Nextjournal / Apr 23 2019

Scholarship for Explorable Research Recipients

We are excited by our global assembly of Nextjournal Scholarship for Explorable Research awardees. The winning teams hail from Europe, Argentina, and the United States, conducting research in the fields of statistical modeling, data-driven policy, and environmental data science.

Their research will form the foundation of an interactive article. Our goal is to change how people engage published science. To paraphrase Bret Victor, rather than "information to be consumed," the resulting notebook will be "used as an environment to think in."

This will be accomplished using Nextjournal's existing technologies and a coordinated development effort with the Nextjournal team. Our notebooks make results and methods reproducible, foster model-driven debate, and provide simple collaborative tools every step of the way.

Exploring and Statistically Learning an Excitable Stochastic-Dynamical Model


Recurring natural events such as West Antarctic ice shelf collapses, the firing of neurons or the rapid population expansions of defoliating insects typically occur neither in a completely periodic nor completely irregular fashion. The cause is the interplay between the noise coming from outside factors interacting with the system in a random or non-predictable way and the rhythm created by the delayed interaction between excitatory and inhibitory components of the system. Stochastic models are mathematical objects which couple rhythm and noise and can express a manifold of possible recurrence patterns.

The FitzHugh-Nagumo model is a versatile yet simple model for the evolution of excitable or oscillatory physical processes. It incorporates a single hidden or latent component, but it can capture a wide spectrum of dynamical processes. Because of its flexibility, this model has been widely used in many scientific fields. There is practical importance to match the free parameters to the real phenomena at hand. The task of statisticians is indeed to fit real data and infer the parameters of the model. In this project we will package a statistical method, provide an interface to visualise and explore the interaction between the parameters and the dynamical patterns created and finally interactively reconnect the inferential results with the natural phenomenon.

The Team

  • Sebastiano Grazzi, Delft Institute of Applied Mathematics, Delft University of Technology, The Netherlands
  • Frank van der Meulen, Delft Institute of Applied Mathematics, Delft University of Technology, The Netherlands
  • Marcin Mider, Department of Statistics, University of Warwick, United Kingdom
  • Dr. Moritz Schauer, Mathematical Institute, Leiden University, The Netherlands

Women Programmers - Chicas en Tecnología


We are Chicas en Tecnología, an Argentine non-profit organization that since 2015 works to close the gender gap in technology. With our programs and initiatives we motivate, train and support the next generation of women leaders in technology.

We believe that what is not measured cannot be changed and this is why we collected, for the first time in our country, gender data for students of all 80 institutions in all the country, including more than 1700 degrees in STEM, for the last 6 years.

Our goal for our Nextjournal project is to make this information accessible and understandable by journalists, teachers, researchers and the general public. We will combine our dataset with sociodemographic and geographic data, comparing careers by region, year and more. We want to map how universities are distributed across the country, which of them have higher rates of gender diversity and share the strategies that they are applying for achieving them. With the help of this scholarship we will be able to allocate the necessary resources to generate and cross our dataset with others, to discover correlations and outliers and, ultimately, to educate the population in Argentina about how gender diversity in tech is evolving in our country.

The Team

Our team is formed by professionals and experts in technology, education, entrepreneurship, design and communication who work as permanent staff, consultants and volunteers. We have a dynamic structure that adapts every year to the objectives proposed by our organization. For this project we will form a team of programmers, data scientists and designers to transform all this data into useful information, though text, visualizations and exploration tools.

Where Should We Apply Biochar?


Biochar is a low-density organic by-product of the thermochemical conversion of biomass that is being evaluated as a soil amendment. Biochar can be used to sequester carbon in soil and also has the potential to boost crop yields when it is used to improve yield-limiting soil properties. These complex interactions have made it challenging to answer the question of where biochar should be applied for the maximum agronomic and economic benefit.

We addressed this challenge by developing an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. We used a probabilistic graphical model to study the relationships between soil and biochar variables and predict the probability and magnitude of crop yield response to biochar application.

Our large spatial-scale modeling revealed that 8.4% to 30% of all U.S. cropland can be targeted and is expected to show a positive yield response to biochar application. Expected yield increases of at least 6.1% and 8.8% are necessary to cover 25% and 10% of U.S. cropland with biochar. It was found that biochar application to areas with high probability of crop yield response in the U.S could offset a maximum of 2% of the current global anthropogenic carbon emissions per year.

Finally, in order to better communicate with our audience we will be using Nextjournal to develop interactive tools allowing users with no or little background in computer science to use and take advantage of our data products.

The Team

  • Hamze Dokoohaki, Postdoc at Boston University
  • Fernando E. Miguez, Associate Professor, Department of Agronomy, Iowa State University
  • David Laird, Professor, Department of Agronomy, Iowa State University
  • Jerome Dumortier, Associate Professor, School of Public and Environmental Affairs, Indiana University - Purdue University Indianapolis

About Nextjournal

Nextjournal is a web-based tool that helps researchers and data scientists streamline their data-driven workflows. It combines code, prose, data and results into interactive, shareable articles.

On-demand cloud-computing and automatic versioning of all content, data and the full computational environment allow for rapid experimentation with your code and data. You can go back in time, any time.

This underlying versioning technology also enables Nextjournal’s articles to be easily shared and remixed by peers as a starting point for their own articles. Other articles’ computational environments can be referenced and re-used while all attribution is automatically stored.

Articles can contain multiple programming languages (currently Julia, R, Bash, Python, and Clojure) for building sophisticated data-driven workflows with rich documentation and narrative in context with code.

Read About Nextjournal to learn more about our team and technology.