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1,663 results for “BIAS”
Pervasive selection biases inferences of the species tree
<p>Supplementary files, scripts, and data of 'Pervasive selection biases inferences of the species tree' by Borges, Boussau, Szöllősi, and Kosiol</p>
Figure S1 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure S1. Map of the Gigantometra gigas mitogenome using Sanger method (GenBank accession number: MF177288). Genes in the outer circle indicate the direction of transcription of the majority strand (J-strand), and those in the inner circle indicate that of the minority strand (N-strand). The GC content, GC skew+, and GC skew- are separately shown in the circle.
Figure 6 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure 6. Two examples of the heteroplasmic sites in Sanger sequencing which correspond to the differently sequenced sites. Panels A and B indicate the sites at which the second-peak is obviously higher than the third-peak and fourth-peak, and the base state of the second-peak can be obtained by at least one result of HTS. The different fluorescence densities of base situated at np 1923 in the cox1 are shown in the panel A, and the panel B shows the nucleotides with amino acids at np 1923 in the results of Sanger and HTS methods. The nucleotides are C in the results of HTS sequencing, while the corresponding nucleotides are T in the results of Sanger method in both positions, and the different nucleotides lead not to the amino acids changed. Panels C and D indicate the site at the unobvious second-peak, which is slightly higher than the third-peak and fourth-peak, and the base state of the second-peak can also be obtained by at least one result of HTS. Panel C shows the unobvious second-peak at np 7125, and the nucleotide and amino acid of the site in the results of Sanger and HTS methods are shown in panel D. The amino acids are listed using single-letter amino acid abbreviations.
Figure 4. Intraspecific pairwise K2P in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure 4. Intraspecific pairwise K2P distance of G. gigas based on barcode fragment size of cox1 (Sanger). The red boxplot shows the genetic distances of individuals in all three collecting sites, and the boxplots (blue, green, and yellow) separately show the distances of individuals within each place (HNYG, HNDL, and VIET). The pink boxplot shows the distances of the corresponding cox1 sequences obtained by the two sequencing methods. Abbreviation: HNYG—Yinggeling Nature Reserve, Hainan; HNDL— Diaoluoshan Nature Reserve, Hainan; VIET—northern Vietnam.
Figure S5 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure S5. The coverage of short fragments at each position in the assembly results of HTS. The three results of HTS method were separately used as reference sequences to be mapped back onto the corresponding HTS scaffolds, and the mitochondrial genes were shown below the corresponding coverage. The scale bar had an indicator at the mean coverage level and the coverage for each nucleotide position was indicated by the height of the blue line.
Figure 3 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure 3. The different nucleotides in the ITS-1 and ITS-2 regions are shown. The result shows the different nucleotides at nucleotide position np 1897 (G nucleotide and T nucleotide) and np 2790 (C nucleotide and T nucleotide) obtained by Sanger and HTS methods.
Figure 1. Gigantometra gigas. A. Female, dorsal view. B. Male, dorsal view. C in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure 1. Gigantometra gigas. A. Female, dorsal view. B. Male, dorsal view. C. The narrow distribution of G. gigas.
Risk of bias assessment for the Cochrane review "Colchicine for the treatment of COVID-19"
<p>Risk of bias assessment and support for judgement with the RoB 2 tool for the Cochrane review "Colchicine for the treatment of COVID-19"</p>
Dataset for "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets"
<p>This dataset is associated with a research article entitled "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets".</p>
Lying in a 3T MRI scanner induces neglect-like spatial attention bias
<p>Exposing subjects to the magnetic field of a 3T MRI scanner stimulates the vestibular organ and thereby induces - besides a VOR - horizontal biases in visual search and in subjective straight ahead, which are similar to those seen in stroke patients with spatial neglect.</p>
Bias-corrected monthly air temperature data over South Siberia for 1979-2020 (CTSS 1.0)
<p>Bias-<strong>C</strong>orrected Air <strong>T</strong>emperature data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CTSS 1.0</strong>) contains monthly air temperature at 2m for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2020. CTSS data were combined from monthly total air temperture data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and temperature data records from ground weather stations (Bulygina et al., 2014). The ERA5 data were scaled according to the derived correction coefficient. The additive coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CTSS reproduces the spatial variability of temperature more precisely than can be done from the weather station observation network. Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at: <a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p><em>Bulygina O.N., Razuvaev V.N., Trofimenko L.T., Shvets N.V., 2014. Description of the monthly air temperature data at weather stations of Russia.</em> Certificate of state registration of the database No. 2014621485 Available at: <a href="http://meteo.ru/data/156-temperature"><em>http://meteo.ru/data/156-temperature</em></a></p>
Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations: video supplements
<p>These are video supplements cited in the manuscript "Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations" that has been submitted to Geoscientific Model Development (GMD; MS No.: gmd-2018-192).</p> <p>The animations (with a 10yr running window) show the development of temperature biases in the deep ocean over a period of 100 years in two pre-industrial configurations with the AWI Climate Model: AWI-CM-LR and AWI-CM-HR. Meridional biases along the 30.5°W transect through the Atlantic Ocean (S3 and S4; animated version of Fig.8 for LR and HR) and maps of along-isopycnal biases (sigma_1=31.8) are shown (S1 and S2). Time axes have been added compared to version 1.</p>
Bias-corrected data from the preoperational MiKlip system for decadal climate predictions used in the PNRA-IPSODES project
<p>This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the Italian research project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output.</p> <p>Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)</p>
Risk of bias in assessed studies for LSR: Antiplatelet agents for the treatment of adults with COVID-19
<p>This is our RoB2 excel sheet, in which detailled information about our risk of bias rating can be found. This concerns the outcomes of studies included in our LSR Antiplatelet agents for the treatment of adults with COVID-19. The first version is published before publication of the review.</p>
cb-oura-1.0 : Generic climate scenarios from bias-adjusted CMIP5 global models
<p><strong>Context </strong></p> <p>The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, <a href="https://www.ouranos.ca/">Ouranos</a> is working to create a set of operational multipurpose climate scenarios. The initial version of “Scénarios Génériques” (generic scenarios, acronym cb-oura-1.0) is used mainly in Ouranos’ work to provide a consistent image of the changing climate over the North East of North America, principally the province of Québec. Cb-oura-1.0 was produced in 2016 by downscaling and bias-adjusting a selection of global climate model simulations available through the CMIP5 program. </p> <p><strong>Climate simulations </strong></p> <table> <caption>Climate simulations in the ensemble</caption> <thead> <tr> <th scope="col">Modeling center</th> <th scope="col">Acronym</th> <th scope="col">Model</th> <th scope="col">RCP</th> <th scope="col">Status*</th> </tr> </thead> <tbody> <tr> <td><strong>College of Global Change and Earth System Science, Beijing Normal University</strong></td> <td>GCESS</td> <td>BNU-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Canadian Centre for Climate Modelling and Analysis</strong></td> <td>CCCMA</td> <td>CanESM2</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Centro Euro-Mediterraneo per I Cambiamenti Climatici</strong></td> <td>CMCC</td> <td>CMCC-CMS</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia</strong></td> <td>CSIRO-BOM</td> <td>ACCESS1.3</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institute for Numerical Mathematics</strong></td> <td>INM</td> <td>INM-CM4</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institut Pierre-Simon Laplace</strong></td> <td>IPSL</td> <td>IPSL-CM5A-LR</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td>IPSL-CM5B-LR</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Met Office Hadley Centre</strong></td> <td>MOHC</td> <td>HadGem2</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)</strong></td> <td>MPI-M</td> <td>MPI-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Norwegian Climate Centre</strong></td> <td>NCC</td> <td>NorESM</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>NOAA Geophysical Fluid Dynamics Laboratory</strong></td> <td>NOAA-GFDL</td> <td>GFDL-ESM2M</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> </tbody> </table> <p>From the complete ensemble of RCP 4.5 and 8.5 driven CMIP5 climate simulations, a selection of 22 simulations (11 per RCP) was made using a clustering ensemble reduction methodology (Casajus et al. 2016). This objective selection method identifies a reduced number of simulations that best represent the overall ensemble. Input criteria for the reduction were the monthly changes between the present (1981-2010) and two future horizons (2041-2070 and 2071-2100), at 15 regions distributed across Canada, for three variables (mean daily maximum temperature, mean daily minimum temperature and total precipitation). An initial selection of 16 simulations shows a distribution projected changes for the 12 (months) x 2 (horizons) x 15 (regions) x 3 (variables) indices that is not statistically different from the complete ensemble. A small number of simulations were subsequently added to have a complete set with both RCPs represented equally (11 members for each emission scenario). </p> <p><strong>Reference dataset </strong></p> <p>The bias-adjustment reference (or target) is a gridded observation dataset produced by Natural Resources Canada (McKenney et al., 2011; et Hutchinson et al. ,2009). It uses the ANUSPLIN interpolation method over station observations to derive daily grids of minimum and maximum temperature, as well as total precipitation for the Canadian landmass. The grid has a resolution of 10 km x 10 km and cover the time period from 1950 to 2013. </p> <p>As this dataset is not available over the United States, it was merged with another observation interpolation dataset produced by Livneh et al. (2015) in order to enable the production of bias-corrected climate scenarios covering a portion of the northern United States. </p> <p><strong>Coverage </strong></p> <p>The final version of this dataset covers a region covering the Atlantic provinces, Québec, Ontario, Manitoba and Saskatchewan and part of the northern United States: From 120°W to 54°W and from 40°N to 62°N. </p> <p>It contains the daily minimum temperature, daily maximum temperature and daily precipitation flux, covering the period 1950 to 2100. </p> <p><strong>Bias-adjustment </strong></p> <p>The global simulations where downscaled to the reference grid using bilinear interpolation and then bias-adjusted with a 1-D quantile mapping method, as described by Gennaretti et al. (2015). A moving window of 31 days was used to adjust each day of the year, using 50 quantiles to define the statistical distributions to match. The long-term linear trends of the temperature variables were preserved explicitly. </p> <p><strong>Climate indicators </strong></p> <p>This dataset is used to in the first versions (up to 1.3) of Ouranos’ <a href="https://www.ouranos.ca/en/climate-portraits">Climate Portraits </a>website. A selection of 26 seasonal and annual climate indicators were computed from the daily scenarios, using the xclim software package (Logan et al. 2022). The "virtual indicator module" used for the computation is made available here in the "indicators.yml" file.</p> <p>On the Climate Portraits website, the information is presented from three aspects: spatial, temporal and summary. This repository stores the reduced ensemble data as shown on the website. Filenames are constructed as "{aspect}_{indicator}_{season}.nc".</p> <ul> <li> <p>Maps (files "spatial_*") : Climate indicators for each bias-adjusted climate simulation and for a given RCP emission scenario are averaged over 30-year horizons. Ensemble percentiles are computed in order to summarize climate model uncertainty. In particular the 10, 25, 50, 75 and 90th percentiles over the 11 members are calculated for each RCP. </p> </li> <li> <p>Timeseries (files "temporal_*") : Climate indicators for each bias-adjusted simulation are averaged spatially over each region for every time step (annual or seasonal). The ensemble statistics are computed by first pooling all regional average values for the 11 members using within a centred 30-year window and then calculating percentile values (same as above) on the pooled data. </p> </li> </ul> <ul> <li> <p>Summary (files "summary_*") : The indicators are averaged over each region and then over 30-year horizons. The ensemble statistics (same as above) are then computed. </p> </li> </ul> <p>In versions 2.x of the app, this data will be presented as "CMIP5".</p> <p><strong>Data availability </strong></p> <p>This repository stores the climate indicator ensemble statistics as shown on the Climate Portraits website and described above. The complete daily dataset is too large for this platform.</p> <p>The complete daily dataset is available through the public THREDDS server of the PAVICS platform maintained by Ouranos. This data might be removed in the future. When this is the case, please contact us for data requests.<br> <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html</a> </p> <p>The annual and season indicators of the Climate Portraits website are available on the same server, along with a few more indicators not shown on the app. <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html</a></p> <p><em>Terms of use</em>: Use of this dataset should be acknowledged as 'Data produced and provided by the Ouranos Consortium on Regional Climatology and Adaptation to Climate Change'. Furthermore, the modeling groups from which the bias-adjusted climate scenarios were constructed must also be acknowledged, please refer to: The Coupled Model Intercomparison Project <a href="https://pcmdi.llnl.gov/mips/cmip5/citation.html.">https://pcmdi.llnl.gov/mips/cmip5/citation.html.</a></p>
Bioclimatic outputs for Last Glacial Maximum South America for LPX, bias-corrected to pollen records
<p>LPX model output for South America for the Last Glacial Maximum. We provide outputs for LPX driven by four GCM simulated climates along with an ensemble average:</p> <ul> <li>MIROC.tar.gz: LPX driven by MIROC3.2</li> <li>FGOALS.tar.gz: driven by FGOALS-1.0g</li> <li>HAD.tar.gz: HadCM3M2</li> <li>CNRM.tar.gz: CNRM-CM33</li> <li>Ensemble.tar.gz: The mean of each output variable for the four models.</li> </ul> <p> </p> <p>Driving data comes from the Palaeoclimate Modelling Intercomparison Project Phase II (PMIP2)<sup>1,2</sup>. See Sato et al. <sup>3</sup> for modelling protocol.</p> <p>Each model’s directory contains “uncorrected” and “corrected” directories. With each of these are bioclimatic maps outputted from LPX and biome information:</p> <ul> <li>fpc.nc: fractional projected cover of all vegetation</li> <li>height.nc: mean height of vegetations</li> <li>gdd.nc: Growing Degree Days base 2</li> <li>tropical.nc: proportion of vegetated areas taken up by tropical trees and c4 grasses</li> <li>temperate.nc: proportion of vegetated areas taken up by temperate trees and c3 grasses</li> <li>evergreen.nc: proportion of tree cover that is composed of evergreen trees</li> <li>biome.nc: the assigned biomes from these data, based on a modified version of Sato et al. <sup>3</sup>. See “biomisation” below.</li> <li>cluster.nc: In “corrected” only. The spatial location of kmean clusters of fpc vs height. See <sup>4</sup> for details.</li> </ul> <p> </p> <p>“Uncorrected” is from Sato et al. <sup>3</sup>. “Corrected” is bias-corrected to match the 42 pollen-core observations taken from Marchant et al. <sup>5</sup> We do this by shifting the total vegetation cover and composition, height, and growing degree day (GDD) DVM output to the closest boundary of the corresponding biome of the pollen core in that specific location. We then extrapolate this correction between pollen-core locations across the Neotropics. See Kelley et al. <sup>4</sup> for details.</p> <p> </p> <p><strong>Biomeisation</strong></p> <p>"Biomeisation.png" displays the scheme. We primarily split biomes by FPCs of 0.3 and 0.6, with biomes > 0.6 split by a height of 10m. Forests (>0.6 FPC and > 10m) is split by GDD, Evergreen FPC (EG) and Tropical or temperate FPC (TR, TM). Likewise, we split FPCs> 0.6 and heights <10m into savanna, woodland and parkland using EG and TR. We additionally assign Tropical savanna >5m to Woodland/Tropical savanna. We divided desert, dry grassland and (shrub)-tundra by FPC of 0.3 and GDD of 350°C. See Kelley et al. <sup>4</sup> for details.</p>
Data for: Five decades of data yield no support for adaptive biasing of offspring sex ratio in wild baboons (Papio cynocephalus)
<p>Over the past 50 years, a wealth of testable, often conflicting, hypotheses has been generated about the evolution of offspring sex ratio manipulation by mothers. Several of these hypotheses have received support in studies of invertebrates and some vertebrate taxa. However, their success in explaining sex ratios in mammalian taxa, and especially in primates, has been mixed. Here, we assess the predictions of four different hypotheses about the evolution of biased offspring sex ratios in the well-studied baboons of the Amboseli basin in Kenya: the Trivers-Willard, female rank enhancement, local resource competition, and local resource enhancement hypotheses. Using the largest sample size ever analyzed in a primate population (n = 1372 offspring), we test the predictions of each hypothesis. Overall, we find no support for adaptive biasing of sex ratios. Offspring sex is not consistently related to maternal dominance rank or biased towards the dispersing sex, nor it is predicted by group size, population growth rates, or their interaction with maternal rank. Because our sample size confers power to detect even subtle biases in sex ratio, including modulation by environmental heterogeneity, these results suggest that adaptive biasing of offspring sex does not occur in this population.</p>
UKCP18 RCM precipitation and temperature bias corrected using ISIMIP3BA change-preserving quantile mapping.
<p>We present bias-corrected UK Climate Projections 2018 (UKCP18; Met Office Hadley Centre, 2018) regional datasets for temperature, precipitation, and potential evapotranspiration (1981-2080). All 12 members of the 12 km ensemble were corrected using quantile mapping and a change-preserving variant (Lange, 2019; Lange, 2020). Both methods effectively reduce biases in multiple statistics, while maintaining projected climatic changes. We provide guidance on using the bias-corrected datasets for climate change impact assessment. Please find a detailed description and evaluation in the metadata and accompanying data paper (Reyniers et al., 2025).</p> <p>---</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, <em>8 March 2022</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). <em>GMD,</em> <em>12</em>(7), 3055-3070.</p> <p>Lange, S. (2020). ISIMIP3BASD (2.4.1). Zenodo. https://doi.org/10.5281/zenodo.3898426</p> <p>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., & He, Y. (2025). Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain. <em>Earth System Science Data</em>, <em>2025, 17(5)</em>, 2113–2133.</p>
The imbalance of wanting and liking contribute to a bias of internal attention towards positive consequences of tobacco smoking
<p>Data set used for the publication entitled "<strong>The imbalance of wanting and liking contribute to a bias of internal attention towards positive consequences of tobacco smoking</strong>".</p> <p>Variable names:</p> <p>sex: gender of respondent; classes: smoker profile; attempt: quit attempt in the past 12 months; imoportance: importance attributed to quit smoking; plan: whether they plan to quit smoking; prob1-prob3: how likely they will not smoke (1) within a year, (2) within 5 years, (3) within 10 years; time: when they plan to quit; nicotine_dependence: Fagerstöm score; smoking_freq: frequency of smoking; smokng_quantity: number of cigarettes on a day they smoke; wb: wanting-before; wd: wanting-during; wa: wanting-after; lb:liking-before; ld: liking-during; la: liking-after; ist-before: wd minus ld; ist_during: wd-ld; ist_after: wa-la; i1-14: importance scores of smoking consequences; regret: regret about start smoking; ease: assumed difficulity of quitting; environment: amont of smokers around; people: how much smoking bothers others around.</p> <p>For more info please email domonkos.file@gmail.com </p>
Deep Reinforcement Learning Enables Better Bias Control in Benchmark for Virtual Screening
<p>This compressed file contains all datasets made for the validation of MUBDsyn.</p><ul><li>datasets_int_val: 17 cases in this folder are derived from <a href="https://github.com/jwxia2014/ULS-UDS">MUBD for GPCRs</a>. MUBDreal was made by <a href="https://github.com/jwxia2014/MUBD-DecoyMaker2.0">MUBD-DecoyMaker2.0</a> and MUBDsyn was made by <a href="https://github.com/taoshen99/MUBDsyn">MUBD-DecoyMakersyn</a>.</li><li>datasets_ext_val_classical_VS: Five cases in this folder are derived from the shared cases of MUV and DUD-E. The active sets of MUV were taken as the input to make corresponding MUBD datasets. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_SI_classical_VS: DeepCoy and TocoDecoy were used to make the datasets corresponding to the same five cases above. The data of DeepCoy was directly retrieved from <a href="https://opig.stats.ox.ac.uk/resources">DeepCoy resources at OPIG</a> while topology decoys of TocoDecoy_9W were made based on the scripts provided at <a href="https://github.com/5AGE-zhang/TocoDecoy">TocoDecoy GitHub Repository</a>. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_ML_VS: Ten cases in this folder are derived from <a href="http://nrlist.drugdesign.fr/">NRLiSt-BDB</a>. Corresponding MUBD datasets were made as described above.</li></ul><p>All these datasets can be used for the reproduction of validation performed in the manuscript or to benchmark various virtual screening methods.</p>
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.