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Journal articles on the topic 'Occurrence datasets'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Waller, John. "Data Location Quality at GBIF." Biodiversity Information Science and Standards 3 (June 13, 2019): e35829. https://doi.org/10.3897/biss.3.35829.

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I will cover how the Global Biodiversity Information Facility (GBIF) handles data quality issues, with specific focus on coordinate location issues, such as gridded datasets (Fig. 1) and country centroids. I will highlight the challenges GBIF faces identifying potential data quality problems and what we and others (Zizka et al. 2019) are doing to discover and address them. GBIF is the largest open-data portal of biodiversity data, which is a large network of individual datasets (> 40k) from various sources and publishers. Since these datasets are variable both within themselves and dataset-

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2

Steeman, Roosmarijn, Wouter Vanreusel, Rutger Barendse, et al. "Waarnemingen.be – Plant occurrences in Flanders and the Brussels Capital Region, Belgium." PhytoKeys 85 (August 8, 2017): 1–10. http://dx.doi.org/10.3897/phytokeys.85.14925.

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Waarnemingen.be - Plant occurrences in Flanders and the Brussels Capital Region, Belgium is a species occurrence dataset published by Natuurpunt. The dataset contains almost 1.2 million plant occurrences of 1,222 native vascular plant species, mostly recorded by volunteers (citizen scientists), mainly since 2008. The occurrences are derived from the database http://www.waarnemingen.be, hosted by Stichting Natuurinformatie and managed by the nature conservation NGO Natuurpunt. Together with the datasets Florabank1 (Van Landuyt and Brosens 2017) and the Belgian IFBL (Instituut voor Floristiek va

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Steeman, Roosmarijn, Wouter Vanreusel, Rutger Barendse, et al. "Waarnemingen.be – Plant occurrences in Flanders and the Brussels Capital Region, Belgium." PhytoKeys 85 (August 8, 2017): 1–10. https://doi.org/10.3897/phytokeys.85.14925.

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Waarnemingen.be - Plant occurrences in Flanders and the Brussels Capital Region, Belgium is a species occurrence dataset published by Natuurpunt. The dataset contains almost 1.2 million plant occurrences of 1,222 native vascular plant species, mostly recorded by volunteers (citizen scientists), mainly since 2008. The occurrences are derived from the database http://www.waarnemingen.be, hosted by Stichting Natuurinformatie and managed by the nature conservation NGO Natuurpunt. Together with the datasets Florabank1 (Van Landuyt and Brosens 2017) and the Belgian IFBL (Instituut voor Floristiek va

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Orozco-Arias, Simon, Ana María Núñez-Rincón, Reinel Tabares-Soto, and Diana López-Álvarez. "Worldwide co-occurrence analysis of 17 species of the genus Brachypodium using data mining." PeerJ 6 (January 14, 2019): e6193. http://dx.doi.org/10.7717/peerj.6193.

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The co-occurrence of plant species is a fundamental aspect of plant ecology that contributes to understanding ecological processes, including the establishment of ecological communities and its applications in biological conservation. A priori algorithms can be used to measure the co-occurrence of species in a spatial distribution given by coordinates. We used 17 species of the genus Brachypodium, downloaded from the Global Biodiversity Information Facility data repository or obtained from bibliographical sources, to test an algorithm with the spatial points process technique used by Silva et

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Aydin, B., D. Kempton, V. Akkineni, R. Angryk, and K. G. Pillai. "Mining spatiotemporal co-occurrence patterns in solar datasets." Astronomy and Computing 13 (November 2015): 136–44. http://dx.doi.org/10.1016/j.ascom.2015.10.003.

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Šturm, Tomaž, and Tomaž Podobnikar. "A probability model for long-term forest fire occurrence in the Karst forest management area of Slovenia." International Journal of Wildland Fire 26, no. 5 (2017): 399. http://dx.doi.org/10.1071/wf15192.

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The aim of this study is to develop a long-term forest fire occurrence probability model in the Karst forest management area of Slovenia. The target area has the greatest forest fire occurrence rates and the largest burned areas in the country. To discover how the forest stand characteristics influence forest fire occurrence, we developed a long-term linear regression model. The geographically weighted regression method was applied to build the model, using forest management plans and land-based datasets as explanatory variables and a past forest fire activity dataset as a predicted variable.

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Neves, Matheus Oliveira, Hugo Cabral, Mariana Pedrozo, Vanda Lucia Ferreira, Mário Ribeiro Moura, and Diego José Santana. "Dataset of occurrences and ecological traits of amphibians from Upper Paraguay River Basin, central South America." Nature Conservation 41 (September 2, 2020): 71–89. http://dx.doi.org/10.3897/natureconservation.41.54265.

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There are many gaps in our biodiversity knowledge, especially in highly diverse regions such as the Neotropics. Basic information on species occurrence and traits are scattered throughout different literature sources, which makes it difficult to access data and ultimately delays advances in ecology, evolution, and conservation biology. We provide species occurrence and trait data for amphibian species in the Upper Paraguay River Basin, central South America. The compiled information is made available through two different datasets that hold (i) 17K species occurrence records and (ii) 30 specie

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Neves, Matheus Oliveira, Hugo Cabral, Mariana Pedrozo, Vanda Lucia Ferreira, Mário Ribeiro Moura, and Diego José Santana. "Dataset of occurrences and ecological traits of amphibians from Upper Paraguay River Basin, central South America." Nature Conservation 41 (September 2, 2020): 71–89. https://doi.org/10.3897/natureconservation.41.54265.

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There are many gaps in our biodiversity knowledge, especially in highly diverse regions such as the Neotropics. Basic information on species occurrence and traits are scattered throughout different literature sources, which makes it difficult to access data and ultimately delays advances in ecology, evolution, and conservation biology. We provide species occurrence and trait data for amphibian species in the Upper Paraguay River Basin, central South America. The compiled information is made available through two different datasets that hold (i) 17K species occurrence records and (ii) 30 specie

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9

Simpson, Annie, and Elizabeth Martín. "Biodiversity Information Serving Our Nation (BISON) now with more than 1/3 billion species occurrences." Biodiversity Information Science and Standards 1 (August 15, 2017): e20285. https://doi.org/10.3897/tdwgproceedings.1.20285.

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Biodiversity Information Serving Our Nation (BISON) is a web-based resource (https://bison.usgs.gov) for finding and accessing occurrence records of species found in the United States (US), its Territories and marine Exclusive Economic Zones. BISON serves as a data aggregator that compiles and standardizes species occurrence data from multiple data providers, and now contains more than 1/3 billion species occurrences. BISON uses the Integrated Taxonomic Information System (ITIS) to standardize species names in searches. It is also the US hub of the Global Biodiversity Information Facility (GBI

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Pisarenko, Olga. "Moss occurrences in Salair-Kuznetsk Region (Altai-Sayan mountain country)." Biodiversity Data Journal 9 (September 15, 2021): e72889. https://doi.org/10.3897/BDJ.9.e72889.

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In the flora of large regions, mosses comprise about a quarter of the total diversity of higher plants. However, now mosses are the least studied group of higher plants. Data on moss species distribution are fragmentary, especially in Russia with its vast expanse and low density of botanists.The author for many years has been studying the bryoflora of various areas of the Salair-Kuznetsk Region. In addition to the herbarium collection, the author's bryological relevés were organised as a Database. It stores all the assembled information for the years about the locations of the species, includi

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Decruw, Cedric, Leen Vandepitte, Ruben Perez, et al. "An Algorithmic Approach to Reducing Taxonomic Detail from Actual Datasets to their Metadata Representation to Increase Findabilty." Biodiversity Information Science and Standards 6 (September 9, 2022): e94603. https://doi.org/10.3897/biss.6.94603.

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The rise in demand for more FAIR (Findable, Accessible, Interoperable, and Reusable) data is being answered by increasingly automated ways to capture, process, publish and register biodiversity datasets. Coupled with the increasing possibilities for detecting hundreds of species in a single sample/event (i.e., eDNA), this results in taxonomic information that is multiple levels of magnitude higher than it was a couple of years ago. This spike in content has an adverse effect on the ability of researchers to find relevant datasets within catalogues, due to the limitations in storing and display

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Marshall, Benjamin M., and Colin T. Strine. "Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media." PeerJ 7 (December 17, 2019): e8059. http://dx.doi.org/10.7717/peerj.8059.

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A species’ distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated

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Liu, Yimo, Wanchang Zhang, Zhijie Zhang, Qiang Xu, and Weile Li. "Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake." Remote Sensing 13, no. 6 (2021): 1157. http://dx.doi.org/10.3390/rs13061157.

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Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector

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Golovanov, Yaroslav, Larisa Abramova, Mikhail Drap, and Maria Lebedeva. "Vascular plants occurrences in the Southern Urals industrial towns (Sterlitamak and Salavat)." Biodiversity Data Journal 10 (March 2, 2022): e77148. https://doi.org/10.3897/BDJ.10.e77148.

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The paper presents datasets of plant species of two industrial cities Sterlitamak and Salavat (Republic of Bashkortostan) is presented. These cities are part of the Southern Bashkortostan urban agglomeration and are amongst the three largest in the Republic. The population of Sterlitamak is about 276,000. There are large oil refineries and other large industrial transport infrastructure facilities. Datasets are prepared on the basis of long-time field research by Ya. Golovanov (2008 - 2016). Technical preparation of the datasets was carried out by M. Lebedeva and M. Drap. The herbarium samples

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Owens, Hannah L., Cory Merow, Brian S. Maitner, Jamie M. Kass, Vijay Barve, and Robert P. Guralnick. "occCite: Tools for querying and managing large biodiversity occurrence datasets." Ecography 44, no. 8 (2021): 1228–35. http://dx.doi.org/10.1111/ecog.05618.

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Yoon, Sungsoo. "The Integrated Dataset for Occurrence of Odonata Species in South Korea." GEO DATA 5, no. 2 (2023): 77–86. http://dx.doi.org/10.22761/gd.2023.0013.

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This study presents an integrated dataset comprising occurrence records for 102 Odonata species in South Korea. The occurrence information of the Odonata species was collected from 10 independent field survey-based datasets, spanning the period from 2014 to 2023 and provided by three agencies: National Institute of Ecology, National Institute of Environmental Research, and Global Biodiversity Information Facility (GBIF). In addition to occurrence data, the dataset includes information on the survey year, IUCN (International Union for Conservation of Nature) Red List classification, satellite-d

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Jiang, Hongyan, Dianjun Fang, Klaus Spicher, Feng Cheng, and Boxing Li. "A New Period-Sequential Index Forecasting Algorithm for Time Series Data." Applied Sciences 9, no. 20 (2019): 4386. http://dx.doi.org/10.3390/app9204386.

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A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for t

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18

Piatinskii, Mikhail M., Dmitrii G. Bitiutskii, Arsen V. Mirzoyan, et al. "The Long-Term Annual Datasets for Azov Sea Basin Ecosystems for 1925–2024 and Russian Sturgeon Occurrences in 2000–2024." Data 10, no. 5 (2025): 57. https://doi.org/10.3390/data10050057.

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The abundance of the Russian sturgeon population in the Sea of Azov declined many times in the XX–XXI centuries. This paper presents long-term annual and spatial occurrence datasets to create statistical and machine learning models to better understand the distribution patterns as well as biological and ecological features. The annual dataset provides annually averaged results of environmental and biotic population estimates obtained by in situ observations for 1925–2024. The spatial occurrence dataset contains raw survey observations with a bottom trawl over the period of 2000–2024. Prelimina

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Levitsky, Victor, Elena Zemlyanskaya, Dmitry Oshchepkov, et al. "A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with MCOT package." Nucleic Acids Research 47, no. 21 (2019): e139-e139. http://dx.doi.org/10.1093/nar/gkz800.

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Abstract Recognition of composite elements consisting of two transcription factor binding sites gets behind the studies of tissue-, stage- and condition-specific transcription. Genome-wide data on transcription factor binding generated with ChIP-seq method facilitate an identification of composite elements, but the existing bioinformatics tools either require ChIP-seq datasets for both partner transcription factors, or omit composite elements with motifs overlapping. Here we present an universal Motifs Co-Occurrence Tool (MCOT) that retrieves maximum information about overrepresented composite

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Rowley, Jodi J. L., and Corey T. Callaghan. "The FrogID dataset: expert-validated occurrence records of Australia’s frogs collected by citizen scientists." ZooKeys 912 (February 17, 2020): 139–51. http://dx.doi.org/10.3897/zookeys.912.38253.

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This dataset represents expert-validated occurrence records of calling frogs across Australia collected via the national citizen science project FrogID (http://www.frogid.net.au). FrogID relies on participants recording calling frogs using smartphone technology, after which point the frogs are identified by expert validators, resulting in a database of georeferenced frog species records. This dataset represents one full year of the project (10 November 2017–9 November 2018), including 54,864 records of 172 species, 71% of the known frog species in Australia. This is the first instalment of the

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Rowley, Jodi J.L., and Corey T. Callaghan. "The FrogID dataset: expert-validated occurrence records of Australia's frogs collected by citizen scientists." ZooKeys 912 (February 17, 2020): 139–51. https://doi.org/10.3897/zookeys.912.38253.

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This dataset represents expert-validated occurrence records of calling frogs across Australia collected via the national citizen science project FrogID (http://www.frogid.net.au). FrogID relies on participants recording calling frogs using smartphone technology, after which point the frogs are identified by expert validators, resulting in a database of georeferenced frog species records. This dataset represents one full year of the project (10 November 2017–9 November 2018), including 54,864 records of 172 species, 71% of the known frog species in Australia. This is the first instalment of the

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Maoz, Zeev, Paul L. Johnson, Jasper Kaplan, Fiona Ogunkoya, and Aaron P. Shreve. "The Dyadic Militarized Interstate Disputes (MIDs) Dataset Version 3.0: Logic, Characteristics, and Comparisons to Alternative Datasets." Journal of Conflict Resolution 63, no. 3 (2018): 811–35. http://dx.doi.org/10.1177/0022002718784158.

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We introduce the new, substantially updated, and revised version of the Dyadic Militarized Interstate Disputes (MIDs) dataset. We discuss the underlying logic of constructing dyadic MIDs and demonstrate that these operations generate significant differences between the actual occurrence and properties of MID dyads and those extracted from machine-generated programs such as EUGene, or from the MID participant dataset. We provide some descriptive measures of dyadic MIDs over the period of 1816 to 2010 and compare some of the key dyadic results on the correlates of MIDs using different datasets.

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Pando, Francisco. "Quantifying quality: the "Apparent Quality Index", a measure of data quality for occurrence datasets." Proceedings of TDWG 1 (August 23, 2017): e20533. https://doi.org/10.3897/tdwgproceedings.1.20533.

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When making an initial assessment of a dataset originating from an unfamiliar source, a user typically relies on the visible properties of the dataset as a whole, such as, the title, the publisher, and the size of the dataset. Aspects of data quality are usually out of view, beyond some intuitions and hard to compare assertions. In 2007 at GBIF Spain we tried to correct that by developing an index that enables a user to assess the quality of Darwin Core datasets published by GBIF-Spain, and to track improvements in quality over time. Our goal was to create an index that is explicit, easy to un

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Shi, Lingfei, and Feng Ling. "Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform." Land 10, no. 5 (2021): 454. http://dx.doi.org/10.3390/land10050454.

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As one of the widely concerned urban climate issues, urban heat island (UHI) has been studied using the local climate zone (LCZ) classification scheme in recent years. More and more effort has been focused on improving LCZ mapping accuracy. It has become a prevalent trend to take advantage of multi-source images in LCZ mapping. To this end, this paper tried to utilize multi-source freely available datasets: Sentinel-2 multispectral instrument (MSI), Sentinel-1 synthetic aperture radar (SAR), Luojia1-01 nighttime light (NTL), and Open Street Map (OSM) datasets to produce the 10 m LCZ classifica

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Halaguru, Basavarajappa Basanth Kumar, and Rajanna Chennamma Haranahalli. "Dataset for classification of computer graphic images and photographic images." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 137–47. https://doi.org/10.11591/ijai.v11.i1.pp137-147.

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The recent advancements in computer graphics (CG) image rendering techniques have made it easy for the content creators to produce high quality computer graphics similar to photographic images (PG) confounding the most naïve users. Such images used with negative intent, cause serious problems to the society. In such cases, proving the authenticity of an image is a big challenge in digital image forensics due to high photo-realism of CG images. Existing datasets used to assess the performance of classification models are lacking with: (i) larger dataset size, (ii) diversified image content

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Mainali, Kumar P., Sharon Bewick, Peter Thielen, et al. "Statistical analysis of co-occurrence patterns in microbial presence-absence datasets." PLOS ONE 12, no. 11 (2017): e0187132. http://dx.doi.org/10.1371/journal.pone.0187132.

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Olusi, T., M.O. Balogun, M.T. Adepoju, et al. "Overcoming Computational Challenges of K-Nearest Neighbors: A Memory-Efficient Approach using Gray Level Co-Occurrence Matrix and Firefly Optimization Technique." Nigerian Research Journal of Engineering and Environmental Sciences 9, no. 2 (2024): 664–80. https://doi.org/10.5281/zenodo.14565971.

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<em>Feature extraction is a critical step in machine learning, especially when dealing with high-dimensional datasets such as medical data. The k-Nearest Neighbors (KNN) algorithm is widely used for feature extraction due to its simplicity and effectiveness, but it often suffers from high memory consumption, particularly with large datasets such as breast cancer dataset. This work addresses the problem of optimizing memory efficiency of KNN, which can limit its applicability in resource-constrained environments, by proposing a novel approach through integrating the Gray Level Co-Occurrence Mat

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Novikov, Andriy, Anastasiia Savytska, Oleksandr Kuzyarin, et al. "Data mobilisation in the LWS Herbarium: success and prospects." Biodiversity Data Journal 12 (January 11, 2024): e117292. https://doi.org/10.3897/BDJ.12.e117292.

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Digitisation of hosted specimens is a crucial task for all herbaria worldwide and is one of the main streams for today. By digitising their collections and publishing the datasets, the herbaria grant access to essential data to a wide research audience and, as a result, involve their collections in scientific work more actively. Digitisation also allows virtual preservation of the collections, which is especially important in conditions of hostilities, when the entire collection can be destroyed or damaged in one moment. This paper describes two datasets recently published in GBIF in the frame

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Basanth Kumar, Halaguru Basavarajappa, and Haranahalli Rajanna Chennamma. "Dataset for classification of computer graphic images and photographic images." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 137. http://dx.doi.org/10.11591/ijai.v11.i1.pp137-147.

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<span lang="EN-US">The recent advancements in computer graphics (CG) image rendering techniques have made it easy for the content creators to produce high quality computer graphics similar to photographic images (PG) confounding the most naïve users. Such images used with negative intent, cause serious problems to the society. In such cases, proving the authenticity of an image is a big challenge in digital image forensics due to high photo-realism of CG images. Existing datasets used to assess the performance of classification models are lacking with: (i) larger dataset size, (ii) diver

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Belitz, Michael, Lillian Hendrick, Michael Monfils, et al. "Aggregated occurrence records of the federally endangered Poweshiek skipperling (Oarisma poweshiek)." Biodiversity Data Journal 6 (September 27, 2018): e29081. https://doi.org/10.3897/BDJ.6.e29081.

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Primary biodiversity data records that are open access and available in a standardised format are essential for conservation planning and research on policy-relevant time-scales. We created a dataset to document all known occurrence data for the Federally Endangered Poweshiek skipperling butterfly [<i>Oarisma poweshiek</i> (Parker, 1870; Lepidoptera: Hesperiidae)]. The Poweshiek skipperling was a historically common species in prairie systems across the upper Midwest, United States and Manitoba, Canada. Rapid declines have reduced the number of verified extant sites to six. Aggregating and cur

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Barve, Vijay. "Elevating the Fitness of Use of GBIF Occurrence Datasets: A proposal for peer review." Biodiversity Information Science and Standards 7 (September 7, 2023): e112237. https://doi.org/10.3897/biss.7.112237.

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Biodiversity data plays a pivotal role in understanding and conserving our natural world. As the largest occurrence data aggregator, the Global Biodiversity Information Facility (GBIF) serves as a valuable platform for researchers and practitioners to access and analyze biodiversity information from across the globe (Ball-Damerow et al. 2019). However, ensuring the quality of GBIF datasets remains a critical challenge (Chapman 2005).The community emphasizes the importance of data quality and its direct impact on the fitness of use for biodiversity research and conservation efforts (Chapman et

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Penev, Lyubomir, Terry Erwin, Jeremy Miller, Vishwas Chavan, Tom Moritz, and Charles Griswold. "Publication and dissemination of datasets in taxonomy: ZooKeys working example." ZooKeys 11, no. 11 (2009): 1–8. https://doi.org/10.3897/zookeys.11.210.

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A concept for data publication and semantic enhancements proposed by ZooKeys and applied in the milestone paper of Miller et al. (2009) is described. For the first time in systematic zoology, an unique combination of data publication and semantic enhancements is applied within the mainstream process of journal publishing, to demonstrate how: (1) All primary biodiversity data underlying a taxonomic monograph are published as a dataset under a separate DOI within the paper; (2) The occurrence dataset is discoverable and accessible through GBIF data portal (data.gbif.org) simultaneously with the

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Fall, Cheikh Modou Noreyni, Christophe Lavaysse, Mamadou Simina Drame, Geremy Panthou, and Amadou Thierno Gaye. "Wet and dry spells in Senegal: comparison of detection based on satellite products, reanalysis, and in situ estimates." Natural Hazards and Earth System Sciences 21, no. 3 (2021): 1051–69. http://dx.doi.org/10.5194/nhess-21-1051-2021.

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Abstract. In this study, the detection and characteristics of dry/wet spells (defined as episodes when precipitation is abnormally low or high compared to usual climatology) drawn from several datasets are compared for Senegal. Here, four datasets are based on satellite data (TRMM-3B42 V7, CMORPH V1.0, TAMSAT V3, and CHIRPS V2. 0), two on reanalysis products (NCEP-CFSR and ERA5), and three on rain gauge observations (CPC Unified V1.0/RT and a 65-rain-gauge network regridded by using two kriging methods, namely ordinary kriging, OK, and block kriging, BK). All datasets were converted to the sam

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Baruffa, Giacomo, Andrea Pieressa, Marco Sorgato, and Giovanni Lucchetta. "Transfer Learning-Based Artificial Neural Network for Predicting Weld Line Occurrence through Process Simulations and Molding Trials." Journal of Manufacturing and Materials Processing 8, no. 3 (2024): 98. http://dx.doi.org/10.3390/jmmp8030098.

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Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from related tasks to enhance model training and performance. This study explores TL’s viability in predicting weld line visibility in injection-molded components using artificial neural networks (ANNs). TL techniques are employed to transfer knowledge between datasets related to differen

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Kabir, Masud, Fatih Unal, Tahir Cetin Akinci, Alfredo A. Martinez-Morales, and Sami Ekici. "Revealing GLCM Metric Variations across a Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications." Electronics 13, no. 12 (2024): 2299. http://dx.doi.org/10.3390/electronics13122299.

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This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These d

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Safavi, Kayvon, and Martin A. Weinstock. "Large Datasets: Common Uses and Caveats." Journal of Cutaneous Medicine and Surgery 3, no. 2 (1998): 90–95. http://dx.doi.org/10.1177/120347549800300208.

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Background: Increasingly, large collections of pre-existing data are being used to analyze the occurrence, burden, and health care resources directed to the management of various skin diseases. Objective: This article discusses a number of different types of large datasets along with their common uses. Various concerns about the use of this information are also discussed. Conclusion: Although large datasets provide significant statistical power with readily available data, there are significant concerns, particularly regarding data quality and statistical analysis. Readers need to be aware of

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Roebeling, R. A., E. L. A. Wolters, J. F. Meirink, and H. Leijnse. "Triple Collocation of Summer Precipitation Retrievals from SEVIRI over Europe with Gridded Rain Gauge and Weather Radar Data." Journal of Hydrometeorology 13, no. 5 (2012): 1552–66. http://dx.doi.org/10.1175/jhm-d-11-089.1.

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Abstract Quantitative information on the spatial and temporal error structures in large-scale (regional or global) precipitation datasets is essential for hydrologic and climatic studies. A powerful tool to quantify error structures in large-scale datasets is triple collocation. In this paper, triple collocation is used to determine the spatial and temporal error characteristics of three precipitation datasets over Europe—that is, the precipitation-properties visible/near infrared (PP-VNIR) retrievals from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board Meteosat

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Sim, Woo-Dam, Jong-Su Yim, and Jung-Soo Lee. "Assessing Land Cover Classification Accuracy: Variations in Dataset Combinations and Deep Learning Models." Remote Sensing 16, no. 14 (2024): 2623. http://dx.doi.org/10.3390/rs16142623.

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This study evaluates land cover classification accuracy through adjustments to the deep learning model (DLM) training process, including variations in loss function, the learning rate scheduler, and the optimizer, along with diverse input dataset compositions. DLM datasets were created by integrating surface reflectance (SR) spectral data from satellite imagery with textural information derived from the gray-level co-occurrence matrix, yielding four distinct datasets. The U-Net model served as the baseline, with models A and B configured by adjusting the training parameters. Eight land cover c

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Ansari, Shaheer, Afida Ayob, Molla Shahadat Hossain Lipu, Aini Hussain, and Mohamad Hanif Md Saad. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries." Energies 14, no. 22 (2021): 7521. http://dx.doi.org/10.3390/en14227521.

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Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is ut

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

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

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Hashim, Zainab, Hanaa Mohsin, and Ahmed Alkhayyat. "Signature verification based on proposed fast hyper deep neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 961–73. https://doi.org/10.11591/ijai.v13.i2.pp961-973.

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Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearanc

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Priyanto, Edi, Enny Itje Sela, Luther Alexander Latumakulita, and Noourul Islam. "Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation." ILKOM Jurnal Ilmiah 15, no. 2 (2023): 373–81. http://dx.doi.org/10.33096/ilkom.v15i2.1676.373-381.

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Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to

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Escribano, Nora, David Galicia, Arturo H. Ariño, and Carmen Escala. "Mammals in the MZNA Vertebrate Collection of University of Navarra, Spain." ZooKeys 634 (November 21, 2016): 137–50. https://doi.org/10.3897/zookeys.634.10207.

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In this paper five datasets are described that provide information about records of mammals in the Vertebrate Collection of the Museum of Zoology of the University of Navarra (MZNA-VERT). The datasets contain 3,466 records belonging to 20 species of mammals sampled across the transition zone between the Atlantic and Mediterranean biogeographical regions (north Iberian Peninsula). The datasets include both distributional data (georeferenced records) and basic biometric data of most of the vouchered specimens stored in the museum facilities. The samples originated mainly within research projects

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Slieker, François, and Niels Raes. "Database of butterfly and moth observations in the Netherlands: research from 1947-2020." Biodiversity Data Journal 10 (April 7, 2022): e78784. https://doi.org/10.3897/BDJ.10.e78784.

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The Natural History Museum Rotterdam (NMR) is a regional natural history museum in The Netherlands that focuses on nature and biodiversity of the city of Rotterdam and its surroundings. Bureau Stadsnatuur Rotterdam (bSR) is part of the NMR and collects, mainly on behalf of third parties, data on the flora and fauna from primarily urban areas. The NMR has received a large amount of observation data (1,363 different species in 886,902 observations), in particular of moths and mainly from the Provinces of Zuid-Holland, Noord-Holland and Noord-Brabant from the period 1947-2020. The observation dat

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Shen, Wei, Zixi Zhao, Zheping Xu, and Yong Zhang. "Occurrence dataset of birds in the Xinjizhou National Wetland Park, Nanjing, China." Biodiversity Data Journal 11 (May 15, 2023): e103497. https://doi.org/10.3897/BDJ.11.e103497.

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Xinjizhou National Wetland Park is located in Jiangning District, Nanjing, Jiangsu, China. With diverse wetland landscape types, Xinjizhou National Wetland Park maintains high biodiversity all year around. Meanwhile, as an important hub on the East Asian-Australasian Flyway, Xinjizhou National Wetland Park also occupies a core ecological position in the middle and lower reaches of the Yangtze River in Jiangsu Province. Therefore, carrying out systematic bird surveys and consequently understanding the distribution of birds and the seasonal variation of their communities can provide important in

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Majoros, Samantha, Tyler Elliott, and Sarah Adamowicz. "CanFlyet: habitat zone and diet trait dataset for Diptera species of Canada and Greenland." Biodiversity Data Journal 13 (March 25, 2025): e129610. https://doi.org/10.3897/BDJ.12.e129610.

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Flies (Diptera) are an ecologically important group that play a role in agriculture, public health and ecosystem functioning. As researchers continue to investigate this order, it is beneficial to link the growing occurrence data to biological traits. However, large-scale ecological trait data are not readily available for fly species. While some databases and datasets include fly data, many ecologically relevant traits for taxa of interest are not included. In this study, we create a dataset containing ecological traits (habitat and diet) for fly species of Canada and Greenland having occurre

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Rehman, Saif Ur, Muhammad Altaf Khan, Habib Un Nabi, Shaukat Ali, Noha Alnazzawi, and Shafiullah Khan. "TKIFRPM: A Novel Approach for Topmost-K Identical Frequent Regular Patterns Mining from Incremental Datasets." Applied Sciences 13, no. 1 (2023): 654. http://dx.doi.org/10.3390/app13010654.

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The regular frequent pattern mining (RFPM) approaches are aimed to discover the itemsets with significant frequency and regular occurrence behavior in a dataset. However, these approaches mainly suffer from the following two issues: (1) setting the frequency threshold parameter for the discovery of regular frequent patterns technique is not an easy task because of its dependency on the characteristics of a dataset, and (2) RFPM approaches are designed to mine patterns from the static datasets and are not able to mine dynamic datasets. This paper aims to solve these two issues by proposing a no

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Sug, Hyontai. "Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information." Journal of Applied Mathematics 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/258054.

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Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. But applying random forests blindly may not produce good results, and a dataset in the domain of rotogravure printing is one of such datasets. Hence, in this paper, some best classification accuracy based on clever application of random forests to predict the occurrence of cylinder bands in rotogravure printing is investigated. Since random forests could generate good results with an appropriate combination of par

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Mao, Rui, Dao-Yi Gong, Tianbao Zhao, Wenshan Wang, and Jing Yang. "Trends in the Frequency of High Relative Humidity over China: 1979–2012*." Journal of Climate 28, no. 24 (2015): 9816–37. http://dx.doi.org/10.1175/jcli-d-14-00840.1.

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Abstract High relative humidity (HRH) is defined as a relative humidity of at least 80%, which is often associated with the occurrence of cloud layers. Thus, the frequency of HRH and its changes in the troposphere may be related to the occurrence frequency of cloud layers and their changes. In this study, trends in the frequency of HRH (defined as days with relative humidity ≥80%) over China from the surface to the midtroposphere (≥400 hPa) from 1979 to 2012 were analyzed using a homogenized humidity dataset for spring (March–May), summer (June–August), autumn (September–November), and winter

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Filippova, Nina, Elena Zvyagina, Elena Rudykina, et al. "DNA-based occurrence dataset on peatland fungal communities studied by metabarcoding in north-western Siberia." Biodiversity Data Journal 12 (March 27, 2024): e119851. https://doi.org/10.3897/BDJ.12.e119851.

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The paper represents the first DNA-based occurrence dataset on peatland fungal communities published for north-western Siberia, the first for Russia and complements several existing datasets on metabarcoding of peat soils globally.The aim of the present publication is to describe the first DNA-based occurrence dataset on fungal communities in peat soils and other substrates studied by the eDNA approach in the Mukhrino raised bog, located in a large paludified area of north-western Siberia. A comparison of the species diversity of larger fungi identified by the conventional approach and by eDNA

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