scholarly journals PERFICT: a Re-imagined Foundation for Predictive Ecology

2021 ◽  
Author(s):  
Eliot McIntire ◽  
Alex Chubaty ◽  
Steve Cumming ◽  
David Andison ◽  
Ceres Barros ◽  
...  
Keyword(s):  
Science Policy ◽  
Data Science ◽  
Ecological Models ◽  
Model Complexity ◽  
Policy Integration ◽  
Model Quality ◽  
The Public ◽  
Scientific Disciplines ◽  
Modeling Paradigm ◽  
Integrated Approaches

Making predictions from ecological models – and comparing these predictions to data – offers a coherent approach to objectively evaluate model quality, regardless of model complexity or modeling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies and the public has been hampered by disparate perspectives on prediction and inadequate integrated approaches. We present an updated foundation for Predictive Ecology that is based on 7 principles applied to ecological models: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows, that are routinely Tested (PERFICT). We outline some benefits of working with these principles: 1) accelerating science; 2) bridging to data science; and 3) improving science-policy integration.

scholarly journals Predictive Ecology: a Re-imagined Foundation and Toolkit for Ecological Models

2021 ◽  
Author(s):  
Eliot McIntrie ◽  
Alex Chubaty ◽  
Steve Cumming ◽  
David Andison ◽  
Ceres Barros ◽  
...  
Keyword(s):  
Science Policy ◽  
Data Science ◽  
Statistical Simulation ◽  
Ecological Models ◽  
Policy Integration ◽  
Management Decisions ◽  
The Public ◽  
Scientific Disciplines ◽  
Predictive Approach ◽  
Scientific Advances

Prediction from models and data in Ecology has a long history and can be made from many types of statistical, simulation, and other classes of models. To date, our ability to use the predictive approach as a tool for developing, validating, updating, integrating and applying models across scientific disciplines and to influence management decisions, policies and the public has been hampered by disparate perspectives on prediction and inadequate tools. We present a coherent perspective that follows a Predictive Ecology approach based on 5 principles: Reusable, Freely available and Interoperable models, built around a Continuous workflow, which are Tested automatically (PERFICT). We describe the SpaDES toolkit that helps implement these principles. We outline some benefits for society of working with these principles, including 1) speeding up scientific advances; 2) data science advances; and 3) improving science-policy integration.

scholarly journals Becoming a steward of data science

2019 ◽  
Author(s):  
Rochelle E. Tractenberg
Keyword(s):  
Professional Identity ◽  
Data Science ◽  
Professional Community ◽  
Undergraduate Program ◽  
The Public ◽  
The Individual ◽  
Taking Responsibility

This article introduces the concept of the steward: the individual to whom the public, and other practitioners, can entrust the integrity of their field. The concept will be defined, particularly with respect to what about stewardship can be demonstrated by the practitioner so that others – including other stewards – can recognize this professional identity. Stewardship is an important aspect of professionalism, and although data science is a very new profession, its growth in terms of the number of practitioners should also include growth in the commitment to integrity in practice. Although an undergraduate program may seem early to begin understanding what this commitment means, and how to generate evidence of that commitment for yourself, those with a strong understanding of stewardship and how to recognize it will be better able to select jobs in contexts where this commitment to integrity is nurtured and valued. Learning about stewardship engages students in taking responsibility for their role in the profession, and so taking responsibility for the profession and the professional community. Once the construct is understood, learners can focus on the nature of the evidence they can compile - as well as the types of activities that can generate that kind of evidence- and on why this is meaningful over their career.

scholarly journals The R package “eseis” – a software toolbox for environmental seismology

2018 ◽  
Vol 6 (3) ◽  
pp. 669-686 ◽  
Author(s):  
Michael Dietze
Keyword(s):  
Data Science ◽  
Earth Science ◽  
Science Research ◽  
Worked Examples ◽  
R Package ◽  
Research Field ◽  
Data Sets ◽  
Research Fields ◽  
Scientific Disciplines ◽  
Analysis Environment

Abstract. Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the intersection of seismology, geomorphology, hydrology, meteorology, and further Earth science disciplines. It amalgamates a wide variety of methods from across these disciplines and ultimately fuses them in a common analysis environment. This overarching scope of environmental seismology requires a coherent yet integrative software which is accepted by many of the involved scientific disciplines. The statistic software R has gained paramount importance in the majority of data science research fields. R has well-justified advances over other mostly commercial software, which makes it the ideal language to base a comprehensive analysis toolbox on. The article introduces the avenues and needs of environmental seismology, and how these are met by the R package eseis. The conceptual structure, example data sets, and available functions are demonstrated. Worked examples illustrate possible applications of the package and in-depth descriptions of the flexible use of the functions. The package has a registered DOI, is available under the GPL licence on the Comprehensive R Archive Network (CRAN), and is maintained on GitHub.

Automated design process for hybrid regression modeling with a one-class SVM

2019 ◽  
Vol 67 (10) ◽  
pp. 843-852 ◽  
Author(s):  
Moritz Böhland ◽  
Wolfgang Doneit ◽  
Lutz Gröll ◽  
Ralf Mikut ◽  
Markus Reischl
Keyword(s):  
Design Process ◽  
Regression Models ◽  
Data Science ◽  
Automated Design ◽  
Model Complexity ◽  
Support Vector ◽  
Local Data ◽  
Benchmark Datasets ◽  
Data Coverage ◽  
First Time

Abstract The accuracy of many regression models suffers from inhomogeneous data coverage. Models loose accuracy because they are unable to locally adapt the model complexity. This article develops and evaluates an automated design process for the generation of hybrid regression models from arbitrary submodels. For the first time, these submodels are weighted by a One-Class Support Vector Machine, taking local data coverage into account. Compared to reference regression models, the newly developed hybrid models achieve significant better results in nine out of ten benchmark datasets. To enable straightforward usage in data science, an implementation is integrated in the open source MATLAB toolbox SciXMiner.

scholarly journals The Sensationalist Discourse of Science Diplomacy: A Critical Reflection

2020 ◽  
Vol 15 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Tim Flink
Keyword(s):  
Science Policy ◽  
Public Discourse ◽  
Critical Reflection ◽  
International Affairs ◽  
Cultural Contexts ◽  
The Public ◽  
Science Diplomacy ◽  
Foreign Politics

Summary For almost twenty years, the concept of science diplomacy has gained momentum in a public discourse that brings together science policy and international affairs. While some policy actions were newly established and others got into the stride of science diplomacy, the public discourse kept proliferating and has greatly enlarged the concept’s meaning. Reviewing one of its most common definitions, this contribution critically reflects on the sensational promises made by advocates and endorsers of science diplomacy. Their framing bears on a popular and romantic image of science that would hold salutary capacities to solve problems no matter how complex and that goes into rhapsodies about scientists as cosmopolitans who would eagerly collaborate with kindred spirits regardless of national and cultural contexts. Apart from the fact that science tends to get instrumentalised for particularistic purposes, these reveries are problematic, as they overbook expectations about science and foreign politics that can hardly be fulfilled.

scholarly journals Naming the Bonaire banded box jelly, Tamoya ohboya, n. sp. (Cnidaria: Cubozoa: Carybdeida: Tamoyidae)

Zootaxa ◽  
2011 ◽  
Vol 2753 (1) ◽  
pp. 53 ◽  
Author(s):  
ALLEN G. COLLINS ◽  
BASTIAN BENTLAGE ◽  
WILLIAM GILLAN ◽  
TARA H. LYNN ◽  
ANDRÉ C. MORANDINI ◽  
...  
Keyword(s):  
New Species ◽  
Mitochondrial Gene ◽  
Southeastern United States ◽  
Detailed Comparison ◽  
Shallow Waters ◽  
The Public ◽  
Scientific Disciplines ◽  
Close Relatives ◽  
Dutch Caribbean ◽  
A New Species

A new species of cubozoan jellyfish has been discovered in shallow waters of Bonaire, Netherlands (Dutch Caribbean). Thus far, approximately 50 sightings of the species, known commonly as the Bonaire banded box jelly, are recorded, and three specimens have been collected. Three physical encounters between humans and the species have been reported. Available evidence suggests that a serious sting is inflicted by this medusa. To increase awareness of the scientific disciplines of systematics and taxonomy, the public has been involved in naming this new species. The Bonaire banded box jelly, Tamoya ohboya, n. sp., can be distinguished from its close relatives T. haplonema from Brazil and T. sp. from the southeastern United States by differences in tentacle coloration, cnidome, and mitochondrial gene sequences. Tamoya ohboya n. sp. possesses striking dark brown to reddish-orange banded tentacles, nematocyst warts that densely cover the animal, and a deep stomach. We provide a detailed comparison of nematocyst data from Tamoya ohboya n. sp., T. haplonema from Brazil, and T. sp. from the Gulf of Mexico.

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