Results for 'Pusat Data Indikator'

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  1.  2
    Madilog: materialisme, dialektika, logika.Tan Malaka, Ronny Agustinus & Pusat Data Indikator - 1999 - Jakarta: Pusat Data Indikator. Edited by Ronny Agustinus.
    Philosophy of dialectical materialism and logic.
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  2.  13
    Sınıf Öğretmenlerinin Matematiksel Modellemeye İlişkin Algılarını Belirlemeye Yö.Pusat Pi̇lten - 2016 - Journal of Turkish Studies 11 (Volume 11 Issue 3):1919-1919.
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  3. The 1 law of "absolute reality"." ~, , Data", , ", , Value", , = O. &Gt, Being", & Human - manuscript
  4. B line.Kbytes Data Flash - 2009 - Nexus 2:3.
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  5.  25
    On board computing system for AMS-02 mission.Data Link Lrdl - 2005 - In Alan F. Blackwell & David MacKay (eds.), Power. Cambridge University Press. pp. x2.
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  6.  12
    Ordinary language analysis as'therapy'eugen Fischer Ludwig-maximilians-university, munich.Austin On Sense-Data - 2006 - Grazer Philosophische Studien 70 (1):67-99.
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  7. Sem tarefa.Turma Disciplina Data Entrega Tarefa, Prova Quimica, Tabela Periodica, Ed Fisica, Terminar Tarefa da, Pesquisar Sobre A. Vida Do, Educador Paulo Freire & Tarefa Guia de Estudo Pagina - 1928 - História 8 (4/09):08.
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  8. An Ecofeminist Philosophical Perspective.".Taking Empirical Data Seriously - 1997 - In Karen Warren (ed.), Ecofeminism: Women, Culture, Nature. Indiana Univ Pr.
     
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  9. Electro Industries/Gauge Tech.Power Meter & Data Acquisition Node - 2003 - Nexus 1250.
     
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  10.  22
    PMR-2450 Projeto de Máquinas agosto/2005 Professores.Julio C. Adamowski, Tarcisio Hess Coelho, Gilberto F. Martha de Souza, Cronograma de Atividades, Data Atividade Tipo de Atividade & C. N. C. Máquina - 2005 - Princípios 9:08.
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  11.  29
    What is data justice? The case for connecting digital rights and freedoms globally.Linnet Taylor - 2017 - Big Data and Society 4 (2).
    The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitly (...)
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  12. Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?Daniel Susser & Laura Y. Cabrera - 2023 - American Journal of Bioethics Neuroscience 15 (2):122-133.
    The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)
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  13. Open science, data sharing and solidarity: who benefits?Ciara Staunton, Carlos Andrés Barragán, Stefano Canali, Calvin Ho, Sabina Leonelli, Matthew Mayernik, Barbara Prainsack & Ambroise Wonkham - 2021 - History and Philosophy of the Life Sciences 43 (4):1-8.
    Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel “Open science, data sharing and solidarity: who benefits?” held at the 2021 Biennial conference of the International Society for the History, Philosophy, (...)
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  14. Privacy, Autonomy, and Personalised targeting: Rethinking How Personal Data is Used.Karina Vold & Jessica Whittlestone - 2020 - In Carissa Veliz (ed.), Report on Data, Privacy, and the Individual in the Digital Age.
    Technological advances are bringing new light to privacy issues and changing the reasons for why privacy is important. These advances have changed not only the kind of personal data that is available to be collected, but also how that personal data can be used by those who have access to it. We are particularly concerned with how information about personal attributes inferred from collected data (such as online behaviour), can be used to tailor messages and services to (...)
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  15. Precision Medicine and Big Data: The Application of an Ethics Framework for Big Data in Health and Research.G. Owen Schaefer, E. Shyong Tai & Shirley Sun - 2019 - Asian Bioethics Review 11 (3):275-288.
    As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...)
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  16.  12
    Real-world Data to Generate Evidence About Healthcare Interventions: The Application of an Ethics Framework for Big Data in Health and Research.Wendy Lipworth - 2019 - Asian Bioethics Review 11 (3):289-298.
    It is increasingly recognised that evidence generated using “real-world data” is crucial for assessing the safety and effectiveness of health-related interventions. This, however, raises a number of issues, including those related to the quality of RWD, and of the scientific methods used to generate evidence from it, and the potential for those gathering and using RWD be driven by commercial, political, professional or personal self-interest. This article is an application of the framework presented in this issue of ABR. Please (...)
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  17.  24
    What can data trusts for health research learn from participatory governance in biobanks?Richard Milne, Annie Sorbie & Mary Dixon-Woods - forthcoming - Journal of Medical Ethics.
    New models of data governance for health data are a focus of growing interest in an era of challenge to the social licence. In this article, we reflect on what the data trust model, which is founded on principles of participatory governance, can learn from experiences of involving and engagement of members of the public and participants in the governance of large-scale biobanks. We distinguish between upstream and ongoing governance models, showing how they require careful design and (...)
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  18.  18
    The Nordic data imaginary.Heta Tarkkala, Karoliina Snell & Aaro Tupasela - 2020 - Big Data and Society 7 (1).
    The Nordic countries aim to have a unique place within the European and global health data economy. They have extensive nationally maintained and centralized health data records, as well as numerous biobanks where data from individuals can be connected based on personal identification numbers. Much of this phenomenon can be attributed to the emergence and development of the Nordic welfare state, where Nordic countries sought to systematically collect large amounts of population data to guide decision making (...)
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  19. What do patterns in empirical data tell us about the structure of the world?James W. McAllister - 2011 - Synthese 182 (1):73-87.
    This article discusses the relation between features of empirical data and structures in the world. I defend the following claims. Any empirical data set exhibits all possible patterns, each with a certain noise term. The magnitude and other properties of this noise term are irrelevant to the evidential status of a pattern: all patterns exhibited in empirical data constitute evidence of structures in the world. Furthermore, distinct patterns constitute evidence of distinct structures in the world. It follows (...)
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  20.  86
    In search of ‘extra data’: Making tissues flow from personal to personalised medicine.Mette N. Svendsen & Clémence Pinel - 2021 - Big Data and Society 8 (2).
    One of the key features of the contemporary data economy is the widespread circulation of data and its interoperability. Critical data scholars have analysed data repurposing practices and other factors facilitating the travelling of data. While this approach focused on flows provides great potential, in this article we argue that it tends to overlook questions of attachment and belonging. Drawing upon ethnographic fieldwork within a Danish data-linkage infrastructure, and building upon insights from archival science, (...)
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  21.  87
    Assessing the Legitimacy of “Open” and “Closed” Data Partnerships for Sustainable Development.Erik Wetter, Mette Morsing & Andreas Rasche - 2021 - Business and Society 60 (3):547-581.
    This article examines the legitimacy attached to different types of multi-stakeholder data partnerships occurring in the context of sustainable development. We develop a framework to assess the democratic legitimacy of two types of data partnerships: open data partnerships and closed data partnerships. Our framework specifies criteria for assessing the legitimacy of relevant partnerships with regard to their input legitimacy as well as their output legitimacy. We demonstrate which particular characteristics of open and closed partnerships can be (...)
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  22.  57
    A Big-Data Approach to Understanding the Thematic Landscape of the Field of Business Ethics, 1982–2016.Ying Liu, Feng Mai & Chris MacDonald - 2019 - Journal of Business Ethics 160 (1):127-150.
    This study focuses on examining the thematic landscape of the history of scholarly publication in business ethics. We analyze the titles, abstracts, full texts, and citation information of all research papers published in the field’s leading journal, the Journal of Business Ethics, from its inaugural issue in February 1982 until December 2016—a dataset that comprises 6308 articles and 42 million words. Our key method is a computational algorithm known as probabilistic topic modeling, which we use to examine objectively the field’s (...)
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  23.  10
    Institutions, infrastructures, and data friction – Reforming secondary use of health data in Finland.Ville Aula - 2019 - Big Data and Society 6 (2).
    New data-driven ideas of healthcare have increased pressures to reform existing data infrastructures. This article explores the role of data governing institutions during a reform of both secondary health data infrastructure and related legislation in Finland. The analysis elaborates on recent conceptual work on data journeys and data frictions, connecting them to institutional and regulatory issues. The study employs an interpretative approach, using interview and document data. The results show the stark contrast between (...)
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  24.  19
    Varieties of Data-Centric Science: Regional Climate Modeling and Model Organism Research.Elisabeth Lloyd, Greg Lusk, Stuart Gluck & Seth McGinnis - 2022 - Philosophy of Science 89 (4):802-823.
    Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric (...)
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  25.  14
    What Are Data Good for Anyway?Nahshon Perez - 2020 - Social Theory and Practice 46 (2):339-364.
    This article develops a typology of usages for empirical data in normative theorizing in ‎contemporary political theory. A typology of usages is indicated, providing definitions, ‘names’ and an analysis for each ‎usage, and points to the typical stage within political theory research for each usage. The typology is built in a casuistic methodology. It includes the following categories: Spotlighting, Definition, ‎‎ Conversion, Institutional clarity, Theoretical clarity, and Theory improvement. The typology creates a novel toolbox that can be adopted by (...)
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  26. No wisdom in the crowd: genome annotation at the time of big data - current status and future prospects.Antoine Danchin - 2018 - Microbial Biotechnology 11 (4):588-605.
    Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in (...)
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  27.  12
    Big Bad Data: Law, Public Health, and Biomedical Databases.Sharona Hoffman & Andy Podgurski - 2013 - Journal of Law, Medicine and Ethics 41 (s1):56-60.
    The accelerating adoption of electronic health record systems will have profound impacts on clinical care. It will also have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs.Much has been written about the risk of EHR privacy breaches. (...)
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  28.  15
    Data privacy protection in scientific publications: process implementation at a pharmaceutical company.Friedrich Maritsch, Ingeborg Cil, Colin McKinnon, Jesse Potash, Nicole Baumgartner, Valérie Philippon & Borislava G. Pavlova - 2022 - BMC Medical Ethics 23 (1):1-10.
    Background Sharing anonymized/de-identified clinical trial data and publishing research outcomes in scientific journals, or presenting them at conferences, is key to data-driven scientific exchange. However, when data from scientific publications are linked to other publicly available personal information, the risk of reidentification of trial participants increases, raising privacy concerns. Therefore, we defined a set of criteria allowing us to determine and minimize the risk of data reidentification. We also implemented a review process at Takeda for clinical (...)
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  29.  9
    Data Stream Quality Evaluation for the Generation of Alarms in the Health Domain.Adriana Marotta, Joaquín Fleitas & Saúl Fagúndez - 2015 - Journal of Intelligent Systems 24 (3):361-369.
    The use of sensors has had an enormous increment in the last years, becoming a valuable tool in many different areas. In this kind of scenario, the quality of data becomes an extremely important issue; however, not much attention has been paid to this specific topic, with only a few existing works that focus on it. In this paper, we present a proposal for managing data streams from sensors that are installed in patients’ homes in order to monitor (...)
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  30.  13
    Arche-writing and data-production in theory-oriented scientific practice: the case of free-viewing as experimental system to test the temporal correlation hypothesis.Juan Felipe Espinosa Cristia, Carla Fardella & Juan Manuel Garrido Wainer - 2021 - History and Philosophy of the Life Sciences 43 (2):1-27.
    Data production in experimental sciences depends on localised experimental systems, but the epistemic properties of data transcend the contingencies of the processes that produce them. Philosophers often believe that experimental systems instantiate but do not produce the epistemic properties of data. In this paper, we argue that experimental systems' local functioning entails intrinsic capacities to produce the epistemic properties of data. We develop this idea by applying Derrida's model of arche-writing to study a case of theory-oriented (...)
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  31.  13
    Research using free text data in medical records could benefit from dynamic consent and other tools for responsible governance.Michael Morrison - 2020 - Journal of Medical Ethics 46 (6):380-381.
    As the capacity to generate, store, aggregate and combine ever greater volumes and types of data about individuals, behaviours and interactions continues to expand apace, so too does the challenge of ensuring suitable and appropriate governance of that data. In broad terms, the challenge is simple; how to ensure the benefits of data, such as improvements in service delivery or individual and collective well-being, while avoiding harms such as discrimination, injustice or placing undue burdens on individuals and (...)
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  32.  20
    Local Model-Data Symbiosis in Meteorology and Climate Science.Wendy Parker - 2020 - Philosophy of Science 87 (5):807-818.
    I introduce a distinction between general and local model-data symbiosis and offer three examples of local symbiosis in the fields of meteorology and climate science. Local model-data symbiosis ref...
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  33.  29
    Automated vehicles, big data and public health.David Shaw, Bernard Favrat & Bernice Elger - 2020 - Medicine, Health Care and Philosophy 23 (1):35-42.
    In this paper we focus on how automated vehicles can reduce the number of deaths and injuries in accident situations in order to protect public health. This is actually a problem not only of public health and ethics, but also of big data—not only in terms of all the different data that could be used to inform such decisions, but also in the sense of deciding how wide the scope of data should be. We identify three key (...)
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  34.  75
    Stopping rules and data monitoring in clinical trials.Roger Stanev - 2012 - In H. W. de Regt, S. Hartmann & S. Okasha (eds.), EPSA Philosophy of Science: Amsterdam 2009, The European Philosophy of Science Association Proceedings Vol. 1, 375-386. Springer. pp. 375--386.
    Stopping rules — rules dictating when to stop accumulating data and start analyzing it for the purposes of inferring from the experiment — divide Bayesians, Likelihoodists and classical statistical approaches to inference. Although the relationship between Bayesian philosophy of science and stopping rules can be complex (cf. Steel 2003), in general, Bayesians regard stopping rules as irrelevant to what inference should be drawn from the data. This position clashes with classical statistical accounts. For orthodox statistics, stopping rules do (...)
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  35.  4
    Facing Big Data: Making sociology relevant.Sophie Mützel - 2015 - Big Data and Society 2 (2).
    Working with computational methods and large textual analysis has been challenging and very rewarding—with all the ups and downs that doing empirical social research entails. In my contribution, I relate some research experiences and reflect upon data construction and the links between theory, data, and methods.
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  36.  19
    Hypocrisy Around Medical Patient Data: Issues of Access for Biomedical Research, Data Quality, Usefulness for the Purpose and Omics Data as Game Changer.Erwin Tantoso, Wing-Cheong Wong, Wei Hong Tay, Joanne Lee, Swati Sinha, Birgit Eisenhaber & Frank Eisenhaber - 2019 - Asian Bioethics Review 11 (2):189-207.
    Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. (...)
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  37. Would you mind being watched by machines? Privacy concerns in data mining.Vincent C. Müller - 2009 - AI and Society 23 (4):529-544.
    "Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis will (...)
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  38.  17
    Data conflict in a multinomial decision task.David W. Martin - 1969 - Journal of Experimental Psychology 82 (1p1):4.
  39. Ontology-based knowledge representation of experiment metadata in biological data mining.Scheuermann Richard, Kong Megan, Dahlke Carl, Cai Jennifer, Lee Jamie, Qian Yu, Squires Burke, Dunn Patrick, Wiser Jeff, Hagler Herb, Herb Hagler, Barry Smith & David Karp - 2009 - In Jake Chen & Stefano Lonardi (eds.), Biological Data Mining. Boca Raton: Chapman Hall / Taylor and Francis. pp. 529-559.
    According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...)
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  40. Social Implications of Big Data and Fog Computing.Jeremy Horne - 2018 - International Journal of Fog Computing 1 (2):50.
    In the last half century we have gone from storing data on 5-1/4 inch floppy diskettes to cloud and now fog computing. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects but why is there so much data on us? Then, we ask about its “interface” through fog computing. Such questions prompt this chapter on the philosophy of big data and fog computing. After (...)
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  41.  21
    The latent space of data ethics.Enrico Panai - forthcoming - AI and Society:1-19.
    In informationally mature societies, almost all organisations record, generate, process, use, share and disseminate data. In particular, the rise of AI and autonomous systems has corresponded to an improvement in computational power and in solving complex problems. However, the resulting possibilities have been coupled with an upsurge of ethical risks. To avoid the misuse, underuse, and harmful use of data and data-based systems like AI, we should use an ethical framework appropriate to the object of its reasoning. (...)
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  42. Political communication in Social Networks Election campaigns and digital data analysis: a bibliographic review.Luca Corchia - 2019 - Rivista Trimestrale di Scienza Dell’Amministrazione (2):1-50.
    The outcomes of a bibliographic review on political communication, in particular electoral communication in social networks, are presented here. The electoral campaigning are a crucial test to verify the transformations of the media system and of the forms and uses of the linguistic acts by dominant actors in public sphere – candidates, parties, journalists and Gatekeepers. The aim is to reconstruct the first elements of an analytical model on the transformations of the political public sphere, with which to systematize the (...)
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  43.  51
    Using first-person data about consciousness.Maja Spener - 2011 - Journal of Consciousness Studies 18 (1):165-179.
    In Describing Inner Experience, Hurlburt and Schwitzgebel explore the proper limits of scepticism about consciousness and the prospect of a scientific investigation of consciousness. Their debate with each other focuses on the question about whether we can trust people's reports about their inner experiences and on Hurlburt's introspective method, DES. I point out that their discussion leaves unclear the crucial question of the aims and objectives of DES. This makes it difficult genuinely to assess DES's merits and the problems for (...)
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  44.  43
    Evidentiary standards and animal data.Kristin Shrader-Frechette - unknown
    Those who wish to deny some instance of environmental injustice often attempt to place inappropriate evidentiary burdens on scientists who show disproportionate pollution effects on vulnerable populations. One such evidentiary standard is the epidemiological-evidence rule (EER). According to EER, legitimate causal inferences about pollution-related harm (and actions to reduce probable environmental injustice) require human-epidemiological data, not merely good animal or laboratory data. This article summarizes the grounds for supporting EER, evaluates central scientific problems with EER, assesses key ethical (...)
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  45.  20
    Researchers' Duty to Share Pre-publication Data: From the Prima Facie Duty to Practice.Christoph Schickhardt, Nelson Hosley & Eva C. Winkler - 2016 - In Mittelstadt Brent & Floridi Luciano (eds.), The ethics of biomedical big data. Springer. pp. 309-337.
    The purpose of this chapter is to offer an ethical investigation into whether researchers have a duty to share pre-published bio-medical data with the scientific community. The central questions of the chapter are the following: do researchers have a prima facie duty to share pre-published data? And if so, what stakes and aspects of a concrete situation need to be taken into consideration in order to assess whether and to what extent researchers’ prima facie duty to share (...) applies? We will argue that based upon their basic duties to benefit society and to promote scientific knowledge, researchers have a prima facie duty to share data. We will also argue that in order to determine whether the prima facie duty applies in practice it is indispensable to take into account the stakes of the persons concerned as well as context dependent aspects. The chapter’s overall goal is to build an analytical and ethical framework that helps to assess with regard to concrete situations whether researchers’ duty to share data applies. To this end we analyse the concept of data sharing and clarify what data sharing might imply in practice. To offer an overview of the different stakeholders’ concerns we will analyse the normative-informational environment in which data producing researchers (to whom the prima facie duty to share data applies) are usually situated. In the last step we focus on the ethically relevant context dependent aspects and illustrate how they affect researchers’ prima facie duty to share data and stakeholders’ potentially conflicting stakes. (shrink)
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  46. Accessing Online Data for Youth Mental Health Research: Meeting the Ethical Challenges.Elvira Perez Vallejos, Ansgar Koene, Christopher James Carter, Daniel Hunt, Christopher Woodard, Lachlan Urquhart, Aislinn Bergin & Ramona Statache - 2019 - Philosophy and Technology 32 (1):87-110.
    This article addresses the general ethical issues of accessing online personal data for research purposes. The authors discuss the practical aspects of online research with a specific case study that illustrates the ethical challenges encountered when accessing data from Kooth, an online youth web-counselling service. This paper firstly highlights the relevance of a process-based approach to ethics when accessing highly sensitive data and then discusses the ethical considerations and potential challenges regarding the accessing of public data (...)
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  47.  24
    Process-Sensitive Naming: Trait Descriptors and the Shifting Semantics of Plant (Data) Science.Sabina Leonelli - 2022 - Philosophy, Theory, and Practice in Biology 14 (16).
    This paper examines classification practices in the domain of plant data semantics, and particularly methods used to label plant traits to foster the collection, management, linkage and analysis of data about crops across locations—which crucially inform research and interventions on plants and agriculture. The efforts required to share data place in sharp relief the forms of diversity characterizing the systems used to capture the biological and environmental characteristics of plant variants: particularly the biological, cultural, scientific and semantic (...)
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  48.  3
    Cross-jurisdictional Data Transfer in Health Research: Stakeholder Perceptions on the Role of Law.Hui Yun Chan, Hui Jin Toh & Tamra Lysaght - forthcoming - Asian Bioethics Review:1-20.
    Large data-intensive health research programmes benefit from collaboration amongst researchers who may be located in different institutions and international contexts. However, complexities in navigating privacy frameworks and data protection laws across various jurisdictions pose significant challenges to researchers seeking to share or transfer data outside of institutional boundaries. Research on the awareness of data protection and privacy laws amongst stakeholders is limited. Our qualitative study, drawn from a larger project in Singapore, revealed insights into stakeholders’ perceptions (...)
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  49.  6
    Squeaky wheels: Missing data, disability, and power in the smart city.Arielle Alferez, Amy Lobben & Shiloh Deitz - 2021 - Big Data and Society 8 (2).
    Data about the accessibility of United States municipalities is infrastructure in the smart city. What is counted and how, reflects the sociotechnical imaginary of a time or place. In this paper we focus on features identified by people with disabilities as promoting or hindering safe pedestrian travel. We use a regionally stratified sample of 178 cities across the United States. The municipalities were scored on two factors: their open data practices, and the degree to which they cataloged the (...)
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  50.  32
    The Tension Between Big Data and Theory in the "Omics" Era of Biomedical Research.Sui Huang - 2018 - Perspectives in Biology and Medicine 61 (4):472-488.
    [Without a theorising], a man might as well go into a gravel-pit and count the pebbles and describe the colours. How odd it is that anyone should not see that all observation must be for or against some view if it is to be of any service!Recent years have seen a steady shift of funding programs in biomedical research towards data collection, analysis, and management and its computational analysis. A sizable majority of new major funding opportunity announcements call for (...)
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