Unified Dataset Research Articles

Data mining techniques on astronomical spectra data – I. Clustering analysis

ABSTRACT Clustering is an effective tool for astronomical spectral analysis, to mine clustering patterns among data. With the implementation of large sky surveys, many clustering methods have been applied to tackle spectroscopic and photometric data effectively and automatically. Meanwhile, the performance of clustering methods under different data characteristics varies greatly. With the aim of summarizing astronomical spectral clustering algorithms and laying the foundation for further research, this work gives a review of clustering methods applied to astronomical spectra data in three parts. First, many clustering methods for astronomical spectra are investigated and analysed theoretically, looking at algorithmic ideas, applications, and features. Secondly, experiments are carried out on unified datasets constructed using three criteria (spectra data type, spectra quality, and data volume) to compare the performance of typical algorithms; spectra data are selected from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey and Sloan Digital Sky Survey (SDSS). Finally, source codes of the comparison clustering algorithms and manuals for usage and improvement are provided on GitHub.

Smart Home’s Energy Management Through a Clustering-Based Reinforcement Learning Approach

Smart homes that contain renewable energy sources, storage systems, and controllable loads will be key components of the future smart grid. In this article, we develop a reinforcement-learning (RL)-based scheme for the real-time energy management of a smart home that contains a photovoltaic system, a storage device, and a heating, ventilation, and air conditioning (HVAC) system. The objective of the proposed scheme is to minimize the smart home’s electricity cost and the residents’ thermal discomfort by appropriately scheduling the storage device and the HVAC system on a daily basis. The problem is formulated as a Markov decision process, which is solved using the deep deterministic policy gradient (DDPG) algorithm. The main contribution of our study compared to the existing literature on RL-based energy management is the development of a clustering process that partitions the training data set into more homogeneous training subsets. Different DDPG agents are trained based on the data included in the derived subsets, while in real time, the test days are assigned to the appropriate agent, which is able to achieve more efficient energy schedules when compared to a single DDPG agent that is trained based on a unified training data set.

Predicting cross-tissue hormone-gene relations using balanced word embeddings.

MotivationInter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing.ResultsWe present a first study to predict from biomedical literature the hormone–gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS* models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone–gene pair is associated or not, and whether an associated gene is involved in the hormone’s production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue–tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well.Availability and implementationFreely available at https://cross-tissue-signaling.herokuapp.com are our model predictions & datasets; https://github.com/BIRDSgroup/BioEmbedS has all relevant code.Supplementary information Supplementary data are available at Bioinformatics online.

Pan-cancer analyses identify DCBLD2 as an oncogenic, immunological, and prognostic biomarker.

Discoidin, CUB, and LCCL domain-containing protein 2 (DCBLD2) is a two-domain transmembrane protein-coding gene located on chromosome 3, the protein expressed by which acts as the membrane receptor of semaphorin and vascular endothelial growth factor during the development of axons and blood vessels. Although several research evidences at the cellular and clinical levels have associated DCBLD2 with tumorigenesis, nothing is known regarding this gene from a pan-cancer standpoint. In this study, we systematically analyzed the influence of DCBLD2 on prognosis, cancer staging, immune characteristics, and drug sensitivity in a variety of cancers based on a unified and standardized pan-cancer dataset. In addition, we performed GO enrichment analyses and KEGG analyses of DCBLD2-related genes and DCBLD2-binding proteins. Our results showed that DCBLD2 is a potential oncogenic, immunological as well as a prognostic biomarker in terms of pan-cancer, and is expected to contribute to the improvement of tumor prognosis and the development of targeted therapy.

Urban Sustainability: Integrating Socioeconomic and Environmental Data for Multi-Objective Assessment

The large concentration of the world’s population in cities, along with rapid urbanization, have brought numerous environmental and socioeconomic challenges to sustainable urban systems (SUS). However, current SUS studies focus heavily on ecological aspects, rely on SUS indicators that are not supported by available data, lack comprehensive analytical frameworks, and neglect SUS regional differences. This paper develops a novel approach to assessing urban sustainability from regional perspectives using commonly enumerated socioeconomic statistics. It integrates land use and land cover change data and ecosystem service values, applies data mining analytics to derive SUS indicators, and evaluates SUS states as trade-offs among relevant SUS indicators. This synthetic approach is called the integrated socioeconomic and land-use data mining–based multi-objective assessment (ISL-DM-MOA). The paper presents a case study of urban sustainability development in cities and counties in Inner Mongolia, China, which face many environmental and sustainable development problems. The case study identifies two SUS types: (1) several large cities that boast well-developed economies, diversified industrial sectors, vital transportation locations, good living conditions, and cleaner environments; and (2) a few small counties that have a small population, small urban construction areas, extensive natural grasslands, and primary grazing economies. The ISL-DM-MOA framework innovatively synthesizes currently available socioeconomic statistics and environmental data as a unified dataset to assess urban sustainability as a total socio-environmental system. ISL-DM-MOA deviates from the current indicator approach and advocates the notion of a data-mining-driven approach to derive urban sustainability dimensions. Furthermore, ISL-DM-MOA diverges from the concept of a composite score for determining urban sustainability. Instead, it promotes the concept of Pareto Front as a choice set of sustainability candidates, because sustainability varies among nations, regions, and locations and differs between political, economic, environmental, and cultural systems.

Systematic Review and Pharmacokinetic Meta-analysis of Doxorubicin Exposure in Transcatheter Arterial Chemoembolizationand Doxorubicin-Eluted Beads Chemoembolization for Treatment of Unresectable Hepatocellular Carcinoma.

Almost 15 years after the introduction of transarterial chemoembolization (TACE) with drug-eluting beads (DEB-TACE) for hepatocellular carcinoma (HCC) therapy, the mean peak plasma concentration (Cmax) and area under the concentration-time curve (AUC) for doxorubicin have still not been systematically reviewed or meta-analyzed. To conduct a systematic review and meta-analysis of available data and establish a reference range for Cmax and AUC of doxorubicin DEB-TACE and TACE, as well as explore the potential influence of microspheres' size and type on these parameters. PubMed, EMBASE, and Web of Science were searched from August 1992 through December 2021. Studies measuring exposure parameters among HCC patients treated with doxorubicin DEB-TACE without restriction on language were included. Two independent reviewers extracted and unified data sets for pooled estimate analysis. The quality of the evidence was assessed via the Grading of Recommendations Assessment, Development and Evaluation framework. The ClinPK Statement checklist and Newcastle-Ottawa Scale (NOS) were used to determine the quality of studies. Out of 666 studies, 246 full-text werereviewed, and 8 studies entered the meta-analysis (120 patients). Cmax and AUC of doxorubicin were 7.52-fold (95% CI 7.65 to 7.42-fold; P < 0.0001) and 1.91-fold (95% CI 1.95 to 1.88-fold; P = 0.0001) lower with DEB-TACE compared to TACE. Significant reduction in pooled standardized mean difference (SMD) of Cmax and AUC was observed with DEB-TACE versus TACE in direct comparison analysis (-2.93; 95% CI -3.60 to -2.26, P < 0.00001, and -1.73 95% CI -2.55 to -0.91, P < 0.0001, respectively). Moreover, in DEB-TACE stratification analysis, small microspheres revealed higher Cmax, AUC and tumor response rate as well as lower complication rate. The heterogeneity could not be completely addressed through sensitivity and stratification analysis. This meta-analysis provides exposure parameters of doxorubicin and justifies the advantage of DEB-TACE over TACE in terms of safety for patients with unresectable HCC. This study showed a marked association between the size of microsphere and exposure parameters of doxorubicin supporting the preference for small microspheres in DEB-TACE. The moderate and low quality of evidence is assigned to the Cmax and AUC, respectively.

Defect Defection for Intelligent Sewer Inspection with Multi-Label Classification

The sewerage system is held as an important part of city infrastructure, while the pipeline inspection technology based on closed-circuit television system (CCTV) is still offline and by human. This paper focus on the research topic of defect detection for intelligent sewer inspection with multi-label classification. We first use the image preprocessing methods to get the unified datasets, then deploy the framework of deep learning, and propose a multi-label classification method for defect detection. The experimental results show that our approach is a scalable and efficient method, and it will be used in the application systems.

Defect Defection for Intelligent Sewer Inspection with Multi-Label Classification

The sewerage system is held as an important part of city infrastructure, while the pipeline inspection technology based on closed-circuit television system (CCTV) is still offline and by human. This paper focus on the research topic of defect detection for intelligent sewer inspection with multi-label classification. We first use the image preprocessing methods to get the unified datasets, then deploy the framework of deep learning, and propose a multi-label classification method for defect detection. The experimental results show that our approach is a scalable and efficient method, and it will be used in the application systems.

Genesis of improved quality in imaging through a national Australian echocardiography registry.

BackgroundDespite rapid technological advances and growth, quality in imaging has not received the focus seen elsewhere in cardiovascular medicine, resulting in significant gaps between guidelines and practice. Contemporary echocardiography practice requires comprehensive real-time data collection to allow dynamic auditing and benchmarking of key performance indices. The American College of Cardiology (ACC) proposed additional data standardisation, structured reporting identifying key data elements and imaging registries. In the absence of an Australian echocardiography registry, we developed a national clinical quality registry (GenesisCare Cardiovascular Outcomes Echo Registry). We hypothesised that measurement and local reporting of data would improve compliance of echo studies with quality guidelines and hence their clinical value.Methods and resultsWe prospectively collected data on 4 099 281 echocardiographic studies entered directly into a central electronic database from 63 laboratories across four Australian states between 2010 and 2021. Real-time auditing of key data elements and introduction of quality improvement pathways were performed to maximise completeness and uniformity of data acquisition and reporting. We compared completeness of key data element acquisition (AV peak velocity, left ventricular ejection fraction, E/e’, LA area, rhythm, RVSP) by time and state using de-identified data. Key performance outcomes benchmarked against the aggregated study cohort and international standards were reported to individual sites to drive quality improvement. Between 2010 and 2014 there were significant improvements in data completeness (72.0%+/-26.8% vs 86.8%+/-13.5%, p=0.02), which were maintained to 2020. In addition, interstate variability fell for both EF and E/e’ (p<0.002).ConclusionsThis large-scale collaboration provides a platform for the development of major quality improvement initiatives in echocardiography. Introduction of local quality assurance programmes via a unified national data set significantly improved the completeness of reporting of key echo quality measures. This in turn significantly improved the quality of, and reduced the interstate variability of, echo data. Developing a centralised database allowed rapid adoption nationally of local quality improvements.

Standardization procedure to provide a unified multi-method elemental compositional dataset, application to ferruginous colouring matters from Namibia

Curation of archaeological materials often leads to carrying out multi-analytical methodologies that combine non-invasive and invasive elemental analyses. Such materials are often analysed with different techniques. It results in the production of complementary but apparently non-compatible compositional datasets that cannot be easily compared. In the present paper, we propose to compare results acquired on geological ferruginous colouring matters from Namibia with analytical techniques (X-Ray Fluorescence spectrometry (XRF), Proton-Induced-X-ray Emission spectrometry (PIXE), Inductively Coupled Plasma coupled to Optical Emission spectrometry (ICP-OES) and Inductively Coupled Plasma coupled to Mass Spectrometry (ICP-MS)). We aim to provide a unified elemental dataset about these ferruginous colouring matters, usually referred to as “ochre” in archaeology. Analysed geological samples come from three distinct tectonostratigraphic zones of Namibia surveyed in the frame of rock art research. When compared directly, three of the four datasets obtained from these measurements appear as non-compatible because of the inter-equipment variability. However, through a simple standardization procedure, we demonstrate that it is possible to unify these datasets. This procedure minimizes the inter-equipment variability, making the inter-zones of provenance predominant and allowing distinctions of the samples according to their sole origin. Beyond shedding new light on the possibility to compare different elemental analytical techniques, this procedure paves the way for complementary multimodal studies of ferruginous colouring matters.

The seasonality of cholera in sub-Saharan Africa: a statistical modelling study

SummaryBackgroundCholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality.MethodsUsing the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010–16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation).Findings24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding.InterpretationWidespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate.FundingUS National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation.

Agitation in Cognitive Disorders: Use of the National Alzheimer's Coordinating Center Uniform Data Set (NACC-UDS) to Evaluate International Psychogeriatric Association Definition

ObjectiveConsensus-based definition of agitation by the International Psychogeriatric Association (IPA) has not been evaluated in community-based samples who are not preselected for behavioral disturbances. Here, we use a well-characterized cohort of community-dwelling older adults with cognitive impairment to assess the IPA criteria associated with agitation to evaluate the construction of this diagnostic entity. MethodsWe used the National Alzheimer Coordinating Center Unified Data Set (NACC-UDS) to construct the IPA consensus-based provisional definition of agitation in cognitive impairment (N = 19,424). We used clinician diagnosis of agitation as a gold standard in those with mild cognitive impairment and dementia and used the Neuropsychiatric Inventory-Questionnaire to define agitation symptoms and standardized assessments of function (including the Functional Assessment Scale and Clinical Dementia Rating Scale Sum of Boxes) to assess “excess disability.” We also examined patterns of psychiatric comorbidities to determine if they were consistent with IPA criteria. ResultsThere was agreement between the selected NPI measure of agitation and clinician judgment (sensitivity = 0.79, specificity = 0.69, Cohen's Kappa = 0.304). More than 84% of those with clinician judgment of agitation and 74% of those meeting the scale-based definition of agitation demonstrated excess social/functional disability. Comorbid psychiatric symptoms were present in 38% of the sample without agitation and higher in those with agitation by either definition. ConclusionAgitation ranges between 15% and 48% in those with cognitive impairment. The pattern of level of excess disability and the presence of comorbid psychiatric symptoms is consistent with the profile of published definitions.

SINBAD, structural, experimental and clinical characterization of STAT inhibitors and their potential applications

The abnormal activation of signal transducer and activator of transcription (STAT) protein family is recognized as cause or driving force behind multiple diseases progression. Therefore, searching for potential treatment strategy is pursued by multiple scientific groups. We consider that providing comprehensive, integrated and unified dataset for STAT inhibitory compounds may serve as important tool for other researchers. We developed SINBAD (STAT INhbitor Biology And Drug-ability) in response to our experience with inhibitory compound research, knowing that gathering detailed information is crucial for effective experiment design and also for finding potential solutions in case of obtaining inconclusive results. SINBAD is a curated database of STAT inhibitors which have been published and described in scientific articles providing prove of their inhibitory properties. It is a tool allowing easy analysis of experimental conditions and provides detailed information about known STAT inhibitory compounds.

Unified Deep Learning Model for Multitask Reaction Predictions with Explanation.

There is significant interest and importance to develop robust machine learning models to assist organic chemistry synthesis. Typically, task-specific machine learning models for distinct reaction prediction tasks have been developed. In this work, we develop a unified deep learning model, T5Chem, for a variety of chemical reaction predictions tasks by adapting the "Text-to-Text Transfer Transformer" (T5) framework in natural language processing (NLP). On the basis of self-supervised pretraining with PubChem molecules, the T5Chem model can achieve state-of-the-art performances for four distinct types of task-specific reaction prediction tasks using four different open-source data sets, including reaction type classification on USPTO_TPL, forward reaction prediction on USPTO_MIT, single-step retrosynthesis on USPTO_50k, and reaction yield prediction on high-throughput C-N coupling reactions. Meanwhile, we introduced a new unified multitask reaction prediction data set USPTO_500_MT, which can be used to train and test five different types of reaction tasks, including the above four as well as a new reagent suggestion task. Our results showed that models trained with multiple tasks are more robust and can benefit from mutual learning on related tasks. Furthermore, we demonstrated the use of SHAP (SHapley Additive exPlanations) to explain T5Chem predictions at the functional group level, which provides a way to demystify sequence-based deep learning models in chemistry. T5Chem is accessible through https://yzhang.hpc.nyu.edu/T5Chem.

A strategy to build a unified data set of moment magnitude estimates for low-to-moderate seismicity regions based on European–Mediterranean data: application to metropolitan France

SUMMARYSeismic hazard assessment (SHA) requires earthquake events characterized in terms of the moment magnitude (Mw) scale both in the earthquake catalogues and in the ground motion databases (SHA data sets). However, published Mw estimates for the same event may actually differ due to the adoption of different computation methods, input data, or seismic networks. The main objective of this paper is to develop a novel strategy to assign a unique Mw value for each earthquake and unified to an Mw reference data set that can be applied to low-to-moderate seismicity regions (Mw &amp;lt; 4.5) such as Metropolitan France, the target region of our study. The strategy consists of first collecting all Mw estimated directly by spectral or temporal inversion in a large area of the European–Mediterranean zone, including regional and specific studies in our target region. Then, the sources of Mw estimates are ranked in five different categories, from global to specific studies. A GCMT (Global Centroid Moment Tensor project) standard Mw value is recomputed and a quality flag is assigned depending on the level of the information provided by the sources. Mw estimates are then compared at the European–Mediterranean scale with those provided by the CMT services, chosen as reference. Mw estimates are subsequently corrected for their systematic deviations by progressively including lower magnitude estimates as one moves from high ranked categories to lower ones at each step of the unification procedure. In this work, the collected data set contains 6752 Mw estimates of 4454 shallow events (depth &amp;lt; 40 km) that occurred between 1963 and 2019 and computed by 34 sources (bulletins, publications,…, etc). A unified Mw value could be attributed to 185 events (2.36 ≤ Mw ≤ 5.35) in France and neighbouring regions and 3351 events (3.06 ≤ Mw ≤ 7.57) in the European–Mediterranean region. Finally, we showed that this Mw data set is consistent with European SHA data sets but leads to slightly larger Mw values compared to the French SHA data sets. We also noted that some variability still subsist in Mw data set compared to the existing SHA data sets, especially for Mw &amp;lt; 5.0. This new unified Mw data set is a first step toward the building of future catalogues. It will contribute to greatly improve the coherence of Mw definitions in the next generation of SHA data sets for both metropolitan France and Europe.

Five decades lead in biomedical research: University of Khartoum tops Sudanese universities in PubMed articles

Introduction: University of Khartoum (U of K) is the largest and oldest university in Sudan (1). The recent rankings of international and local universities made by Webometrics Ranking of world Universities initiative in 2017 have placed University of Khartoum as number one University in Sudan and 1970 in rank Worldwide. This ranking system is based mainly on research activity and publications. Using PubMed we obtained data on all PubMed-indexed articles (from 1966 to 2016) affiliated to the four oldest and most established Sudanese universities: University of Khartoum, University of Gezira, Omdurman Islamic University, and Al-Neelain University. Methods: Using R Statistical environment, we compiled a unified dataset from multiple resources including PubMed data and impact factor data. This unified dataset was then used to calculate multiple article and authorship metrics including total number of articles and annual averages. We also applied text mining techniques to from indexed abstracts in order to explore the prevailing trends and research focus of some the most prolific authors and to visualize this trends though word clouds. Results: Since the earliest date of PubMed indexing in 1966, the University of Khartoum is by far the most affiliated Sudanese university in PubMed. Approaching 300 articles in 2016, the number of PubMed indexed articles has shown a promising rising trend over the past decade. Our results clearly indicate lead of University of Khartoum in published biomedical research. This trend is promising and shows the attention shift towards research activities among the major Sudanese universities in general and University of Khartoum in particular.

A novel DeepMaskNet model for face mask detection and masked facial recognition

Coronavirus disease (COVID-19) has significantly affected the daily life activities of people globally. To prevent the spread of COVID-19, the World Health Organization has recommended the people to wear face mask in public places. Manual inspection of people for wearing face masks in public places is a challenging task. Moreover, the use of face masks makes the traditional face recognition techniques ineffective, which are typically designed for unveiled faces. Thus, introduces an urgent need to develop a robust system capable of detecting the people not wearing the face masks and recognizing different persons while wearing the face mask. In this paper, we propose a novel DeepMasknet framework capable of both the face mask detection and masked facial recognition. Moreover, presently there is an absence of a unified and diverse dataset that can be used to evaluate both the face mask detection and masked facial recognition. For this purpose, we also developed a largescale and diverse unified mask detection and masked facial recognition (MDMFR) dataset to measure the performance of both the face mask detection and masked facial recognition methods. Experimental results on multiple datasets including the cross-dataset setting show the superiority of our DeepMasknet framework over the contemporary models.

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets.

Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This article introduces the largest known international database of ultrasound channel data and describes the associated evaluation methods that were initially developed for the challenge on ultrasound beamforming with deep learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-and-sum beamforming, compared with a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm when formed with networks submitted by the challenge winners. We also describe contributed CUBDL data that may be used for training of future networks. The compiled database includes a total of 576 image acquisition sequences. We additionally introduce a neural-network-based global sound speed estimator implementation that was necessary to fairly evaluate the results obtained with this international database. The integration of CUBDL evaluation methods, evaluation code, network weights from the challenge winners, and all datasets described herein are publicly available (visit https://cubdl.jhu.edu for details).

An End-to-End Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.

A unified dataset of colocated sewage pollution, periphyton, and benthic macroinvertebrate community and food web structure from Lake Baikal (Siberia)

AbstractSewage released from lakeside development can introduce nutrients and micropollutants that can restructure aquatic ecosystems. Lake Baikal, the world's most ancient, biodiverse, and voluminous freshwater lake, has been experiencing localized sewage pollution from lakeside settlements. Nearby increasing filamentous algal abundance suggests benthic communities are responding to localized pollution. We surveyed 40‐km of Lake Baikal's southwestern shoreline from 19 to 23 August 2015 for sewage indicators, including pharmaceuticals, personal care products, and microplastics, with colocated periphyton, macroinvertebrate, stable isotope, and fatty acid samplings. The data are structured in a tidy format (a tabular arrangement familiar to limnologists) to encourage reuse. Unique identifiers corresponding to sampling locations are retained throughout all data files to facilitate interoperability among the dataset's 150+ variables. For Lake Baikal studies, these data can support continued monitoring and research efforts. For global studies of lakes, these data can help characterize sewage prevalence and ecological consequences of anthropogenic disturbance across spatial scales.