La sociología a través de sus publicaciones en revistas de impacto mediante el uso de big data

La sociología como disciplina ha sido definida de formas diversas que intentan abarcar sus dominios y métodos, aunque también se ha considerado inútil intentar definirla e incluso ha sido acusada de estar fragmentada y falta de uniformidad. Al igual que las demás disciplinas científicas, la sociología se puede observar como un sistema social al estar compuesta de complejas relaciones entre actores que incluyen a investigadores, instituciones, revistas y editoriales. Esas relaciones se forman a través de comunicaciones conceptuales y conforman redes que establecen como se organiza la disciplina. Actualmente, el fenómeno del big data ofrece la posibilidad de usar grandes colecciones de datos que permiten analizar la información de los procesos sociales. En concreto, a través de las grandes fuentes de datos bibliométricas la sociología tiene a su alcance ingentes cantidades de datos para mapear y estudiar la evolución de las disciplinas científicas. En este artículo describimos la sociología de los últimos treinta años a través de las publicaciones en las revistas de impacto. Para hacer esto, se emplean datos de revistas de sociología del Journal Citation Reports ampliados con la información de los artículos del Microsoft Academic Graph. Realizamos un análisis descriptivo de las revistas, sus países de origen, lenguas, editoriales y décadas de aparición e impacto. A continuación, evaluamos la evolución temporal del número de artículos y citas, así como la coautoría y el género de los autores. Tras esto, establecemos cuatro grupos de tipos de revistas y estudiamos sus diferencias en las dimensiones anteriores mediante contrastes de hipótesis. Finalmente, representamos las relaciones entre autores y revistas usando una red de afiliación que nos permite detectar grupos de revistas que forman interesantes comunidades temáticas y geográficas. Sociology as a discipline has been defined in diverse ways that attempt to cover the breadth of its domains and methods. Nonetheless, others have considered futile trying to define the discipline and many have accused sociology to be fragmented and lacking unity. Like the other scientific disciplines, sociology can be observed as a social system made up of researchers, institutions, journals and publishers. These relationships are established via conceptual communications which form networks that establish the way in which disciplined are organized. At present, the big data phenomena offers the capacity to use large data collections to analyse social processes. Big scholarly data sources offer sociology immense quantities of data useful to map and study the evolution of scientific disciplines. In this article we characterised the last thirty years of sociology through its publications in impact factor journals. To do this, we use data about the sociology journals from Journal Citation reports augmented with article information from Microsoft Academic Graph. The analysis starts by describing the journals, countries of origin, languages, publishers, the decades in which they appeared and their impact factor. After this, we evaluate the evolution of numbers of articles and citations as well as co-authorship and gender proportion. Subsequently, we establish four groups of journal types and study their differences in the previous dimensions using hypothesis tests. Finally, we represent the relationships between authors and journals using an affiliation network that allows us to detect groups of journals that form interesting thematic and geographic communities.

Cross-lingual citations in English papers: a large-scale analysis of prevalence, usage, and impact

Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation-based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available.

Towards FAIR Data for Low Carbon Energy - Current State and Call for Action

With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It adds to the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based evidence for the choices made in the energy transition. We comprehensively test FAIR data practices in the energy domain with the help of automated and manual tests. We document the state-of-the art and provide insights on bottlenecks from the human and machine perspectives. We propose action items for overcoming the problem with FAIR and open energy data and suggest how to prioritize activities.

AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry

Academia and industry share a complex, multifaceted, and symbiotic relationship. Analysing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonise their efforts is a critical task for several stakeholders. Research publications and patents are an ideal medium to analyze this space, but current datasets of scholarly data cannot be used for such a purpose since they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to the research topics drawn from the Computer Science Ontology. 5.1M publications and 5.6M patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.

Relationships between method-section citation rates and citation contexts: evidence from highly cited references in psychology

PurposeThe Method section of research articles offers an important space for researchers to describe their research processes and research objects they utilize. To understand the relationship between these research materials and their representations in scientific publications, this paper offers a quantitative examination of the citation contexts of the most frequently cited references in the Method section of the paper sample, many of which belong to the category of research material objects.Design/methodology/approachIn this research, the authors assessed the extent to which these references appear in the Method section, which is regarded as an indicator of the instrumentality of the reference. The authors also examined how this central measurement is connected to its other citation contexts, such as key linguistic attributes and verbs that are used in citation sentences.FindingsThe authors found that a series of key linguistic attributes can be used to predict the instrumentality of a reference. The use of self-mention phrases and the readability score of the citances are especially strong predictors, along with boosters and hedges, the two measurements that were not included in the final model.Research limitations/implicationsThis research focuses on a single research domain, psychology, which limits the understanding of how research material objects are cited in different research domains or interdisciplinary research contexts. Moreover, this research is based on 200 frequently cited references, which are unable to represent all references cited in psychological publications.Practical implicationsWith the identified relationship between instrumental citation contexts and other characteristics of citation sentences, this research opens the possibility of more accurately identifying research material objects from scientific references, the most accessible scholarly data.Originality/valueThis is the first large-scale, quantitative analysis of the linguistic features of citations to research material objects. This study offers important baseline results for future studies focusing on scientific instruments, an increasingly important type of object involved in scientific research.Peer reviewThe peer review history for this article is available at: 10.1108/OIR-03-2021-0171

An exploratory analysis: extracting materials science knowledge from unstructured scholarly data

Purpose The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The purpose of this study is to address this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science. Design/methodology/approach The authors conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER) application. This paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach. Findings The results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies. Originality/value To the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.

Diversification of Nigeria Economy and National Development 2005-2012

Economic diversification has been the only solution to Nigeria’s economic challenges with the country in control of diversely untapped natural and human resources. This work has contextually x-rayed some much more considered theoretical paths of economic development through economic diversification and placed the blame for Nigeria’s economic backwardness on political will and lack of commitment to national course of political leaders. Since the diverse policy process of the government had yielded little or no sustainable results, even when the emphasis is to utilize the potentials in non-oil sectors to benefit ever-increasing population. Nigeria is relatively diversified but the positive impact of real diversification through surplus economic gains has not been achieved. A holistic approach to development was adopted in the theoretical framework used in this work that positively affects state, people and their relationship nationally and internationally. The thrust of the theory encourages free trade policy, efficient competition and democratic features to liberalize productivity through various guided legislation in line with Globalized Quality Standard. The research design was descriptive of the observed trend in the economy. It also analyses similar scholarly data collected for accuracy in exposing greatly a multi-sectoral approach in planning, dealing with interdependence using input-output matrix with reference to pre-independence and post-independence era of the national economy. This study looked at the positive intentions of some interventionist programmes and policies of the Government which were short-lived. Few years’ aggregate contribution and sectoral real GDP rate were stated. Recommendations were effectively based on keen interest in multi-sectoral diversification of an economy being the sub-structure that determines the effectiveness of super-structure.