The Role of Provenance Management in Accelerating the Rate of Astronomical Research

The availability of vast quantities of data through electronic archives has transformed astronomical research. It has also enabled the creation of new products, models and simulations, often from distributed input data and models, that are themselves made electronically available. These products will only provide maximal long-term value to astronomers when accompanied by records of their provenance; that is, records of the data and processes used in the creation of such products. We use the creation of image mosaics with the Montage grid-enabled mosaic engine to emphasize the necessity of provenance management and to understand the science requirements that higher-level products impose on provenance management technologies. We describe experiments with one technology, the "Provenance Aware Service Oriented Architecture" (PASOA), that stores provenance information at each step in the computation of a mosaic. The results inform the technical specifications of provenance management systems, including the need for extensible systems built on common standards. Finally, we describe examples of provenance management technology emerging from the fields of geophysics and oceanography that have applicability to astronomy applications.Comment: 8 pages, 1 figure; Proceedings of Science, 201

An open innovation approach to co-produce scientific knowledge: an examination of citizen science in the healthcare ecosystem

Purpose The article applies the citizen science phenomenon - i.e. lay people involvement in research endeavours aimed at pushing forward scientific knowledge - to healthcare. Attention is paid to initiatives intended to tackle the COVID-19 pandemic as an illustrative case to exemplify the contribution of citizen science to system-wide innovation in healthcare. Design/methodology/approach A mixed methodology consisting of three sequential steps was developed. Firstly, a realist literature review was carried out to contextualize citizen science to healthcare. Then, an account of successfully completed large-scale, online citizen science projects dealing with healthcare and medicine has been conducted in order to obtain preliminary information about distinguishing features of citizen science in healthcare. Thirdly, a broad search of citizen science initiatives targeted to tackling the COVID-19 pandemic has been performed. A comparative case study approach has been undertaken to examine the attributes of such projects and to unravel their peculiarities. Findings Citizen science enacts the development of a lively healthcare ecosystem, which takes its nourishment from the voluntary contribution of lay people. Citizen scientists play different roles in accomplishing citizen science initiatives, ranging from data collectors to data analysts. Alongside enabling big data management, citizen science contributes to lay people's education and empowerment, soliciting their active involvement in service co-production and value co-creation. Practical implications Citizen science is still underexplored in healthcare. Even though further evidence is needed to emphasize the value of lay people's involvement in scientific research applied to healthcare, citizen science is expected to revolutionize the way innovation is pursued and achieved in the healthcare ecosystem. Engaging lay people in a co-creating partnership with expert scientist can help us to address unprecedented health-related challenges and to shape the future of healthcare. Tailored health policy and management interventions are required to empower lay people and to stimulate their active engagement in value co-creation. Originality/value Citizen science relies on the wisdom of the crowd to address major issues faced by healthcare organizations. The article comes up with a state of the art investigation of citizen science in healthcare, shedding light on its attributes and envisioning avenues for further development

Quantitative Models in Life Science Business

This open access book explores the field of life science business from a multidisciplinary perspective. Applying statistical, mathematical, game-theoretic, and data science tools to pharmaceutical and biotechnology business endeavors, the book describes value creation, value maintenance, and value realization in the life sciences as a sequence of processes using the quantitative language of applied mathematics. Written by experts from a variety of fields, the contributions illustrate the shift from a deterministic to a stochastic view of the processes involved, offering a new perspective on life sciences economics. The book covers topics such as valuing and managing intellectual property in life science, licensing in the pharmaceutical business, outsourcing pharmaceutical R&D, and stochastic modelling of a pharmaceutical supply chain. The book will appeal to scholars of economics and the life sciences, as well as to professionals in chemical and pharmaceutical industries

Quantitative Models in Life Science Business

This open access book explores the field of life science business from a multidisciplinary perspective. Applying statistical, mathematical, game-theoretic, and data science tools to pharmaceutical and biotechnology business endeavors, the book describes value creation, value maintenance, and value realization in the life sciences as a sequence of processes using the quantitative language of applied mathematics. Written by experts from a variety of fields, the contributions illustrate the shift from a deterministic to a stochastic view of the processes involved, offering a new perspective on life sciences economics. The book covers topics such as valuing and managing intellectual property in life science, licensing in the pharmaceutical business, outsourcing pharmaceutical R&D, and stochastic modelling of a pharmaceutical supply chain. The book will appeal to scholars of economics and the life sciences, as well as to professionals in chemical and pharmaceutical industries

The Sciences of Data – Moving Towards a Comprehensive Systems Perspective

Data science’s rapid development in a dynamically growing data environment endows it with unique characteristics among scientific disciplines, juxtaposing challenges typically encountered in theoretical as well as empirical sciences. This raises questions as to the identification of the most pressing problems for data science, as well as to what constitutes its theoretical foundations. In this contribution, we first describe data science from the perspective of philosophy of science. We argue that the current mode of development of data science is adequately described by what we term the differentiational-expansionist mode. This leads us to conclude that data science concerns the acquisition of scientific theories relating to the application of methods, workflows and algorithms that generate value for users – which we term the integrative view. This definition emphasizes the interdependent nature of human and algorithmic elements in complex data workflows. We then offer four challenges for the future of the field. We conclude that since full control of entire data workflows is unfeasible, attention should be redirected towards the creation of an infrastructure by which data workflows will self-organize in a useful manner

A Three-Fold Perspective of Continuous IT Value Assessment

Assessing Information Technology (IT) value is a priority in the digital age. The topic has been studied for decades, but the difficulty to (1) continuously measure and (2) steer value creation with IT investments still presents challenges. This paper presents an approach to evaluate the value of IT over time. The results include a multidimensional framework for the main phases of (1) dynamic contextualization and (2) data analysis. The results emerge from the first cycle of design science research conducted in a municipality-owned water supply company struggling to monitor and communicate IT value to different stakeholders, particularly the administration board. IT value assessment in digital transformation contexts must result from continuous monitoring in three essential perspectives: net value, goal achievement, and perceived value. The findings can be helpful to Chief Information Officers (CIOs) dealing with the pressure to report value in turbulent environments and justify their increasing IT investments

How non-epistemic values can be epistemically beneficial in scientific classification

The boundaries of social categories are frequently altered to serve normative projects, such as social reform. Griffiths and Khalidi argue that the value-driven modification of categories diminishes the epistemic value of social categories. I argue that concerns over value-modified categories stem from problematic assumptions of the value-free ideal of science. Contrary to those concerns, non-epistemic value considerations can contribute to the epistemic improvement of a scientific category. For example, the early history of the category infantile autism shows how non-epistemic value considerations can contribute to delimiting and establishing infantile autism as a distinct category in mainstream psychiatry. In the case of infantile autism, non-epistemic considerations have led to a new interpretation of existing data, the expansion of research to include biology, and the creation of diagnostic criteria that further contribute to collecting relevant data. Given this case study, it can be argued that non-epistemic values can improve our knowledge of scientific categories

Unleashing the Potential of External Data: A DSR-based Approach to Data Sourcing

External data has become an indispensable pillar in state-of-the-art decision-making and value creation in an enterprise context. Despite the increasing motivation to use external data, information systems (IS) research still lacks an adequate data sourcing perspective. This study aims to address this gap by investigating the practical challenges in this emerging field and developing a reference process for sourcing and managing external data. To this end, we adopt a design science research approach leveraging collaboration with practitioners from nine high-profile companies. Our findings contribute to the scarce body of knowledge on data sourcing in IS by proposing explicit prescriptions in the form of a reference process for sourcing and managing external data

THE INTELLECTUAL CAPITAL AND THE CREATION OF VALUE IN RESEARCH UNITS LINKED TO THE BRAZILIAN MINISTRY OF SCIENCE TECHNOLOGY AND INNOVATION

The study investigated the understanding of the directors and managers of the Research Units (Unidades de Pesquisa – UP) linked to the Brazilian Ministry of Science, Technology and Innovation (Ministério da Ciência, Tecnologia & Inovac¸ão – MCTI) on the creation of value and innovations from Knowledge and Intellectual Capital management, analyzing their understanding of the theme and the way in which the intangible assets – mainly information and knowledge – are transformed by the UP. To develop the objective and research question, it was necessary to (i) characterize the organizational context of the UP, (ii) to discuss the applicability of the relevant conceptual framework to Knowledge Management, Intellectual Capital and Intangible Assets in order to follow the value created within the UP, (iii) demonstrate the recognition of the innovations achieved by the UP and the way they are viewed by the managers, and (iv) portray the understanding of the directors and managers about the Intellectual Capital within the unit in which they are inserted. A descriptive approach was adopted, with functionalist discussion and quantitative research to measure the relationship between the data and test hypotheses about the sample – the interdependence between value creation drivers and the differentiated perception about value creation via intangible drivers. The findings allow to affirm that there are distinct clusters of UP, with greater importance, in general, attributed to Relational Capital