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Fig. 13. Ceraphron naivashae Kieffer, 1913 in A photographic catalog of Ceraphronoidea types at the Muséum national d'Histoire naturelle, Paris (MNHN), with comments on unpublished notes from Paul Dessart
Fig. 13. Ceraphron naivashae Kieffer, 1913, holotype, ♀. A. Lateral view (MNHN EY25360). B. Left antenna (MNHN EY22430). C. Left wing (MNHN EY22431).
Fig. 5. Aphanogmus fumipennis Thomson, 1858 in A photographic catalog of Ceraphronoidea types at the Muséum national d'Histoire naturelle, Paris (MNHN), with comments on unpublished notes from Paul Dessart
Fig. 5. Aphanogmus fumipennis Thomson, 1858, originally the female type of Ceraphron oriphilus Kieffer, 1913, synonymized by Dessart (1966a). A. Lateral habitus of the specimen in ethanol (vial MNHN EY25361). B. Fore wing (slide MNHN EY22433) C. Hind wing (slide MNHN EY22433). D. Left posterior leg (slide MNHN EY22432) E. Antenna (slide MNHN EY22434).
Fig. 4. Ceraphron mymecophilus Kieffer, 1913 in A photographic catalog of Ceraphronoidea types at the Muséum national d'Histoire naturelle, Paris (MNHN), with comments on unpublished notes from Paul Dessart
Fig. 4. Ceraphron mymecophilus Kieffer, 1913, synonymized with Aphanogmus abdominalis (Thomson, 1858). A–B. Syntype, ♂ (MNHN EY22464). Genitalia. A. Dorsal view. B. Ventral view. C. CLSM image showing the male genitalia of a different specimen (PSUCIM_3120), ventral view. Volume rendered media file available at https://doi.org/10.6084/m9.figshare.100875.v2. Arrows point to the cuticular fold on the ventral edge of the harpe that is characteristic of Aphanogmus abdominalis (Thomson, 1858).
Fig. 2. Ceraphron mymecophilus Kieffer, 1913 in A photographic catalog of Ceraphronoidea types at the Muséum national d'Histoire naturelle, Paris (MNHN), with comments on unpublished notes from Paul Dessart
Fig. 2. Ceraphron mymecophilus Kieffer, 1913, synonymized with Aphanogmus abdominalis (Thomson, 1858). Syntype, ♂ (MNHN EY22475). A. Lateral view. B. Dorsal view, with arrow pointing to the fovea on the mesoscutellum characteristic of Aphanogmus abdominalis (Thomson, 1858).
Fig. 1. A in A photographic catalog of Ceraphronoidea types at the Muséum national d'Histoire naturelle, Paris (MNHN), with comments on unpublished notes from Paul Dessart
Fig. 1. A. An image of the glass bail-lid jar containing several Kieffer type specimens collected by Ch. Alluaud and R. Jeannel during an expedition to Africa from 1911 to 1912. The specimens are stored in ethanol, in separate glass vials inside the jar. B. An image of the ethanol vial and labels for Ceraphron alticola Kieffer, 1913 (MNHN EY25359).
Yearly Estimation of Ecological Functional Attributes of vegetation from 2005-2010 over the Peneda Geres National Park
<p>Yearly Estimation of Ecological Functional Attributes of vegetation using MSAVI index based on Landsat data from 2005-2010 over the Peneda Geres National Park</p>
Bacterial Communities Composition across the French National Territory 2
<p>Bacterial Communities Composition across the French National Territory : the dataset is composed of 1798 samples and 1355 bacterial and archaeal genera</p>
National Weather Service Coded Surface Bulletins, 2003- (JSON format)
<p>This dataset contains the Coded Surface Bulletin dataset reformatted as JSON files. The Coded Surface Bulletin dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.</p> <p>Each JSON file contains one top-level object corresponding to one bulletin. The top-level object is composed of name/value pairs with the names bulletinType, createDate, validDate, Highs, Lows, ColdFronts, WarmFronts, OccludedFronts, StationaryFronts, and Troughs. The name/value pairs for bulletinType, createDate, and validDate are always present. The other name/value pairs are only present if there is corresponding data. The value for bulletinType is either "LR" or "HR", for low-resolution or high-resolution, respectively. The values for createDate and validDate are UTC timestamp strings. If the bulletinType value is "LR", the longitudes and latitudes have 1° precision. If the bulletinType value is "HR", the longitudes and latitudes have 0.1° precision.</p> <p>The value associated with the name High in the top-level object is itself an object composed of three name value pairs that describe the geographic locations and surface air pressure levels for one or more high pressure centers. The names of the object elements are lats, lons, and pressures. The values for these are all arrays. For a given object, the arrays will all have the same size. The arrays contain latitudes in degrees, longitudes in degrees, and pressures in millibars. If the arrays contain N elements apiece, the object is describing N pressure centers. The object associated with the name Low in the top-level object is structured in the same way. It describes the geographic locations and surface air pressure levels for one or more low pressure centers.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, OccludedFronts, and Troughs names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front or trough of the given type. Each object is composed of three name/value pairs with the names lats, lons, and strength. The value for the name strength is a string that is one of "weak", "moderate", "strong", or "unstated". The values associated with the names lats and lons are arrays. This pair of arrays represent the vertices of a polyline describing the location of a frontal boundary or trough.</p> <p>The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.</p>
National Weather Service Coded Surface Bulletins, 2003- (netCDF format)
<p>This dataset contains the Coded Surface Bulletin (CSB) dataset reformatted as <a href="https://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. The CSB dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.</p> <p>Each netCDF file contains one year of CSB fronts data represented as spatial map data grids. The times and geospatial locations for the data grid cells are also included. The front data is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded. The front polylines from the CSB dataset are rasterized into the appropriate data grids. Each file conforms to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>.</p> <p>There are two large groupings of the CSB netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.</p> <p>The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.</p> <p>There are a number of files within each group. The files all follow the naming convention codsus_[masked]_<grid>_<subproduct>.nc, where [masked] indicates that the presence of the word <em>masked</em> is optional and <grid> is either <em>merra2-1deg</em> or <em>narr-96km</em>. The <subproduct> element is either the word <em>mask</em> or the sequence <n>wide_<year>, where <n> is the front width and <year> is the year for the data stored in the file.</p> <p>The codsus_<grid>_mask.nc file is a file containing a single data grid that delineates the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year. The WPC meteorologists don't attempt to provide equal levels of attention to every grid cell displayed on their workstations. The files of the form codsus_masked_<grid>_<n>wide_<year>.nc have all had the mask described above applied to exclude parts of fronts that extend past the envelope. The files of the form codsus_<grid>_<n>wide_<year>.nc have no masking applied.</p> <p>The <n>wide portion of the file names takes two forms—<em>1wide</em> and <em>3wide</em>. The fronts in the<em>1wide</em> files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the <em>3wide</em> files were rasterized by drawing the front polylines with a width of 3 grid cells.</p> <p>Within each grid group, there are five subsets of files:</p> <ul> <li>codsus_masked_<grid>_1wide_<year>.nc</li> <li>codsus_masked_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_1wide_<year>.nc</li> <li>codsus_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_mask.nc</li> </ul> <p>The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.</p>
A National Forum on Web Privacy and Web Analytics — Participant Survey Instrument
<p>This survey instrument was administered to participants of the <em>National Forum on Web Privacy and Web Analytics</em>. Results informed the Forum event and Forum deliverables.</p> <p>The <em>National Forum on Web Privacy and Web Analytics </em>was held September 2018 in Bozeman, Montana, where 40 librarians, technologists, and privacy researchers collaborated in producing a practical roadmap for enhancing our analytics practice in support of privacy.</p> <p>More information is available on our project site: <a href="https://osf.io/gnfpu/">https://osf.io/gnfpu/</a>. </p> <p>This project is made possible in part by the Institute of Museum and Library Services, through grant <a href="https://www.imls.gov/grants/awarded/lg-73-18-0100-18"># LG-73-18-0100-18</a>.</p>
Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget: Supplementary Material
<p>Detailed calculations supporting the results in the published paper, <em>Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget</em>, <a href="https://link.springer.com/journal/11027">Mitigation and Adaptation Strategies for Global Change</a>, DOI: <a href="https://doi.org/10.1007/s11027-019-09881-6">10.1007/s11027-019-09881-6</a>.</p> <ul> <li><strong>IE-CO2-Quota-2015.ods</strong>: Spreadsheet/workbook in <a href="http://opendocumentformat.org/">Open Document</a> format. Includes table and charts as presented in the paper. Prepared using <a href="http://www.libreoffice.org">LibreOffice</a> (v 5.0+). Should also be accessible also in Microsoft Excel, but some formatting or functionality may be lost.</li> <li><strong>IE-CO2-Quota-2015.ipynb</strong>: Mathematical background and cross-check of detailed calculations in interactive <a href="https://jupyter.org/">Jupyter notebook</a> format (coding in <a href="https://www.python.org/">python</a>).</li> <li><strong>IE-CO2-Quota-2015-ipynb.html</strong>: Static HTML version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> <li><strong>IE-CO2-Quota-2015-ipynb.pdf</strong>: Static version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> </ul>
Views of the students and prospective teachers of the National and Kapodistrian University of Athens and School of Pedagogical and Technological Education, regarding the necessity of certified pedagogical competence.
<p>views of the students and prospective teachers of the National and Kapodistrian University of Athens and School of Pedagogical and Technological Education, regarding the necessity of certified pedagogical competence.</p>
Code for 'Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'
<p>Code to reproduce the analyses in the study '<strong>Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'</strong></p> <p>The analysis requires two R scripts available here, plus original 2013-4 NHANES data files, downloadable from the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2013).</p> <p>The first R script, 'merging script.r' takes the original NHANES files, extracts the variables required for the study, merges them into a single data frame, and saves this in .csv format. The NHANES files it requires are:</p> <p># Demographics, food insecurity and BMI<br> DEMO_H.XPT<br> FSQ_H.XPT<br> BMX_H.XPT</p> <p># Summary files of food recalls<br> DR1TOT_H.XPT<br> DR2TOT_H.XPT</p> <p># Individual foods files from food recalls<br> DR1FF_H.XPT<br> DR2FF_H.XPT</p> <p>The second R script takes the .csv file output by the merging script, and reproduces the analyses and figures described in the paper.</p> <p>Initially uploaded by Daniel Nettle, April 23rd 2019. Slightly revised versions uploaded August 6th 2019 by Daniel Nettle.</p>
On Farm Demonstration Datasets - National Summaries
<p>This document brings together the national summaries and posters prepared on the basis of the inventory data collected, and 'supra-regional meetings' held in Northern, Southern and Eastern Europe to discuss the implications of the findings for on-farm demonstration in Europe. </p> <p> </p>
Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.
<p>The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in:</p> <p>Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S,<br> Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G<br> (2019) Advancing Coastal Risk Reduction Science and Implementation by<br> Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556.<br> doi: 10.3389/fmars.2019.00556</p> <p>The readme.txt file contains information about data layers.</p>
Fig. 4 in The Trichoptera diversity of Nyungwe National Park, Rwanda, with description of a new species in the family Pisuliidae
Fig. 4. Silvatares laetae Ngirinshuti & Johanson sp. nov., ♂, holotype. A. Right forewing. B. Right hind wing. C. Right forewing, underside, showing long golden hairs. Scale bar = 1 mm.
Spatial partial identity model for spatial capture-recapture analysis of large carnivores in Kasungu National Park, Malawi
<p>Overview:</p> <p>Decline in global carnivore populations has led to increased demand for assessment of carnivore densities in understudied habitats. Spatial capture-recapture is used increasingly to estimate species densities, where individuals are often identified from their unique pelage patterns. However, uncertainty in bilateral individual identification can lead to the omission of capture data and reduce the precision of results. The recent development of the two-flank spatial partial identity model (SPIM), offers a cost-effective approach which can reduce uncertainty in individual identity assignment and provide robust density estimates. We conducted camera trap surveys annually between 2016 and 2018 in Kasungu National Park, Malawi, a primary miombo woodland and a habitat lacking baseline data on carnivore densities. We used SPIM to estimate density for leopard (<em>Panthera pardus</em>) and spotted hyaena (<em>Crocuta crocuta</em>), and report on the status of other large carnivores.</p> <p>Usage notes:</p> <p>These data are to estimate density for leopard and spotted hyaena in KNP, Malawi. They are provided as an example for using the spatial partial identity model for spatial capture-recapture analysis in populations where individuals are partially identified.</p> <p>Methods:</p> <p>Individual leopards and spotted hyaena were identified from photographs using their unique pelage patterns (Henschel & Ray, 2003). A database was maintained of identified individuals, with partial (single flank) or complete (two flank) identities, to build capture histories for SCR analysis. We identified individuals from left flank captures for both species, due to higher numbers of identified left flank individuals recorded during preliminary surveys. Complete identities were added where flanks were certain to come from the same individual (from baited stations outside of survey time, live captures, dual camera trap stations and multiple passes of a single camera trap). Leopards were sexed by visual determination of external genitalia, presence of the dewlap, frontal bossing and overall body size (Henschel & Ray, 2003; Devens <em>et al</em>. 2018). Sexing was not possible for spotted hyaena due to difficulties in determining sex from external genitalia and body size. Capture histories were developed for spatial captures and trap effort, with each day (24 hours) treated as a separate sampling occasion (Goldberg <em>et al</em>. 2015). Trap effort was measured through a binary matrix of active-inactive days, to improve estimates of detection probability, and included the spatial location of each camera location.</p> <p>Density was modelled using the package <em>SPIM </em>(Augustine, 2018) in R v.3.5.2<em> </em>(R Development Core Team, 2018) to resolve the complete identity of individuals from single-flank samples probabilistically (see Augustine <em>et al</em>. 2018 for complete description of spatial partial identity model), and a Bernoulli observation model fitted, whereby an individual may be captured in each trap only once during each sampling occasion (Royle <em>et al</em>. 2013; Augustine <em>et al</em>. 2018). For Markov Chain Monte Carlo simulations, a single chain of 50,000 iterations per single session analysis was undertaken, with a burn-in of 500 iterations and data augmentation of 100-130 individuals for leopard and 125-250 for spotted hyaena. Analysis was conducted with an increasing buffer width from 10,000 to 25,000 metres (leopard) and 10,000 to 40,000 metres (spotted hyaena), using 5,000 metre increments, until density estimates stabilised (Chase-Grey <em>et al</em>. 2013; Devens <em>et al</em>. 2018).</p>
Cairngorm National Park snow cover duration 1960 - 2080
<p>Created for work commissioned by the Cairngorms National Park through ClimateXChange</p> <p>Contains Ordnance Survey data © Crown copyright and database right 2019.</p> <p>Contains Met Office UKCP09 and UKCP18 data licensed under the Open Government Licence v3.0.</p> <p>Downscaling and Correction copyright 2019 The James Hutton Institute.</p>
Figures 1–2 in Vertical stratification of Sphingidae moths (Lepidoptera: Bombycoidea: Sphingidae) in the Tapajós National Forest, Pará, Brazil
Figures 1–2. Map of the location of the sampling unit (red circle): (1) Tapajós National Forest, western Pará (Google Earth satellite image); (2) LBA platform tower located at Forest National Tapajós. Photo: Genilson Rego, 2009.
Figure 6 in Vertical stratification of Sphingidae moths (Lepidoptera: Bombycoidea: Sphingidae) in the Tapajós National Forest, Pará, Brazil
Figure 6. Rarefaction curves of the observed species richness of Sphingidae based on the number of specimens, collected with light traps, in the three strata canopy (C), midstory (M) and understory (U), in the Forest National Tapajós, Pará, Brazil, from May 2019 to February 2020.
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.