jupyter Notebooks. The development of effective documentation and accesible and reusable methods in scientific analysis can make a significant contribution to the reproducibility and understanding of a research activity.  The integration of executable code with blocks of narrative content within notebook systems such as those provided in RStudio and the Jupyter Notebook (and Lab) software environments provides a streamined way to bring these minimum components (data, metadata, code, and software) into a package that can be easily shared with others for review and reuse.

This workshop will provide:  

• A high-level introduction to the notebook interfaces provided for R and Python through the RStudio and Jupyter Notebook environments.
• An introduction to Markdown as a language supported by both systems for adding narrative content to notebooks
• Sample notebooks illustrating structure, content, and output options

 From the master page for this resource, the Reproducibility and Communication Using Notebooks ipynb file provides more information about what is covered in this workshop.  

This is a tutorial about version control, also known as revision control, a method for tracking changes to files and folders within a source code tree, project, or any complex set of files or documents.

Also see ​Advanced Version Control, here: ​https://github.com/unmrds/cc-version-control/blob/master/03-advanced-ver...

In this session, we are going to dig a little deeper into databases as representions of systems and processes. A database with a single table may not feel or function much differently from a spreadsheet. Much of the benefit of using databases results from designing them as models of complex systems in ways that spreadsheets just can't do:

• Inventory control and billing
• Human resources
• Blogging platforms
• Ecosystems

There will be some more advanced SQL statements this time, though we will still be using SQLite. Concepts which will be discussed and implemented in our code include

• Entities and attributes
• Keys
• Relationships
• Normalization