scholarly journals Open Science in Data-Intensive Psychology and Cognitive Science

2018 ◽  
Author(s):  
Alexandra Paxton ◽  
Alexa Mary Tullett
Keyword(s):  
Cognitive Science ◽  
Large Scale ◽  
Scientific Discovery ◽  
Open Science ◽  
Human Cognition ◽  
Incentive Structure ◽  
Data Intensive ◽  
Large Scale Data ◽  
Share Data ◽  
And Behavior

Today, researchers can collect, analyze, and share more data than ever before. Not only does increasing technological capacity open the door to new data-intensive perspectives in cognitive science and psychology (that is, research that takes advantage of complex or large-scale data to understand human cognition and behavior), but increasing connectedness has sparked exponential increases in the ease and practice of scientific transparency. The growing open science movement encourages researchers to share data, materials, methods, and publications with other scientists and the wider public. Open science benefits data-intensive psychological science, the public, and public policy, and we present recommendations to improve the adoption of open science practices by changing the academic incentive structure and by improving the education pipeline. Despite ongoing questions about implementing open-science guidelines, policymakers have an unprecedented opportunity to shape the next frontier of scientific discovery.

scholarly journals Open Science in Data-Intensive Psychology and Cognitive Science

2019 ◽  
Vol 6 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Alexandra Paxton ◽  
Alexa Tullett
Keyword(s):  
Cognitive Science ◽  
Large Scale ◽  
Scientific Discovery ◽  
Open Science ◽  
Human Cognition ◽  
Incentive Structure ◽  
Data Intensive ◽  
Large Scale Data ◽  
Share Data ◽  
And Behavior

Today, researchers can collect, analyze, and share more data than ever before. Not only does increasing technological capacity open the door to new data-intensive perspectives in cognitive science and psychology (i.e., research that takes advantage of complex or large-scale data to understand human cognition and behavior), but increasing connectedness has sparked exponential increases in the ease and practice of scientific transparency. The growing open science movement encourages researchers to share data, materials, methods, and publications with other scientists and the wider public. Open science benefits data-intensive psychological science, the public, and public policy, and we present recommendations to improve the adoption of open science practices by changing the academic incentive structure and by improving the education pipeline. Despite ongoing questions about implementing open science guidelines, policy makers have an unprecedented opportunity to shape the next frontier of scientific discovery.

scholarly journals Sir John Edward Sulston CH. 27 March 1942—6 March 2018

2019 ◽  
Vol 67 ◽  
pp. 421-447
Author(s):  
Robert H. Waterston ◽  
Georgina Ferry
Keyword(s):  
Cell Fate ◽  
Large Scale ◽  
Wellcome Trust Sanger Institute ◽  
Scientific Discovery ◽  
Cell Lineage ◽  
Genome Project ◽  
Biomedical Ethics ◽  
Sanger Institute ◽  
Large Scale Data ◽  
Founding Director

In 2002 Sir John Sulston shared the Nobel Prize for Physiology or Medicine for his contribution to understanding the genetic control of cell fate during the development of the roundworm Caenorhabditis elegans . However, it was his position as one of the leaders of the international and publicly funded Human Genome Project that brought him to public prominence. Both his work on the worm cell lineage and his later commitment to genome sequencing as founding director of the Wellcome Trust Sanger Institute stemmed from his conviction that investing in large-scale data collection would have long-term benefits for future scientific discovery. He was a key figure in promoting the principle, now widely accepted, that genomic data should be universally and freely shared. After retiring from his post at the Sanger Institute he engaged with organizations with interests in biomedical ethics and global equality. He was a loyal and supportive colleague to many, delighting in the international collegiality of the ‘worm community’, of which he was a founding member.

scholarly journals Enabling Large-Scale Biomedical Analysis in the Cloud

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ying-Chih Lin ◽  
Chin-Sheng Yu ◽  
Yen-Jen Lin
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Biomedical Data ◽  
Data Intensive Computing ◽  
Biomedical Analysis ◽  
Data Intensive ◽  
Large Scale Data ◽  
Performance Computing ◽  
Scale Data

Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.

scholarly journals Optimizing Workflow Data Footprint

2007 ◽  
Vol 15 (4) ◽  
pp. 249-268 ◽  
Author(s):  
Gurmeet Singh ◽  
Karan Vahi ◽  
Arun Ramakrishnan ◽  
Gaurang Mehta ◽  
Ewa Deelman ◽  
...  
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Open Science ◽  
Scale Production ◽  
Distributed Resources ◽  
Data Intensive ◽  
Large Scale Production ◽  
Data Files ◽  
The Cost ◽  
Open Science Grid

In this paper we examine the issue of optimizing disk usage and scheduling large-scale scientific workflows onto distributed resources where the workflows are data-intensive, requiring large amounts of data storage, and the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer needed and we demonstrate that workflows may have to be restructured to reduce the overall data footprint of the workflow. We show the results of our data management and workflow restructuring solutions using a Laser Interferometer Gravitational-Wave Observatory (LIGO) application and an astronomy application, Montage, running on a large-scale production grid-the Open Science Grid. We show that although reducing the data footprint of Montage by 48% can be achieved with dynamic data cleanup techniques, LIGO Scientific Collaboration workflows require additional restructuring to achieve a 56% reduction in data space usage. We also examine the cost of the workflow restructuring in terms of the application's runtime.

scholarly journals Building high accuracy emulators for scientific simulations with deep neural architecture search

2021 ◽  
Author(s):  
Muhammad Firmansyah Kasim ◽  
D. Watson-Parris ◽  
L. Deaconu ◽  
S. Oliver ◽  
P. Hatfield ◽  
...  
Keyword(s):  
Large Scale ◽  
Scientific Discovery ◽  
Fusion Energy ◽  
High Energy ◽  
Training Data ◽  
High Energy Density ◽  
Neural Architecture ◽  
Large Scale Data ◽  
Scientific Simulations ◽  
Computational Discovery

Abstract Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate sci-ence, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

scholarly journals Assessing Complex Patterns of Student Resources and Behavior in the Large Scale

2019 ◽  
Vol 683 (1) ◽  
pp. 233-249 ◽  
Author(s):  
Knut Neumann ◽  
Horst Schecker ◽  
Heike Theyßen
Keyword(s):  
Large Scale ◽  
Reliability And Validity ◽  
Performance Tests ◽  
Automated Scoring ◽  
Large Scale Data ◽  
Assessment Design ◽  
Simulation Based ◽  
And Behavior ◽  
Physics Experiments ◽  
Scale Data

Large-scale assessments still focus on those aspects of students’ competence that can be evaluated using paper-and-pencil tests (or computer-administered versions thereof). Performance tests are considered costly due to administration and scoring, and, more importantly, they are limited in reliability and validity. In this article, we demonstrate how a sociocognitive perspective provides an understanding of these issues and how, based on this understanding, an argument-based approach to assessment design, interpretation, and use can help to develop comprehensive, yet reliable and valid, performance-based assessments of student competence. More specifically, we describe the development of a computer-administered, simulation-based assessment that can reliably and validly assess students’ competence to plan, perform, and analyze physics experiments at a large scale. Data from multiple validation studies support the potential of adopting a sociocognitive perspective and assessments based on an argument-based approach to design, interpretation, and use. We conclude by discussing the potential of simulations and automated scoring methods for reliable and valid performance-based assessments of student competence.

Data Stewardship Practices for Earth Observation Transient and Optimized Analysis Platforms

2021 ◽  
Author(s):  
Kaylin Bugbee ◽  
Rahul Ramachandran ◽  
Ge Peng ◽  
Aaron Kaulfus
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Scientific Research ◽  
Open Science ◽  
Heterogeneous Data ◽  
Earth Observation ◽  
Large Scale Data ◽  
Data Stewardship ◽  
Uniform Manner ◽  
Analysis Platform

<p>Access to valuable scientific research data is becoming increasingly more open, attracting a growing user community of scientists, decision makers and innovators. While these data are more openly available, accessibility continues to remain an issue due to the large volumes of complex, heterogeneous data that are available for analysis. This emerging accessibility issue is driving the development of specialized software stacks to instantiate new analysis platforms that enable users to quickly and efficiently work with large volumes of data. These platforms, typically found on the cloud or in a high performance computing environment, are optimized for large-scale data analysis. These platforms can be transient in nature, with a defined life span and a focus on improved capabilities as opposed to serving as an archive of record. </p><p> </p><p>While these transient, optimized platforms are not held to the same stewardship standards as a traditional archive, data must still be managed in a standardized and uniform manner throughout the platform. Valuable scientific research is conducted in these platforms, making these platforms subject to open science principles such as reproducibility and accessibility. In this presentation, we examine the differences between various data stewardship models and describe where transient optimized platforms fit within those models. We then describe in more detail a data and information governance framework for Earth Observation transient optimized analysis platforms. We will end our presentation by sharing our experiences of developing such a framework for the Multi-Mission Algorithm and Analysis Platform (MAAP).</p>

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