Data Science Professional Uncovered: How the EDISON Project will Contribute to a Widely Accepted Profile for Data Scientists

To evaluate efficacy with ublituximab in treatment-naive participants with relapsing multiple sclerosis enrolled in ULTIMATE I and II.

The title of data scientist has been described as one of the sexiest jobs of the 21st century. Numerous efforts have been made to define the job of a data scientist in a qualitative manner by, for example, listing the job functions and required skill sets of data scientists. However, to the best of our knowledge, no attempt has been made to define the term data scientist in a scientific manner. In this paper, we address this issue by using a data-driven approach to answer three questions: 1) What is a proper definition of the term data scientist from a market-demand perspective? 2) Do self-described data scientists meet the market demand? and 3) Finally, how can companies efficiently recruit data scientists that match their openings? To answer these questions, we crawl two data sets for the supply and demand sides. For the former, we collect a set of data scientist user profiles from LinkedIn; for the latter, we collect a set of data scientist job descriptions from Monster. We first parse the set of data scientist job descriptions via natural language processing techniques and derive a scientific definition of the job of a data scientist via a clustering algorithm. Second, we use the same approach to determine that, under the aforementioned definition, self-claimed data scientists on the market would meet the market demand with a high probability. Finally, we introduce a distance-metric learning approach that can be used by companies to find data scientist candidates that match their openings. We achieve an average precision of 12.31%; i.e., one in ten candidates with matching qualifications would accept a given offer. The application of this quantitative approach could significantly reduce the human-resource costs incurred by companies in recruiting matching data scientists.

In this paper, we explored the research status, hotspots, development contexts and trends of Data Science study in the field of editing and publishing in China. In our study, we selected 441 Data Science articles which published in 15 journals of editing and publishing science from 2008 to 2018 in CNKI database. By using Citespace, a bibliometric and visualization software, we analyzed the characteristics of publications, authors and research institutions in this field, plot the knowledge graph of data science study in the field of editing and publishing. According to the analysis, we found Data Science study in the field of editing and publishing has a clear development path in China, and most of the research findings would be divided into three aspects: a) research on data storage and mining; b) research on the application of Big Data in publishing Industry; c) research on collaborative development of data science technology and digital publishing. Although the Data Science method has been applied to solve scientific research problems and guide social production in the field of editing and publishing, the core researcher group is still weak and the study is in the stage of developing. It's going to be the focus of future research that the application of new data science method, such as Artificial Intelligence, to new production and profit model in publishing industry.

Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor-descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.

Data science has emerged as an important discipline in the era of big data and biological and biomedical data mining. As such, we have seen a rapid increase in the number of data science departments, research centers, and schools. We review here ten important leadership roles for a successful academic data science chair, director, or dean. These roles include the visionary, executive, cheerleader, manager, enforcer, subordinate, educator, entrepreneur, mentor, and communicator. Examples specific to leadership in data science are given for each role.

There are various ideas in everyone's mind. But to convert the ideas into reality, requires initiative and action. Petroleum industry has been using different techniques, science and algorithms over the years to tackle the challenges in order to achieve a business objective. This paper shares the implemented approach to support business challenges by developing a Research and development environment to support the business community. The idea was to find out current business user challenges and support their requirements. IBTIKAR (innovation) lab is the result of this idea which is created to provide required In-House infrastructure (Hardware and Software) support within UAE National Oil Company premises. Research and development are integral part of any company/industry for tackling challenges. Accessing various data types and implementing the concept of Big data has been of a great value. Collaboration between business users, Information technology and Data Management is required to have concrete foundation and to prepare roadmap for the future. The application portfolio in an Oil and Gas upstream varies a lot and now a days, almost all the applications /tool come with a programming interface for integrating Open-Source algorithms. It makes it easier to directly connect the open-source tools to existing applications. The type of data types in upstream varies a lot and since these applications are already developed over the years to handle different kind of data, which further makes data analysis tasks achievable in timely manner. The available algorithms in existing tools and those accessed visa Open-source tools are further used for prediction and classification problems. With the advances in the learning algorithms, the modelling has become easier, and it requires very less coding. The challenges are more towards infrastructure support, as the data types and data size has gradually increased. Machine learning and Deep learning requires High end infrastructure to perform the data science using more CPU's, High End GPU's. In order to adapt to the advancing science and technologies, it makes everyone responsible to learn and implement the data science and Artificial intelligence (AI) concepts in daily tasks and projects to support organization strategy and objectives. In order to get the business community ready for AI, availability of Data science tools and infrastructure is required. This will support and allow the business users of any kind of data to try and test various data types using AI and data science. Following approach was taken from the idea initiation to the execution phase: Discussion with Business users to find out current business challenges and impediments in overcoming those challengesDocument project scopes for the discussed business challenges.Discussion with the respective Infrastructure support team to provide the Hardware and software support along with the required data storage space.Secure budget for developing IBTIKAR labDevelop environment for the research and development of various data science projectsIntegration of Open Source and Free software/s with the existing tools and applicationsAgree and support of the respective roles and responsibilitiesAgree of establishing a collaborative environment between business users, Information technology and data management engineers.To have tools covering the life cycle of the machine learning model (from creation to deployment to monitoring) The business users involved throughout the upstream activities, want to use available open-source algorithms and run various Proof of concepts. In order to get value out of data and to extract insight, it is must that business users are empowered with the required infrastructure, trainings, data science tools and Artificial intelligent algorithms in a collaborative Research and development environment. Configuration and implementation of the IBTIKAR environment has aided the business user community in conducting various Proof of concepts and execute process to handle various business objectives and extract value. IBTIKAR environment has delivered value to the business community by providing required High-end resources, a programming environment, available open-source algorithms and it has improved the level of efficiency, collaboration and people engagement.

Judith A. Cook, Ph.D., is Director of Thresholds National Research and Training Center on Rehabilitation and Mental Illness, Chicago, Illinois. Lisa A. Razzano, Ph.D., is Assistant Director of Thresholds National Research and Training Center on Rehabilitation and Mental Illness, Chicago, Illinois. Madison Straiton is Managing Assistant Director of Thresholds National Research and Training Center on Rehabilitation and Mental Illness, Chicago, Illinois. Yvette Ross is a Research Assistant at the Thresholds National Research and Training Center on Rehabilitation and Mental Illness, Chicago, Illinois.___________

The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer science, statistics and mathematics.

To evaluate compliance and persistence with ofatumumab in patients with relapsing multiple sclerosis (RMS) for up to 4 years across the core ofatumumab studies and the ALITHIOS extension study (NCT03650114).

Protective effects of 3,6-bis-(2-piperidinoethoxy) acridine trihydrochloride (CL-246738) in experimental acute Trypanosoma cruzi infection

Tracking Diabetes Therapy Into The 21st Century

The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urban research, but has also allowed for new analytical possibilities. Because of the rapid growth and change in the field, terminology in urban analytics can be vague and unclear. This paper, an abridged synthesis of a panel discussion among scholars in Urban Data Science held at the 2019 American Association of Geographers Conference in Washington, D.C., outlines one discussion seeking a better sense of the conceptual, terminological, social, and ethical challenges faced by researchers in this emergent field. The panel outlines the difficulties of defining what is or is not urban data science, finding that good urban data science must have an expansive role in a successful discipline of “city science.” It suggests that “data science” has value as a “signaling” term in industrial or popular science applications, but which may not necessarily be well-understood within purely academic circles. The panel also discusses the normative value of doing urban data science, linking successful practice back to urban life. Overall, this panel report contributes to the wider discussion around urban analytics and city science and about the role of data science in this domain.

From space to biomedicine: Enabling biomarker data science in the cloud.

The recent advancements in artificial intelligence (AI), and data science more broadly, have led to a proliferation of new methods and tools, such as machine learning (ML), that are used in all kinds of scientific research, from biomedical research through to environmental and education research. Research ethics review bodies are increasingly required to review AI research protocols that cover these different fields of enquiry. Questions have been raised regarding the appropriateness of existing ethics governance principles, practices, and processes to deal with the ethical challenges that AI and data science are introducing to research. Universities and research institutions across the world are trying to understand how to translate and practically implement broad AI ethical principles into research ethics governance guidelines and processes. In this article, we report on an expert stakeholders’ workshop organised at the University of Oxford as part of the process of reviewing its ethics governance for AI research. We describe the workshop and present the reflections and recommendations that emerged from it. The aim of the article is to share the approach taken by the University of Oxford CUREC in reviewing its ethics governance processes, and the insights gained with the broader research community, as a way of contributing to this scarce body of literature, facilitating further dialogue, promoting debate and collaboration on this important issue.

The landscape of research in science and engineering is heavily reliant on computation and data processing. There is continued and expanded usage by disciplines that have historically used advanced computing resources, new usage by disciplines that have not traditionally used HPC, and new modalities of the usage in Data Science, Machine Learning, and other areas of AI. Along with these new patterns have come new advanced computing resource methods and approaches, including the availability of commercial cloud resources. The Coalition for Academic Scientific Computation (CASC) has long been an advocate representing the needs of academic researchers using computational resources, sharing best practices and offering advice to create a national cyberinfrastructure to meet US science, engineering, and other academic computing needs. CASC has completed the first of what we intend to be an annual survey of academic cloud and data center usage and practices in analyzing return on investment in cyberinfrastructure. Critically important findings from this first survey include the following: many of the respondents are engaged in some form of analysis of return in research computing investments, but only a minority currently report the results of such analyses to their upper-level administration. Most respondents are experimenting with use of commercial cloud resources but no respondent indicated that they have found use of commercial cloud services to create financial benefits compared to their current methods. There is clear correlation between levels of investment in research cyberinfrastructure and the scale of both cpu core-hours delivered and the financial level of supported research grants. Also interesting is that almost every respondent indicated that they participate in some sort of national cooperative or nationally provided research computing infrastructure project and most were involved in academic computing-related organizations, indicating a high degree of engagement by institutions of higher education in building and maintaining national research computing ecosystems. Institutions continue to evaluate cloud-based HPC service models, despite having generally concluded that so far cloud HPC is too expensive to use compared to their current methods.