Subjects -> SOCIAL SCIENCES (Total: 1648 journals)
    - BIRTH CONTROL (22 journals)
    - CHILDREN AND YOUTH (262 journals)
    - FOLKLORE (30 journals)
    - MATRIMONY (16 journals)
    - MEN'S INTERESTS (16 journals)
    - MEN'S STUDIES (90 journals)
    - SEXUALITY (56 journals)
    - SOCIAL SCIENCES (937 journals)
    - WOMEN'S INTERESTS (44 journals)
    - WOMEN'S STUDIES (175 journals)

SOCIAL SCIENCES (937 journals)            First | 1 2 3 4 5     

Showing 801 - 136 of 136 Journals sorted alphabetically
Shanlax International Journal of Arts, Science and Humanities     Open Access  
SHS Web of Conferences     Open Access  
Si Somos Americanos     Open Access  
Signos : Investigación en Sistemas de Gestión     Open Access  
Simbiótica     Open Access  
SINTESA : Jurnal Ilmu Sosial dan Ilmu Politik     Open Access  
SN Social Sciences     Hybrid Journal   (Followers: 3)
Social & Legal Studies     Hybrid Journal   (Followers: 16)
Social and Personality Psychology Compass     Hybrid Journal   (Followers: 17)
Social Behavior and Personality : An International Journal     Full-text available via subscription   (Followers: 13)
Social Choice and Welfare     Hybrid Journal   (Followers: 12)
Social Cognition     Full-text available via subscription   (Followers: 20)
Social Compass     Hybrid Journal   (Followers: 7)
Social Development & Security : Journal of Scientific Papers     Open Access  
Social Development Issues     Full-text available via subscription  
Social Education     Full-text available via subscription   (Followers: 4)
Social History Studies     Open Access   (Followers: 2)
Social Influence     Hybrid Journal   (Followers: 7)
Social Inquiry : Journal of Social Science Research     Open Access  
Social Justice Research     Hybrid Journal   (Followers: 24)
Social Landscape Journal     Open Access  
Social Philosophy and Policy     Full-text available via subscription   (Followers: 25)
Social Policy and Society     Hybrid Journal   (Followers: 136)
Social Research : An International Quarterly     Full-text available via subscription   (Followers: 5)
Social Science & Medicine     Hybrid Journal   (Followers: 102)
Social Science Computer Review     Hybrid Journal   (Followers: 13)
Social Science Japan Journal     Hybrid Journal   (Followers: 11)
Social Science Protocols     Open Access  
Social Science Research     Hybrid Journal   (Followers: 31)
Social Science Spectrum     Open Access  
Social Sciences     Open Access   (Followers: 4)
Social Sciences & Humanities Open     Open Access   (Followers: 2)
Social Sciences and Missions     Hybrid Journal   (Followers: 3)
Social Sciences in China     Hybrid Journal   (Followers: 6)
Social Semiotics     Hybrid Journal   (Followers: 7)
Social Studies and the Young Learner     Full-text available via subscription   (Followers: 2)
Social Studies of Science     Hybrid Journal   (Followers: 36)
Social Studies Research and Practice     Hybrid Journal   (Followers: 1)
Social, Humanities, and Educational Studies (SHEs) : Conference Series     Open Access  
Socialiniai tyrimai     Open Access  
Socialium : Revista Cientifica de Ciencias Sociales     Open Access  
Socialvetenskaplig tidskrift     Open Access  
Sociedad e Infancias     Open Access  
Sociedade e Cultura     Open Access  
Sociedade e Estado     Open Access   (Followers: 2)
Sociétés & Représentations     Full-text available via subscription   (Followers: 1)
Society     Open Access   (Followers: 1)
Socio     Open Access  
Socio-analysis     Full-text available via subscription   (Followers: 3)
Socio-Ecological Practice Research     Hybrid Journal  
Sociología y Tecnociencia     Open Access  
Sophia Austral     Open Access  
Soshum : Jurnal Sosial dan Humaniora     Open Access  
Sosio Didaktika : Social Science Education Journal     Open Access  
SosioHumanika: Jurnal Pendidikan Sains Sosial dan Kemanusiaan (Journal of Humanities and Social Sciences Education)     Open Access  
Soundings : An Interdisciplinary Journal     Full-text available via subscription   (Followers: 1)
South Asian Journal of Social Studies and Economics     Open Access  
Sozial Extra     Hybrid Journal   (Followers: 1)
Soziale Passagen     Hybrid Journal  
Sri Lanka Journal of Advanced Social Studies     Open Access  
Sri Lanka Journal of Social Sciences     Open Access  
Studi Magrebini : North African Studies     Full-text available via subscription   (Followers: 2)
Studia Socialia Cracoviensia     Open Access   (Followers: 1)
Studies in Asian Social Science     Open Access  
Studies in Social Justice     Open Access   (Followers: 8)
Sultan Agung Fundamental Research Journal     Open Access  
Suma de Negocios     Open Access   (Followers: 1)
Suomen Sukututkimusseuran Vuosikirja     Open Access  
Survey Research Methods     Open Access   (Followers: 2)
Sustainability     Open Access   (Followers: 24)
Sustainability : Science, Practice, & Policy     Open Access   (Followers: 2)
Symmetry     Open Access  
Symposion : Theoretical and Applied Inquiries in Philosophy and Social Sciences     Open Access  
Søkelys på arbeidslivet (Norwegian Journal of Working Life Studies)     Open Access  
Tangent     Hybrid Journal  
Tapuya : Latin American Science, Technology and Society     Open Access   (Followers: 1)
Technology transfer: innovative solutions in Social Sciences and Humanities     Open Access   (Followers: 2)
TechTrends     Hybrid Journal   (Followers: 8)
Teme : Journal of Social Sciences     Open Access  
Tempo Social     Open Access  
Teoría y Praxis     Open Access  
Textos & Contextos (Porto Alegre)     Open Access  
The Batuk     Open Access   (Followers: 8)
The Equilibrium     Open Access  
The EXceptional Parent     Full-text available via subscription  
The New Yorker     Full-text available via subscription   (Followers: 33)
The Tocqueville Review/La revue Tocqueville     Full-text available via subscription   (Followers: 3)
The Winnower     Open Access  
The Women : Annual Research Journal of Gender Studies     Open Access   (Followers: 7)
Thesis     Open Access   (Followers: 1)
Third Sector Review     Full-text available via subscription   (Followers: 3)
Tidsskrift for kjønnsforskning     Open Access  
Tidsskrift for samfunnsforskning     Open Access  
Tidsskrift for velferdsforskning     Open Access  
Tieteessä Tapahtuu     Open Access  
Tinkazos     Open Access  
Trabajos y Comunicaciones     Open Access  
Trama : Revista de Ciencias Sociales y Humanidades     Open Access  
Trans-pasando Fronteras     Open Access  
Transmodernity : Journal of Peripheral Cultural Production of the Luso-Hispanic World     Open Access   (Followers: 4)
Transmotion     Open Access   (Followers: 20)
Transtext(e)s Transcultures     Open Access  
Trayectorias Humanas Trascontinentales : TraHs     Open Access  
Trivium     Open Access   (Followers: 1)
Tulane Undergraduate Research Journal     Open Access   (Followers: 1)
Twentieth Century Communism     Full-text available via subscription   (Followers: 1)
Twenty-First Century Society: Journal of the Academy of Social Sciences     Hybrid Journal  
UC Merced Undergraduate Research Journal     Open Access  
UC Riverside Undergraduate Research Journal     Open Access  
UED Journal of Social Sciences, Humanities and Education     Open Access  
Ultima Década     Open Access  
Uluslararası Anadolu Sosyal Bilimler Dergisi / International Anatolian Journal of Social Sciences     Open Access  
Umanistica Digitale     Open Access   (Followers: 2)
Uni-pluriversidad     Open Access  
Universidad de La Habana     Open Access  
Universidad y Ciencia     Open Access  
Universidad, Escuela y Sociedad     Open Access   (Followers: 1)
Universitas Científica     Open Access  
Universitas-XXI, Revista de Ciencias Sociales y Humanas     Open Access   (Followers: 1)
University of Mauritius Research Journal     Open Access  
Universum : Revista de Humanidades y Ciencias Sociales     Open Access   (Followers: 1)
UNM Environmental Journals     Open Access  
Unoesc & Ciência - ACSA     Open Access  
VA Engage Journal     Open Access  
Variations : Revue Internationale de Théorie Critique     Open Access   (Followers: 1)
VFAST Transactions on Education and Social Sciences     Open Access  
Vilnius University Proceedings     Open Access  
Voluntas: International Journal of Voluntary and Nonprofit Organizations     Hybrid Journal   (Followers: 16)
Wani : Revista del Caribe Nicaragüense     Open Access  
War & Society     Hybrid Journal   (Followers: 30)
Weather, Climate, and Society     Hybrid Journal   (Followers: 15)
Wellbeing, Space & Society     Open Access   (Followers: 4)
Whatever : A Transdisciplinary Journal of Queer Theories and Studies     Open Access   (Followers: 3)
Women Against Violence : An Australian Feminist Journal     Full-text available via subscription   (Followers: 15)
Workplace : A Journal for Academic Labor     Open Access   (Followers: 1)
World Journal of Social Science     Open Access  
World Journal of Social Science Research     Open Access   (Followers: 2)
Youth Studies Australia     Full-text available via subscription   (Followers: 3)
Zambia Social Science Journal     Open Access   (Followers: 2)
Zeitschrift für Medienwissenschaft     Open Access   (Followers: 2)
Œconomia     Open Access  
Вісник ДонНУЕТ. Серія. Гуманітарні науки     Open Access  
Култура / Culture     Open Access   (Followers: 1)

  First | 1 2 3 4 5     

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The Winnower
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2373-146X
Published by The Winnower Homepage  [1 journal]
  • I'm Steve Bernard, Senior Visual Journalist at the Financial Times. AMA on
           how we mapped the war in Ukraine

    • Authors: (financialtimes
      Abstract: PROOF: there, I work in the graphics department at the Financial Times website/newspaper where I have worked for over 26 years and have seen many changes in this industry over my career. The main focus of this AMA is how we have used maps during the Russia/Ukraine conflict. But you can ask me anything you want with regards to visualising data on maps, telling stories with maps, processes, software,or how I got into data visualisation.Russia’s invasion of Ukraine in maps — latest updatesKherson counter-offensiveData visualisation: how the FT newsroom designs mapsSand castles on Jersey shore: property boom defies US flood riskSuez blockage animationHow to create an animated smoke mapSmall multiple maps showing California's 22 years of dealing with droughtAnimation showing shipments of Russian fossil fuels to Europe since the invasion of UkraineAnimation showing civilian and military targets in Ukraine since the beginning of the Russian invasion3D animation of China’s nitrogen dioxide pollution levels since 2005 Read and Review the full paper at
      PubDate: Fri, 24 Feb 2023 10:50:25 -050
  • We're The Economist's data team. Ask us anything!

    • Authors: (theeconomist
      Abstract: Hi everyone. We're The Economist's data team. We gather, analyse and visualise data for The Economist and produce data-driven journalism. Over the past year we've created many coronavirus trackers, a risk estimator and most recently an excess-mortality model, and we've seen the interest in our work skyrocket. We can answer questions about anything relating to data journalism at The Economist. All of our work can be found on the website here or you can follow us on Twitter for updates. For more exclusive insights, sign up for our free weekly newsletter.Proof:'s=20 Read and Review the full paper at
      PubDate: Thu, 20 May 2021 12:50:44 -040
  • ModelingToolkit, Modelica, and Modia: The Composable Modeling Future in

    • Authors: (Christopher Rackauckas
      Abstract: Let me take a bit of time here to write out a complete canonical answer to ModelingToolkit and how it relates to Modia and Modelica. This question comes up a lot: why does ModelingToolkit exist instead of building on tooling for Modelica compilers' I'll start out by saying I am a huge fan of Martin and Hilding's work and we work very closely with them on the direction of Julia-based tooling for modeling and simulation. ModelingToolkit, being a new system, has some flexibility in the design space it explores, and while we are following a different foundational philosophy, we have many of the same goals.Composable Abstractions for Model TransformationsEverything in the SciML organization is built around a principle of confederated modular development: let other packages influence the capabilities of your own. This is highlighted in a paper about the package structure of DifferentialEquations.jl. The underlying principle is that not everyone wants or needs to be a developer of the package, but still may want to contribute. For example, it's not uncommon that a researcher in ODE solvers wants to build a package that adds one solver to the SciML\n ecosystem. Doing this in their own package for their own academic credit, but with the free bonus that now it exists in the multiple dispatch world. In the design of DifferentialEquations.jl, solve(prob,IRKGL16()) now exists because of their package, and so we add it to the documentation. Some of this work is not even inside the organization, but we still support it. The philosophy is to include every researcher as a budding artist in the space of computational research, including all of the possible methods, and building an infrastructure that promotes a free research atmosphere in the methods. Top level defaults and documentation may lead people to the most stable aspects of the ecosystem, but with a flip of a switch you can be testing out the latest research.The Modelica ecosystem (open standard, OpenModelica, multiple commercial implementations), which started based on the simple idea of equation oriented modeling, has had a huge impact on industry and solved lots of difficult real industrial problems. The modern simulation system designer today, wants much more from their language and compiler stack. For example, in the Modelica language, there is no reference to what transformations are being done to your models in order to make them "simulatable". People know about Pantelides algorithm, and "singularity elimination", but this is outside the language. It's something that the compiler maybe gives you a few options for, but not something the user or the code actively interacts with. Every compiler is different, advances in one compiler do not help your model when you use another compiler, and the whole world is siloed. By this design, it is extremely difficult for an external user to write compiler passes in Modelica which effects this model lowering process. You can tweak knobs, or write a new compiler, or fork OpenModelica and hack on the whole… Read and Review the full paper at
      PubDate: Tue, 18 May 2021 07:57:16 -040
  • GPU-Accelerated ODE Solving in R with Julia, the Language of Libraries

    • Authors: (Christopher Rackauckas
      Abstract: R is a widely used language for data science, but due to performance most of its underlying library are written in C, C++, or Fortran. Julia is a relative newcomer to the field which has busted out since its 1.0 to become one of the top 20 most used languages due to its high performance libraries for scientific computing and machine learning. Julia's value proposition has been its high performance in high level language, known as solving the two language problem, which has allowed allowed the language to build a robust, mature, and expansive package ecosystem. While this has been a major strength for package developers, the fact remains that there are still large and robust communities in other high level languages like R and Python. Instead of spawning distracting language wars, we should ask the question: can Julia become a language of libraries to accelerate these other languages as well'This is definitely not the first time this question was asked. The statistics libraries in Julia were developed by individuals like Douglas Bates who built some of R's most widely used packages like lme4 and Matrix. Doug had written a blog post in 2018 showing how to get top notch performance in linear mixed e\nffects model fitting via JuliaCall. In 2018 the JuliaDiffEq organization had written a blog post demonstrating the use of DifferentialEquations.jl in R and Python\n (the Jupyter of Diffrential Equations). Now rebranded as SciML for Scientific Machine Learning, we looked to expand our mission and bring automated model discovery and acceleration include other languages like R and Python with Julia as the base.With the release of diffeqr v1.0, we can now demonstrate many advances in R through the connection to Julia. Specifically, I would like to use this blog post to showcase:
      The new direct wrapping interface of diffeqr
      JIT compilation and symbolic analysis of ODEs and SDEs in R using Julia and ModelingToolkit.jl
      GPU-accelerated simulations of ensembles using Julia's DiffEqGPU.jlTogether we will demonstrate how models in R can be accelerated by 1000x without a user ever having to write anything but R.A Quick Note Before ContinuingBefore continuing on with showing all of the features, I wanted to ask for support so that we can continue developing these bridged libraries. Specifically, I would like to be able to support developers interested in providing a fully automated Julia installation and static compilation so that calling into Julia libraries is just as easy as any Rcpp library. To show support, the easiest thing to do is to star our libraries. The work of this blog post is build on DifferentialEquations.jl, diffeqr, ModelingToolkit.jl, and DiffEqGPU.jl. Thank you for your patience and now back to our regularly scheduled program.diffeqr v1.0: Direct wrappers for Differential Equation Solving in RFirst let me start with the new direct wrappers of differential equations solvers in R. In the previous iterations of diffeqr, we had relied on specifically designed high… Read and Review the full paper at
      PubDate: Tue, 18 May 2021 07:52:19 -040
  • Generalizing Automatic Differentiation to Automatic Sparsity, Uncertainty,
           Stability, and Parallelism

    • Authors: (Christopher Rackauckas
      Abstract: Automatic differentiation is a "compiler trick" whereby a code that calculates f(x) is transformed into a code that calculates f'(x). This trick and its two forms, forward and reverse mode automatic differentiation, have become the pervasive backbone behind all of the machine learning libraries. If you ask what PyTorch or Flux.jl is doing that's special, the answer is really that it's doing automatic differentiation over some functions.What I want to dig into in this blog post is a simple question: what is the trick behind automatic differentiation, why is it always differentiation, and are there other mathematical problems we can be focusing this trick towards' While very technical discussions on this can be found in our recent paper titled "ModelingToolkit: A Composable Graph Transformation System \nFor Equation-Based Modeling" and descriptions of methods like intrusive uncertainty quantification, I want to give a high-level overview that really describes some of the intuition behind the technical thoughts. Let's dive in!What is the trick behind automatic differentiation' Non-standard interpretationTo understand automatic differentiation in practice, you need to understand that it's at its core a code transformation process. While mathematically it comes down to being about Jacobian-vector products and Jacobian-transpose-vector products for forward and reverse mode respectively, I think sometimes that mathematical treatment glosses over the practical point that it's really about code.Take for example . If we want to take the derivative of this, then we could do , but this misses the information that we actually know analytically how to define the derivative! Using the principle that algorithm efficiency comes from problem information, we can improve this process by directly embedding that analytical solution into our process. So we come to the first principle of automatic differentiation:If you know the analytical solution to the derivative, then replace the function with its derivativeSo if you see and someone calls ``derivative(f,x)``, you can do a quick little lookup to a table of rules, known as primitives, and if it's in your table then boom you're done. Swap it in, call it a day.This already shows you that, with automatic differentiation, we cannot think of as just a function, just a thing that takes in values, but we have to know something about what it means semantically. We have to look at it and identify "this is sin" in order to know "replace it with cos". This is the fundamental limitation of automatic differentiation: it has to know something about your code, more information than it takes to call or run your code. This is why many automatic differentiation libraries are tied to specific implementations of underlying numerical primitives. PyTorch understands ``torch.sin`` as , but it does not understand ``tf.sin`` as , which is why if you place a TensorFlow function into a PyTorch training loop you will get an error thrown about the derivative calculation. This semantic mapping is the reason for libraries like
      PubDate: Tue, 18 May 2021 07:51:46 -040
  • We’re Allison Mccartney and Brittany Harris, data reporters and
           engineers on the Bloomberg News Graphics team. We worked on the 2016 and
           2018 election cycles, and have been focused for the past year (at least!)
           on our data-driven coverage of the 2020 U.S. election. Ask Us Anything!

    • Authors: (bloomberg
      Abstract: For our 2020 graphic tracking real-time results, we worked with data scientist Andrew Therriault to create an exclusive voter-turnout model. Our live election map includes a state-by-state breakdown of the vote, as well as Senate, House and Governors races. We also gave readers the ability to sign up for key race alerts through a new editorial news product, Storythreads. You can read more about the methodology powering our voter turnout model here. Proof: We will be here to answer questions starting at 11:30am ET/8:30am PT Read and Review the full paper at
      PubDate: Thu, 05 Nov 2020 08:50:54 -050
  • How Inexact Models Can Guide Decision Making in Quantitative Systems

    • Authors: (Christopher Rackauckas
      Abstract: Pre-clinical Quantitiative Systems Pharmacology (QSP) is about trying to understand how a drug target effects an outcome. If I effect this part of the biological pathways, how will it induce toxicity' Will it be effective'Recently I have been pulling in a lot of technical collegues to help with the development of next generation QSP tooling. Without a background in biological modeling, I found it difficult to explain the "how" and "why" of pharmacological modeling. Why is it differential equations, and where do these "massively expensive global optimization" runs come from' What kinds of problems can you solve with such models when you know that they are only approximate'To solve these questions, I took a step back and tried to explain a decision making scenario with a simple model, to showcase how playing with a model can allow one to distinguish between intervention strategies and uncover a way forward. This is my attempt. Instead of talking about something small and foreign like chemical reaction concentrations, let's talk about something mathematically equivalent that's easy to visualize: ecological intervention.Basic Modeling and FittingLet's take everyone's favorite ecology model: the Lotka-Volterra model. The model is the following:
      Left alone, the rabbit population will grow exponentially
      Rabbits are eaten wolves in proportion to the number of wolves (number of mouthes to feed), and in proportion to the number of rabbits (ease of food access: you eat more at a buffet!)
      Wolf populations grow exponentially, as long as there is a proportional amount of food around (rabbits)
      Wolves die overtime of old age, and any generation dies at a similar age (no major wolf medical discoveries)The model is then the ODE:using OrdinaryDiffEq, Plotsfunction f(du,u,p,t) du[1] = dx = p[1]*u[1] - p[2]*u[1]*u[2] du[2] = dy = -p[3]*u[2] + p[4]*u[1]*u[2]endu0 = [1.0;1.0]tspan = (0.0,10.0)p = [1.5,1.0,3.0,1.0]prob = ODEProblem(f,u0,tspan,p)sol = solve(prob,Tsit5())plot(sol,label=["Rabbits" "Wolves"])Except, me showing you that picture glossed over a major detail that every piece of the model is only mechanistic, but also contains a parameter. For example, rabbits grow exponentially, but what's the growth rate' To make that plot I chose a value for that growth rate (1.5), but in reality we need to get that from data since the results can be wildly different:p = [0.1,1.0,3.0,1.0]prob = ODEProblem(f,u0,tspan,p)sol = solve(prob,Tsit5())plot(sol)Here the exponential growth rate of rabbits too low to sustain a wolf population, so the wolf population dies out, but then this makes the rabbits have no predators and grow exponentially, which is a common route of ecological collapse as then they will destroy the local ecosystem. More on that later.Data and Model IssuesBut okay, we need parameters from data, but no single data source is great. One gives us a noisy sample of the population yearly, another every month for the first two years and only on the wolves, etc.:function f_true(du,u,p,t) du[1] = dx = p[1]*u[1] -… Read and Review the full paper at
      PubDate: Tue, 24 Mar 2020 16:07:54 -040
  • Hey everybody, I'm Tom Smith from the Office for National Statistics’
           Data Science Campus. We’re using data to help the UK improve people’s
           lives. Ask Me Anything!

    • Authors: (ONS_UK
      Abstract: Hi Reddit, I’m Tom Smith, MD for the UK’s Data Science Campus as part of the Office for National Statistics. I have 20 years’ experience using data and analysis to improve public services and am a life-long data addict. I have a PhD in computational neuroscience and robotics, an MSc in knowledge-based systems and an MA in theoretical physics. I'm currently Chair of the Advisory Board to the United Nations Global Platform for big data & official statistics, Member of Council for the UK Royal Statistical Society, and previously chair of the Environment Agency Data Advisory Group, vice-chair of the Royal Statistical Society Official Statistics section, and a member of the Open Data User Group ministerial advisory group to Cabinet Office.Since the Campus was founded in 2017 we have been working on a huge range of projects including:- using tax returns, ship tracking data and road traffic sensor data to allow early identification of large economic changes;- exploring what internet traffic peaks and troughs can tell us about our lives; - using satellite imagery to detect surface water and assess changes over time, for rapid detection of emerging issues;- launching a hub focused on data science and AI for International Development, located at the Department for International Development (DfID), near Glasgow.- supporting ONS, government and public sector organisations to increase their data science capability. We’re aiming to have 500 trained data science practitioners for UK government by 2021.I'll be here to talk about statistics, data and making the world a better place from 3-5pm GMT today. Proof: me anything! Read and Review the full paper at
      PubDate: Mon, 16 Mar 2020 06:50:40 -040
  • We are survey methodologists, and we’re here to answer all your
           nerdy data questions.

    • Authors: (AAPOR
      Abstract: We’re Jessica Holzberg and Ashley Amaya, both survey research methodologists based in Washington, D.C. Questions abound regarding the value and reliability of survey research, including federal data, and we want to share how we work to uncover insights that impact the lives of everyday Americans. Public opinion research is essential to a healthy democracy and provides information that is crucial to informed policymaking. This research gives voice to the nation’s beliefs, attitudes and desires. Ask us how!We believe in transparency and in ethical survey practices. We also believe some practices are not at all above board. You can ask us about those, too.I’m Jessica, and I am the associate communications chair for the American Association for Public Opinion Research (AAPOR). I use both qualitative and quantitative research methods such as cognitive interviewing, focus groups, web probing and experiments to reduce survey measurement error and improve the clarity of communication around surveys. I particularly like talking about the burden of surveys for respondents, measurement of sexual orientation and gender identity, and issues surrounding privacy and confidentiality.I’m Ashley, and I am a senior research survey methodologist at RTI International. I am also the Editor-in-chief of Survey Practice, an assistant research professor at University of Maryland and University of Mannheim, and a member of AAPOR’s Standards Definitions and Policy Impact Award Committees. I focus on the big picture of any design to make sure that all components (e.g., sampling, data collection modes, questionnaires, analysis) form a cohesive design. I also like talking about alternative sources of data (e.g., administrative records, digital trace data) that can enhance or replace survey data. Proof: us Anything! Read and Review the full paper at
      PubDate: Wed, 20 Nov 2019 12:50:37 -050
  • We’re survey research methodologists based in Washington, D.C. Questions
           abound regarding the value and reliability of survey research, including
           federal data, and we want to share how we work to uncover insights that
           impact the lives of everyday Americans. AMA!

    • Authors: (PHealthy
      Abstract: We’re Jessica Holzberg and Ashley Amaya, both survey research methodologists based in Washington, D.C. Questions abound regarding the value and reliability of survey research, including federal data, and we want to share how we work to uncover insights that impact the lives of everyday Americans. Public opinion research is essential to a healthy democracy and provides information that is crucial to informed policymaking. This research gives voice to the nation’s beliefs, attitudes and desires. Ask us how!We believe in transparency and in ethical survey practices. We also believe some practices are not at all above board. You can ask us about those, too.I’m Jessica, and I am the associate communications chair for the American Association for Public Opinion Research (AAPOR). I use both qualitative and quantitative research methods such as cognitive interviewing, focus groups, web probing and experiments to reduce survey measurement error and improve the clarity of communication around surveys. I particularly like talking about the burden of surveys for respondents, measurement of sexual orientation and gender identity, and issues surrounding privacy and confidentiality.I’m Ashley, and I am a senior research survey methodologist at RTI International. I am also the Editor-in-chief of Survey Practice, an assistant research professor at University of Maryland and University of Mannheim, and a member of AAPOR’s Standards Definitions and Policy Impact Award Committees. I focus on the big picture of any design to make sure that all components (e.g., sampling, data collection modes, questionnaires, analysis) form a cohesive design. I also like talking about alternative sources of data (e.g., administrative records, digital trace data) that can enhance or replace survey data.We will begin answering questions at 1pm EST. Ask Us Anything! Read and Review the full paper at
      PubDate: Wed, 20 Nov 2019 11:51:09 -050
  • Science Discussion Series: What should and shouldn't be done with your
           personal genetic data' Who should benefit' We are researchers and
           advocates who are working on new models for DNA research. Let's discuss!

    • Authors: (ScienceModerator
      Abstract: Hi reddit! We're a group of scientists and advocates who believe that the traditional genetics research model is outdated. We think that people who participate in genetic studies should be involved in decision-making, that research projects should collaborate, that samples should be diverse, and that studies should use real world data. We need these changes to improve our ability to discover treatments and cures for diseases. But at the same time, researchers also need to ensure participant privacy, data security, and give participants the chance to weigh in on and directly benefit from research- medically, informationally, and financially. Let's discuss!With us today are an array of researchers and leaders from a variety of genetics backgrounds working with a company, LunaPBC, on these questions.Dawn Barry (u/Dawn_Barry): I’m the President and Co-founder at LunaPBC, Board Chair at Alzheimer’s Association San Diego/Imperial Chapter, and former VP Applied Genomics at Illumina. The twelve years I spent at Illumina, Inc., I led pioneering teams in preemptive health screening, nutrition security, and transplant diagnostics. I was also the co-founder of the Illumina Understand Your Genome symposium, which is now owned by Genome Medical.Bob Kain (u/Bob_Kain): I’m the Chief Executive Officer and Co-founder at LunaPBC, 2019 World Economic Forum’s Tech Pioneer, and former Chief Engineering Officer at Illumina. During my 15-year tenure, Illumina grew from a research start up of 30 employees to a global genomics leader of 3,000 employees with $1.5 billion in revenue. My team helped reduce the cost of genome sequencing from a million dollars in 2006 to $1,000 in 2015. The products developed enabled new applications for DNA sequencing in agriculture, pathogen identification and precision medicine. Today, I’m building a talented, ethical team with unifying visions to create a world-changing solution and improve the quality of life for all at LunaPBC.Scott Kahn, Ph.D (u/Scott_Kahn): I’m the Chief Information Officer at LunaPBC, Board of Directors at Rady Children’s Institute for Genomic Medicine, and former Chief Information Officer and Vice President Commercial, Enterprise Informatics at Illumina. I’m integrating data privacy and security provisions that comply with GDPR and HIPAA at the world’s first community-owned health database that offer shares of ownership to health data contributors.Kirby Bloom (u/Kirby_Bloom): I’m the Chief Architect at LunaPBC, former Head of Software for Applied Genomics at Illumina, and MIDS candidate at the University of California, Berkeley. I'm helping bridge the gap between research scientists and large scale data analytics by building the tools needed to produce better insights for health discovery.Sharon Terry (u/Sharon-Terry): I’m the President and CEO of the Genetic Alliance, a network transforming health by promoting openness and is founding CEO of PXE International, a research advocacy organization for the genetic condition pseudoxanthoma elasticum (PXE). My memberships and advisories include the International Rare Disease Research Consortium and the Institute of Medicine Science and Policy Board. I was instrumental in the passage of the Genetic Information Nondiscrimination Act. Among other awards I received was the Clinical Research Forum, Foundation’s Annual Award for Leadership in Public Advocacy in 2011, and PMWC 2019 Luminary Award Recipient.Yaniv Erlich, Ph.D (u/Yaniv-Erlich): I'm the creator of DNA.Land, Chief Science Officer of, and until recently, an Associate Professor of Computer Science and Computational Biology at Columbia University. I’m a TEDMED speaker (2018), the recipient of DARPA’s Young Faculty Award (2017), the Burroughs Wellcome Career Award (2013) and the Harold M. Weintraub award (2010).Aristides Patrinos, Ph.D (u/Aristides_Patrinos): I am the Chief Scientist and Director for Research of the NOVIM Group, Former Lead at the Human Genome Project, LunaDNA Advisor, and leading authority on structural biology, genomics, global environmental change, and nuclear medicine. I'm dedicated to the development of synthetic biology and in the development of clean and renewable fuels and chemicals, sustainable food products, and novel medical applications.EDIT:Thank you to everyone who participated in this important discussion about the future of health discovery. We believe the fastest, most impactful change can only happen at the level of community. Your voice matters. Please feel free to continue the conversation at and @LunaDNA_ on Twitter. Until then, it was our pleasure chatting with you!
      PubDate: Thu, 03 Oct 2019 09:50:54 -040
  • Science Discussion Series: Climate Change is in the news so let’s talk
           about it! We’re experts in climate science and science communication,
           let’s discuss!

    • Authors: (ScienceModerator
      Abstract: Hi reddit! This month the UN is holding its Climate Action Summit, it is New York City's Climate Week next week, today is the Global Climate Strike, earlier this month was the Asia Pacific Climate Week, and there are many more local events happening. Since climate change is in the news a lot let’s talk about it!We're a panel of experts who study and communicate about climate change's causes, impacts, and solutions, and we're here to answer your questions about it! Is there something about the science of climate change you never felt you fully understood' Questions about a claim you saw online or on the news' Want to better understand why you should care and how it will impact you' Or do you just need tips for talking to your family about climate change at Thanksgiving this year' We can help!Here are some general resources for you to explore and learn about the climate:
      AAAS just released a report with case studies and videos of how communities and companies (and individuals) in the US are working with scientists to respond to climate change called "How We Respond."
      NASA: Vital Signs of the Planet
      National Academies of Sciences: Climate Change Evidence and Causes
      National Geographic: Seven things to know about Climate ChangeToday's guests are:Emily Cloyd (u/BotanyAndDragons): I'm the director for the American Association for the Advancement of Science Center for Public Engagement with Science and Technology, where I oversee programs including How We Respond: Community Responses to Climate Change (just released!), the Leshner Leadership Institute, and the AAAS IF/THEN Ambassadors, and study best practices for science communication and policy engagement. Prior to joining AAAS, I led engagement and outreach for the Third National Climate Assessment, served as a Knauss Marine Policy Fellow at the National Oceanic and Atmospheric Administration, and studied the use of ecological models in Great Lakes management. I hold a Master's in Conservation Biology (SUNY College of Environmental Science and Forestry) and a Bachelor's in Plant Biology (University of Michigan), am always up for a paddle (especially if it is in a dragon boat), and last year hiked the Tour du Mont Blanc.Jeff Dukes (u/Jeff_Dukes): My research generally examines how plants and ecosystems respond to a changing environment, focusing on topics from invasive species to climate change. Much of my experimental work seeks to inform and improve climate models. The center I direct has been leading the Indiana Climate Change Impacts Assessment (INCCIA); that's available at You can find more information about me at, and more information about the Purdue Climate Change Research Center at R. Sayani (u/Hussein_Sayani): I'm a climate scientist at the School of Earth and Atmospheric Science at Georgia Institute of Technology. I develop records of past ocean temperature, salinity, and wind variability in the tropical Pacific by measuring changes in the chemistry of fossil corals. These past climate records allow us to understand past climate changes in the tropical Pacific, a region that profoundly influences temperature and rainfall patterns around the planet, so that we can improve future predictions of global and regional climate change. Jessica Moerman (u/Jessica_Moerman): Hi reddit! My name is Jessica Moerman and I study how climate changed in the past - before we had weather stations. How you might ask' I study the chemical fingerprints of geologic archives like cave stalagmites, lake sediments, and ancient soil deposits to discover how temperature and rainfall varied over the last several ice age cycles. I have a Ph.D. in Earth and Atmospheric Sciences from the Georgia Institute of Technology and have conducted research at Johns Hop...
      PubDate: Fri, 20 Sep 2019 09:51:14 -040
  • Science discussion series: Small-scale mining provides a huge portion of
           the world’s minerals and metals, but has major effects on health and the
           environment. We are a team of scientists focused on finding solutions to
           these problems, let’s discuss!

    • Authors: (ScienceModerator
      Abstract: Hello, Reddit! We are a team of conservationists and scientists here to discuss artisanal and small-scale mining, its surprising importance to some of our most beloved possessions, and its effects on biodiversity. Let's discuss!Artisanal and small-scale mining (ASM)—the mining of metals and minerals by hand, often using tools as simple as a hammer and a pick––is an enormous part of our industrial supply chain. In fact, ASM is the main source of income for over 40 million people world wide, and is responsible for between 15–20% of all the world’s mineral and metal production. ASM produces huge percentages of the world’s gold, tin, and cobalt supplies, which, as you may know, are all absolutely crucial ingredients to one of our most important possessions- our smart phones.As things stand, there are some serious drawbacks to artisanal and small scale mining. Notably, ASM requires a lot of water to clean the mined materials before they’re ready for sale, and in some cases, numerous poisonous solvents must be used as well. What results is contaminated water, decreased biodiversity, and birth defects—and these are just some of the problems. For these reasons, we’ve been extremely interested lately in potential technological, logistical, and political solutions that could improve the lives of ASM workers and protect their environments. We’re here today with some ideas of our own, but certainly not all the answers. We hope that we can have a lively discussion about artisanal mining, really dig into the issues surrounding it, and maybe even unearth some possible solutions.Today’s discussion is in collaboration with Conservation X Labs, a D.C. based conservation non-profit that acts as an incubator to help innovators and organizations bring great conservation ideas to life and get them out into the world where they can make a difference for the people who need them. Conservation X Labs is putting up $750,000 dollars in prize money in hopes of finding solutions to improve lives of ASM workers and protect their environments.Our discussion-panel guests today are:Alex Dehgan (u/Alex_Dehgan): I am CEO and co-founder of Conservation X Labs. I recently served as the Chief Scientist at USAID, with rank of Assistant Administrator, and co-founded the Global Development Lab. I am also the Chanler Innovator at Duke University and served as Duke’s inaugural David Rubenstein Fellow. Prior to USAID, I worked in multiple positions within the Office of the Secretary, and the Bureau of Near Eastern Affairs, at the Dept. of State, where I used science and conservation as a diplomatic tool for engagement with countries in the Islamic world, including Iran. I also hold a J.D. from the University of California, Hastings, and a B.S. from Duke University, and am the author of The Snow Leopard Project and Other Adventures in Warzone Conservation.Luis E. Fernandez (u/Luis_E_Fernandez): I am the Executive Director of the Amazon Scientific Innovation Center (CINCIA) and an Assistant Professor of Research in the Department of Biology at Wake Forest University. I am a tropical ecologist, as well as an expert in the dynamics and impact of environmental mercury in areas where artisanal mining is common. I have held positions at the Carnegie Institution for Science, Stanford University, the U.S. Department of Energy, and the U.S. Environmental Agency.Nicole M. Smith (u/Nicole_M_Smith): I am a cultural anthropologist with research interests in artisanal and small-scale mining; sustainability and social responsibility, as well as engineering education. I am an Assistant Professor in the Mining Engineering Department at the the Colorado School of Mines. I am currently the PI for a U.S. Department of State-funded project addressing mercury use among Peruvian artisanal and small-scale gold miners, as well as the Co-PI on a National Science Foundation-funded project that applies an interdisciplinary, community-centered approach to understanding ASM systems in Colombia and Peru. I am also a research fellow at the Centre for Social Responsibility in Mining at the Sustainable Minerals Institute at the University of Queensland and a scholarly affiliate with the Gemstone and Sustainable Development Knowledge Hub.James McQuilken (u/James_McQuilken): I am a Program Officer in Pact’s Mines to Markets program, and the Project Manager and Technical Lead on DELVE, a global data initiative between the World Bank and Pact to develop an online platform on artisanal and small-scale mining (ASM). Based in Kigali, Rwanda, I am also the ASM specialist on Sustainable Development of Mining in Rwanda (SDMR). Based on over a year of fieldwork in Ghana, my PhD thesis maps small-scale mining networks of gold and diamond production and develops policy recommendations to improve mineral certification and formalization initiatives in the region.We'll be around ~1pm EST to answer your questions and discuss artisanal and small-scale mining with you! Read and Review the full paper at
      PubDate: Fri, 30 Aug 2019 08:50:46 -040
  • The Essential Tools of Scientific Machine Learning (Scientific ML)

    • Authors: (Christopher Rackauckas
      Abstract: Scientific machine learning is a burgeoning discipline which blends scientific computing and machine learning. Traditionally, scientific computing focuses on large-scale mechanistic models, usually differential equations, that are derived from scientific laws that simplified and explained phenomena. On the other hand, machine learning focuses on developing non-mechanistic data-driven models which require minimal knowledge and prior assumptions. The two sides have their pros and cons: differential equation models are great at extrapolating, the terms are explainable, and they can be fit with small data and few parameters. Machine learning models on the other hand require "big data" and lots of parameters but are not biased by the scientists ability to correctly identify valid laws and assumptions.However, the recent trend has been to merge the two disciplines, allowing explainable models that are data-driven, require less data than traditional machine learning, and utilize the knowledge encapsulated in centuries of scientific literature. The promise is to fuse a priori domain knowledge which doesn't fit into a "dataset", allow this knowledge to specify a general structure that prevents overfitting, reduces the number of parameters, and promotes extrapolatability, while still utilizing machine learning techniques to learn specific unknown terms in the model. This has started to be used for outcomes like automated hypothesis generation and accelerated scientific simulation.The purpose of this blog post is to introduce the reader to the tools of scientific machine learning, identify how they come together, and showcase the existing open source tools which can help one get started. We will be focusing on differentiable programming frameworks in the major languages for scientific machine learning: C++, Fortran, Julia, MATLAB, Python, and R.We will be comparing two important aspects: efficiency and composability. Efficiency will be taken in the context of scientific machine learning: by now most tools are well-optimized for the giant neural networks found in traditional machine learning, but, as will be discussed here, that does not necessarily make them efficient when deployed inside of differential equation solvers or when mixed with probabilistic programming tools. Additionally, composability is a key aspect of scientific machine learning since our toolkit is not ML in isolation. Our goal is not to do machine learning as seen in a machine learning conference (classification, NLP, etc.), and it's not to do traditional machine learning as applied to scientific data. Instead, we are putting ML models and techniques into the heart of scientific simulation tools to accelerate and enhance them. Our neural networks need to fully integrate with tools that simulate satellites and robotics simulators. They need to integrate with the packages that we use in our scientific work for verifying numerical accuracy, tracking units, estimating uncertainty, and much more. We need our neural networks to play nicely with existing packages for delay differential equations or reconstruction of dynamical systems. Otherwise we need to write the entire toolchain from scratch! While writing a neural network framework may be a good undergraduate project with modern tools, writing a neural network framework plus adaptive stiff differential equation… Read and Review the full paper at
      PubDate: Tue, 20 Aug 2019 10:51:06 -040
  • We're The Washington Post data journalists and finished a comprehensive
           project tracking the opioid crisis in America. AMA.

    • Authors: (washingtonpost
      Abstract: Hello r/dataisbeautiful! We are Steven Rich, Aaron Williams and Andrew Ba Tran of The Washington Post’s data and design team!We've compiled a comprehensive database on the sale of pain pills which fueled the opioid epidemic. The Post team sifted through almost 380 million transactions from 2006 through 2012 in the Drug Enforcement Administration’s database and made the data available at state and county levels to help the public understand the national crisisWe're here to talk about the methodology, tracking, how they’ve seen people use their data, and how you can too! Want to take a peek at the data' Here’s how to do it. “The Opioid Files” is an investigative effort to analyze an epidemic that’s claimed the lives of more than 200,000 people since 1996. All of our past coverage can be found here. We start at 1 p.m. Looking forward to answering your questions, and special thanks to the mods for inviting us here! Read and Review the full paper at
      PubDate: Fri, 16 Aug 2019 11:51:02 -040
  • Science Discussion Series: We're scientists from Vanderbilt studying how
           microbes relate to gut health and what this research means for risk of
           disease and developing new treatments. Let’s discuss!

    • Authors: (ScienceModerator
      Abstract: Hi reddit! We’ve known since the 1800’s that pathogenic microbes are the cause of contagious diseases that have plagued humankind. However, it has only been over the last two decades that we have gained an appreciation that the “normal” microbes that live on and around us dramatically impact many chronic and non-contagious diseases that are now the leading causes of death in the world. This is most obvious in the gastrointestinal tract, or gut, where the community of microbes that lives within our guts can affect the likelihood of developing Inflammatory Bowel Disease, Crohn’s Disease, and gastrointestinal cancers. These gut microbes also contribute to metabolic diseases such as obesity and diabetes.In this discussion, a panel of scientists and infectious disease doctors representing the Vanderbilt Institute for Infection, Immunology, and Inflammation (VI4) will answer questions regarding how the microbes in your gut can impact your health and how this information is being used to design potential treatments for a variety of diseases.Mariana Byndloss, DVM, PhD (u/Mariana_Byndloss): I have extensive experience studying the interactions between the host and intestinal microbiota during microbiota imbalance (dysbiosis). I’m particularly interested in how inflammation-mediated changes in gut epithelial metabolism lead to gut dysbiosis and increased risk of non-communicable diseases (namely IBD, obesity, cardiovascular disease, and colon cancer).Jim Cassat, MD, PhD (u/Jim_Cassat): I am a pediatric infectious diseases physician. My research program focuses on the following: Staph aureus pathogenesis, bone infection (osteomyelitis), osteo-immune crosstalk, and how inflammatory bowel disease impacts bone health.Jane Ferguson, PhD (u/Jane_Ferguson): I am an Assistant Professor of Medicine, in the Division of Cardiovascular Medicine. I’m particularly interested in how environment and genetics combine to determine risk of developing cardiovascular disease and diabetes. My group studies how the microbiome interacts with diet, genetic background, and other factors to influence cardiometabolic disease.Maria Hadjifrangiskou, PhD (u/M_Hadjifrangiskou): I am fascinated by how bacteria understand their environment and respond to it and to each other. My lab works to understand mechanisms used by bacteria to sample the environment and use the info to subvert insults (like antibiotics) and persist in the host. The bacteria we study are uropathogenic E. coli, the primary cause of urinary tract infections worldwide. We have identified bacterial information systems that mediate intrinsic antibiotic resistance in this microbe, as well as mechanisms that lead to division of labor in the bacterial community in the gut, the vaginal space and the bladder. In my spare time, I spend time with my husband and 3 little girls, run, play MTG, as well as other nerdy strategy games. Follow me @BacterialTalkYou can follow our work and the work of all the researchers at VI4 on twitter: @VI4ResearchWe'll be around to answer your questions between 1-4 pm EST. Thanks for joining us in this discussion today! Read and Review the full paper at
      PubDate: Mon, 22 Jul 2019 08:50:44 -040
  • Neural Jump SDEs (Jump Diffusions) and Neural PDEs

    • Authors: (Christopher Rackauckas
      Abstract: This is just an exploration of some new neural models I decided to jot down for safe keeping. DiffEqFlux.jl gives you the differentiable programming tools to allow you to use any DifferentialEquations.jl problem type (DEProblem) mixed with neural networks. We demonstrated this before, not just with neural ordinary differential equations, but also with things like neural stochastic differential equations and neural delay differential equations.At the time we made DiffEqFlux, we were the "first to the gate" for many of these differential equations types and left it as an open question for people to find a use for these tools. And judging by the Arxiv papers that went out days after NeurIPS submissions were due, it looks like people now have justified some machine learning use cases for them. There were two separate papers on neural stochastic differential equations, showing them to be the limit of deep latent Gaussian models. Thus when you stick these new mathematical results on our existing adaptive high order GPU-accelerated neural SDE solvers, you get some very interesting and fast ways to learn some of the most cutting edge machine learning methods.So I wanted to help you guys out with staying one step ahead of the trend by going to the next differential equations. One of the interesting NeurIPS-timed Arxiv papers was on jump ODEs. Following the DiffEqFlux.jl spirit, you can just follow the DifferentialEquations.jl tutorials on these problems, implement them, add a neural network, and it will differentiate through them. So let's take it one step further and show an example of how you'd do that. I wanted to take a look at jump diffusions, or jump stochastic differential equations, which are exactly what they sound like. They are a mixture of these two methods. After that, I wanted to show how using some methods for stiff differential equations plus a method of lines discretization gives a way to train neural partial differential equations.Instead of being fully defined by neural networks, I will also be showcasing how you can selectively make parts of a differential equation neuralitized and other parts pre-defined, something we've been calling mixed neural differential equations, so we'll demonstrate a mixed neural jump stochastic differential equation and a mixed neural partial differential equation with fancy GPU-accelerated adaptive etc. methods. I'll then leave as homework how to train a mixed neural jump stochastic partial differential equation with the fanciest methods, which should be easy to see from this blog post (so yes, that will be the MIT 18.337 homework). This blog post will highlight that these equations are all already possible within our framework, and will also show the specific places we see that we need to accelerate to really put these types of models into production.Neural Jump Stochastic Differential Equations (Jump Diffusions)To get to jump diffusions, let's start with a stochastic differential equation. A stochastic differential equation is defined via dX_t = f(t,X_t)dt + g(t,X_t)dW_t which is essentially saying that there is a deterministic term f and a… Read and Review the full paper at
      PubDate: Wed, 05 Jun 2019 12:13:16 -040
  • Science Discussion Series: Batteries seem to power everything today- cell
           phones, cars, homes, even airplanes! We are a team of scientists and
           engineers working on batteries and energy storage, let's discuss!

    • Authors: (ScienceModerator
      Abstract: Hello Reddit! We are a group of scientists and engineers in academia and industry working on batteries and energy storage. Batteries are ubiquitous in our daily lives and we all have complained about them when using our favorite portable electronic devices. They are also critical in enabling the next generation of electric vehicles, such as electric cars and electric airplanes, and large-scale stationary energy storage. Let's discuss anything regarding batteries and other energy storage technologies!Our guests today are:Kristin Persson (u/KPatBerkeley): I am an Associate Professor in Materials Science and Engineering at UC Berkeley, and I direct the Materials Project which is a multi-institution, multi-national effort to compute the properties of all inorganic materials and provide the data and associated analysis algorithms to the world. The Persson group uses their expertise in materials informatics and the high-throughput infrastructure of the Materials Project to design novel photocatalysts, multi-valent battery electrode materials, Li-ion battery electrode materials and electrolytes for beyond-Li energy storage solutions. Twitter: @KPatBerkeleyShirley Meng (u/ShirleyMeng): I received my Ph.D. in Advance Materials for Micro & Nano Systems from the Singapore-MIT Alliance in 2005, after which I worked as a postdoc research fellow and became a research scientist at MIT. I currently hold the Zable Chair Professor in Energy Technologies and professor in NanoEngineering at University of California San Diego (UCSD), and am the principal investigator of the Laboratory for Energy Storage and Conversion (LESC) research group. The LESC research focuses on the direct integration of experimental techniques with first principles computation modeling for developing new materials and architectures for electrochemical energy storage. I am the founding Director of Sustainable Power and Energy Center (SPEC), consisting faculty members from interdisciplinary fields, who all focus on making breakthroughs in distributed energy generation, storage and the accompanying integration-management systems. I have received several prestigious awards, including International Battery Association Research Award (2019), Blavatnik National Awards Finalist (2018), American Chemical Society ACS Applied Materials & Interfaces Young Investigator Award (2018), International Coalition for Energy Storage and Innovation (ICESI) Inaugural Young Career Award (2018), IUMRS-Singapore Young Scientist Research Award (2017), C.W. Tobias Young Investigator Award of the Electrochemical Society (2016), BASF Volkswagen Electrochemistry Science Award (2015) and NSF CAREER Award (2011). I've published more than 170 peer-reviewed journal articles, two book chapters and eight patents, and am the elected Fellow of the Electrochemical Society.Ray Smith (u/thatkindofcell): I did a PhD in battery modeling at MIT focusing on active materials that exhibit phase changes during the charging and discharging process. Now, I do battery modeling research and development work at a San Francisco Bay Area company with particular focus on cell design, charging, and degradation processes.Matt Lacey (u/MattLacey): I graduated from the University of Southampton, United Kingdom, with a Master of Chemistry degree in 2008 and completed my PhD at the same university in 2012 under the supervision of Prof John R. Owen. I joined the Ångström Advanced Battery Centre in 2012 as a postdoc working on lithium-sulfur batteries, and in 2016 became a researcher in the same group. Since 2018 I am also a thematic researcher with the Swedish Electromobility Centre. My research interests centre on the electrochemistry of lithium batteries, particularly on ageing mechanisms. Twitter: @mjlaceyVenkat Viswanathan (u/venkvis): I am faculty at Carnegie Mellon University, working on batteries for electrifying cars, trucks and planes. Find out more -- Twitter: @venkvis; website: Steingart (u/steingart): I am the Stanley Thompson Associate Professor of Chemical Metallurgy in the Departments of Earth and Environmental Engineering and Chemical Engineering at Columbia University, and the co-director of the Columbia Electrochemical Energy Center. My group studies the systematic behavior of electrochemical cells. You may be familiar with my study on the (on-linear) bouncing behavior of AA cells. Twitter: @steingart; websites:, you so much for joining us! We will be around throughout the day, though mostly in the afternoon EST, to discuss energy storage with you! Read and Review the full paper at
      PubDate: Tue, 30 Apr 2019 09:51:06 -040
  • Science discussion series: We are an interdisciplinary group of water
           science professionals and we’re here to discuss safe drinking water. Ask
           us anything!

    • Authors: (ScienceModerator
      Abstract: Hi Reddit! Today's discussion is coming to us from the National Academy of Sciences, Engineering and Medicine (NASEM) and is a collaboration between r/science, LabX, and the Water, Science and Technology Board! We are panel of diverse water science professionals ready to answer your questions about all things related to drinking water.Water is a ubiquitous phenomenon! But its visual abundance—from its constant flow out of taps and fountains to the immensity of our oceans—can mask the fact that 1 in 7 people worldwide lack access to safe drinking water (that’s over 1 billion people!). Furthermore, in places like New Orleans or The Netherlands, water can seem like an impending threat without the proper means to safely contain it, or productively incorporate it into our daily lives. The broader water/society interface raises questions about drinking water’s vulnerability to climate change and society’s vulnerability to ageing infrastructure for adequate and safe sourcing, treatment, and distribution. Poor drinking water quality can result from pollution from sources such as industrial waste, agricultural runoff, corrosion of lead from distribution pipes, or treatment facility contamination by man-made materials such as polyfluoroalkyl substances (PFAS). Emerging innovations in grey/green infrastructure, stewardship programs to reduce man-made contaminants, desalination technology, and international efforts to increase access to safe water on a global scale might help preserve and increase Earth’s water supply and society’s value of it. All of these issues, and more, are on our collective radar and we look forward to discussing them with you. Ask us anything! Our discussion panel guests today are:
      Will Logan (u/Will_Logan_ICIWaRM) is currently the Director at the International Center for Integrated Water Resources Management (ICIWaRM), which is part of the U.S. Army Corps of Engineers. Previously, Will was the Science Attaché for the US Mission to UNESCO and he served for almost a decade on the Water, Science, and Technology Board at the National Academies of Sciences. Will holds a Ph.D. in Earth Sciences/Hydro-geology from Waterloo University and was an Assistant Professor of Hydro-geology at George Washington University.
      Ellen de Guzman (u/Ellen_de_Guzman) is currently the Senior Water Officer in the Middle East and North Africa Bureau at USAID. Ellen has managed projects spanning rural reconstruction, humanitarian and disaster response, alternative livelihoods, food security, agriculture, water and sanitation. Prior to USAID, Ellen worked for the National Academies of Sciences, where she provided policy research support to develop federal policies on managing subsurface water contamination, the Clean Water Act, sustainable water and environmental management in the California Bay-Delta, and invasive species in ballast water.
      Jin Shin (u/Jin_Shin_WSSC) is currently the Water Quality Division Manager at WSSC (Washington Suburban Sanitary Commission), where he has worked for nearly 15 years. The WSSC is one of the largest water and wastewater utilities in the nation, with a service area that spans nearly 1,000 square miles in Prince George’s and Montgomery counties in Maryland. Jin holds a Ph.D. in Environmental Engineering from John Hopkins University, where he was also a lecturer and visiting professor for 6 years.
      Teddi Ann Galligan (u/Teddi_Ann_Galligan) is a community science educator. She draws from firsthand experience living in conditions where safe drinking water was a daily issue, as well as substantial laboratory experience, which includes wastewater analysis for a sustainable sanitation digestion technology, water quality analysis, and clinical laboratory work in low-resource settings. Currently Director of Covalence Science Education, Ms. Galligan has designed and delivered hands-on programs in a wide variety of environments, ranging from classrooms in the United States to open-air community science workshops in Port-au-Prince, Haiti. Teddi Ann was an educator and consultant at the Marian Koshland Science Museum of the National Academy of Sciences for more than a decade, helping visitors use science to address real world community resilience issues associated with climate change.Our guests will be answering questions starting at 8:30 PM EST. Read and Review the full paper at
      PubDate: Wed, 03 Apr 2019 15:51:34 -040
  • Hi, I'm Alan Smith, Data visualisation editor at the Financial Times. I've
           just finished an experimental project at the FT to both visualise and
           sonify the historical yield curve - a large dataset of over 100,000 data
           points. AMA!

    • Authors: (financialtimes
      Abstract: Hi, I'm Alan Smith, Data visualisation editor at the Financial Times. I've just finished an experimental project at the FT to both visualise and sonify the historical yield curve - a large dataset of over 100,000 data points. I've filmed a step-by-step walkthrough of the project. And the end product, a combined animated data visualisation and sonification of four decades of the US yield curve, is available on YouTube'v=GoQBWcNw6IU . My full article is on the FT, website: work has also coincided with the the release of a new open source tool funded by Google* that allows users to make music from spreadsheets. So - is data sonification ready to be the next big thing in data presentation' Can it bring data to new audiences such as including the blind/visually impaired, podcast listeners, and those accessing the web via screenless devices with voice interfaces. Or is it a simple novelty' Ask me anything!
      TwoTone app funded by Google ( Read and Review the full paper at
      PubDate: Thu, 21 Mar 2019 11:50:42 -040
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