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Occurrences and R code for: Dynamic distribution modeling of the Swamp Tigertail dragonfly Synthemis eustalacta (Odonata: Anisoptera: Synthemistidae) over a 20-year bushfire regime
<p>Intensity and severity of bushfires in Australia have increased over the past few decades due to climate change, threatening habitat loss for numerous species. Although the impact of bushfires on vertebrates is well-documented, the corresponding effects on insect taxa are rarely examined, although they are responsible for key ecosystem functions and services. Understanding the effects of bushfire seasons on insect distributions could elucidate long-term impacts and patterns of ecosystem recovery. Here, we investigated the effects of recent bushfires, land-cover change, and climatic variables on the distribution of a common and endemic dragonfly, the swamp tigertail (<em>Synthemis</em> <em>eustalacta</em> (Burmeister, 1839)), which inhabits forests that have recently undergone severe burning. We used a temporally dynamic species distribution modeling approach that incorporated 20 years of community-science data on dragonfly occurrence and predictors based on fire, land cover, and climate to make yearly predictions of suitability. We also compared this to an approach that combines multiple temporally static models that use annual data. We found that for both approaches, fire-specific variables had negligible importance for the models, while percent of tree and non-vegetative cover were the most important. We also found that the dynamic model outperformed the static ones when evaluated with cross-validation. Model predictions indicated temporal variation in area and spatial arrangement of suitable habitat but no patterns of habitat expansion, contraction, or shifting. These results highlight not only the efficacy of dynamic modeling to capture spatiotemporal variables, such as vegetation cover for an endemic insect species, but also provide a novel approach to mapping species distributions with sparse locality records.</p>
OMOP2OBO Condition Occurrence Mappings
<p><strong>OMOP2OBO Condition Occurrence Mappings V1.0</strong></p> <p>These mappings were created by the OMOP2OBO mapping algorithm (see links below). OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions (and N relationships curated in these ontologies).</p> <p>The mappings in this repository were created between OMOP standard condition occurrence concepts (i.e., SNOMED CT) to the Human Phenotype Ontology (HPO) and the (Mondo). The National Library of Medicine's Unified Medical Language System (UMLS) Semantic Types are first used to filter out all concepts that did not have a biological origin (accidents, injuries, external complications, and findings without clear interpretations). Then, the Semantic Type was used to prioritize the mapping of HPO concepts to findings and symptoms and Mondo to Semantic Types indicative of disease. For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.</p> <p><br> <strong>Mapping Details</strong><br> Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.</p> <p><strong><em>Mapping Categories</em></strong></p> <ul> <li><strong>Automatic One-to-One Concept</strong>: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li><strong>Automatic One-to-One Ancestor:</strong> Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li><strong>Automatic One-to-Many Concept: </strong>Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li><strong>Automatic One-to-Many Ancestor:</strong> Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li><strong>Manual One-to-One: </strong>Hand mapping created using expert suggested resources; 1:1</li> <li><strong>Manual One-to-Many:</strong> Hand mapping created using expert suggested resources; 1:Many</li> <li><strong>Cosine Similarity:</strong> score suggested mapping -- manually verified</li> <li><strong>UnMapped:</strong> No suitable mapping or not mapped type</li> </ul> <p><br> <em><strong>Mapping Statistics</strong></em><br> Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:</p> <table align="center"> <thead> <tr> <th scope="col">Mapping Category</th> <th scope="col">HPO</th> <th scope="col">Mondo</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>4767</td> <td>9097</td> </tr> <tr> <td>Automatic One-to-Many Concept</td> <td>150</td> <td>885</td> </tr> <tr> <td>Cosine Similarity</td> <td>1375</td> <td>667</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>13595</td> <td>8911</td> </tr> <tr> <td>Automatic One-to-Many Ancestor </td> <td>38080</td> <td>40224</td> </tr> <tr> <td>Manual</td> <td>5131</td> <td>755</td> </tr> <tr> <td>Manual One-to-Many</td> <td>10326</td> <td>2835</td> </tr> <tr> <td>Unmapped</td> <td>36301</td> <td>46345</td> </tr> </tbody> </table> <p><br> <strong>Provenance and Versioning: </strong>The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020. </p> <p><strong>Caveats:</strong> The deposited files only contain the mappings that were generated automatically by the algorithm. The manually generated mappings will be deposited with the official preprint manuscript. Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.</p> <p> </p> <p><strong>Important Resources and Documentation</strong></p> <ul> <li>GitHub: <a href="https://github.com/callahantiff/OMOP2OBO">OMOP2OBO</a></li> <li>Project Wiki: <a href="https://github.com/callahantiff/OMOP2OBO/wiki">OMOP2OBO - wiki</a></li> <li>Zenodo Community: <a href="https://zenodo.org/communities/omop2obo">OMOP2OBO</a></li> <li>Preprint Manuscript: <a href="https://doi.org/10.5281/zenodo.5716421">10.5281/zenodo.5716421</a></li> </ul>
Samples of solar flares classes, active regions and time of occurrence
<p>This dataset contains samples of solar flares measurements of classes X, M, C and B.</p> <p>For clarification, the flares are classified as follows:</p> <ul> <li>Class X: flares <span class="math-tex">\(>10^{-4} watts/m^{2}\)</span></li> <li>Class M: <span class="math-tex">\(10^{-5} watts/m^{2} <\)</span>flares <span class="math-tex">\(<10^{-4} watts/m^{2}\)</span></li> <li>Class C: <span class="math-tex">\(10^{-6} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-5} watts/m^{2}\)</span></li> <li>Class B: <span class="math-tex">\(10^{-7} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-6} watts/m^{2}\)</span></li> </ul> <p>This dataset was assembled with data from https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares</p> <p>The date (yyyy-mm-dd hh:mm:ss) the authors assembled the data is 2017-11-14 13:48:37</p> <p>The original data source is the National Oceanic & Atmospheric Administration (NOAA), U.S. Departement of Commerce.</p> <p>Data description:</p> <ul> <li><strong>Class</strong>: Class of the flare: X, M, C or B</li> <li><strong>Date</strong>: Date of occurence in yyyy-mm-dd format.</li> <li><strong>AR</strong>: Active Region ID attributed by NOAA.</li> <li><strong>Begin</strong>: time the flare begins in hh:mm:ss format.</li> <li><strong>Max</strong>: time the flare reaches its max value in hh:mm:ss format.</li> <li><strong>End</strong>: time the flare vanishes in hh:mm:ss format.</li> </ul> <p>The dataset has 2,256 tuples divided as follows:</p> <ul> <li>171 tuples with X class flares data (7.58%).</li> <li>572 tuples with M class flares data (25.35%).</li> <li>767 tuples with C class flares data (34%).</li> <li>746 tuples with B class flares data (33.07%).</li> </ul> <p>The data collected refer to the period between August 25, 1996 and May 29, 2017.</p>
Рис. 4. Частота встречаемости водных брюхоногих моллюсков Калининградской области (%). Fig. 4. The occurrence frequency of gastropods of Kaliningrad Region (%). in Spatial distribution of gastropods (Mollusca: Gastropoda) from the Kaliningrad Region (Russia) water bodies
Рис. 4. Частота встречаемости водных брюхоногих моллюсков Калининградской области (%). Fig. 4. The occurrence frequency of gastropods of Kaliningrad Region (%).
Fig. 1 in On the occurrence of Hemiphractus scutatus (Spix, 1824) (Anura: Hemiphractidae) in eastern Amazonia
Fig. 1. Known distribution of (a) genus Hemiphractus (in purple) and (b) Hemiphractus scutatus (dots), highlighting new locality of occurrence in middle Tapajós River region, Pará State, Brazil (red dots) and localities of sequences included in molecular analysis (yellow dots). The region of new records is zoomed in (c), showing the sampling sites where H. scutatus was present (red) and not recorded (white).
Fig. 4 in On the occurrence of Hemiphractus scutatus (Spix, 1824) (Anura: Hemiphractidae) in eastern Amazonia
Fig. 4. Aerial (a) and inside (b) view of the Hemiphractus scutatus habitat (Terra Firme forest) in middle Tapajós River region, Pará State, Brazil, also showing the BR-230 highway.
Fig. 6 in On the occurrence of Hemiphractus scutatus (Spix, 1824) (Anura: Hemiphractidae) in eastern Amazonia
Fig. 6. Maximum likelihood phylogenetic tree of Hemiphractus species based in a fragment of the 16S mtDNA gene, with GenBank accession numbers. Only bootstrap values>80% are shown (5,000 replicates). For Hemiphractus scutatus, sample localities are in parentheses and specimens from middle Tapajós River region, Pará State, Brazil are highlighted.
Fig. 5 in On the occurrence of Hemiphractus scutatus (Spix, 1824) (Anura: Hemiphractidae) in eastern Amazonia
Fig. 5. Variation in number of individuals of Hemiphractus scutatus recorded along its known elevational range (60–3,300 m above mean sea level). The specimens from middle Tapajós River region, Pará State, Brazil are recorded among the lowest known elevation for the species.
Fig. 7 in On the occurrence of Hemiphractus scutatus (Spix, 1824) (Anura: Hemiphractidae) in eastern Amazonia
Fig. 7. Inter and intraspecific genetic distances (mean ± standard deviation of pairwise and K2P distances) calculated for a fragment of 16S mtDNA gene of Hemiphractus species and populations. (Hsc) Hemiphractus scutatus; (Hpr) Hemiphractus proboscideus (Hfa) Hemiphractus fasciatus; (Hhe) Hemiphractus helioi; (Col) Colombia; (Per) Peru; (Tap) middle Tapajós River region, Pará State, Brazil. GenBank accession numbers of sequences are in Table 2.
Fig. 3 in On the occurrence of Hemiphractus scutatus (Spix, 1824) (Anura: Hemiphractidae) in eastern Amazonia
Fig. 3. Dorsal and ventral views of voucher specimens of Hemiphractus scutatus from middle Tapajós River region, Pará State, Brazil. (a) Female, 76.1 mm SVL, INPA-H38116; (b) Male, 57.8 mm SVL, INPA-H38117. Scale bar = 20 mm.
Fig. 2 in On the occurrence of Hemiphractus scutatus (Spix, 1824) (Anura: Hemiphractidae) in eastern Amazonia
Fig. 2. Specimens of Hemiphractus scutatus from middle Tapajós River region, Pará State, Brazil. (a) Female, 76.1 mm SVL, INPA-H38116; (b) Male, 57.8 mm SVL, INPA-H38117; (c) Female, 61.7 mm SVL, INPA-H38118.
Figure 4 in First occurrence of Duboisia (Bovidae, Artiodactyla, Mammalia) from Thailand
Figure 4. Comparison of skulls between Duboisia aff. santeng (A–B: PPN 01-000109) and D. santeng (C–D: MGB.SA 290709) from Pucung (Java). Panels (a) and (c), left lateral view; (b) and (d), anterior view and cross section of the left horn core. Abbreviations: an., anterior; me., medial.
Figure 3 in First occurrence of Duboisia (Bovidae, Artiodactyla, Mammalia) from Thailand
Figure 3. Calvarium with right and left horn cores of Duboisia aff. santeng (PPN 01-000109). (a) Anterior view; (b) cross section of the left horn core (an., anterior; me., medial.); (c) left lateral view; (d) posterior view; (e) schematic drawing of the posterior surface; (f) dorsal view; (g) schematic drawing of the dorsal surface.
Figure 1 in First occurrence of Duboisia (Bovidae, Artiodactyla, Mammalia) from Thailand
Figure 1. Geological map of the Khorat basin, Nakhon Ratchasima, north-eastern Thailand, and the locality of Tha Chang sandpit no. 8 (modified from Department of Mineral Resources, 1999).
Figure 5 in First occurrence of Duboisia (Bovidae, Artiodactyla, Mammalia) from Thailand
Figure 5. Distribution of Boselaphini and zoogeographical barriers between South Asia and South East Asia. Fossil localities: 1, Siwaliks; 2, Bagan; 3, Tha Chang; 4, Java.
Fig. 3 in Occurrence and regional distribution of Aelurostrongylus abstrusus in cats in Germany
Fig. 3 Seasonal distribution of Aelurostrongylus abstrusus-positive feline faecal samples (n 026) in Germany
Fig. 2 in Occurrence and regional distribution of Aelurostrongylus abstrusus in cats in Germany
Fig. 2 Light micrograph of a dehydrated, damaged and morphologically altered first-stage larva (L1) of Aelurostrongylus abstrusus after flotation using a concentrated salt solution with zinc chloride and sodium chloride (specific gravity, 1.3)
Fig. 1 in Occurrence and regional distribution of Aelurostrongylus abstrusus in cats in Germany
Fig. 1 Light micrograph of the posterior end of the first-stage larva (L1) of Aelurostrongylus abstrusus immobilised by heat
Figures 128–129 in Scorpions of the Horn of Africa (Arachnida: Scorpiones). Part XXX. Parabuthus (Buthidae) (Part III), with description of three new species from Somaliland and occurrence of Parabuthus eritreaensis Kovařík, 2003
Figures 128–129: Figure 128. Map showing confirmed distribution of Parabuthus spp. In Djibouti, Eritrea, Ethiopia, Somalia, and Somaliland. Figure 129. Parabuthus eritreaensis, female from Somaliland in vivo habitus.
Figures 103–110 in Scorpions of the Horn of Africa (Arachnida: Scorpiones). Part XXX. Parabuthus (Buthidae) (Part III), with description of three new species from Somaliland and occurrence of Parabuthus eritreaensis Kovařík, 2003
Figures 103–110: Parabuthus quincyae sp. n., male holotype. Figure 103. Carapace and tergites I–IV. Figures 104–105. Sternopectinal area and sternites. Figure 106. Sternite VII and metasoma I–II ventral Figures 107–110. Right legs I–IV, retrolateral aspect.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.