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134 results for “Notebook”
Digital humanities semantic Notebook / video of the presentation
<p>After a theoretical introduction on the concepts of reproducible science and the Web of Data, we will present the methodology of scientific notebooks (e.g. Jupyter Notebooks). In this context, we will present a step-by-step approach to the creation of a scientific notebook in history: we will load a dataset from the Open Data of the institute and then apply filters and calculations on the data. A presentation and analysis layer with graphics will complete our research product with enrichments from external data and vocabularies from the Semantic Web. This will make our output reproducible and documented with text enriched with semantic schemas (schema.org) and disambiguation authorities (GND, dpPedia, Wikidata...), data sources and computer code. The presentation does not require advanced technical knowledge. It is aimed at beginners. The technical demonstration, in the second part, is deliberately simple in content and will be commented on as it goes along.</p>
Data from: Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
<p>The baseline distribution data for all species of a given group in a region can provide fundamental insights into biogeographic questions about historic patterns of species richness, population trends, and extinction. Grasshoppers are one major group of insects for which a continent-wide perspective on their geographic distribution can be obtained. This is because they were extensively surveyed in Australia for 54 years (1936-1989) as part of Commonwealth expeditions to obtain specimens for the Australian National Insect Collection (ANIC). Field notebooks recorded from those surveys, under the direction of ANIC curator and director K. H. L. Key, form the principal source of historic distribution records for grasshoppers in Australia. We digitized all 223 notebooks (2486 pages) and transcribed all the field trips conducted in Western Australia (WA) and Tasmania (47 notebooks, 590 pages). We then carefully geocoded all sampling sites of the transcribed notebooks, following the odometer readings and descriptions of routes from a suitable reference point using historic topographic maps and Google Earth. In total, we extracted 8975 geographic coordinates for 477 species having a confirmed or putative taxonomic name of (only 170 of these species have been formally described). We found that species richness varied spatially, with highest richness in arid interior and north of WA. Historic grasshopper surveys were non-randomly distributed across both WA and Tasmania with the highest survey intensity around coastal regions. Variation was observed among surveyors in terms of the number of species detected per site, between-site distance, and the season of survey being conducted. Overall, however, the dataset is among the most comprehensive continent-wide surveys of Australian invertebrates and will greatly facilitate future work on their ecology, biogeography, conservation, and responses to environmental change.</p>
Example data, notebook script and output
<p>Raw, azimuthally resolved XRD on SRM660a. Correction and integration script. Output integrator descriptions and processed 1 and 2D data.</p> <p> </p>
Notebooks and calculation files for: Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Models
<p>The files with simulation results for JLT submission "Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Modelsr".</p> <p><strong>"3CCF_supermodes"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 3-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"3CCF_modelingJLTPaper" </strong>in order to plot them to get Fig. 3 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"3CCF_modelingJLTPaper"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p> <p> </p> <p> </p> <p><strong>! </strong><em>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</em>A. Ankiewicz, A. Snyder, and X.-H. Zheng, “Coupling between parallel optical fiber cores–critical examination”, Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<em>]:</em></p> <p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p> <p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p> <p> </p> <p><strong>P.s. </strong>In case of any questions or suggestions or if you need more explanations, you are welcome to write me an email ekader@chalmers.se. If it seems like the code does not work or mistakes in simulations are found, I also appreciate letting me know.</p>
R Notebook and Dataset for "Usage-based perspective on argument realisation: A corpus study of Indonesian BUY verbs in applicative construction with -kan" (1.0.0)
<p>This repository contains the dataset and R codes for our paper that has been published in <a href="http://www.aa.tufs.ac.jp/en/publications/nusa">NUSA</a> (<i>Linguistic studies of languages in and around Indonesia</i>) special volume (74) on "Applicatives in Austronesian Languages".</p><h4>How to cite the paper</h4><p>Rajeg, Gede Primahadi Wijaya & I Wayan Arka. 2023. Usage-based perspective on argument realisation: A corpus study of Indonesian BUY verbs in applicative construction with -<i>kan</i>. In Jocelyn Aznar, Christian Döhler & Jozina Vander Klok (eds.), <i>NUSA (special issue on "Applicatives in Austronesian Languages")</i>, vol. 74, 83–114. <a href="https://tufs.repo.nii.ac.jp/records/2000019">https://tufs.repo.nii.ac.jp/records/2000019</a>.</p><h4>Description of the repository</h4><p>The .qmd file contains the R codes used to produce the quantitative analyses in the paper, including the statistical figures. This .qmd file also interweaves some text narratives with the codes. The file is published as a webpage at: <a href="https://gederajeg.github.io/applicative-buy/">https://gederajeg.github.io/applicative-buy/</a></p><p>The raw, annotated concordance data is located in the <a href="https://github.com/gederajeg/applicative-buy/tree/main/data">data</a> directory.</p><p>The statistical figures can also be accessed individually <a href="https://github.com/gederajeg/applicative-buy/tree/main/nusa-applicative-code_files/figure-html">here</a>.</p>
Namelists for the CA20 Dataset and Figure Notebooks
<p>This dataset contains the namelists used to generate the CA20 dataset, as well as notebooks and post-processed data used to generate figures for "A Twenty-Year Analysis of Winds in California for Offshore Wind Energy Production Using WRF v4.1.2" (in review).</p> <p>CA20 is a 20-year offshore wind resource assessment in the Outer Continental Shelf off the coast of California. namelist.wps was used by <a href="https://github.com/wrf-model/WPS">WPS 4.1</a> to generate boundary conditions. namelist.input was used by <a href="https://github.com/wrf-model/WRF">WRF 4.1.2 </a>to carry out the CA20 simulation. Details of the CA20 dataset as well as analysis may be found in the technical report "2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf" <a href="http://doi.org/10.2172/1677466">doi.org/10.2172/1677466</a>. The dataset itself may accessed through the <a href="https://registry.opendata.aws/nrel-pds-wtk/">Registry of Open Data on AWS</a>. </p> <p>Update on July 11, 2023: The previous versions of namelist.input incorrectly listed the surface layer scheme as 1 (MM5) when it should have been 5 (MYNN). This error occurred during manual transcription of a template namelist.</p>
Vibrational-EELS Dataset and data processing routine (Jupyter Notebook) (Laforet et al.)
<p>Contains all the vibrational-EELS data presented in the article, accompanied with the python HyperSpy processing routine used (Jupyter Notebook). </p>
Data from: Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
Open the record for dataset details and reuse information.
Python and Jupyter Notebook for Medical Image Analysis - OpenMRBenelux 2020
<p>Dataset for the workshop "Python and Jupyter Notebook for Medical Image Analysis" at OpenMRBenelux - January 22, 2020 - Nijmegen (The Netherlands)</p>
Complete set of raw and processed datasets, as well as associated Jupyter notebooks for analysis, associated with manuscript entitled: "The MOUSE project: a practical approach for obtaining traceable, wide-range X-ray scattering information"
<p>This dataset is a complete set of raw, processed and analyzed data, complete with Jupiter notebooks, associated with the manuscript mentioned in the title. </p> <p>In the manuscript, we provide a ``systems architecture''-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (<strong>M</strong>ethodology <strong>O</strong>ptimization for <strong>U</strong>ltrafine <strong>S</strong>tructure <strong>E</strong>xploration). Through this project, we aim to provide a comprehensive methodology for obtaining the highest quality X-ray scattering information (at small and wide angles) from measurements on materials science samples. </p>
Envelopes for the article "Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes" and the python notebook that shows the algorithm usage.
<p>The github repository is available at https://github.com/djozinovi/goodnessOfFitEnv</p>
Extended dataset and Mathematica notebook produced by the Integer sequence (A363743): a(n) = floor(sqrt(log_10(n!))).
<p>This integer sequence was registered and published in the On-Line Encyclopedia of Integer Sequences (OEIS.org) Database on August 17 - 2023, under the OEIS code: A363743. </p><p>This sequence can be generally expressed as follows: a(n) = floor(sqrt(log_10(n!))), where n is a non-negative integer. It should be noted that the aforementioned formula is written in accordance with OEIS specific style sheet format. On the other hand, it was possible to represent the general formula on another two forms that the following: </p><p>1) a(n) = floor(sqrt(A034886(n) - 1)).</p><p> 2) a(n) = A000196(A034886(n) -1). </p><p>This dataset verifies the reported properties in the comments section of the OEIS publication. Our evaluation ranges from 0 to n = 5000, in contrast to the publication which ranges from 0 to n = 92. These mentioned properties are the following: </p><p>* Every non-negative integer occurs at least 4-times. </p><p>* Each integer k > 14 appears fewer than k times. </p><p>* The only integers k that occur exactly k times are 11, 13 and 14. </p><p>* This sequence can produce random values between 0 and 1 if we do a(n)/a(n+m) for any non-negative integer m. </p><p>The numerical data showed on this dataset was generated by the following Mathematica program: Array[Floor@ Sqrt[Log10[#!]] &, 5000, 0] The previous program was builded on Mathematica v13.3.0.</p><p> <strong>Note:</strong> More mathematical details, graphics and technical information can be found in the notebook or .nb file provided in this dataset. </p>
Multiverse Notebook: Shifting Data Scientists to Time Travelers (Supplemental Material)
<p>The collected revisions and the results of our manual inspectionspresented in "Multiverse Notebook: Shifting Data Scientist to Time Traveler."</p>
The data and notebook for "Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence"
<p>The data and Jupyter notebook for "Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence."</p> <p><br><strong>Fig_6_code_implementation.ipynb</strong></p> <p>This notebook presents implementation of our closure model in python to predict turbulent transport for all $(r, \mathrm{Pr})$ in Fig. 6(a).</p> <p><br><strong>GSF_r_Pr_scan__Shear_eq_3.h5</strong></p> <p>This file contains data output from the $(r, \mathrm{Pr})$-scan of the closure model, obtained using "Fig_6_code_implementation.ipynb". The h5 data file can be simply read using the following lines of code:</p> <p><br><code>import h5py</code><br><code>hf=h5py.File('~/GSF_r_Pr_scan__Shear_eq_3.h5', 'r')</code><br><code>ux_uy = hf['ux_uy/ux_uy/ux_uy'][()]</code><br><code>ux_th = hf['ux_th/ux_th/ux_th'][()]</code></p> <p><code>Pr_exp = np.linspace(0.02, 7, 28)</code><br><code>Pr_array = 10**(-Pr_exp) #These are the values of Pr for which the transport is computed.</code></p> <p><code>r_exp = np.linspace(0.02, 5, 20)</code><br><code>r_array = 10**(-r_exp) #These are the values of r for which the transport is computed.</code><br><br></p> <p> </p> <blockquote> <p>Authors:</p> <p><strong>B. Tripathi<br></strong>Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>ORCID : 0000-0002-4723-2170<br>Email : btripathi@wisc.edu</p> <p><strong>A.J. Barker</strong><br>Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK<strong><br></strong>ORCID : 0000-0003-4397-7332<br>Email : A.J.Barker@leeds.ac.uk</p> <p><strong>A.E. Fraser<br></strong>Department of Applied Mathematics, University of Colorado, Boulder, Colorado 80309, USA<br>Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder, Colorado 80309, USA<br>Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Colorado 80309, USA<br>ORCID : 0000-0003-4323-2082</p> <p><strong>P.W. Terry<br></strong>Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>ORCID : 0000-0002-4981-9637</p> <p><strong>E.G. Zweibel<br></strong>Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>Department of Astronomy, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>ORCID : 0000-0003-4821-713X</p> </blockquote>
single-cell imaging datasets associated with JupyterLab notebooks
Open the record for dataset details and reuse information.
lr-kallisto Example Colab Notebook
<p>This Zenodo contains the data and resources for processing ONT data with lr-kallisto using seqspec and splitcode for preprocessing. </p>
NOOS - Programming skills, notebook environment for sharing data and code
Open the record for dataset details and reuse information.
Jupyter Notebook Activity Dataset (rsds-20241113)
<h2>List of data</h2> <ul> <li>rsds-20241113.zip: Collection of SQLite database files</li> <li>image.tar.gz: Docker image provided in our data collection experiment</li> <li>redspot-341ffa5.zip: Redspot source code (<a href="https://github.com/tomokinakamaru/redspot/tree/341ffa56cb941b6f1ad74bd50d23fcf0ee96b270" target="_blank" rel="noopener">redspot@341ffa5</a>)</li> </ul> <div> <h2>Extended version of Section 2D of our paper</h2> Redspot is a Jupyter extension (i.e., Python package) that records activity signals. However, it also offers interfaces to read recorded signals. The following shows the most basic usage of its command-line interface:<br> <div> </div> <div><code>redspot replay <path-to-db></code></div> <br> <div>This command generates snapshots (.ipynb files) restored from the signal records. Note that this command does not produce a snapshot for every signal. Since the change represented by a single signal is typically minimal (e.g., one keystroke), generating a snapshot for each signal results in a meaninglessly large number of snapshots. <em><strong>However, we want to obtain signal-level snapshots for some analyses. In such cases, one can analyze them using the application programming interfaces:</strong></em></div> <br> <div><code>from redspot import database</code></div> <div><code>from redspot.notebook import Notebook</code></div> <div><code>nbk = Notebook()</code></div> <div><code>for signal in database.get("path-to-db"):</code></div> <div><code> time, panel, kind, args = signal</code></div> <div><code> nbk.apply(kind, args) # apply change</code></div> <div><code> print(nbk) # print notebook</code></div> <br> <div>To record activities, one needs to run the Redspot command in the recording mode as follows:</div> <br> <div><code>redspot record</code></div> <br> <div>This command launches Jupyter Notebook with Redspot enabled. Activities made in the launched environment are stored in an SQLite file named ``redspot.db'' under the current path.</div> <br> <div>To launch the environment we provided to the participants, one first needs to download and import the image (image.tar.gz). One can then run the image with the following command:</div> <br> <div><code>docker run --rm -it -p8888:8888 <image-name></code></div> <br> <div>Note that the SQLite file is generated in the running container. The file can be downloaded into the host machine via the file viewer of Jupyter Notebook.</div> </div>
Python Notebook and Dataset for Multi-Elves Analysis at the Pierre Auger Observatory
<p>In this directory, you will find the Python notebook <code>multi-elves.ipynb</code> for analyzing the photo traces of the multiple elves detected in the Fluorescence Detector of the Pierre Auger Observatory. Instructions for running the notebook are included in the README file.</p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</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.