Refback is a linkback method that works using the standard HTTP Referer header. Like pingbacks, trackbacks, and webmentions, it attempts to present links of other sites that have linked to you. Unlike other methods, the other site requires no additional support. The implementation works exactly as the other linkbacks do in WordPress.
Learn about the features of Scalar, refreshed with the Scalar 2.0 interface.
This is an intriguing looking tool for potential academic use. I’ll have to find some time to download it and play around.
I’m going on the journey of building a simple, private, self-hosted, cookie-free analytics tool that I’m calling Kownter. I may fail. But it will be fun and interesting! Come along!
Hi, My name is Ross. I’ve been thinking a lot about GDPR lately and considering how I will become compliant with it as I run my business and projects, so I’m looking to slim down the data that I capture about people.
The topics of both analytics and server logs have come up several times. It’s not entirely clear to me that either fall into the category of personal data, but I’ve been considering my use of them anyway.
I use Google Analytics on most sites/projects that I create, but I’m not that sophisticated in my use of it. I’m mostly interested in:
and it occurred to me that I can collect this data without using cookies and without collecting anything that would personally identify someone.
- how many visitors I’m getting and when
- which pages are popular
- where people are coming from
I would also be happier if my analytics were stored on a server in the EU rather than in the US – I can’t find any guarantee that my Google Analytics data is and remains EU-based.
I’m aware that there are self-hosted, open-source analytics solutions like Matomo (previously Piwik) and Open Web Analytics. But they always seem very large and clunky. I’ve tried them and never got to grips with them.
So I wondered: how hard would it be to build my own, simple, high-privacy, cookie-free analytics tool?
Most of today’s global challenges, from online misinformation spreading to Ebola outbreaks, involve such a vast number of interacting players that reductionism delivers little insight. Systems are often non-linear, exhibiting complexity in temporal and spatial domains over large scales, which is a challenge to predictability and comprehension. Strategies must be found to look at the problem as a whole, in all its complexity. Representing the associated data as a complex network, in which nodes and connections between them form complicated patterns, is one such strategy. Network science provides novel tools for analyzing, visualizing and modeling this data thanks to the cross-fertilization of fields as diverse as statistical physics, algebraic topology and machine learning, among the others.
This Channel brings together all aspects of complexity research and includes interdisciplinary topics from network theory to applications in neuroscience and the social sciences.
Announcing the launch of the @PLOS #Complexity Channel https://t.co/RRvsk3O4ok , a home for complexity research and interdisciplinary topics from network theory to applications in neuroscience and the social sciences, feat content from @PLOSONE @PLOSCompBiol @arxiv and more pic.twitter.com/sTRTbRCRu0
— PLOS Channels (@PLOSChannels) June 15, 2018
From the celebrated neurobiologist and primatologist, a landmark, genre-defining examination of human behavior, both good and bad, and an answer to the question: Why do we do the things we do?
Sapolsky's storytelling concept is delightful but it also has a powerful intrinsic logic: he starts by looking at the factors that bear on a person's reaction in the precise moment a behavior occurs, and then hops back in time from there, in stages, ultimately ending up at the deep history of our species and its evolutionary legacy.
And so the first category of explanation is the neurobiological one. A behavior occurs--whether an example of humans at our best, worst, or somewhere in between. What went on in a person's brain a second before the behavior happened? Then Sapolsky pulls out to a slightly larger field of vision, a little earlier in time: What sight, sound, or smell caused the nervous system to produce that behavior? And then, what hormones acted hours to days earlier to change how responsive that individual is to the stimuli that triggered the nervous system? By now he has increased our field of vision so that we are thinking about neurobiology and the sensory world of our environment and endocrinology in trying to explain what happened.
Sapolsky keeps going: How was that behavior influenced by structural changes in the nervous system over the preceding months, by that person's adolescence, childhood, fetal life, and then back to his or her genetic makeup? Finally, he expands the view to encompass factors larger than one individual. How did culture shape that individual's group, what ecological factors millennia old formed that culture? And on and on, back to evolutionary factors millions of years old.
The result is one of the most dazzling tours d'horizon of the science of human behavior ever attempted, a majestic synthesis that harvests cutting-edge research across a range of disciplines to provide a subtle and nuanced perspective on why we ultimately do the things we do...for good and for ill. Sapolsky builds on this understanding to wrestle with some of our deepest and thorniest questions relating to tribalism and xenophobia, hierarchy and competition, morality and free will, and war and peace. Wise, humane, often very funny, Behave is a towering achievement, powerfully humanizing, and downright heroic in its own right.
Pressbooks makes it easy to create professionally designed books & ebooks. Discover how our user friendly epublishing software can help you publish today!
This looks like an interesting platform. Saw it as a subdomain on someone’s personal website, so perhaps it’s self-hostable?
Some fans have noticed that Anthony Bourdain: Parts Unknown was scheduled to come off Netflix US on June 16. As of today, we’ve extended our agreement that will keep Parts Unknown on the service for months to come.— Netflix US (@netflix) June 12, 2018
DO NOT add this as the URL for a bookmark:— Jon Tennant (@Protohedgehog) June 10, 2018
Which when you click on a paywalled research article, automatically takes you to the @Sci_Hub version of it.
And DO NOT try this, see that it works wonderfully, and share it with others.
This suggests some interesting bookmarklet functionality.
Personal and private Web archives are proliferating due to the increase in the tools to create them and the realization that Internet Archive and other public Web archives are unable to capture personalized (e.g., Facebook) and private (e.g., banking) Web pages. We introduce a framework to mitigate issues of aggregation in private, personal, and public Web archives without compromising potential sensitive information contained in private captures. We amend Memento syntax and semantics to allow TimeMap enrichment to account for additional attributes to be expressed inclusive of the requirements for dereferencing private Web archive captures. We provide a method to involve the user further in the negotiation of archival captures in dimensions beyond time. We introduce a model for archival querying precedence and short-circuiting, as needed when aggregating private and personal Web archive captures with those from public Web archives through Memento. Negotiation of this sort is novel to Web archiving and allows for the more seamless aggregation of various types of Web archives to convey a more accurate picture of the past Web.
This workshop will bring together a diverse group of experts in complementary areas of complex systems and will be preceded by a series of weekly webinars. The overarching goal of the activity is to address scientific issues that are relevant to the scientific community and bring to surface possible areas of opportunity for multidisciplinary research in the study of complex systems. The specific goals of the workshop include:
- identifying the most substantive research questions that can be addressed by fundamental complex systems research;
- recognizing community needs, knowledge gaps, and barriers to research progress in this area;
- identifying future directions that cut across disciplinary boundaries and that are likely to lead to transformative multidisciplinary research in complex systems.
The outcomes of the workshop will include the preparation of a report to inform the scientific community at large of the current status and challenges as well as future opportunities in multidisciplinary complex systems research as perceived by the participants of the workshop.
The workshop is motivated by the observation that many processes in natural, engineered, and social contexts exhibit emergent collective behavior and are thus governed by complex systems. Because challenges in understanding, predicting, designing, and controlling complex systems are often common to many domains, a central objective of the workshop is to facilitate the exchange of ideas across different fields and avoid disciplinary boundaries typical of many traditional scientific meetings. The workshop participants will include experts both in theory and in applications as well as a selection of postdoctoral researchers and graduate students from various domains. Because of the cross-disciplinary nature of the workshop, the participants themselves will become aware of the latest developments in fields related to but different from their own. This environment will foster discussions on the state of the art, potential issues, and most promising directions in multidisciplinary complex systems research. The inclusion of early-career researchers will help to promote the transfer of this expertise to the next generation of engineers, mathematicians, and scientists.
h/t to @adilson_motter
The report from the NSF Workshop “Multidisciplinary Complex Systems Research” that I co-chaired with Kim Gray is now available: https://t.co/LC1Df27GyE
— Adilson E. Motter (@adilson_motter) June 5, 2018
I just learned that a second nervous system has now been fully mapped (along with C. elegans) and it too is a small world. All hail the tadpole larva of a sea squirt, and its marvelously tiny connectome of 177 neurons:https://t.co/kO3tlEVq5x— Steven Strogatz (@stevenstrogatz) June 5, 2018
Affordable education. Transparent science. Accessible scholarship.
These ideals are slowly becoming a reality thanks to the open education, open science, and open access movements. Running separate—if parallel—courses, they all share a philosophy of equity, progress, and justice. This book shares the stories, motives, insights, and practical tips from global leaders in the open movement.
It’s not just the book about which there’s so much to find interesting, but the website that’s serving it is well designed, crafted, and very forward thinking in what it is doing.
About the Course:
Probability and statistics have long helped scientists make sense of data about the natural world — to find meaningful signals in the noise. But classical statistics prove a little threadbare in today’s landscape of large datasets, which are driving new insights in disciplines ranging from biology to ecology to economics. It's as true in biology, with the advent of genome sequencing, as it is in astronomy, with telescope surveys charting the entire sky.
The data have changed. Maybe it's time our data analysis tools did, too.
During this three-month online course, starting June 11th, instructors Hector Zenil and Narsis Kiani will introduce students to concepts from the exciting new field of Algorithm Information Dynamics to search for solutions to fundamental questions about causality — that is, why a particular set of circumstances lead to a particular outcome.
Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is a new type of discrete calculus based on computer programming to study causation by generating mechanistic models to help find first principles of physical phenomena building up the next generation of machine learning.
The course covers key aspects from graph theory and network science, information theory, dynamical systems and algorithmic complexity. It will venture into ongoing research in fundamental science and its applications to behavioral, evolutionary and molecular biology.
Students should have basic knowledge of college-level math or physics, though optional sessions will help students with more technical concepts. Basic computer programming skills are also desirable, though not required. The course does not require students to adopt any particular programming language for the Wolfram Language will be mostly used and the instructors will share a lot of code written in this language that student will be able to use, study and exploit for their own purposes.
- The course will begin with a conceptual overview of the field.
- Then it will review foundational theories like basic concepts of statistics and probability, notions of computability and algorithmic complexity, and brief introductions to graph theory and dynamical systems.
- Finally, the course explores new measures and tools related to reprogramming artificial and biological systems. It will showcase the tools and framework in applications to systems biology, genetic networks and cognition by way of behavioral sequences.
- Students will be able apply the tools to their own data and problems. The instructors will explain in detail how to do this, and will provide all the tools and code to do so.
The course runs 11 June through 03 September 2018.
Tuition is $50 required to get to the course material during the course and a certificate at the end but is is free to watch and if no fee is paid materials will not be available until the course closes. Donations are highly encouraged and appreciated in support for SFI's ComplexityExplorer to continue offering new courses.
In addition to all course materials tuition includes:
- Six-month access to the Wolfram|One platform (potentially renewable by other six) worth 150 to 300 USD.
- Free digital copy of the course textbook to be published by Cambridge University Press.
- Several gifts will be given away to the top students finishing the course, check the FAQ page for more details.
Best final projects will be invited to expand their results and submit them to the journal Complex Systems, the first journal in the field founded by Stephen Wolfram in 1987.
About the Instructor(s):
Hector Zenil has a PhD in Computer Science from the University of Lille 1 and a PhD in Philosophy and Epistemology from the Pantheon-Sorbonne University of Paris. He co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. He is also the head of the Algorithmic Nature Group at LABoRES, the Paris-based lab that started the Online Algorithmic Complexity Calculator and the Human Randomness Perception and Generation Project. Previously, he was a Research Associate at the Behavioural and Evolutionary Theory Lab at the Department of Computer Science at the University of Sheffield in the UK before joining the Department of Computer Science, University of Oxford as a faculty member and senior researcher.
Narsis Kiani has a PhD in Mathematics and has been a postdoctoral researcher at Dresden University of Technology and at the University of Heidelberg in Germany. She has been a VINNOVA Marie Curie Fellow and Assistant Professor in Sweden. She co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. Narsis is also a member of the Algorithmic Nature Group, LABoRES.
Hector and Narsis are the leaders of the Algorithmic Dynamics Lab at the Unit of Computational Medicine at Karolinska Institute.
Alyssa Adams has a PhD in Physics from Arizona State University and studies what makes living systems different from non-living ones. She currently works at Veda Data Solutions as a data scientist and researcher in social complex systems that are represented by large datasets. She completed an internship at Microsoft Research, Cambridge, UK studying machine learning agents in Minecraft, which is an excellent arena for simple and advanced tasks related to living and social activity. Alyssa is also a member of the Algorithmic Nature Group, LABoRES.
The development of the course and material offered has been supported by:
- The Foundational Questions Institute (FQXi)
- Wolfram Research
- John Templeton Foundation
- Santa Fe Institute
- Swedish Research Council (Vetenskapsrådet)
- Algorithmic Nature Group, LABoRES for the Natural and Digital Sciences
- Living Systems Lab, King Abdullah University of Science and Technology.
- Department of Computer Science, Oxford University
- Cambridge University Press
- London Mathematical Society
- Springer Verlag
- ItBit for the Natural and Computational Sciences and, of course,
- the Algorithmic Dynamics lab, Unit of Computational Medicine, SciLifeLab, Center for Molecular Medicine, The Karolinska Institute
Course dates: 11 Jun 2018 9pm PDT to 03 Sep 2018 10pm PDT
- A Computational Approach to Causality
- A Brief Introduction to Graph Theory and Biological Networks
- Elements of Information Theory and Computability
- Randomness and Algorithmic Complexity
- Dynamical Systems as Models of the World
- Practice, Technical Skills and Selected Topics
- Algorithmic Information Dynamics and Reprogrammability
- Applications to Behavioural, Evolutionary and Molecular Biology
Another interesting course from the SFI. Looks like an interesting way to spend the summer.