Computational Reproducibility in Computational Social Science

• URL: http://arxiv.org/abs/2307.01918v4
• Date: Wed, 4 Oct 2023 08:10:27 GMT
• Title: Computational Reproducibility in Computational Social Science
• Authors: David Schoch, Chung-hong Chan, Claudia Wagner, Arnim Bleier
• Abstract summary: We argue that computational-x disciplines such as computational social science are also susceptible for the symptoms of the crises. We provide solutions for Computational Social Science that hinder researchers from obtaining the highest level of data.
• Score: 0.8930269507906258
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Replication crises have shaken the scientific landscape during the last decade. As potential solutions, open science practices were heavily discussed and have been implemented with varying success in different disciplines. We argue that computational-x disciplines such as computational social science, are also susceptible for the symptoms of the crises, but in terms of reproducibility. We expand the binary definition of reproducibility into a tier system which allows increasing levels of reproducibility based on external verfiability to counteract the practice of open-washing. We provide solutions for barriers in Computational Social Science that hinder researchers from obtaining the highest level of reproducibility, including the use of alternate data sources and considering reproducibility proactively.

Related papers

• A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
  Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient. In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks. We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
  arXiv  Detail & Related papers  (2024-02-16T18:05:09Z)
• Reproducibility, Replicability, and Repeatability: A survey of reproducible research with a focus on high performance computing [0.0]
  Reproducibility is a fundamental principle in scientific research. Highperformance computing presents unique challenges. This paper provides a comprehensive review of these concerns and potential solutions.
  arXiv  Detail & Related papers  (2024-02-12T09:59:11Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• A Backend Platform for Supporting the Reproducibility of Computational Experiments [2.1485350418225244]
  It is challenging to recreate the same environment using the same frameworks, code, data sources, programming languages, dependencies, and so on. In this work, we propose an Integrated Development Environment allowing the share, configuration, packaging and execution of an experiment. We have been able to successfully reproduce 20 (80%) of these experiments achieving the results reported in such works with minimum effort.
  arXiv  Detail & Related papers  (2023-06-29T10:29:11Z)
• Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues [5.30596984761294]
  A crisis has been recently acknowledged by scientists and this seems to affect even more Artificial Intelligence and Machine Learning. We critically review the current literature on the topic and highlight the open issues. We identify key elements often overlooked in modern Machine Learning and provide novel recommendations for them.
  arXiv  Detail & Related papers  (2023-02-24T15:33:20Z)
• ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery [79.08833067391093]
  Real-world pharmaceutical datasets often exhibit highly imbalanced distribution. We introduce ImDrug, a benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline.
  arXiv  Detail & Related papers  (2022-09-16T13:35:57Z)
• Reproducibility in machine learning for medical imaging [3.1390096961027076]
  This chapter intends at being an introduction to for researchers in the field of machine learning for medical imaging. For each of them, we aim at defining it, at describing the requirements to achieve it and at discussing its utility. The chapter ends with a discussion on the benefits of didactic and with a plea for a non-dogmatic approach to this concept and its implementation in research practice.
  arXiv  Detail & Related papers  (2022-09-12T09:00:04Z)
• Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems [120.297940190903]
  Recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
  arXiv  Detail & Related papers  (2022-05-02T18:00:10Z)
• Constrained Learning with Non-Convex Losses [119.8736858597118]
  Though learning has become a core technology of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced solutions.
  arXiv  Detail & Related papers  (2021-03-08T23:10:33Z)
• Incremental Knowledge Based Question Answering [52.041815783025186]
  We propose a new incremental KBQA learning framework that can progressively expand learning capacity as humans do. Specifically, it comprises a margin-distilled loss and a collaborative selection method, to overcome the catastrophic forgetting problem. The comprehensive experiments demonstrate its effectiveness and efficiency when working with the evolving knowledge base.
  arXiv  Detail & Related papers  (2021-01-18T09:03:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.