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42 results for “corruption”
QUIQI II Dataset - Statistical analyses of motion-corrupted MRI relaxometry data
<p>QUIQI II package includes supporting material for the scientific article by Corbin et al. entitled ‘Statistical analyses of motion-corrupted MRI relaxometry data’.</p> <p>The complete support package for the QUIQI method includes:</p> <ol> <li>A copy of the original analysis code used to compile the results presented in the original scientific publication (doi: 10.5281/zenodo.7612032) </li> <li>A subset of the data used in the original publication for computation of the results. This data also includes a set of analysis results obtained by running the code described in 1. on the provided data.</li> </ol> <p><br> The combination of 1. and 2. allows users to replicate the computation of the provided analysis results.<br> <strong>The material provided here only concerns part 2. of the QUIQI support package described above - subset of the data used in the original publication.</strong></p>
Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience
<p>Dataset and matlab analysis scripts for article: Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience</p>
Supplementary Data to *Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data*
<p>Supplementary code and data to <strong>Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_rad, of which this archive is a snapshot.</p> <p> </p>
MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions
<p><strong>Abstract: </strong>The integration of neural-network-based systems into clinical practice is limited by challenges related to domain generalization and robustness. The computer vision community established benchmarks such as ImageNet-C as a fundamental prerequisite to measure progress towards those challenges. Similar datasets are largely absent in the medical imaging community which lacks a comprehensive benchmark that spans across imaging modalities and applications. To address this gap, we create and open-source MedMNIST-C, a benchmark dataset based on the MedMNIST+ collection, covering 12 datasets and 9 imaging modalities. We simulate task and modality-specific image corruptions of varying severity to comprehensively evaluate the robustness of established algorithms against real-world artifacts and distribution shifts. We further provide quantitative evidence that our simple-to-use artificial corruptions allow for highly performant, lightweight data augmentation to enhance model robustness. Unlike traditional, generic augmentation strategies, our approach leverages domain knowledge, exhibiting significantly higher robustness when compared to widely adopted methods. By introducing MedMNIST-C and open-sourcing the corresponding library allowing for targeted data augmentations, we contribute to the development of increasingly robust methods tailored to the challenges of medical imaging. The code is available at <a href="https://github.com/francescodisalvo05/medmnistc-api">github.com/francescodisalvo05/medmnistc-api</a>.</p> <blockquote> <p>This work has been accepted at the Workshop on Advancing Data Solutions in Medical Imaging AI @ MICCAI 2024 [<a href="https://arxiv.org/pdf/2406.17536" target="_blank" rel="noopener">preprint</a>].</p> </blockquote> <p><strong>Note: </strong>Due to space constraints, we have uploaded all datasets except TissueMNIST-C. However, it can be reproduced via our APIs. </p> <p><strong>Usage: </strong>We recommend using the demo code and tutorials available on our GitHub <a href="https://github.com/francescodisalvo05/medmnistc-api">repository</a>.</p> <p><strong>Citation: </strong>If you find this work useful, please consider citing us:</p> <blockquote> <pre>@article{disalvo2024medmnist, title={MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions}, author={Di Salvo, Francesco and Doerrich, Sebastian and Ledig, Christian}, journal={arXiv preprint arXiv:2406.17536}, year={2024} }</pre> </blockquote> <p><strong>Disclaimer: </strong>This repository is inspired by MedMNIST APIs and the ImageNet-C repository. Thus, please also consider citing <a href="https://www.nature.com/articles/s41597-022-01721-8" rel="nofollow">MedMNIST</a>, the respective source datasets (described <a href="https://medmnist.com/" rel="nofollow">here</a>), and <a href="https://arxiv.org/abs/1903.12261" rel="nofollow">ImageNet-C</a>.</p>
3D motion corrupted PET/MRI phantom raw data
<p>The measurements were all performed on a PET/MRI system from Siemens Healthineers AG (Biograph mMR). An MR-compatible robotic system was used to generate rigid movements of a head-like phantom (translational and more comlpex motion patterns). </p> <p> </p> <p><strong>A README.md is available for further information regarding the dataset.</strong></p>
Data from Commission for the Prevention of Corruption of the Republic of Slovenia: public-to-private transactions
<p>Public spending is often a contentious subject because different political parties have different agendas as to what should be the current national priorities. Of course, the same is true for the public in general. It is thus of interest to determine whether public spending is indeed as biased and capricious as it is often perceived, or whether there nevertheless exist some fundamental principles that guide it. We use data from the Commission for the Prevention of Corruption of the Republic of Slovenia, detailing every transfer of public money to the private sector from January 2003 to May 2020. During this time Slovenia has done business with no less than 248,989 companies. We find that the cumulative distribution of money received per company can be reasonably well explained by means of a power-law or a log-normal fit. We also show evidence for the first-mover advantage, and determine that the attachment rate of public spending to companies over time is roughly linear. These results indicate that Slovenian public spending is to a large extent guided by self organizing principles that, against all odds, go beyond nefarious interests and lobbying.</p>
Data from Commission for the Prevention of Corruption of the Republic of Slovenia: public-to-private transactions
Open the record for dataset details and reuse information.
Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms
<p><strong>Overview </strong></p> <p>This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session.</p> <p> </p> <p><strong>Database</strong></p> <p>The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: <a href="https://pubmed.ncbi.nlm.nih.gov/30094756/">https://pubmed.ncbi.nlm.nih.gov/30094756/</a></p> <p>In addition, PPG signals corrupted by the noise generator at different levels are also included in the database.</p> <p> </p> <p><strong>Realistic noise generator</strong></p> <p>Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper.</p> <p> </p> <p><strong>Data Files</strong></p> <p>The reference PPG signal can be found in <em>Datasets\GoodSignals\PPG</em> and the simultaneously acquired ECG in <em>Datasets\GoodSignals\ECG</em>. The folder <em>Datasets\NoisySignals</em> contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation).</p> <p>Name convention: <em>ppg_stdx_y</em> denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x.</p> <p><em>Datasets\BPMs</em> contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s.</p> <p><strong>Code</strong></p> <p>The folder <em>Code </em>contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator.</p> <p><strong>When referencing this material, please cite:</strong></p> <p>Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 2725. <a href="https://doi.org/10.3390/s21082725">https://doi.org/10.3390/s21082725</a></p>
Old lady sculpture - Corrupted scan
Scanned by iPhone 8+ Processed by RealityScan https://www.capturingreality.com/ Source: Objaverse 1.0 / Sketchfab
Corrupted Stonehenge Ring
Ring of stones imitating the Stonehenge The stones are based off the actual stones used to make the Stonehenge Corrupted with purple bile and energy If you use in a project, please email me and let me know, I'd loved to see what you've done! Source: Objaverse 1.0 / Sketchfab
Corruption_and_intrapreneurship Dataset
<p><span>This dataset contains the same 2 files published in figshare.com but in more-opened formats (csv separated with commas-decimals are points, for the data; and rtf for the text) to facilitate access. </span></p> <p><span> </span></p> <p><span>In the csv you can find the data, and in the rtf you have the description of variables, which are the columns of the data file, and the lines of code for Sintax to run all analyses, both those included and those just mentioned, like for example, the robustness tests. </span></p>
Dataset of 'How Everyday Counterfeit Behavior that Disrupts Self Authenticity May Lead to Corruption Tendencies'
<p>Dataset and JASP Output of 'How Everyday Counterfeit Behavior that Disrupts Self Authenticity May Lead to Corruption Tendencies' (Abraham et al., 2021).</p> <p> </p>
CORRUPTED FILES
<p>Feature fitting code and spectra of olivine samples used in paper "<i>Signal to Noise Ratio and Spectral Sampling Constraints on Olivine Detection and Compositional Determination in the Intermediate Infrared Region: Applications in Planetary Sciences</i>"</p>
Data for "Corruption Risk in Contracting Markets: A Network Science Perspective"
<p>EU public procurement data, scored for corruption risk. Includes deduped issuers and winners. Includes data dictionary. For more information contact: johanneswachs@gmail.com</p>
Anti-Corruption Behavior Index Indonesia 2022-2024
<div> <div> <div> <p>The graph shows Indonesia’s Anti-Corruption Behavior Index for the period 2022-2024, based on data from the Central Statistics Agency (BPS). Here are some key points in the data:</p> <p>• In 2022, urban areas (blue line) had a higher index (~3.96) compared to rural areas (~3.88).</p> <p>• In 2023, the anti-corruption index in both regions did not change significantly, with the rural index remaining stable, while the urban area slightly decreased.</p> <p>• In 2024, there was a sharp decline in both regions, especially in rural areas where the index fell further than urban areas.</p> <p>Overall, the trend in both regions shows a decline in the anti-corruption behavior index from 2022 to 2024, with a more drastic decline in rural areas.</p> </div> </div> </div>
Replication package for: "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty"
<p>Package to the following paper:</p> <p>Leib, M; Köbis, N; Rilke, R M; Hagens, M; Irlenbusch, B (2023) Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty</p>
Data from: Social capital predicts corruption risk in towns
Corruption is a social plague: gains accrue to small groups, while its costs are borne by everyone. Significant variation in its level between and within countries suggests a relationship between social structure and the prevalence of corruption, yet, large-scale empirical studies thereof have been missing due to lack of data. In this paper, we relate the structural characteristics of social capital of settlements with corruption in their local governments. Using datasets from Hungary, we quantify corruption risk by suppressed competition and lack of transparency in the settlement's awarded public contracts. We characterize social capital using social network data from a popular online platform. Controlling for social, economic and political factors, we find that settlements with fragmented social networks, indicating an excess of bonding social capital has higher corruption risk, and settlements with more diverse external connectivity, suggesting a surplus of bridging social capital is less exposed to corruption. We interpret fragmentation as fostering in-group favouritism and conformity, which increase corruption, while diversity facilitates impartiality in public life and stifles corruption.
Replication package for: "Corruption and firm growth: evidence from around the world"
<p>Guriev, S., Fisman, R., Ioramashvili, C., & Plekhanov, A. (2023). Corruption and firm growth: evidence from around the world. The Economic Journal.</p>
Replication package for: Convicting Corrupt Officials: Evidence from Randomly Assigned Cases
<p>This package contains replication files for "Convicting Corrupt Officials: Evidence from Randomly Assigned Cases", by Sebastian Axbard, to be published in the <em>Review of Economic Studies</em>. The package contains all neccesary preparation and analysis code as well as analysis data files. The README file describes how to get access to the part of the raw data that is proprietary. </p>
Why support International redistribution? Corruption and public support for aid in the eurozone: DATA
<p>What factors explain public support for international redistribution? While the European Union has sent billions of taxpayers' money to over indebted euro countries in an attempt to avoid an economic collapse, these transfers have encountered fierce resistance among both donor and recipient constituents. However, we know surprisingly little about why citizens support or oppose redistribution within the EU. This paper suggests that domestic levels of corruption and institutional quality may be one of the most important explanations for the great variation in public support for financial assistance, bailouts and aid. Using recent European Elections Survey data merged with data on regional level quality of government, we show that the effects of institutional quality are consistently stronger than macro-economic factors, including economic development, inequality or levels of public debt. We find strong evidence that citizens in low corrupt contexts are more likely to support financial assistance to fellow member states. The results have implications for future challenges in securing public support for EU economic integration as well as for our understanding of how and why corruption undermines society's collective action capacity.</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.