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.
3,688
datasets available to search
ShareScore release 0.7.1
Dataset results
3,688 results for “Computer”
Dataset: Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled Cluster Theory for GPU-accelerated Computer Architectures
<p>This dataset collects the data (outputs, coordinate files) for the calculations presented in the manuscript "Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled<br> Cluster Theory for GPU-accelerated Computer Architectures".</p>
BUS-BRA: A Breast Ultrasound Dataset for Assessing Computer-aided Diagnosis Systems
<p>The BUS-BRA Dataset is a publicly available dataset of anonymized breast ultrasound (BUS) images of 1,064 patients that underwent routinary breast studies. The BUS dataset contains biopsy-proven tumor cases and BI-RADS annotations in categories 2, 3, 4, and 5. In addition, the dataset also contains ground truth delineations that divide the BUS images into tumoral and normal regions. A full description of the BUS-BRA dataset can be found in the following article:</p> <p>Wilfrido Gómez-Flores, Maria Julia Gregorio-Calas, and Wagner Coelho de Albuquerque Pereira, "BUS-BRA: A Breast Ultrasound Dataset for Assessing Computer-aided Diagnosis Systems," Medical Physics, vol. 51, pp. 3110-3123, 2024, DOI: 10.1002/mp.16812.</p> <p>Any research originating from its usage is required to cite this paper.</p> <p>The Program of Biomedical Engineering of the Federal University of Rio de Janeiro (PEB/COPPE-UFRJ, Brazil) has a copyright on the data and is the principal distributor of the BUS-BRA Dataset. In addition, the Centro de Investigación y de Estudios Avanzados (Cinvestav, Mexico) is involved in an ongoing effort to develop this dataset to aid research efforts in developing, testing, and evaluating algorithms for breast ultrasound analysis.</p>
Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications
<p>This repository contains the dataset for the study of <a href="https://doi.org/10.1093/gigascience/giad113">computational reproducibility of Jupyter notebooks from biomedical publications</a>. Our focus lies in evaluating the extent of reproducibility of Jupyter notebooks derived from GitHub repositories linked to publications present in the biomedical literature repository, PubMed Central. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.</p> <p><strong>Data Collection and Analysis</strong></p> <p>We use the code for reproducibility of Jupyter notebooks from the study done by <a href="../record/2592524">Pimentel et al., 2019</a> and adapted the code from <a href="https://github.com/fusion-jena/ReproduceMeGit">ReproduceMeGit</a>. We provide code for collecting the publication metadata from PubMed Central using <a href="https://biopython.org/docs/1.76/api/Bio.Entrez.html">NCBI Entrez utilities via Biopython</a>.</p> <p>Our approach involves searching PMC using the esearch function for Jupyter notebooks using the query: ``(ipynb OR jupyter OR ipython) AND github''. We meticulously retrieve data in XML format, capturing essential details about journals and articles. By systematically scanning the entire article, encompassing the abstract, body, data availability statement, and supplementary materials, we extract GitHub links. Additionally, we mine repositories for key information such as dependency declarations found in files like requirements.txt, setup.py, and pipfile. Leveraging the GitHub API, we enrich our data by incorporating repository creation dates, update histories, pushes, and programming languages.</p> <p>All the extracted information is stored in a SQLite database. After collecting and creating the database tables, we ran a pipeline to collect the Jupyter notebooks contained in the GitHub repositories based on the code from Pimentel et al., 2019.</p> <p>Our reproducibility pipeline was started on 27 March 2023.</p> <p><strong>Repository Structure</strong></p> <p>Our repository is organized into two main folders:</p> <ul> <li><strong>archaeology</strong>: This directory hosts scripts designed to download, parse, and extract metadata from PubMed Central publications and associated repositories. There are 24 database tables created which store the information on articles, journals, authors, repositories, notebooks, cells, modules, executions, etc. in the db.sqlite database file.</li> <li><strong>analyses</strong>: Here, you will find notebooks instrumental in the in-depth analysis of data related to our study. The db.sqlite file generated by running the archaelogy folder is stored in the analyses folder for further analysis. The path can however be configured in the config.py file. There are two sets of notebooks: one set (naming pattern N[0-9]*.ipynb) is focused on examining data pertaining to repositories and notebooks, while the other set (PMC[0-9]*.ipynb) is for analyzing data associated with publications in PubMed Central, i.e.\ for plots involving data about articles, journals, publication dates or research fields. The resultant figures from the these notebooks are stored in the 'outputs' folder.</li> <li><strong>MethodsWorkflow</strong>: The MethodsWorkflow file provides a conceptual overview of the workflow used in this study.</li> </ul> <p><strong>Accessing Data and Resources:</strong></p> <ul> <li>All the data generated during the initial study can be accessed at https://doi.org/10.5281/zenodo.6802158</li> <li>For the latest results and re-run data, refer to this link.</li> <li>The comprehensive SQLite database that encapsulates all the study's extracted data is stored in the db.sqlite file.</li> <li>The metadata in xml format extracted from PubMed Central which contains the information about the articles and journal can be accessed in pmc.xml file.</li> </ul> <p><strong>System Requirements:</strong></p> <ul> <li>Centos 7 (Documentation: https://www.centos.org/)</li> <li>Conda 4.9.4 (Installation Guide: https://docs.anaconda.com/anaconda/install/linux/)</li> <li>Python 3.7.6 (Download Link: https://www.python.org/downloads/)</li> <li>GitHub account (Get Started: https://github.com/, Requires GitHub Username and Token)</li> <li>gcc 7.3.0 (Installation Guide: https://gcc.gnu.org/install/)</li> <li>lbzip2 (Command: `conda install -c conda-forge lbzip2')</li> </ul> <p><strong>Running the pipeline:</strong></p> <ul> <li>Clone the computational-reproducibility-pmc repository using Git:<br>git clone https://github.com/fusion-jena/computational-reproducibility-pmc.git<br> </li> <li>Navigate to the computational-reproducibility-pmc directory:<br>cd computational-reproducibility-pmc/computational-reproducibility-pmc</li> <li>Configure environment variables in the config.py file:<br>GITHUB_USERNAME = os.environ.get("JUP_GITHUB_USERNAME", "add your github username here")<br>GITHUB_TOKEN = os.environ.get("JUP_GITHUB_PASSWORD", "add your github token here")</li> <li>Other environment variables can also be set in the config.py file.<br>BASE_DIR = Path(os.environ.get("JUP_BASE_DIR", "./")).expanduser() # Add the path of directory where the GitHub repositories will be saved<br>DB_CONNECTION = os.environ.get("JUP_DB_CONNECTION", "sqlite:///db.sqlite") # Add the path where the database is stored.</li> <li>To set up conda environments for each python versions, upgrade pip, install pipenv, and install the archaeology package in each environment, execute:<br>source conda-setup.sh</li> <li>Change to the archaeology directory<br>cd archaeology</li> <li>Activate conda environment. We used py36 to run the pipeline.<br>conda activate py36</li> <li>Execute the main pipeline script (r0_main.py):<br>python r0_main.py</li> </ul> <p><strong>Running the analysis:</strong></p> <ul> <li>Navigate to the analysis directory.<br>cd analyses</li> <li>Activate conda environment. We use raw38 for the analysis of the metadata collected in the study.<br>conda activate raw38</li> <li>Install the required packages using the requirements.txt file.<br>pip install -r requirements.txt</li> <li>Launch Jupyterlab<br>jupyter lab</li> <li>Refer to the Index.ipynb notebook for the execution order and guidance.</li> </ul> <p><strong>References:</strong></p> <ul> <li>Sheeba Samuel, Daniel Mietchen. (2024). Computational reproducibility of Jupyter notebooks from biomedical publications, https://doi.org/10.1093/gigascience/giad113, GigaScience</li> <li>Sheeba Samuel, Daniel Mietchen. (2022). Computational reproducibility of Jupyter notebooks from biomedical publications, https://arxiv.org/pdf/2209.04308.pdf, CoRR abs/2209.04308</li> <li>Sheeba Samuel, & Daniel Mietchen. (2022). Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6802158</li> </ul> <p> </p>
Dataset for "Computer vision assisted decomposition analysis of atom probe tomography data"
<p>Dataset for the article "Computer vision assisted decomposition analysis of atom probe tomography data". APT measurements were performed by Marcus Hans at Materials Chemistry (RWTH Aachen University) using a CAMECA LEAP 4000X HR. Training data was created by Janis A. Sälker.</p> <p>Content:</p> <p>- 13 (V,Al)N and 3 (Ti,Al)N APT reconstructions (.epos file format) and the corresponding range file (.rrng file format).</p> <p>- Training data (images & masks) for 9 labeled (V,Al)N APT samples (h5 file format). Image data with key "image" of shape (2, number_of_slices, 608, 192), where 2 corresponds to the V- and Al-contribution/channel and 608/192 to the height/width of the images. Masks/labels with key "label" of shape (number_of_slices, 608, 192)</p> <p> </p>
A computer vision pipeline for predicting zoonotic reservoir distribution
<p>Shared here is the complete project repository of code and data supporting the manuscript, "Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision."</p> <p>Note that all project subdirectories (with the exception of the "Data" subdirectory) were compressed into single .zip files and thus need to be decompressed upon download. The "Data" subdirectory, which contains numerous large data files, was stored as a split zip archive to facilitate upload to Zenodo and consists of the files "Data.z01", "Data.z02", "Data.z03", and "Data.zip". These files may need to be decompressed using dedicated archiver software (such as The Unarchiver [https://theunarchiver.com/] on macOS). The "Data" subdirectory should be ~17 GB upon decompression. Note that the entire project repository is ~40 GB when uncompressed.</p>
Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time
<p>Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the "decoder" at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called "Adversarial Domain Adaptation Network" (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by factor analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.</p>
Computational mechanisms underlying latent value updating of unchosen actions
<p><span>Current studies suggest that individuals estimate the value of their choices based on observed feedback. Here, we ask whether individuals also update the value of their unchosen actions, even when the associated feedback remains unknown. </span><span>One hundred and seventy-eight</span><span> individuals completed a multi-armed bandit task, making choices to gain rewards. We found robust evidence suggesting latent value updating of unchosen actions based on the chosen action's outcome. Computational modeling results suggested that this effect is mainly explained by a value updating mechanism whereby individuals integrate the outcome history for choosing an option with that of rejecting the alternative. Properties of the deliberation (i.e., duration/difficulty) did not moderate the latent value updating of unchosen actions, suggesting that memory traces generated during deliberation might take a smaller role in this specific phenomenon than previously thought. We discuss the mechanisms facilitating credit assignment to unchosen actions and their implications for human decision-making.</span></p>
Computing the relative affinity of chlorophylls 𝘢 and 𝘣 to light-harvesting complex II
<p>Simulation data for MCCE and FEP on Pea (PDB: 2BHW) and Spinach (PDB: 1RWT) LHCII. </p> <p>MCCE step2_out files and the coordinates for the refitted spinach structure.</p> <p>Simulation data for FEP input/output files.</p>
Parallel window decoding enables scalable fault tolerant quantum computation
<p>Dataset containing raw data presented in the publication <em>"Parallel window decoding enables scalable fault tolerant quantum computation"</em> as well as the stim circuits used to sample circuit-level noise.</p> <p> </p>
Assessing Computational Notebook Understandability through Code Metrics Analysis
<p>Computational notebooks have become the primary coding environment for data scientists. Despite their popularity, research on the code quality of these notebooks is still in its infancy, and the code shared in these notebooks is often of poor quality. Considering the importance of maintenance and reusability, it is crucial to pay attention to the comprehension of the notebook code and identify the notebook metrics that play a significant role in their comprehension. The level of code comprehension is a qualitative variable closely associated with the user's opinion about the code. Previous studies have typically employed two approaches to measure it. One approach involves using limited questionnaire methods to review a small number of code pieces. Another approach relies solely on metadata, such as the number of likes and user votes for a project in the software repository. In our approach, we enhanced the measurement of the understandability level of notebook code by leveraging user comments within a software repository. As a case study, we started with 248,761 Kaggle Jupyter notebooks introduced in previous studies and their relevant metadata. To identify user comments associated with code comprehension within the notebooks, we utilized a fine-tuned DistillBERT transformer. We established a \emph{user comment based criterion} for measuring code understandability by considering the number of code understandability-related comments, the upvotes on those comments, the total views of the notebook, and the total upvotes received by the notebook. This criterion has proven to be more effective than alternative methods, making it the ground truth for evaluating the code comprehension of our notebook set. In addition, we collected a total of 34 metrics for 10,857 notebooks, categorized as script-based and notebook-based metrics. These metrics were utilized as features in our dataset. Using the Random Forest classifier, our predictive model achieved 85% accuracy in predicting code comprehension levels in computational notebooks, identifying developer expertise and markdown-based metrics as key factors.</p> <p> </p>
A dataset of DFT-computed oxygen vacancy formation energies of metal oxides
<p>This dataset contains DFT-computed oxygen vacancy formation energies of different oxygen sites in over 1000 metal oxides present on the Open Quantum Materials Database (OQMD). Entries in the Data.csv file are indexed using the OQMD entry ID of the pristine structure, and list, alongside the oxygen vacancy formation energy, a number of other properties used as features in machine learning models. An exemplary code allowing to test the performance of a random forest regressor using different sets of features to predict the vacancy formation energy, as a function of training set size, is provided in Test_Models.py. More details on the dataset and machine learning models can be found in: arXiv:2309.01160. </p><p> </p><p><strong>Acknowledgments</strong></p><p>The work was funded by the U.S. Department of Energy under Grant DE-EE0008089. S. G. acknowledges the Air Force Office of Scientific Research for support under Award No. FA9550-18-1-0136 (OQMD database). A. G. acknowledges the Center for Hierarchical Materials Design (ChiMaD) under Award No. 70NANB19H005 (ML models). A. J.A. S.-C. acknowledges the financial support to National Agency for Research and Development (ANID)/DOCTORADO BECAS CHILE/2018 - 56180024. A. J.A. S.-C. and T. L. acknowledge funding from the Toyota Research Institute through the Accelerated Materials Design and Discovery program (ML representations). The data was produced relying on the computing power provided by Quest high performance computing facility at Northwestern University.</p>
Distinct value computations support rapid sequential decisions
<p>This behavioral and modeling data was used and described in the following paper:Mah, A., Schiereck, S.S., Bossio, V., Constantinople, C.M. (2023). Distinct value computations support rapid sequential decisions. Nature Communications.The dataset comprises </p><p>1) Behavioral data for the value-based decision making task in rats, and </p><p>2) computational models fit to the rat behavioral data. </p><p>All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Mah et al., 2023 is available at {https://github.com/constantinoplelab/published/tree/main/rat_behavior. Data was analyzed using Matlab 2023a with the following additional toolboxes:<br>Curve Fitter<br>Optimization<br>Signal Analyzer</p><p> </p><p>Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH111926), an Alfred P. Sloan Fellowship, a Klingenstein-Simons Fellowship in Neuroscience, an NIH Director's New Innovator Award (DP2MH126376), an NSF CAREER Award, R01MH125571, and a McKnight Scholars Award to C.M.C. A.M. was supported by 5T90DA043219 and F31MH130121. A.M. and S.S.S. were supported by 5T32MH019524.</p>
Working memory capacity of crows and monkeys arises from similar neuronal computations
Open the record for dataset details and reuse information.
Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
Open the record for dataset details and reuse information.
Data from: A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis
Open the record for dataset details and reuse information.
Data from: Mapping coastal redwoods (<em>Sequoia sempervirens</em>) across their natural range: An updateable and field-validated distribution map using Sentinel satellite data and cloud computing
Open the record for dataset details and reuse information.
A computational mechanism of cue-stimulus integration for pain in the brain
Open the record for dataset details and reuse information.
Computational simulations show proof-of-concept for optogenetic suppression of ectopic activity in cardiac stem cell therapy
Open the record for dataset details and reuse information.
Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging
Open the record for dataset details and reuse information.
Cophylogeny reconstruction allowing for multiple associations through approximate Bayesian computation
Open the record for dataset details and reuse information.
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.