Search

In today's academic environment, social media platforms, ResearchGate in particular, play an essential role in facilitating collaboration and communication among researchers. Even with the wide usage of this site, there remains a significant gap in the availability of structured datasets that focus on researchers and their academic outputs. This lack of accessible data obstructs comprehensive analysis and evaluation of research trends and impact. This study seeks to address this gap by employing web scraping techniques to construct a dataset derived from ResearchGate, a leading platform for academic professionals. The introduced dataset consists of eight key features, including metrics related to publications, citations, and areas of research specialization. The availability of such a dataset not only provides a valuable resource for future research but also enables scholars to analyze research performance, identify collaboration opportunities, and uncover trends in academic productivity. Beyond dataset construction, this paper also details the exploratory data analysis (EDA) conducted to derive insights from the collected data. The K-means clustering algorithm was applied to categorize researchers according to their publication and citation patterns, offering a clearer understanding of academic achievements. The main contribution of this work is the establishment of a researcher’s dataset that can be used in future studies to analyze and examine the efforts and trends of researchers in their scientific work.

Abstract Purpose This paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students’ data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape. Methods The study uses a teaching strategy that combines structured and unstructured data analysis using Python tools and ChatGPT APIs. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking. Results The paper offers insights into how the teaching strategy effectively enhances students’ data analysis and critical thinking skills. It investigates the specific impact of active learning on students’ engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines. Conclusion The key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session’s effectiveness to accumulate in students’ personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era.

This research examines the unique Chinese approaches to implementing the Early Childhood Curriculum (ECC) in Shenzhen and Hong Kong, drawing on School-based Curriculum Development (SBCD) studies. A total of 200 administrators and teachers were interviewed in total, and transcripts from those interviews were examined, cross-checked, and assessed using document analysis and classroom observation. Through interviews that have been conducted by administrators and teachers analyzed by document analysis and classroom observation, the influence of Chinese culture on ECC implementation is explored using the Cultural-Historical Activity Theory (CHAT). An exploratory, inferential, and descriptive statistical approach evaluates the sociocultural mechanism of ECC in Chinese society. The proposed framework utilizes K-Nearest Neighbor (KNN) regression analysis to illustrate how social development leads to cultural fusion and conflicts. The overall sociocultural framework promotes cultural growth and inheritance in China's early childhood education settings.

This study assesses and quantifies the economic and financial impacts of the COVID-19 pandemic during the period of business operation restrictions countrywide (lockdown measures). We examine the strategies adopted by small and medium-sized enterprises (SMEs) to reopen their business operations after lockdown measures had been relaxed or lifted. Data were collected in Rwanda from nearly 244 SMEs across the country, providing firsthand and reliable information on the effects of the pandemic on business performance, with a particular emphasis on wood-based enterprises. We used Exploratory Data Analysis (EDA) and multivariate linear regression methods to measure the pandemic’s effects on employment, sales, and tax payments among SMEs. The findings reveal that firms downsized employment by 36%, with significant deviations within different SME sizes. Small businesses were particularly affected by reduced sales levels due to the pandemic. Although there was an overall reduction in tax payments during the crisis, medium-sized enterprises experienced a more significant decrease in taxes paid to the government by 74.6%. Additionally, regression findings affirm that the COVID-19 effects on SMEs were manifested in reduced sales across all categories of SMEs, reduced employment, and a reduced amount of taxes paid to the government, which further translate to reduced economic performance during COVID-19 period. Furthermore, SME owners utilised various coping mechanisms during the reopening phase, including a reliance on savings and selling assets. The analysis recommends establishing medium-term financing mechanisms and providing technical support for SMEs to ensure a steady and sustainable recovery from the pandemic’s effects, as well as enhancing their resilience to future socio-economic shocks.

Abstract Background Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation. Results We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically. Conclusions ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices—iterative QC, providing a data-driven QC framework compatible with observed biological diversity.

The data concerning land and real estate transactions, which for a long time were stored in various complex and sparsely accessible databases, are now more easily available since the opening of the DVF database ("Demandes de Valeurs Foncières" in French, "Real Estate Transaction Datafiles" in English) in 2019. However, the use of these information resources for analytical purposes requires time for acculturation and significant preparation work that should not be underestimated. This article presents a workflow dedicated to the analysis of open data DVF that we formalized in the R environment and documented in order to ensure replicability. This workflow illustrates the potential of these data to understand the spatial dynamics of residential real estate markets at different scales. Through a case study of the Brittany region and the Rennes urban/metropolitan area, the objective is to propose and discuss some methodological avenues for the preparation, analysis and (carto)graphic representation of these "new data" as well as the challenges and issues associated with their use.

Abstract Background With the enactment of the Brazilian Law Arouca 11,794/2008 and Decree 6.899/2009, there has been an urgent need for changes in the processes related to animal experimentation in Brazil; in particular, there is a need for improvements in enhancements of the lab animal management. To improve the management capacity of the Lab animal facility of the Carlos Chagas Institute’s Laboratory Animals Science (LAS), BioterC software was developed and implemented in 2014 for tracking mouse laboratory colonies. Five years after the implementation of this software, we sought to analyze the information in the database originated from BioterC using the Exploratory Analysis Data methodology (EDA). This article aims to identify animal breeding patterns using a data mining tool (Data Science) with Python programming language. Results The results show that from September 2014 to June 2019, under the license IACUC number LW- 6/17, 15.106 animals were produced. The C57BL/6, BALB/c and Swiss strains were the most frequently produced strains. The distribution of births due to crosses between these strains showed a median of 6 to 10 animals, depending on the genetic homozygosis and heterozygosis of the animal. The median number of days of mating was 35 days. In the sexing period, the records reported a median of 19 days. A total of 393 requests for animals from internal and external laboratories were registered. It was noted that approximately half of the animals produced to meet the demand for orders were discarded. Of the 15,106 animals, 38% were requested for animal experimentation, 58% were discarded and 4% did not have an outcome recorded in the data. Conclusions This volume of data provides an initial view of the information retrieval capabilities contained in BioterC, allowing for unique breeding knowledge by installing laboratory animals.

This paper presents an analysis of cosmic ray intensity in Pakistan air space using spatial interpolation, comparing it with Chinese cosmic ray records from 1984 to 1993. The Exploratory Data Analytic (EDA) approach was applied to compare the cosmic ray fluctuations in both countries. The time series plot of the monthly cosmic rays showed relatively flatter counts in Pakistan than in China. The cosmic ray data for the years 1984 to 1993 fell within Solar Cycle 22, which lasted from 1986 to 1996, with its maximum phase in 1989 to 1991. The cosmic radiation varies between the atmospheric regions of Pakistan and China due to modulations in intensity that are accessible accordingly. It can be explained by purely astrophysical phenomena: (1) the source of emission of cosmic radiation may be different, (2) the rate at which emanation takes place depends on bursts of deep space dynamical objects from their sources that may be affected by solar wind and other solar radiations. Therefore, modulations in intensity are not only due to different geophysical locations. This study will help government organizations to predict and forecast cosmic rays values.

Political alignment identification is an author profiling task that aims at identifying political bias/orientation in people’ writings. As usual in any automatic text analysis, a critical aspect here is having available adequate data sets so that the data mining and machine learning approaches can obtain reliable and informative results. This article makes a contribution in this regard by presenting a new corpus for the study of political alignment in documents of Argentinian journalists. The study also includes several kinds of analysis of documents of pro-government and opposition journalists such as the relevance of terms in each journalist class, sentiment analysis, topic modelling and the analysis of psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count (LIWC) system. From the experimental results, interesting patterns could be observed such as the topics both types of journalists write about, how the sentiment polarities are distributed and how the writings of pro-government and opposition journalists differ in the distinct LIWC categories.

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.

Big data is a field of investigation rich in promises but still complex in renewing spatial mobility analysis. In fact, thinking about mobility through the prism of digital footprints raises many questions regarding the data nature, the accessibility procedures and the methods and techniques of treatment. This paper aims to explore the potential of digital footprints in the analysis of mobility through the example of data from the Carpooling platform BlaBlaCar. By analyzing (temporal and spatial) trips to and from city of Rennes over the course of 5 months, the goal is both to draw a portrait of Carpooling in the capital of Britanny and to give an overview of advantages and limitations of these data in understanding this new form of mobility.

The forms of socio-spatial differentiation found in Lima are for the most part in line with the model of Andean metropolises. There is, for example, an opposition between central areas occupied by the middle and upper classes and the more recent and lower-class areas in the peripheries. There is also a directional expansion of the wealthier areas, in this case, towards the east of the central areas. This model is still very pronounced but seems to have changed since the end of the last century with internal diversification of previously homogeneous units. Our objective is to understand the current spatial organization of social differentiation in Lima. On the basis of data of the last national census of population in 2007 and of a set of aerial photos and satellite images taken since the middle of the 20th century, we build on, at the level of the blocks, various cartographical and statistical indicators concerning the successive layers of the expansion of Lima and its current socio-spatial structure. We conclude from this first, that the urban area is organized according to a scale of social and urban integration which is registered spatially on a gradient, from the center towards the peripheries, in correlation with the time the blocks were built. In a second phase, a series of multidimensional statistical analyses which visually reintroduce each block allows us to moderate this conclusion by the means of the study of the variability inside each layer of expansion. It is thus possible to understand the current territorial dynamics and to formulate the hypothesis of a change in the structure contrasting the center and the peripheries.

An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills—the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008–2012) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients—sigma-(σo), beta-(βo), and gamma-(γo) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with ~70% of overall accuracy, however, the sole and simple use of two specific oil slick’s Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use.

Nowadays, cross-product statistics provide a complete set of procedures for exploratory spatial analysis, applied both to point patterns analysis as well as to map or image interpretation. Derived from the Mantel test in Epidemiology, these statistics developed in two main directions: correlation analysis and spatial auto regression by the generalised Mantel test on the one hand, global and local analysis of spatial dependency with correlograms and local indicators of spatial autocorrelation on the other hand.

Cartography is a discipline which evolves simultaneously with the sciences it uses. Initially, the contributions of the geometry enabled cartographers to represent physical geographical Then, the contributions of the statistics made it possible to represent abstracted phenomena and thems on topographical maps, by structuring information before communicating it graphically and efficiently. Today, the contributions offered by the new data-processing possibilities offer new possibilities. They mainly rely on Geographic Information Systems (GIS) and on the experience of Eploratory Data Analysis (EDA). The main characteristics of this multimedia and interactive cartography can be structured in a synthetic figure. Cartographic evolutions are presented as “added values”, calling upon different senses, with different levels of interactivity. These new possibilities contribute to modify cartography and particullarly its role of communication, as well as the relationship between the actors that emit and the actors that receive the cartographical information.

The process of enlargement of the European Union is continuous, involving in 2004 ten Central or Oriental European Countries (PECO), and later some Mediterranean countries (PTM). But the EU still asks important questions: what are the candidate countries which will be included first? What will this process induce on European relationship? How high is the risk that some other countries may be evicted (in terms of trade) due to other countries integration? This paper aims to understand more precisely the global potential trade evolution between actual and future EU included oriental or Mediterranean countries. We try to enhance economical factors and geographical constraints which explain trade levels between (groups of) countries. We first model the trade flows for each aggregate of countries. Then, we simulate and assess the impact of including other (blocs of) countries to the UE model and both EU and PTM aggregates. Our study uses classical gravity modelling (designed by Bergstrand and Festoc) and compute data (obtained from the Chelem database) mainly from 1993 to 1997. We add to the model the foreign investments in each country, the contiguity levels between countries and the distance separating country centres. Our approach includes flow modelling, simulations of integration processes and a comparison of relative economical elasticities between the different models.

Since the geographical information system revolution of the mid-1980s, there has been a rapid growth in the number of spatially referenced databases. Data management, within these new systems, immediately took up a large place, but today, as Stan Openshaw argued, "analysis is no longer an optional activity". Nevertheless, we may keep in mind that spatial data have very special properties, that create a need for spatial data analysis and spatial. Furthermore, it may be argued that, in a GIS context, user’s needs are mainly of a data exploratory nature. Yet, data-driven tools can hardly be found within classical GIS software, when they are available at all.