THE DARK SIDE OF PUBLISHING Butler, Declan
Nature (London),
03/2013, Volume:
495, Issue:
7442
Journal Article
Peer reviewed
Open access
According to Beall, whose list now includes more than 300 publishers, collectively issuing thousands of journals, the problem is getting worse. In one e-mail that Beall received and shared with ...Nature, a dental researcher wrote that she had submitted a paper to an open-access journal after she "was won over by the logos of affiliated databases on the home page and seemingly prestigious editorial board".
As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in ...biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs.
To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence.
A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method.
The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models.
A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.
The objective of the study was to analyze researchers’ compliance with their data availability statement (DAS) from manuscripts published in open-access journals with the mandatory DAS.
We analyzed ...all articles from 333 open-access journals published during January 2019 by BioMed Central. We categorized types of the DAS. We surveyed corresponding authors who wrote in the DAS that they would share the data. Consent to participate in the study was sought for all included manuscripts. After accessing raw data sets, we checked whether data were available in a way that enabled reanalysis.
Of 3556 analyzed articles, 3416 contained the DAS. The most frequent DAS category (42%) indicated that the data sets are available on reasonable request. Among 1792 manuscripts in which the DAS indicated that authors are willing to share their data, 1669 (93%) authors either did not respond or declined to share their data with us. Among 254 (14%) of 1792 authors who responded to our query for data sharing, only 123 (6.8%) provided the requested data.
Even when authors indicate in their manuscript that they will share data upon request, the compliance rate is the same as for authors who do not provide the DAS, suggesting that the DAS may not be sufficient to ensure data sharing.
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed ...in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured
graph
data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly
attribution
and highlight the major techniques that facilitate digging out the root cause, or the ‘why’, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.
In the late spring and summer of 2020, a local build-your-own salad restaurant chain, along with many mid-size corporations and local non-profit organizations, sent an e-mail statement in response to ...the death of George Floyd by the police. Different from corporations and large institutions, these businesses and organizations — what we collectively term the “salad group” within our sample — associated their product or service (ranging from salads to yoga mats to chocolate) with the project of creating a more local, socially just, and inclusive community. A thematic analysis of 81 crowdsourced organization e-mail messages identified the use of both internal and external appeals for action, although organizations chiefly focused on their internal actions. Our analysis revealed that these e-mails primarily offered solutions that invited or highlighted Black participation in their business enterprises. We describe such statements as salad solidarity, a genre of promotion that simultaneously appeals to consumers and social change. Indeed, the framing of possible external responses as tied to consumer choice — and internal responses as tied to a company’s growth and reach — do not directly address the structural problems that spurred these e-mail campaigns. Consequently, such corporate and digital messaging of social movements provokes questions about the commercialization of political movements and the value that language and digital tools hold in building solidarity. We conclude with observations on how e-mails, and digital platforms more broadly, can and cannot facilitate political change, from the analytical lens of racial capitalism. These findings have broader implications for the study of corporate-social responsibility, networked social movements, and mediated communication.
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