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.

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 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.

Special issue: Life cycle consumption

2020 ◽  
Vol 47 ◽  
pp. 101678
Author(s):  
Thomas Davidoff
Keyword(s):  
Life Cycle ◽  
Special Issue

scholarly journals The INSESS-COVID19 Project. Evaluating the Impact of the COVID19 in Social Vulnerability While Preserving Privacy of Participants from Minority Subpopulations

2021 ◽  
Vol 11 (7) ◽  
pp. 3110
Author(s):  
Karina Gibert ◽  
Xavier Angerri
Keyword(s):  
Social Vulnerability ◽  
Data Science ◽  
Policy Design ◽  
Data Driven ◽  
Informed Decision Making ◽  
Technological Infrastructure ◽  
Moderate Number ◽  
Reliable Diagnosis ◽  
Multivariate Relationships ◽  
The Impact

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized.

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