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134 results for “Notebook”
Output data for the PyCoxMunk example notebooks
<p>The PyCoxMunk python library allows users to calculate the expected sea surface reflectance under a given set of geometric and meteorological conditions. It is designed for use in satellite remote sensing and supports the majority of common satellite platforms.</p> <p> </p> <p>This archive contains data related to the example notebooks distributed with PyCoxMunk. Here you can find:</p> <p>1) An input dataset for the SLSTR example notebook.</p> <p>2) Output files for the SLSTR, SEVIRI and GOES example notebooks.</p>
Data dan R Markdown Notebook untuk "PENONJOLAN PERAN SEMANTIS DAN KONSTRUKSI GRAMATIKAL PASANGAN VERBA -I DAN -KAN: KAJIAN GRAMATIKA KONSTRUKSIONAL BERBASIS KORPUS ATAS MENAWARI/MENAWARKAN"
<p>Repositori <a href="https://r4ds.had.co.nz/workflow-projects.html">RStudio Project</a> yang mengandung data dan kode pemrograman R untuk analisis data dan penulisan makalah berjudul <strong>"Penonjolan Peran Semantis dan Konstruksi Gramatikal Pasangan Verba -<em>i</em> dan -<em>kan</em>: Kajian Gramatika Konstruksional Berbasis Korpus atas <em>Menawari</em>/<em>Menawarkan</em>"</strong>. Makalah ini diterbitkan pada jurnal <a href="https://ojs.linguistik-indonesia.org/index.php/linguistik_indonesia/index"><em>Linguistik Indonesia</em></a> di bulan Agustus, 2023.</p> <ol> <li> <p>Kode pemrograman R dan narasi teks makalah terintegrasi dalam berkas <a href="https://rmarkdown.rstudio.com">R Markdown</a> Notebook dengan nama <a href="https://github.com/gederajeg/profiled-participant-roles/blob/main/manuskrip-2.Rmd"><code>manuskrip-2.Rmd</code></a>.</p> </li> <li> <p>Data konkordansi terdapat pada berkas <a href="https://github.com/gederajeg/profiled-participant-roles/blob/main/menawari.txt"><code>menawari.txt</code></a> dan <a href="https://github.com/gederajeg/profiled-participant-roles/blob/main/menawarkan.txt"><code>menawarkan.txt</code></a>.</p> </li> <li> <p>Luaran visualisasi tersimpan pada direktori <a href="https://github.com/gederajeg/profiled-participant-roles/tree/main/plots"><code>plot</code></a>.</p> </li> </ol>
Outputs of the Jupyter Notebook - Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)
<p>The dataset contains the outputs of the notebook "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)" published in The Environmental Data Science Book.</p>
Microsecond ALEX FRET analysis notebook using FRETbursts - corrections, FRET burst analysis of recurring molecules, FCS, 2CDE & BVA
<p>The herein Python notebook uses FRETbursts (download from here: https://github.com/tritemio/FRETBursts) to show how to analyze microsecond alternating laser excitation (usALEX) confocal-based FRET measurements of freely diffusing single molecules. It includes a step-by-step calculation and implementation of correction factors, donor fluorescence leakage to the acceptor detection channel (Lk), acceptor fluorescence caused by acceptor excitation by the laser intended for donor excitation (Dir), the imbalance in acceptor/donor fluorecence quantum yields and detection efficiencies (Gamma) and the imbalance in donor/acceptor excitation yields (Beta). The notebook implments a global Gamma correction, assuming the Gamma correction factor is constant for all FRET populations, based on the procedure from Lee et al. 2005. Burst search for showing the FRET population is a dual-channel burst search. After correction, the corrected FRET histogram is presented (after burst selection takes into account Beta & Gamma corrected burst sizes). We also present analysis of bursts from recurring molecules, as well as the FCS (a bit irrelevant here, due to the lasr alternation in microeconds), 2CDE & BVA plots, helping in identifying whether a FRET population is a time average of FRET states, occurring faster then molecular diffusion time, or wheather the FRET population is static and represents a single conformational state. The sample data is a result of measurement of 50 pM of hairpin 3 presented in Tsukanov et al. 2013, labeled with ATTO dyes (ATTO 550 & ATTO 647N as donor and acceptor dyes, respectively) - 532 & 640 nm cw excitation, with an alternation period of 50 microseconds. </p>
Modelica Models and Jupyter Notebooks for System Analysis of Glucose Insulin Regulation
<p>This dataset contains source code of Modelica models of Glucose-Insulin regulation using different techniques.</p> <p>Accompanying Jupyter notebook is demo for system analysis (parameter estimation) of artificial data and to match model simulation able to be used in Teaching class.</p> <ul> <li><strong>ModelicaIdentification.ipynb</strong> - default notebook - code contains ellipsis which needs to be replaced as per instruction in text</li> <li><strong>ModelicaIdentificationResolution.ipynb - </strong>notebook - code with exemplar solution to default notebook</li> <li><strong>glucoseinsulin.mo - </strong>Modelica source code</li> <li><strong>PatientInsulinConcentration.csv</strong> - sample data to be fitted against model</li> <li><strong>seminar11hw.GIExperiment.fmu</strong> - FMU exported from Modelica in order to run simulation in Python and PyFMI library</li> </ul> <p>Thanks to the MYBINDER service, the Jupyter notebook can be viewed and executed as</p> <ul> <li><a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/">https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/</a> note that you need to launch terminal first in Jupyter -> New -> Terminal and install pyfmi and matplotlib by:</li> </ul> <pre><code class="language-bash">conda install -c conda-forge pyfmi matplotlib</code></pre> <ul> <li>Most recent version with other models and notebooks <a href="https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/">https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/</a></li> </ul>
Dataset, Survey, and R Notebooks for "Does Surprisal Predict Code Comprehension Difficulty?"
<p>(Version 1.1 Update)</p> <p>- Removed anonymization after reviewing period ended and paper was accepted at Cogsci 2020.</p> <p>- Added Qualtrics Survey in exported form</p> <p>Dataset and R analysis scripts for the paper "Does Surprisal Predict Code Comprehension Difficulty?". For more details, see "ComprehensionREADME.md" in the included zip file.</p>
Over Decomposition Laboratory Notebook (Companion for IPDPS 2017)
<p>This package includes the laboratory notebook (in Org Mode) the culminated to our submission to IPDPS 2017, under the title "Using Simulation to Evaluate and Tune the Performance of Dynamic Load Balancing of an Over-decomposed Geophysics Application". It includes the Ondes3D source code, all the collected data, the execution, extraction and analysis scripts that have been written in R and bash languages. The source file of the paper, also written in Org, is also included.</p>
DistilKaggle: a distilled dataset of Kaggle Jupyter notebooks
<h2><strong>Overview</strong></h2> <p>DistilKaggle is a curated dataset extracted from Kaggle Jupyter notebooks spanning from September 2015 to October 2023. This dataset is a distilled version derived from the download of over 300GB of Kaggle kernels, focusing on essential data for research purposes. The dataset exclusively comprises publicly available Python Jupyter notebooks from Kaggle. The essential information for retrieving the data needed to download the dataset is obtained from the MetaKaggle dataset provided by Kaggle.</p> <h2><strong>Contents</strong></h2> <p>The DistilKaggle dataset consists of three main CSV files:</p> <p><strong>code.csv:</strong> Contains over 12 million rows of code cells extracted from the Kaggle kernels. Each row is identified by the kernel's ID and cell index for reproducibility.</p> <p><strong>markdown.csv:</strong> Includes over 5 million rows of markdown cells extracted from Kaggle kernels. Similar to <strong>code.csv</strong>, each row is identified by the kernel's ID and cell index.</p> <p><strong>notebook_metrics.csv:</strong> This file provides notebook features described in the accompanying paper released with this dataset. It includes metrics for over 517,000 Python notebooks.</p> <h2><strong>Directory Structure</strong></h2> <p>The <strong>kernels</strong> directory is organized based on Kaggle's Performance Tiers (PTs), a ranking system in Kaggle that classifies users. The structure includes PT-specific directories, each containing user ids that belong to this PT, download logs, and the essential data needed for downloading the notebooks.</p> <p>The <strong>utility</strong> directory contains two important files:</p> <p><strong>aggregate_data.py:</strong> A Python script for aggregating data from different PTs into the mentioned CSV files.</p> <p><strong>application.ipynb:</strong> A Jupyter notebook serving as a simple example application using the metrics dataframe. It demonstrates predicting the PT of the author based on notebook metrics.</p> <p><strong>DistilKaggle.tar.gz: </strong>It is just the compressed version of the whole dataset. If you downloaded all of the other files independently already, there is no need to download this file.</p> <h2><strong>Usage</strong></h2> <p>Researchers can leverage this distilled dataset for various analyses without dealing with the bulk of the original 300GB dataset. For access to the raw, unprocessed Kaggle kernels, researchers can request the dataset directly.</p> <h2><strong>Note</strong></h2> <p>The original dataset of Kaggle kernels is substantial, exceeding 300GB, making it impractical for direct upload to Zenodo. Researchers interested in the full dataset can contact the dataset maintainers for access.</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset in your research, please cite the accompanying paper or provide appropriate acknowledgment as outlined in the documentation.</p> <p>If you have any questions regarding the dataset, don't hesitate to contact me at <a href="mailto:mohammad.abolnejadian@gmail.com">mohammad.abolnejadian@gmail.com</a></p> <p>Thank you for using DistilKaggle!</p>
Data and Mathematical notebook for "Fractional-statistics-induced entanglement from Andreev-like tunneling"
<p>The uploaded files "SourceRightON_full.txt", "SourceLeftON_full.txt" and "BothSourcesON_full.txt" contain data for the work entitled "Fractional-statistics-induced entanglement from Andreev-like tunneling".</p> <p> </p> <p>The other file "New_Anyonic_data_fittings v2.nb" is the Mathematica notebook with which we perform the data analysis. When using it, please place three data files (mentioned above) in the Download folder.</p>
FIGURE 1 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 1 First page of field notebook number 156. This trip was conducted from Port Augusta to Norseman, Western Australia, by Ken Key, Murray S Upton and Jim Balderson from 28/9/1963 to 23/10/1963. Plant specimens were identified by Nancy T Burbidge. On 28th of September, they started from Mildura, took Arumpo road at a vehicle odometer 6827 and travelled 6 mi to reach 6833. Site description and general observations for collection at stop 6833: Flat with sparse belah and Callitris robusta to 25 ft on pale brown sandy loam with?Cassia sp. abundant to 8 ft and regrowth. Ground layer of Bassia spp. and Kochia spp. to 6 in. and occasional Kochia?pyramidata to 2 ft barley grass and succulents drying off, considerable bare ground. Return. Grasshoppers collected at this site: Cratilopus sp. 1, Chortoicetes terminifera, Caperrala sp. 1 (j.), Apotropis vittata (j.).
FIGURE 4 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 4 Comparison of survey consistency among different surveyors. (a) Between-site distance maintained by different lead surveyors. (b) The total number of grasshoppers recorded per site by different lead surveyors. Numbers in the middle of boxplot show median value. (c) Seasonal variation in the number of species counted per site by different lead surveyors. (d) Proportion of total surveys conducted in each season by different surveyors. Values on pie charts show the number of surveys conducted by each surveyor. Seasons are indicated by colours.
FIGURE 3 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 3 Spatial bias in historic grasshopper surveys in Western Australia and Tasmania (inset). (a) Thiessen polygon network drawn based on survey sites as centre of each polygon, showing the intensity of survey activity; the smaller polygons, the more intensive the survey activity because each polygon represents a sampling site. Colours in the background represent bioregions. (b) Bioregions bias: Positive scores indicate positive survey bias (higher survey effort than expected from a random allocation) and vice versa. Bars on right side of vertical dash line represent Tasmania. Bioregions were abbreviated as: AvWh, Avon Wheatbelt; BL, Ben Lomond; Ca, Carnarvon; CeKi, Central Kimberley; CeRa, Central Ranges; Co, Coolgardie; Da, Dampierland; EsPl, Esperance plains; F, Furneaux; Ga, Gascoyne; GeSa, Geraldton Sandplains; GiDe, Gibson Desert; GrSaDe, Great Sandy Desert; GrViDe, Great Victorian Desert; Ha, Hampton; JaFo, Jarrah Forest; K, King; LiSaDe, Little Sandy Desert; Ma, Mallee; Mu, Murchison; NoKi, Northern Kimberley; Nu, Nullarbor; OrViPl, Ord Victoria Plain; Pi, Pilbara; SwCoPl, Southwest Coastal Plain; Ta, Tanami; TCH, Tasmanian Central Highlands; TNM, Tasmanian Northern Midlands; TNS, Tasmanian Northern Slopes; TSE, Tasmanian South East; TSR, Tasmanian Southern Ranges; TW, Tasmanian West; ViBo, Victorian Bonaparte; Wa, Warren; Ya, Yalgoo.
FIGURE 2 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 2 Grasshopper species count and survey effort in Western Australia and Tasmania (inset) per 50-km grid cells. Only records with both genus and species (either confirmed based on formal taxonomy for genus and species or putative taxonomy used for genus or species) names were included in these analyses. (a) Species richness (total number of recorded species per cell); (b) total number of survey sites per cell; (c) for each cell, the average number of species per survey site. No survey was conducted in white cells. Bioregions were abbreviated as: Ca, Carnarvon; CeKi, Central Kimberley; Co, Coolgardie; Da, Dampierland; EsPl, Esperance Plains; Ga, Gascoyne; Ha, Hampton; Nu, Nullarbor; Pi, Pilbara; SwCoPl, Southwest Coastal Plains; ViBo, Victorian Bonaparte.
Dataset of Jupyter Notebooks from the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts"
<pre>This archive contains the dataset of properly-licensed Jupyter notebooks from the MSR'22 paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts". The dataset contains 847,881 notebooks stored in the PostgreSQL dump file. You can find the details about the database in the README file. To transform the notebooks into this convenient format and to calcuate the structural metrics, we used our library called Matroskin, which can be found here: <a href="https://github.com/JetBrains-Research/Matroskin">https://github.com/JetBrains-Research/Matroskin</a>. </pre>
Inputs of the Jupyter Notebook - Cosmos-UK soil moisture
<p>The dataset contains the inputs of the notebook "Cosmos-UK soil moisture" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of the public 2013-2019 COSMOS-UK dataset, daily and subhourly observations and metadata for four stations: WYTH1, WADDN, SHEEP and CHIMN. These stations represent the first sites to prototype COSMOS sensors in the UK, see further details in Evans et al. (2016) and they are situated in human-intervened areas (grassland and cropland), except for one in a woodland land cover site.</p> <p>Data from COSMOS-UK up to the end of 2019 are available for download from the UKCEH Environmental Information Data Centre (EIDC). The data are accompanied by documentation that describes the site-specific instrumentation, data and processing including quality control. The full dataset is available for <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">download</a> under the terms of the Open Government License.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Doran Khamis (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/dorankhamis">@dorankhamis</a></p> </li> <li> <p>Matt Fry (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/mattfry-ceh">@mattfry-ceh</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>UK Centre for Ecology & Hydrology (creator)</p> </li> <li> <p>Natural Environment Research Council (support)</p> </li> </ul> <p><em>Dataset reference and documentation</em></p> <ul> <li> <p>S. Stanley, V. Antoniou, A. Askquith-Ellis, L.A. Ball, E.S. Bennett, J.R. Blake, D.B. Boorman, M. Brooks, M. Clarke, H.M. Cooper, N. Cowan, A. Cumming, J.G. Evans, P. Farrand, M. Fry, O.E. Hitt, W.D. Lord, R. Morrison, G.V. Nash, D. Rylett, P.M. Scarlett, O.D. Swain, M. Szczykulska, J.L. Thornton, E.J. Trill, A.C. Warwick, and B. Winterbourn. Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk]. 2021. URL: <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>, <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">doi:10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>.</p> </li> </ul> <p><strong>Further references</strong></p> <ul> <li> <p>Jonathan G. Evans, H. C. Ward, J. R. Blake, E. J. Hewitt, R. Morrison, M. Fry, L. A. Ball, L. C. Doughty, J. W. Libre, O. E. Hitt, D. Rylett, R. J. Ellis, A. C. Warwick, M. Brooks, M. A. Parkes, G. M.H. Wright, A. C. Singer, D. B. Boorman, and A. Jenkins. Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk. <em>Hydrological Processes</em>, 30:4987–4999, 12 2016. <a href="https://doi.org/10.1002/hyp.10929">doi:10.1002/hyp.10929</a>.</p> </li> <li> <p>M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem. Cosmos: the cosmic-ray soil moisture observing system. <em>Hydrology and Earth System Sciences</em>, 16(11):4079–4099, 2012. URL: <a href="https://hess.copernicus.org/articles/16/4079/2012/">https://hess.copernicus.org/articles/16/4079/2012/</a>, <a href="https://doi.org/10.5194/hess-16-4079-2012">doi:10.5194/hess-16-4079-2012</a>.</p> </li> </ul>
Outputs of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series
<p>The dataset contains the outputs of the notebook "Concatenating a gridded rainfall reanalysis dataset into a time series" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Timothy Lam (author), University of Exeter, <a href="https://github.com/timo0thy">@timo0thy</a></p> </li> <li> <p>Marlene Kretschmer (author), University of Reading, <a href="https://github.com/MarleneKretschmer">@MarleneKretschmer</a></p> </li> <li> <p>Samantha Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Rachel Prudden (author), Met Office Informatics Lab, <a href="https://github.com/RPrudden">@RPrudden</a></p> </li> <li> <p>Elena Saggioro (author), University of Reading, <a href="https://github.com/ESaggioro">@ESaggioro</a></p> </li> <li> <p>Nick Homer (reviewer), University of Edinburgh, <a href="https://github.com/NHomer">@NHomer</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>NOAA National Center for Environmental Prediction (creator)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Eugenia Kalnay, Director, NCEP Environmental Modeling Center</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, Roy Jenne, and Dennis Joseph. The ncep/ncar 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3):437 – 472, 1996. URL: <a href="https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml">https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml</a>, <a href="https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2">doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2</a>.</p> </li> </ul> <p><em>Pipeline documentation</em></p> <ul> <li> <p>Marlene Kretschmer, Samantha V. Adams, Alberto Arribas, Rachel Prudden, Niall Robinson, Elena Saggioro, and Theodore G. Shepherd. Quantifying causal pathways of teleconnections. Bulletin of the American Meteorological Society, 102(12):E2247 – E2263, 2021. URL: <a href="https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml">https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml</a>, <a href="https://doi.org/10.1175/BAMS-D-20-0117.1">doi:10.1175/BAMS-D-20-0117.1</a>.</p> </li> </ul>
Analysis of scholarly repositories' availability. Data and notebooks.
<p>These datasets and companion Jupyter notebooks supplement the publication "Knock knock! Who's there?'' A study on scholarly repositories' availability" accepted at TPDL 2022, Padova, Italy.</p>
Python scripts / Jupyter Notebooks and data for training segmentation models on slide scans of diatom preparations from river Menne
<p>This archive contains the Jupyter Notebooks and data used for the deep learning experiments published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>The notebooks are numbered according to the order in which they are to execute. Please refer to the comments and documentation within the notebooks as well as to the manuscript for details. The data (image data, mask data & segmentation ground truth in COCO format for several different tiling strategies) is stored in separate subfolders corresponding with data usage (model training, validation, test) and tiling strategy. Please refer to the "readme" files for detailed information.</p> <p> </p> <p> </p>
Jupiter Notebook and example data for "Prospects for a camera-based detector for Neutron Reflectometry"
<p>We report the outcome of a proof-of-principle (IPTS-29165) neutron reflectivity measurement obtained using a neutron scintillator and a Photonis (brand) camera. We were motivated to test this technology because it provides much better spatial resolution and count rate capability than the BL4A <sup>3</sup>He position sensitive detector (Table 1). The report describes the detector setup, challenges encountered, a reflectivity measurement and next steps.</p> <p>Two example measurements are provided and a Jupyter Notebook to create a NumPy binary file consisting of event positions and times.</p>
Optimal modes for wavefront sensorless adaptive optics. Turbulence- and oocyte-induced phase screens and Mathematica notebooks.
<p>This notebook and phase screens constitute a numerical experiment to calculate the error of the wavefront approximation using first N modes of the a) Zernike and b) Lukosz-Braat polynomials, c) SVD modes obtained with respect to the gradient-dot product, and the d) eigenfunctions of the Laplace operator with the Neumann boundary conditions. It's a complementary material to a paper submitted to Optics Express.</p>
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.