We are happy to announce expanded features for the public sharing of large scale data in the Duke Research Data Repository! The importance of open science for the public good is more relevant than ever and scientific research is increasingly happening at scale. Relatedly, journals and funding agencies are requiring researchers to share the data produced during the course of their research (for instance see the newly released NIH Data Management and Sharing Policy). In response to this growing and evolving data sharing landscape, the Duke Research Data Repository team has partnered with Research Computing and OIT to integrate the Globus file transfer system to streamline the public sharing of large scale data generated at Duke. The new RDR features include:
A streamlined workflow for depositing large scale data to the repository
An integrated process for downloading large scale data (datasets over 2GB) from the repository
New options for exporting smaller datasets directly through your browser
New support for describing and using collections to highlight groups of datasets generated by a project or group (see this example)
Additional free storage (up to 100 GB per deposit) to the Duke community during 2021!
While using Globus for both upload and download requires a few configuration steps by end users, we have strived to simplify this process with new user documentation and video walk-throughs. This is the perfect time to share those large(r) datasets (although smaller datasets are also welcome!).
This post was authored by Shadae Gatlin, DUL Repository Services Analyst and member of the Research Data Curation Team.
Collaborating for openness
The Duke University Libraries’ Research Data Curation team has the privilege to collaborate with exceptional researchers and scholars who are advancing their fields through open data sharing in the Duke Research Data Repository (RDR). One such researcher, Martin Fischer, Ph.D., Associate Research Professor in the Departments of Chemistry and Physics, recently discussed his thoughts on open data sharing with us. A trained physicist, Dr. Fischer describes himself as an “optics person” his work ranges from developing microscopes that can examine melanin in tissues to looking at pigment distribution in artwork. He has published data in the RDR on more than one occasion and says of the data deposit process that, “I can only say, it was a breeze.”
“I can only say, it was a breeze.”
Dr. Fischer recalls his first time working with the team as being “much easier than I thought it was going to be.” When Dr. Fischer and colleagues experienced obstacles trying to setup OMERO, a server to host their project data, they turned to the Duke Research Data Repository as a possible solution to storing the data. This was Dr. Fischer’s first foray into open data publishing, and he characterizes the team as being responsive and easy to work with. Due to the large size of the data, the team even offered to pick up the hard drive from Fischer’s office. After they acquired the data, the team curated, archived, and then published it, resulting in Fischer’s first dataset in the RDR.
Why share data?
When asked why he believes open data sharing is important, Dr. Fischer says that “sharing data creates an opportunity for others to help develop things with you.” For example, after sharing his latest dataset which evaluates the efficacy of masks to reduce the transmission of respiratory droplets, Fischer received requests for a non-proprietary option for data analysis instead of using the team’s data analysis scripts written for the commercial program Mathematica. Peers offered to help develop a Python script, which is now openly available, and for which the developers used the RDR data as a reference. As of January 2021, the dataset has had 991 page views.
Dr. Fischer appreciates the opportunity for research development that open data sharing creates, saying, “Maybe somebody else will develop a routine, or develop something that is better, easier than what we have”. Datasets deposited in the RDR are made publicly available for download and receive a permanent DOI link, which makes the data even more accessible.
“Maybe somebody else will develop a routine, or develop something that is better, easier than what we have.”
In addition to the benefits of long-term preservation and access that publishing data in the RDR provides, Dr. Fischer finds that sharing his data openly encourages a sense of accountability. “I don’t have a problem with other people going in and trying, and making sure it’s actually right. I welcome the opportunity for feedback”. With many research funding agencies introducing policies for research data management and data sharing practices, the RDR is a great option for Duke researchers. Every dataset that is accepted into the RDR is carefully curated to meet FAIR guidelines and optimized for future reuse.
Collaborating with researchers like Dr. Martin Fischer is one of the highlights of working on the Research Data Curation team. We look forward to seeing what fascinating data 2021 will bring to the RDR and working with more Duke researchers to share their data with the world.
Dr. Fischer’s Work in the Duke Research Data Repository:
Wilson, J. W., Degan, S., Gainey, C. S., Mitropoulos, T., Simpson, M. J., Zhang, J. Y., & Warren, W. S. (2019). Data from: In vivo pump-probe and multiphoton fluorescence microscopy of melanoma and pigmented lesions in a mouse model. Duke Digital Repository.https://doi.org/10.7924/r4cc0zp95
Fischer, E., Fischer, M., Grass, D., Henrion, I., Warren, W., Westman, E. (2020). Video data files from: Low-cost measurement of facemask efficacy for filtering expelled droplets during speech. Duke Research Data Repository. V2 https://doi.org/10.7924/r4ww7dx6q
GIS Day is an international celebration of geographic information systems (GIS) technology. The event provides an opportunity for users of geospatial data and tools to build knowledge, share their work, and explore the benefits of GIS in their communities. Since its establishment in 1999, GIS Day events have been organized by nonprofit organizations, universities, schools, public libraries, and government agencies at all levels.
Held annually on the third Wednesday of November, this year GIS Day is officially today. Happy GIS Day! CDVS has participated in Duke GIS Day activities on campus in past years, but with COVID-19, we had to find other ways to celebrate.
A (Virtual) Map Showcase
To mark GIS Day this year, CDVS is launching an ArcGIS StoryMaps showcase! We invite any students, faculty, and staff to submit a story map to highlight their mapping and GIS work. Send us an email at askdata@duke.edu if you would like to add yours to the collection. We are keen to showcase the variety of GIS projects happening across Duke, and we will add contributions to the collection as we receive them. Our first entry is a story map created by Kerry Rork as part of a project for undergraduate students that used digital mapping to study the English CivilWars.
Why Story Maps?
If you aren’t familiar with ArcGIS StoryMaps, this easy-to-use web application integrates maps with narrative text, images, and video. The platform’s compelling, interactive format can be an effective communication tool for any project with a geographic component. We have seen a surge of interest in story maps at Duke, with groups using them to present research, give tours, provide instruction. Check out the learning resources to get started, or contact us at askdata@duke.edu to schedule a consultation with one of our GIS specialists.
As students and classes moved online in the spring of 2020, the Center for Data and Visualization Sciences realized that it was time to expand our existing email (askdata@duke.edu) and lab based consultation services to meet the data demands of online learning and remote projects. Six months and hundreds of online consultations later, we have developed a new appreciation for the online tools that allow us to partner with Duke researchers around the world. Whether you prefer to chat, zoom, or email, we hope to work with you on your next data question!
Chat
Ever had a quick question about how to visualize or manage your data, but weren’t sure where to get help? Having trouble figuring out how to get the data software to do what you need for class/research? CDVS offers roughly thirty hours of chat support each week. Data questions on chat cover our full range of data support. If we cannot resolve a question in the chat session, we will make a referral for a more extended consultation.
Zoom
We’re going to be honest… we miss meeting Duke students and faculty in the Brandaleone Lab in the Edge and consulting on data problems! However, virtual data consultations over zoom have some advantages over an in-person data consultations at the library. With zoom features such as screen sharing, multiple participants, and chat, we can reach both individuals and project teams in a format where everyone can see the screen and sharing resource links is simple. As of October 1st, we have used zoom to consult on questions from creating figures in the R programming languages to advising Bass Connection teams on the best way to visualize their research. We are happy to schedule zoom consultations via email at: askdata@duke.edu.
Just askdata@duke.edu
Even with our new data chat service and video chat services, we are still delighted to advise on questions over email at askdata@duke.edu. As the days grow shorter this fall and project deadlines loom, we look forward to working with you to resolve your data challenges!
John Little is the Data Science Librarian in Duke Libraries Center for Data and Visualizations Sciences. Contact him at askdata@duke.edu.
The Center for Data and Visualization Sciences is and has been open since March! We never closed. We’re answering questions, teaching workshops, have remote virtual machines available, and business is booming.
What’s changed? Due to COVID-19, the CDVS staff are working remotely. While we love meeting with people face-to-face in our lab, that is not currently possible. Meanwhile, digital data wants to be analyzed and our patrons still want to learn. By late spring I began planning to flip my workshops for fall 2020. My main goal was to transform a workshop into something more rewarding than watching the video of a lecture, something that lets the learner engage at their pace, on their terms.
How to flip
Flipping the workshop is a strategy to merge student engagement and active learning. In traditional instruction, a teacher presents a topic and assigns work aimed at reinforcing the lesson.
Background: I offer discrete two-hour workshops that are open to the entire university. There are very few prerequisites and people come with their own level of experience. Since the workshops attract a broad audience, I focus on skills and techniques using general examples that reliably convey information to all learners. In this environment, discipline specific examples risk losing large portions of the audience. As an instructor I must try to leave my expectations of students’ skills and background knowledge — at the door.
In a flipped classroom, materials are assigned and made available in advance. In this way, group Zoom-time can be used for questions and examples. This instruction model allows students to learn at their own pace, pause and rewind videos, practice exercises, or speed up lectures. During the workshop, students can bring questions relevant to their particular point of confusion.
The main instructor goal is to facilitate a topic for student engagement that puts the students in control. This approach has a democratizing effect that allows students to become more active and familiar with the materials. With flipped workshops, student questions appear to be more thoughtful and relevant. When the student is invited to take charge of their learning, the process of investigation becomes their self-driven passion.
For my flipped workshops materials, I offer basic videos to introduce and reinforce particular techniques. I try to keep each video short, less than 25 minutes. At the same time I offer plenty of additional videos on different topical details. More in-depth videos can cover important details that may feel ancillary or even demotivating, even if those details improve task efficiency. Sometimes the details are easier to digest when the student is engaged. This means students start at their own level and gain background when they’re ready. Students may not return to the background material for weeks, but the materials will be ready when they are.
Flipping a consultation?
The Center for Data & Visualization Sciences provides open workshops and Zoom-based consulting. The flipped workshop model aligns perfectly with our consulting services since students can engage with the flipped workshop materials (recordings, code, exercises) at any time. When the student is ready for more information, whether a general question or a specific research question, I can refer to targeted background materials during my consultations. With the background resources, I can keep my consultations relevant and brief while also reducing the risk of under-informing.
For my flipped workshop on R, or other CDVS workshops, please see our workshop page.
Felipe Álvarez de Toledo López-Herrera is a Ph.D. candidate in 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 askdata@duke.edu.
[This blogpost introduces a GitHub Repository that provides resources for developing NER projects in historical languages. Please do not hesitate to use the code and ideas made available there, or contact me if there are any issues we could discuss .]
Understanding Historical Art Markets: an Automated Approach
When the Sevillian painters’ guild archive was lost in the 19th century, with it vanished lists of master painters, journeymen, apprentices and possibly dealers recorded in the guilds’ registration books. Nevertheless, researchers working for over a century in other Sevillian archives have published almost twenty volumes of archival documents. These transcriptions, excerpts and summaries reflect the activities of local painters, sculptors, and architects, among other artisans. I use this evidence as a source of data on early modern Seville’s art market in my dissertation research. For this, I have to extract information from many documents in order to query and discern larger patterns.
Left. Some of the volumes used in this research, in my home library. I have managed to acquire these second-hand; others I have borrowed from libraries. Right. A scan of one of the pages of these books, showing some of the documents from which we extracted data.
Instead of manually keying this information into a spreadsheet or other form of data storage, I chose to scan my sources and test an automated approach using Natural Language Processing. Last semester, within the context of the Humanities Unbounded Digital Humanities Graduate Assistantship, I worked with Named-Entity Recognition (NER), a technique in which computers can be taught to identify named real-world objects in texts. NER models underperform on historical texts because they are trained on modern documents such as news or Wikipedia articles. Furthermore, NLP developers have focused most of their efforts on English language models, resulting in underdeveloped models for other languages. For these reasons, I had to retrain a model to be useful for my purposes. In this blogpost, I give an overview of the process of adapting NER tools for use on non-English historical sources.
Defining Named-Entity Recognition
Named-Entity Recognition (NER) is a set of processes in which a computer program is trained to identify and categorize real-world objects with proper names in a corpus of texts. It can be used to tag names in documents without a standardized structure and label them as people, locations or organizations, among other categories.
Named-entity recognition is the automated tagging of real-world objects with names, such as people, locations, organizations, or monetary amounts, within texts.
Code libraries such as Spacy, NLTK or Stanford CoreNLP provide widely-tested toolkits for NER. I decided that Spacy would be the best choice for my purposes. Though its Spanish model included less label categories, they performed better out-of-the-box. Importantly, the model worked better for certain basic language structures such as recognizing compound names (last names with several components, such as my own). The Spacy library also proved user-friendly for those of us with little coding knowledge. Its pre-programmed data processing pipeline is easy to modify, given that you have a basic understanding of Python. In my case, I had the time and motivation to acquire this literacy.
I sought to improve the model’s performance in two ways. First, I retrained it on a subset of my own data. This improved performance and allowed me to add new label categories such as dates, monetary amounts and objects. Additionally, I added a component that modernized my texts’ spelling to make them more conducive to proper tagging.
Training NER on Historical Spanish Text: Process and Results
To improve the model, I needed training data – a “gold standard” of perfectly-tagged text. First, I ran the model on a set of 400 documents, which resulted in a set of preliminary tags. Then, I corrected these tags with a tool called Dataturks and reformatted the output to work with Spacy. Once this data was ready, I split it 80-20, which means running a training loop on 80% of correctly-tagged texts to adjust the performance of the model, and reserving 20% for testing or evaluating the model on data it had not yet seen.
Final output as stored in my database for one particular document with ID=5.
Finally, I evaluated whether all these changes actually improved the model’s performance, saved the updated model, and exported the output in a format that worked for my own database. For my texts, the model initially worked at around 36% recall (the percentage of true entities that were identified by the model), compared to an 89% recall with modern texts as evaluated by Spacy. After training, recall has increased to 64%. Some tags, such as person or location, perform especially well (85% and 81%, respectively). Though the numbers are not perfect, they show a marked improvement, generated with little training data.
For the 8,607 documents processed, the process has resulted in 59,191 tags referring to people, locations, organizations, dates, objects and money. Next steps include finding descriptors of entities within the text, and modeling relationships between entities appearing in the same document. For now, a look at the detected tags underscores the potential of NER for automating data collection in data-driven humanities research.
The Center for Data and Visualization Sciences is glad to welcome you back to a new academic year! We’re excited to have friends and colleagues returning to the Triangle and happy to connect with Duke community members who will not be on campus this fall.
This fall, CDVS will expand its existing online consultations with a new chat service and new online workshops for all members of the Duke community. Since mid-March, CDVS staff have redesigned instructional sessions, constructed new workflows for accessing research data, and built new platforms for accessing data tools virtually. We look forward to connecting with you online and working with you to achieve your research goals.
In addition to our expanded online tools and instruction, we have redesigned our CDVS-Announce data newsletter to provide a monthly update of data news, events, and workshops at Duke. We hope you will consider subscribing.
Upcoming Virtual CDVS Workshops
CDVS continues to offer a full workshops series for the latest strategies and tools for data focused research. Upcoming workshops for early September include:
R for data science: getting started, EDA, data wrangling
Thursday, Sep 1, 2020 10am – 12pm
This workshop is part of the Rfun series. R and the Tidyverse are a data-first coding language that enables reproducible workflows. In this two-part workshop, you’ll learn the fundamentals of R, everything you need to know to quickly get started. You’ll learn how to access and install RStudio, how to wrangle data for analysis, gain a brief introduction to visualization, practice Exploratory Data Analysis (EDA), and how to generate reports.
Register: https://duke.libcal.com/event/6867861
Research Data Management 101
Wednesday, Sep 9, 2020 10am – 12pm
This workshop will introduce data management practices for 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 using examples that span disciplines. During the workshop, participants will also engage in discussions with their peers on data management concepts as well as learn about how to assess data management tools.
Register: https://duke.libcal.com/event/6874814
R for Data Science: Visualization, Pivot, Join, Regression
Wednesday, Sep 9, 2020 1pm – 3pm
This workshop will introduce data management practices for 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 using examples that span disciplines. During the workshop, participants will also engage in discussions with their peers on data management concepts as well as learn about how to assess data management tools.
Register: https://duke.libcal.com/event/6867914
ArcGIS StoryMaps
Thursday, September 10, 2020 1pm – 2:30pm
This workshop will help you get started telling stories with maps on the ArcGIS StoryMaps platform. This easy-to-use web application integrates maps with narrative text, images, and videos to provide a powerful communication tool for any project with a geographic component. We will explore the capabilities of StoryMaps, share best practices for designing effective stories, and guide participants step-by-step through the process of creating their own application.
Register: https://duke.libcal.com/event/6878545
Assignment Tableau: Intro to Tableau work-together
Friday, September 11, 2020 10am – 11:30am
Work together over Zoom on an Intro to Tableau assignment. Tableau Public (available for both Windows and Mac) is incredibly useful free software that allows individuals to quickly and easily explore their data with a wide variety of visual representations, as well as create interactive web-based visualization dashboards. Attendees are expected to watch Intro to Tableau Fall 2019 online first, or have some experience with Tableau. This will be an opportunity to work together on the assignment from the end of that workshop, plus have questions answered live.
Register: https://duke.libcal.com/event/6878629
While the library may be physically closed, the Duke Research Data Repository (RDR) is open and accepting data deposits. If you have a data sharing requirement you need to meet for a journal publisher or funding agency we’ve got you covered. If you have COVID-19 data that can be openly shared, we can help make these vital research materials available to the public and the research community today. Or if you have data that needs to be under access restrictions, we can connect you to partner disciplinary repositories that support clinical trials data, social science data, or qualitative data.
Speaking of the RDR, we just completed a refresh on the platform and added several features!
Image files can now be previewed via a built in Image Viewer
In-line with data sharing standards, we also assign a digital object identifier (DOI) to all datasets, provide structured metadata for discovery, curate data to further enhance datasets for reuse and reproducibility, provide safe archival storage, and a standardized citation for proper acknowledgement.
Openness supports the acceleration of science and the generation of knowledge. Within the libraries we look forward to partnering with Duke researchers to disseminate their research data! Visit https://research.repository.duke.edu/ to learn more or contact datamanagement@duke.edu with any questions.
One of the attractive features of Tableau for visualization is that it can produce maps in addition to standard charts and graphs. While Tableau is far from being a full-fledged GIS application, it continues to expand its mapping capabilities, making it a useful option to show where something is located or to show how indicators are spatially distributed.
Here, we’re going to go over a few of the Tableau’s mapping capabilities. We’ve recorded a workshop with examples relating to this blog post’s discussion:
Tableau is a visualization tool: Tableau can quickly and effectively visualize your data, but it will not do specialized statistical or spatial analysis.
Tableau makes it easy to import data: A big advantage of Tableau is the simplicity of tasks such as changing variable definitions between numeric, string, and date, or filtering out unneeded columns. You can easily do this at the time you connect to the data (“connect” is Tableau’s term for importing data into the program).
Tableau is quite limited for displaying multiple data layers: Tableau wants to display one layer, so you need to use join techniques to connect multiple tables or layers together. You can join data tables based on common attribute values, but to overlay two geographic layers (stack them), you must spatially join one layer to one other layer based on their common location.
Tableau uses a concept that it calls a “dual-axis” map to allow two indicators to display on the same map or to overlay two spatial layers. If, however, you do need to overlay a lot of data on the same map, consider using proper GIS software.
Overlay spatial files using dual-axis maps
Displaying paths on a map requires a special data structure: In order for tabular data with coordinate values (latitude/longitude) to display as lines on a map, you need to include a field that indicates drawing order. Tableau constructs the lines like connect-the-dots, each row of data being a dot, and the drawing order indicating how the dots are connected.
Using drawing order to create lines from points
You might use this, for instance, with hurricane tracking data, each row representing measurements and location collected sequentially at different times. The illustration above shows Paris metro lines with the station symbol diameter indicating passenger volume. See how to do this in Tableau’s tutorial.
You can take advantage of Tableau’s built-in geographies: Tableau has many built-in geographies (e.g., counties, states, countries), making it easy to plot tabular data that has an attribute with values for these geographic locations, even if you don’t have latitude/longitude coordinates or geographic files — Tableau will look up the places for you! (It won’t, however, look up addresses.)
Tableau also has several built-in base maps available for your background.
Tableau uses the “Web Mercator” projection: This is the same as Google Earth/Maps. Small-scale maps (i.e., large area of coverage) may look stretched out in an unattractive way since it greatly exaggerates the size of areas near the poles.
Useful Mapping Capabilities
Plot points: Tableau works really well for plotting coordinate data (Longitude (X) and Latitude (Y) values) as points. The coordinates must have values in decimal degrees with negative longitudes being east of Greenwich and negative latitudes being south of the equator.
Point data with time slider
Time slider: If you move a categorical “Dimension” variable onto Tableau’s Pages Card, you can get a value-based slider to filter your data by that variable’s values (date, for instance, as in Google Earth). This is shown in the image above.
Heatmap of point distribution: You can choose Tableau’s “Density” option on its Marks card to create a heatmap, which may display the concentration of your data locations in a smoother manner.
Filter a map’s features: Tableau’s Filter card is akin to ArcGIS’s Definition Query, to allow you to look at just a subset of the features in a data table.
Shade polygons to reflect attribute values: Choropleth maps (polygons shaded to represent values of a variable) are easy to make in Tableau. Generally, you’ll have a field with values that match a built-in geography, like countries of the world or US counties. But you can also connect to spatial files (e.g., Esri shapefiles or GeoJSON files), which is especially helpful if the geography isn’t built into Tableau (US Census Tracts are an example).
Filled map using color to indicate values
Display multiple indicators: Visualizing two variables on the same map is always problematic because the data patterns often get hidden in the confusion, but it is possible in Tableau. Use the “dual-axis” map concept mentioned above. An example might be pies for one categorical variable (with slices representing the categories) on top of choropleth polygons that visualize a continuous numeric variable.
Two variables using filled polygons and pies
Draw lines from tabular data: Tableau can display lines if your data is structured right, as discussed and illustrated previously, with a field for drawing order. You could also connect to a spatial line file, such as a shapefile or a GeoJSON file.
Help Resources
We’ve just given an overview of some of Tableau’s capabilities regarding spatial data. The developers are adding features in this area all the time, so stay tuned!
Interested in attending the 2020 RStudio Conference, but unable to travel to San Francisco? With the generous support of RStudio and the Department of Statistical Science, Duke Libraries will host a livestream of the annual RStudio conference starting on Wednesday, January 29th at 11AM. See the latest in machine learning, data science, data visualization, and R. Registration links and information about sessions follow. Registration is required for the first session and keynote presentations. Please see the links in the agenda that follows.
Education Track (registration is not required) Meet you where you R – Lauren Chadwick, R Studio.
Data Science Education in 2022 (Karl Howe and Greg Wilson, R Studio)
Data science education as an economic and public health intervention in East Baltimore (Jeff Leek, Johns Hopkins)
Of Teacups, Giraffes, & R Markdown (Desiree Deleon, Emory)
Location: Edge Workshop Room – Bostock 127
5:15 – 6:45
All About Shiny (registration is not required)
Production-grade Shiny Apps with golem (Colin Fay, ThinkR)
Making the Shiny Contest (Duke’s own Mine Cetinkaya-Rundel)
Styling Shiny Apps with Sass and Bootstrap 4(Joe Cheng, RStudio)
Reproducible Shiny Apps with shinymeta (Carson Stewart, RStudio)
7:00 – 8:30
Learning and Using R (registration is not required)
Learning and using R: Flipbooks (Evangeline Reynolds, U Denver)
Learning R with Humorous Side Projects (Ryan Timpe, Lego Group)
Toward a grammar of psychological Experiments (Danielle, Navaro, University of New South Wales)
R for Graphical Clinical Trial Reporting(Frank Harrell, Vanderbilt)
Data Visualization Track (registration is not required) The Glamour of Graphics (William Chase, University of Pennsylvania)
3D ggplots with rayshader (Dr. Tyler Morgan-Wall, Institute for Defense Analyses)
Designing Effective Visualizations (Miriah Meyer, University of Utah)
Tidyverse 2019-2020 (Hadley Wickham, RStudio)
3:00 – 4:00
Livestream of Rstudio Conference Sessions (registration is not required)
4:00 – 5:30
Data Visualization Track 2 (registration is not required)
Spruce up your ggplot2 visualizations with formatted text (Claus Wilke, UT Austin)
The little package that could: taking visualizations to the next level with the scales package (Dana Seidel, Plenty Unlimited)
Extending your ability to extend ggplot2 (Thomas Lin Pedersen, RStudio)
5:45 – 6:30
Career Advice for Data Scientists Panel Discussion (registration is not required)
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