We invite the Duke University research community to submit data visualizations that interpret or touch on the theme of “Through Time.” Whether you are studying human history, molecular evolution, or the flow of water through a tributary, we invite you to share your data storytelling skills. This competition is an opportunity to both celebrate our rich history and envision our promising future.
Submission Deadline: January 8th, 5pm
Don’t miss this chance to be a part of our centennial celebration and make your mark on history!
In 2024, the GIS software ArcGIS Desktop (also known as ArcMap) will no longer be available through Duke’s education license. Esri has been encouraging users to upgrade to their more modern GIS software, ArcGIS Pro, or cloud-based platforms such as ArcGIS Online. CDVS’s GIS workshop series has not included an ArcMap session for the past several years, and we have been encouraging anyone interested in learning GIS software to start with ArcGIS Pro. You can read more details about the process in Esri’s blog post, ArcMap Retirement in Education Programs.
While the transition away from ArcMap has been moving forward, we occasionally hear from students and faculty who are still using this software. If you have yet to make the switch from ArcMap to ArcGIS Pro or ArcGIS Online, please consider doing so this semester.
Fortunately, there are many resources available to help you navigate the shift. Esri provides dozens of free, self-paced online tutorials about ArcGIS Pro and ArcGIS Online. You may also want to explore their tutorial series Modern GIS. For those looking for a more personal and interactive learning experience, we are offering several GIS workshops in Fall 2023. Finally, the in-depth Migrate to ArcGIS Pro (log-in required) documentation includes a training video and guide that address topics like migrating Python scripts and importing styles from ArcMap.
These guides should explain everything you might want to know (and much more) about the change. If you still have questions or want to learn more about other software options, please don’t hesitate to contact one of our GIS specialists by sending an email to firstname.lastname@example.org.
The Curation Team for the Duke Research Data Repository is happy to present an interview with Dr. Thomas Struhsaker, Retired Adjunct Professor of Evolutionary Anthropology.
Dr. Struhsaker’s dataset, Digitized tape recordings of Red colobus and other African forest monkey species vocalizations, was the 200th dataset to be added to the Duke Research Data Repository. I worked closely with Tom to arrange and describe this collection. He hopes to be adding even more in the near future as he winds down his career. Tom might not know this, but his dataset has been tweeted about 36 times at this point and has been viewed 336 times since August. Ever the humble scientist, I did not know until I saw the tweets that Tom was the winner of the 2022 President’s Award from the American Society of Primatologists (congratulations Tom!).
I started my interview with Dr. Struhsaker as one typically would – by asking him to tell me about himself and his field of research. He laughs and says “Oh boy, where to begin? You’re talking half a century here.” I could listen to Tom talk for hours about his experiences as a young field biologist at a time when primatology was just figuring itself out. Tom went about his work as a naturalist – do not interfere, observe and learn. He spent 25 years in Africa (spanning 56 years from 1962-2018), observing many different species of animals, not just primates. For 18 of these 25 years Tom lived in Uganda as a full-time resident, including during the reign of Idi Amin, one of the most brutal rulers in modern history. Idi Amin aside, Tom thought that the Ugandans were some of the best folks to work with regarding conservation in Africa due to their dedication to higher education (Makerere University) with growing generations of students and the establishment of Kibale National Park. I cannot do Tom’s fascinating life justice in just this short blog post, so I encourage you to read Tom’s 2022 article, The life of a naturalist (full text access available through NetID login) and his memoir, I remember Africa: A field biologist’s half-century perspective (Perkins & Bostock Library – Duke Authors Display – QH31.S79 A3 2021). What I can tell you, at least from my perspective, is that Tom has led a life passionate about nature, wanting to know everything he could from our cohabiters on this planet and how we can best live together. If you would like your own copy it can be purchased here.
Tom recorded these vocalizations between 1969-1992. He thought it was really important to do so because they are key to understanding communication and the social life of primates. Analysis of these recordings led Tom to conclude that among African monkeys vocalizations are relatively stable characters from an evolutionary perspective and, therefore, important in understanding phylogenetic relationships. As for archiving and sharing the recordings of these vocalizations, Tom didn’t initially have that in mind. He instead followed the more traditional academic route of publishing articles including spectrograms, and his conclusions about the meaning of the vocalizations. Over the last two years as Tom began thinking about the legacy of his materials, he realized that while the visual representations are useful to share for analysis, it is just not the same as listening to the sounds themselves. Why not archive them to make it possible for others to hear them?
“He realized that while the visual representations are useful to share for analysis, it is just not the same as listening to the sounds themselves. Why not archive them to make it possible for others to hear them?”
With increasing human populations, deforestation, climate changes, etc., some of these animals (like the Red Colobus) have become critically endangered, and these recordings might be the only way future generations will ever be able to hear these animals. Tom’s recordings were made using reel to reel tapes on very large and heavy tape recorders with 12 D-Cell batteries. Crawling through the forest with these machines in addition to a large boom microphone was no easy feat. With the help of the Macaulay Library (Mr. Matthew Medler in particular), several of the original tapes were digitized to the high-quality WAV files we have in the collection. Tom has also augmented the collection with his own MP3 recordings. He hopes to have more WAV format from Macaulay Library in the future.
Tom did not initially know where to archive these vocalizations as they weren’t in scope for MorphoSource (another Duke-based repository for 3D imaging) where Tom will soon have a collection of red colobus monkey images available. Thanks to a suggestion from his neighbor Ben Donnelly, he reached out to the Duke Research Data Repository Curation Team (thanks for being a great colleague Ben!). This is where I (Jen Darragh), the author, come in.
Tom and I worked together over the course of a couple months to build his data deposit. Perhaps somewhat self-servingly, I asked him how he found the process. He stoked my ego with both a “fantastic, and easy peasy.” He said he would recommend us to anyone as we do our best to make the process as clear and pain-free as possible. Aw shucks Tom. You are one of my favorite depositors to work with, too.
I asked Tom what would he advise for early career researchers and those just getting started in the field when it comes to data sharing and archiving. He said that he is seeing increasing requirements as part of publishing (he’s right) and he’s in favor, as long as the person who collected the data is credited (cite properly!) and consulted when possible (collaboration is good). It’s important to advance the sciences. Repositories help to encourage good citation practices in addition to the preservation of important data for the long-term.
Tom also mentioned some longitudinal data he had collaborative built over the years with colleagues and that continues to be built upon. His experience of archiving his vocalization recordings with us (and his images with MorphoSource) got him thinking that repositories are a wonderful option to ensure that these important materials continue to persist and be used. He has thought of at least three important datasets and plans to reach out to his collaborators about archiving these data either with us in the Duke RDR, or in another formal repository of their choosing.
Tom recently shared with me a collection of photographs that he has taken in the same spot in Kibale from 1976-2018 that shows how the area went from bare grassland to a low stature forest (pre-conservation to post-conservation efforts). He has shared these with his colleagues directly to show the fascinating change over time. He now hopes to share them more broadly through the Duke RDR (forthcoming, we have some processing to do). Perhaps someone will be inspired to animate the images and then share back with us.
To close the interview, I asked Tom what his favorite animal was. I think it’s no surprise that he likes them all; there are so many he likes for different reasons, some subtle, some not (“some insects are damn weird”) and some just do incredibly interesting things. The diversity is what he loves.
Struhsaker, T. T. (2022). Digitized tape recordings of Red colobus and other African forest monkey species vocalizations. Duke Research Data Repository. https://doi.org/10.7924/r4pv6nm9f
You’re probably aware that voting in the United States is managed in a very decentralized manner compared to most other countries. There are limited sources that comprehensively compile local-level results or geographic data showing local voting precincts. We’ll discuss several selected projects have come about to try to pull all this data together to provide one-stop repositories, as well as state and local sources for election data. Some of these are free resources, and some are licensed by us for the use of Duke affiliates.
The Princeton University Library has an excellent guide to elections returns and related data in their Elections and Voting Data Guide: United States (U.S.) and International, compiled by their Politics Librarian, Jeremy Darrington. This is a good first place to look for repositories of voting data, both U.S. and international. We’ll discuss a few of the most useful of these sources that the Duke community has access to.
The CQ Voting and Elections Collection (Duke users only) has results data on Presidential, Congressional, and gubernatorial elections, some back to the 19th century. Results are generally given down to the county level of detail.
Polidata presents presidential election result data by congressional district and county in STATA, Excel, or CSV format, with data dictionaries as text files and documentation in PDF format. The Duke Libraries has obtained some of their data, curating the 1992-2008 District-level Polidata.
Geographic Data (GIS Layers)
Geographies that relate specifically to election data are Congressional or Legislative Districts, as well as voting precincts. The Census Bureau’s Voting Tabulation District (VTD) boundaries closely parallel precincts but are based on the Census Block geographies. They may not exactly match all locally created precincts, but may be all you can get electronically.
NHGIS (National Historical GIS) has the most election-related GIS boundary files, back to 1990 for VTDs, to 2000 for state legislative districts, and into the late 1980s for U.S. Congressional Districts. The Census Bureau has a scattered collection of these as well, at least for more recent years, usually on a state-by-state- or county-by-county (for the VTDs) basis. See either their web interface or their FTP site.
Election Results and GIS layers Together
A good all-in-one source is The United States Elections Project, with lead contributors from the Voting and Election Science Team at the University of Florida and Wichita State University. It includes both election results and GIS shapefiles down to the precinct level, mostly from the last decade (as recently as some 2021 elections). For those interested in redistricting issues and gerrymandering, precinct-level data is essential.
Their data is stored in the Harvard Dataverse, a data publishing platform that includes several election-related projects (election results and sometimes GIS files). It is a rich, if somewhat scattershot, repository with a lot of hidden gems. You can use the Advanced Search interface to find some of these datasets.
State and Local Sources
Sometimes, you need to find state, county, and city sources for election data, either for local elections or for geographically granular data results, like voting precincts. The National Association of Secretaries of State (NASS) website indexes the Secretaries of State websites, which may or may not have actual election results data.
The state elections offices may only have information on registration and on voting locations, but sometimes may include results data. For instance, the North Carolina State Board of Elections has some pretty thorough data at the precinct level for recent years, with good documentation.
Some local governments are good about releasing election data at the precinct level. They may include data for such elections as municipal offices, school districts, and bond initiatives that you’d probably never find compiled at a national site. This example is from Los Angeles County.
If you need statistical or GIS tools to analyze the data, be sure to contact us at email@example.com for advice. Here, I’ll mention the Geocorr utility at the Missouri Census Data Center, which you can use to reaggregate data into different geographic areas. You can create correspondence tables between geographies such as voting tabulation districts or legislative districts and Census geographies, say, if you need to analyze demographics and socioeconomic factors. The correspondence tables include weighting factors indicating the percent of one area within another.
We’ve only scratched the surface on the data sources related to U.S. elections. If you want more suggestions or have specialized needs not covered here, please contact us at firstname.lastname@example.org for other ideas.
This post is part of the Duke Research Data Curation Team’s ‘Researcher Highlight’ series.
In the field of engineering, a key driving motivator is the urge to solve problems and provide tools to the community to address those problems. For Dr. Mark Palmeri, Professor in Biomedical Engineering at Duke University, open research practices support the ultimate goals of this work, and helps get the data into the hands of those solving problems: “It’s one thing to get a publication out there and see it get cited. It’s totally another thing to see people you have no direct professional connection to accessing the data and see it impacting something they’re doing…”
“It’s one thing to get a publication out there and see it get cited. It’s totally another thing to see people you have no direct professional connection to accessing the data and see it impacting something they’re doing…”
With the new NIH data management and sharing policy on the horizon, many researchers are now considering what sharing data looks like for their own work. Palmeri highlighted some common challenges that many researchers will face, such as the inability to share proprietary data when working with industry partners, de-identifying data for public use (and who actually signs off on this process), the growing scope and scale of data in the digital age, and investing the necessary time to prepare data for public consumption. However, two of his biggest challenges relate to the changing pace of technology and the lack of data standards.
When publishing a dataset, you necessarily have a static version of the dataset established in space and time via a persistent identifier (i.e., DOI); however, Palmeri’s code and software outputs are constantly evolving as are the underlying computational environments. This mismatch can result in datasets becoming out of sync with the coding tools, thereby affecting future reuse and ultimately keeping things up-to-date takes time and effort. As Palmeri notes, in the fast-paced culture of academia “no one has time to keep old project data up to snuff.”
Likewise, while certain types of data in medical imaging have standardized formats (e.g., DICOM), for the images Palmeri is creating from raw signal data there are no ubiquitous standards. This creates problems for data reuse. Palmeri remarks that “There’s no data model that exists to say what metadata should be provided, in what units, what major fields and subfields, so that becomes a major strain on the ability to meaningfully share the data, because if someone can’t open it up and know how to parse it and unwrap it and categorize it, you’re sharing gigabytes of bits that don’t really help anyone.” Currently, Dr. Palmeri is working with the Quantitative Imaging Biomarkers Alliance and the International Electrotechnical Commision (IEC) TC87 (Ultrasonics) WG9 (Shear Wave Elastography) to create a public standard for this technology for clinical use.
Regardless of these challenges, Palmeri sees many benefits to publicly sharing data including enhancing “our internal rigor even just that little bit more” as well as opening “new doors of opportunity for new research questions…and then the scope and impact of the work can be augmented.” Dr. Palmeri appreciates the infrastructure provided by the Duke University Libraries to host his data in a centralized and distributed network as well as the ability to cite his data via the DOI. As he notes “you don’t want to just put up something on Box as those services can change year to year and don’t provide a really good preserved resource.” Beyond the infrastructure, he appreciates how the curation team provides “an objective third party [to] look at things and evaluate how shareable is this.”
“you don’t want to just put up something on Box as those services can change year to year and don’t provide a really good preserved resource.”
Within the Duke Research Data Repository, we have a mission to help Duke researchers make their data accessible to enable reproducibility and reuse. Working with researchers, like Dr. Palmeri, to realize a future where open research practices lead to a greater impact for researchers and democratizes knowledge is a core driving motivator. Contact us (email@example.com) with any questions you might have about starting your own data sharing adventure!
Besides his emphasis on clear communication of results, one of the things I admire most about Schwabish is his focus on Microsoft Excel as a legitimate tool for crafting that communication. While not free and open-source, it’s a piece of software that many people have access to, and despite some of its limitations (e.g. reproducibility issues), it is a very capable tool for data processing and visualization. If you want to make lots of people better communicators, teach them how to use the tools they already have!
Of course, visit policyviz.com and the PolicyViz YouTube channel to access the plethora of resources Jon is constantly generating, but to get you started I want to point out a few of my favorites.
I get frustrated that Excel doesn’t have a built-in, easy way to make horizontal dot plots with error bars. (On the med-side they tend to call these Forest plots, although they are useful whenever you have categories and a quantity with confidence intervals. Don’t just create a table – make it visual with a plot!) Jon’s Labeling Dot Plots blog post and accompanying YouTube video was super useful – it taught me a general approach for using scatterplots in Excel to create a variety of chart types that Excel doesn’t support natively! The method is a pain the first time you do it, and I get a bit belligerent because I hate that you have to employ this workaround, but it’s so brilliant and flexible that I’m tempted to teach a CDVS workshop on just this one chart type. More broadly, he also has an Excel Tutorials section of his YouTube channel, and he sells a PDF on his website called A Step-by-Step Guide to Advanced Data Visualization in Excel 2016.One of the best ways to become a better visualizer and communicator is to get feedback on your work and iterate through multiple drafts. To compliment that, it’s wonderful to get an expert’s take on a published visualization, along with proposed alternatives. For years Jon has been publishing brillant visualization redesigns on his blog. He doesn’t just criticize – he shows you alternatives and talks about their strengths and weaknesses. There is also a DataViz Critiques section on his YouTube channel.
In early 2021 he released over 50 daily videos in a series called One Chart at a Time where visualization experts “expand your graphic literacy” and “help you learn about more than just the standard bar, line, and pie chart.”
Along with Alice Feng, Schwabish published in 2021 the “Do No Harm Guide: Applying Equity Awareness in Data Visualization”. You can download the report at urban.org and listen to a talk they gave about it on YouTube to get their reflections on “how data practitioners can approach their work through a lens of diversity, equity, and inclusion … to encourage thoughtfulness in how analysts work with and present their data.”
Years ago I heard the following quote attributed to Seamus Ross from 2007:
Digital objects do not, in contrast to many of their analog counterparts, respond well to benign neglect.
Meaning, you cannot simply leave digital files to their bit-rot tendencies while expecting them to be usable in the future. Digital repositories are part of a solution to this problem. But to review, there are many types of repositories, both digital and analog: repositories of bones, insects, plants, books, digital data, etc. Even among the subset of digital repositories there are many types. Some digital repositories keep your data safe for posterity and replication. Some help you manage the distribution of analysis and code. Knowing about these differences will affect not only the ease of your computational workflow, but also the legacy of your published works.
Version-control repositories and their hubs
The most widely known social coding hubs include GitHub, Bitbucket and GitLab. These hubs leverage Git version-control software to track the evolution of project repositories – typically a software or computational analysis project. Importantly, Git and GitHub are not the same thing but they work well together.
Version control works by monitoring any designated folder or project directory, making that directory a local repository or repo. Among other benefits, using version control enables “time travel.” Interactions with earlier versions of a project are commonplace. It’s simple to retrieve a deleted paragraph from a report written six months ago. However there are many advanced features as well. For example, unlike common file-syncing tools, it’s easy to recreate an earlier state of an entire project directory and every file from a particular point in time. This feature among others makes Git version-control a handy tool in support of many research workflows and the respective outputs: documents, visualizations, dashboards, slides, analysis, code, software, etc.
Git is one of the most popular, open-source, version-control applications; originally developed in 2005 to facilitate the evolution of the world’s most far reaching and successful open-source coding project. Linux is a world-wide collaborative project that spans multiple developers, project managers, natural languages, geographies, and time-zones. While Git can handle large projects, it is extensible and can easily scale up or down to support a wide range of workflows. Additionally, Git is not just for software and code files. Essentially any file on a file system can be monitored with Git: MSWord, PDF files, images, datasets, etc.
There are many ways to share a Git repository and profile your work. The term push refers to a convenient process of synchronizing a repo up to a remote social coding hub. Additional features of a hub include issue tracking, collaboration, hosting documentation, and Kanban Method planning. Conveniently, pushing a repo to GitHub means maintaining a seamless, two-location backup – a push will simultaneously and efficiently synchronize the timeline and file versions. Meanwhile, at a repo editor’s discretion, any collaborator or interested party can be granted access to their GitHub repository.
Many public instances of social-coding hubs operate on a freemium model. At GitHub most users pay nothing. It’s also possible to run a local instance of a coding hub. For example, OIT offers a local instance of GitLab, delivering many of the same features while enabling permissions, authorization, and access Via Duke’s NetID.
While social coding hubs are great tools for distributing files and managing project life-cycles, in and of themselves they do not sufficiently ensure long-term reproducible access to research data. To do that simply synchronize version-control repositories with archival research data repositories.
Research Data Repositories
Preserving the computational artifacts of formal academic works requires a repository focus that is complementary to version-control repositories and social-coding hubs. Nonetheless, version control is not a requirement of a data repository where the goal is long-term preservation. Fortunately, many special-purpose data repositories exist. Discipline-specific research repositories are sometimes associated with academic societies. There also exist more generalized archival research repositories such as Zenodo.org. Additionally, many research universities host institutional research data repositories. Not surprisingly, such a research data repository exists at Duke where the Duke University Libraries promotes and cooperatively shepherds Duke’s Research Data Repository (RDR).
Unlike social coding hubs, data repositories operate under different funding models and are motivated by different horizons. Coding hubs like GitHub do not promise long-term retention, instead they focus on immediate distribution of version-control repos and offer project management features. Research data repositories take a long view centered closer to the artifacts of formal research and publication.
By archiving the data milestones of publication, a deposit in the RDR links a formal publication – book edition, chapter, or serial article, etc. – with the data and code (i.e., a compendium) used to produce a single tangible instance of publication. In turn, the building blocks of computational thinking and research processes are preserved for posterity because the RDR maintains an assurance of long term sustainability.
In the Duke RDR, particular effort is focussed on preserving unique versions of data associated with each formal publication. In this way, authors can associate a digital object identifier, or DOI, with the precise code and data used to draft an accepted paper or research project. Once deposited in the RDR, researchers across the globe can look at these archives to verify, to learn, to refute, to cite, or be inspired toward new avenues of investigation.
By preserving workflow artifacts endemic to publication milestones, research data repositories preserve the record of academic progress. Importantly, the preservation of these digital outcomes or artifacts is strongly encouraged by funding agencies. Increasingly, these archival access points are a requirement for funding, especially among publicly funded research. As such, the Duke RDR exists with aims to preserve and make the academic record accessible, and to create a library of reproducible academic research.
The imperatives for preserving research data are derived from expressly different motives than those driving version-control repositories. Minimally, version-control repositories do not promise academic posterity. However, among the drivers of scholarship is the intentional engagement with the preserved academic record. In reality, while unlikely, your GitHub repository could vanish in the blink of the next Wall Street acquisition. Conversely research data repositories exist with different affordances. These two types of repositories complement each other. Once more, they can be synchronized to enable and preserve digital processes that comprise many forms of data-driven research. Using both types of repositories imply workflows that positively contribute to a scholarly legacy. It is this promise of academic transmission that drives Duke’s RDR, and benefits scholars by enabling access to persistent copies of research.
As we begin the new year, the Center of Data and Visualization Sciences is happy to announce a series of twenty-one data workshops designed to empower you to reach your goals in 2022. With a focus on data management, data visualization, and data science, we hope to provide a diverse set of approaches that can save time, increase the impact of your research, and further your career goals.
While the pandemic has shifted most of our data workshops online, we remain keenly interested in offering workshops that reflect the needs and preferences of the Duke research community. In November, we surveyed our 2021 workshop participants to understand how we can better serve our attendees this spring. We’d like to thank those who participated in our brief email survey and share a few of our observations based on the response that we received.
While some of our workshops participants (11%) prefer in-person workshops and others (24%) expressed a preference for hybrid workshops, a little over half of the survey respondents (52%) expressed a preference for live zoom workshops. Our goal for the spring is to continue offering “live” zoom sessions while continuing to explore possibilities for increasing the number of hybrid and in-person options. We hope to reevaluate our workshops communities preferences later this year and will continue to adjust formats as appropriate.
With the rapid shift to online content in the last two years coupled with a growing body of online training materials, we are particularly interested in how our workshop attendees evaluate online courses and their expectations for these courses. More specifically, we were curious about whether registering for an online session includes more than simply the expectation of attending the online workshop.
While we are delighted to learn that the majority of our respondents (87%) intend to attend the workshop (our turnout rate has traditionally been about 50%), we learned that a growing number of participants had other expectations (note: for this question, participants could choose more than one response). Roughly sixty-seven percent of the sample indicated they expected to have a recording of the session available. While another sixty-six percent indicated that they expected a copy of the workshop materials (slides, data, code) even if they were unable to attend.
Within the Center for Data and Visualization Sciences (CDVS) we pride ourselves on providing numerous educational opportunities for the Duke community. Like many others during the COVID-19 pandemic, we have spent a large amount of time considering how to translate our in-person workshops to online learning experiences, explored the use of flipped classroom models, and learned together about the wonderful (and sometimes not so wonderful) features of common technology platforms (we are talking about you, Zoom).
We also wanted to more easily surface the various online learning resources we have developed over the years via the web. Recognizing that learning takes place both synchronously and asynchronously, we have made available numerous guides, slide decks, example datasets, and both short-form and full-length workshops on our Online Learning Page. Below we highlight 5 online learning resources that we thought others interested in data driven research may wish to explore:
Mapping & GIS: R has become a popular and reproducible option for mapping and spatial analysis. Our Geospatial Data in R guide and workshop video introduce the use of the R language for producing maps. We cover the advantages of a code-driven approach such as R for visualizing geospatial data and demonstrate how to quickly and efficiently create a variety of map types for a website, presentation, or publication.
Data Visualization: Visualization is a powerful way to reveal patterns in data, attract attention, and get your message across to an audience quickly and clearly. But, there are many steps in that journey from exploration to information to influence, and many choices to make when putting it all together to tell your story. In our Effective Data Visualization workshop, we cover some basic guidelines for effective visualization, point out a few common pitfalls to avoid, and run through a critique and iterations of an existing visualization to help you start seeing better choices beyond the program defaults.
Data Science: QuickStart with R is our beginning data science module focusing on the Tidyverse — a data-first approach to data wrangling, analysis, and visualization. Beyond introducing the Tidyverse approach to reproducible data workflows, we offer a rich allotment of other R learning resources at our Rfun site: workshop videos, case studies, shareable data, and code. Links to all our data science materials can also be found collated on our Online Learning page (above).
Data Management: Various stakeholders are stressing the importance of practices that make research more open, transparent, and reproducible including NIH who has released a new data management & sharing policy. In collaboration with the Office of Scientific Integrity, our Meeting Data Management Plan Requirements workshop presents details on the new NIH policy, describes what makes a strong plan, and where to find guidance, tools, resources, and assistance for building funder-based plans.
Data Sources: The U.S. Census has been collecting information on persons and businesses since the late 18th century, and tackling this huge volume of data can be daunting. Our guide to U.S. Census data highlights many useful places to view or download this data, with the Product Comparisons tab providing in chart form a quick overview of product contents and features. Other tabs provide more details about these dissemination products, as well as about sources for Economic Census data.
In the areas of data science, mapping & GIS, data visualization, and data management, we cover many other topics and tools including ArcGIS, QGIS, Tableau, Python for tabular data and visualization, Adobe Illustrator, MS PowerPoint, effective academic posters, reproducibility, ethics of data management and sharing, and publishing research data. Access more resources and past recordings on our online learning page or go to our upcoming workshops list to register for a synchronous learning opportunity.
Open access journals have been around forseveral decades, and almost all researchers have read them or published in them by now. Perhaps less well known are trends toward more openness in sharing of data, methods, code, and other aspects of research – broadly called open scholarship. There are lots of good reasons to make your research outputs as open as possible, and increasing support at Duke for doing it.
There are manydifferent variants of “open” – including goals of making research accessible to all, making data and methods transparent to increase reproducibility and trust, licensing research to enable broad re-use, and engagement with a variety of stakeholders, among other things. All of these provide benefits to the public and they also provide benefits to Duke researchers. There’s growing evidence that openly available publications and data result in more citations and greater impact (Colavizza 2020), and showing one’s work and making it available for replication helps build greater trust. There’s greater potential economic impact when others can build on research more quickly, and more avenues for collaboration and interdisciplinary engagement.
Recognizing the importance of making research outputs quickly and openly available to other researchers and the public, and supporting greater transparency in research, many funding agencies are now encouraging or requiring it. NIH has had a public access policy for over a decade, and NSF and other agencies have followed with similar policies. NIH has also released a newData Management and Sharing policythat goes into effect in 2023 with more robust and clearer expectations for how to effectively share data. In Europe, government research funders back a program calledPlan S, and in the United States, the recently passed U.S. Innovation and Competition Act (S. 1260) includes provisions that instruct federal agencies to provide free online public access to federally-funded research “not later than 12 months after publication in peer-reviewed journals, preferably sooner.”
The USICA bill aims to maximize the impact of federally-funded research by ensuring that final author manuscripts reporting on taxpayer-funded research are:
Deposited into federally designated or maintained repositories;
Made available in open and machine-readable formats;
Made available under licenses that enable productive reuse and computational analysis; and
Housed in repositories that ensure interoperability and long-term preservation.
Duke got a head start on supporting researchers in making their publications open access in 2010, when Academic Council adopted an open access policy, which since then has been part of theFaculty Handbook (Appendix P). The policy provides the legal basis for Duke faculty to make their own research articles openly available on a personal or institutional website via a non-exclusive license, while also making it possible to comply with any requirements imposed by their journal or funder. Shortly after the policy was adopted, Duke Libraries worked with the Provost’s office to implement a service making open access easy for Duke researchers. DukeSpace, a repository integrated with theScholars@Duke profile system, allows you to add a publication to your profile and deposit it to Duke’s open access archive in a single step, and have the open access link included in your citations alongside the link to the published version.
Duke Libraries also support aresearch data repository and services to help the Duke community organize, describe, and archive their research data for open access. This service, with support from the Provost’s office, provides both the infrastructure and curation staff to help Duke researchers make their dataFAIR (Findable, Accessible, Interoperable, and Reusable). By publishing datasets with digital object identifiers (DOIs) and data citations, we create a value chain where making data available increases their impact and positions them as standalone research objects. The importance of data sharing specifically is also being formalized at Duke through the currentResearch Data Policy Initiative, which has a stated mission to “facilitate efficient and quality research, ensure data quality, and foster a culture of data sharing.” Together the Duke community is working to develop services, processes, procedures, and policies that broaden our contributions to society through public access to the outputs of our research.