All Learning Resources

There are several important elements to digital preservation, including data protection, backup and archiving. In this lesson, these concepts are introduced and best practices are highlighted with case study examples of how things can go wrong. Exploring the logistical, technical and policy implications of data preservation, participants will be able to identify their preservation needs and be ready to implement good data preservation practices by the end of the module. This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.

Data citation is a key practice that supports the recognition of data creation as a primary research output rather than as a mere byproduct of research. Providing reliable access to research data should be a routine practice, similar to the practice of linking researchers to bibliographic references. After completing this lesson, participants should be able to define data citation and describe its benefits; to identify the roles of various actors in supporting data citation; to recognize common metadata elements and persistent data locators and describe the process for obtaining one, and to summarize best practices for supporting data citation. This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
 

Conversations regarding research data often intersect with questions related to ethical, legal, and policy issues for managing research data. This lesson will define copyrights, licenses, and waivers, discuss ownership and intellectual property, and describe some reasons for data restriction. After completing this lesson, participants will be able to identify ethical, legal, and policy considerations that surround the use and management of research data. The 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.

The Environmental Data Initiative (EDI) data repository is a metadata-driven archive for environmental and ecological research data described by the Ecological Metadata Language (EML). This webinar will provide an overview of the PASTA software used by the repository and demonstrate the essentials of uploading a data package to the repository through the EDI Data Portal. 

Project Jupyter, evolved from the IPython environment, provides a platform for interactive computing that is widely used today in research, education, journalism, and industry. The core premise of the Jupyter architecture is to design tools around the experience of interactive computing, building an environment, protocol, file format and libraries optimized for the computational process when there is a human in the loop, in a live iteration with ideas and data assisted by the computer.

The Jupyter Notebook, a system that allows users to compose rich documents that combine narrative text and mathematics together with live code and the output of computations in any format compatible with a web browser (plots, animations, audio, video, etc.), provides a foundation for scientific collaboration. The next generation of the Jupyter web interface, JupyterLab, will combine in a single user interface not only the notebook but multiple other tools to access Jupyter services and remote computational resources and data.  A flexible and responsive UI allows the user to mix Notebooks, terminals, text editors, graphical consoles and more, presenting in a single, unified environment the tools needed to work with a remote environment.  Furthermore, the entire design is extensible and based on plugins that interoperate via open APIs, making it possible to design new plugins tailored to specific types of data or user needs.

JupyterHub enables Jupyter Notebook and JupyterLab to be used by groups of users for research collaboration and education. We believe JupyterHub provides a foundation on which to build modern scientific gateways that support a wide range of user scenarios, from interactive data exploration in high-level languages like Python, Julia or R, to the education of researchers and students whose work relies on traditional HPC resources.

The presenter discusses the benefits and applications of Jupyter Notebooks.

Scroll to the bottom of the page to view the webinar. Presentation slides are also available on the same page. 

Short video discussing the "Research Lifecycle at University of Central Florida," a useful diagram for understanding the typical flow of a research project.

Taxonomic data can be messy and challenging to work with. Incorrect spelling, the use of common names, unaccepted names, and synonyms, contribute to ambiguity in what a taxon actually is. The taxonomyCleanr R package helps you resolve taxonomic data to a taxonomic authority, get accepted names and taxonomic serial numbers, as well as create metadata for your taxa in the Ecological Metadata Language (EML) format.

The Experimental Design Assistant (EDA) (https://eda.nc3rs.org.uk) is a free web-based tool that was developed by the NC3Rs (https://www.nc3rs.org.uk). It guides researchers through the design and analysis of in vivo experiments. The EDA allows users to build a stepwise visual representation of their experiment, providing feedback and dedicated support for randomization, blinding and sample size calculation. This demonstration will provide an introduction to the tool and provide guidance on getting started. Ultimately, the use of a tool such as the EDA will lead to carefully designed experiments that yield robust and reproducible data using the minimum number of animals consistent with scientific objectives. 

Licensing your research outputs is an important part of practicing Open Science. After completing this course, you will:

• Know what licenses are, how they work, and how to apply them 
• Understand how different types of licenses can affect research output reuse
• Know how to select the appropriate license for your research 

This tutorial explains how to use the “Habitat-Phenotype Relation Extractor for Microbes” application available from the OpenMinTeD platform. It also explains the scientific issues it addresses, and how the results of the TDM process can be queried and exploited by researchers through the Florilège application.  

In recent years, developments in molecular technologies have led to an exponential growth of experimental data and publications, many of which are open, however accessible separately. Therefore, it is now crucial for researchers to have bioinformatics infrastructures at their disposal, that propose unified access to both data and related scientific articles. With the right text mining infrastructures and tools, application developers and data managers can rapidly access and process textual data, link them with other data and make the results available for scientists.

The text-mining process behind Florilege has been set up by INRA using the OpenMinTeD environment. It consists in extracting the relevant information, mostly textual, from scientific literature and databases. Words or word groups are identified and assigned a type, like  “habitat” or “taxon”.

Sections of the tutorial:
1. Biological motivation of the Florilege database
2. Florilège Use-Case on OpenMinTeD (includes a description of how to access the Habitat-Phenotype Relation Extractor for Microbes application)
3. Florilege backstage: how is it build?
4. Florilège description
5. How to use Florilege ?

Data-driven research is becoming increasingly common in a wide range of academic disciplines, from Archaeology to Zoology, and spanning Arts and Science subject areas alike. To support good research, we need to ensure that researchers have access to good data. Upon completing this course, you will:

• Understand which data you can make open and which need to be protected
• Know how to go about writing a data management plan
• Understand the FAIR principles
• Be able to select which data to keep and find an appropriate repository for them
• Learn tips on how to get maximum impact from your research data

This talk is the first in a series of NSF's Office of Advanced Cyberinfrastructure (OAC) webinars. Dr. Deelman describes the challenges of developing and sustaining cyberinfrastructure capabilities that have impact on scientific discovery and that innovate in the changing cyberinfrastructure landscape. The recent multi-messenger observation triggered by LIGO and VIRGO’s first detection of gravitational waves produced by colliding neutron stars is a clear display of the increasing impact of dependable research cyberinfrastructure (CI) on scientific discovery.

Today’s cyberinfrastructure—hardware, software, and workforce—underpins the entire scientific workflow, from data collection at instruments, through complex analysis, to simulation, visualization, and analytics. The Pegasus project in an example of a cyberinfrastructure effort that enables LIGO and other communities to accomplish their scientific goals. In addition, it delivers robust automation capabilities to researchers at the Southern California Earthquake Center (SCEC) studying seismic phenomena, to astronomers seeking to understand the structure of the universe, to material scientists developing new drug delivery methods, and to students seeking to understand human population migration.
 

The ISRIC Spring School aims to introduce participants to world soils, soil databases, software for soil data analysis and visualisation, digital soil mapping and soil-web services through two 5-day courses run in parallel.  Target audiences for the Spring School include soil and environmental scientists involved in (digital) soil mapping and soil information production at regional, national and continental scales; soil experts and professionals in natural resources management and planning; and soil science students at MSc and PhD level.  Examples courses include "World Soils and their Assessment (WSA) and Hands-on Global Soil Information Facilities (GSIF).  Data management topics are included within the course topics.

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

The programme comprises:

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

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

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

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

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

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.

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.

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.