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1,663 results for “BIAS”
Inductive biases of neural network modularity in spatial navigation
<p>The brain may have evolved a modular architecture for reward-based learning in daily tasks, with circuits featuring functionally specialized modules that match the task structure. We propose that this architecture enables better learning and generalization than architectures with less specialized modules. To test this hypothesis, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the architecture that largely segregates computations of state representation, value, and action into specialized modules enables more efficient learning and better generalization. The behavior of agents with this modular architecture also resembles macaque behaviors more closely. Investigating the latent state computations in these agents, we discovered that the learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to a Kalman filter. These results shed light on the possible rationale for the brain's modular specializations and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.</p>
Dataset for "Is Wikipedia Politically Biased?"
<p><span><span>·<span> </span></span></span><span>This work aims to determine whether there is evidence of political bias in English Wikipedia articles.</span></p> <p><span><span>·<span> </span></span></span><span>Wikipedia is one of the most visited domains on the Web, attracting hundreds of millions of unique users per month. Wikipedia content is also routinely used for training Large Language Models (LLMs), which are the core engines driving cutting edge AI systems.</span></p> <p><span><span>·<span> </span></span></span><span>To study political bias in Wikipedia content, we analyze the sentiment (<em>positive</em>, <em>neutral</em> or <em>negative</em>) with which a set of target terms (N=1,628) with political connotations (i.e. names of recent U.S. presidents, U.S. congressmembers, U.S. Supreme Court Justices, or Prime Ministers of Western countries) are used in Wikipedia articles. </span></p> <p><span><span>·<span> </span></span></span><span>We do not cherry pick the set of terms to be included in the analysis but instead use publicly available pre-existing lists of terms from Wikipedia and other sources.</span></p> <p><span><span>·<span> </span></span></span><span>We find a mild to moderate tendency in Wikipedia articles to associate public figures politically aligned right-of-center with more negative sentiment than left-of-center public figures.</span></p> <p><span><span>·<span> </span></span></span><span>These favorable associations for left-leaning public figures are apparent for names of recent U.S. Presidents, U.S. Supreme Court Justices, U.S. Senators, U.S. House of Representatives Congressmembers, U.S. State Governors, Western countries’ Prime Ministers, and prominent U.S. based journalists and media organizations.</span></p> <p><span><span>·<span> </span></span></span><span>Despite being common, these politically asymmetrical sentiment associations are not ubiquitous. We find no evidence of them in the sentiment with which names of U.K. MPs and U.S. based think tanks are used in Wikipedia articles.</span></p> <p><span><span>·<span> </span></span></span><span>We also find larger associations of negative emotions (i.e. <em>anger</em> and <em>disgust</em>) with right-leaning public figures and positive emotion (i.e. <em>joy</em>) with left-leaning public figures.</span></p> <p><span><span>·<span> </span></span></span><span>The trends just described constitute suggestive evidence of political bias embedded in Wikipedia articles.</span></p> <p><span><span>·<span> </span></span></span><span>We also find some of the aforementioned sentiment political associations embedded in Wikipedia articles popping up in OpenAI’s language models. This is suggestive of the potential for biases in Wikipedia content percolating into widely used AI systems.</span></p> <p><span><span>·<span> </span></span></span><span>Wikipedia’s <em>neutral point of view policy</em> (NPOV) aims for articles in Wikipedia to be written in an impartial and unbiased tone. Our results suggest that Wikipedia’s <em>neutral point of view policy</em> is not achieving its stated goal of political viewpoint neutrality.</span></p> <p><span><span>·<span> </span></span></span><span>This report highlights areas where Wikipedia can improve in how it presents political information. Nonetheless, we want to acknowledge Wikipedia’s significant and valuable role as a public resource. We hope this work inspires efforts to uphold and strengthen Wikipedia’s principles of neutrality and impartiality.</span></p> <p>The set of 1,653 target terms used in our analysis, the sample of Wikipedia paragraphs where they occur (as of 2022) and their sentiment and emotion annotations are provided in the files:</p> <p>- WikipediaParagraphsWithTargetNGramsAndSentiment.csv</p> <p>- WikipediaParagraphsWithTargetNGramsAndEmotion.csv</p>
Media Bias Aware Simulation Dataset
<p>We utilized the Hyperpartisan News Detection Dataset, released with the SemEval-2019 Task 4 Hyperpartisan detection task, due to its extensive bias labels. To ensure accurate bias labels, we used the Overlap-checking (1:1) model, retaining only articles where the distant supervision bias labels matched the model's predictions. This validation process resulted in 409,757 articles.</p> <p>These articles span from 1960 to 2018, with a sparse distribution in earlier years. We focused on articles from May 1, 2017, to December 31, 2017, resulting in a subset of 72,940 news articles, ensuring a consistent daily news flow. We processed this subset by removing HTML tags and special characters and generating news summaries using PEGASUS. We used Latent Dirichlet Allocation (LDA) to categorize the articles into 20 news themes, based on perplexity scores.</p> <p>This dataset was then fed into the simulation framework, with a cut-off date of June 24, 2017.</p> <ul> <li><strong>News Recommendation Dataset</strong>: Includes user-item interaction records from May 1 to June 24, providing users' reading histories and interacted news articles. <ul> <li><strong>Training Split</strong>: Data from May 1 to June 17, used to train news recommendation algorithms.</li> <li><strong>Evaluation Split</strong>: Data from June 17 to June 24, used to evaluate the trained recommendation algorithms.</li> </ul> </li> <li> <p><strong>Candidate News Dataset</strong>: News articles published from June 25 to December 31, presented to users during simulations.</p> </li> </ul> <p>For more information, please visit <a href="https://github.com/ruanqin0706/UserRecSimulation" target="_new" rel="noreferrer">https://github.com/ruanqin0706/UserRecSimulation</a>.</p>
Supplementary data and code "Wealth over Woe: global biases in hydro-hazard research"
<p>Supplementary data and code "Wealth over Woe: global biases in hydro-hazard research". V0.2</p>
FIGURE 3 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 3. Percentage of taxonomic groups in the KUMIP Spence Shale collection by specimen counts.
Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets
<h1><strong>Introduction</strong></h1> <p>We provide DEMs from Pléiades stereo images and TanDEM-X acquired in March 2021 and their difference DEM (dDEM) used to estimate the elevation bias due to radar penetration on Grosser Aletschgletscher, Switzerland. Additionally, we provide the reference DEM (i.e., swissALTI3D) used for the Pléiades DEM co-registration and TanDEM-X production, stable terrain masks used for the co-registration and the glacier outline</p> <p>These are supplementary datasets associated to the publication by Bannwart et al., (2024a): “Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland”, Journal of Glaciology. DOI: <a href="https://doi.org/10.1017/jog.2024.37">https://doi.org/10.1017/jog.2024.37</a></p> <h1><strong>Datasets and Citation</strong></h1> <p>The following datasets have been used and/or produced as part of the study above. Below you find a description of the individual datasets and their generation, including a citation example for each dataset. In general, when one or more datasets are used, both the individual dataset as well as the study (Bannwart et al. 2024) need to be cited. If you refer to the entire dataset, cite the Zenodo entry as well as the study.</p> <ul> <li> <p>TanDEM-X_DEM_6m_EPSG_32632_non_coreg.tif</p> </li> <li> <p>TanDEM-X_DEM_6m_EPSG_32632_coreg.tif</p> </li> <li> <p>Pleiades_DEM_2m_EPSG_32632_non_coreg.tif</p> </li> <li> <p>Pleiades_DEM_6m_EPSG_32632_coreg.tif </p> </li> <li> <p>swissALTI3D_2m_EPSG_32632.tif</p> </li> <li> <p>dDEM_non_corrected_6m_EPSG_32632.tif</p> </li> <li> <p>dDEM_corrected_6m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_Pléiades_2m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_TanDEM-X_6m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_TanDEM-X_modified_6m_EPSG_32632.tif</p> </li> <li> <p>glacier_outline_2021.shp</p> </li> </ul> <h1><strong>Data and Methods</strong></h1> <h2>TanDEM-X DEMs (non co-registered and co-registered):</h2> <p>We used an InSAR DEM generated from TanDEM-X Coregistered Single look Slant range Complex (CoSSC) data provided by the German Aerospace Center (DLR) through the project XTI_GLAC7746. The DEM originates from a single bistatic X-band (9.65 GHz) InSAR acquisition in the polarisations VV and HH from 30 March 2021 at 5:45 Central Europe Time. We used the operational processing system of the TanDEM-X mission to generate the DEM. The removal of artefacts resulted in data voids. The TanDEM-X DEM was then co-registered with the Pléiades DEM (see publication).</p> <p>Resolution: 6 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong></p> <p>TanDEM-X © DLR 2024</p> <p>DLR-IMF: TanDEM-X Payload Ground Segment - CoSSC Generation and Interferometric Considerations, German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF) Oberpfaffenhofen, Germany, Issue 1.0, available at: https://tandemx-science.dlr.de/ (last access: 3 May 2024), doc. TD-PGS-TN-3129.</p> <p>DLR-EOC: TanDEM-X Ground Segment – DEM Products Specification Document, German Aerospace Center (DLR) – Earth Observation Center (EOC), EOC, DLR, Oberpfaffenhofen, Germany, 3.2 Edn., available at: https://tandemx-science.dlr.de/ (last access: 3 May 2024), doc. TD-GS-PS-0021, 2018.</p> <p><strong>Citation example:</strong></p> <p>“The TanDEM-X DEM (DLR 2024; DLR-IMF 2024; DLR-EOC 2024) from the study of Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC-SA 4.0 International License (Attribution-NonCommercial-ShareAlike).</p> <h2>Pléiades DEMs (non co-registered and co-registered):</h2> <p>The stereo pair was acquired on 31 March 2021 at 10:40 Central Europe Time. The images were processed with the NASA Ames Stereo Pipeline (ASP, Beyer and others, 2018) to generate the DEM with a ground-sampling distance of 2 m using the Semi-Global-Matching algorithm and the processing parameters from Deschamps-Berger and others (2020) without ground control points (GCPs). The Pléiades DEM was then co-registered with the reference DEM (see publication). To calculate the DEM differencing, the Pléiades DEM was resampled using bilinear interpolation to 6 m to match the resolution of the TanDEM-X DEM.</p> <p>Resolution: 2 m (non co-registered), 6 m (co-registered)</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong></p> <p>Pléiades © CNES 2023, distribution AIRBUS DS</p> <p>Berthier, E., Lebreton, J., Fontannaz, D., Déprez, A., Michéa, D., Malet, J.-P., LEGOS-OMP / Data Terra (ForM@Ter-Theia) (2024). Pléiades Glacier Observatory Data Products. EOST. (Collection). doi: 10.25577/313a-a978</p> <p><strong>Citation example:</strong></p> <p>“The Pléiades DEM (CNES 2023; Berthier et al. 2024) from the study of Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h2>swissALTI3D: </h2> <p>We used the swissALTI3D DEM from swisstopo (Swiss Federal Office of Topography (2021a) as a reference DEM for (a) the co-registration of the Pléiades DEM and (b) the generation of the TanDEM-X DEM (i.e., phase unwrapping). This swissALTI3D DEM has a resolution of 2 m and was generated using aerial images acquired between July and September 2017. The downloaded version was provided in orthometric heights and swiss coordinate system CH1903+ / LV95 (EPSG:2056). Therefore, we converted to WGS84 UTM projection (EPSG:32632) using cubic interpolation and to ellipsoidal heights using the Swiss geoid model provided by swisstopo (Swiss Federal Office of Topography, 2021b). The geoid is provided in the geographic coordinate system (EPSG:4258), which was projected to UTM using the cubic method.</p> <p>Resolution: 2 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong> </p> <p>Swiss Federal Office of Topography (2021). swissALTI3D. Das hochpräzise digitale Höhenmodell der Schweiz. Bundesamt für Landestopografie swisstopo, ed. Wabern. <a href="https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html">https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html</a>.</p> <p><strong>Citation example:</strong></p> <p>“The swissALTI3D (Swiss Federal Office of Topography 2021) as used in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License (Attribution).</p> <h2>DEM differencing (dDEM, non corrected and corrected):</h2> <p>We used DEM differencing to quantify the elevation difference (dh) between the TanDEM-X DEM and the Pléiades DEM. To calculate the dDEM we subtracted the Pléiades DEM from the TanDEM-X DEM (after co-registration). Values larger than ±50m in the dDEM are considered outliers and removed. Further, on the tongue of Grosser Aletschgletscher we encountered a bias in the dDEM originating from the phase unwrapping during the TanDEM-X DEM production. This resulted in unrealistic positive elevation differences at the tongue (up to 50 m). Therefore, on-glacier, we additionally removed positive differences larger than 5 m. For direct comparison of the elevation differences with the GPR, snow pit and snow core measurements one has to assume a signal propagation velocity within the snow and ice medium, which will lead to a smaller height of ambiguity in the snow volume and a reduced actual penetration depth. As a measure for the actual radar penetration depth the previously calculated dDEM due to signal penetration is scaled proportionally with (approx. 78%). The resulting file is called “dDEM_corrected”. For more information we refer to the publication.</p> <p>Resolution: 6 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong> </p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899</p> <p><strong>Citation example:</strong></p> <p>“The dDEM (Bannwart et al. 2024b) as produced in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC-SA 4.0 International License (Attribution-NonCommercial-ShareAlike).</p> <h2>Stable terrain masks:</h2> <p>The stable terrain masks have been used to co-register the Pléiades DEM to the swissALTI3D and the TanDEM-X DEM to the Pléiades DEM. The two masks were created using the Pléiades orthophoto and a supervised classification method following the approach by Deschamps-Berger C and others (2020). The masks consist of off-glacier areas where no changes are expected between the two DEMs, and only snow-free pixels were retained. The masks are a binary file with a resolution of 2m and 6 m, respectively, in UTM32N projection. To evaluate the sensitivity of the radar penetration bias to co-registration, we tested two co-registration algorithms on two stable terrain masks used to co-register the TanDEM-X DEM. In the modified stable mask, we manually removed pixels in the sparse forest and dark shadow areas based on a visual inspection of the Pléiades orthophoto.<strong> </strong></p> <p>Resolution: 2 m (mask used to co-register Pléiades DEM to the swissALTI3D), 6 m (masks used to co-register the TanDEM-X DEM to the Pléiased DEM)</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset:</strong></p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899<a href="https://doi.org/10.5281/zenodo.11071899"> </a></p> <p><strong>Citation example:</strong></p> <p>“The stable terrain mask to co-register the Pléiades DEM (Bannwart et al. 2024b) in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h2>Glacier outline:</h2> <p>The outline as a shapefile of Grosser Aletschgletscher was manually adjusted to the glacier’s outline in 2021 based on the GLIMS outlines from 2015 (Paul and others, 2019) and the Pléiades orthophoto.</p> <p><strong>Citation of individual dataset:</strong></p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899</p> <p>Paul, F. (submitter), Azzoni, R.S., Fugazza, D., Le Bris, R., Nemec, J., Paul, F., Rabatel, A., Ramusovic, M.; Rastner, P., Schaub, Y., Schwaizer (nee Bippus), G (analysts). (2019) GLIMS Glacier Database. Boulder, Co. National Snow and Ice Data Center. http://dx.doi.org/10.7265/N5V98602.</p> <p><strong>Citation example:</strong></p> <p>“The glacier outline of Grosser Aletschgletscher in 2021 (Bannwart et al 2024b; Paul et al. 2019) as produced in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h1>Acknowledgements</h1> <p>We thank swisstopo for providing the swissALTI3D and CNES for their support.</p> <p> </p>
Scripts for "Evaluation and Attribution of a Warm Winter Bias Over Arctic Sea Ice in a Climate Model"
<p>The scripts used to generate the main figures and results of the work entitled ''Evaluation and Attribution of a Warm Winter Bias Over Arctic Sea Ice in a Climate Model'' by Michalezyk et al., submitted for publication in JAMES - AGU in 2024.</p> <p>If you have any questions, please contact Nicolas MICHALEZYK : nicolas.michalezyk@locean.ipsl.fr</p>
Role of Troposphere-Convection-Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model version 1 (CESM1)
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Data for ''A note on systematic biases in the ocean due to the air-sea flux calculation in coupled models''
<p>Data used to in a JAMES publication.</p> <p> </p> <p>Plotting routines can be found at: https://github.com/RafaelAbel/Coarse_Graining</p> <p>Manuscript DOI: tba</p>
dataset for Biases in the Visual and Haptic Subjective Vertical Reveal the Role of Proprioceptive/Vestibular Priors in Child Development
<p>dataset for "Biases in the Visual and Haptic Subjective Vertical Reveal the Role of Proprioceptive/Vestibular Priors in Child Development" publication. </p>
Weak effects of geolocators on small birds: a meta-analysis controlled for phylogeny and publication bias
<p>Data for our article "<strong>Weak effects of geolocators on small birds: a meta-analysis controlled for phylogeny and publication bias</strong>" by Brlík et al. </p>
Simulated quantification files for "Swimming downstream" workflow (bias 1-6 + uniform 7-12)
<p>Simulated quantification files for "Swimming downstream" workflow <a href="https://doi.org/10.12688/f1000research.15398.3">https://doi.org/10.12688/f1000research.15398.3</a></p> <p>Salmon (0.11.3) with Gibbs samples and kallisto (0.44.0) quantification files for the samples in two condition groups and two balanced batches: with realistic bias (1-6) and samples with uniform coverage (7-12). 24 samples in total. Reference: Gencode v28 human transcripts.</p>
Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA Data Archive
<p>[This repository contains the source data for the workflow presented in the manuscript "<strong>Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA</strong>". The workflow can be found here: http://gitlabscottgroup.med.usherbrooke.ca/gaspard/snakemake_blockbuster ]</p> <p>The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 115 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new tRNA and snoRNA genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distort the perceived architecture of the human transcriptome.</p>
Data supplementing the article "Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities" K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to "Frontiers in Ecology and Evolution" journal
<p>These data supplement the article "Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities" K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to "Frontiers in Ecology and Evolution" journal.</p> <p>The directory contains the following files:</p> <p><strong>464_samples_fastq_files_(mothur).rar </strong>- contains the 464 fastq files proceed together during the Mothur bioinformatics treatments to produce the OTUs and ISUs tables. As the contig and the demultiplexing steps were performed by the sequencing platform, there is 1 fastq file per sample. From this 464 samples OTU/ISU tables, only information regarding 76 samples were used in this study and are listed in the "<strong>76_samples_list_(mothur).xlsx" </strong>file<strong>.</strong></p> <p><strong>76_samples_list_(mothur).xlsx </strong>- contains the information regarding the 76 samples used to create the OTUs and ISUs tables presented in the paper.</p> <p><strong>76_samples_R1_R2_fastq_files(DADA2).rar - </strong>contains the raw demultiplexed fastq files (R1.fastq and R2.fastq) for each of the 76 samples used in this study to produce the ESVs table using the DADA2 bioinformatics pipeline.</p>
Open dataset for the research of "Assessing accuracy improvement of integrating digital footprints into gridded population mapping: spatiotemporal variations and data bias"
<p>Result datasets for "Assessing accuracy improvement of integrating digital footprints into gridded populationmapping:spatiotemporal variations and data bias":</p> <ol> <li> S1 is the results for gridded population mapping using different methods.</li> <li> S2 is the aggregate results of population mapping at county level.</li> <li> S3 is the results for intraday variation of population disaggregation accuracy,</li> <li> S4 is the data bias of different digital footprints.</li> </ol>
KuafuPrimer: Machine learning facilitates the design of 16S rRNA gene primers with minimal bias in bacterial communities
<p>KuafuPrimer is a machine learning-aided method that learns community characteristics from several samples to design 16S rRNA gene primers with minimal bias for microbial communities. It is built on <strong>Python 3.9.0</strong>, <strong>Pytorch 1.12.0</strong>. Here are some large size files required to run KuafuPrimer, and users need to download and put them in correct directories before running the program.</p> <ol> <li>Silva_ref_data.zip: processed files of silva dataset that should be put in <code>Model_data/Silva_ref_data/</code>.</li> <li>DeepAnno16_publicated_model.zip: parameters of the trained DeepAnno16 model that should be put in <code>Model_data/DeepAnno16_publicated_model/</code> .</li> </ol> <p>For more information, please refer to https://github.com/zhanghaoyu9931/KuafuPrimer.</p>
Dataset of the simulations in the GōMartini 3: From large conformational changes in proteins to environmental bias corrections
<p>This section describes the data files for the nanomechanics of SARS-CoV-2 RBD in complex with a potent nanobody (H11-H4) by GōMartini approach.</p> <p>(1) The <strong>data_4iu3.tar.xz</strong> file has all the files necessary to reproduce the pulling simulations of PDB ID 4IU3 using GōMartini methodology. The files are described below.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------</p> <p>https://www.rcsb.org/structure/4iu3</p> <p>Cohesin-dockerin -X domain complex from Ruminococcus flavefacience</p> <p> </p> <p>https://github.com/rams-research/OVrCSU</p> <p>http://pomalab.ippt.pan.pl/GoContactMap/</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------</p> <p>4iu3.pdb : All-atoms PDB file</p> <p>4iu3.map : All-atoms OVrCSU contac map file</p> <p>A.pdb,B.pdb : All-atoms Structures of Cohesin and Dockering, later referenced as molA and molB</p> <p>index.ndx : base index file</p> <p>martini_v3*.itp : Martini3 force field definitions</p> <p> </p> <p>GoMartini, topology files:</p> <p> molA.itp</p> <p> molA_exclusions_VirtGoSites.itp</p> <p> molB.itp</p> <p> molB_exclusions_VirtGoSites.itp</p> <p> go-table_VirtGoSites_interface.itp</p> <p> go-table_VirtGoSites.itp</p> <p> topol.top</p> <p> </p> <p>GoMartini, GROMACS MDP equilibration and final GRO files:</p> <p> Pre-equilibration:</p> <p> min.mdp, min.gro</p> <p> npt.mdp, npt,gro</p> <p> nvt.mdp, nvt.gro</p> <p> Equilibration MD run:</p> <p> md-corr.log</p> <p> md-corr.mdp</p> <p> md-corr.edr</p> <p> md-corr.gro</p> <p> md-corr.tpr</p> <p> </p> <p>Topology and force field definitions:</p> <p> GGG_pull/BB-part-def_VirtGoSites.itp</p> <p> GGG_pull/go-table_VirtGoSites.itp</p> <p> GGG_pull/index.ndx</p> <p> GGG_pull/martini_v3.0.4.itp</p> <p> GGG_pull/martini_v3.0_ions.itp</p> <p> GGG_pull/martini_v3.0_phospholipids.itp</p> <p> GGG_pull/martini_v3.0_solvents.itp</p> <p> GGG_pull/molA_exclusions_VirtGoSites.itp</p> <p> GGG_pull/molA.itp</p> <p> GGG_pull/molB_exclusions_VirtGoSites.itp</p> <p> GGG_pull/molB.itp</p> <p> </p> <p>Pulling MD runs from 100 runs ({1-100}), GROMACS files:</p> <p> GGG_pull/md_pull_{1-100}-trajectory-protein.pdb : Trajectory in PDB format</p> <p> GGG_pull/md_pull_{1-100}_pullf.xvg : force profile</p> <p> GGG_pull/md_pull_{1-100}_pullx.xvg : distance profile</p> <p> GGG_pull/md_pull_{1-100}.log : log files</p> <p> GGG_pull/md_pull_{1-100}.edr : EDR files</p> <p> GGG_pull/md_pull_{1-100}.tpr : TPR files</p> <p> </p> <p>2) The <strong>data_6zh9_1.tar.xz</strong>, <strong>data_6zh9_2.tar.xz</strong>,<strong> data_6ZH9_GM2.tar.xz</strong>,<strong> </strong>and<strong> data_6ZH9_GM3.tar.xz </strong>files contain</p> <p>The dataset comprises a total of 50 replicas, each with a total of 751 snapshots, and the mdp and tpr files, which are located in the CG_Pull/Trajectories folder, for the RBD-H11-H4 complex. The gro, mdp and tpr files for NVT and NPT equilibrations are included.</p> <p>The force and displacement xvg files for each replica are included in the GC_Pull folder (pullf*.xvg and pullx*.xvg, respectively). The output files from GōMartini are included.</p> <p>The system consists of a single-domain antibody (i.e. nanobody) and the receptor-binding domain (RBD) portion of the SARS-CoV-2 spike protein. In this regard, the nanobody named H11-H4 and the RBD form a mechanostable protein complex with PDB ID: 6ZH9. The entire system was modeled by the Martini 3 force field. The same protocol as for the XMod-Doc:Coh complex was employed. The GōMartini model requires a total of 715 contacts divided into 404 and 285 for the RBD and the H11-H4 respectively and the protein complex interface was represented by 26 contacts. The value of the dissociation energy of the LJ potentials was set to a value of ε = 15.0 kJ/mol. The dimensions of the water box for the RBD-Nb complex were 16x12x90 nm3 with a 0.15 M NaCl. There were 135,669 CG water beads representing 542,676 water molecules.</p> <p>To conduct the nanomechanical studies, the positions of RBD residues GLY-526, PRO-527, and LYS-528 were kept frozen in the z-axis and the coordinates of residues SER-126, SER-127, and LYS-128 in H11-H4 were kept frozen in x- and y-axis, and were chosen for SMD simulation at constant speed.</p> <p>Two different protocols according to all-atom SMD simulation were considered. i) in <strong>data_6zh9_1.tar.xz: </strong>Restraints were applied to the backbone atoms of the RBD with a spring contact of kb =1000 kJ/(mol·nm2) to avoid large deformation and the residue in the SMD protocol with velocity equal to 5x10-3 nm/ps was coupled to another harmonic potential with a spring constant equal to 600 kJ/(mol·nm2) and ii) <strong>data_6zh9_2.tar.xz: </strong>Pulling velocity was equal to 1x10-4 nm/ps was coupled to another harmonic potential with a spring constant equal to 600 kJ/(mol·nm2). </p> <p>The supplementary files <strong>data_6ZH9_GM2.tar.xz</strong> and <strong>data_6ZH9_GM3.tar.xz</strong> followed a similar protocol as <strong>data_6ZH9_2.tar.xz</strong>, with the following differences: the box size was set to 10 × 10 × 60 nm, and the spring constant was adjusted to 37.6 kJ/(mol·nm²). For the GōMartini 2 model, the default value of ε = 9.414 kJ/mol was used.</p> <p>3) The <strong>IDP_GoMartini3.tar.gz </strong>file has the following folders:</p> <p>a) Atomistic</p> <p>The _atomistic_ directory contains subfolders for each of the IDPs tested. Each subdirectory contains the topology for the protein and an initial starting structure. There is also a folder of the mdp files used. In each simulation, the correct temperature as described in the manuscript was applied.</p> <p>b) Martini</p> <p>The _martini_ directory contains subfolders for each of the IDPs tested. Each subdirectory contains topology for the corresponding protein model tested, and the starting structure used. The TEMP variable in the mdp files was replaced with the experimentally corresponding temperature in each simulation.</p> <p>c) Condensate</p> <p>The _condensate_ directory contains two systems. The artifical IDP (_aIDP_) condensate system of Dzuricky et al. and the short peptides of Abbas et al. (_peptides_)</p> <p>_aIDP_ has two systems comparing the native and optimised topologies for the phase separation of the aIDP.</p> <p>_peptides_ contains a single system with topologies for protonated and deprotonated synthetic peptides, as well as the starting structures, and topology files for the differently rescaled protein-water interactions used.</p> <p>4) The WALPDATA.tar.gz has the following folders:</p> <p>a) ProteinITPs has all GROMACS itp files for the WALP α-helices – 16, 19, 23, and 27 denoted as WALP16, WALP29, WALP23 and WALP27 as well as the GōMartini inputs</p> <p>b) mdps and the script for preparing the mebrane for the WALP peptides PrepareMembranePeptideSim.ipynb</p> <p>5) The <strong>BetaSheet_GoMartini.tar.gz</strong> file has the following folders:</p> <p>molecule_0.itp is the itp file for beta strand 1.<br>molecule_1.itp is the itp file for beta strand 2.</p> <p>martini.itp is the Martini forcefield itp used.</p> <p>rCSU.map is the rCSU map generated.<br>OV.map is the OV map generated.</p> <p><br>GoMartini files:</p> <p> molecule_0_exclusions_VirtGoSites.itp <br> molecule_0_BB-part-def_VirtGoSites.itp<br> molecule_0_go-table_IDPsolubility.itp <br> molecule_0_go-table_VirtGoSites.itp <br> molecule_0_go4view_harm.itp <br> BB-part-def_VirtGoSites.itp <br> go-table_VirtGoSites.itp </p> <p><br>mdp files used:</p> <p> min.mdp <br> rel_310.mdp <br> prod_310.mdp </p> <p> </p> <p>rada_sheet_canonical.pdb is the PDB file of the AA RADA16 structure.<br>rada_cg.pdb is the PDB file of the Martini RADA16 structure.</p> <p> </p> <p>Inside folder Negative_Control is the topology file for<br>the system with no structural or interaction bias.</p> <p><br>Inside folder GoVirt_Bias is the topology file for<br>the system with a GoMartini model applied between the<br>two strands.</p> <p><br>Inside folder Scaled_Water is the topology file for<br>the system with GoVirt water interaction scaling.</p> <p>6) The <strong>md_inputs_go_IDPs_PETER.tar.gz</strong> file has the following folders:</p> <p>Directories "a1", "hst5", and "pnt" contain the input files for proteins with Go potentials. And in the "controls" directory, input files for the same proteins, without Go potentials.</p> <p>To run the simulations, the user can run the following commands:</p> <p>gmx grompp -f md.mdp -p topol.top -c pre_md.gro -o md<br>gmx mdrun -s md.tpr -deffnm md</p> <p>Additionally, atomistic structures are available in .gro format - {protein}_atomistic.gro</p> <p>7) The <strong>repository_Hafez.zip </strong></p> <p>In the folders 1AOH, 1TIT, 1UBQ, 3W0K, AQP1, and Ist2, you will find the necessary files (ITP, MDP, and GRO) to repeat the simulations.</p> <p>In the Martini3FF folder, you can find the MARTINI3 force field files.</p> <p>8) The <strong>Hairpin-M3-15.tar.xz</strong> and <strong>Hairpin-M2.tar.xz</strong> and <strong>Hairpin-M2-15.tar.xz</strong> </p> <p>contains the information mdp, itp and trajectories generated for the study of folding simulation with GoMartini implemented in 2017 and the latest implementation on top of the Martini 3 force field.</p> <p>The Hairpin-M2, Hairpin-M2-15, and Hairpin-M3-15 folders contain all the necessary files to reproduce the pulling simulations of PDB ID 1GB1 (a novel, highly stable fold of the immunoglobulin binding domain of streptococcal protein G) using the GōMartini 2 and GōMartini 3 methodologies, respectively.</p> <p>Each folder contains:</p> <p>A.pdb: All-atom PDB file of the unfolded structure.<br>B.pdb: All-atom PDB file of the folded structure.<br>A-CG.pdb: Martini coarse-grained representation of the unfolded structure.<br>B-CG.pdb: Martini coarse-grained representation of the folded structure.<br>A-GC.gro: GROMACS format file of the unfolded structure.<br>B-GC.gro: GROMACS format file of the folded structure.<br>system.top: Topology file.</p> <p><br>Force Fields and Topologies:<br>GōMartini 2:<br>martini_v2.2.itp: Martini 2 force field for proteins.<br>martiniv2.2_ions.itp: Martini 2 force field for ions.<br>Protein_A.itp: GōMartini 2 topology for the folded structure.<br>Protein_A_unfolded.itp: GōMartini 2 topology for the unfolded structure.</p> <p><br>GōMartini 3:<br>martini_v3.0.0.itp: Martini 3 force field for proteins.<br>martini_v3.0.0_ions_v1.itp: Martini 3 force field for ions.<br>martini_v3.0.0_solvents_v1.itp: Martini 3 force field for solvents.<br>go_martini.itp: GōMartini 3 topology for the folded structure.<br>go_martini_unfold.itp: GōMartini 3 topology for the unfolded structure.<br>go_molecule1.itp: Additional GōMartini 3 topology file.<br>A.map: Contact map for the unfolded structure.<br>B.map: Contact map for the folded structure.</p> <p>GROMACS Files for Equilibration and MD Runs:</p> <p>Equilibration:<br>minimization.mdp, minimization.gro: Files for energy minimization.<br>nvt.mdp, nvt.gro, nvt.xtc, nvt.tpr: Files for NVT equilibration.<br>npt.mdp, npt.gro, npt.xtc, npt.tpr, npt_dry.tpr: Files for NPT equilibration (dry simulations without waters or ions).</p> <p>Folding MD Runs:<br>dynamic.mdp: File for folding molecular dynamics runs.<br>Folding MD Runs (1-100):<br>MD/hairpin-M2_fit.xtc*: Trajectories (without waters or ions, ε = 9.414 kJ/mol).<br>MD/hairpin-M2-15_fit*.xtc*: Trajectories (without waters or ions, ε = 15.0 kJ/mol).</p> <p> </p> <p>MD/hairpin-CG-M3-_fit*.xtc: Trajectories (without waters or ions, ε = 15.0 kJ/mol).</p> <p>Data Analysis Scripts:<br>MD/Distances_native_WT.ipynb: Jupyter notebook for backbone distance analysis.<br>MD/all_native_graphs.ipynb: Jupyter notebook for native contact analysis.<br>MD/total_contacts_column.ipynb: Python script for contact statistics.</p>
Consistency Bias Data Sets
<p>This is the data set correspond to the calculation of Consistency Bias. This has been submitted for publication to a journal and will be linked directly to the manuscript when published.</p>
Dataset for the numerical simulation in the article "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"
<p>This dataset includes the essential source code and the corresponding numerical data for the self-assembly of a M6L4 square-based pyramid (SP) complex. </p> <p>The associated study is described in </p> <p><strong>"Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"</strong>, by T. Abe, S. Takahashi, H. Sato, and S. Hiraoka.</p>
Genomic localization bias of secondary metabolite gene clusters and association with histone modifications in Aspergillus
<p>Table S4 (Distribution of Orthologous groups) associated with the publication 'Genomic localization bias of secondary metabolite gene clusters and association with histone modifications in Aspergillus' is deposited at Zenodo.</p>
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