Base de dados : MEDLINE
Pesquisa : L01 [Categoria DeCS]
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  1 / 409 MEDLINE  
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[PMID]:29023441
[Au] Autor:Bonham KS; Stefan MI
[Ad] Endereço:Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts, United States of America.
[Ti] Título:Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology.
[So] Source:PLoS Comput Biol;13(10):e1005134, 2017 Oct.
[Is] ISSN:1553-7358
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance.
[Mh] Termos MeSH primário: Autoria
Biologia
Biologia Computacional
Ciência da Informação
Publicações/estatística & dados numéricos
[Mh] Termos MeSH secundário: Biologia/organização & administração
Biologia/estatística & dados numéricos
Escolha da Profissão
Biologia Computacional/organização & administração
Biologia Computacional/estatística & dados numéricos
Feminino
Seres Humanos
Ciência da Informação/organização & administração
Ciência da Informação/estatística & dados numéricos
Distribuição por Sexo
Mulheres
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1710
[Cu] Atualização por classe:171105
[Lr] Data última revisão:
171105
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171013
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pcbi.1005134


  2 / 409 MEDLINE  
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[PMID]:28244257
[Au] Autor:Grant MJ
[Ad] Endereço:Health Information and Libraries Journal.
[Ti] Título:Ten years of reviews.
[So] Source:Health Info Libr J;34(1):1-4, 2017 Mar.
[Is] ISSN:1471-1842
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:The March 2017 issue of the Health Information and Libraries Journal marks the 10 year anniversary of the inaugural review published in the journal's review series. The review series was conceived to meet the growing appetite of health library and information workers to access synthesised evidence to inform their practice; something we'd already been doing to support medics in their practice. This editorial looks back on the 10 years and the inspiration which saw the development of a typology of review types and associated methodologies to address the lack of consistent guidelines on the features a review should incorporate.
[Mh] Termos MeSH primário: Ciência da Informação/normas
Revisão da Pesquisa por Pares/normas
Publicações
[Mh] Termos MeSH secundário: Seres Humanos
[Pt] Tipo de publicação:EDITORIAL
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170912
[Lr] Data última revisão:
170912
[Sb] Subgrupo de revista:H
[Da] Data de entrada para processamento:170301
[St] Status:MEDLINE
[do] DOI:10.1111/hir.12175


  3 / 409 MEDLINE  
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[PMID]:28336805
[Au] Autor:Floridi L; Taddeo M
[Ad] Endereço:Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford OX1 3JS, UK luciano.floridi@oii.ox.ac.uk.
[Ti] Título:What is data ethics?
[So] Source:Philos Trans A Math Phys Eng Sci;374(2083), 2016 Dec 28.
[Is] ISSN:1364-503X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:This theme issue has the founding ambition of landscaping as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data ethics builds on the foundation provided by computer and information ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations-the interactions among hardware, software and data-rather than on the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Because of such complexity, data ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of data science and its applications within a consistent, holistic and inclusive framework. Only as a macroethics will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments.This article is part of the themed issue 'The ethical impact of data science'.
[Mh] Termos MeSH primário: Ciência da Informação/ética
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170629
[Lr] Data última revisão:
170629
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170325
[St] Status:MEDLINE


  4 / 409 MEDLINE  
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[PMID]:28336804
[Au] Autor:Grindrod P
[Ad] Endereço:Mathematical Institute, University of Oxford, Oxford, UK grindrod@maths.ox.ac.uk.
[Ti] Título:Beyond privacy and exposure: ethical issues within citizen-facing analytics.
[So] Source:Philos Trans A Math Phys Eng Sci;374(2083), 2016 Dec 28.
[Is] ISSN:1364-503X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:We discuss the governing forces for analytics, especially concerning citizens' behaviours and their transactions, that depend on which of three of operation an institution is in (corporate, public sector/government and academic). We argue that aspirations and missions also differ by sphere even as digital spaces have drawn these spheres ever closer together. We propose that citizens' expectations and implicit permissions for any exploitation of their data require the perception of a fair balance of benefits, which should be transparent (accessible to citizens) and justifiable. We point out that within the most analytics does not concern identity, targeted marketing nor any direct interference with individual citizens; but instead it supports strategic decision-making, where the data are effectively anonymous. With the three spheres we discuss the nature of models deployed in analytics, including 'black-box' modelling uncheckable by a human mind, and the need to track the provenance and workings or models. We also examine the recent evolution of personal data, where some behaviours, or tokens, identifying individuals (unique and yet non-random) are partially and jointly owned by other individuals that are themselves connected. We consider the ability of heavily and lightly regulated sectors to increase access or to stifle innovation. We also call for clear and inclusive definitions of 'data science and analytics', avoiding the narrow claims of those in technical sub-sectors or sub-themes. Finally, we examine some examples of unethical and abusive practices. We argue for an ethical responsibility to be placed upon professional data scientists to avoid abuses in the future.This article is part of the themed issue 'The ethical impact of data science'.
[Mh] Termos MeSH primário: Ciência da Informação/ética
Privacidade
[Mh] Termos MeSH secundário: Regulamentação Governamental
Seres Humanos
Ciência da Informação/legislação & jurisprudência
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170629
[Lr] Data última revisão:
170629
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170325
[St] Status:MEDLINE


  5 / 409 MEDLINE  
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[PMID]:28336802
[Au] Autor:Vayena E; Tasioulas J
[Ad] Endereço:Health Ethics and Policy Lab, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, 8001 Zurich, Switzerland effy.vayena@uzh.ch.
[Ti] Título:The dynamics of big data and human rights: the case of scientific research.
[So] Source:Philos Trans A Math Phys Eng Sci;374(2083), 2016 Dec 28.
[Is] ISSN:1364-503X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities-the new capabilities and risks-of the rapidly evolving digital environment.This article is part of the themed issue 'The ethical impact of data science'.
[Mh] Termos MeSH primário: Direitos Humanos
Ciência da Informação
Pesquisa
[Mh] Termos MeSH secundário: Seres Humanos
Ciência da Informação/ética
Ciência da Informação/legislação & jurisprudência
Internacionalidade
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170629
[Lr] Data última revisão:
170629
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170325
[St] Status:MEDLINE


  6 / 409 MEDLINE  
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[PMID]:28336799
[Au] Autor:Leonelli S
[Ad] Endereço:Department of Sociology, Philosophy and Anthropology, Exeter Centre for the Study of the Life Sciences, University of Exeter, Byrne House, St Germans Road, EX4 4PJ Exeter, UK s.leonelli@exeter.ac.uk.
[Ti] Título:Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems.
[So] Source:Philos Trans A Math Phys Eng Sci;374(2083), 2016 Dec 28.
[Is] ISSN:1364-503X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:The distributed and global nature of data science creates challenges for evaluating the quality, import and potential impact of the data and knowledge claims being produced. This has significant consequences for the management and oversight of responsibilities and accountabilities in data science. In particular, it makes it difficult to determine who is responsible for what output, and how such responsibilities relate to each other; what 'participation' means and which accountabilities it involves, with regard to data ownership, donation and sharing as well as data analysis, re-use and authorship; and whether the trust placed on automated tools for data mining and interpretation is warranted (especially as data processing strategies and tools are often developed separately from the situations of data use where ethical concerns typically emerge). To address these challenges, this paper advocates a participative, reflexive management of data practices. Regulatory structures should encourage data scientists to examine the historical lineages and ethical implications of their work at regular intervals. They should also foster awareness of the multitude of skills and perspectives involved in data science, highlighting how each perspective is partial and in need of confrontation with others. This approach has the potential to improve not only the ethical oversight for data science initiatives, but also the quality and reliability of research outputs.This article is part of the themed issue 'The ethical impact of data science'.
[Mh] Termos MeSH primário: Ciência da Informação/ética
Internacionalidade
Conhecimento
Responsabilidade Social
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170922
[Lr] Data última revisão:
170922
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170325
[St] Status:MEDLINE


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[PMID]:28336798
[Au] Autor:Drew C
[Ad] Endereço:Cabinet Office, London, UK cat.drew@cabinetoffice.gov.uk.
[Ti] Título:Data science ethics in government.
[So] Source:Philos Trans A Math Phys Eng Sci;374(2083), 2016 Dec 28.
[Is] ISSN:1364-503X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Data science can offer huge opportunities for government. With the ability to process larger and more complex datasets than ever before, it can provide better insights for policymakers and make services more tailored and efficient. As with all new technologies, there is a risk that we do not take up its opportunities and miss out on its enormous potential. We want people to feel confident to innovate with data. So, over the past 18 months, the Government Data Science Partnership has taken an open, evidence-based and user-centred approach to creating an ethical framework. It is a practical document that brings all the legal guidance together in one place, and is written in the context of new data science capabilities. As part of its development, we ran a public dialogue on data science ethics, including deliberative workshops, an experimental conjoint survey and an online engagement tool. The research supported the principles set out in the framework as well as provided useful insight into how we need to communicate about data science. It found that people had a low awareness of the term 'data science', but that showing data science examples can increase broad support for government exploring innovative uses of data. But people's support is highly context driven. People consider acceptability on a case-by-case basis, first thinking about the overall policy goals and likely intended outcome, and then weighing up privacy and unintended consequences. The ethical framework is a crucial start, but it does not solve all the challenges it highlights, particularly as technology is creating new challenges and opportunities every day. Continued research is needed into data minimization and anonymization, robust data models, algorithmic accountability, and transparency and data security. It also has revealed the need to set out a renewed deal between the citizen and state on data, to maintain and solidify trust in how we use people's data for social good.This article is part of the themed issue 'The ethical impact of data science'.
[Mh] Termos MeSH primário: Governo
Ciência da Informação/ética
[Mh] Termos MeSH secundário: Segurança Computacional/ética
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170629
[Lr] Data última revisão:
170629
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170325
[St] Status:MEDLINE


  8 / 409 MEDLINE  
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[PMID]:28336797
[Au] Autor:Mulligan DK; Koopman C; Doty N
[Ad] Endereço:University of California, Berkeley, School of Information, and Berkeley Center for Law & Technology, Berkeley, CA 94720, USA dmulligan@berkeley.edu.
[Ti] Título:Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy.
[So] Source:Philos Trans A Math Phys Eng Sci;374(2083), 2016 Dec 28.
[Is] ISSN:1364-503X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:The meaning of privacy has been much disputed throughout its history in response to wave after wave of new technological capabilities and social configurations. The current round of disputes over privacy fuelled by data science has been a cause of despair for many commentators and a death knell for privacy itself for others. We argue that privacy's disputes are neither an accidental feature of the concept nor a lamentable condition of its applicability. Privacy is essentially contested. Because it is, privacy is transformable according to changing technological and social conditions. To make productive use of privacy's essential contestability, we argue for a new approach to privacy research and practical design, focused on the development of conceptual analytics that facilitate dissecting privacy's multiple uses across multiple contexts.This article is part of the themed issue 'The ethical impact of data science'.
[Mh] Termos MeSH primário: Ciência da Informação
Privacidade
[Mh] Termos MeSH secundário: Ciência da Informação/legislação & jurisprudência
Comunicação Interdisciplinar
Privacidade/legislação & jurisprudência
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170629
[Lr] Data última revisão:
170629
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170325
[St] Status:MEDLINE


  9 / 409 MEDLINE  
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[PMID]:28268963
[Au] Autor:Friedenberg DA; Bouton CE; Annetta NV; Skomrock N; Mingming Zhang; Schwemmer M; Bockbrader MA; Mysiw WJ; Rezai AR; Bresler HS; Sharma G
[Ti] Título:Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.
[So] Source:Conf Proc IEEE Eng Med Biol Soc;2016:3084-3087, 2016 08.
[Is] ISSN:1557-170X
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.
[Mh] Termos MeSH primário: Interfaces Cérebro-Computador
Encéfalo/fisiopatologia
Ciência da Informação/métodos
Quadriplegia/fisiopatologia
[Mh] Termos MeSH secundário: Algoritmos
Eletroencefalografia
Seres Humanos
Masculino
Córtex Motor/fisiopatologia
Processamento de Sinais Assistido por Computador
Fatores de Tempo
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1706
[Cu] Atualização por classe:171121
[Lr] Data última revisão:
171121
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170309
[St] Status:MEDLINE
[do] DOI:10.1109/EMBC.2016.7591381


  10 / 409 MEDLINE  
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[PMID]:27088862
[Au] Autor:Heneberg P
[Ad] Endereço:Third Faculty of Medicine, Charles University in Prague, Praha, Czech Republic.
[Ti] Título:From Excessive Journal Self-Cites to Citation Stacking: Analysis of Journal Self-Citation Kinetics in Search for Journals, Which Boost Their Scientometric Indicators.
[So] Source:PLoS One;11(4):e0153730, 2016.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Bibliometric indicators increasingly affect careers, funding, and reputation of individuals, their institutions and journals themselves. In contrast to author self-citations, little is known about kinetics of journal self-citations. Here we hypothesized that they may show a generalizable pattern within particular research fields or across multiple fields. We thus analyzed self-cites to 60 journals from three research fields (multidisciplinary sciences, parasitology, and information science). We also hypothesized that the kinetics of journal self-citations and citations received from other journals of the same publisher may differ from foreign citations. We analyzed the journals published the American Association for the Advancement of Science, Nature Publishing Group, and Editura Academiei Române. We found that although the kinetics of journal self-cites is generally faster compared to foreign cites, it shows some field-specific characteristics. Particularly in information science journals, the initial increase in a share of journal self-citations during post-publication year 0 was completely absent. Self-promoting journal self-citations of top-tier journals have rather indirect but negligible direct effects on bibliometric indicators, affecting just the immediacy index and marginally increasing the impact factor itself as long as the affected journals are well established in their fields. In contrast, other forms of journal self-citations and citation stacking may severely affect the impact factor, or other citation-based indices. We identified here a network consisting of three Romanian physics journals Proceedings of the Romanian Academy, Series A, Romanian Journal of Physics, and Romanian Reports in Physics, which displayed low to moderate ratio of journal self-citations, but which multiplied recently their impact factors, and were mutually responsible for 55.9%, 64.7% and 63.3% of citations within the impact factor calculation window to the three journals, respectively. They did not receive nearly any network self-cites prior impact factor calculation window, and their network self-cites decreased sharply after the impact factor calculation window. Journal self-citations and citation stacking requires increased attention and elimination from citation indices.
[Mh] Termos MeSH primário: Pesquisa Biomédica
Interpretação Estatística de Dados
Fator de Impacto de Revistas
Publicações Periódicas como Assunto
[Mh] Termos MeSH secundário: Bibliometria
Seres Humanos
Ciência da Informação
Manuscritos como Assunto
Editoração
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1609
[Cu] Atualização por classe:170220
[Lr] Data última revisão:
170220
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160419
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0153730



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