Duke University Libraries has partnered with the Qualitative Data Repository (QDR) as an institutional member to provide qualitative data sharing, curation, and preservation services to the Duke community. QDR is located at Syracuse University and has staff and infrastructure in place to specifically address some of the unique needs of qualitative data including curating data for future reuse, providing mediated access, and assisting with Data Use Agreements.
Duke University Libraries has long been committed to helping our scholars make their research openly accessible and stewarding these materials for the future. Over the past few years, this has included launching a new data repository and curation program, which accepts data from any discipline as well as joining the Data Curation Network. Now through our partnership with QDR we can further enhance our support for sharing and archiving qualitative data.
Qualitative data come in a variety of forms including interviews, focus groups, archival materials, textual documents, observational data, and some surveys. QDR can help Duke researchers have a broader impact through making these unique data more widely accessible.
“Founded and directed by qualitative researchers, QDR is dedicated to helping researchers share their qualitative data,” says Sebastian Karcher, QDR’s associate director. “Informed by our deep understanding of qualitative research, we help researchers share their data in ways that reflect both their ethical commitments and do justice to the richness and diversity of qualitative research. We couldn’t be more excited to continue our already fruitful partnership with Duke University Libraries”
Through this partnership, Duke University Libraries will have representation on the governance board of QDR and be involved in the latest developments in managing and sharing qualitative data. The libraries will also be partnering with QDR to provide virtual workshops in the spring semester at Duke to enhance understanding around the sharing and management of qualitative research data.
Felipe Álvarez de Toledo López-Herrera is a Ph.D. candidate at the Art, Art History, and Visual Studies Department at Duke University and a Digital Humanities Graduate Assistant for Humanities Unbounded, 2019-2020. Contact him at email@example.com.
Over the 2019-2020 academic year, I am serving as a Humanities Unbounded graduate assistant in Duke Libraries’ Center for Data and Visualization Sciences. As one of the three Humanities Unbounded graduate assistants, I will partner on Humanities Unbounded projects and focus on developing skills that are broadly applicable to support humanities projects at Duke. In this blog post, I would like to introduce myself and give readers a sense of my skills and interests. If you think my profile could address some of the needs of your group, please reach out to me through the email above!
My own dissertation project began with a data dilemma. 400 years ago, paintings were shipped across the Atlantic by the thousands. They were sent by painters and dealers in places like Antwerp or Seville, for sale in the Spanish colonies. But most of these paintings were not made to last. Cheap supports and shifting fashions guaranteed a constant renewal of demand, and thus more work for painters, in a sort of proto-industrial planned obsolescence.As a consequence, the canvas, the traditional data point of art history, was not a viable starting point for my own research, rendering powerless many of the tools that art history has developed for studying painting. I was interested in examining the market for paintings as it developed in Seville, Spain from 1500-1700; it was a major productive center which held the idiosyncratic role of controlling all trade to the Spanish colonies for more than 200 years. But what could I do when most of the work produced within it no longer exists?
This problem drives my research here at Duke, where I apply an interdisciplinary, data-driven approach. My own background is the product of two fields: I obtained a bachelor’s degree in Economics in my hometown of Barcelona, Spain in 2015 from the Universitat Pompeu Fabra, and simultaneously attended art history classes in the University of Barcelona. This combination found a natural mid-way point in the study of art markets. I came to Duke to be a part of DALMI, the Duke, Art, Law and Markets Initiative, led by Professor Hans J. Van Miegroet, where I was introduced to the methodologies of data-driven art historical research.
Documents in Seville’s archives reveal a stunning diversity of production that encompasses the religious art for which the city is known, but also includes still lives, landscapes and genre scenes whose importance has been understated and of which few examples remain [Figures 1 & 2]. But analysis of individual documents, or small groups of them, yields limited information. Aggregation, with an awareness of the biases and limitations in the existing corpus of documents, seems to me a way to open up alternative avenues for research. I am creating a database of painters in the city of Seville from 1500-1699, where I pool known archival documentation relating to painters and painting in this city and extract biographical, spatial and productive data to analyze the industry. I explore issues such as the industry’s size and productive capacity, its organization within the city, reactions to historical change and, of course, its participation in transatlantic trade.
This approach has obliged me to become familiar with a wide range of digital tools. I use OpenRefine for cleaning data, R and Stata for statistical analysis, Tableau for creating visualizations and ArcGIS for visualizing and generating spatial data (see examples of my own work below [Figures 3-4]). I have also learned the theory behind relational databases and am learning to use MySQL for my own project; similarly, for the data-gathering process I am interested in learning data-mining techniques through machine learning. I have been using a user-friendly software called RapidMiner to simplify some of my own data gathering.
Thus, I am happy to help any groups that have a data set and want to learn how to visualize it graphically, whether through graphs, charts or maps. I am also happy to help groups think about their data gathering and storage. I like to consider data in the broadest terms: almost anything can be data, if we correctly conceptualize how to gather and utilize it realistically within the limits of a project. I would like to point out that this does not necessarily need to result in visualization; this is also applicable if a group has a corpus of documents that they want to store digitally. If any groups have an interest in text mining and relational databases, we can learn simultaneously—I am very interested in developing these skills myself because they apply to my own project.
Help you consider potential data sources and the best way to extract the information they contain
Help you make them usable: teach you to structure, store and clean your data
And of course, help you analyze and visualize them
With Tableau: for graphs and infographics that can be interactive and can easily be embedded into dashboards on websites.
With ArcGIS: for maps that can also be interactive and embedded onto websites or in their Stories function.
Help you plan your project through these steps, from gathering to visualization.
Once again, if you think any of these areas are useful to you and your project, please do not hesitate to contact me. I look forward to collaborating with you!
With the launch of the Duke University Energy Intiative (EI) several years ago, the Center for Data and Visualization Sciences (CDVS) has seen an increased demand for all sorts of data and information related to energy generation, distribution, and pricing. The EI is a university-wide, interdisciplinary hub that advances an accessible, affordable, reliable, and clean energy system. It involves researchers and students from the Pratt School of Engineering, the Nicholas School of the Environment, the Sanford School of Public Policy, the Duke School of Law, the Fuqua School of Business, and departments in the Trinity College of Arts & Sciences.
The EI website provides links to energy-related data sources, particularly datasets that have proven useful in Duke energy research projects. We will discuss below some more key sources for finding energy-related data.
Energy resources and potentials
The sources for locating energy data will vary depending on the type of energy and the spot on the source-to-consumption continuum that interests you.
The US Department of Energy’s (DoE’s) Energy Information Administration (EIA) has a nice outline of energy sources, with explanations of each, in their Energy Explainedweb pages. These include nonrenewable sources such as petroleum, gas, gas liquids, coal, and nuclear. The EIA also discusses a number of renewable sources such as hydropower (e.g., dams, tidal, or wave action), biomass (e.g., waste or wood), biofuels (e.g., ethanol or biodiesel), wind, geothermal, and solar. Hydrogen is another fuel source discussed on these pages.
Besides renewability, a you might be interested in a source’s carbon footprint. Note that some of the sources the EIA lists as renewables may be carbon creating (such as biomass or biofuels), and some non-renewables may be carbon neutral (such as nuclear). Any type of energy source clearly has environmental implications, and the Union of Concerned Scientists has a discussion of the Environmental Impacts of Renewable Energy Technologies.
For more on renewables, check out the NREL (National Renewable Energy Laboratory), which disseminates GIS data relating to renewable energy in the US (e.g., wind speeds, wave energy, solar potential), along with some international data. The DoE’s Open Data Catalog is also particularly strong on datasets (tabular and GIS) relating to renewables. The data ranges from very specific studies to US nationwide data.
For visualizing energy-related map layers from selected non-US countries, the Renewable Energy Data Explorer (REexplorer) provides an online mapping tool. Most layers can be downloaded as GIS files. The International Renewable Energy Agency (IRENA) also has statistics on renewables. Besides downloadable data, summary visualizations can be viewed online using Tableau Dashboards.
Price and production data
The US DOE “Energy Economy” web pages will introduce you to all things relating to the economics of energy, and their EIA (mentioned above) is the main US source for fossil fuel pricing, from both the production and the retail standpoint.
Internationally, the OECD’s International Energy Agency (IEA) collects supply, demand, trade, production and consumption data, including price and tax data, relating to oil, gas, and coal, as well as renewables. In the OECD iLibrary go to Statistics tab to find many detailed IEA databases as well as PDF book series such as World Energy Balances, World Energy Outlook, and World Energy Statistics. For more international data (particularly in the developing world), you might want to try Energydata.info. This includes geospatial data and a lot on renewables, especially solar potential.
Finally, a good place to locate tabular data of all sorts is the database ProQuest Statistical Insight. It indexes publications from government agencies at all levels, IGOs and NGOs, and trade associations, usually providing the data tables or links to the data.
Infrastructure (Generation, Transportation/Distribution, and Storage)
Energy storage can include the obvious battery technologies, but also pumped hydroelectric systems and even more novel schemes. The US DoE has a Global Energy Storage Database with information on “grid-connected energy storage projects and relevant state and federal policies.”
For data or information relating to individual companies in the energy sector, as well as for more qualitative assessments of industry segments, you can begin with the library’s Company and Industry Research Guide. This leads to some of the key business sources that the Duke Libraries provide access to.
Trade associations that promote the interests of companies in particular industries can provide effective leads to data, particularly when you’re having trouble locating it from government agencies and IGOs/NGOs. If they don’t provide data or much other information on their websites, be sure to contact them to see what they might be willing to share with academic researchers. Most of the associations below focus on the United States, but some are global in scope.
You are invited to stop by the Edge Workshop Room on Mondays for a new Rfun program, the R Open Labs, 6-7pm, Sept. 16 through Oct. 28. No need to register although you are encouraged to double-check the R Open Labs schedule/hours. Bring your laptop!
This is your chance to polish R skills in a comfortable and supportive setting. If you’re a bit more advanced, come and help by demonstrating the supportive learning community that R is known for.
No Prerequisites, but please bring your laptop with R/RStudio installed. No skill level expected. Beginners, intermediate, and advanced are all welcome. One of the great characteristics of the R community is the supportive culture. While we hope you have attended our Intro to R workshop (or watched the video, or equivalent). This is an opportunity to learn more about R and to demystify some part of R that your find confusing.
What are Open Labs
Open labs are semi-structured workshops designed to help you learn R. Each week brief instruction will be provided, followed by time to practice, work together, ask questions and get help. Participants can join the lab any time during the session, and are welcome to work on unrelated projects.
The Open Labs model was established by our colleagues at Columbia and adopted by UNC Chapel Hill. We’re giving this a try as well. Come help us define our direction and structure. Our goal is to connect researchers and foster a community for R users on campus.
How do I Get Started?
Attend an R Open Lab. Labs occur on Mondays, 6pm-7pm in the Edge Workshop Room in the Bostock Library. In our first meeting we will decide, as a group, which resource will guide us. We will pick one of the following resources…
R for Data Science by Hadley Wickham & Garrett Grolemund (select chapters, workbook problems, and solutions)
Please bring a laptop with R and R Studio installed. If you have problems installing the software, we can assist you with installation as time allows. Since we’re just beginning with R Open Labs, we think there will be time for one-on-one attention as well through learning and community building.
How to install R and R Studio
If you are getting started with R and haven’t already installed anything, consider using using these installation instructions. Or simply skip the installation and use one of these free cloud environments:
We’ll start at the beginning, however, R Open Labs recommends that you attend our Intro to R workshop or watch the recorded video. Being a beginner makes you part of our target audience so come ready to learn and ask questions. We also suggest working through materials from our other workshops, or any of the resource materials listed in the Attend an R Open Lab section (above). But don’t let lack of experience stop you from attending. The resources mentioned above will be the target of our learning and exploration.
As data driven research has grown at Duke, Data and Visualization Services receives an increasing number of requests for partnerships, instruction, and consultations. These requests have deepened our relationships with researchers across campus such that we now regularly interact with researchers in all of Duke’s schools, disciplines, and interdepartmental initiatives.
In order to expand the Libraries commitment to partnering with researchers on data driven research at Duke, Duke University Libraries is elevating the Data and Visualization Services department to the Center for Data and Visualization Sciences (CDVS). The change is designed to enable the new Center to:
Expand partnerships for research and teaching
Augment the ability of the department to partner on grant, development, and funding opportunities
Develop new opportunities for research, teaching, and collections – especially in the areas of data science, data visualization, and GIS/mapping research
Recognize the breadth and demand for the Libraries expertise in data driven research support
Enhance the role of CDVS activities within Bostock Libraries’ Edge Research Commons
We believe that the new Center for Data and Visualization Sciences will enable us to partner with an increasingly large and diverse range of data research interests at Duke and beyond through funded projects and co-curricular initiatives at Duke. We look forward to working with you on your next data driven project!
The Data and Visualization Services (DVS) Department can help you locate and extract many types of data, including data about companies and industries. These may include data on firm location, aggregated data on the general business climate and conditions, or specific company financials. In addition to some freely available resources, Duke subscribes to a host of databases providing business data.
Directories of Business Locations
You may need to identify local outlets and single-location companies that sell a particular product or provide a particular service. You may also need information on small businesses (e.g., sole proprietorships) and private companies, not just publicly traded corporations or contact information for a company’s headquarters. A couple of good sources for such local data are the ReferenceUSA Businesses Database and SimplyAnalytics.
From these databases, you can extract lists of locations with geographic coordinates for plotting in GIS software, and SimplyAnalytics also lets you download data already formatted as GIS layers. Researchers often use this data when needing to associate business locations with the demographics and socio-economic characteristics of neighborhoods (e.g., is there a lack of full-service grocery stores in poor neighborhoods?).
Government surveys ask questions of businesses or samples of businesses. The data is aggregated by industry, location, size of company, and other criteria and typically include information on the characteristics of each industry, such as employment, wages, and productivity.
Macroeconomic indicators relate to the overall business climate, and a good source for macro data is Global Financial Data. Its data series includes many stock exchange and bond indexes from around the world.
Private firms also collect market research data through sample surveys. These are often from a consumer perspective, for instance to help gauge demand for specific products and services. Be aware that the numbers for small geographies (e.g., Census Tracts or Block Groups) are typically imputed from small nationwide samples, based on correlations with demographic and socioeconomic indicators. Examples of resources with such data are SimplyAnalytics (with data from EASI and Simmons) and Statista (mostly national-level data).
You may be interested in comparing numbers between companies, ranking them based on certain indicators, or gathering time-series data on a company to follow changes over time. Always be aware of whether the company is a publicly traded corporation or is privately held, as the data sources and availability of information may vary.
Helping Duke students, staff and faculty to locate data is something that we in Data and Visualization Services often do. In this blog post I will walk you through a sample search and share some tips that I use when I search for data and statistics.
“Hi there, I am looking for motorcycle registration numbers and sales volumes by age and sex for the United States.”
BREAKING DOWN THE QUESTION:
There are two types of data needed: motorcycle registration data and motorcycle sales data. There are two criteria that the data should be differentiated by: owner’s age and owner’s gender.
There is a geographic component: United States.
One criteria that is not given is time. When a time frame isn’t provided, I assume that what is needed is the most current data available. Something to consider is that “current” often will still be a year or more old. It takes time for data to be gathered, cleaned and published.
***Pro-tip: When you are looking for data consider who/what/when and where – adding in those components makes it easier to construct your search.***
WHERE AND HOW DO I SEARCH?
If I do not immediately have a source in mind (and sometimes even if I do, just to hit all the bases) I will use Google and structure my search as follows: motorcycle sales and registration by age and gender united states.
***Pro-tip: You can use Google (or search engine of your choice) to search across things we subscribe to and the open Web, but you will need to be connected via a Duke IP address***
One of the first results returned is from a database we subscribe to called Statistia. This source gives me the number of motorcycle owners by age in 2018, which answers part of the question, but does not include sales information or gender breakdown.
Another top result is a report on Motorcycle Trends in the United States from the Bureau of Transportation Statistics (BTS). Unfortunately, the report is from 2009 and the data cited in the article are from 2003-2007. A search of the BTS site does not yield any thing more current. However, when I check the source list at the bottom of the report, there are several listed that I will check directly once I’ve finished looking through my search results.
***Pro-tip: Always look for sources of data in reports and figures, even if the data are old. Heading to the source can often yield more current information.***
A third result that looks promising is from a motorcycling magazine: Motorcycle Statistics in America: Demographics Change for 2018. The article reports on statistics from the 2018 owner surveys conducted by the Motorcycle Industry Council (which is one of the sources that the Bureau of Transportation report listed). This article provides the percent of males and females that own motorcycles as well as the median age of motorcycle owners. While this is pretty close to the data needed, it is worthwhile to look into the Motorcycle Industry Council. Experience has taught me, however, that industry data typically is neither open nor freely available.
CHECKING THE COMMON SOURCE
When I go to the Motorcycle Industry Council (MIC) Web site I find that they do, indeed, have a statistical report that comes out every year which gives a comprehensive overview of the motorcycle industry. If you are not a member, you can buy a copy of the report, but it is expensive (nearly $500).
***Pro-tip: Always check the original source even if you anticipate that there may be a paywall – it’s a good idea to evaluate all sources to ensure that they are credible and authoritative.***
MAKING A DECISION
In this instance, I would ultimately advise the person to use the statistics reported in the article Motorcycle Statistics in America: Demographics Change for 2018. Secondary sources aren’t ideal, and can sometimes be complicated to cite, but when you can’t get access to the primary source and that primary source is the authority, it is your best bet.
***Pro-tip: If you are using a secondary source, you should name the original source in text. For example: Data from the 2018 Motorcycle Industry Council Owner Survey (as cited by Ultimate Motorcycling, 2019) but include a citation to the secondary source in your reference list according to the formatting of the style you are using.
In closing, the data you want might not always be the data you use – either due to the data being proprietary, restricted, or perhaps just doesn’t exist or doesn’t exist in the form you need and/or are able to use. When this happens, take a moment to think on your research question and determine if you have the time and the resources needed to continue pursuing your question as it stands (purchasing, requesting, applying for, or collecting your own data), or if you need to broaden or change your focus to incorporate the resources you do find in a meaningful way.
The Open Science Framework (OSF) is a data and project management platform developed by the Center for Open Science that is designed to support the entire research lifecycle. OSF has a variety of features including file management and versioning, integration with third-party tools, granular permissions and sharing capabilities, and communication functionalities. It also supports growing scholarly communication formats including preprints and preregistrations, which enable more open and reproducible research practices.
In early 2017, Duke University became a partner institution with the OSF. As a partner institution, Duke researchers cansign into the OSF using their NetIDandaffiliate a project with Duke, which allows it to be displayed on the Duke OSF page. After 2 years of supporting OSF for Institutions here at Duke, the Research Data Management (RDM) team wanted to gain a better perspective surrounding how our community was using the tool and their perceptions.
As of March 10, 2019, Duke has 202 users that have signed into the system using their Duke credentials (and there are possibly more users that are authenticating using personal email accounts). Of these users, 177 total projects have been created and affiliated with Duke. Forty-six of these projects are public and 132 remain private. Duke users have also registered 80 Duke affiliated projects, 62 of which are public and 18 are embargoed. A registration is a time-stamped read-only copy of an OSF project that can be used to preregister a research design, to create registered reports for journals, or at the conclusion of a project to formally record the authoritative copy of materials.
But what do OSF users think of the tool and how are they using it within their workflows? A few power users shared their thoughts:
Optimizing research workflows: A number of researchers noted how the OSF has helped streamline their workflows through creating a “central place that everyone has access to.” OSF has helped “keeping track of the ‘right’ version of things” and “bypassing the situation of having different versioned documents in different places.” Additionally, the OSF has supported “documenting workflow pipelines.”
Facilitating collaboration: One of the key features of the OSF is that researchers, regardless of institutional affiliation, can contribute to a project and integrate the tools they already use. Matt Makel, Director of Research at TIP, explains how OSF supports his research – “I collaborate with many colleagues at other institutions. OSF solves the problem of negotiating which tools to use to share documents. Rather than switching platforms across (or worse, within) projects, OSF is a great hub for our productivity.”
Offering an end-to-end data management solution: Some research groups are also using OSF in multiple stages of their projects and for multiple purposes. As one researcher expressed – “My research group uses OSF for every project. That includes preregistration and archiving research materials, data, data management and analysis syntax, and supplemental materials associated with publications. We also use it to post preprints to PsyArXiv.”
It also surfaced that OSF supported an ideological perception regarding a shift in the norms of scholarly communication. As Elika Bergelson, Crandall Family Assistant Professor in Psychology and Neuroscience, aptly put it “Open science is the way of the future.” Here within Duke University Libraries, we aim to continue to support these shifting norms and the growing benefits of openness through services, platforms, and training.
If you have other questions about using the OSF in a project, the RDM team is available for consultations or targeted demonstrations or trainings for research teams. We also have an OSF project that can help you understand the basic features of the tool.
Developing interactive maps that incorporate text, images, video, and audio can be time-consuming and require specialized technical skills. Fortunately, at Duke we have access to Esri Story Maps, a web-based tool that helps you quickly design engaging narratives around your maps, no coding required.
We have seen a variety of creative uses of Story Maps at Duke, including:
Presentations to communicate research
Student assignments, as an alternative to a midterm or paper
Tours and guides of campus
Tutorials to explain a topic with a spatial component
Portfolios to showcase projects that include maps
Christine Liu, a graduate student in the Department of Art, Art History & Visual Studies, created this Story Map to illustrate a journey through Kraków under Nazi occupation.
If you are interested in building a Story Map, we recommend first spending some time exploring Esri’s curated gallery of stories to find inspiration and understand the platform’s capabilities. You can also review their collection of resources, which includes training videos, FAQs, and useful advice.
A goal of Duke University Libraries (DUL) is to support the growing and changing needs of the Duke research community. This can take many forms. Within Data and Visualization Services, we provide learning opportunities, consulting services, and computational resources to help Duke researchers implement their data-driven research projects. Monitoring and assessing new tools and platforms also helps DUL stay in tune with changing research norms and practices. Today the increasing focus on the importance of transparency and reproducibility has resulted in the development of new tools and resources to help researchers produce and share more reproducible results. One such tool is Code Ocean.
Code Ocean is a computational reproducibility platform that employs Docker technology to execute code in the cloud. The platform does two key things—it integrates the metadata, code, data and dependencies into a single ‘compute capsule’, ensuring that the code will run—and it does this in a single web interface that displays all inputs and results. Within the platform, it is possible to develop, edit or download the code, run routines, and visualize, save or download output, all from a personal computer. Users or reviewers can upload their own data and test the effects of changing parameters or modification of the code. Users can also share their data and code through the platform. Code Ocean provides a DOI for all capsules facilitating attribution and a permanent connection to any published work.
In order to help us understand and evaluate the usefulness of the Code Ocean platform to the Duke research community, DUL will be offering trial access to the Code Ocean cloud-based computational reproducibility platform starting on October 1, 2018. To learn more about what is included in the trial access and to sign up to participate, visit the Code Ocean pilot portal page.