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130 results for “BITs”

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zenodo52/100

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 8-bit Sub-Volumes

This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of each dataset is 500x1000x1000. Below is a summary of the pixel-sizes and associated datasets on Zenodo. <blockquote> Key: <ul> <li>160695 = 0.3125 Micron = https://zenodo.org/records/13327692</li> <li>169066 = 0.8125 Micron = https://zenodo.org/records/13327682</li> <li>169067 = 1.625 Micron = https://zenodo.org/records/13327651</li> <li>169068 = 2.6 Micron = https://zenodo.org/records/12206815</li> </ul> </blockquote> The purpose of this dataset is to provide an easy to download sub-volumes of the larger (>50GB) datasets in the above Zenodo entries. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#

opencc-by-4.0Nov 2024View details →

zenodo48/100

Dataset of Measurement and conceptualization of maternal PTSD following childbirth: Psychometric properties of the City Birth Trauma Scale – French version (City BiTS-F)

The City Birth Trauma Scale (City BiTS-F) was developed to assess posttraumatic stress disorder following childbirth (PTSD-FC), based on the PTSD criteria of the DSM-5. Recent studies investigating the latent factor structure of PTSD-FC symptoms in women reported mixed results. Given that no validated French questionnaire exists to measure PTSD-FC symptoms, this study first aimed to validate the French version of the CBTS (City BiTS-F). Second, it aims to establish the latent factor structure of PTSD-FC. This dataset contains data on the mental health (i.e., PTSD-CB, depression, anxiety) of 541 mothers who gave birth during the last 12 months. Sociodemegraphic data such as maternal age, marital status, educational level, parity, gravidity, weeks of gestation, type of delivery, history of traumatic childbirth, or history of traumatic event is available.   This dataset is related to: Sandoz, V., Hingray, C., Stuijfzand, S., Lacroix, A., El Hage, W., & Horsch, A. (2022). Measurement and conceptualization of maternal PTSD following childbirth: Psychometric properties of the City Birth Trauma Scale—French Version (City BiTS-F). Psychological Trauma: Theory, Research, Practice, and Policy, 14(4), 696–704. <a href="https://psycnet.apa.org/doi/10.1037/tra0001068">https://doi.org/10.1037/tra0001068</a>

opencc-by-4.0Jan 2021View details →

OpenNeuro44/100

EEG: Continuous gameplay of an 8-bit style video game

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →

zenodo44/100

Distinguishing between high entropy bit streams

This dataset contains the curated files, classified by type and extension so that other researchers can compute their features and replicate outcomes.    A total of 5 datasets (i.e., one according to each file size denoted as 64, 128, 256, 512, and 1024) each one consisting of exactly 50% encrypted and 50% compressed files. -The encryption algorithms used to generate the files were: AES(128 / 192 / 256) and Camelia(128 / 192 / 256   -In the case of compressed files: ZIP RAR BZIP2 GZIP   -Different source files were considered to generate the encrypted and compressed files.  COCO Dataset (http://cocodataset.org/home)  Microsoft Research (https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/) ArXiv (https://arxiv.org/) Project Gutenberg (https://www.gutenberg.org/) Several classical music symphonies in MP3 format  YouTube-8M dataset Binaries extracted from system32 in Win10 x64 and sbin from Ubuntu 16.04

opencc-by-4.0Oct 2021View details →

zenodo44/100

Data for Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX

Dataset for M Kloewer, S Hatfield, M Croci, PD Dueben and TN Palmer, 2021. Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16, in review. This dataset contains data from simulations with <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> with varying number formats and with or without a compensated time integration. All other parameters are shared between simulations. All .tar.gz are packed folders of the same name that contain netCDF files presenting velocities u,v, sea surface height eta, and tracer sst (sea surface temperature) <ul> <li>run0002. Float16 simulation with compensated summation in the time integration.</li> <li>run0003. Float16 simulation without compensated summation in the time integration.</li> <li>run0004. Float64 reference simulation (without compensated summation in the time integration).</li> <li>run0005. Float16/32 mixed-precision simulation. No compensated time integration.</li> </ul> Additionally, parameter.txt in each run summarizes all model parameters and progress.txt was created to monitor the progress of the data output during simulation. The file benchmarking.jld2 stores data for the benchmarking of the different runs as Julia's <a href="https://github.com/JuliaIO/JLD2.jl">JLD2 format</a> (a subset of HDF5). This dataset was created using the Isambard UK National Tier-2 HPC Service operated by GW4 and the UK Met Office, and funded by the Engineering and Physical Sciences Research Council EPSRC. For more details, see <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> or the preprint <a href="http://doi.org/10.1002/essoar.10507472.2">Kloewer et al, 2021</a>.

opencc-by-4.0Nov 2021View details →

zenodo44/100

Infrared Video of Bone Drilling - Supplementary material for article "Thermal Evaluation of Bone Drilling: Assessing Drill Bits and Sequential Drilling"

Video 1 shows sequential bone drilling with 5 drill bits (⌀2.0 mm, ⌀2.5 mm, ⌀3.2 mm, ⌀3.7 mm, and ⌀4.1 mm) used in series following the manufacturer's recommended spindle speeds. Video 2 shows bone drilling with a single drill bit (⌀2.0 mm) with a spindle speed of 1500 rpm. These videos are supplementary to the article, "Thermal Evaluation of Bone Drilling: Assessing Drill Bits and Sequential Drilling" published in the journal Bioengineering.

opencc-by-4.0Aug 2024View details →

zenodo40/100

Reproduction Package (VirtualBox Image) for the POPL 2024 Article `Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations`

This is the artifact for the ACM PACMPL article Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations. We provide our artifact as an easy-to-use VirtualBox image, which contains the benchmarks, our tools for bit-vector synthesis, and the scripts for generating the results showcased in the paper.

opencc-by-4.0Nov 2023View details →

zenodo40/100

Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.

The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.It consists of the following layers:<ul><li>conv1: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li>relu1: Rectified Linear Unit (3@28x28).</li><li>max1: Subsampling buy max pooling (3@14x14).</li><li>conv2: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li>relu2: Rectified Linear Unit (6@14x14).</li><li>max2: Subsampling buy max pooling (6@7x7).</li><li>fc1: Fully connected (294, 147)</li><li>fc2: Fully connected (147, 10)</li></ul>The fault hypotheses for this work include the occurrence of:<ul><li>S0/S1: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li>BF: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul>In the memory cells containing all the parameters of the CNN:  <ul><li>w: weights (float32)</li><li>b: biases (float32)</li></ul>All the images (10000) from the MNIST dataset have been used as workload.The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.The parity check matrices (H = P I) that define these ECCs are:<ul><li>SEC(32, 26) (Hamming) under a classic policy (see methodology below):</li></ul>        11010010001000011101101000 100000        10101001000100011011010100 010000        01100100100010010110110010 001000        00011100010001001110001101 000100        00000011110000100001111011 000010        00000000001111100000000111 000001<ul><li>SEC(23, 18) (Hamming) under a conservative policy (see methodology below):</li></ul>        111100001111000000 10000        110011101000111000 01000        101011010100100110 00100        010110110010010101 00010        001101110001001011 00001<ul><li>SEC(13, 9) (Hamming) under an aggressive policy (see methodology below):</li></ul>        110111000 1000        101100110 0100        011010101 0010        111001011 0001<ul><li>DEC(32, 21) (low redundancy and reduced overhead DEC) under a classic policy (see methodology below):</li></ul>        111000011001010010000 10000000000        110110000011101000000 01000000000        101011000110000010001 00100000000        100101101000110001000 00010000000        011010101100100000100 00001000000        010101010100001001010 00000100000        001100110010010100100 00000010000        000011110001000110010 00000001000        000000001111001101001 00000000100        000000000000111100111 00000000010        000000000000000011111 00000000001<ul><li>DEC(28, 18) (low redundancy and reduced overhead DEC) under a conservative policy (see methodology below):</li></ul>        111111000000000000 1000000000        110100111100000000 0100000000        110000100011110000 0010000000        001110010011001100 0001000000        101100001010101010 0000100000        010001001101010110 0000010000        001011000101101001 0000001000        101000011000110101 0000000100        010001110000011011 0000000010        000010100110000111 0000000001<ul><li>DEC(17, 9) (low redundancy and reduced overhead DEC) under an aggressive policy (see methodology below):</li></ul>        111110000 10000000        111001100 01000000        110101010 00100000        101010110 00010000        101101001 00001000        100110101 00000100        100011011 00000010        110000111 00000001This dataset contains the raw data obtained from:<ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li>no_ecc folder: Results obtained for the original (not protected) version of the CNN.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li>locating_sensitive_bits folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li>sec/classic, sec/conservative, and sec/aggressive folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li>dec/classic, dec/conservative, and dec/aggressive folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is golden_run.csv file.To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:<ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul>The analysis of the obtained results may help in determining which bits can be used to store an ECC:<ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul>Accordingly, three different policies have been identified for deploying an ECC using these bits:<ul><li>Classic policy: The ECC protects as much bits as possible.</li><li>Conservative policy: The ECC protects all those bits that may affect the prediction of the network.</li><li>Aggressive policy: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.Each experiment consisted in:<ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li>IMGID: Integer number identifying the considered image (1-9999).</li><li>TENSORID: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li>ELEMID: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li>MASK: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li>FAULT: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li>SOFTMAX: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li>PRED: Integer number representing the category predicted for the processed image.</li><li>LABEL: integer number representing the actual category for the processed image.</li></ul>

opencc-by-4.0Nov 2023View details →

zenodo40/100

Dataset: Bit Origin Ltd (BTOG) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

zenodo40/100

Dataset: Bit Digital, Inc. (BTBT) Stock Performance

opencc-zeroJun 2024View details →

zenodo40/100

Dataset: Global X Blockchain & Bitcoin Strategy ETF (BITS) Stock Performance

opencc-zeroJun 2024View details →

zenodo40/100

BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 10. Graphic containing the results for 1000 bits

The distribution obtained depending on the length of the input string shows that time depends on the input length, but for lengths even closer together, the times are also close (this can be seen in Figure 8 for 20 bits inputs). Time does not change so much depending on which of the 14 different 16th degree primitive polynomials has been used.

opencc-by-4.0Aug 2016View details →

zenodo40/100

BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 9. Graphic containing the results for 1000 bits

The next two graphics show the obtained results from the execution of the main program for each of the 14 degrees, 16th primitive polynomials for three different situations depending on the lengths of the entrance data polynomial. The lengths of the input polynomials were 20. 30. 40, 50, 100 and 1000 bits. The maximum number of sequences is 216-1(Solomon, 1967).

opencc-by-4.0Aug 2016View details →

zenodo40/100

BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 8. Graphic containing the results for 20 bits

opencc-by-4.0Aug 2016View details →

zenodo40/100

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data

In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data.

opencc-by-4.0Apr 2018View details →

zenodo40/100

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 14. Symbol 8 regeneration

As we can see, here we were able to recover the lost middle portion of character ‘A’ using our genetic algorithm. If we can apply some noise filtering technique, the result would be far better. In figure 14, 15 there are another two examples.

opencc-by-4.0Apr 2018View details →

zenodo40/100

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 11. Initial population

In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.

opencc-by-4.0Apr 2018View details →

zenodo40/100

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 10. Block example

First, we have to convert the pixelated small image to the original size image, and then divide these into the blocks as it done in extraction time. Then we have to add or subtract random bits from each of these blocks to equal each block hamming bit to original hamming bit number. And then GA is applied to match these randomness to original image hamming bits in per row and column. In figure 10 there is a 4-block example which regenerates randomly using total block bit count. Now we will try to match their row and column bits of information with the extracted data which is described in later section.

opencc-by-4.0Apr 2018View details →

zenodo40/100

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 6. Methodology

Our method will first resize the image using normal image resizing option provided by operating system or standard library and attach the extra 2 array of data which contains no of 1 in original image in each row and column. Also, the total no of 1 in that image will be present too.

opencc-by-4.0Apr 2018View details →

zenodo40/100

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 16. Failed Generations

For some images there is a chance to get stuck where fitness function maxed, but we are not near to the original image like in figure 16, both row and column fitness matched. But image lost a key portion from original image, in these cases we should increase the weight of fitness function Fx(X), which will solve the issue.

opencc-by-4.0Apr 2018View details →

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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