1,721,020 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
Accelerating computational discovery of porous solids through improved navigation of energy structure function maps
Full text linkWhile energy-structure-function (ESF) maps are a powerful new tool for in silico materials design, the cost of acquiring an ESF map for many properties is too high for routine integration into high-throughput virtual screening workflows. Here, we propose the next evolution of the ESF map. This uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost. We use this approach to obtain a two orders of magnitude speedup on an ESF study that focused on the discovery of molecular crystals for methane capture, saving more than 500,000 central processing unit hours from the original protocol. By accelerating the acquisition of insight from ESF maps, we pave the way for the use of these maps in automated ultrahigh-throughput screening pipelines by greatly reducing the opportunity risk associated with the choice of system to calculate.</p
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
Computational data related to "Digital Navigation of Energy–Structure–Function Maps for Hydrogen-Bonded Porous Molecular Crystals"
No full textComputational data related to landscapes of predicted crystal structures reported in Digital Navigation of Energy–Structure–Function Maps for Hydrogen-Bonded Porous Molecular Crystals </span
Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals
No full textPorous molecular crystals are a promising class of functional materials, but their a priori design is challenging. We demonstrated recently that energy–structure–function (ESF) maps can aid in the targeted discovery of porous molecular crystals via prediction of the stable crystalline arrangements along with their functions of interest. Here, we compute ESF maps for a series of molecules that comprise either a triptycene or a spiro-biphenyl core, functionalized with six different hydrogen-bonding moieties. By quantifying the intermolecular hydrogen bonding and intermolecular stacking for the structures on the ESF maps, we show that the positioning of the hydrogen bonding sites, as well as their number, has a profound influence on the shape of the resulting ESF maps. This reveals promising structure–function spaces for future experimental efforts. To assist with the navigation and interpretation of these ESF maps, we developed an interactive browser-based visualization tool (https://www.interactive-esf-maps.app) for interrogating the correlations, dependencies, and relationships between the various dimensions of the data. We also demonstrate a simple and general approach to representing and inspecting the high-dimensional data of an ESF map; this involves learning two-dimensional embeddings of the high-dimensional ESF data by applying unsupervised learning to engineered descriptors, or to numerical representations, that encode the crystal structures. Within this unified framework, ESF maps can be efficiently navigated to identify ‘landmark’ structures that are energetically favourable or functionally interesting. This is a step toward the automated analysis of ESF maps, which is an important goal for closed-loop, autonomous searches for molecular crystals with useful functions
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
Development of Chemistry-Informed Machine Learning Tools for the Analysis of Powder X-Ray Diffraction Data
Full text linkThe collection of powder X-ray diffraction (PXRD) data is a commonly employed technique in
the analysis of new materials, especially those formed as crystalline powders. Developments of
high-throughput and automated techniques have the potential to accelerate the process of data
acquisition of PXRD data, which will increase the data available in structural databases. The
emergence of big data in this area merits the development of methods which can be used to perform
analysis on, or gain insights into the structural data of materials in an automated fashion.
In this work, a series of tools known as the Python Powder X-Ray Diffraction Toolkit
(PyPDT) were developed to prepare for the inevitable “big data” generation in the area of
PXRD. This work can be broken down into 3 tools; a physics-informed synthetic PXRD
data generator, clustering and dimensionality reduction tools for representing and classifying
PXRD data, and a data visualisation tool to view the results of the PXRD classification
tool, alongside the underlying PXRD data. These tools hold the potential to identify novel
structures or experimental anomalies present in unlabelled experimental data by leveraging
existing structural data from databases, whilst allowing for dynamic visualisation and analysis
of the machine learnt representation alongside the PXRD data. This introduces a level of
transparency and interpretability to the process, providing users with increased confidence
and validity of the results. The process of developing, and examples of using these tools on
real-world examples of experimental significance, are presented in this work and potential
areas where further developments can be made are highlighted
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