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4,068 results for “geographic”
Geographic variation of tree height of Pinus nigra Arn. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus nigra</em> Arn. planted in common gardens in France, Germany and Spain, between years 1968 and 2009. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 194,642 individual tree height data measurements <em> </em>with 15 common gardens and 78 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p> <p> </p>
Geographic variation of tree height of Pinus pinaster Aiton gathered from common gardens in Europe and North-Africa
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinaster</em> Aiton planted in common gardens in France, Morocco and Spain, between years 1966 and 1992. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 123,801 individual tree height data measurements <em> </em>with 14 common gardens and 182 different genetic units. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover
<p>Zenodo deposit for Pozzi et al (2024) AJB</p> <p>The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover</p> <p><strong> _____________________________________________________________________________________________________________________________________</strong></p> <p><strong>The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover</strong></p> <p>Adrien C.M. Pozzi<sup>1,2</sup>, Ruth G. Shaw<sup>1</sup>, Georgiana May<sup>1,3</sup></p> <p><sup>1</sup> Department of Ecology, Evolution and Behavior, University of Minnesota Twin-Cities, St Paul, MN 55108. <sup>2</sup> Current affiliation: Universite Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, 69622 Villeurbanne, France. ORCID: 0000-0001-6765-4293. <sup>3</sup> Correspondence: Georgiana May (gmay@umn.edu)</p> <p><em><strong>Keywords:</strong></em></p> <p>conservation; <em>Dalea</em> spp.; habitat fragmentation; mutualism; nitrogen-fixing symbiosis; population level variation; native prairie legume; restoration; rhizobia</p> <p><em><strong>Description:</strong></em></p> <p>This Zenodo deposit is part of the MN LCCMR Healthy Prairies project granted to R. Shaw and G. May, UMN Twin-Cities. It contains the following files:</p> <p><span><span>·<span> </span></span></span>“Metadata & data” spreadsheet. <em>Contains metadata and data about the Twin Valley experiment (including the position of plants, an intermediary census, growth and nodulation traits for harvested plants, data on bacterial isolates from root nodules, and source modifiers for GenBank accessions OQ732394-OQ732572).</em></p> <p><span><span>·<span> </span></span></span>“Blast alignments output.txt” text file. <em>Contains ouput of Blastn alignements for the 16S rRNA genes of bacterial isolates against the rRNA_typestrains/16S_ribosomal_RNA 16S ribosomal RNA (Bacteria and Archaea type strains) database, to determine the genus as recommended by GenBank during sequence submission.</em></p> <p><span><span>·<span> </span></span></span>“General script.R”. <em>R script of the general statistical analyses produced for publication.</em></p> <p><span><span>·<span> </span></span></span>“General environment.RData”.<em> The companion RData (environment) of the above R script.</em></p> <p><span><span>·<span> </span></span></span>“Trait model script.R”. <em>R script of the plant trait models produced for publication</em></p> <p><span><span>·<span> </span></span></span>“Trait model environment.RData”. <em>The companion RData (environment) of the above R script.</em></p> <p><strong><em>Acknowledgments:</em></strong></p> <p>The authors thank members of the May Lab (Mai Beauclaire, Em Daily, Mara Demers, Kane Keller, Cedric Ndinga-Muniania, Liam Vertal, Monica Watson) for their help in field and lab work. We thank members of the Healthy Prairies project (Shelby Flint, Anna Peschel, Bill Peterson) who provided much of the infrastructure that made this project possible, as well as help in setting up field experiments. We also thank volunteer undergrads from University of Minnesota Morris (Amelia Nelson, Emily Job, Lily Fulton) for assistance in measuring harvested plants and counting nodules. Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR) Project 00086965, Healthy Prairies.</p> <p> </p>
Geographic Information System for marine aquaculture in Argentina
<p>Planning the use of marine areas for aquaculture through the development of Geographic Information Systems (GIS) has taken on great importance recently . This is because GIS allows decision-making through the analysis and integration of a large amount of data of various kinds gathered in a single database. This system allows the incorporation of information on optimal environmental conditions for farm species and relevant data to develop strategies throughout the entire production chain, from service providers and inputs to the final marketing of the product. The recommended actions of the strategic guidelines for a more sustainable and competitive EU aquaculture in 2021–2030 (EC 2021) stated explicitly the need to “<em>Develop a more detailed guidance document on the planning for space and access to water for marine, freshwater and land-based aquaculture</em>”, highlighting the importance of the GIS.</p> <p>Here you will find 4 files with the following information:<br>1) <strong><em>Metadata.doc</em></strong> file with the details of the metadata used to diagram the GIS layers.<br>2) <em><strong>GIS.gpkg</strong></em> file with each of the layers in raster and vector format.<br>3) <em><strong>Land-based model.gpkg</strong></em> file with examples of GIS modeling for land-based facilities.<br>4) <strong><em>Open-water model.gpkg</em></strong> file with examples of GIS modeling for facilities in open systems.</p> <p> </p>
Biometry data to reveal geographic variation in Bramblings Fringilla montifringilla
<p><strong>Abstract</strong></p> <p>The Brambling <em>Fringilla montifringilla</em> has a large breeding distribution across the entire Palaearctic taiga region. Birds in the Far East are more brightly coloured and formerly separated as subspecies <em>subcuneolata</em> (S. Cramp & C. M. Perrins 1994; Handbook of the Birds of Europe, the Middle East and North Africa. The Birds of the Western Palearctic, Vol. 8. Oxford University Press, Oxford). To reveal possible geographical variation in the size of the wing, primary feathers, bill, and in the extent of the partial post-juvenile moult, we present measurements taken from 579 skins of the Natural History Museum, Tring UK, Natural History Museum of Denmark, Copenhagen, The Arctic University Museum of Norway, Tromsø, Natural History Museum, University of Oslo, Zoologisches Forschungsmuseum Alexander Koenig, Bonn, Swedish Museum of Natural History, Stockholm, and Finnish Museum of Natural History, Helsinki.</p> <p>I thank I. C. J. Galbraith for allowing us to measure the birds in the collection of the Natural History Museum, Tring UK, and the following museums and their curators for sending Brambling specimens to Switzerland more than 35 years ago: Natural History Museum of Denmark, Copenhagen (Jon Fieldså), The Arctic University Museum of Norway, Tromsø (Hans-Petter Mannvik and Wim Vader), Natural History Museum, University of Oslo (Tore Slagsvold), Zoologisches Forschungsmuseum Alexander Koenig, Bonn (Renate van den Elzen), Swedish Museum of Natural History, Stockholm (Bo Fernholm), and Finnish Museum of Natural History, Helsinki (Ann Forstén). I thank Raffael Winkler, Natural History Museum Basel for managing these specimen exchanges. I thank Susanne Jenni-Eiermann for help with measuring the large collection of the Natural History Museum at Tring).</p> <p>I thank Mark Adams (Natural History Museum, Tring UK), Peter A. Hosner (Natural History Museum of Denmark, Copenhagen), Geir Rudolfsen (The Arctic University Museum of Norway, Tromsø), Jan T. Lifjeld (Natural History Museum, University of Oslo), Till Töpfer (Zoologisches Forschungsmuseum Alexander Koenig), Ulf Johansson (Swedish Museum of Natural History, Stockholm), and Hanna Laakkonen (Finnish Museum of Natural History, Helsinki) for updating the collection numbers of the specimens and giving permission to present these data here.</p> <p>Explanations of the variables can be found in the Excel-file.</p> <p> </p> <p> </p>
1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate
<p>1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate.</p> <p> </p> <p>How to use</p> <pre>from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5))</pre> <p> </p> <p>Files:</p> <ul> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/towns.csv">towns.csv</a> - city information</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/regions.csv">regions.csv</a> - list of Russian Federation regions</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/alt_city_names.json">alt_city_names.json</a> - alternative city names</li> </ul> <p> </p> <p>Сolumns (towns.csv):</p> <p>Basic info:</p> <ul> <li><code>city</code> - city name (several cities have alternative names marked in <code>alt_city_names.json</code>)</li> <li><code>population</code> - city population, thousand people, Rosstat estimate as of 1.1.2020</li> <li><code>lat,lon</code> - city geographic coordinates</li> </ul> <p>Region:</p> <ul> <li><code>region_name</code> - subnational region (oblast, republic, krai or AO)</li> <li><code>region_iso_code</code> - <a href="https://en.wikipedia.org/wiki/ISO_3166-2:RU">ISO 3166 code</a>, eg <code>RU-VLD</code></li> <li><code>federal_district</code>, eg <code>Центральный</code></li> </ul> <p>City codes:</p> <ul> <li><code>okato</code></li> <li><code>oktmo</code></li> <li><code>fias_id</code></li> <li><code>kladr_id</code></li> </ul> <p> </p> <p>Data sources</p> <ul> <li>City list and city population collected from Rosstat publication <a href="https://rosstat.gov.ru/folder/210/document/13206">Регионы России. Основные социально-экономические показатели городов</a> and parsed from publication Microsoft Word files.</li> <li>City list corresponds to <a href="https://ru.wikipedia.org/wiki/%D0%A1%D0%BF%D0%B8%D1%81%D0%BE%D0%BA_%D0%B3%D0%BE%D1%80%D0%BE%D0%B4%D0%BE%D0%B2_%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8">this Wikipedia article</a>.</li> <li>Alternative dataset is <a href="https://github.com/hflabs/city">wiki-based Dadata city dataset</a> (no population data).</li> </ul> <p> </p> <p>Comments</p> <p> </p> <p>City groups</p> <ul> <li> <p><code>Ханты-Мансийский</code> and <code>Ямало-Ненецкий</code> autonomous regions excluded to avoid duplication as parts of <code>Тюменская область</code>.</p> </li> <li> <p>Several notable towns are classified as administrative part of larger cities (<code>Сестрорецк</code> is a municpality at Saint-Petersburg, <code>Щербинка</code> part of Moscow). They are not and not reported in this dataset.</p> </li> </ul> <p> </p> <p>By individual city</p> <ul> <li><code>Белоозерский</code> not found in Rosstat publication, but <a href="https://github.com/epogrebnyak/ru-cities/issues/5#issuecomment-886179980">should be considered a city as of 1.1.2020</a></li> </ul> <p> </p> <p>Alternative city names</p> <ul> <li> <p>We suppressed letter "ё" <code>city</code> columns in towns.csv - we have <code>Орел</code>, but not <code>Орёл</code>. This affected:</p> <ul> <li><code>Белоозёрский</code></li> <li><code>Королёв</code></li> <li><code>Ликино-Дулёво</code></li> <li><code>Озёры</code></li> <li><code>Щёлково</code></li> <li><code>Орёл</code></li> </ul> </li> <li> <p><code>Дмитриев</code> and <code>Дмитриев-Льговский</code> are the same city.</p> </li> </ul> <p><code>assets/alt_city_names.json</code> contains these names.</p> <p> </p> <p>Tests</p> <pre><code>poetry install poetry run python -m pytest </code></pre> <p> </p> <p>How to replicate dataset</p> <p> </p> <p>1. Base dataset</p> <p>Run:</p> <ul> <li>download data stro rar/get.sh</li> <li>convert <code>Саратовская область.doc</code> to docx</li> <li>run make.py</li> </ul> <p>Creates:</p> <ul> <li><code>_towns.csv</code></li> <li><code>assets/regions.csv</code></li> </ul> <p> </p> <p>2. API calls</p> <p>Note: do not attempt if you do not have to - this runs a while and loads third-party API access.</p> <p>You have the resulting files in repo, so probably does not need to these scripts.</p> <p>Run:</p> <ul> <li><code>cd geocoding</code></li> <li>run coord_dadata.py (needs token)</li> <li>run coord_osm.py</li> </ul> <p>Creates:</p> <ul> <li>coord_dadata.csv</li> <li>coord_osm.csv</li> </ul> <p> </p> <p>3. Merge data</p> <p>Run:</p> <ul> <li>run merge.py</li> </ul> <p>Creates:</p> <ul> <li>assets/towns.csv</li> </ul> <p> </p>
Artificial reefs geographical location matters more than shape, age and depth for sessile invertebrate colonization in the Gulf of Lion (NorthWestern Mediterranean Sea)
<p>Artificial reefs (ARs) have been used to support fishing activities. Sessile invertebrates are essential components of trophic networks within ARs, supporting fish productivity. However, colonization by sessile invertebrates is possible only after effective larval dispersal from source populations, usually in natural habitat. While most studies focused on short term colonization by pioneer species, we propose to test the relevance of geographic location, shape, age and depth of immersion on the ARs long term colonization by species found in natural stable communities in the Gulf of Lion. We recorded the presence of five sessile invertebrates species, with contrasting life history traits and regional distribution in the natural rocky habitat, on ARs with different shapes deployed during two immersion time periods (1985 and the 2000s) and in two depth ranges (<20m and >20m). At the local level (~5kms), neither shape, depth nor immersion duration differentiated ARs assemblages. At the regional scale (>30kms), colonization patterns differed between species, resulting in diverse assemblages. This study highlights the primacy of geographical positioning over shape, immersion duration and depth in ARs colonization, suggesting it should be accounted for in maritime spatial planning.</p>
Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru
<p>Datasets with information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.</p> <p>Data processed from National Statistical System (INEI) publications.</p> <p>2025.</p>
Geographical Distribution Maps of Western Palearctic Weevil Taxa
<p>The electronic supplement belongs to the research article Schütte A, Stüben PE, Astrin JJ (2023) Molecular Weevil Identification Project: A Thoroughly Curated Barcode Release of 1300 Western Palearctic Weevil Species (Coleoptera: Curculionoidea) - Biodiversity Data Journal 11.</p> <p>The ZIP file contains 613 distribution maps from Western Palearctic weevil taxa. The distribution maps showing Europe originate from the Curculio Institute's website (www.curci.de). Additional information on distribution range and known synonyms were based on the information from the Löbl catalogs (Löbl & Smetana 2011, Löbl & Smetana 2013). The maximum distribution range of each species was measured in km with Google Earth's ruler function.</p> <p>An unzip software is needed to access the *.JPG files within the *.ZIP file. Microsoft operating systems support *.zip files natively since Windows XP. MAC operating systems offer the "archive utility" to access *.zip files. Android users must install an app like Winzip, WinRAR, or 7ZIP. The iOS 13 operating system and onwards allow unzipping *.zip archives natively (iPhone and iPad). The *.zip filetype support can be installed on Linux operating systems via the terminal command: "sudo apt-get install unzip". Command to unzip: "unzip \*.zip". The *.JPG files can be opened with any picture viewer or internet browser.</p> <p>References<br> Löbl L, Smetana A (2011) Catalogue of the Coleoptera. Vol. 7, Curculionoiodea I, Stenstrup, Apollo Books, 373 pp.<br> Löbl L, Smetana A (2013) Catalogue of the Coleoptera. Vol. 8, Curculionoiodea II, Leiden & Boston, Brill, 700 pp.</p>
LauNuts: A Knowledge Graph to identify and compare geographic regions in the European Union
<p><strong>LauNuts</strong> is a RDF Knowledge Graph consisting of:</p> <ul> <li>Local Administrative Units (LAU) and</li> <li>Nomenclature of Territorial Units for Statistics (NUTS)</li> </ul> <p><a href="https://w3id.org/launuts">https://w3id.org/launuts</a></p>
Geographic Scope of Randomized Clinical Trials from Africa
<p><strong>Overview</strong></p> <p>This map reports the geographic scope of randomized controlled trials (RCTs) conducted in Africa, as found in PubMed. The map highlights a discrepancy between actual trial location and how trials are reported: although reports of research from Africa are often labeled as being "African" in scope, RCTs are rarely continent-wide and many countries are not represented in even a single study. The intent of the map is to visualize actual trial locations, hopefully leading to a more accurate portrayal of RCT study sites on the African continent.</p> <p><strong>Data Source and Tools</strong></p> <p>Data for the map was extracted from PubMed and the map was created in ArcGIS Online.</p> <p><strong>Audience</strong></p> <p>The map was created for an original research poster at the 2022 International Congress on Peer Review and Scientific Publication (see "Attribution" below). Its audience is researchers, clinicians, policy makers, librarians, scholarly communications stakeholders, and editors in chief interested in improving the accuracy of the reported scope of research in Africa to better inform health care research, policy, and resource distribution.</p> <p><strong>Design</strong></p> <p>A PubMed search using Medical Subject Headings and keywords representing Africa, African, and RCTs was run to identify citations published between 1968 and February 2022. The citation titles and abstracts were screened against established inclusion/exclusion criteria, with included studies continuing through a full text review and data extraction process. Total RCT representation per country was then mapped in ArcGIS with higher RCT counts represented by darker shading. The map highlights the variance in RCT representation across the continent, with some countries represented in up to 80 RCTs and many countries represented in none.</p> <p><strong>Attribution</strong></p> <p>Folafoluwa Olutobi Odetola and Marisa L. Conte conceived the research question, crafted the PubMed search, and analyzed the citation data; Tyler Nix created the map. Special thanks to Caroline Kayko (University of Michigan) for her input in the creation of the map. </p> <p>See the original research poster at:</p> <p>Odetola, FO; Conte, ML. Geographical Scope of Randomized Clinical Trials from Africa. [Poster]. 9th International Congress on Peer Review and Scientific Publication, September 8-10, 2022, Chicago, IL. Available from: <a href="https://peerreviewcongress.org/abstract/geographical-scope-of-randomized-clinical-trials-from-africa/">https://peerreviewcongress.org/abstract/geographical-scope-of-randomized-clinical-trials-from-africa/</a></p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>
Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs (Raw data)
<p>Datasets for the analysis developed in the Article "<em><strong>Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs</strong></em>". For more information, please refer to the original publication.</p> <p>Network_Structural_Index_&_SpogeTraits.csv <- Structural Index for the sponge-dwelling fauna network, sponge accumulated area and sponges’ morphology.</p> <p>NWTA_CoralReefs_Sponges_ interactions.csv <- Relationship between host sponges and guest fauna in the Northwester Atlantic coral reefs</p> <p>NWTA_CoralReefs_Sponge_reacords.csv <- Sponge species incidence records in the Northwester Atlantic coral reefs</p> <p>sponges_morphological_description.csv <- Sponge morphological standardization</p> <p>Network.html <- Interactive sponge-dwelling fauna network</p> <p>Enjoy!<br> </p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at ZIP-code level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct ZIP-code areas in Germany. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each ZIP-code area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles provided by ESRI Deutschland (<a href="https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0">https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0</a>). These shapefiles link the air quality data to precise ZIP-code areas .</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each ZIP-code polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p> <p>Generated using Copernicus Atmosphere Monitoring Service Information 2013-2022</p>
PIE LTER geographic information regarding vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich and a reference marsh (Rough Meadows) in Rowley, Massachusetts.
A description of the vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich, MA and a reference marsh (Rough Meadows) in Rowley, MA.
Geographical gradients of genetic diversity and differentiation among the southernmost marginal populations of Abies sachalinensis revealed by EST-SSR polymorphism
Research Highlights: We detected the longitudinal gradients of genetic diversity parameters, such as the number of alleles, effective number of alleles, heterozygosity, and inbreeding coefficient, and found that these might be attributable to climatic conditions, such as temperature and snow depth. Background and Objectives: Genetic diversity among local populations of a plant species at its distributional margin has long been of interest in ecological genetics. Populations at the distribution center grow well in favorable conditions, but those at the range margins are exposed to unfavorable environments, and the environmental conditions at establishment sites might reflect the genetic diversity of local populations. This is known as the central-marginal hypothesis in which marginal populations show lower genetic variation and higher differentiation than do central populations. In addition, genetic variation in a local population is influenced by phylogenetic constraints and the population history of selection under environmental constraints. In this study, we investigated this hypothesis in relation to Abies sachalinensis, a major conifer species in Hokkaido. Materials and methods: A total of 1,189 trees from 25 natural populations were analyzed using 19 EST-SSR loci. Results: The eastern populations; namely, those in the species distribution center, showed greater genetic diversity than did the western peripheral populations. Another important finding is that the southwestern marginal populations were highly differentiated from the other populations. Conclusions: These differences might be due to genetic drift in the small and isolated populations at the range margin. Therefore, our results indicated that the central-marginal hypothesis held true for the southernmost A. sachalinensis populations in Hokkaido.
Figures 1–5. Oreodera pergeri. 1–4 in A new species, new geographical records, and taxonomic notes in Oreodera Audinet-Serville, 1835 (Coleoptera: Cerambycidae: Lamiinae)
Figures 1–5. Oreodera pergeri. 1–4) Holotype male. 1) Dorsal habitus. 2) Ventral habitus. 3) Lateral habitus. 4) Head, frontal view. 5) Paratype female, dorsal habitus.
Fig. 2 in Missing geographic link: minute lady beetles (Coleoptera: Coccinellidae: Microweiseinae) from Mount Wilhelm, New Guinea
Fig. 2. Morphology of Scymnomorphus species. A–H – S. bimaculatus sp. nov.: A – abdomen, female; B – antenna; C – ovipositor; D – spermatheca; E – tegmen; inner view; F – tegmen, lateral view; G – penis, lateral view; H – abdomen, male. I–P – S. kausi sp. nov.: I – abdomen, female; J – antenna; K – ovipositor; L – spermatheca; M – tegmen, inner view; N – tegmen, lateral view; O – penis, lateral view; P – abdomen, male (arrows indicate glandular opening pores).
Figure 15 in Geographic variation in host selection in the spider wasps Entypus unifasciatus (Say) and Tachypompilus ferrugineus (Say) (Hymenoptera: Pompilidae)
Figure 15. Combined geographic distribution of 39 host species of Lycosidae, Trechaleidae, Pisauridae, Ctenidae, Zoropsidae, Agelenidae and Sparassidae for Entypus unifasciatus and Tachypompilus ferrugineus based on ~9040 SCAN collection records and online images. Northwestern Mexico is undersampled and Colorado is oversampled on this map. Note scarcity of records from the Pacific Northwest.
Figures 7–12. Tachypompilus ferrugineus, 7 in Geographic variation in host selection in the spider wasps Entypus unifasciatus (Say) and Tachypompilus ferrugineus (Say) (Hymenoptera: Pompilidae)
Figures 7–12. Tachypompilus ferrugineus, 7) Female with immobilized Rabidosa rabida (Lycosidae), adult female, Meadowlands Nature Area, Bergen County, NJ. Photograph © Natalie Gregorio. 8) Female with immobilized Dolomedes albineus (light morph) (Pisauridae), adult female, Wolfskin District, Oglethorpe County, GA. Photograph © Wayne Hughes. 9) Female with immobilized Dolomedes albineus (dark morph) (Pisauridae), adult or subadult female, Azle, Tarrant County, TX. Photograph © Tracey Fandre. 10) Female with immobilized Agelenopsis?naevia (Agelenidae), adult female, Mansfield, Tarrant County, TX. Photograph © Don McMillan. 11) Female with immobilized Cupiennius coccineus (Trechaleidae), adult female, Rancho Naturalista, Cartago Province, Costa Rica. Photograph © Debbie Hall. 12) Female with immobilized Phoneutria boliviensis (Ctenidae), adult female, Playa Paunch near Bluff Beach, Isla Colón, Bocas del Toro Province, Panama. Photograph © Ray Hamilton.
Figures 1–6. Entypus unifasciatus. 1 in Geographic variation in host selection in the spider wasps Entypus unifasciatus (Say) and Tachypompilus ferrugineus (Say) (Hymenoptera: Pompilidae)
Figures 1–6. Entypus unifasciatus. 1) Entypus unifasciatus unifasciatus (Say), female, with immobilized Hogna sp., subadult female (Lycosidae), Clark County, IN. Photograph © David Brown. 2) Entypus unifasciatus unifasciatus, female, with immobilized Dolomedes albineus (Pisauridae) (light morph), adult female, Acadiana Park Nature Station, Lafayette, Lafayette Parish, LA. Photograph © James Beck. 3) Entypus unifasciatus cressoni (Townes), female, with immobilized Olios giganteus (Sparassidae), adult female, Gilbert Riparian Reserve, Maricopa County, AZ. Photograph © Kelly Gibson. 4) Entypus unifasciatus cressoni, female, with immobilized Tigrosa sp. (Lycosidae), adult or subadult female, Santa Elena Canyon, Chihuahua State, Mexico. Photograph © Aaron Balam. 5) Entypus unifasciatus cressoni, female, with immobilized?Ctenus sp. (Ctenidae), adult female, Amozoc, Puebla State, Mexico. Photograph © Luis Fuentes. 6) Entypus unifasciatus cressoni, female, with immobilized Cupiennius salei (Trechaleidae), adult or subadult female, Zihuateutla, Bosque Mesófilo Xecotepec, Puebla State, Mexico. Photograph © A. D. Hernández-Saint Martin.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.