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1,663 results for “BIAS”
Figure 2 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Figure 2 - Sample-based OTU richness as recovered by different barcode-primer pair combinations. Error bars denote standard error; different letters indicate statistically different groups.
Figure 1 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Figure 1 - Map of ribosomal DNA indicating variable regions as well as primers used and/or discussed in this study. Primers pairs used for HTS are highlighted.
Figure 3 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Figure 3 - Rarefied OTU accumulation curves for samples based on the (a) ITS1 and (b) ITS2 barcodes and their 95% confidence intervals.
Figure 6 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Figure 6 - Relative abundance of fungal classes in the amplicon and metagenomics data sets divided into SSU, ITS, and LSU subsets averaged over different barcodes (amplicon data) and 14 shared samples. Asterisks in the margins indicate significant differences in recovery of classes among SSU, ITS, and LSU of metagenomics (right) and amplicon (left) data sets. Asterisks in the center indicate significant differences between the metagenomics and amplicon-bases approaches.
Figure 7 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Figure 7 - Differences in sequence length in the ITS1 and ITS2 barcodes of 16 most abundant fungal classes as revealed based on amplicon libraries in this study. Columns, asterisks, and error bars represent mean and median values and standard deviation, respectively. Numbers inside bars indicate the number of sequences analyzed (n). Taxa are ordered by average length of the ITS1 region.
Figure 4 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Figure 4 - Relationship between connectance and adjusted coefficient of determination (R2adj) for floristic variables across different barcode-primer pair combinations based on (a) Bray-Curtis distance and (b) Hellinger distance. Pointed line indicates correlation in the ITS1Fngs-ITS2 data set (filled circles), covering eight connectance classes (C<0.45). Open circles, other ITS1 and ITS2 primer pairs; triangles, SSU barcodes; rectangles, LSU barcodes.
Data from: The advantage of male-biased flower production in andromonoecious plants under intensive predispersal seed predation
<p><span>1) Not only mutualistic plant–pollinator interactions but also antagonistic plant–herbivore interactions can be a selective force on sex allocation in angiosperms. In this study, we investigate how predispersal seed predation affects the reproductive success and floral gender of andromonoecious herbs on a natural snowmelt gradient.</span></p> <p><span>2) The developing fruits of an alpine herb (<em>Peucedanum</em> <em>multivittatum</em>: Apiaceae) were intensively predated by lepidopteran larvae (<em>Phaulernis</em> <em>fulviguttella</em>: Epermeniidae) in the early-snowmelt populations, where flowering occurred from mid to late July. In the late-snowmelt populations, where flowering occurred after early August, seed predation was negligible due to the oviposition of the predator moths being concentrated in early summer. The moths tended to oviposit on plants with more perfect flowers and taller inflorescences, whereas the number of male flowers was independent of their oviposition preference. </span></p> <p><span>3) Responding to the oviposition behavior, the proportion of male flowers was the largest and floral stems were the shortest in the early-snowmelt population suffering from intensive predation damage. The contribution of perfect flowers to intact seed production significantly decreased with earlier flowering along the snowmelt gradient. Fitness measurements using genetic markers revealed that the increase in flower number resulted in greater success as a pollen donor within a population. Thus, plants can ameliorate the risk of predation damage to sired seeds by wider pollen dispersal. </span></p> <p><span>4) Synthesis: Taken together, the greater production of male flowers at the expense of perfect flowers is advantageous under intensive predation pressure owing to the reduction of predation damage (female fitness) and the improvement of siring success (male fitness). These results revealed that predispersal seed predation acts as a selective force that promotes male-biased sex allocation in andromonoecious plants.</span></p>
Supplementary material 2 from: Reyes-Puig C, Mancero E (2022) Beyond the species name: an analysis of publication trends and biases in taxonomic descriptions of rainfrogs (Amphibia, Strabomantidae, Pristimantis). ZooKeys 1134: 73-100. https://doi.org/10.3897/zookeys.1134.91348
Complete translation of the manuscript
Supplementary material 1 from: Reyes-Puig C, Mancero E (2022) Beyond the species name: an analysis of publication trends and biases in taxonomic descriptions of rainfrogs (Amphibia, Strabomantidae, Pristimantis). ZooKeys 1134: 73-100. https://doi.org/10.3897/zookeys.1134.91348
Complete database of Pristimantis descriptions
Current nest box designs may not be optimal for the larger forest dormice; pre-hibernation increase in body mass might lead to sampling bias in ecological data
<p>Biologists commonly use nest boxes to study small arboreal mammals, including forest dormouse (Dryomys nitedula). Hibernating dormouse species often experience pronounced seasonal variations in body mass, which might lead to sampling biases if it is not taken into account when designing nest boxes. In our study of forest dormouse, we noticed that the entrance hole of nest boxes had been gnawed on. We hypothesized that this behavior was exhibited by individual dormice who had higher body mass and, therefore, were unable to pass through the entrance holes. To test our hypothesis, we categorized individual dormice present inside nest boxes based on their body mass; then compared the seasonal body mass dynamics with the timing of the gnawing behavior. We also compared nest box occupancy by forest dormouse before and after the gnawing behavior. Interestingly, we found that the gnawing behavior was displayed exclusively when part of the dormouse population increased considerably in body mass, which supports our hypothesis. Additionally, nest box occupancy decreased significantly from 20% before to 4.6% after the gnawing behavior. We suggest that researchers use nest boxes with entrance holes larger than 4 cm in future studies of forest dormouse to prevent the possible exclusion of the conspecifics that have higher body mass before hibernation. This type of sampling bias can probably happen in studies of other species, such as fat dormouse, that similarly show pronounced seasonal variations in body mass. We recommend that biologists consider the seasonal body mass dynamics of the target species when designing nest boxes to minimize bias in ecological data and improve management actions.</p>
Appendix 7 detailed risk of bias assessment of the included studies
<p>The file contains the detailed risk of bias assessment of the included studies in the master thesis "NON-PHARMACOLOGICAL INTERVENTIONS FOR THE MANAGEMENT OF PAIN IN PATIENTS WITH SPINAL CORD INJURY – A SYSTEMATIC REVIEW"</p>
Biased tracers as a probe of beyond-ΛCDM cosmologies
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Biased tracers as a probe of beyond-ΛCDM cosmologies" (<a href="https://arxiv.org/pdf/2206.14179.pdf">https://arxiv.org/pdf/2206.14179.pdf</a>).<br> <br> This paper has also been published in A&A and can be found in Volume 668, December 2022, Article Number A56, which can be accessed at this link: <a href="https://www.aanda.org/articles/aa/full_html/2022/12/aa44405-22/aa44405-22.html">https://www.aanda.org/articles/aa/full_html/2022/12/aa44405-22/aa44405-22.html</a>. The paper is part of the Cosmology (including clusters of galaxies) section and has a DOI of <a href="https://doi.org/10.1051/0004-6361/202244405">https://doi.org/10.1051/0004-6361/202244405</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution, gevolution, Pylians and CLASS, Latfield2, and Rockstar.</li> <li><strong>data</strong>: This directory includes the data for the power spectra and halos for different simulations.</li> <li><strong>figures_Jupyter_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>simulation_settings</strong>: This directory includes the settings files that were used to run the simulations.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "figures_Jupyter_notebooks" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "codes" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "data" directory to access the simulation data.</li> <li>Use the simulation settings files in the "simulation_settings" directory to replicate the simulations.</li> </ol> <p>Note: Some of the simulations may require high computational resources and may take a significant amount of time to run.<br> <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Meta-data of published work titled 'Effect of selection bias on Automatic Colonoscopy Polyp Detection'
<p>Meta-data of published work titled 'Effect of selection bias on Automatic Colonoscopy Polyp Detection' includes:</p> <p>1. Gastrolab-Polyp test dataset </p> <p>2. App release of the developed real-time polyp detector </p> <p>3. Meta-data prepared during test set analysis of the developed app and Polyp detector app on Google Store for five existing datasets like Polypgen, Kvasir, and CVC-Clinic, etc. </p> <p>For more details : https://www.sciencedirect.com/science/article/abs/pii/S1746809423003488</p> <p> </p>
Radiation sensitive MOSFETs irradiated with various positive gate biases (raw data from journal article)
<p>This upload contains raw data from the manuscript "Radiation sensitive MOSFETs irradiated with various positive gate biases". The manuscript was published in the Journal of Radiation Research and Applied Sciences, vol. 14, no. 1 (2021): 353-357; DOI: https://doi.org/10.1080/16878507.2021.1970921</p> <p>The upload consists of a .pdf file of the manuscript and .opj files with raw data related to the figures in the manuscript. </p> <p>This work was partly supported by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558) and the Ministry of Education, Science and Technology Development of the Republic of Serbia (Project No. 43011).</p>
Supplementary material 1 from: Liu M, Burridge CP, Clarke LJ, Baker SC, Jordan GJ (2023) Does phylogeny explain bias in quantitative DNA metabarcoding? Metabarcoding and Metagenomics 7: e101266. https://doi.org/10.3897/mbmg.7.101266
The bioinformatic pipeline of processing metabarcoding data
Supplementary material 3 from: Liu M, Burridge CP, Clarke LJ, Baker SC, Jordan GJ (2023) Does phylogeny explain bias in quantitative DNA metabarcoding? Metabarcoding and Metagenomics 7: e101266. https://doi.org/10.3897/mbmg.7.101266
Scoring scheme for mismatch between DNA template and primers
Supplementary material 2 from: Liu M, Burridge CP, Clarke LJ, Baker SC, Jordan GJ (2023) Does phylogeny explain bias in quantitative DNA metabarcoding? Metabarcoding and Metagenomics 7: e101266. https://doi.org/10.3897/mbmg.7.101266
Composition and biomass of 24 studied samples, and HTS read abundance of studied species
Data and code for "Logistic and preference bias in participatory science butterfly observations"
<p>This repository contains all code needed to replicate the analyses performed in "Identification ease, wing pattern diversity, and family explain taxonomic bias in butterfly observations" by Goldstein, Stoudt, Lewthwait, Shirey, Mendoza, and Guzman.</p> <p> </p> <p>Components</p> <p>The main code to be executed is contained in the "code" folder and should be run in numeric order (00 through 03) with two helper scripts.</p> <p>All but one of the data inputs can be found in the "data" folder. The iNaturalist data is required but not published within this directory. It may be obtained from GBIF at <a href="https://doi.org/10.15468/dl.rhmxtn">this link</a> and should be saved into the "data" folder.</p> <p>Note that, at the request of eButterfly, we have removed location information for eButterfly observations of sensitive species in this repository. Please contact eButterfly directly for the uncensored data.</p> <p> </p> <p>Other notes</p> <p>The code in the "01" file takes a long time to run (all of the GAMs for each species). You can skip ahead to "02" to run meta-analysis code on the species-level results, which we provide in "output" as "estimated_indices_fromGAMs.csv".</p>
Listening test data for "Investigating Range-Equalizing Bias in Mean Opinion Score Ratings of Synthesized Speech"
<p>This is the listening test data for the paper published at Interspeech 2023:<br>"Investigating Range-Equalizing Bias in Mean Opinion Score Ratings of Synthesized Speech"<br>Erica Cooper and Junichi Yamagishi<br>doi: 10.21437/Interspeech.2023-1076</p><p>Please cite this paper if you use this data in your work.</p><p>The audio data used in this study comes from past editions of the Blizzard Challenge, Voice Conversion Challenge, and published samples from ESPnet-TTS. Audio samples are not included in this dataset but instructions for obtaining them are included.</p>
Attention-Bias Modification Treatment for PTSD
ClinicalTrials.gov study NCT01888653. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.