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RAW DATA form - Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs
<p><strong>Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs</strong></p> <p>Ghada BinKhamis, Agnès Léger, Steven L. Bell, Garreth Prendergast, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>doi: 10.1097/AUD.0000000000000648</strong></p> <p><em>(<strong>Please site above article)</strong></em></p> <p> </p> <p><strong>Description of raw EEG (speech-ABR) data main folder, subfolders, and raw EEG files</strong></p> <p><strong>Folder Information</strong></p> <p><strong>Main folder:</strong></p> <ul> <li>Contains 144 subfolders with raw data from 12 participants</li> </ul> <p><strong>Subfolder names:</strong></p> <ul> <li>Each subfolder starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12</li> </ul> </li> </ul> <ul> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>And finally the background condition <ul> <li>quiet, noise</li> </ul> </li> </ul> <p><strong>Example subfolder names:</strong></p> <ul> <li><em>S01 40ms da noise:</em>Participant number 1, speech-ABRs in response to the 40ms [da] in background noise</li> <li><em>S07 170ms ga quiet:</em>Participant number 7, speech-ABRs in response to the 170ms [ga] in quiet</li> </ul> <p><strong>Each participant has 12 subfolders:</strong></p> <ol> <li>S__ 40ms da quiet </li> <li>S__ 40ms da noise</li> <li>S__ 50ms da quiet</li> <li>S__ 50ms da noise</li> <li>S__ 50ms ba quiet</li> <li>S__ 50ms ba noise</li> <li>S__ 50ms ga quiet</li> <li>S__ 50ms ga noise</li> <li>S__ 170ms da quiet</li> <li>S__ 170ms da noise</li> <li>S__ 170ms ba quiet</li> <li>S__ 170ms ga quiet</li> </ol> <p><strong>Each participant subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong></p> <ul> <li>Each ‘.mat’ file starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12 </li> </ul> </li> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>Next is ‘noise’ if background condition was noise</li> <li>Next is the stimulus polarity <ul> <li>Pos for positive/standard</li> <li>Neg for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally is the recording number for that polarity <ul> <li>R1 is the first recording</li> <li>R2 is the second recording</li> </ul> </li> </ul> <p><strong>Example ‘.mat’ file name:</strong></p> <ul> <li><em>S04 50 ba Neg R1:</em>Participant number 4, speech-ABR in response to the 50ms [ba] in quiet, reversed polarity stimulus, recording number one </li> <li><em>S02 40 da noise Pos R2:</em>Participant number 2, speech-ABR in response to the 40ms [da] in background noise, standard/positive stimulus, recording number two</li> </ul> <p> </p> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example: <ul> <li>xunits: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li>start: ‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li>points: <strong>1800</strong>is the number of sample points for speech-ABRs to the 40ms da, this number will be <strong>2200</strong>for the speech-ABRs to the 50ms stimuli (ba, da, ga), and <strong>4600</strong>for the speech-ABRs to the 170ms stimuli (ba, da, ga)</li> <li>chans: 2 is the number of channels (channel 2 is the ipsilateral channel)</li> <li>frames: 3000 is the number of epochs</li> </ul> </li> <li>The last filed <strong>‘values’</strong>is what contains the raw EEG data (1800x2x3000) <ul> <li><strong>1800 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>3000 </strong>is the number of epochs</li> <li>The field <strong>‘values’ </strong>for speech-ABRs to the 50ms stimuli is <strong>2200x2x3000 </strong>and for speech-ABRs to the 170ms stimuli is <strong>4600x2x3000</strong>.</li> </ul> </li> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> </ul> <p><strong>Data is recorded in Volts and will need to be converted to Micro Volts</strong></p> <p><strong>Date of data collection</strong>: May to November 2016</p>
Supporting files for "Estimating the number of contributors to DNA mixtures provides a novel tool for ecology"
<p>Supporting files for Sethi et al. Methods in Ecology and Evolution, in press. Contents include an alternative likelihood formulation for a DNA mixture estimator, associated R script to implement the likelihood, pcr multiplex conditions for yellow perch, and a supplemental figure.</p>
Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface
<p>The data are used in the paper "Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface" which was submitted to the journal-Water Resources Research. The paper used particles with different sizes, experimental bed is reproduced and the scalar transfer from water to sediment is modelled. We found that The transfer factor changes to turbulent diffusion at the sediment-water interface as the roughness Reynolds number increases. And the turbulent Schmidt number is higher near the SWI and decrease to 0.5 at the SWI, ranging from 0.5 to 1 within the water depth.</p>
Test Instrument Example for ASE'18 paper 'Assessing the Type Annotation Burden" : Corrected Question Numbers
<p>Artifact for ASE'18 paper "Assessing the Type Annotation Burden". This PDF shows an example test instrument (a Qualtrics survey) with all 20 questions (20 code artifacts).</p> <p> </p> <p>A picture of the options in the drop-down box is shown on the last page (note: the order of elements in the drop-down was randomized for each test.</p>
Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA Data Archive
<p>[This repository contains the source data for the workflow presented in the manuscript "<strong>Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA</strong>". The workflow can be found here: http://gitlabscottgroup.med.usherbrooke.ca/gaspard/snakemake_blockbuster ]</p> <p>The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 115 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new tRNA and snoRNA genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distort the perceived architecture of the human transcriptome.</p>
data set related to article Typical numerosity adaptation despite selectively impaired number acuity in dyscalculia
<p>This record contains raw data related to article Typical numerosity adaptation despite selectively impaired number acuity in dyscalculia</p>
Numbers of Single-Cell β-Actins
<p>The dataset is an Excel composed of ten columns representing absolute numbers of single-cell β-actins from cell types of A549 (column A, N<sub>cell</sub> = 14,242), Hep G2 (column B, N<sub>cell</sub> = 35,932), MCF 10A (column C, N<sub>cell</sub> = 16,650), HeLa (column D, N<sub>cell</sub> = 26,151), PC3 (column E, N<sub>cell</sub> = 11,922), SACC-83 (column F, N<sub>cell</sub> = 13,616), CAL 27 (column G, N<sub>cell</sub> = 7,271), CAL 27-LN2 (column H, N<sub>cell</sub> = 6,222), Oral Tumour Patient I (column I, N<sub>cell</sub> = 359) and Oral Tumour Patient II (column J, N<sub>cell</sub> = 175), respectively.</p>
Number of bovine animals tested by age group, reporting country and target group, 2018
<p>The tables contain the number of bovine animals tested by age group, reporting country and target group, 2018</p>
Pangenome graph analysis reveals extensive effector copy-number variation in spinach downy mildew
<p>Data produced for the comparison of six <em>Peronospora effusa</em> isolates. For each isolate, we provide the genome assemblies, gene and repeat annotation, effector clustering, and gene variation. Additionally, we provide the repeat library that was used to annotate the transposable elements for each isolate and the pangenome graph.</p> <p>DOI: https://doi.org/10.1101/2024.05.30.596583 </p>
Prognostic value of a modified pathological staging system for gastric cancer based on the number of retrieved lymph nodes and metastatic lymph node ratio raw data
<p><span>Clinical data from the US Surveillance, Epidemiology, and End Results (SEER) Program from 2010-2015 (https://seer.cancer.gov/) was extracted and analyzed as training set, data from 2016-2017 was adopted as internal validation set. Data from The Cancer Genome Atlas Program (TCGA) (https://portal.gdc.cancer.gov/) and prognosis data from Gastrointestinal surgery Department, Third Affiliated Hospital of Sun Yat-sen University were applied as external validation sets. </span></p> <p><span>Screening criteria for gastric cancer cases were as follow: exclusion of cases with only autopsy or death certificate, cases where initial tumor location was not stomach, patients with stage 0 and stage IV, cases without radical surgery, non-adenocarcinoma cases, death cases within one month after operation, and cases with unknown lymph node information and AJCC TNM stage.</span></p> <p><span>The study analyzed various factors such as age of diagnosis (<50 years, 50-69 years, >69 years), gender, race (white, black, other), AJCC T stage (T1-T4b), AJCC TNM stage (I-III), primary tumor location (stomach body, antrum/pylorus, cardia/fundus, greater gastric recurve, lesser gastric recurve, overlapping area, NOS), Clinical features such as tumor size (≥5cm,<5cm, unknown), tumor grade (I-IV), chemotherapy, radiotherapy, number of lymph nodes retrieved and number of metastases, and lymph node positive rate. The populations of American Indian/Alaskan and Asian/Pacific Islander were classified as "other" due to small sample sizes. Tumor grade was also analyzed, with grades I-IV representing highly differentiated, moderately differentiated, poorly differentiated, and signed-ring cell carcinoma, respectively. Overall survival (OS) is the time from cancer diagnosis to death from any cause, while disease-specific survival (DSS) is the time from cancer diagnosis to death specifically due to the disease.</span></p> <p><strong><span> </span></strong></p>
Effects of Different Momentum Ratios and Reynolds Number in a T-junction with an Upstream Elbow
<p>In accordance with EPSRC funding requirements, this supplementary dataset provides all supporting data used to create figures in the following reference:</p> <p>Wong, Y. H., Lampunio, L., Duan, Y., Eaton, M. D., & Bluck, M. J. (2024). Effects of different momentum ratios and Reynolds number in a T-junction with an upstream elbow. <em>Nuclear Engineering and Design</em>, <em>428</em>. https://doi.org/10.1016/j.nucengdes.2024.113523</p> <div> </div> <p> </p>
Fig. 5 in Methods for collecting large numbers of exuviae from Coptotermes (Blattodea: Rhinotermitidae) termite colonies
Fig. 5. (A) Coptotermes gestroi exuviae (approximately 550); (B) close up of exuviae.
Multisensory Number Channels Derived from Individual Differences
<p>This is the dataset related to the article: "Multisensory Number Channels Derived from Individual Differences"</p> <p>The file contains subjects data for the three experiments</p> <p>sub_ID is the participant's identification number<br><br>The subsequent columns indicate Wfs for each tested numerosity in each experiment.</p> <p>M = Digit to Actions Task<br>F = Digit to Visual Sequence Task<br>D = Visual Dot estimation Task<br><br>E.g. M10 indicates the Wf measured for that subjects with tested number 10 in the Motor condition </p>
The number of HAdV species B and HAdV species C positive cases by age group, and their positivity rates among ARTI cases.
<p><strong><span>The number of HAdV species B and HAdV species C positive cases by age group, and their positivity rates among ARTI cases.</span></strong></p>
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<pre>#A fMRI dataset in response to large number of short natural dynamic facial expression videos<br>Natural facial expressions dataset (NFED),a dataset of functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments.<br>The dataset contains raw data, pre-processed volume data,pre-processed surface data and suface-based analyzed data.<br>To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047<br><br>## Preprocess procedure<br>The MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/volume_pre-process/**<br>Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment.<br>## GLM of main experiment<br>We utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR).<br><br>The regressors in the GLMsingle toolbox mainly include the following categories:<br>1.Experimental Design Matrix: This is constructed based on the experimental design, including experimental conditions, task events, etc., and is used to estimate the BOLD (Blood - Oxygen - Level - Dependent) signal response.<br>2.Data-driven nuisance regressors: The data-driven nuisance regressors used in the GLMdenoise technique. These are identified by analyzing the data itself and are used to remove noise and improve the accuracy of beta estimation.<br>3.Physiological noise regressors: May include indicators of physiological signals such as heart rate and respiration, and are used to correct the influence of physiological noise on the BOLD signal.<br>4.Movement parameter regressors: Usually include head movement parameters, such as translation and rotation, and are used to correct signal changes caused by head movement.<br>5.Polynomial regressors: Polynomial terms used to o characterize the baseline signal level.<br><br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/GLMsingle-main-experiment/matlab/NFED_GLMsingle.m**<br><br>#### retinotopic mapping<br><br>The fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code.<br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_prf.m**<br><br>#### fLoc experiment<br><br>We used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_floc.m**<br><br><br>## Validation<br>### Basic quality control<br>The fundamental quality control suggests that the data displays good quality. To evaluate the quality of the structural data obtained from NFED, we employed four crucial metrics:Coefficient of Joint Variation(CJV), Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio in Grey Matter (SNR_GM), and Signal-to-Noise Ratio in White Matter (SNR_WM). Specifically, the CJV is between white matter(WM) and grey matter(GM).The CNR assesses the relationship between the contrast of GM and WM with the noise present in the image. The SNR assesses the connection between the mean signal measurements and the noise present in the image. The SNR assessment is conducted individually for GM and WM.For quality control of the NFED’s functional scans, we assessed the amount of head motion for each participant and the temporal signal-to-noise ratio (tSNR) of the time-series data, separately.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/T1_Image-quality-metrics/T1data/noiseeval/cal_SNRindex.m**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/FD/FD.py**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/tSNR/tSNR.py**<br>### The visual cortex exhibits reliable BOLD responses to natural facial expression videos stimuli in main experiment.<br>In the main experiment, there were 5 participants who accomplished 10 sessions, each consisting of of 4 training runs and 2 test runs, making a total of 60 runs(40 training and 20 test runs) in all. For each run, 30 (videos)*2 (repetitions) x 3 (seconds) betas were acquired. Z-score normalization was performed on the raw betas at each voxel for each run.The betas were then averaged across stimulus repetitions to generate a vector of betas. In general, 44 runs (40 training runs and 4 test runs)*90 betas were acquired. Hence, the test-retest reliability of responses to these videos in main experiment were evaluated through computing the Pearson correlation between the 90 betas obtained from the even runs and odd runs on each vertex.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/reliability_face/reliability_face.py**<br>### noise celling<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/noise_celling/sub-xx" store the intermediate files required for running the program<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/noise_celling/Noise_Ceiling.py**<br><br>### Correspondence between human brain and DCNN<br>The code are available at https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/dnnbrain/**<br><br>### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brain<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program.<br>**code:https://openneuro.org/datasets/ds005047/derivatives/validation/code/semantic_metadata/**<br><br>## results<br> The results can be viewed at "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual".<br><br>## Whole-brain mapping<br>The whole-brain data mapped to the cerebral cortex as obtained from the technical validation.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/results/show_results_allbrain/Showresults.m**<br><br>## Mannually prepared environment<br>We provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file.<br><br>## stimuli<br>The video stimuli used in the NFED experiment are saved in the "stimuli" folders.<br><br></pre>
Number Plate Number Identification Dataset
<p>This dataset is created for identification of Numbers on the Number Plate/Licence Plate. This dataset is collected from various vehicles in the campus of Bapatla Engineering College, and Bapatla town, Andhra Pradesh, India. The dataset comprises 1700 images captured using the cameras Realme 8i (50MP),IQOO Z9 5G (50MP),Vivo T2X 5G (50MP), and Redmi Note 13 Pro 5G (200MP). The dataset is highly diversified in terms of the angle of capturing, distance, and visibility levels. Hence, it may be used to validate the models i.e., Number Plate Number recognition, Image to Text conversion and Optical Character Recognition. The zipped file contains images folder and an excel file. The excel file consists of the image name and the concerned actual licence plate number. The dataset is not resized to retain the original resolution. However, those who want to apply various CNN models may resize and use them. </p>
Dataset for hydrate particle number and area calculation, and hydrate coarsening videos
<p>There are three series (24 files) of data on day 4, 14, 24, 34, 44, 54, and 64:</p> <p>1. Hydrate particle number in open area (7 files):</p> <p>· 4 days-particle number.pptx</p> <p>· 14 days-particle number.pptx</p> <p>· 24 days-particle number.pptx</p> <p>· 34 days-particle number.pptx</p> <p>· 44 days-particle number.pptx</p> <p>· 54 days-particle number.pptx</p> <p>· 64 days-particle number.pptx</p> <p> </p> <p>2. Hydrate saturation calculation in porous media (15 files):</p> <p>· 4 days-area pixels.xlsx</p> <p>· 4 days-area pixels.pptx</p> <p>· 14 days-area pixels.xlsx</p> <p>· 14 days-area pixels.pptx</p> <p>· 24 days-area pixels.xlsx</p> <p>· 24 days-area pixels.pptx</p> <p>· 34 days-area pixels.xlsx</p> <p>· 34 days-area pixels.pptx</p> <p>· 44 days-area pixels.xlsx</p> <p>· 44 days-area pixels.pptx</p> <p>· 54 days-area pixels.xlsx</p> <p>· 54 days-area pixels.pptx</p> <p>· 64 days-area pixels.xlsx</p> <p>· 64 days-area pixels.pptx</p> <p>· Particle number and SH variation.xlsx</p> <p> </p> <p>3. Videos of the hydrate coarsening and sintering process (2 files):</p> <p>· Hydrate coarsening and sintering in open area.mp4</p> <p>· Hydrate coarsening and sintering in porous media.mp4</p>
Unique Property Reference Number (UPRN) indicator: Flood risk (UPRN_1_1)
<p>This indicator covers three main flooding risks in the UK: fluvial (rivers and streams), coastal and surface water. The indicators were computed for all the Unique Property Reference Numbers (UPRNs) in the NHS Cheshire and Merseyside Integrated Care Board region (North West England). This region is formed by the following Local Authorities: Chester and Cheshire East, Cheshire West, Halton, Knowsley, Liverpool, Sefton, St. Helens, Warrington and Wirral.</p> <p> </p>
Unique Property Reference Number (UPRN) indicator: Tree canopy coverage (UPRN_3_1)
<p>This is an indicator with the tree canopy coverage for various distances (15m, 25m, 150m, 300m and 500m) from Unique Property Refence Numbers (UPRNs) in the Office for National Statistics (ONS) UPRN directory v2024.07 (Epoch 111). This metric is based on <em>Bluesky’s National Tree Map™,</em> which is the only tree dataset to include trees in Great Britain and the Republic of Ireland that are 3 metres and taller.</p> <h3>Available files</h3> <ul> <li><strong>UPRN_3_1_tree_canopy_coverage_cm_with_coords.csv</strong>: covers the Cheshire and Merseyside region (England) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_cm.csv</strong>: covers the Cheshire and Merseyside region (England).</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian_with_coords.csv</strong>: covers the Lothian region (Scotland) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian.csv</strong>: covers the Cheshire and Merseyside region (England).</li> </ul> <p> </p> <h3>Fields</h3> <table> <tbody> <tr> <td> <p><strong>Column name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>UPRN</p> </td> <td> <p>Unique Property Reference Number as per the Office for National Statistics UPRN Directory (ONSUD) v2024.07 (Epoch 111).</p> </td> </tr> <tr> <td> <p>n_trees_15m</p> </td> <td> <p>Number of trees (above 3m) within 15m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_15m</p> </td> <td> <p>Percentage of area covered by tree canopy within 15m from the UPRN. From a maximum area of approximately 707m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_25m</p> </td> <td> <p>Number of trees (above 3m) within 25m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_25m</p> </td> <td> <p>Percentage of area covered by tree canopy within 25m from the UPRN. From a maximum area of approximately 1963m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_150m</p> </td> <td> <p>Number of trees (above 3m) within 150m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_150m</p> </td> <td> <p>Percentage of area covered by tree canopy within 150m from the UPRN. From a maximum area of approximately 70686m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_300m</p> </td> <td> <p>Number of trees (above 3m) within 300m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_300m</p> </td> <td> <p>Percentage of area covered by tree canopy within 300m from the UPRN. From a maximum area of approximately 282743m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_500m</p> </td> <td> <p>Number of trees (above 3m) within 500m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_500m</p> </td> <td> <p>Percentage of area covered by tree canopy within 500m from the UPRN. From a maximum area of approximately 785398m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>latitude (only version with coordinates)</p> </td> <td> <p>Latitude of the UPRN, given in decimal degrees, where N is positive and S is negative.</p> </td> </tr> <tr> <td> <p>longitude (only version with coordinates)</p> </td> <td> <p>Longitude of the UPRN, given in decimal degrees, where E is positive and W is negative.</p> </td> </tr> </tbody> </table> <p> </p> <h3><strong>Regions</strong></h3> <p><strong>Cheshire and Merseyside (Endgland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>Cheshire East</li> <li>Cheshire West and Chester</li> <li>Halton</li> <li>Knowsley</li> <li>Liverpool</li> <li>Sefton</li> <li>St. Helens</li> <li>Warrington</li> <li>Wirral</li> </ul> <p> </p> <p><strong>Lothian (Scotland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>East Lothian</li> <li>City of Edinburgh</li> <li>Midlothian</li> <li>Westlothian</li> </ul> <p> </p>
Depth and Number of added SWAPs for newly developed routing code for QAOA quantum circuits
<p>We developed a qubit routing algorithm with polynomial classical run time for the Quantum Approximate Optimization Algorithm (QAOA). The algorithm follows a two step process. First, it obtains a near-optimal solution, based on Vizing's theorem for the edge coloring problem, consisting of subsets of the interaction gates that can be executed in parallel on a fully parallelized all-to-all connected QPU. Second, it proceeds with greedy application of SWAP gates based on their net effect on the distance of remaining interaction gates on a specific hardware connectivity graph. Our algorithm strikes a balance between optimizing for both the circuit depth and total SWAP gate count. We show that it improves upon existing state-of-the-art routing algorithms for QAOA circuits defined on <span><span><span><span>k</span></span></span></span>-regular as well as Erdös-Renyi problem graphs of sizes up to <span><span><span><span>N</span><span>≤</span><span>400</span></span></span></span>. This repository contains data and a ipython notebook used for plotting the results presented in the paper</p>
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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.
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.