scholarly journals Perspectives on Marine Data Science as a Blueprint for Emerging Data Science Disciplines

2021 ◽  
Vol 8 ◽  
Author(s):  
Maria-Theresia Verwega ◽  
Carola Trahms ◽  
Avan N. Antia ◽  
Thorsten Dickhaus ◽  
Enno Prigge ◽  
...  

Earth System Sciences have been generating increasingly larger amounts of heterogeneous data in recent years. We identify the need to combine Earth System Sciences with Data Sciences, and give our perspective on how this could be accomplished within the sub-field of Marine Sciences. Marine data hold abundant information and insights that Data Science techniques can reveal. There is high demand and potential to combine skills and knowledge from Marine and Data Sciences to best take advantage of the vast amount of marine data. This can be accomplished by establishing Marine Data Science as a new research discipline. Marine Data Science is an interface science that applies Data Science tools to extract information, knowledge, and insights from the exponentially increasing body of marine data. Marine Data Scientists need to be trained Data Scientists with a broad basic understanding of Marine Sciences and expertise in knowledge transfer. Marine Data Science doctoral researchers need targeted training for these specific skills, a crucial component of which is co-supervision from both parental sciences. They also might face challenges of scientific recognition and lack of an established academic career path. In this paper, we, Marine and Data Scientists at different stages of their academic career, present perspectives to define Marine Data Science as a distinct discipline. We draw on experiences of a Doctoral Research School, MarDATA, dedicated to training a cohort of early career Marine Data Scientists. We characterize the methods of Marine Data Science as a toolbox including skills from their two parental sciences. All of these aim to analyze and interpret marine data, which build the foundation of Marine Data Science.

2021 ◽  
pp. 089484532110172
Author(s):  
Ruth Noppeney ◽  
Anna M. Stertz ◽  
Bettina S. Wiese

Obtaining a doctorate offers various career options. This study takes a person-centered approach to identify interest profiles. Career goals (professorate, entrepreneur, etc.) were assessed at two time points (1-year interval) in a sample of doctoral students and doctorate holders from the STEM fields in German-speaking areas ( NT 1 = 2,077). Latent profile analysis revealed that a four-profile solution provided the best data fit: At T1, 33.0% of the participants aimed for a management position in industry, 16.9% pursued an academic career, 30.1% were interested in activities without leadership responsibilities, and 20.1% had a relatively flat career-goal profile. Latent transition analysis indicated that most changes occurred for those classified into the flat profile, while strong interest in a management career was very stable over time. Additionally, the attainment of the doctorate seemed to be a good predictor for profile membership: Doctorate holders were more likely to be clearly dedicated to an academic career.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mario Zanfardino ◽  
Rossana Castaldo ◽  
Katia Pane ◽  
Ornella Affinito ◽  
Marco Aiello ◽  
...  

AbstractAnalysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.


2021 ◽  
Vol 14 (1) ◽  
pp. 205979912098776
Author(s):  
Joseph Da Silva

Interviews are an established research method across multiple disciplines. Such interviews are typically transcribed orthographically in order to facilitate analysis. Many novice qualitative researchers’ experiences of manual transcription are that it is tedious and time-consuming, although it is generally accepted within much of the literature that quality of analysis is improved through researchers performing this task themselves. This is despite the potential for the exhausting nature of bulk transcription to conversely have a negative impact upon quality. Other researchers have explored the use of automated methods to ease the task of transcription, more recently using cloud-computing services, but such services present challenges to ensuring confidentiality and privacy of data. In the field of cyber-security, these are particularly concerning; however, any researcher dealing with confidential participant speech should also be uneasy with third-party access to such data. As a result, researchers, particularly early-career researchers and students, may find themselves with no option other than manual transcription. This article presents a secure and effective alternative, building on prior work published in this journal, to present a method that significantly reduced, by more than half, interview transcription time for the researcher yet maintained security of audio data. It presents a comparison between this method and a fully manual method, drawing on data from 10 interviews conducted as part of my doctoral research. The method presented requires an investment in specific equipment which currently only supports the English language.


Author(s):  
Joanne Pransky

Purpose – This article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach – The interviewee is Dr Yoky Matsuoka, the Vice President of Nest Labs. Matsuoka describes her career journey that led her from a semi-professional tennis player who wanted to build a robot tennis buddy, to a pioneer of neurobotics who then applied her multidisciplinary research in academia to the development of a mass-produced intelligent home automation device. Findings – Dr Matsuoka received a BS degree from the University of California, Berkeley and an MS and PhD in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT). She was also a Postdoctoral Fellow in the Brain and Cognitive Sciences at MIT and in Mechanical Engineering at Harvard University. Dr Matsuoka was formerly the Torode Family Endowed Career Development Professor of Computer Science and Engineering at the University of Washington (UW), Director of the National Science Foundation Engineering Research Center for Sensorimotor Neural Engineering and Ana Loomis McCandless Professor of Robotics and Mechanical Engineering at Carnegie Mellon University. In 2010, she joined Google X as one of its three founding members. She then joined Nest as VP of Technology. Originality/value – Dr Matsuoka built advanced robotic prosthetic devices and designed complementary rehabilitation strategies that enhanced the mobility of people with manipulation disabilities. Her novel work has made significant scientific and engineering contributions in the combined fields of mechanical engineering, neuroscience, bioengineering, robotics and computer science. Dr Matsuoka was awarded a MacArthur Fellowship in which she used the Genius Award money to establish a nonprofit corporation, YokyWorks, to continue developing engineering solutions for humans with physical disabilities. Other awards include the Emerging Inventor of the Year, UW Medicine; IEEE Robotics and Automation Society Early Academic Career Award; Presidential Early Career Award for Scientists and Engineers; and numerous others. She leads the development of the learning and control technology for the Nest smoke detector and Thermostat, which has saved the USA hundreds of billions of dollars in energy expenses. Nest was sold to Google in 2013 for a record $3.2 billion dollars in cash.


Langmuir ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 727-728
Author(s):  
Jacinta C. Conrad ◽  
Noshir S. Pesika ◽  
Daniel K. Schwartz

2020 ◽  
Author(s):  
Jörn Lötsch ◽  
Alfred Ultsch

Abstract Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treatment-related effects on the data if the effects were not reflected by the central tendency. "Impact” is a novel nonparametric measure of effect size obtained as the sum of two separate components and includes (i) the change in the central tendency of the group-specific data, normalized to the overall variability, and (ii) the difference in the probability density of the group-specific data. Results obtained on artificial data and empirical biomedical data showed that impact outperforms Cohen's d by this additional component. It is shown that in a multivariate setting, while standard statistical analyses and Cohen’s d are not able to identify effects that lead to changes in the form of data distribution, “Impact” correctly captures them. The proposed effect size measure shares the ability to observe such an effect with machine learning algorithms. It is numerically stable even for degenerate distributions consisting of singular values. Therefore, the proposed effect size measure is particularly well suited for data science and artificial intelligence-based knowledge discovery from (big) and heterogeneous data.


Chapter 56 provides advice on early career planning, with specific reference to Foundation Programme applications, Academic Foundation Programme applications, and career taster opportunities. The Foundation Programme application process is summarized, with details about the types of application, timeline of application, online submission, educational performance measures used, situational judgement tests, and top tips to maximize the chance of a successful outcome. The situational judgement test forms a significant part of the overall score: the chapter covers example questions and the rationale for the preferred response. Academic Foundation Programmes allow additional scope and funding for research and form the early stages of the academic career pathway. Career taster weeks allow an opportunity to look closely at a career of interest by spending a week in that specialty. Advice on how to organize a taster week, what to ask about, and top tips in organizing your own career taster are provided. A comprehensive list of resources is provided for the reader.


Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 73 ◽  
Author(s):  
HyunKi Lee ◽  
Sasha Madar ◽  
Santusht Sairam ◽  
Tejas G. Puranik ◽  
Alexia P. Payan ◽  
...  

In recent years, there has been a rapid growth in the application of data science techniques that leverage aviation data collected from commercial airline operations to improve safety. This paper presents the application of machine learning to improve the understanding of risk factors during flight and their causal chains. With increasing complexity and volume of operations, rapid accumulation and analysis of this safety-related data has the potential to maintain and even lower the low global accident rates in aviation. This paper presents the development of an analytical methodology called Safety Analysis of Flight Events (SAFE) that synthesizes data cleaning, correlation analysis, classification-based supervised learning, and data visualization schema to streamline the isolation of critical parameters and the elimination of tangential factors for safety events in aviation. The SAFE methodology outlines a robust and repeatable framework that is applicable across heterogeneous data sets containing multiple aircraft, airport of operations, and phases of flight. It is demonstrated on Flight Operations Quality Assurance (FOQA) data from a commercial airline through use cases related to three safety events, namely Tire Speed Event, Roll Event, and Landing Distance Event. The application of the SAFE methodology yields a ranked list of critical parameters in line with subject-matter expert conceptions of these events for all three use cases. The work concludes by raising important issues about the compatibility levels of machine learning and human conceptualization of incidents and their precursors, and provides initial guidance for their reconciliation.


2019 ◽  
Vol 21 (4) ◽  
pp. 1182-1195
Author(s):  
Andrew C Liu ◽  
Krishna Patel ◽  
Ramya Dhatri Vunikili ◽  
Kipp W Johnson ◽  
Fahad Abdu ◽  
...  

Abstract Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.


2012 ◽  
Vol 12 ◽  
pp. 247-256
Author(s):  
Bruce J. MacFadden

Undergraduate paleontology education typically consists of formal coursework involving the classroom, laboratory, and field trips. Other opportunities exist within informal science education (ISE) that can provide students with experiences to broaden their undergraduate education. ISE includes out-of-school, “free-choice,” and/or lifelong learning experiences in a variety of settings and media, including museums, science and nature centers, national and state parks, science cafes, as well as an evergrowing variety of web-based activities. This article discusses ISE as it pertains to university paleontology education and presents examples. Students can participate in the development and evaluation of exhibits as well as assist in the implementation of museum-related educational programs with paleontological content. They also can work or intern as explainers either “on the floor” of museums, or as interpreters at science-related parks. ISE-related activities can also provide opportunities to engage in citizen science and other outreach initiatives, e.g., with undergraduates assisting in fossil digs with public (volunteer) participation and giving talks to fossil clubs. During these activities, students have the opportunity to communicate about controversial topics such as evolution, which is neither well understood nor universally accepted by the general public. Engagement in these kinds of activities provides students with a combination of specialized STEM content (paleontology, geology) and ISE practice that may better position them to pursue nontraditional careers outside of the academic arena. Likewise, for students intending to pursue an academic career, ISE activities make undergraduate students better equipped to conduct Broader Impact activities as early career professionals.


Sign in / Sign up

Export Citation Format

Share Document