1,720,978 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Are anonymised databases truly anonymous? An introduction
Full text linkRecently, there has been an increased number of requests from funders, regulators and publishers, for clinical researchers to share their research data with others, within the existing legal framework, once the primary analysis has been completed. Existing research data could be used to expand medical and scientific knowledge by investigating questions outside the original study scope. It could be used to facilitate individual participant data (IPD) meta-analysis, verify results and to investigate novel methodologies for data analysis. IPD can only be shared if it is fully anonymised. However, it is hard to completely anonymise data while still leaving it in an analysable and usable form. Methods have been developed for data anonymization but it is not known whether study participants could potentially be re-identified, and in what circumstances re-identification is more likely. For example, studies are increasingly using social media, through which it is possible that study participants may unwittingly identify themselves or others.
A video of this presentation can be viewed at https://media.ed.ac.uk/media/0_wmufn83
Are anonymised datasets from clinical trials truly anonymous?
No full textBACKGROUND:
Funders, regulators and publishers are increasingly requesting that clinical trial researchers share their research data with others, once the primary analysis has been completed. Existing clinical trial data could significantly contribute to expanding medical and scientific knowledge by investigating questions beyond the original study scope, facilitating individual participant data (IPD) meta-analysis, verifying results, and exploring novel methodologies for data analysis. Anonymisation of IPD before sharing can offer a way to safeguard participants' privacy. While there are several recommendations and guidance available for attempting data anonymisation prior to sharing, completely anonymising data while keeping it usable remains challenging. Moreover, many anonymised datasets are already publicly available for secondary research. However, it remains unclear whether study participants could potentially be at risk of re-identification, and under what circumstances re-identification is more likely to occur.
METHODS:
In the first phase of this PhD research, a systematic scoping review was conducted to gather publications that reported recommendations on anonymisation for enabling data sharing from clinical trials, to understand what guidance was available to researchers and how publicly available anonymised datasets from clinical trials might have been compiled. Two reviewers, Aryelly Rodriguez with Chris Tuck or Alastair Murray independently assessed titles, abstracts, and full texts for eligibility. One reviewer extracted data from selected papers using thematic synthesis, which was then reviewed by a second reviewer for accuracy. Results were summarised through narrative analysis. Moving on to the second phase, I collected a broad selection of publicly available anonymised datasets that have been made available for research purposes extending beyond their original scope, to explore the characteristics of these anonymised datasets, assess the feasibility of applying re-identification risk scores to them, and determine how these scores could be useful. I estimated their re-identification risk scores with three equations designed for calculation of such scores based on the information in the entire dataset. These equations are commonly applied to routinely collected health records and only generate numerical values ranging from 0 (lowest risk) to 1 (maximum risk), without attempting to re-identify individuals within the datasets. Subsequently, I calculated the re-identification risk scores for each dataset, using the three equations. This analysis explored the characteristics of the datasets associated with increased or decreased risk scores, and compared the risk scores to evaluate their practicality for implementation. In the third and final phase of this PhD research, I used an online exploratory cross-sectional descriptive survey that consisted of both open-ended and closed questions to gather the UK researchers’ views regarding their experiences with the de-identification, anonymisation, release methods and re-identification risk estimation for clinical trials datasets.
RESULTS:
The systematic scoping review identified 59 eligible articles (from 43 studies) for inclusion. From these articles, three distinct themes emerged: anonymisation, de-identification and pseudonymisation. The articles also showed that the most commonly recommended anonymisation techniques are removal of direct participant identifiers, and the careful evaluation and modification of indirect identifiers to minimise the risk of identification. Anonymisation of datasets in conjunction with controlled access was the most recommended method for data sharing.
For the next phase, I contacted data holders and followed their local procedures to access the anonymised datasets. I identified 86 potentially eligible datasets from 18 repositories and successfully secured 76 of them. After full evaluation, 70 datasets met the inclusion criteria and were included in the analysis, representing 14 out of the 18 repositories. Thirty-one datasets were shared with minimal restrictions (open access), while 39 were shared with varying levels of restrictions before access was granted (controlled access). Datasets had, on average, four identifiers and mean risk scores ranging from 0.47 to 0.91. The most common pieces of information present in the datasets that, when combined, may indirectly identify a participant were sex (80%) and age (72.9%). For the final phase, the exploratory survey had 38 responses to invitation from June 2022 to October 2022. Thirty-five participants (92%) used internal documentation, institutional standard operating procedures and/or published guidance to de-identify/anonymise clinical trials datasets. De-identification followed by anonymisation and then fulfilling data holders’ requirements before access was granted (controlled access) was the most common process for releasing the datasets as reported by 18 (47%) participants. Eleven participants (29%) had previous knowledge of re-identification risk estimation but had not used this. Experiences in the process of de-identifying/anonymising the datasets and maintaining such datasets were mostly negative, the main reported issues were lack of resources, guidance, and training.
CONCLUSIONS:
There is no single standardised set of recommendations on how to anonymise clinical trial datasets for sharing. However, the systematic scoping review showed a developing consensus on techniques used to achieve anonymisation. Researchers in clinical trials still consider that anonymisation techniques by themselves are insufficient to protect participant privacy, and they need to be paired with controlled access. The second phase of this research confirmed that clinical trial datasets are very rich in personal details and using re-identification risk scores as a measure of this richness is feasible. These scores could inform the anonymisation process of clinical trials datasets to release them for secondary research. We proposed a strategy for incorporating these scores into the decision-making process for releasing clinical trials datasets. Finally, the majority of responders to the survey reported using documented processes for de-identification and anonymisation. However, our survey results clearly indicate that there are still gaps in the areas of guidance, resources and training to fulfil sharing requests of de-identified/anonymised datasets, and that re-identification risk estimation is an underdeveloped area. This work will be of interest to the clinical trials research community, funders and publishers seeking to improve the process of anonymisation and foster data sharing
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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