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Supplementary material 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
Table S4. Data set of the ITS1 barcode.: Explanation note: Data set of the ITS1 barcode.
Data and scripts (1) for Storkey et al, "Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models", GMD (2024)
<p><br> ================================================================<br> Data and scripts for producing plots from Storkey et al (2024):<br> "Resolution dependence of interlinked Southern Ocean biases in<br> global coupled HadGEM3 models"<br> ================================================================</p> <p>The plots in the paper consist of 10-year mean fields from the third <br>decade of the spin up and timeseries of scalar quantities for the first<br>150 years of the spin up. The data to produce these plots are stored<br>in the MEANS_YEARS_21-30 and TIMESERIES_DATA directories respectively.</p> <p>Note that due to the size limit on records on Zenodo, the 10-year mean <br>output from the N216-ORCA12 integration has been stored as a separate<br>record.</p> <p>Scripts to produce the plots are in SCRIPT, with section definitions<br>in SECTIONS. Bespoke plotting scripts are included in SCRIPT. They use<br>python 3 including the Matplotlib, Iris and Cartopy packages. The <br>plotting of the timeseries data used the Marine_Val VALSO-VALTRANS <br>package which is available here:</p> <p> https://github.com/JMMP-Group/MARINE_VAL/tree/main/VALSO-VALTRANS </p> <p>Much of the processing of the model output data was performed with the<br>CDFTools package, which is available here:</p> <p> https://github.com/meom-group/CDFTOOLS</p> <p>and the NCO package:</p> <p> https://web.mit.edu/course/13/13.715/nco-2.8.1/doc/</p>
Attenuated sex-related DNA methylation differences in cancer highlight the magnitude bias mediating existing disparities
<p>This is supplementary data for the manuscript, "Attenuated sex-related DNA methylation differences in cancer highlight the magnitude bias mediating existing disparities"</p>
Risk of bias graph
Open the record for dataset details and reuse information.
FIGURE 5 in Anthropologically introduced biases in natural history collections, with a case study on the invertebrate paleontology collections from the middle Cambrian Spence Shale Lagerstätte
FIGURE 5. Composition of the Spence Shale fauna by number of genera, comparing the most recent faunal list based on Kimmig et al., 2019; and results from the PBDB. Genera in the PBDB not present on the most recent list are colored in red. The proportion of genera from each taxonomic group is expressed as a percentage above each count.
FIGURE 2 in Anthropologically introduced biases in natural history collections, with a case study on the invertebrate paleontology collections from the middle Cambrian Spence Shale Lagerstätte
FIGURE 2. Location of the Spence Shale A. Location in the western United States. B. Topographic relief showing the main mountain ranges and collecting localities. The Wasatch Range contains the High Creek and Blacksmith Fork localities, and the Bear River Range contains Emigration Canyon, Oneida and the type locality Spence Gulch. C. Enlargement of the Wellsville Mountain Range (MH=Miners Hollow, CFC=Calls Fort Canyon, DC=Donation Canyon, CC=Cataract Canyon, AC=Antimony Canyon, HC=Hansen Canyon). Modified from Whitaker et al., 2020.
FIGURE 3 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 3. Remains of Paracentrotus lividus from Level A of the Is Mesas deposit (Late Pleistocene). A, 1) Fragment of test consisting of ambulacral and interambulacral plates still sutured together and showing interplate fracturing; 2) Isolated plates; 3) Fragments; 4) Spines. B, Large portion of ambulacral column showing intraplate fragmentation. C, Large portion of interambulacral column. D, Fragmented interambulacral plate. E, Madreporite. F, Hemipyramid. G, Rotula. H, Complete spine. A-C, H Scale bars equal 1 cm. E-G Scale bars equal 0.5 cm.
FIGURE 1 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 1. Location of A, Late Pleistocene deposit of Is Mesas and B, Sa Mesa Longa Beach, Sardinia, Italy.
FIGURE 2. A in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 2. A, Field view of the Upper Pleistocene deposit of Is Mesas. B, Stratigraphic section of the deposit.
Figure S1. Funnel Plot of Logit Transformation of Proportions Against its Standard Errors for Detecting Publication Bias
<p>Figure S1. Funnel Plot of Logit Transformation of Proportions Against its Standard Errors for Detecting Publication Bias, Provided as Supplementary Material for Assessing Publication Bias in the Study of Worldwide Prevalence of Internet Addiction Among Medical Students by Zhandos Salpynov and Co-Authors.</p> <p> </p>
The Intergroup Bias of Empathy among Incoming Medical Students
<p>Dataset linked to the short communication named: "The Intergroup Bias of Empathy among Incoming Medical Students".</p>
Attentional bias for physical activity: eye-tracking data
<p><strong>Dataset related to the paper entitled "Physically active individuals look for more: An eye tracking study of attentional bias". </strong></p> <p>This dataset includes:</p> <p>1) A workbook</p> <p>2) Raw data of the behavioral outcome (i.e., reaction times) of the visual dot probe task</p> <p>"dpt_sample1_27_05_2019.csv" for the sample 1.</p> <p>"dpt_sample2_27_05_2019.csv" for the sample 2.</p> <p>3) Raw data of the eye-tracking outcomes (i.e., Gaze Data)</p> <p>4) Self-reported data</p> <p>"data_all_SR.Rdata" for the sample 1</p> <p>"data_SR1_inhib.RData" for the sample 2</p> <p>4) R script for the data management of the raw RT</p> <p>"Data_management_DPT_RT.R". This script leads to the file "dpt_behavioral_both_sample.RData", which merges the behavioral data of both sample with the self-reported data.</p> <p>5) Raw data of the eye-tracking outcomes</p> <p>"Data_management_DPT_Gaze_13_06_2019". This script leads to the file "dpt_both_sample_EyeTrack.RData", which merge the gaze data of both sample with the self-reported data.</p> <p>6) R script for the models tested on the behavioral and eye-tracking outcomes</p> <p>"Models_zenodo"</p> <p>7) the visual dot probe task (e-prime script)</p> <p>"DPT_Eyetracker.7z". This file contains the e-prime script as well as the images used in the visual dot probe task</p>
Fig. 5 in Biases in parasite biodiversity research: why some helminth species attract more research than others
Fig. 5. Box plots (median and interquartile range) showing the number of citations received by species descriptions following their publication as a function of (A) their IUCN conservation status and (B) their IUCN human use status. Data from the Zoological Record™ database. Extreme values (25 species descriptions with more than 50 citations) are excluded to avoid distorting the figures.
Risk of bias assessment for Corticosteroids for leptospirosis treatment
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A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics
<p><strong>Motivation</strong></p> <p>Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections. </p> <p><strong>Data Record</strong></p> <p><strong>CMIP6</strong></p> <p>We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal.</p> <p>Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2] approach respectively.</p> <p>The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target.</p> <p>The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where:</p> <ul> <li> <p>"VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively),</p> </li> <li> <p>"GCM" denotes the CMIP6 GCM name,</p> </li> <li> <p>"X" indicates the warming target compared to the preindustrial period,</p> </li> <li> <p>"yyyy" represents the year index (0001-0027) of the 27-year sample</p> </li> </ul> <p><strong>Table 1 </strong>CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT.</p> <div> <table> <tbody> <tr> <td> <p>GCM</p> </td> <td> <p>Realization</p> </td> <td> <p>GCM grid spacing</p> </td> <td> <p>Ta</p> </td> <td> <p>Tw</p> </td> <td> <p>WBGT</p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.1ox1.125o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>r1i1p2f1</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-CM2-SR5</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CNRM-CM6-1</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> </td> </tr> <tr> <td> <p>EC-Earth3</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.7ox0.7o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>GFDL-ESM4</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.0ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-LL</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-MM</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>0.55ox0.83o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KACE-1-0-G</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KIOST-ESM</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.9ox1.9o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC-ES2L</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC6</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.93ox0.93o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-LR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.85ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> </tbody> </table> </div> <p><strong>ERA5</strong></p> <p>We also provide hourly Tw and WBGT derived from ERA5 reanalysis during 1950-2023 to enable analyses of heat stress changes from historical period to a warmer climate.</p> <p><strong> </strong></p> <p><strong>Data Access</strong></p> <p>An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus. The bias-corrected CMIP6 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?origin_id=6538f53a-1ea7-4c13-a0cf-10478190b901&origin_path=%2F">Globus Link1</a>, and the ERA5 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?destination_id=63242aea-d3e0-4aa4-9372-0e19dd0c6539&destination_path=%2F">Globus Link2</a>. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID)—"6538f53a-1ea7-4c13-a0cf-10478190b901" for CMIP6, and “63242aea-d3e0-4aa4-9372-0e19dd0c6539” for ERA5 dataset—in Globus.</p> <p><strong>Dataset Validation</strong></p> <p>We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts.</p> <p>For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.</p> <p><strong><br><br><br></strong></p>
Investigating Gender Bias in Large Language Models Through Text Generation
<p>Presented at the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024). Published in the ACL Anthology.</p>
Table 1 in Removal of historical taxonomic bias and its impact on biogeographic analyses: a case study of Neotropical tardigrade fauna
<p><b>Table 1.</b> Details regarding species accumulation curves (SACs) of each biogeographic area for all data (‘false cosmopolitan’ and ‘indigenous species’) and only ‘indigenous data’. Estimated total species’ richness (asymptote) and residual sum-of-squares (RSS) were obtained when fitting each curve to an asymptotic model.</p><table><tbody><tr><th><b>Area</b></th><th><b>Dataset</b></th><th><b>Number of surveys</b></th><th><b>Observed richness</b></th><th><b>Estimated total number of species (asymptote)</b></th><th><b>Residual sum-of-squares (RSS)</b></th></tr></tbody><tbody><tr><th>Neotropical region</th><td>All data (‘false cosmopolitan’ and ‘indigenous species’)</td><td>103</td><td>186</td><td>245</td><td>187.600</td></tr><tr><td>‘Indigenous data</td><td>78</td><td>96</td><td>155</td><td>15.750</td></tr><tr><th>Andean region</th><td>All data (‘false cosmopolitan’ and ‘indigenous species’)</td><td>50</td><td>105</td><td>141</td><td>40.710</td></tr><tr><td>‘Indigenous data</td><td>41</td><td>43</td><td>70</td><td>2.428</td></tr><tr><th>South America transition zone</th><td>All data (‘false cosmopolitan’ and ‘indigenous species’)</td><td>38</td><td>66</td><td>105</td><td>3.543</td></tr><tr><td>‘Indigenous data</td><td>24</td><td>26</td><td>65</td><td>0.078</td></tr><tr><th>Mexican transition zone</th><td>All data (‘false cosmopolitan’ and ‘indigenous species’)</td><td>13</td><td>41</td><td>84</td><td>0.004</td></tr><tr><td>‘Indigenous data’</td><td>6</td><td>11</td><td>55</td><td><0.001</td></tr></tbody></table>
A comprehensive model for separating systematic bias and noise in metabolomic timecourse data -- A nonlinear B-spline mixed effect approach
<p>Code for the analysis and validation of the systematic error correction model presented.</p>
Energy distribution of negatively and positively charged ions in a magnetron sputtering discharge with a tungsten cathode and a positively biased anode in an argon/oxygen gas mixture.
<p>The energy distribution of negatively and positively charged ions in a magnetron discharge with a positively biased anode is investigated. A tungsten cathode operated in an argon/oxygen gas mixture is employed. The magnetron is operated either in direct current (DCMS) or in high power impulse magnetron sputtering (HiPIMS) mode.</p>
Data from: Origin of tensile strength of a woven sample cut in bias directions
Textile fabrics are highly anisotropic, so that their mechanical properties including strengths are a function of direction. An extreme case is when a woven fabric sample is cut in such a way where the bias angle and hence the tension loading direction is around 45° relative to the principal directions. Then, once loaded, no yarn in the sample is held at both ends, so the yarns have to build up their internal tension entirely via yarn–yarn friction at the interlacing points. The overall fabric strength in such a sample is a result of contributions from the yarns being pulled out and those broken during the process, and thus becomes a function of the bias direction angle θ, sample width W and length L, along with other factors known to affect fabric strength tested in principal directions. Furthermore, in such a bias sample when the major parameters, e.g. the sample width W, change, not only the resultant strengths differ, but also the strength generating mechanisms (or failure types) vary. This is an interesting problem and is analysed in this study. More specifically, the issues examined in this paper include the exact mechanisms and details of how each interlacing point imparts the frictional constraint for a yarn to acquire tension to the level of its strength when both yarn ends were not actively held by the testing grips; the theoretical expression of the critical yarn length for a yarn to be able to break rather than be pulled out, as a function of the related factors; and the general relations between the tensile strength of such a bias sample and its structural properties. At the end, theoretical predictions are compared with our experimental data.
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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.