Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The ...complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making.
Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.
The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.
CRISP-DM(CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still ...the de facto standard for developing data mining and knowledge discovery projects. However, undoubtedly the field has moved on considerably in twenty years, with data science now the leading term being favoured over data mining . In this paper we investigate whether, and in what contexts, CRISP-DM is still fit for purpose for data science projects. We argue that if the project is goal-directed and process-driven the process model view still largely holds. On the other hand, when data science projects become more exploratory the paths that the project can take become more varied, and a more flexible model is called for. We suggest what the outlines of such a trajectory-based model might look like and how it can be used to categorise data science projects (goal-directed, exploratory or data management). We examine seven real-life exemplars where exploratory activities play an important role and compare them against 51 use cases extracted from the NIST Big Data Public Working Group. We anticipate this categorisation can help project planning in terms of time and cost characteristics.
This consensus statement reflects the deliberations of an international group of stakeholders with a range of expertise in public involvement and engagement (PI&E) relating to data-intensive health ...research. It sets out eight key principles to establish a secure role for PI&E in and with the research community internationally and ensure best practice in its execution. Our aim is to promote culture change and societal benefits through ensuring a socially responsible trajectory for innovations in this field.
Our key premise is that the public should not be characterised as a problem to be overcome but a key part of the solution to establish socially beneficial data-intensive health research for all.
Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the ...characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.
ZusammenfassungEin Anspruch des mathematischen Modellierungsunterrichts in der Schule sollte es sein, besonders aktuelle Probleme und interessante neue Technologien aus dem Alltag der Schüler/innen ...einzubeziehen. Dies gilt insbesondere, wenn sie eine didaktische Reduktion auf elementare (schul-)mathematische Inhalte leicht zulassen. Künstliche Intelligenz (KI) zieht sich durch verschiedene Bereiche von Wissenschaft und Technik und verbirgt sich insbesondere hinter zahlreichen Anwendungen unseres Alltags.In diesem Beitrag wird diskutiert, wie ein zeitgemäßer Mathematikunterricht durch die Modellierung realer, schülernaher Probleme aus dem Bereich KI bereichert werden kann. Dazu werden zwei Methoden und deren didaktische Reduktion für den Einsatz in einem computergestützten Mathematikunterricht vorgestellt.Bei der problemorientierten Diskussion beider Methoden werden zwei alltägliche Problemstellungen in den Blick genommen: Zum einen Klassifizierungsprobleme und deren Lösung mithilfe der sogenannten Stützvektormethode (SVM), die auf der Berechnung des Abstandes von Punkten zu Hyperebenen beruht; zum anderen Empfehlungssysteme, die auf einer Matrix-Faktorisierung basieren können.Zu beiden Problemstellungen wurden digitale Lernmaterialien für Oberstufenschüler/innen entwickelt, die im Rahmen von eintägigen Workshops zur mathematischen Modellierung bereits mehrfach erprobt wurden. Die digitale Umsetzung als Jupyter Notebooks wird abschließend beschrieben und steht den Leser/innen als Open Educational Resources unter einer Creative Commons Lizenz zur Verfügung.
The Centre for Data and Knowledge Integration for Health (CIDACS) was created in 2016 in Salvador, Bahia-Brazil with the objective of integrating data and knowledge aiming to answer scientific ...questions related to the health of the Brazilian population. This article details our experiences in the establishment and operations of CIDACS, as well as efforts made to obtain high-quality linked data while adhering to security, ethical use and privacy issues. Every effort has been made to conduct operations while implementing appropriate structures, procedures, processes and controls over the original and integrated databases in order to provide adequate datasets to answer relevant research questions. Looking forward, CIDACS is expected to be an important resource for researchers and policymakers interested in enhancing the evidence base pertaining to different aspects of health, in particular when investigating, from a nation-wide perspective, the role of social determinants of health and the effects of social and environmental policies on different health outcomes.
New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in ...the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking informed consent. While such practice may not be at odds with Twitter’s terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in ‘legal’ accounts of the permissible use of these data in research publications. To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms.
Data Science Nasution, Mahyuddin K M; Salim Sitompul, Opim; Budhiarti Nababan, Erna
Journal of physics. Conference series,
06/2020, Volume:
1566, Issue:
1
Journal Article
Peer reviewed
Open access
The presence of new science does not necessarily occur just like that. Every science starts from interests, discussion, and looks for a basic foundation, but in general the main foundation of science ...is mathematics. Data science includes structured and systematic knowledge about data. However, many other sciences that has a relationship with the data in question, ranging from statistics to computer science. This paper aims to reveal the obstacle and limitations of other science into a data science completely, on that basis the definition of data sciences needs to be elaborated, then confirm data science as new science and not depend directly on several other sciences.
PDF
