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1,049 results for “robustness”
New methods for robust continuous wave T1ρ relaxation preparation
<p>This dataset contains all the raw source data, MATLAB analysis functions and files, and article figures that comprise the study:</p> <p><br> New methods for robust continuous wave T1ρ relaxation preparation</p> <p>NMR in Biomedicine, 2022. </p> <p>Pala S(1), Hänninen NE(1,2), Nykänen O(1), Liimatainen T(2,3), Nissi MJ(1,2)*</p> <p>(1) Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland<br> (2) Research Unit of Medical Imaging, Physics and Technology, University of Oulu, POB 5000, FI-90014 Oulu, Finland<br> (3) Department of Radiology, Oulu University Hospital, Oulu, Finland</p> <p>*Corresponding author:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p><br> Keywords: contrast, T1ρ relaxation, field inhomogeneity, rotating frame of reference, bloch simulation. </p> <p>Included folders and files are:<br> - article_figures: all figures published in the manuscript and in the supplementary document (.svg format)<br> - experimental Data: .fid files of the MRI measurements ordered, the script for comparison of methods and supporting files (ROIs and masks), and corresponding mono-exponential fitted T1 rho maps.<br> - macro_files: VnmrJ macro files for setting up the measurements using all the pulse schemes discussed in the manuscript and supplementary documents.<br> - RF_Pulse_sequences: VnmrJ RF waveforms used in the study, generated using the included Matlab functions<br> - matlab_functions: Matlab functions used for creating the RF waveforms (T1rho pulse schemes) and for data analysis with subfolders and files:<br> - aedes_plugins: plugins for aedes (http://aedes.uef.fi) and scripts to read all raw data files for calculation of relaxation time maps - miscellaneous_functions: Pulse generation functions and other functions (Pulse generation, sequence reading and display, and pulse simulation scripts) and also scripts for RMS calculation.<br> - PulseTrain_Generator.m: GUI tool for using the various included scripts for creating the RF waveforms used in the study<br> - README.txt: this file describing the contents of the dataset</p> <p><br> See more info in separate readme files included in sub-folders.<br> </p>
Data from: Clustering Deviation Index (CDI): A robust and accurate internal measure for evaluating scRNA-seq data clustering
<div> <div> <p>The clustering of cells has been widely used to explore the heterogeneity of cell populations in single-cell RNA-sequencing (scRNA-seq). We proposed a parametric model for monoclonal and polyclonal scRNA-seq data to evaluate clustering results. Based on the parametric model, we proposed a metric (CDI) to quantify the goodness-of-fit of cell clustering to the data. Here we presented CT26.WT and T-CELL as two datasets to examine the performance of our model and metric. CT26.WT contains wild-type CT26 cells from the murine colorectal carcinoma cell line, and cells in CT26.WT are highly homogeneous. T-CELL contains T-cells from tumor tissue of mice three weeks after 4T1 tumor injection. From these datasets and public datasets, we validated our model and benchmarked our metric.</p> </div> </div>
Code and data for N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling"
<p>Simulation code and data used in N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling."</p>
DeepProjection: Specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning
<p>The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify the 3D stack content and rapidly and robustly predict binary masks containing the target content, e.g., tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract the local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as fully documented Python package.</p>
Robustness assessment via simulation-based fault injection of the implementation level models of the LEON3, MC8051, and PIC microcontrollers in presence of stuck-at, bit-flip, pulse, and delay fault models
<p>This dataset package contains the results of fault injection experiments for RTL and implementation-level models 3 microprocessors (LEON3, MC8051, PIC).</p> <p><strong> 1. Package contents:</strong><br> - raw traces of fault injection experiments: *.lst files<br> - analysis results grouped by fault models: *.html files in the /REPORT subfolders<br> - summary for each targeted HDL model and fault model: index.html in each design folder</p> <p>To facilitate the navigation through the contents, it is organized as a tree of *.html pages. The root page is 'index.html' in the archive root.<br> Additionally, to observe the raw traces for each experiment in the convenient form, the *.lst files are processed on the fly by custom python-script, returning the interactive *.html page. Each observation trace is a table, where: <br> - each row represents an observation vector, comprising {simulation time stamp}, {flags}, {internal state}, {outputs}.<br> - each cell is highlighted:<br> a) green if matches with reference trace (fault-free simulation),<br> b) red if mismatches with reference trace, denoting error for internals / failure for outputs,<br> c) violet in case of vector whose timestamp was not in reference (unexpected transition).<br> These highlighting options can be customized by modifying the linked *.css files.</p> <p> <br> <strong> 2. Installation</strong><br> 2.1 Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p>2.2 Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise install the relevant 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.<br> <br> 2.2 Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the resulting line:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p>2.3 Unpack the *.zip package into the folder on the Web Server. For instance 'Web-server root folder'/ExperimentalResults.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p>2.4 In the web-browser navigate to the root directory of extracted package:<br> http://localhost/ExperimentalResults/index.html</p> <p> </p> <p><strong>3. How to read the contents</strong><br> The root page contains links to different analysis reports, for each HDL design under study and considered fault models. <br> The pages on the first tree level, represent the summary for each injection campaign, describing the rate of failure modes, number of experiments, latencies, and supplementary info. <br> The pages on the second tree level are the detailed analysis reports for each experiment, detailing the fault target, parameters of injected fault, detected failure mode, number of errors, etc. <br> The cells of the first column are highlighted a) in green if injection did not cause the failure, b) in red otherwise. The links in this first column navigate to the detailed traces for each experiment. <br> The latter requires that Web-server is configured to execute the python-scripts (see section 2 - Installation); otherwise the raw traces (*.lst files in ./results folders) can be observed by any text editor (notepad++, etc.).<br> </p>
Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease.
<p>Contains all intermediate data files for the article <strong>Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease, </strong>Quinn-Bohmann et al. 2024. </p> <p>NP and OP refers to nasopharyngeal or oropharyngeal samples, respectively. As some study identifiers are duplicated across these groups, qiime_NP and qiime_OP should be unzipped in distinct directories. The same applies for metadata data files. </p> <p>Analyses of these data can be found at https://github.com/Gibbons-Lab/2023_URTmetaanalysis/tree/main. </p>
Modelling input data for the case studies of the paper "Strategic bidding in light-robust day-ahead electricity markets".
<p><span>This data package includes the modelling input data to replicate the results of the case studies included in the paper "Strategic bidding in light-robust day-ahead electricity markets". </span></p> <p><span>This supplementary data package includes the following files:</span></p> <p><span><span>-<span> </span></span></span><span>Meta Data – Input data: Dataset containing the input data for the strategic bidding behavior problem. It includes bids from conventional, demand and stochastic players and the scenarios for system imbalance and real-time production.</span></p> <p><span><span>-<span> </span></span></span><span>Readme.txt: Includes a detailed description of the data packages</span></p> <p><span> </span></p> <p><span>Sources of data:</span></p> <p><span>* Ordoudis, C., Pinson, P., Morales, J. M., & Zugno, M. (2016). An updated version of the IEEE RTS 24-bus system for electricity market and power system operation studies. Technical University of Denmark.</span></p> <p><span>* Silva-Rodriguez, L., Sanjab, A., Fumagalli, E., Virag, A., & Gibescu, M. (2022). A light robust optimization approach for uncertainty-based day-ahead electricity markets. Electric Power Systems Research, 212, 108281. https://doi.org/10.1016/J.EPSR.2022.108281<span> </span></span></p> <p><span>* Derived (scaled down) from Elia. (2024). Open data. Retrieved from https://www.elia.be/en/grid-data/open-data<span> </span></span></p> <p><span>* Own data<span> </span></span></p> <p><span><span> </span></span></p>
Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California
<p>We combined two climate-based distribution models with three finer-scale suitability models to identify habitat for pronghorn recovery in California now and into the future.</p> <p>Location: California, United States </p> <p>Methods: We used a consensus approach to identify areas of suitable climate now (1980-2010) and future (2031-2060) for pronghorn in California. We compared the results of models from two separate hypotheses about their historical ecology in the state, specifically the migration hypothesis and the niche reduction hypothesis. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat models: (1) an ensemble model using Random Forests, Maxent, Classification and Regression Trees, and a Generalized Linear Model; (2) a step selection function; and (3) an expert-driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. </p> <p>Results: Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be on suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat also depended on the methods used, but areas of consensus among all three exist in large patches throughout the state.</p> <p>Main Conclusions: Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multi-method, multi-hypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest.</p>
Supplemental Data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud."
<p>All supplemental data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud."<br><br>Abstract:<br>We propose Adaptive Container Service (ACS), a new paradigm for deploying bioinformatics workflows in cloud computing environments. By encapsulating the entire workflow within a single virtual container, combined with automatic workflow checkpointing and dynamic migration to appropriately scaled containers, ACS-based deployment demonstrates several key advantages over alternative strategies: it enables optimal resource provision to any workflow that comprise of multiple applications with diverse computing needs; it provides protection against application-agnostic out-of-memory (OOM) errors or spot instance interruptions; and it reduces efforts required for workflow development, optimization, and management because it runs workflows with minimal or no code modifications. Proof-of-concept experiments show that ACS avoided both under- and over-provisioning in monolithic single-container deployment. Despite being deployed as a single container, it achieved comparable resource utilization efficiency as optimized Nextflow-managed, multi-modular workflows. Analysis of over 18,000 workflow runs demonstrated that ACS can effectively reduce workflow failures by two-thirds. These findings suggest that ACS frees developers from navigating the complexity of deploying robust workflows and rightsizing compute resources in the cloud, leading to significant reduction in workflow development time and savings in cloud computing costs.<br><br>Contains the following directories:<br>Fig2-bbtools: running metrics for BBTools<br>Fig3-rna-seq: running metrics for RNA-Seq<br>Fig4-Job_records: meta data and running metrics of 18,000+ jobs</p>
Repurposing Type I-A CRISPR-Cas3 for a robust diagnosis of human papillomavirus (HPV)
<p>R-loop-triggered collateral single-stranded DNA (ssDNA) nuclease activity within Class 1 Type I CRISPR-Cas systems holds immense potential for nucleic acid detection. However, the hyperactive ssDNase activity of Cas3 introduces unwanted noise and false-positive results. In this study, we identified a novel Type I-A Cas3 variant derived from <em>Thermococcus siculi</em>, which remains in an auto-inhibited state until it is triggered by Cascade complex and R-loop formation. This Type I-A CRISPR-Cas3 system not only exhibits an expanded protospacer adjacent motif (PAM) recognition capability but also demonstrates remarkable intolerance towards mismatched sequences. Furthermore, it exhibits dual activation modes—responding to both DNA and RNA targets. The culmination of our research efforts has led to the development of the Hyper-Active-Verification Establishment (HAVE, 惠父). This innovation enables swift and precise <em>human papillomavirus</em> (HPV) diagnosis in clinical samples, providing a robust molecular diagnostic tool based on the Type I-A CRISPR-Cas3 system. Our findings contribute to understanding type I-A CRISPR-Cas3 system regulation and facilitate the creation of advanced diagnostic solutions with broad clinical applicability.</p>
A Comprehensive Central Kurdish Sound Dataset for Robust Automatic Speech Recognition (Part 1).
<p>Exploring the intricacies of Speech Recognition Technology (SRT), our dataset encompasses a wide range of age demographics, spanning from adolescents to individuals in their fifties. This diverse dataset comprises a substantial collection of raw data, amounting to 1,739,089 entries. Within this dataset, a meticulous curation process has yielded a total of 1,683 hours of data, providing a thorough examination of language acquisition patterns across different age cohorts within the Central Kurdish linguistic domain.</p>
Towards Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance
<p>This repository contains the implementation of <strong>Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance</strong>.</p> <p>The provided Python script demonstrates the construction of the MCMC (Markov Chain Monte Carlo) method and illustrates how to use MCMC for generating hemolysis distributions. Please note that the actual hemolysis calculations should be performed using your preferred CFD (Computational Fluid Dynamics) software.</p> <p>If there are any questions, please contact:</p> <p>blum@ame.rwth-aachen.de </p>
Robustness assessment through iterative statistical fault injection: LEON3 processor as a case study
<p><strong>Dataset exemplifies an approach of iterative statistical fault injection to assess the robustness of HDL models.</strong></p> <p>Contents:<br> 1. Results of exhaustive fault injection experiments (bit-flip faults) into LEON3 processor model;<br> 2. Interactive querying interface, allowing to obtain custom samples from exhaustive results, and visualize them;<br> 3. Python scripts simulating 3 approaches to statistical fault injection: conservative, error-driven, time-driven.</p> <p> </p> <p><strong>Installation guide:</strong></p> <p> 1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p> 4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p> 5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html</p>
Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules
<p>The content of this repository accompanies the publication "Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules" and contains the complete dataset produced for the fentanyl CSP. </p> <p>Three types of datasets are present : Generation, ML-Reranker and GRACE. <br>Generation refers to all the molecular crystal structures that GRACE has generated using the tailor made force field. It's the starting pool of the reranking exercise, and it contains all the structures that will be selected by the reranking processes.<br>ML-Reranker refers to the data generated by the algorithm proposed in our manuscript. The configurations and energies are obtained by selecting from structures from the generation pool and relaxing their coordinates.<br>GRACE dataset contains the configurations which a user obtains at the end of a standard GRACE reranking procedure. Since GRACE follows differet convergence and minimization critera, the structures obtained in this dataset can differ (non-substantially) from the equivalents found in the ML-Reranker. </p> <p>*.data : contains the indices, energy of the crystal structure (kcal/mol) and, in case of the ml-reranker dataset, the indices mapping the obtained landscape to their generating pool.</p> <p>*.xyz : contains ASE formatted, extended-xyz list of structures corresponding to each exercise.</p> <p>Authors:</p> <p>Andrea Anelli, Hanno Dietrich, Philipp Ectors, Frank Stowasser, Tristan Bereau, Marcus Neumann, Joost van den Ende</p>
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
Laser assisted fabrication of mechanochemically robust Ti3Au intermetallic at Au-Ti interface
<p>Description:</p> <p> a. Materials Properties used in the Finite Element Analysis (FEA): </p> <ol> <li><strong>specific_enthalpy.zip</strong> :The specific enthalpy (h) data for Au and Ti are presented in the table of temperature (K) and specific enthalpy (J/kg) columns. specific_h_au0001.dat file corresponds to gold and specific_h_ti0001.datcorresponds to Titanium. In each of these .dat files, the temperature values are provided in the first column whereas specific enthalpy values are provided in the second column.</li> <li><strong>viscosity.zip folder</strong>: The viscosity (mu) data for Au and Ti are presented in the table of temperature (K) and viscosity (Pa S) columns. mu_Au.dat file corresponds to gold and mu_Ti.dat corresponds to Titanium. In each of these .dat files, the temperature values are provided in the first column whereas viscosity values are provided in the second column. Solid regimes are assumed to be of high viscosity values. </li> </ol> <p> b. <strong>information_total_geometry_microscale.csv</strong>: This is the data related to finite element analysis. The information geometrical dimensions of the overall geometry (combined geometry consisting of bottom titanium layer and upper gold layer) and the numbers of 1D, 2D and 3D mesh elements is provided in this file. This is the data associated with Fig. 8(a) in the paper.</p> <p>c. <strong>top_and_bottom_layers_comparison_microscale.csv</strong>: The data in this file is presented to emphasize the role of creating finite element meshes of different densities. This file only presents the information about the geometrical dimensions and 3D mesh elements. This file specifically compares the number of mesh elements between the top gold layer and bottom titanium layer. The gold layer (2 micrometers in height) is 25 times thinner than the titanium layer (50 micrometers). However, the number of 3D volume elements in the titanium layer is only 2.19 times more than that of gold layer. So, the upper Au layer has relatively fine meshing and lower Ti layer has relatively coarse meshing. </p> <p> <br>d. <strong>radial_distance_irradiance_intensity_data.csv</strong>: The data is related to laser related aspects in the finite element analysis. This file consists of the data showing the distribution of irradiance (W/m^2) with the radial distance (r) from the center of the beam spot for a flat top heat source described with the expression I = \frac{P}{3.14*r0^2} U*|r0-r| . In this work, laser power = 100.0 W and beam spot radius (r0) = 122.5 micrometers. The 3D visualization of this data is provided at: </p> <p> </p> <p>e. l<strong>aser_material_interface.csv</strong>: This file outlines the major concepts how the laser heat source has been interfaced with the material domain in the finite element analysis. the data is in the string format.</p> <p> </p> <p>f. <strong>initial_configurations_of_molecular_slabs.zip</strong>: The configuration of molecular slabs of HCP Ti and Simple Cubic Ti3Au intermetallics are respectively presented as two separated files.</p> <p>g. <strong>nanoindentation-force-ti3au-imc.csv</strong>: The data of indentation depth (Angstrom) and force (nN) in the upper active region for the Ti3Au slab are presented. </p> <p>h. <strong>nanoindentation-force-titanium.csv</strong>: The data of indentation depth (Angstrom) and force (nN) in the upper active region for the Ti slab are presented. </p> <p> </p> <p> </p> <p> </p>
Supplementary Data and Software for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance"
<p>The file includes supplementary data for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance". If you would like to re-use the software provided please cite the following two items.</p> <p>R. Klus, J. Talvitie, J. Equi, G. Fodor, J. Torsner, and M. Valkama, “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and<br>Performance,” IEEE Transactions on Vehicular Technology, 2024.</p> <p>R Klus et al. (2024). Supplementary materials for “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance”. version v1, 25.06.2024, [Online]. Available: https://doi.org/10.5281/zenodo.12204892</p> <p>If you have any questions about this package, please do not hesitate to contact Roman Klus (roman.klus@tuni.fi).</p>
Double Robust Inference for Continuous Updating GMM
<p>Overview<br>The codes and data in the replication package are for replicating Figures 1-12, Table I in the paper “Double Robust Inference for Continuous Updating GMM” and Figures A1-A6 in the Online Appendix to the paper. Thus, the replication package reproduces: All tables and figures in the paper and its online appendix.<br>Computational requirements (software and hardware)<br>•<br>MATLAB (codes were run with MATLAB R2023a).<br>•<br>Dell Latitude 5501 Notebook (codes were run with Windows 10), or MacBook Pro (codes were run with macOS Sonoma).<br>Instructions to replicators<br>•<br>Step 1: Save all the codes and data to the same folder.<br>•<br>Step 2: Run “master_file.m” in MATLAB for replication. The resulting figures and table will be saved to the “output” folder, using the same names as in the paper/appendix.<br>Content of the replication package<br>Figure/Table<br>Expected Running Time<br>Comments<br>master_file.m<br>200 minutes<br>A master file that generates all results.<br>Figure_1_2.m<br>1 second<br>Simulated contour lines might slightly differ.<br>Figure_3_4_5_6.m<br>12 seconds<br>Figure_7.m<br>5 minutes<br>Data in “app_LLM.xls”<br>Figure_8.m<br>15 minutes<br>Data in “app_LLM_He.xls”<br>Figure_9.m<br>3 minutes<br>Data in “app_LLM.xls”<br>Figure_10.m<br>65 minutes<br>Figure_11.m<br>10 minutes<br>Data in “data_KZ2019.xls”<br>Figure_12.m<br>1 second<br>Data in “data_CRRA.xls”<br>Table_I.m<br>65 minutes<br>Data in “app_LLM.xls”, “app_LLM_He.xls”<br>Output is also saved in “output/Table_I.txt”.<br>Figure_A1.m<br>1 minute<br>Figure_A2.m<br>10 minutes<br>Data in “data_KZ2019.xls”<br>Figure_A3.m<br>4 minutes<br>Figure_A4.m<br>15 minutes<br>Data in “data_CRRA.xls”<br>Figure_A5.m<br>1 second<br>Data in “school1.dat” “school2.dat” “school3.dat”<br>Figure_A6.m<br>1 second<br>Simulated contour lines might slightly differ.<br>Notes: The other programs in the package are used by the programs listed above.</p>
Data from: Robust quantification of fish early life CO2 sensitivities via serial experimentation
Despite the remarkable expansion of laboratory studies, robust estimates of single species CO2 sensitivities remain largely elusive. We conducted a meta-analysis of 20 CO2 exposure experiments conducted over six years on offspring of wild Atlantic silversides (Menidia menidia) to robustly constrain CO2 effects on early life survival and growth. We conclude that early stages of this species are generally tolerant to CO2 levels of ~ 2,000 µatm, likely because they already experience these conditions on diel to seasonal time scales. Still, high CO2 conditions measurably reduced fitness in this species by significantly decreasing average embryo survival (-9%) and embryo + larval survival (-13%). Survival traits had much larger coefficients of variation (>30%) than larval length or growth (3-11%). CO2 sensitivities varied seasonally and were highest at the beginning and end of the species' spawning season (April-July), likely due to the combined effects of transgenerational plasticity and maternal provisioning. Our analyses suggest that serial experimentation is a powerful, yet underutilized tool for robustly estimating small but true CO2 effects in fish early life stages.
Robustness of the Dorsal morphogen gradient with respect to morphogen dosage
In multicellular organisms, the timing and placement of gene expression in a developing tissue assigns the fate of each cell in the embryo in order for a uniform field of cells to differentiate into a reproducible pattern of organs and tissues. This positional information is often achieved through the action of spatial gradients of morphogens. Spatial patterns of gene expression are paradoxically robust to variations in morphogen dosage, given that, by definition, gene expression must be sensitive to morphogen concentration. In this work we investigate the robustness of the Dorsal/NF-κB signaling module with respect to perturbations to the dosage of maternally-expressed dorsal mRNA. The Dorsal morphogen gradient patterns the dorsal-ventral axis of the early Drosophila embryo, and we found that an empirical description of the Dorsal gradient is highly sensitive to maternal dorsal dosage. In contrast, we found experimentally that gene expression patterns are highly robust. Although the components of this signaling module have been characterized in detail, how their function is integrated to produce robust gene expression patterns to variations in the dorsal maternal dosage is still unclear. Therefore, we analyzed a mechanistic model of the Dorsal signaling module and found that Cactus, a cytoplasmic inhibitor for Dorsal, must be present in the nucleus for the system to be robust. Furthermore, active Toll, the receptor that dissociates Cactus from Dorsal, must be saturated. Finally, the vast majority of robust descriptions of the system require facilitated diffusion of Dorsal by Cactus. Each of these three recently-discovered mechanisms of the Dorsal module are critical for robustness. These mechanisms synergistically contribute to changing the amplitude and shape of the active Dorsal gradient, which is required for robust gene expression. Our work highlights the need for quantitative understanding of biophysical mechanisms of morphogen gradients in order to understand emergent phenotypes, such as robustness.
ScienceDex guides
Understand access before you commit
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.