Scientific Datasets Research Articles

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists.

Biodiversity monitoring usually involves drawing inferences about some variable of interest across a defined landscape from observations made at a sample of locations within that landscape. If the variable of interest differs between sampled and nonsampled locations, and no mitigating action is taken, then the sample is unrepresentative and inferences drawn from it will be biased. It is possible to adjust unrepresentative samples so that they more closely resemble the wider landscape in terms of "auxiliary variables." A good auxiliary variable is a common cause of sample inclusion and the variable of interest, and if it explains an appreciable portion of the variance in both, then inferences drawn from the adjusted sample will be closer to the truth. We applied six types of survey sample adjustment-subsampling, quasirandomization, poststratification, superpopulation modeling, a "doubly robust" procedure, and multilevel regression and poststratification-to a simple two-part biodiversity monitoring problem. The first part was to estimate the mean occupancy of the plant Calluna vulgaris in Great Britain in two time periods (1987-1999 and 2010-2019); the second was to estimate the difference between the two (i.e., the trend). We estimated the means and trend using large, but (originally) unrepresentative, samples from a citizen science dataset. Compared with the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although standard uncertainty intervals generally did not cover the true values. Completely unbiased inference is not possible from an unrepresentative sample without knowing and having data on all relevant auxiliary variables. Adjustments can reduce the bias if auxiliary variables are available and selected carefully, but the potential for residual bias should be acknowledged and reported.

Support Vector Machines Principles and Actually Example

With the development of big data technology, it is necessary to quickly and accurately mine the information contained in data. In this paper, we study the selection of kernel function types and the selection of kernel function parameters for support vector machines under classification and regression problems, and experimentally verify their regression prediction performance and classification performance on scientific datasets. Firstly, the scientific data mining model is constructed, the model design principle is clarified, and then linear kernel function, Gaussian kernel function, and polynomial kernel function are selected for the comparison experiments, and the performance classification and regression prediction of the data are finally obtained.

Progress and trends in healthcare marketing strategy (2018–2022): A descriptive and bibliometric analysis of the Web of Science (WOS) dataset

PurposeHealth-care marketing typically entails a coordinated set of outreach and communications designed to attract consumers (patients in the health-care context) who require services for a better health outcome and guide them throughout their health-care journey to achieve a higher quality of life. The purpose of this study is to understand the progress and trends in healthcare marketing strategy (HMS) literature between 2018 and 2022, with a special emphasis on the pre- and post-Covid-19 periods.Design/methodology/approachThe authors examine 885 HMS-related documents from the WOS database between 2018 and 2022 that were extracted using a keyword-based search strategy. After that, the authors present the descriptive statistics related to the corpus. Finally, the authors use author co-citation analysis (ACA) and bibliographic coupling (BC) techniques to examine the corpus.FindingsThe authors present the descriptive statistics as research themes, emerging sub-research areas, leading journals, organisations, funding agencies and nations. Further, the bibliometric analysis reveals the existence of five thematic clusters: Cluster 1: macroeconomic and demographic determinants of healthcare service delivery; Cluster 2: strategies in healthcare marketing; Cluster 3: socioeconomics in healthcare service delivery; Cluster 4: data analytics and healthcare service delivery; Cluster 5: healthcare product and process innovations.Research limitations/implicationsThis study provides an in-depth analysis of the advancements made in HMS-related research between 2018 and 2022. In addition, this study describes the evolution of research in this field from before to after the Covid-19 pandemic. The findings of this study have both research and practical significance.Originality/valueTo the best of the authors’ knowledge, this is the first study of its kind to use bibliometric analysis to identify advancements and trends in HMS-related research and to examine the pattern before and after Covid-19 pandemic.

Global Topology of 3D Symmetric Tensor Fields.

There have been recent advances in the analysis and visualization of 3D symmetric tensor fields, with a focus on the robust extraction of tensor field topology. However, topological features such as degenerate curves and neutral surfaces do not live in isolation. Instead, they intriguingly interact with each other. In this paper, we introduce the notion of topological graph for 3D symmetric tensor fields to facilitate global topological analysis of such fields. The nodes of the graph include degenerate curves and regions bounded by neutral surfaces in the domain. The edges in the graph denote the adjacency information between the regions and degenerate curves. In addition, we observe that a degenerate curve can be a loop and even a knot and that two degenerate curves (whether in the same region or not) can form a link. We provide a definition and theoretical analysis of individual degenerate curves in order to help understand why knots and links may occur. Moreover, we differentiate between wedges and trisectors, thus making the analysis more detailed about degenerate curves. We incorporate this information into the topological graph. Such a graph can not only reveal the global structure in a 3D symmetric tensor field but also allow two symmetric tensor fields to be compared. We demonstrate our approach by applying it to solid mechanics and material science data sets.

RmdnCache: Dual-Space Prefetching Neural Network for Large-Scale Volume Visualization.

Volume visualization plays a significant role in revealing important intrinsic patterns of 3D scientific datasets. However, these datasets are often large, making it challenging for interactive visualization systems to deliver a seamless user experience because of high input latency that arises from I/O bottlenecks and limited fast memory resources with high miss rates. To address this issue, we have proposed a deep learning-based prefetching method called RmdnCache, which optimizes the data flow across the memory hierarchy to reduce the input latency of large-scale volume visualization. Our approach accurately prefetches the content of the next view to fast memory using learning-based prediction while rendering the current view. The proposed deep learning architecture consists of two networks, RNN and MDN in respective spaces, which work together to predict both the location and likelihood distribution of the next view for defining an optimal prefetching range. Our method outperforms existing state-of-the-art prefetching algorithms in reducing overall input latency for visualizing real-world large-scale volumetric datasets.

Predicting invasive species distributions using incremental ensemble-based pseudo-labeling

Control strategies for preventing the spread of invasive species require their accurate geographical distribution. Species distribution models (SDMs) that can predict potential habitats of invasive species and thereby derive habitat suitability maps have become a valuable tool for supporting regulatory strategies. To date, machine learning (ML)-based approaches have outperformed profile and statistical-based approaches in terms of species distribution prediction accuracy. However, ML-based approaches often suffer from poor predictive performance when there is insufficient labeled data. Recently, pseudo-labeling (PL), a semi-supervised learning method, has been proposed to alleviate this problem, but pseudo-labels generated using a single-teacher model with very few labeled data points are generally biased and not suitable for training SDMs. In this paper, we propose a novel prediction scheme for invasive species distributions using incremental ensemble-based PL (SDP-EPL). We first build an ensemble-based teacher using multiple conventional SDMs and then incrementally construct a training set, starting with very few labeled data points, by repeating the following process: (i) generating pseudo-labels for unlabeled data using the teacher model, (ii) appending the pseudo-labeled data representing high or low habitat suitability to the training set, and (iii) training the teacher model using the updated training set. We then train a student SDM using the training set. Based on extensive experiments using citizen science datasets for three species, we show that the proposed scheme outperforms other commonly used SDMs in terms of diverse evaluation metrics and achieves performance improvements of up to 14.61% and 5.45% compared to the baseline and state-of-the-art models, respectively. We also demonstrate the effectiveness of the ensemble teacher model and incremental labeling in terms of predictive accuracy.

Opportunistic citizen science data reveals habitat selection of lesser black-backed gulls in Central Europe.

This study is the first attempt to find a relation between the opportunistic observation data of an easily identifiable but locally rare bird, in our case the lesser black-backed gull (Larus fuscus fuscus), and the Corine Land Cover habitat map by applying a resource selection function model to reveal the habitat selection of the species. We provide a scientific analysis using the valuable citizen science dataset sourced from the www.birding.hu website, collected by hundreds of volunteers over a span of more than a decade in Hungary. Birds selected for landfills, river and lake habitats and showed a significant but much smaller attraction towards urban areas, while they selected against arable lands. We found that in the case of rare and moderately common birds, we can obtain a representative picture of the habitat selection of the species, even if the data are collected by non-standardized means.

Using citizen science to identify Australia’s least known birds and inform conservation action

ABSTRACT Citizen science is a popular approach to biodiversity surveying, whereby data that are collected by volunteer naturalists may help analysts to understand the distribution and abundance of wild organisms. In Australia, birdwatchers have contributed to two major citizen science programs, eBird (run by the Cornell Lab of Ornithology) and Birdata (run by Birdlife Australia), which collectively hold more than 42 million records of wild birds from across the country. However, these records are not evenly distributed across space, time, or taxonomy, with particularly significant variation in the number of records of each species in these datasets. In this paper, we explore this variation and seek to determine which Australian bird species are least known as determined by rates of citizen science survey detections. We achieve this by comparing the rates of survey effort and species detection across each Australian bird species’ range, assigning all 581 species to one of the four groups depending on their rates of survey effort and species observation. We classify 56 species into a group considered the most poorly recorded despite extensive survey effort, with Coxen’s Fig Parrot Cyclopsitta coxeni, Letter-winged Kite Elanus scriptus, Night Parrot Pezoporus occidentalis, Buff-breasted Buttonquail Turnix olivii and Red-chested Buttonquail Turnix pyrrhothorax having the very lowest numbers of records. Our analyses provide a framework to identify species that are poorly represented in citizen science datasets. We explore the reasons behind why they may be poorly represented and suggest ways in which targeted approaches may be able to help fill in the gaps.

Truncated Weibull–exponential distribution: methods and applications

This paper introduces a truncated Weibull-exponential distribution and provides a thorough insight into its mathematical characteristics. These characteristics include moments, generating functions, inverse distribution function, and entropy. Various measures are also discussed about the distribution's reliability. A simulation study is carried out to assess the stability and consistency of the maximum likelihood estimates of the parameters. Finally, two social sciences data sets are used to assess the distribution's relevance in modeling real-world situations.

Corrigendum: Identifying the wetlands of international importance in Beibu Gulf along the East Asian – Australasian Flyway, based on multiple citizen science datasets

Corrigendum: Identifying the wetlands of international importance in Beibu Gulf along the East Asian – Australasian Flyway, based on multiple citizen science datasets

Analysis of intellectual development behind organizational change management (2012-2022): A bibliometric study

Change is a menace or delight, it depends on how well it is managed. Change management in organization is neither new nor common. The aim of this study is to identify impactful articles assessing organisational change management (OCM) and to analyse the evolution of the intellectual structure that updates the existing knowledge set on the same. A scientific dataset that comprises bibliographic details of OCM Scopus publications from 2012 to 2022 has been downloaded to run a bibliometric test. Performance analysis and science mapping revealed influential constituents, the thematic evolution, and trending areas in and around organisational change management (OCM). The intellectual development of a topic can be done every 10 years to make the readers understand the topic’s future trend and recent method. Likewise, this study for the decade 2012–2022 enables researchers to understand the trends and methods of OCM. The conclusions drawn from the analysis can be considered current knowledge on OCM or recent trends in OCM. Knowledge stock analyses of OCM were done until 2011. This study is a continuation that adds value to the previous study and is considered to be the first attempt for the period 2012–2022.

The EPOS multi-disciplinary Data Portal for integrated access to solid Earth science datasets

The European Plate Observing System (EPOS) is a long-term initiative aimed at integrating research infrastructures for solid Earth science in Europe. EPOS provides a sustainable, multidisciplinary user-oriented platform - the EPOS Data Portal - that facilitates data integration, access, use, and re-use, while adhering to the FAIR principles. The paper describes the key governance, community building, and technical aspects for achieving multidisciplinary data integration through the portal. It also outlines the key portal features for aggregating approximately 250 data sources from more than ten different scientific communities. The main architectural concepts underpinning the portal, namely the rich-metadata, the service-driven data provision, and the usage of semantics, are outlined. The paper discusses the challenges encountered during the creation of the portal, describes the community engagement process, and highlights the benefits to the scientific community and society. Future work includes expanding portal functionalities to include data analysis, processing, and visualization and releasing the portal as an open-source software package.

Reconstruction of stochastic dynamics from large streamed datasets.

The complex dynamics of physical systems can often be modeled with stochastic differential equations. However, computational constraints inhibit the estimation of dynamics from large time-series datasets. I present a method for estimating drift and diffusion functions from inordinately large datasets through the use of incremental, online, updating statistics. I demonstrate the validity and utility of this method by analyzing three large, varied synthetic datasets, as well as an empirical turbulence dataset. This method will hopefully facilitate the analysis of complex systems from exceedingly large, "big data" scientific datasets, as well as real-time streamed data.

Dissemination effect of data papers on scientific datasets

AbstractOpen data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research.

Mixed population trends inside a California protected area: Evidence from long‐term community science monitoring

Protected areas are one of the most widespread and accepted conservation interventions, yet population trends of species within protected areas are rarely compared with regional trends to gain insight into their effectiveness. Here, we leverage two long‐term community science datasets, finding mixed effects of protected areas on long‐term bird population trends. We analysed 31 years of bird transect data recorded by community volunteers across all major habitats of Stanford University's Jasper Ridge Biological Preserve to determine the population trends for a sample of 66 species. We found that nearly a third of species experienced long‐term declines, and on average, species declined by 12%. Further, we averaged species trends by conservation status and key life history attributes to identify correlates and possible drivers of these trends. Observed increases in some cavity‐nesters and declines of scrub‐associated species suggest that long‐term fire suppression may be a key driver, reshaping bird communities through changes in forest and chaparral structure and composition. Additionally, we compared our results with those of the North American Breeding Bird Survey's Central California Coast region (n = 55 species) to place Jasper Ridge in a broader context. Most species experienced similar directional population trends inside and outside of the preserve and only eight species (14.5%) did better inside this small protected area. Therefore, we must identify relevant management strategies for declining populations and explicitly consider how existing protected areas target and manage each species. Further, this analysis underlines the importance of local and national community science for revealing long‐term bird population trends.

Geometric Affinity Propagation for Clustering With Network Knowledge

Clustering data into meaningful subsets is a major task in scientific data analysis. To date, various strategies ranging from model-based approaches to data-driven schemes, have been devised for efficient and accurate clustering. One important class of clustering methods that is of a particular interest is the class of exemplar-based approaches. This interest primarily stems from the amount of compressed information encoded in these exemplars that effectively reflect the major characteristics of the respective clusters. Affinity propagation (AP) has proven to be a powerful exemplar-based approach that refines the set of optimal exemplars by iterative pairwise message updates. However, a critical limitation is its inability to capitalize on known networked relations between data points often available for various scientific datasets. To mitigate this shortcoming, we propose geometric-AP, a novel clustering algorithm that effectively extends AP to take advantage of the network topology. Geometric-AP obeys network constraints and uses max-sum belief propagation to leverage the available network topology for generating smooth clusters over the network. Extensive performance assessment reveals a significant enhancement in the quality of the clustering results when compared to benchmark clustering schemes. Especially, we demonstrate that geometric-AP performs extremely well even in cases where the original AP fails drastically.

Document Classification with Contextually Enriched Word Embeddings

The text classification task has a wide range of application domains for distinct purposes, such as the classification of articles, social media posts, and sentiments. As a natural language processing application, machine learning and deep learning techniques are intensively utilized in solving such challenges. One common approach is employing the discriminative word features comprising Bag-of-Words and n-grams to conduct text classification experiments. The other powerful approach is exploiting neural network-based (specifically deep learning models) through either sentence, word, or character levels. In this study, we proposed a novel approach to classify documents with contextually enriched word embeddings powered by the neighbor words accessible through the trigram word series. In the experiments, a well-known web of science dataset is exploited to demonstrate the novelty of the models. Consequently, we built various models constructed with and without the proposed approach to monitor the models' performances. The experimental models showed that the proposed neighborhood-based word embedding enrichment has decent potential to use in further studies.

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data

Errors in measurements are key to weighting the value of data, but are often neglected in machine learning (ML). We show how convolutional neural networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise—in other words, each data point has a different error bar. This model mimics the nature of scientific data sets, such as those from astrophysical surveys, where noise arises as a realization of random processes with known underlying distributions. The classification of these objects can then be performed using standard statistical techniques (e.g. least squares minimization), as well as ML techniques. This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties. We show that, when each data point is subject to different levels of noise (i.e. noises with different distribution functions, which is typically the case in scientific data sets), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least squares method—and sometimes even surpassing it. Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.

Absolute radiometric calibration evaluation of the thermal infrared spectrometer onboard SDGSAT-1

ABSTRACTA thermal infrared spectrometer is an important sensor onboard the SDGSAT-1 dedicated to serving the 2030 Agenda for Sustainable Development of the United Nations. Field campaigns were conducted from January to December 2022 to evaluate its radiometric calibrations. A radiance-based calibration method was used to derive the spectral radiance of the lake surface around the satellite overpass using a boat-deployed TIR spectrometer or radiometer. The radiometric calibration conditions were quantified by examining the temperature bias between the satellite-based brightness temperature and the predicted at-sensor brightness temperature. Our results indicated that the average brightness temperature bias reached 0.772 K, −1.032 K, and −0.300 K, with RMSEs of 1.00, 1.10, and 0.43 K in bands 1, 2 and 3, respectively. Eighty percent of the field experiments achieved a high calibration accuracy with temperature biases of less than 1 K, and the calibration accuracy of band 3 was better than that of other bands. Atmospheric conditions and surface characterization play important roles in determining the calibration accuracy through surface observations and atmospheric transfer simulations. With the increasing need for SDGSAT-1 products, periodic field experiments are needed to monitor and improve the operational radiometric calibration of satellite instruments to create high-resolution and reasonable scientific datasets.

Investigation of Turkish Students' School Engagement through Random Forest Methods Applied to TIMSS 2019: A Problem of School Psychology

In this study, in line with TIMSS 2019 data, 8th grade students' school engagement in the Turkish education system and various variables in the field of science and mathematics, which are thought to be related to school engagement, were examined with the Random Forest method, a method amongst data mining methods. This research focuses on student bullying, which includes psychological factors at school, such as whether students like or dislike their lessons, their confidence in science, their self-confidence, absenteeism, and the effects of teachers' teaching methods on SE. The sample of the study consisted of 3872 students in the science data set and 3802 students in the mathematics data set, which remained as a result of the lost data deletion and assignment processes from 4077 students who originally participated in the application. The open-source Python infrastructure was used in the analysis of the data. Orange 3.32 data mining program was employed for model setup. The model performance criteria MSE, RMSE, MAE and R2 values obtained as a result of the analysis. In both areas, the variables that contribute to the prediction of students' school engagement were ranked according to their importance levels, starting from the most important, also interpreted and discussed. It was observed that the performance criteria of the established model have values close to zero in the field of science (MSE: 2.775 RMSE: 1.666 MAE: 1.267) and mathematics (MSE: 2.240 RMSE: 1.497 MAE: 1.131). Variables explain school engagement at the rate of 69.6% in science and 75.7% in mathematics. The order of importance of the variables in both areas showed a great similarity. Student bullying was obtained as the most important variable. Prospective studies can also be planned towards collecting more in-depth data deploying qualitative data collection methods under a qualitative research model that also includes the opinions of self, peers, teachers and parents. For the education policies that the TES should produce toward increasing SE, it is weighty to reduce and prevent student bullying in schools with sustainable practices. Exclusively school administrations should focus on student bullying with the help of counselors, diagnose the problems and take measures in and out of school according to the types of bullying.