Data management involves developing strategies and processes to ensure that research data are well organized, formatted, described, and documented, during a project’s lifecycle to support the potential sharing and archiving of resultant data. General good practices and resources are highlighted in this guide and short video.
This workshop provides an overview of Duke's Research Data Repository. The general functionalities of the platform and tips for submitting data are discussed as well as how repositories can help researchers comply with funder and journal policies as well as meet growing standards around data stewardship and sharing, such as the FAIR Guiding Principles. New features are also demonstrated including a new integration with the Globus platform to support transferring large-scale data.
Humanists work with various media, content and materials (sources) as part of their research. These sources can be considered data. This workshop introduces data management practices for humanities researchers to consider and apply throughout the research lifecycle. Good data management practices pertaining to planning, organization, documentation, storage and backup, sharing, citation, and preservation will be presented through a humanities lens with discipline-based, concrete examples.
The Open Science Framework (OSF) is a free, open source project management tool developed and maintained by the Center for Open Science. The OSF can help scholars manage their workflow, organize their materials, and share all or part of a project with the broader research community. This workshop will demonstrate some of the key functionalities of the tool including how to structure your materials, manage permissions, version content, integrate with third-party tools (such as Box, GitHub, or Mendeley), share materials, register projects, and track usage. This workshop was presented in the Spring of 2018.
In the course of your research you may collect, interact with or analyze data that are classified as “Sensitive” or ""Restricted"" according to Duke's data classification standard. In this workshop we will examine common sensitive data types, how Duke’s IRB and Information Technology Security Office (ITSO) expects you to protect that data throughout your project’s lifecycle and the resources available to you for sensitive data storage and analysis, data de-identification, and data archiving and sharing.
Data management practices help researchers take care of their data throughout the entire research process from the planning phase to the end of a project when data might be shared or “published” within a repository. This workshop provides hands-on experience where participants will learn strategies for how to prepare data for publishing by “curating” an example dataset and identifying common data issues using the Data Curation Network “CURATE” model. Participants will also learn about the overall role of repositories within the data sharing landscape and learn strategies for locating and assessing repositories.
This workshop explores the many different ethical issues that can arise with data management and sharing and strategies to address those issues to ensure that goals set by publishers and funders around reproducibility and reuse can be met. How are researchers expected to comply with data sharing policies and practices when they do not actually own the data or ensure disclosure protection for human participants? Likewise how can researchers ethically collect, handle, and share data from certain communities, such as Indigenous People? Topics covered will include proper consent procedures, de-identification, the impact of privacy laws on data sharing, and the application of diversity and equity principles to open science and data sharing.
This workshop was a collaboration between the Duke Office of Scientific Integrity and the Duke University Libraries. There are many federal and private funders who require data management plans as part of a grant application, including NIH who recently released a new Data Management and Sharing Policy that takes effect in 2023 and will apply to all grants. This workshop covers the components of a data management plan, what makes a strong plan and how to adhere to it, and where to find guidance, tools, resources, and assistance for building funder-based plans. We also discuss how to make data management plans actionable and meaningful living documents to support research integrity, reproducibility, reuse, and verification of results.
Enhance your reproducible workflows using modern computational techniques supporting functional data life-cycle managment
- Quick-start: data wrangling (Part 1) & visualization, pivots, and joins (Part 2)
- ggplot2 visualization
- Databases: remote SQL queries with R
- GIS: Mapping
- GIS: guide to geospatial data
- Git and GitHub: version control
- Reports: slides and websites with R Markdown
- Reproducible Workflow with R and GitHub
- Sentiment Analysis
- Shiny applications
- Slides with xaringan
- Twitter data with rtweet
- Web Scraping
The Python programming language is a great option for exploration, analysis and visualization of tabular (spreadsheet) data, such as spreadsheets and CSV files. This series of workshops will take you through some practical examples, from basic to advanced, using the Pandas module to load and transform data for analysis and visualization. There is also a video motivating why Humanities scholars could benefit from learning Python, showing examples of work that would have been very hard to do in other ways.
Open Refine allows for easy exploration of data. Define facets within data, identify data inconsistencies, quickly clean and transform data. Open Refine is an often intuitive but powerful tool for normalizing data. Use this before importing the dataset into a presentation application (e.g. mapping, charting, or analyzing.)
Regular Expressions are a powerful method of finding patterns in text. For example: find all words ending in ""ing""; all words which begin with a capital letter; all telephone area codes that begin with either the numbers 7 or 8; all email addresses which contain ""duke.edu"". Many programming languages use regular expressions as a means to support pattern matching.
Many research projects involve textual data, and computational advances now provide the means to engage in various types of automated text analysis that can enhance these projects. Understanding what analysis techniques are available and where they can appropriately be applied is an important first step to beginning a text analysis project. This hands-on approach to text analysis will give a quick overview of small- and large-scale text-based projects before addressing strategies for organizing and conducting text analysis projects. Tools for data collection, parsing and eventual analysis will be introduced and demonstrated. The workshop will focus on acquiring and preparing text sources for small-scale projects and text-based visualizations, but many of the techniques will be useful for larger projects as well. For this introduction, the focus will primarily be on using Graphical User Interface (GUI) tools like Microsoft Excel and Google Refine, instead of programming languages and command line approaches.
Preexisting clean data sets such as the General Social Survey (GSS) or Census data, for example, are readily available, cover long periods of time, and have well documented codebooks. However, some people want to gather their own data. Recent tools and techniques for finding and compiling data from webpages, whole websites or social media sources have become more accessible. But these techniques provide a different layer of complexity.
The purpose of this workshop is to demonstrate simple steps in Excel that you can take to transform a single spreadsheet (such as a master copy of your data that you used to facilitate the gathering process) into a series of normalized tables that can be used to populate a relational database model using, for example, MySQL. In order to accomplish this, we first identify entities and corresponding attributes of those entities within the master datasheet, and then create separate tables for each entity that can be connected to one another by foreign keys (columns that reference, by means of an identification code, columns present in other tables). This workshop does not require any coding experience, but it is recommended that users are familiar with Excel basics.
In the first workshop, held in Spring 2021, Angela Zoss focuses on ggplot2, a library for R that creates clear and well-designed visualizations and that plays well with other tidyverse packages. We get up and running quickly with ggplot2, going through a variety of examples to learn how to understand, modify, and create ggplot2 visualizations. Building basic skills with visualization will improve your ability to create quick, exploratory visualizations for data analysis as well as more formal, outward-facing visualizations for presentations or publications. The second video covers more advanced topics like dealing with categorical data.
ArcGIS Online (AGOL) is a companion to the ArcGIS client that allows members of a group to store and share spatial data online and that can be used independently or in conjunction with the client. We'll discuss aspects of the AGOL organizational account, adding and accessing content, creating map and feature services, creating and sharing web maps and presentations, publishing web applications, and using analysis tools.
R has become a popular and reproducible option for supporting spatial and statistical analysis. This hands-on workshop will demonstrate how to plot x/y coordinates; how to generate thematic choropleths with US Census and other federal data; import, view and produce shapefiles; and create leaflet maps for viewing on the web.