Data Science newsletter – June 5, 2020

GROUP CURATION: N/A

Hundreds of published studies over the last decade have claimed it’s possible to predict an individual’s patterns of thoughts and feelings by scanning their brain in an MRI machine as they perform some mental tasks.

But a new analysis by some of the researchers who have done the most work in this area finds that those measurements are highly suspect when it comes to drawing conclusions about any individual person’s brain.

Watching the brain through a functional MRI machine (fMRI) is still great for finding the general brain structures involved in a given task across a group of people, said Ahmad Hariri, a professor of psychology and neuroscience at Duke University who led the reanalysis.

DeepMind is shifting its focus from building “AI agents” that can play games to building AI agents that can have real world impact, particularly in areas of science like biology.

The University of Illinois’ National Center for Supercomputing Applications received a $10 million federal grant to deploy a next-generation computing program called Delta.

Funded by the National Science Foundation, Delta will be about as powerful as the Blue Waters supercomputer, at a fraction of the cost and size, NCSA Director Bill Gropp said.

“If you’ve seen Blue Waters and its aisle after aisle of cabinets, we’re probably looking at just one aisle of cabinets,” he said. “There’s some things that Blue Waters will be a lot better at, and there’s some things that this machine will be better at.”

The Governance Lab (The GovLab) at the NYU Tandon School of Engineering announced a partnership with the Organisation for Economic Co-operation and Development that will focus on addressing a topic of growing public concern: disinformation.

The new collaboration is part of The 100 Questions Initiative, an effort to identify the most important societal questions for which greater access to data and data science methods could find answers; in our current climate, some of the most pressing questions involve the spread of deceptive or unproven information.

A Purdue University data science and machine learning innovator wants to help organizations and users get the most for their money when it comes to cloud-based databases. Her same technology may help self-driving vehicles operate more safely on the road when latency is the primary concern.

Somali Chaterji, a Purdue assistant professor of agricultural and biological engineering who directs the Innovatory for Cells and Neural Machines [ICAN], and her team created a technology called OPTIMUSCLOUD.

The system is designed to help achieve cost and performance efficiency for cloud-hosted databases, rightsizing resources to benefit both the cloud vendors who do not have to aggressively over-provision their cloud-hosted servers for fail-safe operations and to the clients because the data center savings can be passed on them.

A research group made up of academics from across the globe have published a paper arguing that “cross-cultural cooperation” on AI ethics and governance is vital if the technology is to “bring about benefit worldwide.”

The experts — from Cambridge University’s Leverhulme Centre for the Future of Intelligence, Peking University’s Center for Philosophy and the Future of Humanity, and the Beijing Academy of Artificial Intelligence — specifically want to see cooperation across different domains, disciplines, and cultures, as well as different nations.

“Such cooperation will enable advances to be shared across different parts of the world, and will ensure that no part of society is neglected or disproportionately negatively impacted by AI,” wrote researcher Jess Whittlestone in a blog post this week that summarizes the paper.

Researchers from North Carolina State University and the University at Buffalo have developed a technology called “Artificial Chemist,” which incorporates artificial intelligence (AI) and an automated system for performing chemical reactions to accelerate R&D and manufacturing of commercially desirable materials.

In proof-of-concept experiments, the researchers demonstrated that Artificial Chemist can identify and produce the best possible quantum dots for any color in 15 minutes or less. Quantum dots are colloidal semiconductor nanocrystals, which are used in applications such as LED displays.

However, the researchers are quick to note that Artificial Chemist can identify the best material to meet any suite of measurable properties – not just quantum dots.

The World Health Organization and a number of national governments have changed their Covid-19 policies and treatments on the basis of flawed data from a little-known US healthcare analytics company, also calling into question the integrity of key studies published in some of the world’s most prestigious medical journals.

A Guardian investigation can reveal the US-based company Surgisphere, whose handful of employees appear to include a science fiction writer and an adult-content model, has provided data for multiple studies on Covid-19 co-authored by its chief executive, but has so far failed to adequately explain its data or methodology.

Data it claims to have legitimately obtained from more than a thousand hospitals worldwide formed the basis of scientific articles that have led to changes in Covid-19 treatment policies in Latin American countries. It was also behind a decision by the WHO and research institutes around the world to halt trials of the controversial drug hydroxychloroquine. On Wednesday, the WHO announced those trials would now resume.

Two of the world’s leading medical journals – the Lancet and the New England Journal of Medicine – published studies based on Surgisphere data. The studies were co-authored by the firm’s chief executive, Sapan Desai.

Retraction made after Guardian investigation found inconsistencies in data

Masks should not be a political issue. They are a public health issue. But they seem to have stirred up a whole mess of fuss for various reasons. I hope I can break it down simply here and demonstrate their importance in reducing SARS-CoV2 infections in our communities.

Weather forecasting has improved significantly in recent decades. Thanks to advances in monitoring and computing technology, today’s 5-day forecasts are as accurate as 1-day forecasts were in 1980. Artificial intelligence could revolutionize weather forecasts again. In a new study, Arcomano et al. present a machine learning model that forecasts weather in the same format as classic numerical weather prediction models.

The computational biologist Bruno Correia used to have a rule in his lab: No machine learning allowed. He didn’t consider it real science. Now Correia has used it to detect potential interactions between proteins — the complex folded molecules responsible for many biological processes — 40,000 times faster than conventional methods. The journal Nature Methods featured his system on its cover in February 2020. Correia said of his early reluctance to embrace machine learning, “I was wrong, and I’m glad I was wrong.” … Correia’s system, called MaSIF (short for molecular surface interaction fingerprinting), avoids the inherent complexity of a protein’s 3D shape by ignoring the molecules’ internal structure. I

It’s not always the Traffic Stop. Sometimes I think about the time that guy in the pick up truck spit on me, or the confrontation with the police while I was house hunting. A few weeks ago, I was sitting on a curb in my embarrassingly sprawling neighborhood and someone in a truck drove by, stopped maybe 40 yards away, backed up and stared at me for five or six seconds, then left. “What was that about? What could it have been about?”

My son is 12, all arms and legs. Studies tell me that when he walks down the street, he’ll be more likely mistaken for 16. His 12-year old movements will be threatening. I know: I’ve already had that conversation with teachers.

Yesterday, I sat in my car with him and we talked about what’s going on, and this is neither the first nor second such conversation. He doesn’t quite believe what’s happening, but he believes it more every time we have this conversation. I know he’ll believe it more the next time we are here, the next time someone utters a racial epithet around him. This is a reality I went through, and I know it’s a reality he’ll go through.

The National Institutes of Health (NIH) plays a critical role in funding scientific endeavors in biomedicine. Funding innovative science is an essential element of the NIH’s mission, but many have questioned the NIH’s ability to fulfill this aim. Based on an analysis of a comprehensive corpus of published biomedical research articles, we measure whether the NIH succeeds in funding work with novel ideas, which we term edge science. We find that edge science is more often NIH funded than less novel science, but with a delay. Papers that build on very recent ideas are NIH funded less often than are papers that build on ideas that have had a chance to mature for at least 7 y. We have three further findings. First, the tendency to fund edge science is mostly limited to basic science. Papers that build on novel clinical ideas are not more often NIH funded than are papers that build on well-established clinical knowledge. Second, novel papers tend to be NIH funded more often because there are more NIH-funded papers in innovative areas of investigation, rather than because the NIH funds innovative papers within research areas. Third, the NIH’s tendency to have funded papers that build on the most recent advances has declined over time. In this regard, NIH funding has become more conservative despite initiatives to increase funding for innovative projects. Given our focus on published papers, the results reflect both the funding preferences of the NIH and the composition of the applications it receives.

This morning real-world behavioral data analysis company Evidation Health announced a research initiative that will analyze behavior and symptom data to develop an early warning algorithm for COVID-19.

Funded by the Biomedical Advanced Research and Development Authority (BARDA) and the Bill and Melinda Gates Foundation, the effort will work with 4YouandMe – a nonprofit that helps individuals share their health data for medical research – to collect self-reported and wearable-collected data from 300 participants. This study group will be made up of healthcare workers, first responders and other individuals at high risk of coronavirus infection.

In a recent post, we defined three key attributes of a concept we call Serious Data Science: Credibility, Agility and Durability. In this post, we’ll drill into the challenge of delivering credible insights to your stakeholders, and how to address that challenge.

Ultimately, organizations use data science to discover valuable insights and then apply those insights intelligently. Such applications might include making a better decision, improving a process, or otherwise changing how things are usually done. However, to make this happen, the organization must do at least two things:

Communicate these insights to the right decision-maker, stakeholder, or system (we’ll talk more about that in our next Serious Data Science post on being Agile).

Convince decision makers to trust the insight and accept its implications. If decision makers lack this trust, then they will likely ignore the recommendation, and fall back on “the way we’ve always done things.”

DataCite is a community of libraries, research institutions, and data centers that house repositories. We often receive questions about how exactly we work with repositories, so we wanted to take this opportunity to answer your top 10 questions.

The Center for Global Ethnography announces Doing Ethnography Remotely, a video-interview series focusing on how researchers have used remote methods in their work.

Through interviews with six ethnographers, the Center’s co-directors Sharika Thiranagama (Anthropology) and Sylvia Yanagisako (Anthropology) explore how scholars have used digital and analog tools to study communities online and how they have accessed social spaces from a geographic distance.

From across the disciplines, the researchers speak candidly about past projects and how digital tools and techniques have shaped their research questions and findings. Each interview concludes with practical advice aimed at graduate students interested in deploying remote methods in their own work.