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1,663 results for “BIAS”
Biased RNG Bit Sequences, Dependent RNG Bit Sequences
<p>Matlab PRNG generated biased bit sequences without correlations and unbiased bit sequences with bit correlations; used for validation of the tests in Publication: https://doi.org/10.5281/zenodo.1286723</p>
Data archive for 'Cognitive bias in relation to developmental history and stress response in European starlings (Sturnus vulgaris)'
<p>Data files and R code for Gott et al. 'Cognitive bias in relation to developmental history and stress response in European starlings (Sturnus vulgaris)'.</p> <p>One .csv file gives the trial-by-trial data from the cognitive bias experiment. The other gives individual-level summary variables. Both are used by the R script.</p> <p>Uploaded 9th September 2018</p>
Bias correction of simulated Brazilian wind power generation based on reanalysis data
<p>Available data:</p> <p>- Brazilian wind power generation time series derived from MERRA-2 reanalysis data with wind speed and wind power bias correction.</p> <p>- Wind speed correction factors derived from INMET wind speeds (http://www.inmet.gov.br/portal/) as well as wind power correction factors dervied from ONS wind power generation time series are also provided.</p> <p>- Simulation of about 38 years of wind power generation with fixed capacity.</p> <p>Data used for validation:</p> <p>- Historical wind power generation data, which were used for validation of simulated time series, can be found at the ONS homepage (http://ons.org.br/Paginas/resultados-da-operacao/historico-da-operacao/geracao_energia.aspx).</p> <p> </p> <p>Other Links:</p> <p>- Information on this will soon be found here: https://refuel.world/</p> <p>- Code for generating time series, validation and analysis: https://github.com/KatharinaGruber/BrazilWind</p> <p>- Master thesis belonging to data: https://doi.org/10.5281/zenodo.1471221</p>
Dataset from Computational modeling of anthocyanin pathway evolution: Biases, hotspots, and trade-offs
<p>This directory contains the scripts used to run simulations, Jupyter notebook with complete analysis, and serialized (pickled) raw simulated dataset from our paper "<em>Computational modeling of anthocyanin pathway evolution: Biases, hotspots, and trade-offs</em>". These materials are referenced in the main text and supplemental text of the publication. The purpose of this repository is to facilitate replication of our analysis by any interested parties. </p>
Webis-Bias-Flipper-18
<p>The Webis Bias Flipper 2018 (Webis-Bias-Flipper-2018) comprises 2781 events from allsides.com as of June 1st, 2012 till February 10, 2018. For each event, the title, the summary, all news portals belonging to the event, and the links to the news portals with respective bias were recorded. After that, we crawled the news portals with the given links to retrieve their headlines and the content of all articles, because the content is not provided on allsides.com. For each event we collected the corresponding news articles. A total of 6458 news articles are collected.</p>
Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results
<p><strong>Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results </strong></p> <p> </p> <p>This archive contains the data file for the five CPIC simulations run for paper Larsen et al 2019. [1, 2]. The data files contents and format are described.</p> <p> </p> <p>Each data file is stored in HDF5 format written with h5py [3, 4].</p> <p>The files named field_potential_bias_XX_data.h5 contain the electric field components and electric potential with the following structure:</p> <blockquote> <p>+</p> <p>:|____Author (str [50])</p> <p>:|____Bias (str [3])</p> <p>:|____DOI (str [22])</p> <p>:|____Date (str [14])</p> <p>:|____Description (str [81])</p> <p>:|____File_Creator (str [44])</p> <p>:|____License (str [2140])</p> <p>|____E (h5py._hl.dataset.Dataset (3, 101, 101, 101))</p> <p> :|____Description (str [41])</p> <p> :|____Format (str [94])</p> <p> :|____Units (str [3])</p> <p>|____XYZ (h5py._hl.dataset.Dataset (3, 101))</p> <p>|____phi (h5py._hl.dataset.Dataset (101, 101, 101))</p> <p> </p> </blockquote> <p>See the metadata within the file for units of each variable.</p> <p> </p> <p>The files named detector_bias_XX_data.h5 contain the particles collected at the simulated detector in a mix of physical and CPIC units with the following structure:</p> <blockquote> <pre>+</pre> <pre>:|____Author (str [50])</pre> <pre>:|____Bias (str [4])</pre> <pre>:|____DIO (str [22])</pre> <pre>:|____Date (str [14])</pre> <pre>:|____Description (str [81])</pre> <pre>:|____Detector_area (str [11])</pre> <pre>:|____File_Creator (str [44])</pre> <pre>:|____License (str [2140])</pre> <pre>:|____Timestep (str [12])</pre> <pre>:|____Timestep_description (str [114])</pre> <pre>:|____electron_temperature (str [6])</pre> <pre>:|____photoemission_electron_energy (str [4])</pre> <pre>:|____potential_at_boundary (str [3])</pre> <pre>:|____proton_electron_mass_ratio (str [4])</pre> <pre>:|____proton_temperature (str [7])</pre> <pre>:|____reference_density (str [9])</pre> <pre>|____Energy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [32])</pre> <pre> :|____Units (str [2])</pre> <pre>|____Velocity (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Velocity_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vx (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vx_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vy_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vz (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vz_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____phi (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre> :|____Units (str [3])</pre> <pre>|____q (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [47])</pre> <pre> :|____Units (str [4])</pre> <pre>|____species (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [26])</pre> <pre>|____theta (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [37])</pre> <pre> :|____Units (str [3])</pre> <pre>|____timestep (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre>|____weight (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [38])</pre> <pre> :|____Units (str [4])</pre> </blockquote> <p> </p> <p>The information in parentheses are the datatype and size, square brackets, [], denote a single element of that many characters and parentheses, (), denote the stored array size. Lines with a leading colon, :, are metadata fields to make the data files more usable.</p> <p> </p> <p>1. Delzanno, G.L., et al., <em>CPIC: a curvilinear particle-in-cell code for plasma–material interaction studies.</em> IEEE Transactions on Plasma Science, 2013. <strong>41</strong>(12): p. 3577-3587.</p> <p>2. Meierbachtol, C.S., et al., <em>An electrostatic Particle-In-Cell code on multi-block structured meshes.</em> Journal of Computational Physics, 2017. <strong>350</strong>: p. 796-823.</p> <p>3. Folk, M., A. Cheng, and K. Yates. <em>HDF5: A file format and I/O library for high performance computing applications</em>. in <em>Proceedings of supercomputing</em>. 1999.</p> <p>4. Collette, A., et al., <em>h5py/h5py 2.9.0.</em> 2018.</p> <p> </p> <p> </p>
How Confidence in Prior Attitudes, Social Tag Popularity, and Source Credibility Shape Confirmation Bias Toward Antidepressants and Psychotherapy in a Representative German Sample: Randomized Controlled Web-Based Study
<p>ABSTRACT</p> <p>Background: In health-related, Web-based information search, people should select information in line with expert (vs nonexpert) information, independent of their prior attitudes and consequent confirmation bias.</p> <p>Objective: This study aimed to investigate confirmation bias in mental health–related information search, particularly (1) if high confidence worsens confirmation bias, (2) if social tags eliminate the influence of prior attitudes, and (3) if people successfully distinguish high and low source credibility.</p> <p>Methods: In total, 520 participants of a representative sample of the German Web-based population were recruited via a panel company. Among them, 48.1% (250/520) participants completed the fully automated study. Participants provided <em>prior attitudes</em> about antidepressants and psychotherapy. We manipulated (1) <em>confidence</em> in prior attitudes when participants searched for blog posts about the treatment of depression, (2) <em>tag popularity</em> —either psychotherapy or antidepressant tags were more popular, and (3) <em>source credibility</em> with banners indicating high or low expertise of the tagging community. We measured <em>tag</em> and <em>blog post</em> selection, and <em>treatment</em><em>efficacy ratings</em> after navigation.</p> <p>Results: Tag popularity predicted the proportion of selected antidepressant tags (beta=.44, SE 0.11; <em>P</em><.001) and blog posts (beta=.46, SE 0.11; <em>P</em><.001). When confidence was low (−1 SD), participants selected more blog posts consistent with prior attitudes (beta=−.26, SE 0.05; <em>P</em><.001). Moreover, when confidence was low (−1 SD) and source credibility was high (+1 SD), the efficacy ratings of attitude-consistent treatments increased (beta=.34, SE 0.13; <em>P</em>=.01).</p> <p>Conclusions: We found correlational support for defense motivation account underlying confirmation bias in the mental health–related search context. That is, participants tended to select information that supported their prior attitudes, which is not in line with the current scientific evidence. Implications for presenting persuasive Web-based information are also discussed.</p> <p>Trial Registration: ClinicalTrials.gov NCT03899168; https://clinicaltrials.gov/ct2/show/NCT03899168 (Archived by WebCite at http://www.webcitation.org/77Nyot3Do)</p> <p>J Med Internet Res 2019;21(4):e11081</p> <p>doi:10.2196/11081</p>
Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation
<p>ABSTRACT</p> <p>Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective.</p> <p>Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts’ evaluations.</p> <p>Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts’ blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants’ treatment recommendation after information search.</p> <p>Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; <em>t</em><sub>173</sub>=7.67, <em>P</em><.001, <em>d</em>=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (<em>F</em><sub>2,168</sub>=10.61, <em>P</em><.001, partial eta squared=0.112), blog post selection (<em>F</em><sub>2,168</sub>=6.55, <em>P</em>=.002, partial eta squared=0.072), and treatment efficacy ratings (<em>F</em><sub>2,168</sub>=8.48, <em>P</em><.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (<em>F</em><sub>1,168</sub>=4.32, <em>P</em>=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (<em>P</em>s>.33).</p> <p>Conclusions: We conclude that the psychotherapy bias is most effectively attenuated—and even eliminated—when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion.</p>
Bias-corrected simluated wind power generation time series for Brazil
<p>Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.</p> <p>The data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: <a href="https://doi.org/10.1016/j.energy.2019.116212">10.1016/j.energy.2019.116212</a></p> <p>Code for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr</p> <p> </p> <p>The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.</p> <p>"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).</p> <p>"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).</p> <p>"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).</p> <p> </p> <p>The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.</p>
Fig. 1 in Lice community structure infesting Trinomys iheringi (Thomas, 1911) - Ocurrence, sex bias and climatic variables on tropical island
Fig. 1. Location of capture of Trinomys iheringi in Dois Rios Village, Ilha Grande, Rio de Janeiro State, Brazil, between April 2013, and December 2015.
Fig. 2 in Lice community structure infesting Trinomys iheringi (Thomas, 1911) - Ocurrence, sex bias and climatic variables on tropical island
Fig. 2. Distribution of Gyropus (m.) martini stages on Trinomys iheringi rodents. Host sex (F = female, M = male) and capture months (Aug = August, Dec = December, Feb = February, Jul = July, Nov = November) in Ilha Grande State Park, RJ, Brazil. The numbers along the x axis represent the number of lice life stages: male/female/nymph 1/nymph 2/nymph 3.
Fig. 4 in Lice community structure infesting Trinomys iheringi (Thomas, 1911) - Ocurrence, sex bias and climatic variables on tropical island
Fig. 4. Probability of lice occurrence on Trinomys iheringi as a function of humidity and sex (A), and humidity and age class (B) in Ilha Grande State Park, RJ, Brazil. The letters in the upper part of the graph represent the presence of lice on rodents, and the letters in the lower part of the graph indicate the absence of lice on rodents.
Fig. 3 in Lice community structure infesting Trinomys iheringi (Thomas, 1911) - Ocurrence, sex bias and climatic variables on tropical island
Fig. 3. Relationship between the natural logarithm of body mass and the natural logarithm of body length for Trinomys iheringi individuals infected and uninfected by lice in Ilha Grande State Park, RJ, Brazil. Open circles and the continuous line refer to uninfected individuals, while solid circles and the dashed line refer to infected individuals.
Fig. 1 in Ecology of bat flies in Singapore: A study on the diversity, infestation bias and host specificity (Diptera: Nycteribiidae)
Fig. 1. Pteropodid fruit bats in Singapore with their ectoparasitic Nycteribiidae bat flies. Cynopterus brachyotis (a) and Leptocyclopodia ferrarii (b); Eonycteris spelaea (c) and Eucampsipoda sundaica (d); Penthetor lucasi (e), Eucampsipoda penthetoris (f), and Archinycteribia octophthalma (g).
Fig. 2 in Ecology of bat flies in Singapore: A study on the diversity, infestation bias and host specificity (Diptera: Nycteribiidae)
Fig. 2. Posterior mean intensity of the three species of bats and 89% HDPI (High Density Posterior Interval).
Fig. 6 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 6. Percent similarity among bins averaged to 1 degree bins. A. om7 interval. B. om8 interval. C. om9 interval. The thicker the line, the greater the similarity between the two cells connected by the line.
Fig. 4 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 4. Correlations between richness per map and number of localities. A. om7 interval. B. om8 interval. C. om9 interval. The gap in the distribution of points for the om8 interval highlights the discontinuity between a group of maps with few taxa at a few localities and other maps with a large number of localities and high richness.
Fig. 5 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 5. Rarefaction curves for each interval, based on number of occurrences. The confidence envelope of the species richness for om9 departs significantly from those of om7 and om8 above 50 occurrences, but the significantly higher species−richness of om8 only becomes apparent at sample sizes of around 250 specimens, indicating that a few, rare taxa are boosting richness in the om8 interval.
Fig. 2 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 2. Distribution of Muschelkalk ammonoid localities used in this study plotted on a map of modern Germany. The overall geographic spread of localities does not change greatly over time.
Fig. 3 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 3. Correlations between richness per map and number of occurrences. A. om7 interval. B. om8 interval. C. om9 interval.
ScienceDex guides
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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.