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.
334
datasets available to search
ShareScore release 0.7.1
Dataset results
334 results for “python”
Raw Python Code Corpus
<p>A raw code corpus for the Python programming language i.e., includes only the Python source files of each repository without any preprocessing.<br> The corpus was used to generate the Python training, validation, testing, and BPE encoding sets for the experiments performed in the paper: Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.</p>
Data and Python script for article "A text mining analysis of the climate change literature in industrial ecology'
<p>The data and Python script are part of the forum article "A text mining analysis of the climate change literature in industrial ecology" authored by Dayeen, F.R., Sharma, A.S., and Derrible, S., and published in the <em>Journal of Industrial Ecology</em> in 2020.</p> <p>The Python script and instructions are included in the LiTCoF_v1.00-py.zip file. The original data is available in two formats: .csv and .pkl.</p> <p>Updates of the script will be posted at https://github.com/csunlab/LiTCoF and at https://csun.uic.edu/codes/LiTCoF.html. The data is also available at https://csun.uic.edu/datasets.html#AbstractsIE.</p> <p>Feel free to contact any of the authors for information and questions about the data and code.</p>
Data Files for PyGDSM: Python interface to the Global Diffuse Sky Model
<p>HDF5 data files for PyGDSM: Python interface to Global Diffuse Sky Models</p> <p>PyGDSM is a Python interface for the Global Diffuse Sky Models (GDSM ascl:1011.010). GDSM are models of diffuse galactic radio emission, constructed from a variety of all-sky surveys spanning the radio band (e.g. Haslam and WMAP). PyGDSM uses the Global Sky Model (GSM2008) of <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2966.2008.13376.x/abstract">Oliveira-Costa et. al., (2008)</a>, <a href="http://arxiv.org/abs/1605.04920">Zheng et. al., (2016)</a> model GSM2016, and <a href="https://lda10g.alliance.unm.edu/LWA1LowFrequencySkySurvey/">LWA1 Low Frequency Sky Model</a> (LFSM). The PyGDSM module provides visualization utilities, file output in FITS format, and the ability to generate observed skies for a given location and date. PyGDSM requires <a href="https://healpy.readthedocs.org/en/latest/">Healpy</a>, PyEphem (ascl:1112.014), and AstroPy (ascl:1304.002).</p>
Dataset for the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems"
<p>This dataset contains additional material related to the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems". The article is available at <a href="https://doi.org/10.1063/1.5144298">https://doi.org/10.1063/1.5144298</a> (open access).<br> <br> Note that the current version of the dataset is not complete. The complete dataset will be uploaded as soon as possible.</p>
Community Docker Hub images and the third-party (JavaScript, Python and Ruby) packages installed in them
<p>This dataset comes with the replication package provided for a study that we carried out on third-party JavaScript, Python and Ruby packages installed in DockerHub images.</p> <p>The replication package can be found in: <a href="https://github.com/neglectos/3dPartyPackages_Docker">https://github.com/neglectos/3dPartyPackages_Docker</a>/</p>
Polish is quantitatively different on quartzite flakes used on different worked materials [Python analysis]
<p>This upload includes the following files related to the Python analysis:<br> 1. Raw data as a XLSX table (processing-quartzite-final-2020-04-29.xlsx) is the output from R Script #1 (see <a href="https://doi.org/10.5281/zenodo.3979139">https://doi.org/10.5281/zenodo.3979139</a>), even though the filename is slightly different.</p> <p>Plus, for each analysis (full and restricted datasets), included in the corresponding ZIP archive:<br> 2. Jupyter notebooks of the analysis (Classification_RandSplitFeature_Revision_VXX.ipynb) rendered to HTML file (Classification_RandSplitFeature_Revision_VXX.html)<br> 3. Dataframe including the artificially filled datapoints<br> 4. Output of the analysis as PDF:<br> • Confusion matrices ("CM")<br> • Decision trees on selected features ("DecisionTreeSel")<br> • Balanced accuracy vs. maximum depth ("depth")<br> • Mutual information ("MI")<br> • Pairplots of selected features ("pairplot")<br> • Performance on training sets ("performance")</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Python code for Monte Carlo Simulation of dynein stepping
<p>This project is described in: Three-color single-molecule imaging reveals conformational dynamics of dynein undergoing motility (2020) and can be found on BioRxiv: (link to follow)</p> <p>Here, we provide the custom python code that is used simulate the stepping of dynein based on experimental data. Details on how the code works are given in the script itself. Moreover, we provided a pdf, which shows plots of all the input data.</p> <p>There are three different python scripts:</p> <ul> <li>Monte-Carlo-simulation_Dynein-stepping.py</li> <li>Monte-Carlo-simulation_Dynein-stepping_flexible-ring-position.py</li> <li>Monte-Carlo-simulation_Dynein-stepping_fixed-ring-angle.py</li> </ul> <p>And six folders with different experimental input data:</p> <ol> <li>Experimental-data-to-run-simulation_MT</li> <li>Experimental-data-to-run-simulation_MT_MTBD-distance-independent-angle</li> <li>Experimental-data-to-run-simulation_MT_no-forward-bias</li> <li>Experimental-data-to-run-simulation_MT_no-leading-trailing</li> <li>Experimental-data-to-run-simulation_MT_no-left-right</li> <li>Experimental-data-to-run-simulation_MT_no-stepping-bias</li> </ol> <p>The first python script can be used to generate the stepping movies (Supplementary Movies 3-10). The second python script is used to generate stepping traces and all other plots. The last python script is a special version of number one and two as it simulates the stepping of dynein for a fixed stalk-microtubule angle. It can generate stepping movies as well as stepping traces and all other plots.</p> <p>In order to simulate stepping of dynein for a wild-type condition, the first experimental dataset should be used as input (this dataset is also used for the fixed angle simulation). The other five datasets are used to simulate stepping of dynein when specific rules are ignored:</p> <ul> <li>for an on-axis distance-dependent bias to take more forward than backward steps (dataset #3),</li> <li>a distance-dependent bias to close the gap between the motor domains along the on- and off-axis when taking a step (dataset #4 and 5, respectively), </li> <li>a higher probability for the trailing domain instead of the leading domain to take the next step (dataset #6), and </li> <li>the relative movement between AAA ring and MTBD (for fixed angle see comments above and for MTBD on-axis distance independent angle changes dataset #2)</li> </ul>
Pythons in the Eocene of Europe reveal a much older divergence of the group in sympatry with boas
<p class="BodyA">Extant large constrictors, pythons and boas, have a wholly allopatric distribution that has been interpreted largely in terms of vicariance in Gondwana. Here we describe a stem pythonid based on complete skeletons from the early-middle Eocene of Messel, Germany. The new species is close in age to the divergence of Pythonidae from North American <i>Loxocemus </i>and corroborates a Laurasian origin and dispersal of pythons. Remarkably, it existed in sympatry with the stem boid <i>Eoconstrictor</i>. These occurrences demonstrate that neither dispersal limitation nor strong competitive interactions were decisive in structuring biogeographic patterns early in the history of large, hyper-macrostomatan constrictors and exemplify the synergy between phylogenomic and paleontological approaches in reconstructing past distributions.</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p> <p> </p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project (v1.12.0)
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</p>
Interaction data for the Axelrod Python Project (v1.13.0)
<p>This contains interaction data for the Axelrod Python project tournaments. Results are available here: http://axelrod-tournament.readthedocs.org/</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.