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zenodo36/100

Nanotyrannus - young T-Rex - Shape- Fix

EDIT 2: Did a new shape fix with less shape deformers than before, with a rectangular bounding box, using only 4 corners at a time to deform it either horizontally or vertically. Only increased the amount of deformer points on the box in order to fix a rotation issue at the tip of the snout. It turned out looking slimmer than the previous model and a whole lot less stretched out of shape and I think it looks much better now. EDIT: With the permission of the original uploader I have made the model free and available for download. The 3D model was originally uploaded in the link below, but I just wanted to see if basically just removing some of the vertical skewing of the piece would make it look better and I think it did: https://sketchfab.com/3d-models/nanotyrannus-lancensis-young-t-rex-7b0967fa27674d959647868686b6717b Besides the shape fix I also scaled it up to about full size, with the limited information I could find on the real skull's size. Source: Objaverse 1.0 / Sketchfab

opencc-byJul 2020View details →

zenodo36/100

Lincoln Bust Fixed

A scan of the bust of Abraham Lincoln located on the first floor of Morris Library at Southern Illinois University Carbondale. I have fixed some of the problems with the original scan using Blender, as a way to learn some Blender skills. Source: Objaverse 1.0 / Sketchfab

opencc-byJun 2022View details →

zenodo36/100

libs-github-api: fix "lines" mistake — GitHub API reports bytes, not lines

Correct mistake in several places, including data file and scripts.

opencc-zeroMay 2015View details →

zenodo36/100

Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples

Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice. This dataset includes the FACS, 10X 3', Parse, 10X Flex, and Hive data and analysis.

opencc-by-4.0Aug 2024View details →

zenodo36/100

Replication package for the paper :The Relationship Between Different Python Argument-Passing Mechanisms and Fixes: An Empirical Study

Abstract: Modern programming languages,  such as Python, have introduced a variety of constructs and syntactical elements to make software development more efficient and concise. Examples include lambda functions, comprehension collections, or mechanisms to facilitate the passing of arguments to a function. While many of such constructs may, in principle, be beneficial for developers, recent studies have shown that certain programming constructs may affect program understanding and even induce more fixes than other changes.  This paper studies the effect of different Python argument-passing mechanisms to investigate their relationship with code proneness to be fixed. Specifically, we study the fix-proneness for what concerns function definitions and invocations. This is done by analyzing the evolutionary history of 200 Python projects, for a total of about 3M functions and 12M call sites. While there are varying effects for what concerns parameter declaration mechanisms, we found evidence that keyword-based argument passing is less defect-prone than positional argument passing, and this is not affected by size-related confounding factors.

opencc-by-4.0Sep 2024View details →

zenodo36/100

Kerr Modes: Phase fixed gravitational QNMs and TTMs

These HDF5 files contain data for gravitational modes of the Kerr geometry.  Each file contains the complex mode frequency, angular separation constant, and spheroidal expansion coefficients along a sequences of solutions parameterized by the dimensionless angular momentum of the Kerr black hole.  For each mode along each sequence, the spheroidal expansion coefficients have been phase fixed using the SL-C phase choice to guarantee that the phase smoothly connects to the spherical limit along each sequence. The spin-weight -2 Quasinormal Modes are contained in the files named KerrQNM_nn.h5, where nn is the 2-digit overtone number.  Each file for 0<=nn<=15 contains all the modes for 2<=l<=16, while the each file for 16<=nn<=32 contains all the modes for 2<=l<=4.  The QNM data was constructed using the methods outlined in Cook & Zalutskiy, Phys. Rev. D 90 (2014) pp. 124021 (DOI: <a href="https://doi.org/10.1103/PhysRevD.90.124021">https://doi.org/10.1103/PhysRevD.90.124021</a>). The spin-weight -2 Left Total-Transmission Modes are contained in the files named KerrTTML_nn.h5, and the spin-weight +2 Right Total-Transmission Modes are contained in the files named KerrTTMR_nn.h5.   The 2-digit overtone number nn is used to designate one of three known TTM families as described in Cook & Lu, Phys. Rev. D 107 (2023) pp. 044043 (DOI: <a href="https://doi.org/10.1103/PhysRevD.107.044043">https://doi.org/10.1103/PhysRevD.107.044043</a>).  Each file contains all the modes for 2<=l<=8. All of the data have been constructed using the <a href="https://github.com/cookgb/KerrModes/releases/">publicly available KerrModes Mathematica paclets</a>. The <a href="http://github.com/cookgb/HDF5KerrModes">publicly available HDF5KerrModes Mathematcia paclet</a> can be used to read the data in all of these files, create plots of the mode frequencies and separation constants, and plots of the spin-weighted spheroidal function associated with the modes. Data in this version (v4) have changed from v3 because of a change in the way that the angular eigenvalues are sorted.  This change causes some solutions along some sequences to have a different index.  When this index changes, this can change the included phase factor which is used to convert the spheroidal expansion coefficients to the SL-Ind phase choice.  Such changes are present in a small number of solutions along Quasinormal Mode sequences with overtones in the range 18<=nn<=32, and along all of the Total-Transmission Mode sequences.  However, no changes have been made to the complex mode frequencies, the angular separation constants, or the stored spheroical expansion coefficients stored in any file, with one exception.  The last 7 expansion coefficients for one solution along the l=8, m=6, n=2 Left Total-Transmission Mode sequence were removed for consistency with the corresponding Right Total-Transmission Mode. Note that the data contained in each column of the original 2 versions of the data (Versions 1 and v2) are different from subsequent versions!  The first 5 columns of data are consistent between versions.  However, beginning in Version v3, 3 new columns were inserted before the expansion coefficients.  These new columns containt the 0-based index of the eigenvalue and the complex phase factor needed to convert the subsequent expansion coefficients from the stored SL-C phase choice to the SL-Ind phase choice.

openodc-byOct 2024View details →

zenodo36/100

Using UV stimuli to evoke prey capture strikes in head-fixed zebrafish larvae

Hunting in larval zebrafish begins with eye convergence and orienting turns, proceeds to approach swims, and ends with the strike, where larvae consume the prey. Here, we describe a protocol to present UV stimuli to zebrafish, which greatly increases the occurrence of hunting initiation and strikes. We also describe how we record and analyze strike behavior in head-fixed larvae. Our goals are to increase the robustness of prey capture, and to allow other labs to implement strike behavioural essay

opencc-by-4.0Nov 2023View details →

zenodo36/100

SPS fixed line experiment - experimental data

Experimental turn-by-turn beam position monitor (BPM) data of an example shot of a kicked beam in the CERN Super Proton Synchrotron from 2018. The set contains data from 4 consecutive BPMs (vertical-horizontal-vertical-horizontal) with about 90 degree phase advance between them (in the respective plane). The vertical beam position data has been rotated to bring the measurement to the location of the first horizontal monitor. Like this, the scaled Poincaré surface of section can be reconstructed.

opencc-by-4.0Aug 2023View details →

zenodo36/100

NANCY SNS-JU Project - Fronthaul network of fixed topology Usage Scenario - Dataset 1

In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected plays the role of ML algorithm-specific data preparation. The dataset contains time-series, collected transmitting a video content through the Italtel "VTU - video streaming and transcoding application", that can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters. The data collected are related to the observation of some of the resources involved in the Usage Scenario: “Fronthaul network of fixed topology – Direct Connectivity”. In the Italtel Italian in-lab testbed, a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided. Two different scenarios were set-up, related to downstream and upstream video flows. The variety of collected features ranges from radio front-end metrics to physical server operating system and network function metrics. The dataset consists of raw network traffic and extracted flow-based data captured in separate files. Each file captured is associated to a 10min video streaming of the “Big Buck Bunny” video. This video was transmitted on two different bands, N3 and N78, with different resolutions, 480p, 720p, 1080p; both in uplink (UL) and in downlink (DL); the type of protocol monitored is “HTTP protocol”; in case of N78 band, data related to the resource usage were also captured, for a total of more that 100 data files. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G/B5G network planning and provisioning. The video streaming dataset includes data directly measured while watching the video on the mobile devices and data directly measured while generating downstream video stream traversing the gNB (i.e., downstream scenario), and vice versa (i.e., upstream scenario). In each experiment, we fixed the location of the UE and the gNB. The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.

opencc-by-4.0Dec 2022View details →

zenodo36/100

Negative Complement of a Set of Vulnerability-Fixing Commits: Supplementary Material

<div> <div>EASE 2024 - Industry track</div> <div>This archive contains accompanying materials for the paper: </div> <div>Rocío Cabrera Lozoya, Antonino Sabetta, Tommaso Aiello. "Negative Complement of a Set of Vulnerability-Fixing Commits: Method and Dataset" </div> <div>submitted to the Industry track at EASE 2024 - https://conf.researchr.org/track/ease-2024/ease-2024-industry</div> <div>It contains the following folders and files:</div> <div>- data</div> <div> - commit_pairs_final.csv : Dataset of 534 commit pairs corresponding to a positiive (security-relevant) commit and a negative sample obtained by the approach described in the paper.</div> <div> - single_positive.csv : Contains a single positive instance (taken from the MSR2019 dataset) which can be used to test the generate_negative_complement.py script.</div> <div>- scripts</div> <div> - generate_negative_complement.py : Takes in a single .java file and obfuscates its developer-defined identifiers.</div> <div> - obfuscate_java_file.py : Generates a negative complement for a security-relevant dataset. The ouput is written to couple_dataset.csv.</div> <div> - requirements.txt : Requirements needed in the virtual environment to successfully run the previous scripts.</div> </div>

opencc-by-4.0Dec 2023View details →

zenodo36/100

MineCPP: Mining Bug Fix Pairs and Their Structures

Modern software repositories serve as valuable sources of information for understanding and addressing software bugs. In this paper, we present MineCPP, a tool designed for large-scale bug-fixing dataset generation, extending the capabilities of a recently proposed approach, namely Minecraft. MineCPP not only captures bug locations and types across multiple programming languages but introduces novel features like offset of a bug in a buggy source file, the sequence of syntactic constructs up to and including the location of the bug, etc. We discuss architectural and operational aspects of MineCPP, and show how it can be used to automatically mine GitHub repositories. A Graphical User Interface (GUI) further enhances user experience by providing interactive visualizations and quantitative analyses, facilitating fine-grained insights about the structure of bug fix pairs. MineCPP serves as a helpful solution for researchers, practitioners, and developers seeking comprehensive bug-fixing datasets and insights into coding practices. Tool demonstration is available at https://youtu.be/ln99irvbADE

opencc-by-4.0Jan 2024View details →

zenodo36/100

VFDelta Vulnerability Fix Dataset

The vulnerability fix dataset of VFDelta contains two datasets used in our study: VFM_2021 and VFM_2023. Refer to our paper for the detail of dataset. We are working on model publishing; please always check for the newer version. <ul> <li>vfm_2021_train_val.parquet</li> <li>vfm_2021_test.parquet</li> <li>vfm_2023_train_val.parquet</li> <li>vfm_2023_test.parquet</li> <li>eval_vfdelta.ipynb</li> </ul> The commit-level dataset provides meta information of "repo_name", "commit_id", "label". train_val file contains "partition" for the split of training and validation set, and test file contains "vfdelta_predict_proba" which is the predict probability of each commit by VFDelta.  To evaluate VFDelta's performance on vfm_2021 and vfm_2023, run <code>eval_vfdelta.ipynb</code>, use the column vfdelta_predict_proba as our prediction result. You can also use them to compare the performance of your prediction results with VFDelta's.

openmit-licenseMar 2024View details →

zenodo36/100

Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.

The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example: <ul> <li> wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854). </li> <li> wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the x-coordinate, one for spread in the y-coordinate. </li> </ul>   As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 180, 150, 120, 90, 60, 30, 0 min ago.  Wavelengths should be in the order indicated by the subdirectory name.  The numpy array itself should contain normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().   To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().   To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to z-score units).

opencc-by-4.0Sep 2024View details →

zenodo36/100

Sample data and fixed files for running 'fv3aerorad' in GSI

The tarball, "fv3aerorad.tar.gz", contains the sample data and fixed files to run the regression test, 'fv3aerorad', in community Gridpoint Statistical Interpolation (GSI; https://doi.org/10.5281/zenodo.5735601).

opencc-by-4.0Nov 2021View details →

zenodo36/100

Data and code for: Fixed effects or random effects in statistical models? fewer than five levels of a grouping factor

This is code (and simulated data from that code) to assess how sample size and the numbers of levels of random effects influence parameter estimates of fixed effects in linear mixed-effects models.

opencc-by-4.0Apr 2021View details →

dryad36/100

Data From: Fixed depth Hamiltonian simulation via Cartan decomposition

Simulating quantum dynamics on classical computers is challenging for large systems due to the significant memory requirements. Simulation on quantum computers is a promising alternative, but fully optimizing quantum circuits to minimize limited quantum resources remains an open problem. We tackle this problem presenting a constructive algorithm, based on Cartan decomposition of the Lie algebra generated by the Hamiltonian, that generates quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model, where a O(n^2)-gate circuits naturally emerge. Compared to product formulas with significantly larger gate counts, our algorithm drastically improves simulation precision. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.

opencc-zeroJul 2022View details →

zenodo36/100

Code and data for N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling"

Simulation code and data used in N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling."

opencc-by-4.0Oct 2022View details →

zenodo36/100

Famous Vehicles Fixed FREE

Introducing the Famous Vehicles Fixed made by ACBRadio, the programs used to make this object are as follows: Blender 2.93 & G.I.M.P 2.10.4…The 'Textures' inclued in this object are the following: Diffuse Solid Color…One single image of a 256 bit color palette…free of Charge Backdrop image by Micah Boerma over on https://www.pexels.com 360 backdrop's total poly count: Triangles: 960 Vertices: 482 Note: Have receipt to represent ownership...major change, interiors removed :D Source: Objaverse 1.0 / Sketchfab

opencc-byAug 2021View details →

zenodo36/100

Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"

To cite it: <code>@misc{bfdataset,   author = {Martin Monperrus},   title = {Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"},   year = 2017,   doi = {10.5281/zenodo.1004734},   url = {https://doi.org/10.5281/zenodo.1004734} }</code> <pre>  </pre>

openother-openOct 2017View details →

zenodo36/100

Soft tissue changes in extraction versus non-extraction treatment with fixed orthodontic appliances: a systematic review and meta-analysis

Export of the dataset used in Stata to conduct all meta-analyses reported in the published paper.

opencc-by-4.0Nov 2017View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record