Relevant bibliographies by topics / Sampling-event datasets

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Academic literature on the topic 'Sampling-event datasets'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Sampling-event datasets"

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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): 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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Dissertations / Theses on the topic "Sampling-event datasets"

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Audoux, Thomas. "Approches expérimentales pour l’étude et la caractérisation des dépôts humides d’aérosols atmosphériques par les précipitations." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7332.

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Dans le cadre de mon travail de thèse, je me suis intéressé aux aérosols atmosphériques et à leur transfert de l’atmosphère vers les surfaces terrestres par les précipitations. La stratégie générale que j’ai suivie repose sur l’observation des dépôts humides sur différentes échelles de temps, interannuelle d’une part et intraévènementielle de l’autre. Elle repose aussi sur leur observation dans des environnements marqués en termes de charge et de composition en aérosols, mais aussi de dynamiques atmosphériques et de précipitations. Le fait de combiner des mesures à la fois sur la composition d
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Conference papers on the topic "Sampling-event datasets"

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Ghislain VLAVONOU, Davy, Isckyros Gangbo, Thierry Nsabimana, et al. "IDS with hybrid sampling technique: combination over and under-sampling technique and comparison with deep convolutional approach." In Intelligent Human Systems Integration (IHSI 2024) Integrating People and Intelligent Systems. AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004488.

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Digital is constantly evolving with the appearance of connected objects and on top of the popularization today of artificial intelligence. One of the direct inductions remains the excessive proliferation of various kinds of attacks in computer systems. Hackers exploit these vulnerabilities to break in and attack systems with increasingly complex attacks. The consequences of intrusions are destructive and ruinous for businesses and organizations such as electronic ransom ware, data alteration and loss, financial and brand image loss.It is important for those involved in computer systems to equi
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Liu, Yongzan, Lin Liang, Olga Podgornova, Smaine Zeroug, Takashi Mizuno, and Joel Le Calvez. "Automated Microseismic Event Detection for Downhole Distributed Acoustic Sensing Data Processing." In 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/arma-2023-0797.

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ABSTRACT Distributed acoustic sensing (DAS) can provide high-resolution measurements owing to the closely spaced sensing channels over a long distance and high sampling rate – attributes that are beneficial to seismic monitoring and analysis. However, DAS arrays generally suffer from a lower signal-to-noise ratio (SNR). Additionally, dense spatial and temporal measurements can result in extremely large data volumes that hinder efficient data storage, transmission, and processing. Automatically and accurately detecting seismic events in continuous DAS data is challenging. In this study, we pres
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Wang, Junzhe, Shyam Kareepadath Sajeev, Evren Ozbayoglu, Silvio Baldino, Yaxin Liu, and Haorong Jing. "Reducing NPT Using a Novel Approach to Real-Time Drilling Data Analysis." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215028-ms.

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Abstract Early detection and characterization of anomalous events during drilling operations are critical to avoid costly downtime and prevent hazardous events, such as a stuck pipe or a well control event. A key aspect of real-time drilling data analysis is the capability to make precise predictions of specific drilling parameters based on past time series information. The ideal models should be able to deal with multivariate time series and perform multi-step predictions. The recurrent neural network with a long short-term memory (LSTM) architecture is capable of the task, however, given tha
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Mohamad, Mustafa A., and Themistoklis P. Sapsis. "Efficient sampling for extreme event statistics of the wave loads on an offshore platform." In SNAME 30th American Towing Tank Conference. SNAME, 2017. http://dx.doi.org/10.5957/attc-2017-0049.

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We develop a method for the evaluation of extreme event statistics associated with nonlinear dynamical systems, using a very small number of samples. From an initial dataset of design points, we formulate a sequential strategy that provides the ‘next-best’ data point (set of parameters) that when evaluated results in improved estimates of the probability density function (pdf) for a scalar quantity of interest. The approach utilizes Gaussian process regression to perform Bayesian inference on the parameter-to-observation map describing the quantity of interest. We then approximate the desired
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Ameri, Farhad, Evan Wallace, Reid Yoder, and Frank Riddick. "Agri-Food Supply Chain Traceability Supported by a Formal Ontology: A Grain Elevator to Processor Use Case." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-108860.

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Abstract Traceability of food products to their sources is critical for quick responses to food emergencies. However, having the complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. Food trace-ability is challenging for a variety of reasons including diversity and heterogenicity of participants, complexity of the supply chain and its processes and lack of a common understanding of steps in a supply chain, and incompleteness of data, and unwillingness of actors to expose information of their internal operations. The objec
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Campolettano, Eamon T., John M. Scanlon, and Kristofer D. Kusano. "Representative Cyclist Collision Injury Risk Distributions for a Dense-Urban US ODD Using Naturalistic Dash Camera Data." In WCX SAE World Congress Experience. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2645.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Automated driving systems (ADS) are designed toward safely navigating the roadway environment, which also includes consideration of potential conflict with other road users. Of particular concern is understanding the cumulative risk associated with vulnerable road users (VRUs) conflicts and collisions. VRUs represent a population of road users that have limited protection compared to vehicle occupants. These severity distributions are particularly useful in evaluating ADS real-world performance with respect to the existi
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