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Dataset results
134 results for “Notebook”
Daten und Juypter Notebooks zum Artikel "Aus der Vogelperspektive: Quantitative Einsichten in die Datenbank der Sammlung Giesbrecht"
<p>Dieses Datenset enthält den serialisierten Dataframe und alle Jupyter Notebooks, um die im Artikel vorgelegten Analysen nachvollziehen zu können und sie zu reproduzieren. Die Notebooks "Keyword Extraction" und "Wordcloud" zeigen weitere, im Artikel nicht beschriebene Möglichkeiten, mit den Daten zu arbeiten.</p>
R notebooks to reproduce all analyses from the manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis"
<p>This package contains all R notebooks to reproduce the analyses from our manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis".</p> <p>In the zip file you find</p> <ul> <li>several rds files in the data folder: They contain grandR objects of both simulated and real SLAM-seq data sets. You can delete them and create them again by either just "knitting" the notebooks (which will generate all data necessary for this notebook and save it into the data folder), or by executing the generateAllDataFiles.R script ("Rscript generateAllDataFiles.R"), which will generate all rds files that do not exist).</li> <li>several R notebooks (Rmd): "Knitting" them will generate all figures from the manuscript. Without the data files (rds), this will be slow!</li> <li>knit_all.bash: Execute to "knit" all notebooks</li> <li>clean.bash: Clear the output of "knitting" the notebooks</li> </ul> <p> </p>
Datasets, scripts and Jupyter Notebook for "Two distinct magma storage regions at Ambrym volcano detected by satellite geodesy", Geophysical Research Letters
<p>This repository includes scripts and files necessary to create Figure 1 (<strong>S1.zip </strong>and <strong>plot_TS_Ambrym_2019_2022.py</strong>) in "Two distinct magma storage regions at Ambrym volcano detected by satellite geodesy", <em>Geophysical Research Letters</em>. We also include the Jupyter Notebook used to run the EnKF data assimilation (<strong>enkf_notebook.zip) </strong>and produce Figures 2 and 3. The Jupyter Notebook and files used to produce Figure 4b,c can be found on <a href="http://github.com/tshreve/jupyterNBs/">GitHub</a>.</p> <p>This version corrects a bug in the code used to plot the cross-sections in Figure 3 with <strong>enkf_notebook.zip</strong>.</p>
MOON: Assisting Students in Completing Educational Notebook Scenarios (Artifacts)
<p>This repository contains the artifacts supporting the paper "<em>MOON: Assisting Students in Completing Educational Notebook Scenarios</em>" published in the <em>IEEE Symposium on Visual Languages and Human-Centric Computing</em> (VL/HCC) 2023.</p> <ul> <li><strong>moon.zip</strong> contains the source code of MOON at the time of publication</li> <li><strong>notebooks.zip</strong> contains the example Jupyter notebook used as illustration in the paper as well as the notebook manipulated by students in the evaluation</li> <li><strong>analysis.zip</strong> contains the Jupyter notebooks used to analyze the raw data extracted in the evaluation as well as the results of the user study conducted with students</li> </ul>
Evaluating a Digital Memory Notebook App to Improve Functional Independence
ClinicalTrials.gov study NCT03453554. IPD Sharing: NO. Countries: 1. Publications: 3.
The value of increased spatial resolution of pesticide usage data for assessing risk to endangered species: Data, notebooks, and results
Open the record for dataset details and reuse information.
tashley/particle_tracking_data: Added Jupyter notebook
<p>Data and code related to the manuscript "Probability distributions of particle hop distance and travel time over equilibrium mobile bedforms" (Ashley et al, in revision)</p>
Dataset of the Floating Objects Detection Notebook
<p>This dataset contains the data used in the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery", published in the ocean modelling section of The Environmental Data Science Book.</p>
Error Identification Strategies for Python Jupyter Notebooks
<p>Replication package for the paper "<strong>Error Identification Strategies for Python Jupyter Notebooks</strong>".</p>
Facilitating Sensemaking in Computational Notebooks
Open the record for dataset details and reuse information.
Intersecting Narratives of Resistance: Aimé Césaire's Critique of Colonization in "Notebook of a Return to the Native Land"
Open the record for dataset details and reuse information.
Supplement notebooks for "scBoolSeq: Linking scRNA-Seq Statistics and Boolean Dynamics"
<p>See https://github.com/bnediction/scBoolSeq-supplementary</p>
FAIRmat Tutorial 2: Electronic lab notebooks and FAIR data management
<p>This tutorial is reviewing the need of using Electronic Lab Notebooks (ELNs) in materials science research labs, synthesis, and experimental characterization facilities. A special emphasis is put on efficiently collecting all metadata and perform a FAIR data management which not only facilitates organizing the work better and making the processes in the lab more performant, but also guarantees the possibility of data reuse via offering machine readability and machine interpretability.</p> <p>The tutorial is also addressing the questions: Which kind of choices are available and how to set up such ELN system for a laboratory? A demonstration of such a lab setup will also be provided on the first day.</p> <p>On the second day, three more presentations will guide you through the details of implementing such a setup for your own lab. For this purpose, we have chosen relevant, but simple use cases which can help you in mapping our strategies and solutions onto your institute.</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
GENiPPI: data, code, models and notebooks
<p>Interface-aware molecular generative framework for protein-protein interaction modulators</p>
Facilitating Sensemaking in Computational Notebooks
Open the record for dataset details and reuse information.
Facilitating Sensemaking in Computational Notebooks
Open the record for dataset details and reuse information.
Figure 5 from: Bloom D, Thomer A, Vaidya G, Guralnick R, Russell L (2012) From documents to datasets: A MediaWiki-based method of annotating and extracting species observations in century-old field notebooks. ZooKeys 209: 235-253. https://doi.org/10.3897/zookeys.209.3247
Figure 5 - An example of how a location (Big Thompson Creek near Loveland), a date (Sunday, June 10, 1906), and a taxon (Cottonwood, genus Populus) are grouped from across multiple pages.
Figure 4 from: Bloom D, Thomer A, Vaidya G, Guralnick R, Russell L (2012) From documents to datasets: A MediaWiki-based method of annotating and extracting species observations in century-old field notebooks. ZooKeys 209: 235-253. https://doi.org/10.3897/zookeys.209.3247
Figure 4 - Editing a notebook page on Wikisource. This screenshot shows side-by-side transcription and wiki markup syntax.
Figure 2 from: Bloom D, Thomer A, Vaidya G, Guralnick R, Russell L (2012) From documents to datasets: A MediaWiki-based method of annotating and extracting species observations in century-old field notebooks. ZooKeys 209: 235-253. https://doi.org/10.3897/zookeys.209.3247
Figure 2 - Index page for Notebook #1. Each Index page corresponds to a multipage file. The Index page displays volume metadata and links to sections of the notebook, while also providing links out to each notebook page and color-coding to determine which pages have been already transcribed and proofed.
Figure 1 from: Bloom D, Thomer A, Vaidya G, Guralnick R, Russell L (2012) From documents to datasets: A MediaWiki-based method of annotating and extracting species observations in century-old field notebooks. ZooKeys 209: 235-253. https://doi.org/10.3897/zookeys.209.3247
Figure 1 - Web browser view of a scanned page of Henderson's journal displayed side-by-side with transcriptions and annotations using the MediaWiki Proofread Page extension.
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.