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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 Global Biodiversity Information Facility (GBIF). As part of this project, we compiled two extensive datasets – a sampling-event and occurrence dataset and a taxonomic checklist – for the dung beetles of Sabah, Bornean Malaysia. The sampling-event dataset documents 2,627 unique sampling events and 21,348 dung beetle occurrence records for Sabah. The taxonomic checklist includes 156 confirmed dung beetle species and 36 synonyms, totalling 192 records. These datasets have been made open access through the GBIF portal, which we hope will enhance the understanding of dung beetle taxonomy and their distributions in Southeast Asia.All data presented in this paper comprises of available information pertaining to the dung beetles of Sabah.
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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. The new scheme transforms the byte sequence of an event into a fixed-size vector through content-defined chunking and feature hashing. Then, a clustering algorithm is applied to the vectors, and only a few samples from each cluster are selected for manual labeling. The experimental results demonstrate that the new scheme can select only 2% of the data for labeling without degrading the F1-score of the machine learning model. Two datasets, a private dataset from a real security operations center and a public dataset from the Internet for experimental reproducibility, are used.
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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-event data are rare in Italy.This open-access dataset includes 2,295 specimens for a total of 21 Asilidae and 65 Syrphidae species. Information about the collection (e.g. place, date, methods applied, collector) and the identification (e.g. species name, author, taxon ID) of the species is provided. Given the current biodiversity crisis, the publication of checklists, sampling-event data and datasets on insect communities in open-access repositories is highly recommended, as it represents the opportunity to share biodiversity information amongst different stakeholders. Moreover, such data are also a valuable source of information for nature reserve managers responsible for monitoring the conservation status of protected and endangered species and habitats and for evaluating the effects of conservation actions over time.
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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, gardens, quarries, small urban woodlands, grasslands and riparian habitats. Carabids were collected by pitfall traps during most of the vegetative season (mostly from late April - early May to at least early October) for 13 seasons between 1994 and 2015. In total, the dataset contains 189 carabid species and 79,091 specimens. The dataset provides information about species composition and abundance, habitat distribution, seasonal and long-term dynamics of carabid beetles in environments of different degrees of urbanisation.This dataset is the first sampling-event dataset about carabids in various urban habitats published through GBIF.
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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 various biased sampling methods and evaluates their performance on different datasets using four different discovery techniques. Our experiments show that it is possible to considerably speed up discovery techniques using biased sampling without losing the resulting process model quality. Furthermore, due to the implicit filtering (removing outliers) obtained by applying the sampling technique, the model quality may even be improved.
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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 understudied and ecologically degraded freshwater systems. Historical pollution events and contemporary anthropogenic pressures – agricultural intensification, hydrological fragmentation and invasive species - have severely compromised wetland integrity. During mid-December 2005 and November to December 2006, standardised surveys employed fixed-radius point counts (158 sites) with the component counting method to enhance accuracy.We present the first comprehensive waterbird dataset for the Anhui and Jiangsu part of the Huai He floodplain, comprising 44 species (32,517 individuals) recorded across 30 wetlands during 2005–2006 surveys. All occurrence data adhere to Darwin Core standards and are accessible via the Global Biodiversity Information Facility, providing spatial-temporal baselines for abundance and distributional data for waterbirds in this region.
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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(Wilkinson et al. 2016) for LTER data would result in better data integration and reutilization to support knowledge discovery and innovation in ecological research. The Global Biodiversity Information Facility (GBIF 2019a). is the largest repository of species distribution data in the world, providing access to more than a billion records from over 43,000 datasets. GBIF is a good example of FAIR principles implementation: GBIF data is highly standardized, using Darwin Core (Wieczorek et al. 2012) for data and ecological metadata language (EML, Fegraus et al. 2005) for metadata, allowing record-level search; and has implemented globally unique and persistent identifiers for datasets and downloads. Relevant in this context is that GBIF has recently introduced a new data format intended for monitoring projects and sampling event protocols (GBIF 2019b). In this presentation, we explore the suitability of GBIF data formats and workflows to serve LTER datasets, and the work it may take to transform typical LTER datasets into these formats. For this exercise, we use some datasets available via the DEIMS platform, corresponding to the same territory, (Sierra Nevada, Spain (e.g. Bonet 2016, Bonet 2018) and transform them into the GBIF's sample-based Event core publish them in the GBIF data network, and then perform an analysis to assess how the standardized datasets work in practice, both among themselves and also with typical "occurrence-based" GBIF datasets. Finally, we discuss our findings and make recommendations for the GBIF and LTER communities.
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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 invasion in Ukraine. Surveyed areas in Kherson and Mykolaiv Regions, including established monitoring plots, were severely affected by military actions in 2022. Therefore, collected data are of significant value in the context of biodiversity documentation. The knowledge about the biodiversity of this area will help to assess the environmental impact of the war and plan restoration of the damaged or destroyed habitats. The first preliminary analysis of collected data demonstrates the biodiversity richness and conservation value of studied grassland habitats.We provide sampling-event datasets with 7467 occurrences, which represent 708 taxa (vascular plants, bryophytes and lichens) collected in 275 vegetation relevés. Amongst them, vascular plants are represented by 6665 occurrences (610 taxa), lichens - 420 (46) and bryophytes - 381 (51). Several new species were reported for the first time at the national or regional level. In particular, one vascular plant species (<i>Torilis pseudonodosa</i>) and two lichen species (<i>Cladonia conista</i>, <i>Endocarpon loscosii</i>) were new to Ukraine. One vascular plant (<i>Stipa tirsa</i>), two species of bryophytes (<i>Rhynchostegium megapolitanum</i>, <i>Ptychostomum torquescens</i>) and three species of lichens (<i>Cladonia cervicornis</i>, <i>C. symphycarpa</i>, <i>Involucropyrenium breussi</i>) were recorded for the first time for the Kherson Region. Additionally, these datasets contain occurrences of taxa with narrow distribution, specialists of rare habitat types and, therefore, represented by a low number of occurrences in relevant biodiversity databases and particularly in GBIF. This publication highlights the diversity of natural vegetation and its flora in southern Ukraine and raises conservation concerns.
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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 presence of marine species in space and time. Data collected for biological studies however often include more than just presence or abundance. Physical and chemical measurements are often taken concomitantly providing insights into the environmental conditions the species live in. Details on the nature of the sampling methods, equipment used and effort can also be of major importance. Based on requirements from the growing OBIS community for data archiving and scientific applications, OBIS completed the OBIS-ENV-DATA project in 2017 to enhance its data standard by accommodating additional data types (De Pooter et al. 2017). The proposed standard allows for the management of sampling methodology, animal tracking and telemetry data, and environmental measurements such as nutrient concentrations, sediment characteristics and other abiotic parameters measured during sampling. The new OBIS data standard builds on the Darwin Core Archive and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of an Event Core in combination with an Occurrence Extension and an enhanced MeasurementOrFact Extension Fig. 1. This new structure enables the linkage of measurements or facts - quantitative or qualitative properties - to both sampling events and species occurrences, and includes additional fields for property standardization. The OBIS standard also embraces the use of the new Darwin Core term parentEventID, enabling a sampling event hierarchy. During the follow-up project "OBIS-Event Data", the format will be further fine-tuned during two workshops with two different communities of practice. The first workshop (April 2018) will focus on animal tagging and tracking data, while the second one (October 2018) will tackle macro- and meiobenthos data. The OBIS-Event Data project will also develop the first data products and applications based on the standard and make these tools part of the core OBIS data system output. We believe that the adoption of this new data standard by the international community will be key to improving the effectiveness of the knowledge base and will enhance integration and management of critical data needed to understand ecological and biological processes in the ocean.
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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 comprises 587 sampling events and measurements of various parameters, including water temperature, electrical conductivity, stable oxygen and hydrogen isotopes, dissolved organic carbon concentration and 14C, colored and fluorescent dissolved organic matter, dissolved inorganic and total nutrients, and dissolved elemental and ion concentrations. Sampling consistency and continuity and data quality were ensured through simple sampling protocols, real-time communication, and collaboration with local and international partners. The data are available as a collection of datasets separated by parameter groups and periods at https://doi.org/10.1594/PANGAEA.913197 (Juhls et al., 2020b). To our knowledge, this dataset provides an unprecedented temporal resolution of an Arctic river's biogeochemistry. This makes it a unique baseline on which future environmental changes, including changes in river hydrology, at temporal scales from precipitation event to seasonal to interannual can be detected.
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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 which samples are collected at constant time intervals over the rainfall event and sampling volumes are pre-set as proportional to the runoff volume discharged between two consecutive sample leads to the most representative results. Recommended sampling time intervals are of 5 min for Berlin and Chassieu (resp. 100 and 185 ha area) and 10 min for Graz and Ecully (resp. 335 and 245 ha area), with relative sampling errors between 7% and 20% and uncertainties in sampling errors of about 5%. Uncertainties related to sampling volumes, TSS laboratory analyses and beginning/ending of rainstorm events are reported as the most influent sources in the uncertainties of sampling errors and EMCs.
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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 important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation. This study proposes a software architecture, named Rainbow, as the reference architecture, also, it develops an algorithm for adaptive sampling. The algorithm was implemented in the Rainbow architecture and tested using two datasets; the results show the proper operation of the architecture as well as the algorithm. In conclusion, the Rainbow software architecture has the potential to be used as a framework for adaptive sampling algorithms, and the developed algorithm allows adaptive sampling based on the changes in the signal.
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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 and thematic nodes, and the central OBIS database. However, due to limitations of the DwC-A schema, this data format is currently not suitable for storing sampling event details or sample related measurements, as well as biotic measurements. In order to overcome this problem, OBIS has created a new extension type based on the existing MeasurementOrFacts extension (De Pooter et al. 2017). This ExtendedMeasurementOrFacts extension adds an occurrenceID field, which allows linking biotic measurements to occurrences, even if the archive contains an event table and sample level measurements or facts. In addition, identifiers for measurement types, values and units can now be added in the new measurementTypeID, measurementValueID and measurementUnitID fields. These identifiers link to vocabularies such as the BODC NERC Vocabulary, and greatly improve the interoperability and reusability of the OBIS datasets.
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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 collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling events and species occurrences, and includes additional fields for property standardization. We also embrace the use of the new parentEventID DwC term, which enables the creation of a sampling event hierarchy. We believe that the adoption of this recommended practice as a new data standard for managing and sharing biological and associated environmental datasets by IODE and the wider international scientific community would be key to improving the effectiveness of the knowledge base, and will enhance integration and management of critical data needed to understand ecological and biological processes in the ocean, and on land.
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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 the impact of short text length on text representation quality, the Doc2vec embedding model is utilized to represent short text, yielding a one-dimensional dense vector. Additionally, the dimensions of the input layer and convolution kernel of TextCNN are adjusted. Subsequently, a short text event extraction model based on TF-IDF and TextRank is proposed to extract crucial information, for instance, names and research-related institutions, from events and facilitate the identification of potential public opinion events related to suspected scientific integrity issues. Results of experiments have demonstrated that utilizing SMOTE to balance the dataset is able to improve the classification results of TextCNN classifiers. Compared to traditional classifiers, TextCNN exhibits greater robustness in addressing the problems of imbalanced datasets. However, challenges such as low information content, non-standard writing, and polysemy in short texts may impact the accuracy of event extraction. The framework can be further optimized to address these issues in the future.
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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 approaches (MISR, AATSR), polarisation measurements (POLDER), single-view approaches using solar wavelengths (OMI, MODIS), and the thermal infrared spectral region (SEVIRI, AIRS). Differences between instruments, together with the comparison of different retrieval algorithms applied to measurements from the same instrument, provide a unique insight into the performance and characteristics of the various techniques employed. As well as the intercomparison between different satellite products, the AODs have also been compared to co-located AERONET data. Despite the fact that the agreement between satellite and AERONET AODs is reasonably good for all of the datasets, there are significant differences between them when compared to each other, especially over land. These differences are partially due to differences in the algorithms, such as assumptions about aerosol model and surface properties. However, in this comparison of spatially and temporally averaged data, it is important to note that differences in sampling, related to the actual footprint of each instrument on the heterogeneous aerosol field, cloud identification and the quality control flags of each dataset can be an important issue.
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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 accessibility of much-needed historical datasets on macro-invertebrates.Here, we present sampling event data on invertebrates from four grasslands taken out of agricultural production over the span of several decades, effectively displaying a chronosequence on the effects of agricultural extensification. The data were collected by means of a standardised sampling design using pyramid traps, pitfall traps and soil samples.The raw data presented in this data paper have not been published before. They consist of 20,000+ records of nearly 70,000 specimens from 121 taxonomic groups. The data were collected using a standardised field study set-up and specimens were identified by taxonomic specialists. Most groups were identified up to family level, with eight groups identified up to species level. The occurrence data are complemented by information on plant composition, meteorological data and soil physical characteristics. The dataset has been registered in the Global Biodiversity Information Facility (GBIF): http://doi.org/10.15468/7n499e
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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 spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates, based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns of the design storms from a rather random (short-duration events, 15 min) to a more structured, orographically influenced pattern (long-duration events, 24 h) seems to be much more realistic in the weather-radar-based product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.
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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; Marchantiophyta, 16), macro- and microalgae (Ochrophyta, 546 occurrences; Chlorophyta, 193; Charophyta, 153; Cyanobacteria, 139; Cryptophyta, 86; Myzozoa, 33; Euglenozoa, 27; Rhodophyta, 8; Bigyra, 1) and aquatic invertebrates (Arthropoda, 1408 occurrences; Annelida, 487; Mollusca, 263; Platyhelminthes, 36; Cnidaria, 11). This paper summarises previously unpublished materials in a standardised form.The paper summarises the data collected during the long-term phytodiversity studies in a series of lakes of different types (Vologda Region, north-western Russia). Data on algae and invertebrates diversity were obtained in 60 different plant communities of aquatic, semi-aquatic and coastal plants or their combinations. A total of 6787 occurrences were included in the dataset, published in the global biodiversity database (GBIF) for the first time. According to the GBIF taxonomic backbone, the dataset comprised 837 taxa, including 711 lower-rank taxa (species, subspecies, varieties, forms). New records of 47 species rare and protected in the Vologda Region are given: 43 species of plants, three species of animals and one species of Cyanobacteria.
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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 detection), is proposed. First, in view of the trend features of a time series, a self-adaptive segmenting algorithm is proposed to divide a time series into unfixed-length segmentations based on the trends. Then, by clustering the segmentations and mapping the clusters to different identical symbols, a symbol sequence is built. Finally, based on unfixed-length segmentations, the multigranularity events in the discrete symbol sequence are detected using a tree structure. The SSED is compared to two previous methods with ten public datasets. In addition, the SSED is applied to the public transport systems in Xiamen, China, using bus-speed time-series data. The experimental results show that the SSED can achieve higher efficiency and accuracy than existing algorithms.
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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 uncertainty in the regional stress drop, and lack of site-specific velocity characterization. We use two band-limited κ estimation approaches, the acceleration and displacement spectrum (AS and DS), applied above and below the source corner frequency ( fc), respectively. For band-limited approaches, the key requirement is an estimate of fc, which—apart from the event magnitude readily available in the flatfile—also heavily depends on the highly uncertain stress drop. By considering lower and upper bounds on regional stress drop, we propose a new method to quickly and automatically screen such very large datasets to identify all possible recordings for which band-limited κ approaches can be used. Combining them produces better-quantify estimates of κ and its epistemic uncertainties for this challenging dataset. The mean κ0 values combining the two methods are 13 ± 23 ms for horizontal ground motion.
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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 a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.
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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 training the network using seismic data from individual stations, in this study, we train a CNN model jointly using records of multiple stations. Because the CNN automatically synthesizes information among multiple stations, the detector can more reliably detect seismic events and is less affected by spurious signals. The CNN is trained using aftershock data of the 2013 Mw 6.6 Lushan earthquake. We have applied it on two very different datasets of Gofar transform fault, East Pacific Rise and Changning shale gas field in southern Sichuan basin, China. The tests show that the trained CNN has strong generalization ability and is flexible with the number of available stations, different instrument types, and different data sampling rates. It can detect many more events than the conventional short-term average/long-term average detector and is more efficient than template-matching methods.
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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 retrospective examination of the 2013 challenge data sets and a multimodal MR image from 19 malignant gliomas patients verified the present strategy. When compared to training on entirely marked outcomes, the time to mark and train is reduced by more than 70 and 180 seconds respectively. This considerably facilitates the creation and ongoing extension of annotated large datasets in a variety of circumstances and imaging environments; this is an important step in the actual deployment of tissue categorization learning algorithms.
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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. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.
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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 classification tasks and psycholinguistic statistical modeling. Using diverse datasets-including stationary, remote, and VR eye-tracking data across multiple sampling frequencies-our findings show significant differences in downstream performance. For ML tasks, I-DT generally outperforms I-VT, with I-VT being highly sensitive to threshold choices. In psycholinguistic analysis, results confirm established findings only when thresholds align with established fixation metrics, emphasizing the importance of appropriate threshold selection for meaningful analysis. Our code is publicly available: https://github.com/aeye-lab/eye-movement-preprocessing.
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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 fit extreme-value distributions for evaluation of return period wind speeds. We demonstrate the capability of the model to replicate observed behaviour of TCs, including coastal landfall rates which are of significant importance for risk assessments.
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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 and published through GBIF.org. This work continues these activities. The article combines descriptions of three new sampling-event datasets about the various types of anthropogenic load on the diversity and the abundance of Collembola, small arthropods involved in the destruction of organic residues in the soil:Collembola of winter wheat fields in the Kaluga Region: conservation treatment versus conventional one (Kuznetsova et al. 2020). The following variants were studied: 1) treatment with organic fertilizers and tillage, without mineral fertilizers and pesticides, 2) the same but without tillage, only disking; 3) with mineral fertilizers, pesticides and tillage. Special multi-scale sampling design was used. The material was collected on July 24–26, 2019 in Kaluga region, European part of Russia. Data on 2226 records on 7302 specimens of 32 species in 6 fields in 486 soil cores are presented.Collembola of broadleaved forests along gradient of urbanization in Moscow (Kuznetsova and Ageeva 2020). Sampling plots were placed in oak and lime forests located at different distances from the center of Moscow. The material was collected in different seasons of 1990–1991. Data on 1737 records on 6873 specimens of 64 species (17 series of sampling, 720 soil cores) are presented.Collembola in clear cutting areas of Arkhangelsk region: spatial and temporal series of the data (Kuznetsova and Klyueva 2020). Sampling plots were in birch forests of different ages with spruce underbrush and in old spruce forest. The study was carried out in July of 1970–1971 and 1984 in Arkhangelsk region, European part of Russia. In 1970, cores were taken at sites where the forest was restored 15, 30 and 80 years after clear cuttings, as well as in a 180-year old spruce forest. In 1984, sampling was repeated in two plots. Data on 1468 records on 18788 specimens of 47 species (7 series of sampling, 720 soil cores) are presented.These datasets contribute to filling gaps in the global biodiversity distribution of the Collembola. All datasets present new information about effects of agricultural treatments, urbanization and clear cutting on springtail diversity and abundance in ecosystems of European part of Russia.
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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. Radiomics data were extracted from the gross tumor volumes (GTVs) identified on the planning CT, with gray level being discretized using three different bin widths (8, 16, and 32). The data extracted for the groups with and without distant metastasis were subsequently balanced using three different algorithms including synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and borderline SMOTE. From these different combinations, a total of nine radiomics datasets were derived. Top features that minimized redundancy while maximizing relevance to the endpoint were selected individually and collectively for the nine radiomics datasets to build support vector machine (SVM) based predictive classifiers. Performance of the developed classifiers was evaluated by receiver operating characteristic (ROC) curve analysis. (3) Results: of the 225 locally advanced HPV+ OPSCC patients being studied, 9.3% had developed distant metastases at last follow-up. SVM classifiers built for the nine radiomics dataset using either their own respective top features or the top consensus ones were all able to differentiate the two cohorts at a level of excellence or beyond, with ROC area under curve (AUC) ranging from 0.84 to 0.95 (median = 0.90). ROC comparisons further revealed that the majority of the built classifiers did not distinguish the two cohorts significantly better than each other. (4) Conclusions: radiomics demonstrated discriminative ability in distinguishing patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone and may serve to risk stratify this patient population with the purpose of guiding the appropriate therapy.
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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 alone and in the absence of land use change at any given site by examining a series of soil monitoring sites across a basalt landscape in north-west New South Wales under a range of land use types over a 3-year period (March–April 2008 and March–April 2011), where a significant rainfall event had occurred in the intervening time (2010). Across the dataset, woodland soils contained the largest carbon concentration (SOC%) and total organic carbon stock (TOCs) compared with other non-wooded land use systems, which themselves were statistically similar. However, larger carbon quantities were restricted largely to the surface (0–10cm) soil layers. Between 2008 and 2011, significant increases in SOC% and TOCs were detected, but again these were restricted to the woodland sites. No change in particulate organic carbon (POC) was detected between the two sampling times, but both humic organic carbon (HOC) and resistant organic carbon (ROC) increased in woodland soils between the two sampling times. Increased HOC we attribute to microbial processing of soil carbon following the 2010–11 rainfall event. However, we suggest that increased ROC results from limitations in mid-infrared calibration datasets and estimations. We conclude that the quantity of soil carbon and its component fractions is, indeed, driven by climatic factors, but that these effects are moderated by aboveground land use and SOC inputs.
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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. Matched with each other, ERA5 vertical velocity allows the constraint of the OceanRAIN precipitation. Despite the inhomogeneous sampling along ship tracks, OceanRAIN agrees with ERA5 on the average latitudinal distribution of precipitation with fairly good seasonal sampling. However, the 99th percentile of OceanRAIN precipitation follows a super Clausius–Clapeyron scaling with a SST that exceeds 8.5 % K−1 while ERA5 precipitation scales with 4.5 % K−1. The sensitivity decreases towards lower precipitation percentiles, while OceanRAIN keeps an almost constant offset to ERA5 due to higher spatial resolution and temporal sampling. Unlike over land, we find no evidence for a decreasing precipitation event duration with increasing SST. ERA5 precipitation reaches a local minimum at about 26 ∘C that vanishes when constraining vertical velocity to strongly rising motion and excluding areas of weak correlation between precipitation and vertical velocity. This indicates that instead of moisture limitations as over land, circulation dynamics rather limit precipitation formation over the ocean. For the strongest rising motion, precipitation scaling converges to a constant value at all precipitation percentiles. Overall, high resolutions in observations and climate models are key to understanding and predicting the sensitivity of oceanic precipitation extremes to a change in SST.
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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. leonardiana were each monophyletic. Individuals of H. liebrechtsiana, however, were paraphyletic. They clustered into two distinct geographic clades (Gabon and Democratic Republic of Congo), with the Gabonese clade being most closely related to the individuals of H. danckelmaniana. The latter might be due to introgression in areas of distributional overlap between these two species, as shown in earlier phylogeographic studies. A recent hybridisation event between H. danckelmaniana and H. liebrechtsiana is documented here in a single individual by incongruence in the nr and p dataset. Overall, the study provides support for H. leonardiana being sister to all other species of this genus. To confirm the absence of hybridisation in H. leonardiana further sampling is proposed in the respective areas of distributional overlap with its sister species.
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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 retrievals over bright surfaces. These include multi-angle approaches (MISR, AATSR), polarisation measurements (POLDER), single-view approaches using solar wavelengths (OMI, MODIS), and the thermal infrared spectral region (SEVIRI, AIRS). Differences between instruments, together with the comparison of different retrieval algorithms applied to measurements from the same instrument, provide a unique insight into the performance and characteristics of the various techniques employed. As well as the intercomparison between different satellite products, the AODs have also been compared to co-located AERONET data. Despite the fact that the agreement between satellite and AERONET AODs is reasonably good for all of the datasets, there are significant differences between them when compared to each other, especially over land. These differences are partially due to differences in the algorithms, such as assumptions about aerosol model and surface properties. However, in this comparison of spatially and temporally averaged data, at least as significant as these differences are sampling issues related to the actual footprint of each instrument on the heterogeneous aerosol field, cloud identification and the quality control flags of each dataset.
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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 with the synthetic minority over-sampling technique, for highly class imbalanced to balanced datasets. Spatial covariance greatly impacts ERFs’ perceived performance, as shown through simulation and case studies. In case studies from the Hawaii deep-set longline fishery, giant manta ray Mobula birostris syn. Manta birostris and scalloped hammerhead Sphyrna lewini presence had high spatial covariance and high model test performance, while false killer whale Pseudorca crassidens had low spatial covariance and low model test performance. Overall, we find ERFs have 4 advantages: (1) reduced successive partitioning effects; (2) prediction uncertainty propagation; (3) better accounting for interacting covariates through balancing; and (4) minimization of false positives, as the majority of Random Forests within the ensemble vote correctly. As ERFs can readily mitigate rare event bias without requiring large presence sample sizes or imparting considerable balancing bias, they are likely to be a valuable tool in bycatch and species distribution modeling, as well as spatial conservation planning, especially for protected species where presence can be rare.
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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 show that the concatenated dataset is model and framework dependent, indicating a general paucity of signal. Our Bayesian calibrated divergence estimates point toward a mosquito radiation in the mid-Jurassic and an Aedes radiation from the mid-Cretaceous on. We observe, however a strong chronological incongruence between mitochondrial and nuclear data, the latter providing divergence times within the Aedini significantly younger than the former. We show that this incongruence is consistent over different datasets and taxon sampling and that may be explained by either peculiar evolutionary event such as different levels of saturation in certain lineages or a past history of hybridization throughout the genus. Overall, our updated picture of Aedini phylogeny, reveal a strong nuclear-mitochondrial incongruence which may be of help in setting the research agenda for future phylogenomic studies of Aedini mosquitoes.
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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 embedding model based on the continuous bag of words method is proposed to learn the video embeddings, integrated with a well-designed discriminative negative sampling approach, which helps emphasize the convincing clips in the embedding while weakening the influence of the confusing ones. Second, an aggregated distribution pooling method is proposed to capture the semantic distribution of kernel modes in videos. Finally, our well-trained model can generate compact video representations by direct inference, which provides our model with a better generalization ability compared with those of previous methods. We performed extensive experiments on event detection and the mining of representative event parts. Experiments on TRECVID MED11 and CCV datasets demonstrated the effectiveness of our method. Our method could capture the semantic distribution of kernel modes in videos and shows powerful potential to discover and better describe complex video patterns.
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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 with a diameter of 10 µm or less (PM10) is sampled simultaneously by a dust monitor with a sampling frequency of 1 Hz. The wall-normal array had 11 sonic anemometers and monitors spaced logarithmically from z=0.9 to 30 m, where the spacing is about 2 m between the sonic anemometer and dust monitor at the same height. Based on its nonstationary feature, an SDS event can be divided into three stages, i.e., ascending, stabilizing and descending stages, in which the dynamic mechanism of the wind and dust fields might be different. This is preliminarily characterized by the classical Fourier power analysis. Temporal evolution of the scaling exponent from Fourier power analysis suggests a value slightly below the classical Kolmogorov value of -5/3 for the three-dimensional homogeneous and isotropic turbulence. During the stabilizing stage, the collected PM10 shows a very intermittent pattern, which can be further linked with the burst events in the turbulent atmospheric boundary layer. This dataset is valuable for a better understanding of SDS dynamics and is publicly available in a Zenodo repository at https://doi.org/10.5281/zenodo.5034196 (Li et al., 2021a).
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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 of species. SPARSE is spatially explicit and can accommodate nested spatial structures from multiple spatial scales, including sampling designs where multiple sites within a larger area have been surveyed and the larger area can again be nested in an even larger region. Each site in SPARSE is represented either by a point, line (for transects) or polygon, stored in an ESRI shapefile. SPARSE implements a new combination of our own field definitions with Darwin Core biodiversity data standard and its Humboldt core extension. The use of Humboldt core also makes SPARSE suitable for biodiversity data with temporal replication.We provide an example use of the SPARSE framework by digitising data on birds from the Czech Republic, from 348 sites and 524 sampling events, with 15,969 unique species-per-event observations of presence, abundance or population density. To facilitate use without the need for a high-level database expertise, the Czech bird example is implemented as MS Access .accdb file, but can be ported to other database engines. The example of Czech birds complements other bird datasets from the Czech Republic, specifically the four gridded national atlases and the breeding bird survey which cover a similar temporal extent, but different locations and spatial scales.
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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 datasets that originate from different sources. For this reason, we developed recommendations for plankton imagery data management, which can promote the ability to make these datasets as FAIR (Findable, Accessible, Interoperable and Reusable principles), as possible. The workflow presented here could inspire other Biodiversity Information Standards TDWG communities working with (automated) imagery data (e.g., camera traps) such as the <u>Audubon Core</u> and <u>Machine Observations</u> Interest Group.The recommended data format follows the <u>OBIS-ENV-DATA</u> format (De Pooter et al. 2017), a Darwin Core-based approach to standardise biodiversity data (Wieczorek et al. 2012) used in <u>EurOBIS</u>, the European node of the Ocean Biodiversity Information System (<u>OBIS</u>) and <u>EMODnet Biology</u>, the European Marine Biodiversity Data Network. However, this format does not include sufficient information for imagery data, therefore we propose the use of additional <u>Darwin Core terms</u>. For example, by including the terms <u>identifiedBy</u>, <u>identificationVerificationStatus</u> and <u>identificationReferences</u> in the Occurrence table, more clarity is reported regarding the uncertainty of the classification made by an algorithm. Thus, data providers can publish manually validated datasets or datasets produced by fully automated plankton identification workflows; and users can choose to use validated or not validated data. See in Suppl. material 1 a practical example on how to report an imagery dataset following the best practices.Moreover, the OBIS-ENV-DATA format allows the ingestion of additional information thanks to the use of the Darwin Core (DwC) <u>Extended Measurement Or Facts</u> or eMoF extension in the <u>DwC Event core</u>. The eMoF stores biotic, abiotic and sampling measurements and facts that are related to the Event and Occurrence table. An important aspect of this extension is that it includes standardised terms and controlled vocabularies, such as the British Oceanographic Data Centre (<u>BODC) vocabularies</u>, to standardise parameters that are not covered by DwC. The advantages of these is to unambiguously report information and to include those measurements that cannot be reported in the Event and Occurrence table (e.g., reporting abundance or biomass of plankton), and that are crucial to investigate ecosystem functioning questions. As a consequence, biodiversity data aggregators can extend their scope beyond species occurrence data. Fig. 1 summarises a typical dataflow that goes from imagery data acquisition to publication in several steps:Images are cropped and classified with software. This can be done in <u>EcoTaxa</u>, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its <u>API</u>.Data is submitted to EurOBIS via the <u>IPT</u> (Integrated Publishing Toolkit).Data is quality controlled by the <u>BioCheck tool</u>. Data in EurOBIS can flow to EMODnet Biology, OBIS and <u>GBIF</u> (Global Biodiversity Information Facility).Plankton imagery instrument operators now have the possibility to format their data following the best practices and recommendations for plankton imagery data management (Martin-Cabrera et al. 2022). After a dataset is formatted following these guidelines, it can be submitted to the international biodiversity data aggregators, EurOBIS, EMODnet Biology and GBIF. Additionally a (semi) automated dataflow is presented where data providers can classify images in EcoTaxa and export the data in the required formats using an API before submission to EurOBIS. The next steps are to disseminate these best practices, encouraging plankton imagery data generators to implement these workflows to share their data easily, enriching these data portals and encouraging cross collaborations to create data products covering broader geographic scales and plankton species.
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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 small set of points to access in each period of interest. Then, the perturbation module adds noise to the sampled data points to guarantee privacy. Next, the reconstruction module re-assembles non-sampled data points from the perturbed sample points. Finally, the post-processing module uses convex optimization over the privacy-preserving output of the previous modules, as well as the privacy-preserving answers of additional queries on the data stream, to improve accuracy by redistributing the added noise. OptStream is evaluated on a test case involving the release of a real data stream from the largest European transmission operator. Experimental results show that OptStream may not only improve the accuracy of state-of-the-art methods by at least one order of magnitude but also supports accurate load forecasting on the privacy-preserving data.
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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-independent evaluation approach which means largely ignoring the relations between words while sampling clusters/topics. It definitely leads to the decrease of overall model accuracy performance, especially for short-length text documents such as comments, microblogs, etc. in social networks. To tackle these existing problems, in this paper we propose a novel approach of graph-of-words (GOWs) based text stream clustering, called GOW-Stream. The application of common GOWs which are generated from each document batch while sampling clusters/topics can support to overcome the word-independent evaluation challenge. Our proposed GOW-Stream is promising to significantly achieve better text stream clustering performance than recent state-of-the-art baselines. Extensive experiments on multiple benchmark real-world datasets demonstrate the effectiveness of our proposed model in both accuracy and time-consuming performances.
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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) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins.
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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 information about Power Spectral Density and Sound Pressure Level. The strategy involves generating these key metrics from spectrograms, enabling both short-term (micro) and long-term (macro) studies. A proposal for efficient data processing is presented, structured in three steps: the first focuses on generating key metrics to replace space-consuming raw signals, the second addresses the treatment of these metrics for long-term studies, and the third outlines the creation of event detectors from the processed metrics. A comprehensive overview of the essential features for analyzing acoustic signals is provided, along with considerations for the future design of marine observatories. The necessary calculations and processes are detailed, demonstrating the potential of these methods to address the current data storage and processing limitations in long-term acoustic monitoring.
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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 faults not observed at the ground surface, estimating maximum fault-rupture lengths, inferring maximum short-term displacement rates and improving earthquake hazard assessment. We find that fault displacement rates become increasingly irregular (both faster and slower) on shorter timescales, leading to incomplete sampling of the active fault population. Surface traces have been recognised for <50% of the active faults and along ≤50% of their lengths. The similarity of along-strike displacement profiles for short and long time-intervals suggests that fault lengths and maximum single-event displacements have not changed over the last 3.6 Ma. Therefore, rate changes are likely to reflect temporal adjustments in earthquake-recurrence intervals due to fault interactions and associated migration of earthquake activity within the rift.
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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 using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (<i>t</i>) in our proposed ensemble always yields minimum number (<i>m</i>) of leafs keeping pre-processing computation to <i>n</i> &times; <i>t</i> log <i>m</i> compared to <i>N<sup>2</sup></i> for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.
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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 and monitoring efforts. These kinds of studies provide very rich data that can enable inferences about species absence, but their reliability depends on the methodology implemented, the survey effort and completeness. The information about these elements has often been regarded as metadata and captured in an unstructured manner, thus making their full use very challenging.Unlocking and integrating inventory data requires data standards that can facilitate capture and sharing of data with the appropriate depth. The Darwin Core standard (Wieczorek et al. 2012) currently enables reporting some of the information contained in inventories, particularly using Darwin Core Event terms such as samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort. However, it is limited in its ability to accommodate spatial, temporal, and taxonomic scopes, and other key aspects of the inventory sampling process, such as direct or inferred measures of sampling effort and completeness. The lack of a standardized way to share inventory data has hindered their mobilization, integration, and broad reuse. In an effort to overcome these limitations, a framework was developed to standardize inventory data reporting: Humboldt Core (Guralnick et al. 2018). Humboldt Core identified three types of inventories (single, elementary, and summary inventories) and proposed a series of terms to report their content. These terms were organized in six categories: dataset and identification; geospatial and habitat scope; temporal scope; taxonomic scope; methodology description; and completeness and effort. While originally planned as a new TDWG standard and being currently implemented in Map of Life (https://mol.org/humboldtcore/), ratification was not pursued at the time, thus limiting broader community adoption.In 2021 the TDWG Humboldt Core Task Group was established to review how to best integrate the terms proposed in the original publication with existing standards and implementation schemas. The first goal of the task group was to determine whether a new, separate standard was needed or if an extension to Darwin Core could accommodate the terms necessary to describe the relevant information elements. Since the different types of inventories can be thought of as Events with different nesting levels (events within events, e.g., plots within sites), and after an initial mapping to existing Darwin Core terms, it was deemed appropriate to start from a Darwin Core Event Core and build an extension to include Humboldt Core terms. The task group members are currently revising all original Humboldt Core terms, reformulating definitions, comments, and examples, and discarding or adding new terms where needed. We are also gathering real datasets to test the use of the extension once an initial list of revised terms is ready, before undergoing a public review period as established by the TDWG process.Through the ratification of Humboldt Core as a TDWG extension, we expect to provide the community with a solution to share and use inventory data, which improves biodiversity data discoverability, interoperability and reuse while lowering the reporting burden at different levels (data collection, integration and sharing).
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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, effluent from the secondary treatment, and chlorine-disinfected effluent of four municipal wastewater treatment plants were analyzed by a quantitative polymerase chain reaction (PCR) approach, and the probabilistic distributions of log reduction (LR) were estimated by a Bayesian estimation algorithm. The mean values of LR in the secondary treatment units ranged from 0.9 and 2.2, whereas those in the free chlorine disinfection units were from −0.1 and 0.5. The LR value in the secondary treatment was virus type and unit process dependent, which raised the importance for accumulating the data of virus LR values applicable to the multiple-barrier system, which is a global concept of microbial risk management in wastewater reclamation and reuse.
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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 Global Biodiversity Information Facility (GBIF). As part of this project, we compiled two extensive datasets – a sampling-event and occurrence dataset and a taxonomic checklist – for the dung beetles of Sabah, Bornean Malaysia. The sampling-event dataset documents 2,627 unique sampling events and 21,348 dung beetle occurrence records for Sabah. The taxonomic checklist includes 156 confirmed dung beetle species and 36 synonyms, totalling 192 records. These datasets have been made open access through the GBIF portal, which we hope will enhance the understanding of dung beetle taxonomy and their distributions in Southeast Asia. All data presented in this paper comprises of available information pertaining to the dung beetles of Sabah.
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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 14,166 manually annotated RGB frames, along with a substantial event count of 21.88 million and 20.80 million events from two stereo-configured event-based cameras. Notably, this densely annotated 3D spatial-temporal event-based segmentation benchmark for tabletop objects represents a pioneering initiative, providing event-wise depth, and annotated instance labels, in addition to corresponding RGBD frames. By releasing ESD, our aim is to offer the research community a challenging segmentation benchmark of exceptional quality.