Kid-edited journal pushes scientists for clear writing on complex topics

Curated on Posted on by Stefaan Verhulst

Article by Mark Johnson: “The reviewer was not impressed with the paper written by Israeli brain researcher Idan Segev and a colleague from Switzerland.

“Professor Idan,” she wrote to Segev. “I didn’t understand anything that you said.”

Segev and co-author Felix Schürmann revised their paper on the Human Brain project, a massive effort seeking to channel all that we know about the mind into a vast computer model. But once again the reviewer sent it back. Still not clear enough. It took a third version to satisfy the reviewer.

“Okay,” said the reviewer, an 11-year-old girl from New York named Abby. “Now I understand.”

Such is the stringent editing process at the online science journal Frontiers for Young Minds, where top scientists, some of them Nobel Prize winners, submit papers on gene-editing, gravitational waves and other topics — to demanding reviewers ages 8 through 15.

Launched in 2013, the Lausanne, Switzerland-based publication is coming of age at a moment when skeptical members of the public look to scientists for clear guidance on the coronavirus and on potentially catastrophic climate change, among other issues. At Frontiers for Young Minds, the goal is not just to publish science papers but also to make them accessible to young readers like the reviewers. In doing so, it takes direct aim at a long-standing problem in science — poor communication between professionals and the public.

“Scientists tend to default to their own jargon and don’t think carefully about whether this is a word that the public actually knows,” said Jon Lorsch, director of the National Institute of General Medical Sciences. “Sometimes to actually explain something you need a sentence as opposed to the one word scientists are using.”

Dense language sends a message “that science is for scientists; that you have to be an ‘intellectual’ to read and understand scientific literature; and that science is not relevant or important for everyday life,” according to a paper published last year in Advances in Physiology Education.

Frontiers for Young Minds, which has drawn nearly 30 million online page views in its nine years, offers a different message on its homepage: “Science for kids, edited by kids.”..(More)”.

Report on the Future of Conferences

Curated on Posted on by Stefaan Verhulst

Arxiv Report by Steven Fraser and Dennis Mancl: “In 2020, virtual conferences became almost the only alternative to cancellation. Now that the pandemic is subsiding, the pros and cons of virtual conferences need to be reevaluated. In this report, we scrutinize the dynamics and economics of conferences and highlight the history of successful virtual meetings in industry. We also report on the attitudes of conference attendees from an informal survey we ran in spring 2022…(More).

The ethical and legal landscape of brain data governance

Curated on Posted on by Stefaan Verhulst

Paper by Paschal Ochang , Bernd Carsten Stahl, and Damian Eke: “Neuroscience research is producing big brain data which informs both advancements in neuroscience research and drives the development of advanced datasets to provide advanced medical solutions. These brain data are produced under different jurisdictions in different formats and are governed under different regulations. The governance of data has become essential and critical resulting in the development of various governance structures to ensure that the quality, availability, findability, accessibility, usability, and utility of data is maintained. Furthermore, data governance is influenced by various ethical and legal principles. However, it is still not clear what ethical and legal principles should be used as a standard or baseline when managing brain data due to varying practices and evolving concepts. Therefore, this study asks what ethical and legal principles shape the current brain data governance landscape? A systematic scoping review and thematic analysis of articles focused on biomedical, neuro and brain data governance was carried out to identify the ethical and legal principles which shape the current brain data governance landscape. The results revealed that there is currently a large variation of how the principles are presented and discussions around the terms are very multidimensional. Some of the principles are still at their infancy and are barely visible. A range of principles emerged during the thematic analysis providing a potential list of principles which can provide a more comprehensive framework for brain data governance and a conceptual expansion of neuroethics…(More)”.

The Strength of Knowledge Ties

Curated on Posted on by Stefaan Verhulst

Paper by Luca Maria Aiello: “Social relationships are probably the most important things we have in our life. They help us to get new jobs, live longer, and be happier. At the scale of cities, networks of diverse social connections determine the economic prospects of a population. The strength of social ties is believed one of the key factors that regulate these outcomes. According to Granovetter’s classic theory about tie strength, information flows through social ties of two strengths: weak ties that are used infrequently but bridge distant groups that tend to posses diverse knowledge; and strong ties, that are used frequently, knit communities together, and provide dependable sources of support.

For decades, tie strength has been quantified using the frequency of interaction. Yet, frequency does not reflect Granovetter’s initial conception of strength, which in his view is a multidimensional concept, such as the “combination of the amount of time, the emotional intensity, intimacy, and services which characterize the tie.” Frequency of interaction is traditionally used as a proxy for more complex social processes mostly because it is relatively easy to measure (e.g., the number of calls in phone records). But what if we had a way to measure these social processes directly?

We used advanced techniques in Natural Language Processing (NLP) to quantify whether the text of a message conveys knowledge (whether the message provides information about a specific domain) or support (expressions of emotional or practical help), and applied it to a large conversation network from Reddit composed by 630K users resident in the United States, linked by 12.8M ties. Our hypothesis was that the resulting knowledge and support networks would fare better in predicting social outcomes than a traditional social network weighted by interaction frequency. In particular, borrowing a classic experimental setup, we tested whether the diversity of social connections of Reddit users resident in a specific US state would correlate with the economic opportunities in that state (estimated with GDP per capita)…(More)”.

We need data infrastructure as well as data sharing – conflicts of interest in video game research

Curated on Posted on by Stefaan Verhulst

Article by David Zendle & Heather Wardle: “Industry data sharing has the potential to revolutionise evidence on video gaming and mental health, as well as a host of other critical topics. However, collaborative data sharing agreements between academics and industry partners may also afford industry enormous power in steering the development of this evidence base. In this paper, we outline how nonfinancial conflicts of interest may emerge when industry share data with academics. We then go on to describe ways in which such conflicts may affect the quality of the evidence base. Finally, we suggest strategies for mitigating this impact and preserving research independence. We focus on the development of data infrastructure: technological, social, and educational architecture that facilitates unfettered and free access to the kinds of high-quality data that industry hold, but without industry involvement…(More)”.

ResearchDataGov

Curated on Posted on by Stefaan Verhulst

“ResearchDataGov.org is a product of the federal statistical agencies and units, created in response to the Foundations of Evidence-based Policymaking Act of 2018. The site is the single portal for discovery of restricted data in the federal statistical system. The agencies have provided detailed descriptions of each data asset. Users can search for data by topic, agency, and keywords. Questions related to the data should be directed to the owning agency, using the contact information on the page that describes the data. In late 2022, users will be able to apply for access to these data using a single-application process built into ResearchDataGov. ResearchDataGov.org is built by and hosted at ICPSR at the University of Michigan, under contract and guidance from the National Center for Science and Engineering Statistics within the National Science Foundation.

The data described in ResearchDataGov.org are owned by and accessed through the agencies and units of the federal statistical system. Data access is determined by the owning or distributing agency and is limited to specific physical or virtual data enclaves. Even though all data assets are listed in a single inventory, they are not necessarily available for use in the same location(s). Multiple data assets accessed in the same location may not be able to be used together due to disclosure risk and other requirements. Please note the access modality of the data in which you are interested and seek guidance from the owning agency about whether assets can be linked or otherwise used together…(More)”.

A Landscape of Open Science Policies Research

Curated on Posted on by Stefaan Verhulst

Paper by Alejandra Manco: “This literature review aims to examine the approach given to open science policy in the different studies. The main findings are that the approach given to open science has different aspects: policy framing and its geopolitical aspects are described as an asymmetries replication and epistemic governance tool. The main geopolitical aspects of open science policies described in the literature are the relations between international, regional, and national policies. There are also different components of open science covered in the literature: open data seems much discussed in the works in the English language, while open access is the main component discussed in the Portuguese and Spanish speaking papers. Finally, the relationship between open science policies and the science policy is framed by highlighting the innovation and transparency that open science can bring into it…(More)”

Data Analysis for Social Science: A Friendly and Practical Introduction

Curated on Posted on by Stefaan Verhulst

Book by Elena Llaudet and Kosuke Imai: “…provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.

  • Analyzes real-world data using the powerful, open-sourced statistical program R, which is free for everyone to use
  • Teaches how to measure, predict, and explain quantities of interest based on data
  • Shows how to infer population characteristics using survey research, predict outcomes using linear models, and estimate causal effects with and without randomized experiments
  • Assumes no prior knowledge of statistics or coding
  • Specifically designed to accommodate students with a variety of math backgrounds
  • Provides cheatsheets of statistical concepts and R code
  • Supporting materials available online, including real-world datasets and the code to analyze them, plus—for instructor use—sample syllabi, sample lecture slides, additional datasets, and additional exercises with solutions…(More)”.

How AI That Powers Chatbots and Search Queries Could Discover New Drugs

Curated on Posted on by Stefaan Verhulst

Karen Hao at The Wall Street Journal: “In their search for new disease-fighting medicines, drug makers have long employed a laborious trial-and-error process to identify the right compounds. But what if artificial intelligence could predict the makeup of a new drug molecule the way Google figures out what you’re searching for, or email programs anticipate your replies—like “Got it, thanks”?

That’s the aim of a new approach that uses an AI technique known as natural language processing—​the same technology​ that enables OpenAI’s ChatGPT​ to ​generate human-like responses​—to analyze and synthesize proteins, which are the building blocks of life and of many drugs. The approach exploits the fact that biological codes have something in common with search queries and email texts: Both are represented by a series of letters.  

Proteins are made up of dozens to thousands of small chemical subunits known as amino acids, and scientists use special notation to document the sequences. With each amino acid corresponding to a single letter of the alphabet, proteins are represented as long, sentence-like combinations.

Natural language algorithms, which quickly analyze language and predict the next step in a conversation, can also be applied to this biological data to create protein-language models. The models encode what might be called the grammar of proteins—the rules that govern which amino acid combinations yield specific therapeutic properties—to predict the sequences of letters that could become the basis of new drug molecules. As a result, the time required for the early stages of drug discovery could shrink from years to months.

“Nature has provided us with tons of examples of proteins that have been designed exquisitely with a variety of functions,” says Ali Madani, founder of ProFluent Bio, a Berkeley, Calif.-based startup focused on language-based protein design. “We’re learning the blueprint from nature.”…(More)”.

Explore the first Open Science Indicators dataset

Curated on Posted on by Stefaan Verhulst

Article by Lauren Cadwallader, Lindsay Morton, and Iain Hrynaszkiewicz: “Open Science is on the rise. We can infer as much from the proliferation of Open Access publishing options; the steady upward trend in bioRxiv postings; the periodic rollout of new national, institutional, or funder policies. 

But what do we actually know about the day-to-day realities of Open Science practice? What are the norms? How do they vary across different research subject areas and regions? Are Open Science practices shifting over time? Where might the next opportunity lie and where do barriers to adoption persist? 

To even begin exploring these questions and others like them we need to establish a shared understanding of how we define and measure Open Science practices. We also need to understand the current state of adoption in order to track progress over time. That’s where the Open Science Indicators project comes in. PLOS conceptualized a framework for measuring Open Science practices according to the FAIR principles, and partnered with DataSeer to develop a set of numerical “indicators” linked to specific Open Science characteristics and behaviors observable in published research articles. Our very first dataset, now available for download at Figshare, focuses on three Open Science practices: data sharing, code sharing, and preprint posting…(More)”.