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208 results for “Iteration”
Iterative Bleaching Extends multi-pleXity (IBEX) imaging method, mouse spleen
<p>This dataset was acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in: “IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues“, A. Radtke et al., 2020, <a href="https://doi.org/10.1073/pnas.2018488117">doi:10.1073/pnas.2018488117</a>.</p> <p>It is comprised of a three cycle IBEX experiment performed on mouse spleen sections labeled with the nuclear marker JOJO-1 and membrane label CD4 AF594. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 mm), y (0.284 mm), and z (1 mm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p> </p> <p>Markers per channel in each of the three cycles:</p> <ol> <li>spleen_panel1.nrrd (6 channels): B220 PE, CD8 BV421, IgD AF700, CD4 AF594, JOJO, Foxp3 eF660</li> <li> <p>spleen_panel2.nrrd (7 channels): CD169 PE, F480 BV421, MHCII AF700, CollIV AF488, JOJO, CD11c AF647, CD4 AF594</p> </li> <li> <p>spleen_panel3.nrrd (7 channels): CD31 PE, CD68 BV421, Ki67 AF700, CD45 AF488, CD4 AF594, JOJO, CD3 AF647</p> </li> </ol> <p>The panels can be registered using the code available on github: <a href="https://github.com/niaid/sitk-ibex">https://github.com/niaid/sitk-ibex</a></p> <p>To view these multi-channel images, in <a href="http://teem.sourceforge.net/nrrd/format.html">nrrd format</a>, use the <a href="https://imagej.net/Fiji">Fiji viewer</a>. The data is stored in XYZC order.</p>
Iterative Mapping of Probabilities
<p>This repository contains data and scripts for implementing the Iterative Mapping of Probabilities (IMP) algorithm proposed in the preprint submitted to the International Journal of Applied Earth Observation and Geoinformation (JAG). The framework aims to improve the accuracy of land cover mapping by iteratively refining class maps to match independent area statistics. The experiment focuses on generating classification maps for five countries (Belgium, Czechia, Germany, Luxembourg, Netherlands) based on input probability rasters.</p> <h2>Usage</h2> <ol> <li><strong>Create a project folder</strong> where you'll store the files.</li> <li><strong>Download all the files</strong> to the project folder.</li> <li><strong>Extract the countries data</strong> into the project folder (be.zip=Belgium, cz=Czechia, de.zip=Germany, lu.zip=Luxembourg, nl.zip=Netherlands).<br>(Note: ensure the folder structure matches the "Data Description" section provided below) </li> <li><strong>Install Dependencies</strong> by navigating to the project folder in your terminal and install the necessary dependencies by running:<br>(Note: make sure you have Python installed on your system)<br><code>pip install -r ./requirements.txt</code></li> <li><strong>Run the script</strong> using the following command in the terminal:<br><code>python ./main.py</code></li> </ol> <h2>Data Description</h2> <p>After downloading and decompressing the files, the data must have the following structure.</p> <ul> <li><strong>area_estimates.csv: </strong>The area estimates for each land cover provided by Eurostat.</li> <li><strong>[country_code]/</strong><br> <ul> <li><strong>[model]/:</strong><br> <ul> <li><strong>classified_highest_likelihood/: </strong>Contains classification maps generated using the maximum likelihood mapping algorithm.</li> <li><strong>classified_proportional/: </strong>Stores classification maps produced using the Iterative Mapping of Probabilities algorithm.</li> <li><strong>iterations/: </strong>Stores images representing the iteration number in which each pixel was classified using the iterative proportional algorithm.</li> <li><strong>probabilities/: </strong>Contains input probability rasters for both mapping algorithms.</li> </ul> </li> </ul> </li> <li><strong>main.py</strong>: Python script implementing the Iterative Mapping of Probabilities framework.</li> <li><strong>requirements.txt</strong>: List of required libraries to run the script.</li> <li><strong>graphical_abstracl.pdf (optional)</strong>: Illustration on the IMP algorithm.</li> </ul> <h2>Script Explanation:</h2> <p>The script <strong>main.py</strong> implements IMP algorithm and process land use and land cover classification maps from probability rasters. These probabilities were generated for different countries and used two diffrent models (local and general). Please, refer to the paper for more details on how these models were trained.</p> <h3>Script Steps:</h3> <ol> <li><strong>Data Preparation:</strong><br>Loads <code>area_estimate.csv</code> file containing area estimates for different land cover classes in various countries and years.</li> <li><strong>Parameter Setup:</strong><br>Sets up parameters for each country, year, and model combination.<br>Each parameter set includes the country code, year, model type (local or general), and a list of land cover class codes.<br>(Note: you can change this section to set up parametersto process just some countries)</li> <li><strong>Processing Maps:</strong><br>Iterates over each parameter set and:<br> <ol> <li>Loads reference proportions of land cover classes for the specified country, year, and model.</li> <li>Loads probabilities from raster images.</li> <li>Runs the Iterative Mapping of Probabilities algorithm using the loaded probabilities and reference proportions.</li> <li>Saves the resulting land use and land cover classification map as an output.</li> </ol> </li> </ol> <h3>Script Inputs:</h3> <p>- CSV file containing area estimates for land cover classes (<code>./area_estimates.csv</code>).<br>- Probability raster images generated by classification models stored in <code>./[country_code]/[model]/probabilities/</code> folders.</p> <h3>Script Outputs:</h3> <p>Land use and land cover classification maps obtained by running the Iterative Mapping of Probabilities algorithm for each parameter set. The outputs are stored in <code>./[country_code]/[model]/classified_proportional_user/</code> folders.</p> <h2>Citation</h2> <p>If you use this code or data in your research, please cite the corresponding paper:</p> <p><em>Witjes, M., Herold, M., & de Bruin, S. (2024). Iterative Mapping of Probabilities: A data fusion framework for generating accurate land cover maps that match area statistics. Journal of Applied Earth Observation and Geoinformation (JAG), in review.</em></p> <h2>License</h2> <p>The code in this repository is licensed under the MIT License.</p> <p>The data provided in this repository is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
Dataset for the published article "ITER relevant multi-emissive sheaths at normal magnetic field inclination"
<p>The data contained in the zip files constitute the main research data of the publication entitled as "<a href="https://iopscience.iop.org/article/10.1088/1741-4326/acaabd">ITER relevant multi-emissive sheaths at normal magnetic field inclination</a>" [1]. All the datasets constitute post-processed output from the 2D3V SPICE2 Particle-In-Cell (PIC) code. All the PIC simulations have been performed by M. Komm and A. Podolnik. The input is specified by the plasma density, the electron temperature and the surface temperature. The plasma parameters are relevant to partially mitigated ITER edge-localized modes (ELMs). The output concerns the incident plasma current densities, the emitted electron current densities and their standard deviation, the normal wall electrostatic field, the average electron incident energy, the average electron incident angle with respect to the wall normal and the virtual cathode depth. </p> <p>The assumptions below are followed in all simulations: (i) The Bohm pre-sheath structure is unaltered by the escaping emitted electrons, since the ions are injected at the plasma boundary with a speed distribution satisfying the Bohm criterion. (ii) Irrespective of the emission, the wall is biased with respect to the plasma boundary with a magnitude fixed by the ambipolarity of the plasma fluxes. (iii) The sheath is collisionless. (iv) The wall is perfectly planar. (v) A homogeneous quasi-neutral plasma boundary and an infinite emitting wall with a homogeneous prescribed surface temperature are considered.</p> <p>Sheaths that form between plasma-facing components (PFCs) and standard scrape-off-layer plasmas can be described by the classical model of one-dimensional magnetized multi-positive ion sheaths. There are various conditions that need to be satisfied for this model to be valid such as negligible cross-field drifts, low collisionality and weak electron emission.</p> <p>In contemporary metallic tokamaks, the weak emission condition is violated in the divertor region during intra-ELM as well as inter-ELM periods; thermionic emission being an effective electron emission mechanism from hot tungsten PFCs. As a result of the localized ELM-wetted area, the incident plasma currents can be assumed to remain nearly ambipolar and thus the non-ambipolar current should be equal to the emitted current that escapes to the Bohm pre-sheath. This escaping current density generates a strong volumetric Lorentz force that drives melt layer motion leading to macroscopic PFC erosion. At very elevated surface temperatures, the nominal thermionic current densities are so large that they become incompatible with the classical Bohm pre-sheath structure. As a consequence, space charge accumulation in the sheath leads to the formation of a virtual cathode that limits the escaping thermionic current to a constant value causing the recapture of a fraction of the thermo-electrons. Thus, there is a transition from a monotonic to a non-monotonic potential profile, with the latter known as the space-charge limited (SCL) regime of the emissive sheath. In the case of oblique magnetic field inclination angles, the SCL transition is still realized, but further complications arise due to the suppression of the nominal thermionic current by recapture during Larmor gyration. In contemporary tokamaks, this transition generally occurs at temperatures below the tungsten melting point, thus particular attention has been paid to the SCL sheaths, since they nearly exclusively surround the molten tungsten PFCs. The thermionic emissive sheath in the SCL regime has been thoroughly investigated in our previous works, where an accurate semi-empirical expression for the limited value of the escaping thermionic current as function of the plasma conditions and magnetic field inclination angle was constructed on the basis of systematic PIC simulations [2-4].</p> <p>On the other hand, during ITER intra-ELM periods, the predicted elevated electron temperatures and high plasma densities of the pre-sheath edge should have a strong impact on the emissive sheath established above hot tungsten PFCs. In particular, the high plasma electron temperatures could enable significant contributions from electron-induced electron emission (secondary electron emission and electron backscattering), the intense normal surface electrostatic fields indicate that thermionic emission is coupled with field emission (in the Schottky regime) and the strong plasma currents suggest that virtual cathodes are formed at much higher surface temperatures (so that the monotonic potential profile regime is of primary interest for melt motion). In order to explore this novel multi-emissive sheath regime, a a comprehensive tungsten electron emission model has been implemented that features accurate analytical descriptions of the yields, energy and angular distributions for the processes of field-assisted thermionic emission, secondary electron emission and electron backscattering [5]. In the present publication [1], at normal magnetic field inclinations, highly accurate analytical semi-empirical expressions are provided for the secondary electron emission current, electron backscattering current and thermionic current in the monotonic regime as well as for the total escaping current in the SCL regime. These semi-empirical expressions have been benchmarked against comprehensive PIC simulations, whose primary post-processed data are provided herein.</p> <p>[1] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "ITER relevant multi-emissive sheaths at normal magnetic field inclination", Nucl. Fusion 63 (2023) 026007.<br> [2] M. Komm, S. Ratynskaia, P. Tolias, J. Cavalier, R. Dejarnac, J. P. Gunn and A. Podolnik, "On thermionic emission from plasma-facing components in tokamak-relevant conditions", Plasma Phys. Control. Fusion 59 (2017) 094002.<br> [3] M. Komm, P. Tolias, S. Ratynskaia, R. Dejarnac, J. P. Gunn, K. Krieger, A. Podolnik, R. A. Pitts and R. Panek, "Simulations of thermionic suppression during tungsten transient melting experiments", Phys. Scr. T170 (2017) 014069.<br> [4] M. Komm, S. Ratynskaia, P. Tolias and A. Podolnik, "Space-charge limited thermionic sheaths in magnetized fusion plasmas", Nucl. Fusion 60 (2020) 054002.<br> [5] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "Origin and nature of the emissive sheath surrounding hot tungsten tokamak surfaces", Nucl. Mater. Energy 25 (2020) 100818.</p> <p> </p>
Travellers baseline, pre and post-questionnaires of the 1st iteration phase
<p>The dataset contains the travellers’ baseline-, pre- and post-questionnaires of the first evaluation phase of the MyCorridor project. The column pre-evaluation indicates whether the respondent took part in the baseline- or pre-questionnaires (Column B-FF). Whereas all respondents were asked to participate in the same post-questionnaire survey (Column FG-MI). The questions in the pre-questionnaires are related to the background of the respondents, mobility wants & needs, online consumer experience, MaaS awareness, MyCorridor platform pre-acceptance, computer literacy, online consumer attitude and behaviour, online shopping needs and wishes and MyCorridor platform pre-acceptance. The questions in the post-questionnaires are related to the evaluation of the app, the interaction experience, the value, usability and acceptance.</p>
Raw X-Ray CT data of CFC-Cu ITER monoblock mock-up
<p>Raw X-Ray CT data for CFC-Cu ITER monoblock mock-up. The monoblock was manufactured at Politecnico di Torino, Italy (Dr Valentina Casalegno) and X-ray tomography scanning was performed at the Manchester X-ray Imaging Facility, University of Manchester, UK (Dr Llion Evans).</p> <p>This data was used for the publication Evans, Ll.M. et al. "Transient Thermal Finite Element Analysis of CFC-Cu ITER Monoblock Using X-ray Tomography Data", Fusion Engineering and Design 2015. DOI: 10.1016/j.fusengdes.2015.04.048</p>
Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides
<p>We provide here the underlying data of the publication "Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides". Please find the abstract below.</p> <p><strong>Background: </strong>Cellodextrin phosphorylase (CdP; EC 2.4.1.49) catalyzes the iterative β-1,4-glycosylation of cellobiose using α-D-glucose 1-phosphate as the donor substrate. Cello-oligosaccharides (COS) with a degree of polymerization (DP) of up to 6 are soluble while those of larger DP self-assemble into solid cellulose material. The soluble COS have attracted considerable attention for their use as dietary fibers that offer a selective prebiotic function. An efficient synthesis of soluble COS requires good control over the DP of the products formed. A mathematical model of the iterative enzymatic glycosylation would be important to facilitate target-oriented process development.<br> <strong>Results: </strong>A detailed time-course analysis of the formation of COS products from cellobiose (25 mM, 50 mM) and α-D-glucose 1-phosphate (10–100 mM) was performed using the CdP from <em>Clostridium cellulosi</em>. A mechanism-based, Michaelis–Menten type mathematical model was developed to describe the kinetics of the iterative enzymatic glycosylation of cellobiose. The mechanistic model was combined with an empirical description of the DP-dependent self-assembly of the COS into insoluble cellulose. The hybrid model thus obtained was used for kinetic parameter determination from time-course fits performed with constraints derived from initial rate data. The fitted hybrid model provided excellent description of the experimental dynamics of the COS in the DP range 3–6 and also accounted for the insoluble product formation. The hybrid model was suitable to disentangle the complex relationship between the process conditions used (i.e., substrate concentration, donor/acceptor ratio, reaction time) and the reaction output obtained (i.e., yield and composition of soluble COS). Model application to a window-of-operation analysis for the synthesis of soluble COS was demonstrated on the example of a COS mixture enriched in DP 4.<br> <strong>Conclusions:</strong> The hybrid model of CdP-catalyzed iterative glycosylation is an important engineering tool to study and optimize the biocatalytic synthesis of soluble COS. The kinetic modeling approach used here can be of a general interest to be applied to other iteratively catalyzed enzymatic reactions of synthetic importance.</p>
Alphafold2_ab_initio iterative predictions for folding intermediate identification
<p>PDB ids starts from 1 and 8, rmsds, plddts, t-sne embeddings.</p> <p>Check related biorxiv preprint: AlphaFold2 knows some protein folding principles; DOI: https://doi.org/10.1101/2024.08.25.609581.</p>
Iterative Bleaching Extends Multiplexity (IBEX) Knowledge-Base
<p>The Iterative Bleaching Extends Multiplexity (IBEX) imaging method is an iterative immunolabeling and chemical bleaching method that enables highly multiplexed imaging of diverse tissues. Development of the <a href="https://doi.org/10.1038/s41596-021-00644-9">IBEX method</a> and <a href="https://github.com/niaid/imaris_extensions">related software</a> was led by Dr. Andrea Radtke and Dr. Ziv Yaniv. <a href="https://doi.org/10.1073/pnas.2018488117">IBEX</a> and related methods, <a href="https://doi.org/10.1073/pnas.1708981114">Ce3D</a>, <a href="https://doi.org/10.1111/imr.13052">Ce3D-IBEX</a>, <a href="https://doi.org/10.1073/pnas.2018488117">Opal-plex</a>, were originally developed in the laboratory of <a href="https://www.niaid.nih.gov/research/ronald-n-germain-md-phd">Dr. Ronald N. Germain</a>, US National Institutes of Health.</p><p>The IBEX Imaging Community is an international group of scientists committed to sharing knowledge related to multiplexed imaging in a transparent and collaborative manner. This open, global repository is a central resource for reagents, protocols, panels, publications, software, and datasets. In addition to IBEX, we support standard, single cycle multiplexed imaging (Multiplexed 2D imaging), volume imaging of cleared tissues with clearing enhanced 3D (Ce3D), highly multiplexed 3D imaging (Ce3D-IBEX), and extension of the IBEX dye inactivation protocol to the Leica Cell DIVE (Cell DIVE-IBEX). This dataset contains the current state of knowledge with respect to the IBEX microscopy imaging protocol.</p><p>How to use the Knowledge-Base:</p><ol><li>Save a copy to your computer.</li><li>To find a reagent: Open the reagent_resources.csv file found in the data directory. Use a spreadsheet application to filter the columns based on target name, target species, vendor, etc.</li><li>To view a complete list of fluorescent probes tested by the IBEX imaging community: Open the fluorescent_probes.csv file. This file reports the spectral properties and inactivation conditions of each fluorescent probe.</li><li>To import publications cited in the Knowledge-Base, import the publications.bib file found in the data directory to your reference manager.</li><li>To view a local copy of the website: Open the index.md file found in the docs directory using a markdown editor such as the free <a href="https://code.visualstudio.com/">Visual Studio Code</a>.</li><li>To view supporting information for a reagent (images, publications, notes): Open a specific target-conjugate-orcid combination under the docs-supporting_material directory structure using a markdown editor. This can also be visualized from the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/reagent_resources.html">Reagent Resources page</a> and filtered using a catalog number or other unique identifier in your web browser.</li></ol><p></p><p>Join the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/">online IBEX Imaging community</a> and contribute your knowledge. For more details on how to contribute, see <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/contrib.html">these instructions</a>.</p><p>This research was supported by:</p><ul><li>The Intramural Research Program of the NIH, National Institute of Allergy and Infectious Diseases and National Cancer Institute, under grants 1ZIAAI001290-02, 1ZIAAI000545-33, 1ZIAAI000758-24, 1ZIAAI000974-16, 1ZIAAI001034-14.</li><li> The Wellcome Trust, under grant 224586/Z/21/Z.</li><li> The National Institute of Allergy and Infectious Diseases, NIH, under grant 1ZIAAI001343-01.</li></ul><p></p>
Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"
<p>LM is the linear inversion seafloor topography model</p> <p>NLM is the nonlinear inversion seafloor topography model</p> <p>PM is the prior seafloor topography model</p>
Current Harmonics Minimization of PMSM Based on Iterative Learning Control and Neural Networks: Motor Data
<p>The provided motor data corresponds to an electrical machine with 24 stator slots and 16 poles. As is common in electrical machines, this motor generates unwanted flux and current harmonics. However, the accompanying paper presents an effective solution to suppress these harmonics through the combined use of Iterative Learning Control (ILC) and Neural Networks (NNs).</p> <p>The ILC method demonstrates proficient compensation for harmonics during operations with constant speed and current reference values. Additionally, Neural Networks are trained with data derived from ILC, proving to be highly effective in suppressing harmonics even during transient operation. The simulation model used in the study is based on flux and torque maps, dependent on dq-currents and the electrical angle. These maps are obtained from Finite Element Method (FEM) simulations of an interior permanent magnet synchronous machine (IPM) and are openly published here, intended to facilitate other researchers in making direct comparisons with their own methodologies.</p> <p>Simulation results presented in the paper confirm that the integration of ILC and NNs leads to superior elimination of current harmonics during transient operations compared to using ILC alone.<br> If you use the provided maps and motor data, kindly cite the associated paper for reference: https://doi.org/10.3390/machines11080784, https://www.mdpi.com/2075-1702/11/8/784</p>
Service providers pre and post-questionnaires of the 1st and 2nd iteration phase
<p>The dataset contains information on the pre- and post-questionnaires of the service providers of the first and second evaluation period. Each spreadsheet indicates the source of the data (pre or post). The questions are related to the background of the respondents, previous experience, constraints, the service registration tool use and performance, the use of its supportive documentation, learnability, sustainability and maintainability, installability, changeability, effort and usability. Respondents were also asked about the potential of MyCorridor. The column pilot_round (Column CK) indicates whether the questionnaires were completed in the first or second pilot evaluation round.</p>
Traveller pre and post-questionnaires of mainstream and in depth users of the 2nd iteration phase
<p>The dataset contains the travellers’ pre- and post-questionnaires of the second evaluation phase in the MyCorridor project, which was a semi-real-world test. Each spreadsheet indicates the source of the data (pre, post, mainstream or in depth users). The questions are related to easiness to use the app, usefulness of the App, social desirability, attitude towards public transport, sharing modes and general mind sets, perceived accessibility to local transport, perceived accessibility to innovative mobility services and perceived overall trustworthiness, safety and security when using transport services.</p>
Data and code for the paper "Precision Groundwater Modeling: when cokriging meets evolutionary and iterative algorithms"
<ul> <li>exemplary dataset for 2019 yearly water table measurements in Northeaster Italy</li> <li>MATLAB code for the pre-processing GA-driven and the post-processing iterative validation part</li> </ul>
Supplementary Material for "Aiding the Design of Critical Software Systems by Iterative Exploration of Distinct Requirement Violation Scenarios"
<p>This dataset provides artifacts about an industrial case study of a Steer-by-Wire system. It collects models of the system modeled in the open-source Gamma Statechart Composition Framework. You can find more information about the framework here: <a href="https://github.com/ftsrg/gamma">https://github.com/ftsrg/gamma</a>.</p>
Live Iterative Ptychography
<p>This deposition contains data and code for live-updating ptychographic reconstruction with ePIE, an iterative ptychography method, during ongoing data acquisition.</p> <p>Corresponding paper: <a href="https://doi.org/10.1093/mam/ozae004">https://doi.org/10.1093/mam/ozae004</a> and <a href="https://doi.org/10.48550/arXiv.2308.10674">https://doi.org/10.48550/arXiv.2308.10674</a></p>
F I G U R E 6 A in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation
F I G U R E 6 A midpoint rooted, maximum likelihood phylogeny using IQ-TREE of the 20 biological species found in this study. The phylogeny was estimated using 1000 ultrafast bootstraps (ufBS) from the three-gene concatenated sequence alignment. Coloured boxes and borders correspond to the colours used under the 5% cut-off (Figure S1). Only ufBS of 90 or greater are shown. Species sharing the same colour were found to share at least one nuclear allele.
F I G U R E 3 A 16S in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation
F I G U R E 3 A 16S neighbour-joining tree constructed under an HKY substitution model used for identifying putative species.
F I G U R E 5 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation
F I G U R E 5 TCS haplotype network coloured by putative species identified at the 4% cut-off. (a) EF1γ. (b) 28S.
F I G U R E 1 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation
F I G U R E 1 Map of sampling localities across Australia identified by putative species under the 4% threshold.
F I G U R E 4 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation
F I G U R E 4 Species delimitation summary. Boxes in white represent which initial putative species specimens are assigned to as estimated under different barcode gap cut-offs. Letters within boxes represent different putative species hypotheses. Boxes in green are where the nuclear loci are consistent with being a distinct biological species, with no evidence of allele sharing. Boxes in grey are where putative species are found to share alleles of the nuclear loci. Boxes in orange are where putative species share internal haplotypes but also have alleles unique to each putative species (neotypy). Boxes in black represent the putative species found to be supported under different initial putative species thresholds.
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.