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1,663 results for “BIAS”
Data from: Population closure and the bias-precision trade-off in Spatial Capture-Recapture
1. Spatial capture-recapture (SCR) is an increasingly popular method for estimating ecological parameters. This method often relies on data collected over relatively long sampling periods. While longer sampling periods can yield larger sample sizes and thus increase precision of estimates, they also increase the risk of violating the closure assumption, thereby potentially introducing bias. The sampling period characteristics are therefore likely to play an important role in this bias-precision tradeoff. Yet few studies have studied this tradeoff and none has done so for SCR models. 2. In this study, we explored the influence of the length and timing of the sampling period on the bias-precision tradeoff of SCR population size estimators. Using a continuous time-to-event approach, we simulated populations with a wide range of life histories and sampling periods before quantifying the bias and precision of population size estimates returned by SCR models. 3. While longer sampling periods benefit the study of slow-living species (increased precision and lower bias), they lead to pronounced over-estimation of population size for fast living species. In addition, we show that both bias and uncertainty increase when the sampling period overlaps the species' reproductive season. 4. Based on our findings, we encourage investigators to carefully consider the life history of their study species when contemplating the length and the timing of the sampling period. We argue that SCR (and non-spatial capture-recapture) studies can safely extend the sampling period to increase precision, as long as it is timed to avoid peak recruitment periods. The simulation framework we propose here can be used to guide decisions regarding the sampling period for a specific situation.
Data from: Selection biases the prevalence and type of epistasis along adaptive trajectories
The contribution to an organism's phenotype from one genetic locus may depend upon the status of other loci. Such epistatic interactions among loci are now recognized as fundamental to shaping the process of adaptation in evolving populations. Although little is known about the structure of epistasis in most organisms, recent experiments with bacterial populations have concluded that antagonistic interactions abound and tend to de-accelerate the pace of adaptation over time. Here, we use the NK model of fitness landscapes to examine how natural selection biases the mutations that substitute during evolution based on their epistatic interactions. We find that, even when beneficial mutations are rare, these biases are strong and change substantially throughout the course of adaptation. In particular, epistasis is less prevalent than the neutral expectation early in adaptation and much more prevalent later, with a concomitant shift from predominantly antagonistic interactions early in adaptation to synergistic and sign epistasis later in adaptation. We observe the same patterns when re-analyzing data from a recent microbial evolution experiment. These results show that when the order of substitutions is not known, standard methods of analysis may suggest that epistasis retards adaptation when in fact it accelerates it.
Data from: Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry
We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modular and non-modular models. A series of simulations demonstrated that the modules reduced the effect of the bias in the distribution of muscle pulling directions, as well as in the distribution of torques associated with individual cortical neurons, which led to a more rapid adaptation to multi-directional force generation. These results suggest that modularity is crucial not only for reducing musculoskeletal redundancy but also for overcoming mechanical bias due to the musculoskeletal geometry allowing for faster adaptation to certain external environments.
Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
Approximate Bayesian Computation (ABC) is a powerful tool for model-based inference of demographic population histories from large genetic data sets. For most organisms its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent-based ABC approach where GBS data were modeled with either a population mutation parameter (θ) or with a fixed sites (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50,000 loci a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1,000 loci for migration and split time in simple population divergence models. In more complex models posterior distributions were wide and almost reverted to the uninformative prior even with 50,000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite-likelihood method. Bottleneck scenarios proved particularly difficult and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor allele frequency filters – usual practice for GBS data – negatively affected nearly all estimates. With this in mind, we used a combination of FS and θ approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a range-wide bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC-coalescence framework and proposes a best-practice approach.
Data from: Sex-biased gene flow among elk in the greater Yellowstone ecosystem
We quantified patterns of population genetic structure to help understand gene flow among elk populations across the Greater Yellowstone Ecosystem. We sequenced 596 base pairs of the mitochondrial (mt)DNA control region of 380 elk from eight populations. Analysis revealed high mtDNA variation within populations, averaging 13.0 haplotypes with high mean gene diversity (0.85). The genetic differentiation among populations for mtDNA was relatively high (FST = 0.161; P = 0.001) compared to genetic differentiation for nuclear microsatellite data (FST = 0.002; P = 0.332), which suggested relatively low female gene flow among populations. The estimated ratio of male to female gene flow (m_m/m_f = 46) was among the highest we have seen reported for large mammals. Genetic distance (for mtDNA pair-wise FST) was not significantly correlated with geographic (Euclidean) distance between populations (Mantel's r = 0.274, P = 0.168). Large mtDNA genetic distances (e.g. FST > 0.2) between some of the geographically closest populations (<65 km) suggested behavioral factors and/or landscape features might shape female gene flow patterns. Given the strong sex-biased gene flow, future research and conservation efforts should consider the sexes separately when modeling corridors of gene flow or predicting spread of maternally transmitted diseases. The growing availability of genetic data to compare male versus female gene flow provides many exciting opportunities to explore the magnitude, causes, and implications of sex-biased gene flow likely to occur in many species.
XCH4 surface biases enhanced by aerosols
<p>Results and Jupyter notebook to produce the figures used in "Surface reflectance biases in XCH4 retrievals from the 2.3 µm band are enhanced in the presence of aerosols".</p>
Data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al.
<p>The data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al. The datasets are dictionaries and are saved with .npy extensions. The datasets can be loaded in Python with expressions like: 'scenario_base = np.load(f"{filepath}scenario_base_hierarchical.npy",allow_pickle="TRUE").item()'.</p>
Grouping‐Induced Numerosity Biases Vary with Autistic‐Like Personality Traits
<p>Data set for generating the figures</p>
Full data for 'Ubiquitous Non-Majorana Zero-Bias Conductance Peaks in Nanowire Devices'
<p>This repository contains experimental data for the following paper:<br> Ubiquitous Non-Majorana Zero-Bias Conductance Peaks in Nanowire Devices.<br> Authors: J. Chen, B. D. Woods, P. Yu, M. Hocevar, D. Car, S. R. Plissard, E. P. A. M. Bakkers, T. D. Stanescu, and S. M. Frolov.<br> Content of this repository: </p> <p>Readme file. </p> <p>/RawData/<br> Original data obtained at the time of measurement for devices 0520-840-NW5,0520-840-2,0520-840-NW1,0510-197.</p> <p>/Measurement notes/<br> All the measurement data was summarized in powerpoints, catagorized by the name of the device. Data in the main text was measured on devices 0524-840-NW5 and 0520-840-2, Data in the supplementary information was measured on all the devices.</p> <p>/Data of paper figures/<br> All the organized data files for the figures in the main text and supplementary information.</p> <p>Data file types:<br> data_NNN.dat - the original data file obtained at the time of the experiment<br> dataNNN.py - the original QTLab data acquisition script saved with data<br> data_NNN.set - settings of measurement instruments at the time of measurement<br> data_NNN.meta - auxillary file necessary for plotting data using SpyView (see below) <br> data_NNN.MTX - a simple 2D/3D matrix format developed for Spyview</p> <p>NNN stands for dataset number, automatically indexed by QTLab</p> <p>How to plot data:</p> <p>1) Spyview - a free data plotting program written by Gary Steele</p> <p>Data in this repository can be simply dropped into Spyview for plotting. </p> <p>Spyview also produces and can read .mtx files which are available for some of the data in this repository.</p> <p>https://nsweb.tn.tudelft.nl/~gsteele/spyview/</p> <p><br> 2) QTPlot - a Python plotter written by Ruben van Gulik</p> <p>Data in this repository can be directly opened with QTPlot, which will read axis labels.</p> <p>https://github.com/Rubenknex/qtplot</p> <p>Note: requires PyQT4</p>
Supplementary File_Ivermectin_Risk of Bias Excel Tool (Version 2)
<p>Supplementary material (Risk of Bias Excel Tool (Version 1)) for the updated Cochrane Review "Ivermectin for preventing and treating COVID-19".</p>
Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model
<h3>Dataset</h3> <p>1. observation data over 14 weather stations</p> <p>Variables: hourly near-surface 2-min average wind speed, wind direction </p> <p>2. ECMWF-IFS forecast data over 14 weather stations</p> <p>Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)</p> <p>Table 1. ECMWF-IFS forecast data at surface level</p> <div> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Temperature at 2 m</p> </td> <td> <p>2t</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Sea surface temperature</p> </td> <td> <p>sst</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Dewpoint temperature at 2 m</p> </td> <td> <p>2d</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Convective precipitation in the past hour</p> </td> <td> <p>cp</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Mean sea level pressure</p> </td> <td> <p>msl</p> </td> <td> <p>hPa</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 10 m</p> </td> <td> <p>10u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 10 m</p> </td> <td> <p>10v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 10 m</p> </td> <td> <p>10ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 10 m</p> </td> <td> <p>10wd</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 100 m</p> </td> <td> <p>100u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 100 m</p> </td> <td> <p>100v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 100 m</p> </td> <td> <p>100ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 100 m</p> </td> <td> <p>100wd</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> </div> <div> </div> <p>Table 2. ECMWF-IFS forecast data at upper level</p> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Relative humidity at xxx hPa</p> </td> <td> <p>r_Lxxx</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Temperature at xxx hPa</p> </td> <td> <p>t_Lxxx</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Vertical velocity of wind at xxx hPa</p> </td> <td> <p>w_Lxxx</p> </td> <td> <p>Pa s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Zonal component of wind at xxx hPa</p> </td> <td> <p>u_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind at xxx hPa</p> </td> <td> <p>v_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at xxx hPa</p> </td> <td> <p>ws_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at xxx hPa</p> </td> <td> <p>wd_Lxxx</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> <div> </div> <p>3. key variables constructed by feature engineering</p> <p>(1) sort-term statistics, including <em>maximum, minimum, mean </em>and <em>variance</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during the next 48 hours,</p> <p> (2) long-term statistics, including <em>mean </em>and <em>deviation</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),</p> <p> (3) thermodynamic factors, including the low-level wind shear between <em>10ws</em> and <em>100ws</em>, vertical wind shear between 200 hPa and 850 hPa<em>, </em>the differences between <em>sst</em><em> </em>and <em>2t</em><em>.</em></p> <h3>Scripts</h3> <p>1. Random Forest model training code</p> <p>2. LightGBM model training code</p> <p>3. XGBoost model training code</p> <p>4. TabNet-MTL model training code</p> <p> </p>
Figure 2 from: Joseph P (2018) Eliminating disparities and implicit bias in health care delivery by utilizing a hub-and-spoke model. Research Ideas and Outcomes 4: e26370. https://doi.org/10.3897/rio.4.e26370
Figure 2 Colorectal cancer mortality rates during implementation of the nurse navigator system.
Figure 1 from: Joseph P (2018) Eliminating disparities and implicit bias in health care delivery by utilizing a hub-and-spoke model. Research Ideas and Outcomes 4: e26370. https://doi.org/10.3897/rio.4.e26370
Figure 1 Disparity in LDL cholesterol testing between black and white populations.
Conservation, acquisition, and functional impact of sex-biased gene expression in mammalian tissues
<p>Processed data and code for</p> <p>Sahin Naqvi, Alexander K. Godfrey, Jennifer F. Hughes, Mary L. Goodheart, Richard N. Mitchell, & David C. Page <br> <br> <strong>Conservation, acquisition, and functional impact of sex-biased gene expression in mammalian tissues</strong></p> <p>Expression values</p> <ul> <li>gtex.filt.salmon.tximport.unadj.tpm.txt.gz Unadjusted TPM values for filtered GTEx samples</li> <li>gtex.filt.salmon.tximport.unadj.counts.txt.gz Unadjusted counts for filtered GTEx samples</li> <li>gtex.filt.salmon.tximport.adj.counts.txt.gz PCA- and histology-adjusted counts for filtered GTEx samples</li> <li>cyno.salmon.tximport.tpm.txt.gz Cynomolgus macaque TPM values</li> <li>cyno.salmon.tximport.counts.txt.gz Cynomolgus macaque counts</li> <li>mouse.salmon.tximport.tpm.txt.gz Mouse TPM values</li> <li>mouse.salmon.tximport.counts.txt.gz<a href="http://pagelab.wi.mit.edu/page/papers/Naqvi_et_al_2019/exprvals/mouse.salmon.tximport.counts.txt.gz"> </a>Mouse counts</li> <li>rat.salmon.tximport.tpm.txt.gz Rat TPM values</li> <li>rat.salmon.tximport.counts.txt.gz Rat counts</li> <li>dog.salmon.tximport.tpm.txt.gz Dog TPM values</li> <li>dog.salmon.tximport.counts.txt.gz Dog counts</li> </ul> <p>Metadata</p> <ul> <li>human.metadata.txt Human metadata (abbreviated version of GTEx metadata)</li> <li>histeval.rds Novel histological evaluations for 6 tissues (.rds file to read into R)</li> <li>nonhuman.metadata.txt Non-human metadata</li> </ul> <p>Scripts</p> <ul> <li>filterSamples.R<a href="http://pagelab.wi.mit.edu/page/papers/Naqvi_et_al_2019/scripts/filterSamples.R"> </a>R code to filter GTEx samples based on medical history and cause of death</li> <li>cadjust_exprvals.Rmd<a href="http://pagelab.wi.mit.edu/page/papers/Naqvi_et_al_2019/scripts/pcadjust_exprvals.Rmd"> </a>R code to perform PCA and histology-based adjustment of expression values in GTEx data</li> <li>choosePCs.R<a href="http://pagelab.wi.mit.edu/page/papers/Naqvi_et_al_2019/scripts/choosePCs.R"> </a>Helper function for 'pcadjust_exprvals.Rmd'</li> <li>perform_sexdiff.Rmd R code to perform linear modeling of sex differences across 12 tissues and 5 species. Uses 'getSexBiasStats.R'</li> <li>getSexBiasStats.R<a href="http://pagelab.wi.mit.edu/page/papers/Naqvi_et_al_2019/scripts/getSexBiasStats.R"> </a>Helper function for 'perform_sexdiff.Rmd'</li> </ul> <p>Intermediate files</p> <ul> <li>one2oneorth_emblids.txt One-to-one orthologs across the 5 species</li> <li>one2oneorth_60spectis_beta.txt Gene x tissue-species matrix of estimates of sex bias (beta)</li> <li>one2oneorth_60spectis_beta_se.txt Gene x tissue-species matrix of estimates of sex bias (beta) standard error</li> <li>one2oneorth_60spectis_mashr_pm.txt Gene x tissue-species matrix of mashr posterior estimates of sex bias (posterior mean)</li> <li>one2oneorth_60spectis_mashr_lfsr.txt Gene x tissue-species matrix of mash local false sign rate</li> <li>salmon.starref.tximport.voom.spec5orth.sfa_F.out Sparse factors learned from the gene x tissue-species beta matrix, used as input to mashr</li> </ul> <p>Output files</p> <ul> <li>sexbias.conserved.matrix.txt Gene x tissue matrix of conserved sex bias. 1 indicates male bias, -1 female bias (same for other matrices in this section)</li> <li>sexbias.primategain.matrix.txt Gene x tissue matrix of sex bias gained in primates</li> <li>sexbias.primateloss.matrix.txt<a href="http://pagelab.wi.mit.edu/page/papers/Naqvi_et_al_2019/sexbias.primateloss.matrix.txt"> </a>Gene x tissue matrix of sex bias lost in primates</li> <li>sexbias.rodentgain.matrix.txt Gene x tissue matrix of sex bias gained in rodents</li> <li>sexbias.rodentloss.matrix.txt Gene x tissue matrix of sex bias lost in rodents</li> <li>sexbias.humangain.matrix.txt Gene x tissue matrix of sex bias gained in human</li> <li>sexbias.cynogain.matrix.txt Gene x tissue matrix of sex bias gained in cyno</li> <li>sexbias.mousegain.matrix.txt Gene x tissue matrix of sex bias gained in mouse</li> <li>sexbias.ratgain.matrix.txt Gene x tissue matrix of sex bias gained in rat</li> <li>sexbias.doggain.matrix.txt Gene x tissue matrix of sex bias gained in dog</li> <li>sexbias.multiplegain.matrix.txt Gene x tissue matrix of sex bias likely gained in multiple lineages</li> <li>sexbias.multipleloss.matrix.txt Gene x tissue matrix of sex bias likely lost in multiple lineages</li> <li>sexbias.complex.matrix.txt Gene x tissue matrix of sex bias with complex patterns across species that could not be categorized into gains or losses</li> </ul> <p>Data from other studies</p> <ul> <li>liang2017.human.skin.txt Limma/voom output of sex differences in human skin, from Liang et al, 2017</li> <li>lindholm2017.human.muscle.txt Limma/voom output of sex differences in human muscle, from Lindholm et al, 2017</li> <li>li2017.marin2017.mouse.heart.txt Limma/voom output of sex differences in mouse heart, combining Li et al, 2017 and Marin et al, 2017</li> <li>li2017.marin2017.mouse.liver.txt Limma/voom output of sex differences in mouse liver, combining Li et al, 2017 and Marin et al, 2017</li> <li>li2017.mouse.adrenal.txt Limma/voom output of sex differences in mouse adrenal gland, from Li et al, 2017</li> <li>li2017.mouse.brain.txt Limma/voom output of sex differences in mouse brain, from Li et al, 2017</li> <li>li2017.mouse.lung.txt Limma/voom output of sex differences in mouse lung, from Li et al, 2017</li> <li>li2017.mouse.muscle.txt Limma/voom output of sex differences in mouse muscle, from Li et al, 2017</li> <li>li2017.mouse.spleen.txt Limma/voom output of sex differences in mouse spleen, from Li et al, 2017</li> <li>yang2006.mouse.muscle.geo2r.txt GEO2R output of sex differences in mouse muscle, from Yang et al, 2006</li> <li>franco2010.mouse.lung.geo2r.txt Lim GEO2R output of sex differences in mouse lung, from Franco et al, 2010</li> </ul>
Idiosyncratic choice bias and feedback-induced bias differ in their long-term dynamics
<p>Overview<br>-------------------<br>This repository is associated with the paper "Idiosyncratic choice bias and feedback-induced bias differ in their long-term dynamics".</p> <p>All data and analyses required to reproduce our results are available in the stabilityFeedback folder.</p> <p>Data<br>-------------------<br>Response data and inter-session-delays are stored as sorted tables ("sortedTable_") and assign tables ("assignTable_"), respectively, by experiments (stabilityFeedback/stability folder and stabilityFeedback/feedback folder) and delay groups: <br>"stabilityFeedback/[EXPERIMENT_NAME]/[TABLES_TYPE]/[TABLE_TYPE]_[EXPERIMENT_NAME]_[DELAY_NAME].csv"</p> <p>Analysis<br>-------------------<br>The code required to reproduce all analyses and related figures is available in the stabilityFeedback/stabilityFeedbackICB.mlx MATLAB (R2023b) Live Editor file (also available as stabilityFeedbackICB.pdf).</p> <p>All figures are saved in the stabilityFeedback/figures folder in both MATLAB FIG and PDF formats. File names for the vertical task match those used in the paper, while file names for the horizontal task include " - appendix." at the end.</p> <p>For Binomial tests, we use the myBinomTest custom function, reference: Matthew Nelson (2015). https://www.mathworks.com/matlabcentral/fileexchange/24813-mybinomtest-s-n-p-sided MATLAB Central File Exchange. Retrieved February 9, 2016.</p> <p>Cite<br>-------------------<br>If you use code from this repository, please cite it using the Zenodo DOI: 10.5281/zenodo.13388598</p> <p>Contributors<br>-------------------<br>This code was authored by Lior Lebovich, 2024.</p>
Figure 1 from: Torralba-Burrial A, Merino-Sáinz I, Anadón A (2014) The relevance, biases, and importance of digitising opportunistic non-standardised collections: A case study in Iberian harvestmen fauna with BOS Arthropod Collection datasets (Arachnida, Opiliones). ZooKeys 404: 71-89. https://doi.org/10.3897/zookeys.404.6520
Figure 1 - Distribution of specimens included in this subset.
Sentiment analysis of media's political bias in micro-blogging - A computational framework for optimized recommendation systems
<p>This dataset contains tweets from four Pakistani news channels, namely DAWN, GEO, ARY, and 24News, collected using the twarc command line tool with a Twitter academic researcher account. The tweets were collected between Nov, 2015, and April 4, 2022, and relate to three major political parties in Pakistan, PTI, PMLN, and PPP through their official names in the relevant news channel page tweets. </p>
Non-local transport signatures of topological superconductivity in a phase-biased planar Josephson junction - code
<p>Here are the codes to generate the whole data of the paper.</p>
Raw data for Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias
Open the record for dataset details and reuse information.
Attentional Bias Retraining in Veterans
ClinicalTrials.gov study NCT02041572. IPD Sharing: NO. Countries: 1. Publications: 0.
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.