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Fig. 1 in Taxonomy of some Galeommatoidea (Mollusca, Bivalvia) associated with deep-sea echinoids: A reassessment of the bivalve genera Axinodon Verrill & Bush, 1898 and Kelliola Dall, 1899 with descriptions of new genera Syssitomya gen. nov. and Ptilomyax gen. nov.
Fig. 1. Holotype of Axinodon symmetros Verrill & Bush, 1898, USNM 35175. A-B. SEM of hinges of right and left valves. C-D. SEM of internal of right and left valves. E. Photo micrograph of internal of right valve. F-G. SEM of external of right and left valves. H. SEM of prodissoconch.
Fig. 2 in Taxonomy of some Galeommatoidea (Mollusca, Bivalvia) associated with deep-sea echinoids: A reassessment of the bivalve genera Axinodon Verrill & Bush, 1898 and Kelliola Dall, 1899 with descriptions of new genera Syssitomya gen. nov. and Ptilomyax gen. nov.
Fig. 2. Holotype of Kellia symmetros Jeffreys, 1876, USNM 170626. A-B. SEM of hinges of right and left valves. C-D. SEM of internal of right and left valves. E-F SEM of external of right and left valves. G-H. photo micrographs of internal and external of right valve.
Characteristics of Marginalised Rural Areas in Europe and the Mediterranean Region: Shapeflie and associated attributes
<p>The H2020 project on Social Innovation in Marginalised Rural Areas (SIMRA) focused on understanding social innovation and innovative governance in agriculture, forestry and rural development, and how to boost them, particularly in marginalised rural areas across Europe, with a focus on the Mediterranean region (including non-EU). Its geographic focus was on Marginalised Rural Areas (MRAs), which had not previously been defined.</p> <p>The analysis of the rural areas of Europe and the Mediterranean area required data of consistent spatial and temporal resolutions for variables of three types: physical geography, infrastructure (spatial marginality), and socio-economic (societal marginality). There few datasets of relevance that exist for the entire area, creating a need to derive spatial datasets and produce associated maps of the characteristics that contribute to marginality or marginalization.</p> <p>The outputs comprise new spatial datasets at resolutions compatible with the underlying information (e.g. 1km2, NUTS 3, NUTS 2, and local authorities in North Africa and the eastern Mediterranean), enabling comparisons between such areas. The associated maps and a tabulation of the characteristics for the entire area of interest to SIMRA are reported in Price et al. (2017).</p> <p>This spatial dataset contains the characteristics of the Marginalised Rural Areas as attributes in a Shapefle for use in a Geographic Information System. Details of the attributes in the Shapefle, and their values, are provided in the MS Excel spreadsheet downloadable with this dataset.</p> <p>Reference:</p> <p>Price, M., Miller, D.R., McKeen, M., Slee, W. and Nijnik, M. 2017. Categorisation of marginalised rural areas (MRAs). Deliverable 3.1, Social Innovation in Marginalised Rural Areas (SIMRA). Report to the European Commission, pp. 57. 10.5281/zenodo.3625493</p> <p> </p> <p>The boundaries in the spatial dataset are complied from: Nomenclature of Territorial Units for Statistics (NUTS) 2013 European Commission, © EuroGeographics, © FAO (UN), © TurkStat Source: European Commission – Eurostat/GISCO© for administrative boundaries. All other boundary data were extracted from the GADM database (www.gadm.org), version 2.8, November 2015. They can be used for non-commercial purposes only. It is not allowed to redistribute these data, or use them for commercial purposes, without prior consent. See the website for more information.<br> </p>
Fig. 7 in Discovery of termitophilous rove beetles associated with Formosan subterranean termite Coptotermes formosanus in Taiwan, with the first larval description for the tribe Termitohospitini (Coleoptera: Staphylinidae)
Fig. 7. Japanophilus hojoi Maruyama & Iwata, 2002, larval instar 1. A–B – pronotum; C – mesonotum; D – hatching spines of mesonotum; E – metanotum; F – hatching spines of metanotum; G – left foreleg, anterior view. Abbreviations: I–X – abdominal segments; A – anterior setae; Ad – anterodorsal setae; Al – anterolateral setae; Av – anteroventral seta; C – campaniform sensilla; Cx – coxa; D – dorsal setae; Da-d – discal setae, rows a–d; Fe – femur; Hs – hatching spines; L – lateral setae; P – posterior setae; Pd – posterodorsal setae; P1 – posterolateral setae; Pv – posteroventral setae; Tb – tibia; Tr – trochanter; Ts – tarsungulus; V – ventral setae.
Fig. 5 in Discovery of termitophilous rove beetles associated with Formosan subterranean termite Coptotermes formosanus in Taiwan, with the first larval description for the tribe Termitohospitini (Coleoptera: Staphylinidae)
Fig. 5. Japanophilus hojoi Maruyama & Iwata, 2002, larval instar 1, head. A – dorsal view; B – lateral view; C – ventral view. Abbreviations: Ec – epicranial campaniform sensilla; Ed – epicranial dorsal seta; El – epicranial lateral setae; Em – epicranial marginal setae; Es – epicranial suture; Fd – frontal dorsal setae; Fl – frontal lateral setae; Fm – frontal marginal seta; L – lateral setae; P – posterior (epicranial) setae; T – temporal setae; V – ventral seta; Vc – ventral campaniform sensilla; Vl – ventral lateral setae.
Figure 1 in Is Geckobiella stamii (Acari: Pterygosomatidae) a hyperparasite or phoretic on Amblyomma dissimile (Acari: Ixodidae) associated with Iguana iguana from Panama?
Figure 1 Map of the locations where Geckobiella stamii was found associated withAmblyomma dissimile. 1: Corozal, Panama province; 2: Bugaba, Chiriquí province; 3: Tonosí, Los Santos province.
Data associated with Lark et al. 2020: U.S. cropland conversion (2008-16)
<p>Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found <strong><a href="https://zenodo.org/record/3905556#.XvLXQ21Kipo">here</a></strong>.</p> <ul> <li>Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. <em>Nature Communications </em>(In review)</li> </ul> <p>Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:</p> <ul> <li><em><strong>mtr</strong></em> = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period: <ol> <li>"<em>stable non-cropland</em>" -- areas of consistent non-cropland throughout the duration of the study period.</li> <li>"<em>stable cropland</em>" -- areas of consistent cropland throughout the duration of the study period.</li> <li>"<em>cropland expansion</em>" -- areas converted to crop production between 2008 and 2016.</li> <li>"<em>cropland abandonment</em>" -- areas converted away from crop production between 2008 and 2016.</li> <li>"<em>intermittent cropland/confusion</em>" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion. </li> </ol> </li> <li><em><strong>ytc</strong></em> = "year to cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.</li> <li><em><strong>yfc</strong></em> = "year from cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season. </li> <li><em><strong>bfc</strong></em> = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fc</strong></em> = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>bfnc</strong></em> = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fnc</strong></em> = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> </ul> <p>Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (<em>mtr</em> = 3) and those of stable croplands (<em>mtr </em>= 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).</p> <ul> <li><strong>corn_relDiff_local.tif </strong>= local yield differential (%) of corn grain.</li> <li><strong>corn_relDiff_national.tif</strong> = national yield differential (%) of corn grain.</li> <li><strong>soy_relDiff_local.tif</strong> = local yield differential (%) of soybeans.</li> <li><strong>soy_relDiff_national.tif</strong> = national yield differential (%) of soybeans.</li> <li><strong>wheat_relDiff_local.tif</strong> = local yield differential (%) of wheat.</li> <li><strong>wheat_relDiff_national.tif</strong> = national yield differential (%) of wheat.</li> </ul>
Figure 10 in Chinese psyllids in the genus Cacopsylla (Hemiptera: Sternorrhyncha: Psylloidea) associated with Spiraea (Rosaceae)
Figure 10. Photographs of dried specimens of Cacopsylla spp., adult. (a–b) C. falcata sp. nov.; (c–d) C. hyalinonemae Li and Yang, 1989; (e–f) C. nocturna sp. nov.; (g–h) C. qilianensis sp. nov.; (i–j) C. spiraeicola (Li, 2011). a, c, e, g, i. lateral view; b, d, f, h, j. dorsal view. Scale bar = 1 mm.
Figure 2 in Chinese psyllids in the genus Cacopsylla (Hemiptera: Sternorrhyncha: Psylloidea) associated with Spiraea (Rosaceae)
Figure 2. Cacopsylla falcata sp. nov., fifth instar immature. (a) Overall view, dorsal aspect on the left half, ventral aspect on the right half; (b) tarsal arolium; (c) anal pore field. Scale bar: a = 0.31 mm; b = 0.031 mm; c = 0.124 mm.
Figure 3. Cacopsylla hyalinonemae Li in Chinese psyllids in the genus Cacopsylla (Hemiptera: Sternorrhyncha: Psylloidea) associated with Spiraea (Rosaceae)
Figure 3. Cacopsylla hyalinonemae Li and Yang, 1989, adult. (a) Head, front view, antennae removed; (b) male terminalia, in profile, ignoring distal segment of aedeagus and phallobase; (c) inner view of paramere; (d) distal segment of aedeagus; (e) female terminalia, in profile; (f) fore wing; (g) hind wing; (h) distal two segments of antenna. Scale bar: a = 0.286 mm; b = 0.155 mm; c, d, h = 0.124 mm; e = 0.167 mm; f, g = 0.714 mm.
Figure 1 in Chinese psyllids in the genus Cacopsylla (Hemiptera: Sternorrhyncha: Psylloidea) associated with Spiraea (Rosaceae)
Figure 1. Cacopsylla falcata sp. nov., adult. (a) Head, front view, antennae removed; (b) male terminalia, in profile, ignoring distal segment of aedeagus and phallobase; (c) inner view of paramere; (d) distal segment of aedeagus; (e) female terminalia, in profile; (f) fore wing; (g) distal two segments of antenna. Scale bar: a = 0.25 mm; b, e = 0.155 mm; c, d, g = 0.124 mm; f = 0.625 mm.
Figure 8 in Chinese psyllids in the genus Cacopsylla (Hemiptera: Sternorrhyncha: Psylloidea) associated with Spiraea (Rosaceae)
Figure 8. Microscopic photograph of fore wing membrane of Cacopsylla spp., showing texture of surface spinules. (a) C. falcata sp. nov.; (b) C. hyalinonemae Li and Yang, 1989; (c) C. nocturna sp. nov.; (d) C. qilianensis sp. nov.; (e) C. spiraeicola (Li, 2011).
Data from: Using genetic relatedness to understand heterogeneous distributions of urban rat-associated pathogens
<p>Urban Norway rats (<i>Rattus norvegicus</i>) carry several pathogens transmissible to people. However, pathogen prevalence can vary across fine spatial scales (i.e., by city block). Using a population genomics approach, we sought to describe rat movement patterns across an urban landscape, and to evaluate whether these patterns align with pathogen distributions. We genotyped 605 rats from a single neighborhood in Vancouver, Canada and used 1,495 genome-wide single nucleotide polymorphisms to identify parent-offspring and sibling relationships using pedigree analysis. We resolved 1,246 pairs of relatives, of which only 1% of pairs were captured in different city blocks. Relatives were primarily caught within 33 meters of each other leading to a highly leptokurtic distribution of dispersal distances. Using binomial generalized linear mixed models we evaluated whether family relationships influenced rat pathogen status with the bacterial pathogens <i>Leptospira interrogans</i>, <i>Bartonella tribocorum</i>, and <i>Clostridium difficile</i>, and found that an individual's pathogen status was not predicted any better by including disease status of related rats. The spatial clustering of related rats and their pathogens lends support to the hypothesis that spatially restricted movement promotes the heterogeneous patterns of pathogen prevalence evidenced in this population. <span>Our findings also highlight the utility of evolutionary tools to understand movement and rat-associated health risks in urban landscapes.</span></p>
Figure 13 in Description, molecular phylogeny, and natural history of a new kleptoparasitic species of gelechiid moth (Lepidoptera) associated with Melastomataceae galls in Brazil
Figure 13. Seasonal abundance of cecidogenous (Palaeomystella fernandesi, dashed line) and kleptoparasite (Locharcha opportuna, solid line) larvae in galls (total = 164 and 169 individuals, respectively) induced on Tibouchina sellowiana plants at CPCN Pró-Mata, from April 2012 through June 2013. Arabic numbers from 1 to 14 represent 30-day sampling intervals. Upper horizontal bars indicate host plant phenological phases: red, flowering; green, fruiting; blue, dormancy; black, forming new shoots.
Figure 7 in Description, molecular phylogeny, and natural history of a new kleptoparasitic species of gelechiid moth (Lepidoptera) associated with Melastomataceae galls in Brazil
Figure 7. Locharcha opportuna pupa, in dorsal (A), ventral (B) and lateral (C) views, respectively. Scale bar = 1 mm.
Figure 2 in Phoretic behaviour of Attacobius attarum (Roewer, 1935) (Araneae: Corinnidae: Corinninae) dispersion not associated with predation?
Figure 2. Behavioural repertoire of Attacobius attarum for dispersion in Atta sexdens: (A) female spider in bunch of loose soil from the nest of leaf-cutting ant, in search of a winged male; (B) approximation of female of leaf-cutting ants before the mating flight; (C) climbing of the spider to the dorsal region of winged female; (D) spider detail on the back of the queen; (E, F) winged male and female of leaf-cutting ants are preparing for the mating flight with phoretic spiders on their pronota.
Knocking on giants' doors I: The associated modelled physical parameters
<p>The dataset contains the modelled gas-phase metallicities and gas masses based on SED-derived dust and stellar properties for 300 ALMA detected dusty star-forming galaxies analysed in Donevski et al. A&A accepted, arXiv:2008.09995. </p>
The Psychological Burden of the COVID-19 Pandemic and Its Associated Factors among the Frontline Doctors of Bangladesh: A Cross-sectional Study-Extended Data
<p>Using this document, we tried to assess the mental health status of the frontline doctors of Bangladesh during Coronavirus 2019 pandemic.</p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Accounting for environmental variation in co‐occurrence modelling reveals the importance of positive interactions in root‐associated fungal communities
<p>Understanding the role of interspecific interactions in shaping ecological communities is one of the central goals in community ecology. In fungal communities, measuring interspecific interactions directly is challenging because these communities are composed of large numbers of species, many of which are unculturable. An indirect way of assessing the role of interspecific interactions in determining community structure is to identify the species co-occurrences that are not constrained by the environmental conditions. In this study, we investigated co-occurrences among root-associated fungi, asking whether fungi co-occur more or less strongly than expected based on the environmental conditions and the host plant species examined. For this purpose, we generated molecular data on root-associated fungi of five plant species evenly sampled along an elevational gradient at a high Arctic site. We analysed the data using a joint species distribution modelling approach that allowed us to identify those co-occurrences that could be explained by the environmental conditions and the host plant species, as well as those co-occurrences that remained unexplained and thus more likely reflect interactive associations. Our results indicate that positive interactions play an important role in shaping microbial communities in arctic plant roots. In particular, we found that mycorrhizal fungi are especially prone to positively co-occur with other fungal species. Our results bring new understanding to the structure of arctic interaction networks by suggesting that interactions among root-associated fungi are predominantly positive.</p>
ScienceDex guides
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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.