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13,117 results for “Maps”
MAP 195 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 195. Distribution of Acontias kgalagadi in Angola.
MAP 237 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 237. Distribution of Typhlacontias rudebecki in Angola.
MAP 181 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 181. Distribution of Chamaesaura anguina oligopholis in Angola.
MAP 125 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 125. Distribution of Afrogecko ansorgii in Angola.
MAP 43 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 43. Distribution of Hyperolius gularis in Angola.
MAP 4 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 4. Distribution of Xenopus muelleri in Angola.
MAP 161 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 161. Distribution of Monopeltis perplexus in Angola.
MAP 368 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 368. Distribution of Thrasops flavigularis in Angola.
MAP 334 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 334. Distribution of Naja mossambica in Angola.
MAP 309 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 309. Distribution of Psammophis jallae in Angola.
Dataset for the implicational map of indefinite pronouns
<p>This dataset is the basis of the implicational map in Haspelmath (1997) (Indefinite pronouns, Oxford University Press), which became well-known in the later literature.</p>
Excel mapping tools for 2017 zoonoses data reporting
<p>The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.</p> <p>You can choose between the dynamic or the manual version of the tool.</p>
Training data for 'Genetic map RADSeq ' tutorial (Galaxy Training Material)
<p>The data provided here are part of a study published by Amores<em> et al.</em> (2011) (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3176089/">doi 10.1534/genetics.111.127324</a>), exploiting massively parallel DNA sequencing to develop meiotic maps by genotyping F<sub>1</sub> offspring of a single female and a single male spotted gar (<em>Lepisosteus oculatus</em>).</p>
KPI Mappings
<p>The data is generated from deployments of a virtualised media processing function using different allocations of the vCPU and memory. The specific combinations are as follows:</p> <p> </p> <pre><code>| Name | RAM | Disk | Ephemeral | VCPUs | +------------+-------+------+-----------+-------+ | i1.small | 1024 | 80 | 0 | 1 | | i1.medium | 2048 | 80 | 0 | 1 | | i1.large | 4096 | 80 | 0 | 1 | | i1.xlarge | 8192 | 80 | 0 | 1 | | i2.small | 1024 | 80 | 0 | 2 | | i2.medium | 2048 | 80 | 0 | 2 | | i2.large | 4096 | 80 | 0 | 2 | | i2.xlarge | 8192 | 80 | 0 | 2 | | i3.small | 1024 | 80 | 0 | 4 | | i3.medium | 2048 | 80 | 0 | 4 | | i3.large | 4096 | 80 | 0 | 4 | | i3.xlarge | 8192 | 80 | 0 | 4 | </code></pre> <p><br> The instances of virtualized media processing function are stressed using a workload generator (HAMMER) with the following profile.</p> <ul> <li>5 second steps of 5 incremental users for 50 steps resulting in a final simulated user loads of 250 users.</li> </ul> <p>The telemetry data in the dataset is collected using the open source snap telemetry framework for each unique deployment. The dataset also integrates selected metrics as measured by Hammer such as through and latency for each unique deployment.</p> <p>The dataset contains two iterations for each unique deployment of the of the virtualized media processing function</p> <p>This data can used as input into analytics process such as KPI mapping.</p> <p> </p>
TEXTELSEM mapping data package
<p>Datapackage for TEXTELSEM-Project's Workshop</p> <p>«Le projet TEXTELSEM, SIG et cartographie appliquée », Paris, April 3<sup>rd</sup> 2017</p>
Coordinates of genetic variants used for QTL mapping
<p>Column names</p> <ol> <li>chr - chromosome name</li> <li>pos - position</li> <li>snp_id - variant id</li> <li>ref - reference allele</li> <li>alt - alternate allele</li> <li>type - variant type (SNP or INDEL)</li> <li>AC - alternate allele count</li> <li>AN - total allele count</li> </ol>
Soil moisture maps of Ukraine based on SMAP satellite data, vegetation season 2018
<p>A set of soil moisture maps of Ukraine based on SMAP satellite data</p> <p>Product: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture V001</p> <p>Vegetation season 2018</p> <p> </p>
3D Dataset "Computation of Exact g-Factor Maps in 3D GRAPPA Reconstructions"
<p>Datasets used in the paper entitled "", containing the following acquisitions:</p> <p> <strong>1) Simulated abdomen data set</strong>: we have synthetized a 3D volume using the simulation environment XCAT based on the extended cardio-torso phantom. We simulated a T1-weighted acquisition using the following acquisition parameters: TE/TR=1.5/3ms, flip angle=60º, acquisition matrix size=60x60x32. A 32-coil acquisition was simulated by modulating the image using artificial sensitivity maps coded for each coil. The noise-free coil images were transformed into the \bk--space and corrupted with synthetic Gaussian noise characterized by the matrices <span class="math-tex">\(\Gamma_k\)</span>and <span class="math-tex">\(C_k\)</span> with SNR=25 for each coil, and the correlation coefficient between coils was set to <span class="math-tex">\(\rho\)</span>=0.1$. For statistical purposes, 4000 realizations of each image were used.</p> <p><br> <strong>2) Water phantom acquisition</strong>: A MR phantom sphere with solution (GE Medical Systems, Milwaukee, WI) was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition with 100 realizations of the same fully-encoded k-space sampling was used. Acquisition parameters included: coronal view, TE/TR=0.96/3.69ms, flip angle=12º, field of view=22x22$x30.7<span class="math-tex">\(cm³\)</span>, acquisition matrix size=60x60x32, bandwidth=62.5KHz. We corrected for <span class="math-tex">\(B_0\)</span> field drift related phase variations and magnitude decay by a pre-processing step. First we estimated the phase-shift between realizations from the center of the k-space as a cubic function of time and removed it afterwards. And, second, we estimated the magnitude-decay in the k-space as a linear function and substracted it in order not to affect the noise.</p> <p><br> <strong>3) In vivo acquisition</strong>: in order to assess the feasibility of the proposed method, after obtaining the approval fo the local institutional review board (IRB), a volunteer was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition of a fully-encoded \bk--space sampling was used. Acquisition parameters included: coronal view, TE/TR=2.2/5.7ms, flip angle=12º,field of view=22x22x22<span class="math-tex">\(cm³\)</span>, matrix size=220x220x220, bandwidth=62.5$KHz.</p>
Fig. 1. Phylogenetic relationships of species of Eusurbus and Zentamyia. Tree generated from morpho- logical phylogenetic analysis, unambiguous apomorphies mapped on branches, black circles indicate non- homoplasious changes.
Fig. 1. Phylogenetic relationships of species of Eusurbus and Zentamyia. Tree generated from morpho- logical phylogenetic analysis, unambiguous apomorphies mapped on branches, black circles indicate non- homoplasious changes.
Google Categories (ProductsServices) to DMOZ mapping
<p>This deposit contains the RDF version of a subset of the Google Categories dataset (named ProductsServices, available at https://developers.google.com/adwords/api/docs/appendix/productsservices.csv - last access: 2018-06-29) and the RDF version of a subset of the DMOZ categories dump.</p> <p>Categories have been modeled as SKOS Concept in both datasets.</p> <p>There is also a mapping file that contains links between instances described in these datasets. Links have been created manually (we adopted the SKOS vocabulary).</p>
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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.