Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “Python Notebook”
Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain - Datasets and Python notebooks
<p>This dataset and the associated Python notebooks and R-code are related to the publication "Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain".</p>
Supporting Jupyter Python notebook for "A new class of efficient randomized benchmarking protocols"
<p>Python notebook containing the code used to generate the data for figure 2 in the appendix of "A new class of efficient randomized benchmarking protocols" (arXiv:1806.02048).</p>
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>
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>
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>
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>
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>
Additional 1000 notebooks for the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts"
<p>Additional 1000 notebooks for the review of the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts".</p> <p>The notebooks can be processed using Matroskin tool from the supplementary materials. To do that, place:</p> <p>- the folder "1k_notebooks_dataset" into the folder ".../databases/datasets"</p> <p>- the file "mapping_of_1k_notebooks.json" into the folder ".../databases/mappings"</p>
Python notebooks as a pedagogical tool to teach NOT data reduction
<p>We will present a series of seven Python Jupyter Notebooks designed to teach master-level students the basic steps of data reduction for observations with the Alhambra Faint Object Spectrograph and Camera (ALFOSC). This pedagogical tool, which translates IRAF tasks into the widespread Python language, has been successfully deployed for the course “Observational Astrophysics II” at the Department of Astronomy of Stockholm University. Each notebook introduces the students to one specific task of the data reduction explaining the purpose of that task, how it is implemented and guiding the students through the completion of the task. With a hands-on approach, this allows the students to understand the reason behind each step and to test directly what is the effect of each step, thanks to interactive plots of the data and the intermediate products. In addition to its educational value, this material can be expanded to reach the quality needed for scientific works. Therefore it could offer a starting point for developing a personalised data reduction for a given scientific problem. A complete version of the material is publicly available at https://github.com/astrojuggler/data-reduction-Obs-II-course. This project has been financed by Stockholm University - Department of Astronomy with a grant earned by Professor Matthew Hayes.</p>
Dataset and Jupyter notebook for "pyDARN: A Python Software for Visualizing SuperDARN Radar Data"
<p>SuperDARN radar dataset and Jupyter notebook used to generate figures for "pyDARN: A Python Software for Visualizing SuperDARN Radar Data".</p>
Error Identification Strategies for Python Jupyter Notebooks
<p>Replication package for the paper "<strong>Error Identification Strategies for Python Jupyter Notebooks</strong>".</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.