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295 results for “approximation”
Optimized Coefficients for the Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function
<p>This is a supplementary dataset for the publication:</p> <p>I. M. Tanash and T. Riihonen, "Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function with Optimized Coefficients," in<em> IEEE Communications Letters</em>, in press.</p> <p>The dataset contains the sets of the optimized coefficients for the novel GKL minimax approximations and bounds of the Gaussian Q-function, and the optimized coefficients for the GKL approximations in terms of the total error. The corresponding optimized coefficients are found up to 10 terms (N=10) for the two variations of the absolute error and for the relative error in terms of the minimax and the total errors.</p> <p>The Matlab function (func_extract_coef.m) extracts the required set of optimal coefficients from the provided dataset according to the selected optimization_criterion, error_type, number of terms, the bound or approximation type, and the variation. See help func_extract_coef for more information.</p> <p>A Matlab script (Example.m) is also provided as an example to illustrate the use of the provided Matlab function in extracting the required coefficients from the dataset, to calculate and plot the corresponding minimax absolute error function which is shown by figure Example.jpg. Another example is given in the same script to extract the coefficients of the total relative error.<br> </p>
Supplementary Data to *Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data*
<p>Supplementary code and data to <strong>Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_rad, of which this archive is a snapshot.</p> <p> </p>
F4TCNQ and TTF on approximate supported graphene, 3D surface adsorbate search with BOSS
<p>These datasets were employed in the manuscript:</p> <p>[1] J. Järvi, M. Todorović, and P. Rinke, Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111), <em>Phys. Rev. B</em> 105, 195304 (2022). DOI: 10.1103/PhysRevB.105.195304</p> <p>The two datasets feature DFT sampling of adsorption configurations for F<sub>4</sub>TCNQ and TTF on approximate supported graphene (Gr) using the Bayesian Optimization Structure Search (BOSS) method. The 3D configurational searches were conducted for each molecule over two molecular translations (<em>x, y</em>) and an in-plane rotation (<em>gamma</em>). The search range of on-surface translation (<em>x, y</em>) is [0, 1] in fractional coordinates of the Gr unit cell. The in-plane rotation (<em>gamma</em>) is done in the range [0, 180] deg. Energy (<em>E</em><sub>ads</sub>) is the adsorption energy of F<sub>4</sub>TCNQ or TTF on the approximate supported Gr, computed with DFT. For additional details on search coordinates and DFT calculations please refer to [1].<br> <br> The 3D models of the adsorption energy surfaces were constructed with 500 energy points for each molecule, the full lists are provided in the datasets. Each calculation is denoted with an ID number (001-500). The raw data for each calculation can be found in the NOMAD repository with the corresponding ID, DOI: 10.17172/NOMAD/2022.04.07-1</p>
Convolutional Neural Networks for LPV-Approximations of Semi-discrete Navier-Stokes Equations
<pre><code>A `python` module with * a dynamic setup of *Convolutional Neural Networks* in `PyTorch` * an interface to `FEniCS` to generate data from FEM simulations of flows and * a numerical realization of FEM norm in the training neural networks developed to design very low-dimensional LPV approximations of incompressible Navier-Stokes equations.</code></pre> <p> </p> <p><code>These files contain the core module </code>and the scripts that produce the numerical examples of the paper with <a href="https://doi.org/10.3389/fams.2022.879140">doi:10.3389/fams.2022.879140</a></p> <p> </p> <pre><code>> Benner, Heiland, Bahmani (2022): *Convolutional Neural Networks for Very Low-dimensional LPV Approximations of Incompressible Navier-Stokes Equations* </code></pre> <p> </p>
FEX3-ECG/Charts01: Least Squares Approximation of ECG Signals with Rational Functions
<p> </p> <p> We introduce a new algorithm for "Least Squares Approximation of ECG Signals with Rational Functions". Detailed description here: <a href="https://doi.org/10.5281/zenodo.7628747">https://doi.org/10.5281/zenodo.7628747</a> . The following is one result of the approximations and its charts.</p> <p><br> The original ECG signals:<br> DOI: <a href="https://doi.org/10.13026/C28C71">https://doi.org/10.13026/C28C71</a><br> License: Open Data Commons Attribution License v1.0</p> <p>We approached the following signal from the database above:<br> Patient009, the file: S0035_RE.XYZ, vy(Frank lead system)<br> The location of the QRS: 8669th point (8.669 sec)</p> <p>That is:<br> The approximated signal (P QRS T) section: 1:1139, the location of the QRS 371<br> The first signal point = 8299th data point.<br> Detailes:<br> locations of data points : 8299 8300 8301 .. 8669 .. 9437<br> indexes of signal points : 1 2 3 .. 371 .. 1139<br> values of signal points : 291 282 307 .. 352</p> <p>The signal described above is approximated by the following parameters of rational function in the example_1a.m short program.</p> <p><em>The </em><em>grids of charts:</em><br> X axis: 40ms (The sampling rate is 1000Hz)<br> Y axis: 0.1mV (200 A/D units)<br> This is on the original medical ECG paper: 1mm x 1mm.</p> <p> </p> <p><strong>FILES of Charts01.zip:<br> example_1a.m</strong> This file contains a short script of charts.(GNU Octave or MATLAB?)<br> Input: no. <strong><em>The parameters are in this</em></strong><strong><em> short</em></strong><strong><em> program.</em></strong><br> Output: Figure_b_1 and Figure_b_2<br> <strong>PQRST_sgnl_a.m</strong> Subroutine (of example_1a.m)</p> <p><strong>Figure_b_1.jpg</strong> First output chart of the example_1a.m<br> blue An approximation of the P wave<br> red An approximation of the QRS wave<br> yellow An approximation of the T wave<br> magenta An approximation of the Ta wave</p> <p><strong>Figure_b_2.jpg</strong> Second output chart of the example_1a.m<br> blue An approximation of all the waves (the sum of the above)</p> <p><strong>data_a_c.csv</strong> Result spreadsheet of the approximation program (decimal comma)<br> <strong>data_a_</strong><strong>p</strong><strong>.csv</strong> Result spreadsheet of the approximation program (decimal point)<br> Columns:<br> R relative QRS relative indexing<br> Original The original ECG signal<br> Approx An approximation of the ECG signal<br> Err220128 Error and noise (+date: yymmdd)<br> BL174355 Baseline (+time: hhmmss)<br> P(+Ta) An approximation of the P wave (+Ta wave)<br> QRS An approximation of the QRS wave<br> T An approximation of the T wave<br> V__idy 4 The length of the ECG vector (normalized, max. = 1mV)</p> <p><strong>image_a_</strong><strong>1</strong><strong>.gif</strong> First chart of the columns in the data_a_?.csv (MS Excel)<br> Original<br> Approx<br> Err220128<br> BL174355</p> <p><strong>image_a_</strong><strong>2</strong><strong>.gif</strong> Second chart of the columns in the data_a_?.csv (MS Excel)<br> P(+Ta)<br> QRS<br> T</p> <p> Kobzos, Laszlo<br> Location: HU (Budapest)<br> email: zehu.kola.ci@gmail.com</p> <p> </p>
SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information
<p>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular).</p> <p>The dataset consists of 777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation.</p> <p>The structure of the data is as follows:<br> 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks.<br> 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials.<br> Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type.</p> <p>1-Road-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>1-Road-Mask</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>2-NoRoad-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p> </p>
Comparing four hard-sphere approximations for the low-temperature WCA melting line
<p>Data presented in "Comparing four hard-sphere approximations for the low-temperature WCA melting line".</p> <p>Abstract of paper:</p> <p>By combining interface-pinning simulations with numerical integration of the Clausius-Clapeyron equation we determine accurately the melting-line coexistence pressure and fluid/crystal densities of the Weeks-Chandler-Andersen (WCA) system covering four decades of temperature. The data are used for comparing the melting-line predictions of the Boltzmann, Andersen-Weeks-Chandler, Barker-Henderson, and Stillinger hard-sphere approximations. The Andersen-Weeks-Chandler and the Barker-Henderson theories give the most accurate predictions, and they both work excellently in the zero-temperature limit for which analytical expressions are derived here.</p>
Text-fig. 21. Femur head from White Patch Bone Site belonging to a large mammal approximately the size of a pygmy hippopotamus, probably an embrithopod. View of ligamentary fossa. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 21. Femur head from White Patch Bone Site belonging to a large mammal approximately the size of a pygmy hippopotamus, probably an embrithopod. View of ligamentary fossa.
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015). in First Water Lily, A Leaf Of Nymphaea Sp., From The Miocene Clarkia Flora, Northern Idaho, Usa: Occurrence, Taphonomic Observations, Floristic Implications
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015).
Figure 4. Approximate distributions and associated divergence times for A in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 4. Approximate distributions and associated divergence times for A) Peromyscus maniculatus-like ancestor; B) P. melanotis-like ancestor; C) P. gambelii/keeni/sejugis/sp.-like ancestor; D) P. polionotus-like ancestor; E) P. sonoriensis-like ancestor; F) P. labecula and P. maniculatus - like ancestor; G) P. keeni/sp.-like ancestor; and H) P. keeni-like, P. gambelii-like, P. sejugis-like, and P. sp.-like ancestors. Divergence times were estimated from the BEAST analysis (Version 2.4, Bouckaert et al. 2014) of the mitochondrial cytochrome-b gene dataset (see Fig. 3). Shading schemes that correspond to species distributions are shown in the inset.
Cophylogeny reconstruction allowing for multiple associations through approximate Bayesian computation
<p>Phylogenetic tree reconciliation is extensively employed for the examination of coevolution between host and symbiont species. An important concern is the requirement for dependable cost values when selecting event-based parsimonious reconciliation. Although certain approaches deduce event probabilities unique to each pair of host and symbiont trees, which can subsequently be converted into cost values, a significant limitation lies in their inability to model the <em>invasion</em> of diverse host species by the same symbiont species (termed as a spread event), which is believed to occur in symbiotic relationships. Invasions lead to the observation of multiple associations between symbionts and their hosts (indicating that a symbiont is no longer exclusive to a single host), which are incompatible with the existing methods of coevolution. </p> <p>Here, we present a method called AmoCoala (an enhanced version of the tool Coala) that provides a more realistic estimation of cophylogeny event probabilities for a given pair of host and symbiont trees, even in the presence of spread events. We expand the classical 4-event coevolutionary model to include 2 additional spread events (vertical and horizontal spreads) that lead to multiple associations. In the initial step, we estimate the probabilities of spread events using heuristic frequencies. Subsequently, in the second step, we employ an approximate Bayesian computation (ABC) approach to infer the probabilities of the remaining 4 classical events (cospeciation, duplication, host switch, and loss) based on these values.</p> <p>By incorporating spread events, our reconciliation model enables a more accurate consideration of multiple associations. This improvement enhances the precision of estimated cost sets, paving the way to a more reliable reconciliation of host and symbiont trees. To validate our method, we conducted experiments on synthetic datasets and demonstrated its efficacy using real-world examples. Our results showcase that AmoCoala produces biologically plausible reconciliation scenarios, further emphasizing its effectiveness.The software is accessible at <a href="https://github.com/sinaimeri/AmoCoala" rel="noopener">https://github.com/sinaimeri/AmoCoala</a>.</p>
Supplementary Data: UFBoot2: Improving the Ultrafast Bootstrap Approximation
<p>Supplementary Data<br> UFBoot2: Improving the Ultrafast Bootstrap Approximation<br> doi: https://doi.org/10.1101/153916<br> http://www.biorxiv.org/content/early/2017/06/22/153916</p> <p>This record contains PANDIT based dataset and TreeBASE dataset (Nguyen et al. 2015) which are analyzed by different bootstrap methods in the study "UFBoot2: Improving the Ultrafast Bootstrap Approximation". The PANDIT based dataset (compressed in file data_pandit.tar.gz) is used to benchmark the accuracy of bootstrap estimates. The TreeBASE dataset (compressed in file data_treebase.tar.gz) is used to benchmark runtimes. </p> <p>After being uncompressed, the PANDIT based dataset comprises:</p> <ul> <li>5,690 numbered directories corresponding to 5,690 DNA MSAs simulated by Seq-Gen (Rambaut and Grass 1997), where the model parameters and true tree were inferred from the original MSAs downloaded from the PANDIT database (Whelan et al. 2006). Note that the numbering of these directories is not consecutive because we kept only MSAs that can be tested under the mild and severe model violations as defined in the UFBoot paper (Minh et al. 2013).</li> <li>In each numbered directory N, there are three files: (1) data.N contains the simulated MSA in PHYLIP format; (2) model.N contains the best-fit model detected from the corresponding original MSA; (3) tree.N contains the tree (in Newick format) inferred from the corresponding original MSA. tree.N and model.N are used by Seq-Gen to simulate the MSA in data.N.</li> </ul> <p>After being uncompressed, the TreeBASE dataset comprises 115 files corresponding to 115 MSAs. There are:</p> <ul> <li>70 DNA MSAs in PHYLIP format. These files follow the naming scheme dna_[number of sequences]_[number of sites].phy.</li> <li>45 protein MSAs in PHYLIP format. These files follow the naming scheme prot_[number of sequences]_[number of sites].phy.</li> </ul>
FIGURE 1. The Farallon Islands, approximately 40 in A New Species of Whip-Like Gorgonian Coral in the Genus Swiftia from the Gulf of the Farallones in Central California, with a Key to Eastern Pacific Species in California (Cnidaria, Octocorallia, Plexauridae)
FIGURE 1. The Farallon Islands, approximately 40 miles west of San Francisco, California, and 70 miles southeast of the type locality of Swiftia farallonesica sp. nov., in the Greater Farallones National Marine Sanctuary.
Fig. 1 in How many threatened lice are there? An approximation to the red list of the Spanish Phthiraptera
Fig. 1. Felicola (Lorisicola) isidoroi. Adult male, habitus. This is the holotype of the species and is deposited in the collection of the Museo Nacional de Ciencias Naturales (CSIC) in Madrid, Spain. Photography by Jean-Claude Stahl (Te Papa Tongarewa Museum, Wellington, New Zealand).
Duhumbi Phonology - Approximant rhymes
<p>These data present the arguments for the analysis of the Duhumbi approximant rhymes /oj ~ uj, ej ~ aj, aw ~ ow/ rather than distinctive diphthong phonemes. The zip files contain sound files for illustration.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Test polynomials for approximate GCD algorithms
<p>Dataset of test data (Tables 1–3) used in the paper:</p> <p>Akira Terui, GPGCD: An iterative method for calculating approximate GCD of univariate polynomials.<br> Theoretical Computer Science, Volume 479 (Symbolic-Numerical Algorithms), April 2013, 127-149.</p> <ul> <li><a href="https://doi.org/10.1016/j.tcs.2012.10.023">doi:10.1016/j.tcs.2012.10.023</a></li> <li><a href="https://arxiv.org/abs/1207.0630">arXiv:1207.0630</a></li> </ul>
Approximate map of Sulemaana Kantè's post-1941 travels and history of residence in West Africa
<p>Approximate map of Kantè’s post-1941 travels and history of residence in West Africa. Originally appeared in the following:</p> <p>Donaldson, Coleman. 2017. “Clear Language: Script, Register and the N’ko Movement of Manding-Speaking West Africa.” Doctoral Dissertation, Philadelphia, PA: University of Pennsylvania. Philadelphia, PA. <a href="https://repository.upenn.edu/dissertations/AAI10681364/">https://repository.upenn.edu/dissertations/AAI10681364/</a>.</p> <p>Created with data from the following sources:</p> <p>Amselle, Jean-Loup. 2003. “Peut-on être musulman sans être arabe? : A propos du N’ko malinké d’Afrique de l’Ouest.” In <em>Islam et villes en Afrique au sud du Sahara: entre soufisme et fondamentalisme</em>, edited by Adriana Piga and Costanza Ventura, 257–69. Paris: Karthala.</p> <p>Kántɛ, Sùlemáana. 1968. <em>Ɲìninkalibá’ 5 n’à jáabi’</em> ߢߌ߬ߣߌ߲߬ߞߊ߬ߟߌ߬ߓߊ ߅ ߣߴߊ߬ ߖߊ߯ߓߌ [Five Big Questions and Their Answer]. Edited by Bàbá Màmádi Jàanɛ.</p> <p>Oyler, Dianne White. 2005. <em>The History of the N’ko Alphabet and Its Role in Mandé Transnational Identity: Words as Weapons</em>. Cherry Hill, NJ: Africana Homestead Legacy Publishers.</p> <p>Sangaré, Mahmoud. 2011. <em>Jón yé Sòlomána Kántɛ́ dí?</em> ߖߐ߲߫ ߧߋ߫ ߛߟߏ߬ߡߣߊ߫ ߞߊ߲ߕߍ߫ ߘߌ߫؟ [Who Is Sulemaana Kantè?]. Bamako, Mali.</p> <p>--</p> <p>Blog: <a href="https://ajami.hypotheses.org/">https://ajami.hypotheses.org/</a><br> Project: <a href="https://www.manuscript-cultures.uni-hamburg.de/ajami/index_e.html">https://www.manuscript-cultures.uni-hamburg.de/ajami/index_e.html</a></p>
Skeleton of Struthiomimus altus. Genotype specimen, Amer. Mus. 5339. One-tenth natural size In this panel mount the animal is placed approximately as found. The pollex is too closely appressed to the other digits, see Fig. 3. in Skeletal Adaptations of Ornitholestes, Struthiomimus, Tyrannosaurus
Skeleton of Struthiomimus altus. Genotype specimen, Amer. Mus. 5339. One-tenth natural size In this panel mount the animal is placed approximately as found. The pollex is too closely appressed to the other digits, see Fig. 3.
Figs.: 1–4. Nectarinella manauara sp. nov. (1) Head, anterior view; (2) posterior view, red arrow points to vestigial occipital carina; (3) mesosoma, dorsal view, (4) lateral view; (5) head of N. championi, posterior view, red arrow points to vestigial occipital carina; (6) head of paratype of N. xavantinensis, anterior view; approximately same scale as (5). in Nectarinella manauara, new species and record of the genus from Brazilian Amazonia (Hymenoptera, Vespidae, Polistinae)
Figs.: 1–4. Nectarinella manauara sp. nov. (1) Head, anterior view; (2) posterior view, red arrow points to vestigial occipital carina; (3) mesosoma, dorsal view, (4) lateral view; (5) head of N. championi, posterior view, red arrow points to vestigial occipital carina; (6) head of paratype of N. xavantinensis, anterior view; approximately same scale as (5).
Frasnian, lateral (B1), ventral (B2), anterior (B3), posterior (B4), and dorsal (B5) views of a rounded exfoliated shell, 27.7 mm wide, 25.6 mm long, and about 14.5 mm thick. C. PUM05008, sample PY4, Panxi section, probably Middle Frasnian, lateral (C1), dorsal (C2), and ventral (C3) views of the sectioned specimen (Fig. 6). D. PUM05009, sample PY5, Panxi section, probably Middle Frasnian, ventral beak broken, showing small conjunct deltidial plates (note that true foramen (approximately dashed line) takes up only a small part at the bottom of the seen later enlarged hole). E. PUM05010, sample PY5, Panxi section, probably Middle Frasnian, posterior (E1), lateral (E2), anterior (E3), ventral (E4), and dorsal (E5) views, 26.7 mm wide, 27.7 mm long, 18.5 mm thick, adpressed ventral beak. F. PUM05011, sample GC22, Caiziyan section, Early Frasnian, dorsal view. in Early and Middle Frasnian brachiopod faunas and turnover on the South China shelf
Frasnian, lateral (B1), ventral (B2), anterior (B3), posterior (B4), and dorsal (B5) views of a rounded exfoliated shell, 27.7 mm wide, 25.6 mm long, and about 14.5 mm thick. C. PUM05008, sample PY4, Panxi section, probably Middle Frasnian, lateral (C1), dorsal (C2), and ventral (C3) views of the sectioned specimen (Fig. 6). D. PUM05009, sample PY5, Panxi section, probably Middle Frasnian, ventral beak broken, showing small conjunct deltidial plates (note that true foramen (approximately dashed line) takes up only a small part at the bottom of the seen later enlarged hole). E. PUM05010, sample PY5, Panxi section, probably Middle Frasnian, posterior (E1), lateral (E2), anterior (E3), ventral (E4), and dorsal (E5) views, 26.7 mm wide, 27.7 mm long, 18.5 mm thick, adpressed ventral beak. F. PUM05011, sample GC22, Caiziyan section, Early Frasnian, dorsal view.
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.