- Détails du magazine
- Première publication
- 30 Mar 2017
- Période de publication
- 4 fois par an
- Accès libre
Pages: 1 - 12
In this contribution we provide two new co-authorship indicators based on fractional counting.
Based on the idea of fractional counting we reflect on what should be an acceptable indicator for co-authorship between two entities. From this reflection we propose an indicator, the co-authorship score, denoted as cs, using the harmonic mean. Dividing this new indicator by the classical co-authorship indicator based on full counting, leads to a co-authorship intensity indicator.
We show that the indicators we propose have many necessary or at least highly desirable properties for a proper cs-score. It is pointed out that the two new indicators can be used for countries, but also for institutions and other pairs of entities. A small example shows the feasibility of the co-authorship score and the co-authorship intensity indicator.
The indicators are not yet tested in real cases.
As the notions of co-authorship and collaboration have many aspects, we think that our contribution may help policy management to take yet another aspect into account as part of a multi-faceted description of research outcomes.
The indicators we propose cover yet another aspect of co-authorship.
- Country studies
- Fractional counting
- Harmonic mean
- Co-authorship intensity
- Accès libre
Pages: 13 - 34
Attention deficit hyperactivity disorder (ADHD) is a common behavioural condition. This article introduces a new data science method, word association thematic analysis, to investigate whether ADHD tweets can give insights into patient concerns and online communication needs.
Tweets matching “my ADHD” (n=58,893) and 99 other conditions (n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1: A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.
The themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image.
This is a single case study and the results may differ for other topics.
Health professionals should be sensitive to patients’ needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.
Word association thematic analysis can give new insights into the (self-reported) patient perspective.
- Word association thematic analysis
- Social behaviour
- Social web
- Accès libre
Pages: 35 - 49
Given the ubiquitous presence of the internet in our lives, many individuals turn to the web for medical information. A challenge here is that many laypersons (as “consumers”) do not use professional terms found in the medical nomenclature when describing their conditions and searching the internet. The Consumer Health Vocabulary (CHV) ontology, initially developed in 2007, aimed to bridge this gap, although updates have been limited over the last decade. The purpose of this research is to implement a means of automatically creating a hierarchical consumer health vocabulary. This overall purpose is improving consumers’ ability to search for medical conditions and symptoms with an enhanced CHV and improving the search capabilities of our searching and indexing tool HIVE (Helping Interdisciplinary Vocabulary Engineering).
The research design uses ontological fusion, an approach for automatically extracting and integrating the Medical Subject Headings (MeSH) ontology into CHV, and further convert CHV from a flat mapping to a hierarchical ontology. The additional relationships and parent terms from MeSH allow us to uncover relationships between existing terms in the CHV ontology as well. The research design also included improving the search capabilities of HIVE identifying alternate relationships and consolidating them to a single entry.
The key findings are an improved CHV with a hierarchical structure that enables consumers to search through the ontology and uncover more relationships.
There are some cases where the improved search results in HIVE return terms that are related but not completely synonymous. We present an example and discuss the implications of this result.
This research makes available an updated and richer CHV ontology using the HIVE tool. Consumers may use this tool to search consumer terminology for medical conditions and symptoms. The HIVE tool will return results about the medical term linked with the consumer term as well as the hierarchy of other medical terms connected to the term.
This is a first attempt in over a decade to improve and enhance the CHV ontology with current terminology and the first research effort to convert CHV's original flat ontology structure to a hierarchical structure. This research also enhances the HIVE infrastructure and provides consumers with a simple, efficient mechanism for searching the CHV ontology and providing meaningful data to consumers.
- Consumer Health Vocabulary
- Ontological fusion
- Medical ontologies
- Accès libre
National Lists of Scholarly Publication Channels: An Overview and Recommendations for Their Construction and Maintenance
Pages: 50 - 86
This paper presents an overview of different kinds of lists of scholarly publication channels and of experiences related to the construction and maintenance of national lists supporting performance-based research funding systems. It also contributes with a set of recommendations for the construction and maintenance of national lists of journals and book publishers.
The study is based on analysis of previously published studies, policy papers, and reported experiences related to the construction and use of lists of scholarly publication channels.
Several countries have systems for research funding and/or evaluation, that involve the use of national lists of scholarly publication channels (mainly journals and publishers). Typically, such lists are
The conclusions and recommendations of the study are based on the authors’ interpretation of a complex and sometimes controversial process with many different stakeholders involved.
The recommendations and the related background information provided in this paper enable mutual learning that may feed into improvements in the construction and maintenance of national and other lists of scholarly publication channels in any geographical context. This may foster a development of responsible evaluation practices.
This paper presents the first general overview and typology of different kinds of publication channel lists, provides insights on expert-based versus metrics-based evaluation, and formulates a set of recommendations for the responsible construction and maintenance of publication channel lists.
- Publication channel lists
- Research funding
- Scholarly communication
- Journal ranking
- Accès libre
Pages: 87 - 119
This paper aims to point out the scientific development and research density of renewable energy sources such as photovoltaic, wind, and biomass, using a mix of computational tools. Based on this, it was possible to verify the existence of new research trends and opportunities in a macro view regarding management, performance evaluation, and decision-making in renewable energy generation systems and installations.
A scientometric approach was used based on a research protocol to retrieve papers from the Scopus database, and through four scientometric questions, to analyze each area. Software such as the Science Mapping Analysis Software Tool (SciMAT) and Sci2 Tool were used to map the science development and density.
The scientific development of renewable energy areas is highlighted, pointing out research opportunities regarding management, studies on costs and investments, systemic diagnosis, and performance evaluation for decision-making in businesses in these areas.
This paper was limited to the articles indexed in the Scopus database and by the questions used to analyze the scientific development of renewable energy areas.
The results show the need for a managerial perspective in businesses related to renewable energy sources at the managerial, technical, and operational levels, including performance evaluation, assertive decision making, and adequate use of technical and financial resources.
This paper shows that there is a research field to be explored, with gaps to fill and further research to be carried out in this area. Besides, this paper can serve as a basis for other studies and research in other areas and domains.
- Solar energy
- Photovoltaic energy
- Wind energy
- Biomass energy
- Accès libre
Pages: 120 - 138
The purpose of this exploratory study is to provide modern local governments with potential use cases for their open data, in order to help inform related future policies and decision-making. The concrete context was that of the Växjö municipality located in southeastern Sweden.
The methodology was two-fold: 1) a survey of potential end users (n=151) from a local university; and, 2) analysis of survey results using a theoretical model regarding local strategies for implementing open government data.
Most datasets predicted to be useful were on: sustainability and environment; preschool and school; municipality and politics. The use context given is primarily research and development, informing policies and decision making; but also education, informing personal choices, informing citizens and creating services based on open data. Not the least, the need for educating target user groups on data literacy emerged. A tentative pattern comprising a technical perspective on open data and a social perspective on open government was identified.
In line with available funding, the nature of the study was exploratory and implemented as an anonymous web-based survey of employees and students at the local university. Further research involving (qualitative) surveys with all stakeholders would allow for creating a more complete picture of the matter.
The study determines potential use cases and use contexts for open government data, in order to help inform related future policies and decision-making.
Modern local governments, and especially in Sweden, are faced with a challenge of how to make their data open, how to learn about which types of data will be most relevant for their end users and what will be different societal purposes. The paper contributes to knowledge that modern local governments can resort to when it comes to attitudes of local citizens to open government data in the context of an open government data perspective.
- Open data
- Open government
- Accès libre
Pages: 139 - 153
This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.
Data for openness and coverage have been collected from the Open Data Inventory 2018 (ODIN), by Open Data Watch; institutional trust is built up as a formative construct based on the European Social Survey (ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling (SEM).
The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens’ confidence is more than twice than the impact of openness.
This paper analyzes the causal effect of Open Government Data (OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.
Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.
In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.
- Open government data
- Open data
- Public policy
- Accès libre
Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network
Pages: 154 - 177
The goal of this study is to explore whether deep learning based embedded models can provide a better visualization solution for large citation networks.
Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic OpenOrd method with different edge cutting strategies and parameters.
The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps’ layout has very high stability.
The computational and time costs of training are very high for network embedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested.
This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliometric analysis tasks.
This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer, more stable science map. We also designed a practical evaluation method to investigate and compare maps.
- Essential science indicators
- Bibliometric networks
- Network embedding
- Science mapping
- Accès libre
Pages: 178 - 192
This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.
The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over Sampling (ROS), Random Under Sampling (RUS), Synthetic Minority Oversampling TEchnique (SMOTE), ADAptive SYNthetic Sampling (ADASYN), Edited Nearest Neighbor (ENN), and Condensed Nearest Neighbor (CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is identified.
This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced data.
The testing was carried out with limited dataset and needs to be tested with a larger dataset.
This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance.
This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.
- Imbalanced data
- Sampling methods
- Machine learning