How to Make Your Research Reproducible

Science is about standing on the shoulders of giants – making new discoveries and building new applications by reusing foundations laid by others. But however collaborative a process science is, we don’t always make it easy. Have you ever tried but failed to reproduce someone else's reported research results? How about your own results? According to scientific research, this is not uncommon in academia, which has led to the phenomenon being dubbed as "the reproducibility crisis". And reproducibility is just the first step of reusability.

In this webinar, we will discuss the challenges in reproducibility and reusability of scientific data, methods, and computer code. What do the terms really mean and how can we take steps to improve our work to take them into account.

https://www.aalto.fi/en/events/how-to-make-your-research-reproducible-oct-24-2024

#postdoc researcher to join the Inverse Complexity Lab at IT:U, Linz, Austria.

https://skewed.de/lab/call.html

Deadline is 30 Nov 2024.

Large language models and transformers: perspectives from physics, neuroscience and theory

https://youtu.be/qXEuY6qLISE

#postdoc at PITT Computational Social Dynamics Lab (PICSO LAB)

https://docs.google.com/forms/d/e/1FAIpQLScMjkriluvf48Jc7YCVvDJhYm90XkNKUzCjjEbeqlvziRFWNQ/viewform

Doyne Farmer on Chaos, Crashes, and Economic Complexity | Sean Carroll's Mindscape Podcast

https://www.preposterousuniverse.com/podcast/2024/10/21/293-doyne-farmer-on-chaos-crashes-and-economic-complexity/

DDSA provides funding for national and international bachelor’s and master’s degree students, PhD students and postdoctoral researchers to spend time with new research groups of interest in Denmark.

The purpose of the grant is to give students and young researchers the opportunity to form the basis for a future PhD or Postdoc fellowship application in collaboration with a potential supervisor from a Danish university or a Danish research institution.

https://ddsa.dk/visitgrant/

How to Make Your Research and Code Reproducible and Reusable?

https://youtu.be/SyQl8kJvSxs

00:00 Introduction
01:42 Beginning of the presentation by Mika Jalava
03:25 What is research reproducibility?
06:01 Reproducibility crisis
07:43 Why is reproducibility so important? Who should care?
10:15 What deters reproducibility?
18:33 Importance of the whole computational environment
20:17 What is "computational environment"?
26:02 Random effects
29:06 Human-side of the reproducibility crisis
33:07 Requirements for reproduction
35:58 What can we do to improve reproducibility?
39:23 Practical take-home

How are people able to map knowledge from one domain to another? I'll report a series of studies showing that people's cross-domain mappings were best predicted by similarity along abstract dimensions such as valence, complexity, and genderedness - a finding that could be reliably simulated by language models. In an ongoing study, we further asked what allows people to process cross-domain mappings so easily by drawing insights from a network perspective.

https://www.youtube.com/watch?v=DkiCE8rBi9k

Hopfield network: How are memories stored in neural networks? [Nobel Prize in Physics 2024]

https://youtu.be/piF6D6CQxUw

Open #PhD and #postdoc positions in neuroscience

https://www.keshavarzilab.com/

5 lectures on Theory of Large Deviations and Applications by Pierpaolo Vivo

https://t.co/1grOMbOv3X
https://t.co/38SeBWgYBr
https://t.co/nxn1XgFDCX
https://t.co/YED2gx2EQr
https://t.co/4ZlIIvE96p

a #postdoc to work on a project modeling the knowledge space and the role of scientific funding in technological advancement.

https://osome.iu.edu/research/blog/postdoctoral-fellow-opening

Reality-inspired voter models: A mini-review
Sidney Redner

This mini-review presents extensions of the voter model that incorporate various plausible features of real decision-making processes by individuals. Although these generalizations are not calibrated by empirical data, the resulting dynamics are suggestive of realistic collective social behaviors.

https://www.sciencedirect.com/science/article/pii/S1631070519300325

Opinion dynamics in social networks: From models to data
Antonio F. Peralta, János Kertész, Gerardo Iñiguez

Opinions are an integral part of how we perceive the world and each other. They shape collective action, playing a role in democratic processes, the evolution of norms, and cultural change. For decades, researchers in the social and natural sciences have tried to describe how shifting individual perspectives and social exchange lead to archetypal states of public opinion like consensus and polarization. Here we review some of the many contributions to the field, focusing both on idealized models of opinion dynamics, and attempts at validating them with observational data and controlled sociological experiments. By further closing the gap between models and data, these efforts may help us understand how to face current challenges that require the agreement of large groups of people in complex scenarios, such as economic inequality, climate change, and the ongoing fracture of the sociopolitical landscape.

https://arxiv.org/abs/2201.01322

From classical to modern opinion dynamics
Hossein Noorazar, Kevin R. Vixie, Arghavan Talebanpour, Yunfeng Hu

In this age of Facebook, Instagram and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment are of interest to large numbers of organizations and people, some of whom are resource rich. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or large corporations that are trying to bend sentiment -- often quite surreptitiously, or it is more open and above board, like researchers that want to spread the news of some finding or some business interest that wants to make a large group of people aware of genuinely helpful innovations that they are marketing, what is at stake is often significant. In this paper we review many of the classical, and some of the new, social interaction models aimed at understanding opinion dynamics. While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas. It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done.

https://arxiv.org/abs/1909.12089

#PhD students at Berkeley:
if you are interested in ML applied to health, inequality, or social science, and mention Emma Pierson in your app.

More details on work/how to apply: https://cs.cornell.edu/~emmapierson/

Steven Strogatz: Global Synchronization: New Theorems, New Puzzles

https://youtu.be/0l-UwTLiIX4

#Postdoc Position: Multimodal Signalling in Face-to-Face Communication at the Donders Centre for Cognition

https://www.ru.nl/en/working-at/job-opportunities/postdoc-position-multimodal-signalling-in-face-to-face-communication-at-the-donders-centre-for-cognition

Fast unfolding of communities in large networks: 15 years later

The Louvain method was proposed 15 years ago as a heuristic method for the fast detection of communities in large networks. During this period, it has emerged as one of the most popular methods for community detection: the task of partitioning vertices of a network into dense groups, usually called communities or clusters. Here, after a short introduction to the method, we give an overview of the different generalizations, modifications and improvements that have been proposed in the literature, and also survey the quality functions, beyond modularity, for which it has been implemented. Finally, we conclude with a discussion on the limitations of the method and perspectives for future research.

https://iopscience.iop.org/article/10.1088/1742-5468/ad6139

#PhD position in Digital Chemistry

project on combining traditional expert features with deep learning for reaction prediction.
https://www.jobs.ethz.ch/job/view/JOPG_ethz_4etBDEk8lO4Z8W71Q8