Search (429 results, page 2 of 22)

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Date

22. 5.2021 12:43:05

Source

Open Password. 2021, Nr.935 vom 16.06.2021 [https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzMwNSwiMjNiZDFkOWY4Nzg5IiwwLDAsMjc1LDFd]

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Abstract

This article is the first in a trilogy about the concept "science". Section 1 considers the historical development of the meaning of the term science and shows its close relation to the terms "knowl­edge" and "philosophy". Section 2 presents four historic phases in the basic conceptualizations of science (1) science as representing absolute certain of knowl­edge based on deductive proof; (2) science as representing absolute certain of knowl­edge based on "the scientific method"; (3) science as representing fallible knowl­edge based on "the scientific method"; (4) science without a belief in "the scientific method" as constitutive, hence the question about the nature of science becomes dramatic. Section 3 presents four basic understandings of the scientific method: Rationalism, which gives priority to a priori thinking; empiricism, which gives priority to the collection, description, and processing of data in a neutral way; historicism, which gives priority to the interpretation of data in the light of "paradigm" and pragmatism, which emphasizes the analysis of the purposes, consequences, and the interests of knowl­edge. The second article in the trilogy focus on different fields studying science, while the final article presets further developments in the concept of science and the general conclusion. Overall, the trilogy illuminates the most important tensions in different conceptualizations of science and argues for the role of information science and knowl­edge organization in the study of science and suggests how "science" should be understood as an object of research in these fields.

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Abstract

This study examines trends in open access article processing charges (APCs) from 2011 to 2021, building on a 2011 study by Solomon and Björk. Two methods are employed, a modified replica and a status update of the 2011 journals. Data are drawn from multiple sources and datasets are available as open data. Most journals do not charge APCs; this has not changed. The global average per-journal APC increased slightly, from 906 to 958 USD, while the per-article average increased from 904 to 1,626 USD, indicating that authors choose to publish in more expensive journals. Publisher size, type, impact metrics and subject affect charging tendencies, average APC, and pricing trends. Half the journals from the 2011 sample are no longer listed in DOAJ in 2021, due to ceased publication or publisher de-listing. Conclusions include a caution about the potential of the APC model to increase costs beyond inflation. The university sector may be the most promising approach to economically sustainable no-fee OA journals. Universities publish many OA journals, nearly half of OA articles, tend not to charge APCs and when APCs are charged, the prices are very low on average.

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Abstract

Conceptual models are artifacts representing conceptualizations of particular domains. Hence, multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language's constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. However, to support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis by machines, these catalogs must be built following generally accepted quality requirements for scientific data management. Especially, all scientific (meta)data-including models-should be created using the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. In this initial release, the catalog includes over a hundred models, developed in a variety of contexts and domains. The paper also discusses the research implications for (ontology-driven) conceptual modeling of such a resource.

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Abstract

In recent years, cross-modal retrieval has been a popular research topic in both fields of computer vision and natural language processing. There is a huge semantic gap between different modalities on account of heterogeneous properties. How to establish the correlation among different modality data faces enormous challenges. In this work, we propose a novel end-to-end framework named Dual Multi-Angle Self-Attention (DMASA) for cross-modal retrieval. Multiple self-attention mechanisms are applied to extract fine-grained features for both images and texts from different angles. We then integrate coarse-grained and fine-grained features into a multimodal embedding space, in which the similarity degrees between images and texts can be directly compared. Moreover, we propose a special multistage training strategy, in which the preceding stage can provide a good initial value for the succeeding stage and make our framework work better. Very promising experimental results over the state-of-the-art methods can be achieved on three benchmark datasets of Flickr8k, Flickr30k, and MSCOCO.

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Abstract

This text proposes a multidisciplinary reflection on the subject of ethics, based on philosophical approaches, using Spinoza's work, Ethics, as a foundation. The power of Spinoza's geometric reasoning and deterministic logic, compatible with formal grammars and programming languages, provides a favorable framework for this purpose. In an information society characterized by an abundance of data and a diversity of perspectives, complex thinking is an essential tool for developing an ethical construct that can deal with the uncertainty and contradictions in the field. Acknowledging the natural complexity of ethics in interpersonal relationships, the use of AI techniques appears unavoidable. Artificial intelligence in KOS offers the potential for processing complex questions through the formal modeling of concepts in ethical discourse. By formalizing problems, we hope to unleash the potential of ethical analysis; by addressing complexity analysis, we propose a mechanism for understanding problems and empowering solutions.

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Abstract

Open data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research.

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Abstract

The data paper, an emerging scholarly genre, describes research data sets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this article addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility. Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research articles and they describe data-centric research processes in a through way. This article also discusses how our results could inform a better data publication ecosystem in the future.

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Abstract

This article presents and discusses different kinds of data documents, including data sets, data studies, data papers and data journals. It provides descriptive and bibliometric data on different kinds of data documents and discusses the theoretical and philosophical problems by classifying documents according to the DIKW model (data documents, information documents, knowl­edge documents and wisdom documents). Data documents are, on the one hand, an established category today, even with its own data citation index (DCI). On the other hand, data documents have blurred boundaries in relation to other kinds of documents and seem sometimes to be understood from the problematic philosophical assumption that a datum can be understood as "a single, fixed truth, valid for everyone, everywhere, at all times".

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Abstract

This paper presents findings from an interview study of research data managers in academic data archives. Our study examined policies and professional autonomy with a focus on dilemmas encountered in everyday work by data managers. We found that dilemmas arose at every stage of the research data lifecycle, and legacy data presents particularly vexing challenges. The iFields' emphasis on knowledge organization and representation provides insight into how data, used by scientists, are used to create knowledge. The iFields' disciplinary emphasis also encompasses the sociotechnical complexity of dilemmas that we found arise in research data management. Therefore, we posit that iSchools are positioned to contribute to data science education by teaching about ethics and infrastructure used to collect, organize, and disseminate data through problem-based learning.

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Abstract

Purpose The purpose of this paper is to propose a theoretical framework to illustrate the archival knowledge applied by archivists in their personal archiving (PA) and the mechanism of the application of archival knowledge in their PA. Design/methodology/approach The grounded theory methodology was adopted. For data collection, in-depth interviews were conducted with 21 archivists in China. Data analysis was performed using the open coding, axial coding and selective coding to organise the archival knowledge composition of PA and develops the awareness-knowledge-action (AKA) integration model of archival knowledge application in the field of PA, according to the principles of the grounded theory. Findings The archival knowledge involved in the field of PA comprises four principal categories: documentation, arrangement, preservation and appraisal. Three interactive factors involved in archivists' archival knowledge application in the field of PA behaviour: awareness, knowledge and action, which form a pattern of awareness leading, knowledge guidance and action innovation, and archivists' PA practice is flexible and innovative. The paper underscored that it is need to improve archival literacy among general public. Originality/value The study constructs a theoretical framework to identify the specialised archival knowledge and skills of PA which is able to provide solutions for non-specialist PA and develops an AKA model to explain the interaction relationships between awareness, knowledge and action in the field of PA.

Date

22. 1.2021 14:20:27

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Abstract

The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long-term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of "at-risk" students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real-world education datasets demonstrate that the MASTER framework outperforms other state-of-art methods.

Date

27.12.2022 18:34:22

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Abstract

Lena Forsén, the real human behind the Lenna image, was first published in Playboy in 1972. Soon after, USC engineers searching for a suitable test image for their image processing research sought inspiration from the magazine. They deemed Lenna the right fit and scanned the image into digital, RGB existence. From here, the story of the image follows the story of the internet. Lenna was one of the first inhabitants of ARPANet, the internet's predecessor, and then the world wide web. While the image's reach was limited to a few research papers in the '70s and '80s, in 1991, Lenna was featured on the cover of an engineering journal alongside another popular test image, Peppers. This caught the attention of Playboy, which threatened a copyright infringement lawsuit. Engineers who had grown attached to Lenna fought back. Ultimately, they prevailed, and as a Playboy VP reflected on the drama: "We decided we should exploit this because it is a phenomenon." The Playboy controversy canonized Lenna in engineering folklore and prompted an explosion of conversation about the image. Image hits on the internet rose to a peak number in 1995.

Content

"Having known Lenna for almost a decade, I have struggled to understand what the story of the image means for what tech culture is and what it is becoming. To me, the crux of the Lenna story is how little power we have over our data and how it is used and abused. This threat seems disproportionately higher for women who are often overrepresented in internet content, but underrepresented in internet company leadership and decision making. Given this reality, engineering and product decisions will continue to consciously (and unconsciously) exclude our needs and concerns. While social norms are changing towards non-consensual data collection and data exploitation, digital norms seem to be moving in the opposite direction. Advancements in machine learning algorithms and data storage capabilities are only making data misuse easier. Whether the outcome is revenge porn or targeted ads, surveillance or discriminatory AI, if we want a world where our data can retire when it's outlived its time, or when it's directly harming our lives, we must create the tools and policies that empower data subjects to have a say in what happens to their data. including allowing their data to die."

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Abstract

The importance of involving humans in the data science process has been widely discussed in the literature. However, studies lack details on how to involve humans in the process. Using a design science approach, this paper proposes and evaluates a human-supervised data science framework in the context of local governments. Our findings suggest that the involvement of a stakeholder group, public managers in this case, in the process of data science project enhanced quality of data science outcomes. Public managers' detailed knowledge on both the data and context was beneficial for improving future data science infrastructure. In addition, the study suggests that local governments can harness the value of data-driven approaches to policy and decision making through focalized investments in improving data and data science infrastructure, which includes culture and processes necessary to incorporate data science and analytics into the decision-making process.

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Abstract

The benefits of open data for helping to address societal problems and strengthen communities are well recognized, and unfortunately previous studies found that smaller communities are often excluded from the current data ecosystem because of existing technological, technical, cognitive, and practical barriers. This study aims to investigate the process of communities' data use for community development and decision-making-focusing on the opportunities and challenges of data for communities. From the interviews with 25 staff from community-based organizations (CBOs) in nine small, medium, and large cities in the United States, the findings of this study describe data's role in supporting communities' development while reporting several major challenges that hinder CBOs data use: difficulty accessing data, limitations of open data (un-local nature, excluding essential data from being open), limited data capacity (especially in data literacy skills), and difficulties using and accessing existing data infrastructures. Our findings suggest opportunities for addressing these challenges, particularly by creating educational programming, building partnerships within data ecosystems, and bringing community voices forward in current data ecosystems, which are critical to realizing data's potential for all citizens.

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Abstract

Data Science can be considered a technique or a science. As a technique, it is more interested in the "what" than in the "why" of data. It does not need theories that explain how things work, it just needs the results. As a science, however, working strictly from data and without theories contradicts the post-empiricist view of science. In this view, theories come before data and data is used to corroborate or falsify theories. Nevertheless, one of the most controversial statements about Data Science is that it is a science that can work without theories. In this conceptual paper, we focus on the science aspect of Data Science. How is Data Science as a science? We propose a three-phased view of Data Science that shows that different theories have different roles in each of the phases we consider. We focus on when theories are used in Data Science rather than the controversy of whether theories are used in Data Science or not. In the end, we will see that the statement "Data Science works without theories" is better put as "in some of its phases, Data Science works without the theories that originally motivated the creation of the data."

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Abstract

There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing "negative" citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation-based scores, for instance, the h-index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of "negative" citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated.

Date

6. 1.2020 18:10:22

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Abstract

Purpose The purpose of the paper is to analyse the interactions of bridging users in Twitter discussions about vaccination. Design/methodology/approach Conversational threads were collected through filtering the Twitter stream using keywords and the most active participants in the conversations. Following data collection and anonymisation of tweets and user profiles, a retweet network was created to find users bridging the main clusters. Four conversations were selected, ranging from 456 to 1,983 tweets long, and then analysed through content analysis. Findings Although different opinions met in the discussions, a consensus was rarely built. Many sub-threads involved insults and criticism, and participants seemed not interested in shifting their positions. However, examples of reasoned discussions were also found. Originality/value The study analyses conversations on Twitter, which is rarely studied. The focus on the interactions of bridging users adds to the uniqueness of the paper.

Date

20. 1.2015 18:30:22

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Abstract

Purpose The purpose of the paper is to analyse the interactions of bridging users in Twitter discussions about vaccination. Design/methodology/approach Conversational threads were collected through filtering the Twitter stream using keywords and the most active participants in the conversations. Following data collection and anonymisation of tweets and user profiles, a retweet network was created to find users bridging the main clusters. Four conversations were selected, ranging from 456 to 1,983 tweets long, and then analysed through content analysis. Findings Although different opinions met in the discussions, a consensus was rarely built. Many sub-threads involved insults and criticism, and participants seemed not interested in shifting their positions. However, examples of reasoned discussions were also found. Originality/value The study analyses conversations on Twitter, which is rarely studied. The focus on the interactions of bridging users adds to the uniqueness of the paper.

Date

20. 1.2015 18:30:22

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Date

22. 5.2021 12:43:05

Source

Open Password. 2021, Nr.928 vom 31.05.2021 [https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzI5OSwiMjc2N2ZlZjQwMDUwIiwwLDAsMjY4LDFd]