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Title Authors PubMed ID
1 Neural topic modeling on hyperspheres: Spherical representation learning with von Mises-Fisher mixtures Guo D; Luo Z; Bouguila N; Fan W; 41791177
ENCS
2 Spatio-temporal distribution of AOD and its response to regional energy consumption and air pollution factors in China Su Y; Chen X; Guo J; Yang A; 41308902
ENCS
3 Disentangled representation learning for multi-view clustering via von Mises-Fisher hyperspherical embedding Li Z; Luo Z; Bouguila N; Su W; Fan W; 40664160
ENCS
4 No species left behind: borrowing strength to map data-deficient species Sharma S; Winner K; Pollock LJ; Thorson JT; Mäkinen J; Merow C; Pedersen EJ; Chefira KF; Portmann JM; Iannarilli F; Beery S; de Lutio R; Jetz W; 40571432
BIOLOGY
5 Strategies to Reduce Uncertainties from the Best Available Physicochemical Parameters Used for Modeling Novel Organophosphate Esters across Multimedia Environments Xing C; Ge J; Chen R; Li S; Wang C; Zhang X; Geng Y; Jones KC; Zhu Y; 40105294
CHEMBIOCHEM
6 Exon junction complexes regulate osteoclast-induced bone resorption by influencing the NFATc1 m6A distribution through the "shield effect" Sun B; Yang JG; Wang Z; Wang Z; Feng W; Li X; Liu SN; Li J; Zhu YQ; Zhang P; Wang W; 40051055
ENCS
7 Spatial Variations of Atmospheric Alkylated Polycyclic Aromatic Hydrocarbons across the Western Pacific to the Southern Ocean: Unexpected Increasing Deposition Zhu FJ; Lu XM; Jia JW; Zhang X; Xing DF; Cai MH; Kallenborn R; Li YF; Muir DCG; Zhang ZF; Zhang X; 40025703
CHEMBIOCHEM
8 In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability Khaked AA; Oishi N; Roggen D; Lago P; 39860799
ENCS
9 Asymmetric autocatalytic reactions and their stationary distribution Gallinger C; Popovic L; 39679357
MATHSTATS
10 Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions Sun Y; Wang C; 38493601
ENCS
11 The infimum values of two probability functions for the Gamma distribution Sun P; Hu ZC; Sun W; 38261930
MATHSTATS
12 Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency Al-Bazzaz H; Azam M; Amayri M; Bouguila N; 37837127
ENCS
13 The evolution of plasticity at geographic range edges Usui T; Lerner D; Eckert I; Angert AL; Garroway CJ; Hargreaves A; Lancaster LT; Lessard JP; Riva F; Schmidt C; van der Burg K; Marshall KE; 37183152
BIOLOGY
14 Tide-induced infiltration and resuspension of microplastics in shorelines: Insights from tidal tank experiments Feng Q; Chen Z; An C; Yang X; Wang Z; 37084574
ENCS
15 Identifying climate change refugia for South American biodiversity Sales LP; Pires MM; 36919472
BIOLOGY
16 Human Activity Recognition with an HMM-Based Generative Model Manouchehri N; Bouguila N; 36772428
ENCS
17 Species compositions mediate biomass conservation: the case of lake fish communities Arranz I; Fournier B; Lester NP; Shuter BJ; Peres-Neto PR; 34905222
BIOLOGY
18 Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images Bourouis S; Alharbi A; Bouguila N; 34460578
ENCS
19 Formation of oil-particle aggregates: Impacts of mixing energy and duration Ji W; Boufadel M; Zhao L; Robinson B; King T; An C; Zhang BH; Lee K; 34252767
ENCS
20 Grape seed extract supplementation along with a restricted-calorie diet improves cardiovascular risk factors in obese or overweight adult individuals: A randomized, placebo-controlled trial. Yousefi R, Parandoosh M, Khorsandi H, Hosseinzadeh N, Madani Tonekaboni M, Saidpour A, Babaei H, Ghorbani A 33044768
HKAP
21 The Odonata of Quebec: Specimen data from seven collections. Favret C, Moisan-De Serres J, Larrivée M, Lessard JP 32174757
CONCORDIA
22 Diversity, evolution, and classification of virophages uncovered through global metagenomics. Paez-Espino D, Zhou J, Roux S, Nayfach S, Pavlopoulos GA, Schulz F, McMahon KD, Walsh D, Woyke T, Ivanova NN, Eloe-Fadrosh EA, Tringe SG, Kyrpides NC 31823797
BIOLOGY
23 Aegilops tauschii Genome Sequence: A Framework for Meta-analysis of Wheat QTLs. Xu J, Dai X, Ramasamy RK, Wang L, Zhu T, McGuire PE, Jorgensen CM, Dehghani H, Gulick PJ, Luo MC, Müller HG, Dvorak J 30670607
BIOLOGY

 

Title:The Odonata of Quebec: Specimen data from seven collections.
Authors:Favret CMoisan-De Serres JLarrivée MLessard JP
Link:https://www.ncbi.nlm.nih.gov/pubmed/32174757?dopt=Abstract
DOI:10.3897/BDJ.8.e49450
Publication:Biodiversity data journal
Keywords:AnisopteraZygopteraCanadadamselflydistributiondragonflynatural history collectionspecimen digitisation
PMID:32174757 Category:Biodivers Data J Date Added:2020-03-17
Dept Affiliation: CONCORDIA
1 University of Montreal, Montreal, Canada University of Montreal Montreal Canada.
2 Quebec Ministry of Agriculture, Fisheries and Food, Quebec City, Canada Quebec Ministry of Agriculture, Fisheries and Food Quebec City Canada.
3 Insectarium of Montreal, Montreal, Canada Insectarium of Montreal Montreal Canada.
4 Concordia University, Montreal, Canada Concordia University Montreal Canada.

Description:

The Odonata of Quebec: Specimen data from seven collections.

Biodivers Data J. 2020;8:e49450

Authors: Favret C, Moisan-De Serres J, Larrivée M, Lessard JP

Abstract

Background: The Odonata, dragonflies and damselflies, constitute one of the more charismatic and better-studied orders of insects. The approximately 6,000 extant species on Earth can be variously found on all continents, except Antarctica. A relatively stable taxonomy, a relative ease of species identification and an aquatic immature stage has made the Odonata a taxon of interest in documenting the symptoms of global environmental change, especially at higher latitudes. The Odonata fauna of the north-temperate Canadian province of Quebec includes 150 species, many of which are at the northern limits of their geographic distribution.

New information: Quebec hosts multiple entomological specimen depositories, including seven publicly-accessible research collections. One of these, the University of Montreal's Ouellet-Robert Entomological Collection, houses an exceptionally large collection of Odonata. An initial specimen data capture project for this collection gathered 31,595 Quebec Odonata occurrence records, but several Quebec species were missing and geographic coverage was biased towards the Montreal region. To complement this dataset, we undertook to digitise the Odonata records of six other public research collections. They are, in order of Quebec Odonata collection size, the Laval University Entomological Collection, McGill University's Lyman Entomological Museum, the Insectarium of Montreal Research Collection, the Quebec Government's Insect Collection, Bishop's University's Insect Collection and the Laurentian Forestry Centre's René-Martineau Insectarium. Of the 40,447 total specimen occurrence records, 36,951 are identified to the species level, including 137 of the 150 species officially-recorded in Quebec and 2 non-nominotypical subspecies. We here summarise the data and highlight the strengths and weaknesses of the datasets. The complete dataset is available with this publication (Suppl. material 1), whereas the specimen data associated with each collection are available as Darwin Core archives at Canadensys.net and will be updated as appropriate.

PMID: 32174757 [PubMed]





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