Water bilayers on ZnO(101̄0) surfaces: data-driven structural search

RSC Advances ◽  
2016 ◽  
Vol 6 (37) ◽  
pp. 30928-30936 ◽  
Author(s):  
Hugh F. Wilson ◽  
Amanda S. Barnard
Keyword(s):  
Ab Initio ◽  
High Stability ◽  
Data Science ◽  
Data Driven ◽  
Science Methods ◽  
Large Set ◽  
Atomic Structures ◽  
Use Of Data ◽  
Structural Search

We demonstrate an approach for the use of data science methods for structural search for high-stability atomic structures in ab initio simulation, via the analysis of a large set of candidate structures.

scholarly journals Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

2021 ◽  
Vol 7 (4) ◽  
pp. 208
Author(s):  
Mor Peleg ◽  
Amnon Reichman ◽  
Sivan Shachar ◽  
Tamir Gadot ◽  
Meytal Avgil Tsadok ◽  
...  
Keyword(s):  
Health Policy ◽  
Data Science ◽  
Research Community ◽  
Remote Access ◽  
Data Driven ◽  
Multidisciplinary Teams ◽  
Science Methods ◽  
Research Environments ◽  
Future Data ◽  
The Government

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects.

scholarly journals mSphere of Influence: Predicting Immune Responses and Susceptibility to Influenza Virus—May the Data Be with You

mSphere ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Irene Ramos
Keyword(s):  
Influenza Virus ◽  
Immune Responses ◽  
Ab Initio ◽  
Data Science ◽  
Viral Infections ◽  
Science Methods ◽  
Molecular Signatures ◽  
Host Interactions ◽  
Link Type ◽  
Human Immune

ABSTRACT Irene Ramos works in the field of immunology to viral infections. In this mSphere of Influence article, she reflects on how “Global analyses of human immune variation reveal baseline predictors of postvaccination responses” by Tsang et al. (Cell 157:499–513, 2014, https://doi.org/10.1016/j.cell.2014.03.031) and “A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection” by Fourati et al. (Nat Commun 9:4418, 2018, https://doi.org/10.1038/s41467-018-06735-8) made an impact on her by highlighting the importance of data science methods to understand virus-host interactions.

Things and Data in Recent Biology

2018 ◽  
Vol 48 (5) ◽  
pp. 648-658
Author(s):  
Soraya de Chadarevian
Keyword(s):  
Life Cycle ◽  
Biological Material ◽  
Data Science ◽  
Data Driven ◽  
Special Issue ◽  
Data Link ◽  
Use Of Data ◽  
Data Practices ◽  
In Silico Biology ◽  
Biological World

There is much talk about data-driven and in silico biology, but how exactly does it work? This essay reflects on the relation of data practices to the biological things from which they are abstracted. Looking at concrete examples of computer use in biology, the essay asks: How are biological things turned into data? What organizes and limits the combination, querying, and re-use of data? And how does the work on data link back to the organismic or biological world? Considering the life cycle of data, the essay suggests that data remain linked to the biological material and the concrete context from which they are extracted and to which they always refer back. Consequently, the transition to data science is never complete. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

scholarly journals Vernacular Visualization Practices

2019 ◽  
Author(s):  
Jaime Snyder
Keyword(s):  
Human Computer Interaction ◽  
Data Science ◽  
Evaluation Criteria ◽  
Visual Representations ◽  
Data Driven ◽  
Use Of Data ◽  
Methodological Approaches ◽  
The Creation ◽  
Computer Interaction

In this essay, I define and discuss vernacular visualization practices: the creation and use of data-driven visual representations by individuals untrained in design or data science, in contrast to formal or expert visualization practices. Vernacular visualization practices reflect local and situated resources, requirements, evaluation criteria, and final outputs. In these contexts, individuals sometimes choose to make design choices that differ from those vetted by experts. These differences surface opportunities for deeper understanding of visualizations in communicating data-driven information to diverse audiences and among stakeholders with heterogenous expertise and needs. To support this argument, I discuss the origins of the concept of vernacular visualization practices, highlight related trends in human-computer interaction (HCI) research, and then briefly describe some methodological approaches for studying them.

scholarly journals DATA DRIVEN PERFORMANCE EVALUATION IN SHIPBUILDING

Brodogradnja ◽  
2020 ◽  
Vol 71 (4) ◽  
pp. 39-51
Author(s):  
Umran Bilen ◽  
◽  
Sebnem Helvacioglu
Keyword(s):  
Cost Estimation ◽  
Structural Design ◽  
Data Science ◽  
Rapid Development ◽  
Estimation Algorithm ◽  
Data Driven ◽  
Medium Size ◽  
Analysis Techniques ◽  
Use Of Data ◽  
Evaluation Of Performance

Rapid development in data science keeps paving the way for use of data for many purposes in shipbuilding, both for product development and production, such as Industry 4.0 have been developing many industries. Similar to other industries the evaluation of performance in shipbuilding is the key to success which is closely connected to productivity and lowered costs. Data mining and analysis techniques are used to create effective algorithms to evaluate the performance, also by means of cost estimation based on parametric methods. However, it is usually not very clear how data are collected, organised and prepared for analysing and deriving valuable knowledge as well as algorithms. In most of the cases, having this data requires either continuous investment in expensive software or expensive external expertise which are generally not available for small and medium size shipyards. In this study, considering the needs of the small and medium sized shipyards, a step-by-step methodology is proposed which could be easily applied with widely available low budget software. The application is demonstrated with a case to evaluate the performance of early phase structural design with a data driven cost estimation algorithm.

Ab initio band theory approach to electron energy loss near edge structures

1988 ◽  
Vol 46 ◽  
pp. 506-507
Author(s):  
Xudong Weng ◽  
O.F. Sankey ◽  
Peter Rez
Keyword(s):  
Ab Initio ◽  
Local Density ◽  
Interpolation Method ◽  
Atomic Orbital ◽  
Plane Waves ◽  
Large Set ◽  
Theory Approach ◽  
Band Theory ◽  
Atomic Orbitals ◽  
Pseudopotential Calculations

Single electron band structure techniques have been applied successfully to the interpretation of the near edge structures of metals and other materials. Among various band theories, the linear combination of atomic orbital (LCAO) method is especially simple and interpretable. The commonly used empirical LCAO method is mainly an interpolation method, where the energies and wave functions of atomic orbitals are adjusted in order to fit experimental or more accurately determined electron states. To achieve better accuracy, the size of calculation has to be expanded, for example, to include excited states and more-distant-neighboring atoms. This tends to sacrifice the simplicity and interpretability of the method.In this paper. we adopt an ab initio scheme which incorporates the conceptual advantage of the LCAO method with the accuracy of ab initio pseudopotential calculations. The so called pscudo-atomic-orbitals (PAO's), computed from a free atom within the local-density approximation and the pseudopotential approximation, are used as the basis of expansion, replacing the usually very large set of plane waves in the conventional pseudopotential method. These PAO's however, do not consist of a rigorously complete set of orthonormal states.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa
Keyword(s):  
Physical Phenomenon ◽  
Urban Pollution ◽  
Pollutant Dispersion ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Thermal Fields ◽  
Use Of Data ◽  
Sustainability Challenges ◽  
New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

Assessing Nickel Titanium Binary Systems Using Structural Search Methods and Ab Initio Calculations

2021 ◽  
Vol 125 (2) ◽  
pp. 1578-1591
Author(s):  
Logan Lang ◽  
Adam Payne ◽  
Irais Valencia-Jaime ◽  
Matthieu J. Verstraete ◽  
Alejandro Bautista-Hernández ◽  
...  
Keyword(s):  
Ab Initio Calculations ◽  
Ab Initio ◽  
Binary Systems ◽  
Nickel Titanium ◽  
Search Methods ◽  
Structural Search
Sign in / Sign up

Export Citation Format

Share Document