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Artykuły w czasopismach na temat „Sampling-event datasets”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Yim, Marx, Xin Rui Ong, Li Yuen Chiew, and Eleanor Slade. "A comprehensive synthesis of dung beetle records (Coleoptera, Scarabaeidae, Scarabaeinae) from Sabah, Malaysia." Biodiversity Data Journal 12 (September 12, 2024): e126697. https://doi.org/10.3897/BDJ.12.e126697.

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Dung beetles play key roles in terrestrial ecosystems, contributing to many important ecosystem process and functions, such as nutrient recycling, parasite control and seed dispersal. Due to their tight associations with mammals and their responses to environmental change, they are also frequently used as environmental and biological indicators. Despite their importance, knowledge about dung beetles in Southeast Asia is limited. To address this information gap, we established a databasing project - "Mobilising data on ecologically important insects in Malaysia and Singapore" - funded by the Gl

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Jang, Woohyuk, Hyunmin Kim, Hyungbin Seo, Minsong Kim, and Myungkeun Yoon. "SELID: Selective Event Labeling for Intrusion Detection Datasets." Sensors 23, no. 13 (2023): 6105. http://dx.doi.org/10.3390/s23136105.

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A large volume of security events, generally collected by distributed monitoring sensors, overwhelms human analysts at security operations centers and raises an alert fatigue problem. Machine learning is expected to mitigate this problem by automatically distinguishing between true alerts, or attacks, and falsely reported ones. Machine learning models should first be trained on datasets having correct labels, but the labeling process itself requires considerable human resources. In this paper, we present a new selective sampling scheme for efficient data labeling via unsupervised clustering. T

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Lenzi, Alice, Daniele Birtele, Silvia Gisondi, et al. "Robber flies and hover flies (Insecta, Diptera, Asilidae and Syrphidae) in beech forests of the central Apennines: a contribution to the inventory of insect biodiversity in Italian State Nature Reserves." Biodiversity Data Journal 11 (May 11, 2023): e101327. https://doi.org/10.3897/BDJ.11.e101327.

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The present paper describes a sampling-event dataset on species belonging to two families of Diptera (Syrphidae and Asilidae) collected between 2012 and 2019 in two Italian beech forests located in the central Apennines. The reference dataset consists of an annotated checklist and has been published on Zenodo. Syrphidae and Asilidae are two widespread and key ecological groups, including predator, pollinator and saproxylic species. Despite their pivotal role in both natural and man-made ecosystems, these families are still poorly known in terms of local distribution and open-access sampling-ev

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Aleksanov, Victor, Sergey Alekseev, and Maxim Shashkov. "Ground beetles (Carabidae) in urban habitats of Kaluga City (Russia)." Biodiversity Data Journal 10 (January 19, 2022): e76100. https://doi.org/10.3897/BDJ.10.e76100.

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Ground beetles (Carabidae, Coleoptera) are one of the most species-rich and well-studied insect families. However, the number of published datasets is disproportionately low against the biodiversity of this group. According to GBIF, only a fifth of the percentage of all published data covers ground beetles. This article describes a sampling-event dataset providing primary data on ground beetles collected in urban and suburban habitats in Kaluga, a typical central Russian city. We surveyed habitats of different land-use types and the extent and intensity of anthropogenic influence: yards, garde

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Fani Sani, Mohammadreza, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "The impact of biased sampling of event logs on the performance of process discovery." Computing 103, no. 6 (2021): 1085–104. http://dx.doi.org/10.1007/s00607-021-00910-4.

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AbstractWith Process discovery algorithms, we discover process models based on event data, captured during the execution of business processes. The process discovery algorithms tend to use the whole event data. When dealing with large event data, it is no longer feasible to use standard hardware in a limited time. A straightforward approach to overcome this problem is to down-size the data utilizing a random sampling method. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper systematically evaluates

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Wijethunge, Iromi Kusum, Jingpeng Cao, Fanjuan Meng, Zheping Xu, Qingshan Zhao, and Lei Cao. "Occurrence dataset from the waterbird survey of the middle and lower Huai He floodplain, China." Biodiversity Data Journal 13 (May 22, 2025): e158384. https://doi.org/10.3897/BDJ.13.e158384.

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The Huai He floodplain in Anhui and Jiangsu Provinces, an important component of the East Asian-Australasian Flyway (EAAF), sustains critical wetland habitats for migratory waterbirds, including four threatened species on the IUCN Red List: critically endangered <i>Aythya baeri</i> (Radde, 1863), endangered <i>Anser cygnoides</i> (Linnaeus, 1758) and vulnerable <i>Melanitta fusca</i> (Linnaeus, 1758) and <i>Aythya ferina</i> (Linnaeus, 1758). Despite its biogeographic significance as a transitional zone between the Yangtze and Yellow River floodplains, this region remains one of China's most u

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Pando, Francisco, and Francisco Bonet. "Making LTER Data FAIR: A workbench using DEIMS datasets and GBIF Tools." Biodiversity Information Science and Standards 3 (June 19, 2019): e37257. https://doi.org/10.3897/biss.3.37257.

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DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry, Wohner et al. 2019) is one of the largest repositories of long-term ecological research (LTER) datasets. It provides sophisticated searching tools by metadata elements and identifiers for all the 930 contained datasets, most of them from European sites. Whereas datasets' metadata are highly structured and searchable, datasets themselves have little standardization in terms of content, identifiers or license, making data integration difficult or cumbersome. Adopting the data FAIR guiding principles(Wilkinso

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Skobel, Nadiia, Dariia Borovyk, Denys Vynokurov, et al. "Biodiversity surveys of grassland and coastal habitats in 2021 as a documentation of pre-war status in southern Ukraine." Biodiversity Data Journal 11 (March 6, 2023): e99605. https://doi.org/10.3897/BDJ.11.e99605.

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This paper presents two sampling-event datasets with occurrences of vascular plants, bryophytes and lichens collected in May-June 2021 in southern Ukraine. We aimed to collect high-quality biodiversity data in an understudied region and contribute it to international databases and networks. The study was carried out during the 15th Eurasian Dry Grassland Group (EDGG) Field Workshop in southern Ukraine and the Dark Diversity Network (DarkDivNet) sampling in the Kamianska Sich National Nature Park. By chance, these datasets were collected shortly before the major escalation of the Russian invasi

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De, Pooter Daphnis, Ward Appeltans, Nicolas Bailly, et al. "Expanding the Ocean Biogeographic Information System (OBIS) beyond species occurrences." Biodiversity Information Science and Standards 1 (August 11, 2017): e20196. https://doi.org/10.3897/tdwgproceedings.1.20196.

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The Ocean Biogeographic Information System (OBIS) aims to integrate smaller, isolated datasets into a larger, more comprehensive picture of life in our oceans. Therefore, OBIS provides a gateway to many datasets containing information on where and when marine species have been observed. The datasets within OBIS are contributed by a network of hundreds of institutes, projects and individuals, all with the common goal to gain scientific knowledge and to make these data and knowledge easily available to the public. Until recently, OBIS had solely focused on biogeographic data, in the form of pres

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Juhls, Bennet, Anne Morgenstern, Jens Hölemann, et al. "Lena River biogeochemistry captured by a 4.5-year high-frequency sampling program." Earth System Science Data 17, no. 1 (2025): 1–28. https://doi.org/10.5194/essd-17-1-2025.

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Abstract. The Siberian Arctic is warming rapidly, causing permafrost to thaw and altering the biogeochemistry of aquatic environments, with cascading effects on the coastal and shelf ecosystems of the Arctic Ocean. The Lena River, one of the largest Arctic rivers, drains a catchment dominated by permafrost. Baseline discharge biogeochemistry data are necessary to understand present and future changes in land-to-ocean fluxes. Here, we present a high-frequency 4.5-year-long dataset from a sampling program of the Lena River's biogeochemistry, spanning April 2018 to August 2022. The dataset compri

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Sandoval, Santiago, Jean-Luc Bertrand-Krajewski, Nicolas Caradot, Thomas Hofer, and Günter Gruber. "Performance and uncertainties of TSS stormwater sampling strategies from online time series." Water Science and Technology 78, no. 6 (2018): 1407–16. http://dx.doi.org/10.2166/wst.2018.415.

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Abstract The event mean concentrations (EMCs) that would have been obtained by four different stormwater sampling strategies are simulated by using total suspended solids (TSS) and flowrate time series (about one minute time-step and one year of data). These EMCs are compared to the reference EMCs calculated by considering the complete time series. The sampling strategies are assessed with datasets from four catchments: (i) Berlin, Germany, combined sewer overflow (CSO); (ii) Graz, Austria, CSO; (iii) Chassieu, France, separate sewer system; and (iv) Ecully, France, CSO. A sampling strategy in

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Algabroun, Hatem. "Dynamic sampling rate algorithm (DSRA) implemented in self-adaptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling." Microsystem Technologies 26, no. 4 (2019): 1067–74. http://dx.doi.org/10.1007/s00542-019-04631-9.

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Abstract With the recent digitalization trends in the industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of being installed in inaccessible locations for wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize the sampling with a sufficient rate to catch important information without excessive energy consumption, and one way to achieve sufficient sampling is using adaptive sampling for sensors. As software plays an im

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Provoost, Pieter, Pooter Daphnis De, Ward Appeltans, et al. "Expanding the Ocean Biogeographic Information System (OBIS) beyond species occurrences." Biodiversity Information Science and Standards 1 (August 22, 2017): e20515. https://doi.org/10.3897/tdwgproceedings.1.20515.

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Data providers in the Ocean Biogeographic Information System (OBIS) network are not just recording species occurrences, they are also recording sampling methodology details and measuring environmental and biotic variables. In order to make OBIS an effective data sharing platform, it needs to be able to store and exchange these data in such a way that they can easily be interpreted by end users, as well as by the tools which will be created to search, analyze and visualize the integrated data. OBIS makes use of Darwin Core Archives (DwC-A) for exchanging data between data providers, regional an

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De, Pooter Daphnis, Ward Appeltans, Nicolas Bailly, et al. "Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences." Biodiversity Data Journal 5 (January 9, 2017): e10989. https://doi.org/10.3897/BDJ.5.e10989.

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The Ocean Biogeographic Information System (OBIS) is the world's most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the coll

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Zou, Zongfeng, Xiaochen Ji, and Yingying Li. "A Framework Model of Mining Potential Public Opinion Events Pertaining to Suspected Research Integrity Issues with the Text Convolutional Neural Network Model and a Mixed Event Extractor." Information 15, no. 6 (2024): 303. http://dx.doi.org/10.3390/info15060303.

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With the development of the Internet, the oversight of research integrity issues has extended beyond the scientific community to encompass the whole of society. If these issues are not addressed promptly, they can significantly impact the research credibility of both institutions and scholars. This article proposes a text convolutional neural network based on SMOTE to identify short texts of potential public opinion events related to suspected scientific integrity issues from common short texts. The SMOTE comprehensive sampling technique is employed to handle imbalanced datasets. To mitigate t

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Carboni, E., G. E. Thomas, A. M. Sayer, et al. "Intercomparison of desert dust optical depth from satellite measurements." Atmospheric Measurement Techniques 5, no. 8 (2012): 1973–2002. http://dx.doi.org/10.5194/amt-5-1973-2012.

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Abstract. This work provides a comparison of satellite retrievals of Saharan desert dust aerosol optical depth (AOD) during a strong dust event through March 2006. In this event, a large dust plume was transported over desert, vegetated, and ocean surfaces. The aim is to identify the differences between current datasets. The satellite instruments considered are AATSR, AIRS, MERIS, MISR, MODIS, OMI, POLDER, and SEVIRI. An interesting aspect is that the different algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. These include multi-angle appro

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Gerrits, Gijs, and Lia Hemerik. "Sampling data of macro-invertebrates collected in grasslands under restoration succession in a lowland stream-valley system." Biodiversity Data Journal 12 (July 23, 2024): e125462. https://doi.org/10.3897/BDJ.12.e125462.

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Publication of data from past field studies on invertebrate populations is of high importance, as there is much added value for them to be used as baselines to study spatiotemporal population and community dynamics in these groups. Therefore, a dataset consisting of occurrence data on epigaeic invertebrates collected in 1996 was standardised into the Darwin core format and cross-checked in order to make it publicly available following FAIR data principles. With publication, it can contribute to the biodiversity assessment of terrestrial invertebrates, thereby improving the availability and acc

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18

Hänsler, Andreas, and Markus Weiler. "Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events." Hydrology and Earth System Sciences 26, no. 19 (2022): 5069–84. http://dx.doi.org/10.5194/hess-26-5069-2022.

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Abstract. Spatially explicit quantification on design storms is essential for flood risk assessment and planning. Due to the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations only that have a substantially long time coverage. To achieve a regional picture, these station-based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spa

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Philippov, Dmitriy, Ksenya Ivicheva, Nadezhda Makarenkova, Igor Filonenko, and Aleksandra Komarova. "Biodiversity of macrophyte communities and associated aquatic organisms in lakes of the Vologda Region (north-western Russia)." Biodiversity Data Journal 10 (January 20, 2022): e77626. https://doi.org/10.3897/BDJ.10.e77626.

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This paper provides current data on the biodiversity of boreal lakes of the Vologda Region (north-western Russia), including macrophytes (vascular plants and macroscopic algae) and macrophyte inhabitants (invertebrates and microalgae). The raw data, given in two datasets (Sampling event dataset and an Occurrence dataset) and presented in the form of GBIF-mediated data, were collected from 139 lakes (macrophytes between 2005 and 2021, macrophyte inhabitants between 2014 and 2020). The dataset contains materials on the diversity of vascular plants (Tracheophyta, 3225 occurrences; Bryophyta, 155;

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Li, Haibo, and Yongbo Yu. "Detecting a multigranularity event in an unequal interval time series based on self-adaptive segmenting." Intelligent Data Analysis 25, no. 6 (2021): 1407–29. http://dx.doi.org/10.3233/ida-205480.

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Analyzing the temporal behaviors and revealing the hidden rules of objects that produce time series data to detect the events that users are interested in have recently received a large amount of attention. Generally, in various application scenarios and most research works, the equal interval sampling of a time series is a requirement. However, this requirement is difficult to guarantee because of the presence of sampling errors in most situations. In this paper, a multigranularity event detection method for an unequal interval time series, called SSED (self-adaptive segmenting based event de

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Ktenidou, Olga-Joan, Norman A. Abrahamson, Walter J. Silva, Robert B. Darragh та Tadahiro Kishida. "The search for hard-rock kappa (κ) in NGA-East: A semi-automated method for large, challenging datasets in stable continental regions". Earthquake Spectra 37, № 1_suppl (2021): 1391–419. http://dx.doi.org/10.1177/87552930211019763.

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This article describes the work undertaken within the Next Generation Attenuation (NGA)-East project with the aim of estimating κ0 (the site-specific component of the high-frequency decay parameter, κ) for rock sites in Central and Eastern North America (CENA), using the project’s shallow crustal dataset. We introduce a methodology to address the numerous challenges in CENA: a large dataset in a low-seismicity stable continental region, with poor magnitude and distance coverage, undesirable recording sensor characteristics (low sampling rates leading to poor high-frequency resolution), high un

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Zhang, Pengdi, Abhishek Venketeswaran, Ruishu F. Wright, Nageswara Lalam, Enrico Sarcinelli, and Paul R. Ohodnicki. "Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification." Sensors 23, no. 12 (2023): 5410. http://dx.doi.org/10.3390/s23125410.

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This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for

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Yang, Shaobo, Jing Hu, Haijiang Zhang, and Guiquan Liu. "Simultaneous Earthquake Detection on Multiple Stations via a Convolutional Neural Network." Seismological Research Letters 92, no. 1 (2020): 246–60. http://dx.doi.org/10.1785/0220200137.

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Abstract In recent years, as the amount of seismic data has grown rapidly, it is very important to develop a fast and reliable event detection and association algorithm. Generally, event detection is first performed on individual stations followed by event association through linking phase arrivals to a common event generating them. This study considers earthquake detection as the problem of image classification and convolutional neural networks (CNNs), as some of the widely used deep-learning tools in image processing, can be well used to solve this problem. In contrast to existing studies tr

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Meenakshi, R., R. Ramakrishnan, A. Chirputkar, and K. Somasundaram. "An efficient Supervised Learning Models for Multimodal MR Brain Image classification." CARDIOMETRY, no. 25 (February 14, 2023): 149–53. http://dx.doi.org/10.18137/cardiometry.2022.25.149153.

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The practicality of distinct vehicular communication tissue classifiers is based on lighting training records that replicate a place or purchase circumstance. The use of transfer learning technologies to address sampling mistakes caused by sparse annotations during supervised learning on automated tumour segmentation is recommended. The comprehensive record of a recognised event might be rather extensive. The suggested method is based on a simple and sparse description, and it effectively corrects systematic sampling mistakes for diverse tissue types using domain correction methodologies. A re

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de Aguiar, Everton Luiz, André Eugenio Lazzaretti, Bruna Machado Mulinari, and Daniel Rodrigues Pipa. "Scattering Transform for Classification in Non-Intrusive Load Monitoring." Energies 14, no. 20 (2021): 6796. http://dx.doi.org/10.3390/en14206796.

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Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Neverthel

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Reich, David R., Paul Prasse, and Lena A. Jäger. "Evaluating Gaze Event Detection Algorithms: Impacts on Machine Learning-based Classification and Psycholinguistic Statistical Modeling." Proceedings of the ACM on Human-Computer Interaction 9, no. 3 (2025): 1–15. https://doi.org/10.1145/3725835.

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Eye movements offer valuable, non-invasive insights into cognitive processes and are widely used in both psycholinguistic research and machine-learning applications, such as assessing reading comprehension and cognitive load. These applications typically rely on fixations and saccades detected through gaze event algorithms, which may be either proprietary or open-source. The impact of different gaze event detection algorithms on subsequent analysis is underexplored and often overlooked. This study investigates how two threshold-based algorithms, I-DT and I-VT, influence both machine-learning c

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Arthur, William C. "A statistical–parametric model of tropical cyclones for hazard assessment." Natural Hazards and Earth System Sciences 21, no. 3 (2021): 893–916. http://dx.doi.org/10.5194/nhess-21-893-2021.

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Abstract. We present the formulation of an open-source, statistical–parametric model of tropical cyclones (TCs) for use in hazard and risk assessment applications. The model derives statistical relations for TC behaviour (genesis rate and location, intensity, speed and direction of translation) from best-track datasets, then uses these relations to create a synthetic catalogue based on stochastic sampling, representing many thousands of years of activity. A parametric wind field, based on radial profiles and boundary layer models, is applied to each event in the catalogue that is then used to

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Kuznetsova, Nataliya, and Natalya Ivanova. "Diversity of Collembola under various types of anthropogenic load on ecosystems of European part of Russia." Biodiversity Data Journal 8 (October 30, 2020): e58951. https://doi.org/10.3897/BDJ.8.e58951.

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Despite the key role played by soil organisms in the functioning of terrestrial ecosystems and provisioning of ecosystem services (Barrios 2007, Bardgett and Putten 2014) available open data on soil biodiversity are incongruously scarce (Eisenhauer 2017, Cameron 2018). This is especially true for Russia, but contrasts long traditions of soil zoological research and large volumes of data that were collected during the second half of the 20<sup>th</sup> century for the territory of the former USSR. Last year 41,928 georeferenced occurrences of soil-dwelling arthropods Collembola were digitized a

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Rich, Benjamin, Jianfeng Huang, Yidong Yang, et al. "Radiomics Predicts for Distant Metastasis in Locally Advanced Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma." Cancers 13, no. 22 (2021): 5689. http://dx.doi.org/10.3390/cancers13225689.

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(1) Background and purpose: clinical trials have unsuccessfully tried to de-escalate treatment in locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) with the goal of reducing treatment toxicity. The aim of this study was to explore the role of radiomics for risk stratification in this patient population to guide treatment. (2) Methods: the study population consisted of 225 patients with locally advanced HPV+ OPSCC treated with curative-intent radiation or chemoradiation therapy. Appearance of distant metastasis was used as the endpoint event. Ra

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Wilson, Brian R., Dacre King, Ivor Growns, and Manoharan Veeragathipillai. "Climatically driven change in soil carbon across a basalt landscape is restricted to non-agricultural land use systems." Soil Research 55, no. 4 (2017): 376. http://dx.doi.org/10.1071/sr16205.

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Soils represent a significant component of the global terrestrial carbon cycle. Historical soil carbon depletion resulting from soil and land management offers an opportunity to store additional carbon to offset greenhouse gas emissions as part of our international response to climate change. However, our ability to reliably measure, estimate and predict soil carbon storage is hindered by a range of sources of variability, not least of which is change through time. In the present study, we assessed temporal changes in soil organic carbon (SOC) and its component fractions in response to climate

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Burdanowitz, Jörg, Stefan A. Buehler, Stephan Bakan, and Christian Klepp. "The sensitivity of oceanic precipitation to sea surface temperature." Atmospheric Chemistry and Physics 19, no. 14 (2019): 9241–52. http://dx.doi.org/10.5194/acp-19-9241-2019.

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Abstract. Our study forms the oceanic counterpart to numerous observational studies over land concerning the sensitivity of extreme precipitation to a change in air temperature. We explore the sensitivity of oceanic precipitation to changing sea surface temperature (SST) by exploiting two novel datasets at high resolution. First, we use the Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) as an observational along-track shipboard dataset at 1 min resolution. Second, we exploit the most recent European Reanalysis version 5 (ERA5) at hourly resolution on a 31 km grid. M

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LEY, ALEXANDRA CAROLINE, and MARTIN RÖSER. "Phylogeny of the climber genus Haumania (Marantaceae) endemic to the tropical lowland rainforest in Central Africa." Phytotaxa 379, no. 2 (2018): 143. http://dx.doi.org/10.11646/phytotaxa.379.2.2.

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The genus Haumania (Marantaceae) consists of three described species of perennial climbers endemic to the tropical lowland rainforest in Central Africa. To unravel their phylogenetic relationship to each other, we used variation among DNA sequences of two nuclear ribosomal (nr) and four plastid (p) markers in five to seven accessions per species sampled across their respective distribution range. Maximum parsimony and Bayesian analyses were applied. All datasets and analyses corroborated the monophyly of the genus. Within the genus, individuals of the species H. danckelmaniana and H. leonardia

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Carboni, E., G. E. Thomas, A. M. Sayer, et al. "Desert dust satellite retrieval intercomparison." Atmospheric Measurement Techniques Discussions 5, no. 1 (2012): 691–746. http://dx.doi.org/10.5194/amtd-5-691-2012.

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Abstract. This work provides a comparison of satellite retrievals of Saharan desert dust aerosol optical depth (AOD) during a strong dust event through March 2006. In this event, a large dust plume was transported over desert, vegetated, and ocean surfaces. The aim is to identify and understand the differences between current algorithms, and hence improve future retrieval algorithms. The satellite instruments considered are AATSR, AIRS, MERIS, MISR, MODIS, OMI, POLDER, and SEVIRI. An interesting aspect is that the different algorithms make use of different instrument characteristics to obtain

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Siders, ZA, ND Ducharme-Barth, F. Carvalho, et al. "Ensemble Random Forests as a tool for modeling rare occurrences." Endangered Species Research 43 (October 8, 2020): 183–97. http://dx.doi.org/10.3354/esr01060.

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Relative to target species, priority conservation species occur rarely in fishery interactions, resulting in imbalanced, overdispersed data. We present Ensemble Random Forests (ERFs) as an intuitive extension of the Random Forest algorithm to handle rare event bias. Each Random Forest receives individual stratified randomly sampled training/test sets, then down-samples the majority class for each decision tree. Results are averaged across Random Forests to generate an ensemble prediction. Through simulation, we show that ERFs outperform Random Forest with and without down-sampling, as well as

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Zadra, Nicola, Annapaola Rizzoli, and Omar Rota-Stabelli. "Chronological Incongruences between Mitochondrial and Nuclear Phylogenies of Aedes Mosquitoes." Life 11, no. 3 (2021): 181. http://dx.doi.org/10.3390/life11030181.

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One-third of all mosquitoes belong to the Aedini, a tribe comprising common vectors of viral zoonoses such as Aedes aegypti and Aedes albopictus. To improve our understanding of their evolution, we present an updated multigene estimate of Aedini phylogeny and divergence, focusing on the disentanglement between nuclear and mitochondrial phylogenetic signals. We first show that there are some phylogenetic discrepancies between nuclear and mitochondrial markers which may be caused by wrong taxa assignment in samples collections or by some stochastic effect due to small gene samples. We indeed sho

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Han, Tingting, Yuankai Qi, and Suguo Zhu. "A Continuous Semantic Embedding Method for Video Compact Representation." Electronics 10, no. 24 (2021): 3106. http://dx.doi.org/10.3390/electronics10243106.

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Video compact representation aims to obtain a representation that could reflect the kernel mode of video content and concisely describe the video. As most information in complex videos is either noisy or redundant, some researchers have instead focused on long-term video semantics. Recent video compact representation methods heavily rely on the segmentation accuracy of video semantics. In this paper, we propose a novel framework to address these challenges. Specifically, we designed a novel continuous video semantic embedding model to learn the actual distribution of video words. First, an emb

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Li, Xuebo, Yongxiang Huang, Guohua Wang, and Xiaojing Zheng. "High-frequency observation during sand and dust storms at the Qingtu Lake Observatory." Earth System Science Data 13, no. 12 (2021): 5819–30. http://dx.doi.org/10.5194/essd-13-5819-2021.

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Abstract. Partially due to global climate change, sand and dust storms (SDSs) have occurred more and more frequently, yet a detailed measurement of SDS events at different heights is still lacking. Here we provide a high-frequency observation from the Qingtu Lake Observation Array (QLOA), China. The wind and dust information were measured simultaneously at different wall-normal heights during the SDS process. The datasets span the period from 17 March to 9 June 2016. The wind speed and direction are recorded by a sonic anemometer with a sampling frequency of 50 Hz, while particulate matter wit

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Tschernosterová, Kateřina, Eva Trávníčková, Florencia Grattarola, Clara Rosse, and Petr Keil. "SPARSE 1.0: a template for databases of species inventories, with an open example of Czech birds." Biodiversity Data Journal 11 (November 23, 2023): e108731. https://doi.org/10.3897/BDJ.11.e108731.

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Here, we introduce SPARSE (acronym for "SPecies AcRoss ScalEs"), a simple and portable template for databases that can store data on species composition derived from ecological inventories, surveys and checklists, with emphasis on metadata describing sampling effort and methods. SPARSE can accommodate resurveys and time series and data from different spatial scales, as well as complex sampling designs. SPARSE focuses on inventories that report multiple species for a given site, together with sampling methods and effort, which can be used in statistical models of true probability of occurrence

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Martin-Cabrera, Patricia, Perez Ruben Perez, Jean-Olivier Irrison, et al. "Establishing Plankton Imagery Dataflows Towards International Biodiversity Data Aggregators." Biodiversity Information Science and Standards 6 (September 7, 2022): e94196. https://doi.org/10.3897/biss.6.94196.

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Over the last decade, plankton research has experienced extensive developments in automatic image acquisition for identifying and quantifying plankton species. This information is useful for the reporting of plankton occurrences and ecological data. Imagery instruments can vary in the way they sample (benchtop or <em>in situ</em> imagers) and the particle's size range they target (see Lombard et al. (2019) for an extensive comparison of instruments and specifications). However, due to the wide variety of instruments and their (automatic) output data and formats, it is challenging to integrate

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Fioretto, Ferdinando, and Pascal Van Hentenryck. "OptStream: Releasing Time Series Privately." Journal of Artificial Intelligence Research 65 (July 25, 2019): 423–56. http://dx.doi.org/10.1613/jair.1.11583.

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Many applications of machine learning and optimization operate on data streams. While these datasets are fundamental to fuel decision-making algorithms, often they contain sensitive information about individuals, and their usage poses significant privacy risks. Motivated by an application in energy systems, this paper presents OptStream, a novel algorithm for releasing differentially private data streams under the w-event model of privacy. OptStream is a 4-step procedure consisting of sampling, perturbation, reconstruction, and post-processing modules. First, the sampling module selects a smal

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Vo, Tham, and Phuc Do. "GOW-Stream: A novel approach of graph-of-words based mixture model for semantic-enhanced text stream clustering." Intelligent Data Analysis 25, no. 5 (2021): 1211–31. http://dx.doi.org/10.3233/ida-205443.

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Recently, rapid growth of social networks and online news resources from Internet have made text stream clustering become an insufficient application in multiple domains (e.g.: text retrieval diversification, social event detection, text summarization, etc.) Different from traditional static text clustering approach, text stream clustering task has specific key challenges related to the rapid change of topics/clusters and high-velocity of coming streaming document batches. Recent well-known model-based text stream clustering models, such as: DTM, DCT, MStream, etc. are considered as word-indep

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Pan, Phoebe, Anusha Srirenganathan Malarvizhi, and Chaowei Yang. "Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures." Atmosphere 16, no. 2 (2025): 127. https://doi.org/10.3390/atmos16020127.

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Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3)

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Diego-Tortosa, Dídac, Danilo Bonanno, Manuel Bou-Cabo, et al. "Effective Strategies for Automatic Analysis of Acoustic Signals in Long-Term Monitoring." Journal of Marine Science and Engineering 13, no. 3 (2025): 454. https://doi.org/10.3390/jmse13030454.

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Hydrophones used in Passive Acoustic Monitoring generate vast amounts of data, with the storage requirements for raw signals dependent on the sampling frequency, which limits the range of frequencies that can be recorded. Since the installation of these observatories is costly, it is crucial to maximize the utility of high-sampling-rate recordings to expand the range of survey types. However, storing these large datasets for long-term trend analysis presents significant challenges. This paper proposes an approach that reduces the data storage requirements by up to 85% while preserving critical

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Mouslopoulou, Vasiliki, Andrew Nicol, John Walsh, John Begg, Dougal Townsend, and Dionissios Hristopulos. "Fault-slip accumulation in an active rift over thousands to millions of years and the importance of paleoearthquake sampling." Journal of Structural Geology 36 (March 26, 2012): 71–80. https://doi.org/10.1016/j.jsg.2011.11.010.

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The catastrophic earthquakes that recently (September 4th, 2010 and February 22nd, 2011) hit Christchurch, New Zealand, show that active faults, capable of generating large-magnitude earthquakes, can be hidden beneath the Earth’s surface. In this article we combine near-surface paleoseismic data with deep (<5 km) onshore seismic-reflection lines to explore the relations between fault growth over short (<27kyr) and long (>1Ma) timescales in the Taranaki Rift, New Zealand. Our analysis shows that the integration of different timescale datasets provides a basis for identifying active fau

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Iyer, V., S. Shetty, and S. S. Iyengar. "STATISTICAL METHODS IN AI: RARE EVENT LEARNING USING ASSOCIATIVE RULES AND HIGHER-ORDER STATISTICS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 119–30. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-119-2015.

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Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing

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Sica, Yanina, and Paula Zermoglio. "Unlocking Inventory Data Capture, Sharing and Reuse: The Humboldt Extension to Darwin Core." Biodiversity Information Science and Standards 5 (September 13, 2021): e74275. https://doi.org/10.3897/biss.5.74275.

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Biodiversity inventories, i.e., recording multiple species at a specific place and time, are routinely performed and offer high-quality data for characterizing biodiversity and its change. Digitization, sharing and reuse of incidental point records (i.e., records that are not readily associated with systematic sampling or monitoring, typically museum specimens and many observations from citizen science projects) has been the focus for many years in the biodiversity data community. Only more recently, attention has been directed towards mobilizing data from both new and longstanding inventories

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Ito, Toshihiro, Tsuyoshi Kato, Makoto Hasegawa, et al. "Evaluation of virus reduction efficiency in wastewater treatment unit processes as a credit value in the multiple-barrier system for wastewater reclamation and reuse." Journal of Water and Health 14, no. 6 (2016): 879–89. http://dx.doi.org/10.2166/wh.2016.096.

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The virus reduction efficiency of each unit process is commonly determined based on the ratio of virus concentration in influent to that in effluent of a unit, but the virus concentration in wastewater has often fallen below the analytical quantification limit, which does not allow us to calculate the concentration ratio at each sampling event. In this study, left-censored datasets of norovirus (genogroup I and II), and adenovirus were used to calculate the virus reduction efficiency in unit processes of secondary biological treatment and chlorine disinfection. Virus concentration in influent,

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Omachi, Yuji, Nen Saito, and Chikara Furusawa. "Rare-Event Sampling Analysis Uncovers the Fitness Landscape of the Genetic Code." February 22, 2023. https://doi.org/10.5281/zenodo.7663206.

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Yim, Marx, Xin Rui Ong, Li Yuen Chiew, and Eleanor Slade. "A comprehensive synthesis of dung beetle records (Coleoptera, Scarabaeidae, Scarabaeinae) from Sabah, Malaysia." Biodiversity Data Journal 12 (September 12, 2024). http://dx.doi.org/10.3897/bdj.12.e126697.

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Dung beetles play key roles in terrestrial ecosystems, contributing to many important ecosystem process and functions, such as nutrient recycling, parasite control and seed dispersal. Due to their tight associations with mammals and their responses to environmental change, they are also frequently used as environmental and biological indicators. Despite their importance, knowledge about dung beetles in Southeast Asia is limited. To address this information gap, we established a databasing project - “Mobilising data on ecologically important insects in Malaysia and Singapore” - funded by the Gl

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Huang, Xiaoqian, Sanket Kachole, Abdulla Ayyad, Fariborz Baghaei Naeini, Dimitrios Makris, and Yahya Zweiri. "A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment." Scientific Data 11, no. 1 (2024). http://dx.doi.org/10.1038/s41597-024-02920-1.

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AbstractEvent-based cameras are commonly leveraged to mitigate issues such as motion blur, low dynamic range, and limited time sampling, which plague conventional cameras. However, a lack of dedicated event-based datasets for benchmarking segmentation algorithms, especially those offering critical depth information for occluded scenes, has been observed. In response, this paper introduces a novel Event-based Segmentation Dataset (ESD), a high-quality event 3D spatial-temporal dataset designed for indoor object segmentation within cluttered environments. ESD encompasses 145 sequences featuring

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