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CT dataset for "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer" Part 2
<p>To develop and validate a radiomics-based method for lung cancer detection, chest CT images of patients with pulmonary nodules from clinical 5 centers were collected. We hope this large-scale dataset could facilitate both clinical research for automatic lung cancer detection and diagnoses, and engineering research for 3D detection, segmentation and classification. Due to size limit of zenodo.org, we split the whole CT images sets into 2 parts; This is the Part 2.</p> <p>This dataset is a research effort of thousands of hours by experienced thoracic surgeons, and radiologists. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p>
Warehouse Item Picking Optimization
<h3>Dataset Description</h3> <p>This dataset contains four files that provide data related to warehouse orders and address reassignment optimization. The files are as follows:</p> <ol> <li><strong><code>orders.csv</code></strong>:<br>This file includes orders over a period of three months (Date). Each order is represented by an order number (Order_num) and contains multiple items identified by Product_ID. Each item is associated with an address (Address), which represents its location in the warehouse.</li> <li> <p><strong><code>addresses_avg.csv</code></strong>:<br>This file lists all addresses (Address) in the warehouse along with the average daily number of accesses to each address (product_count). An address in the warehouse is specified by a combination of an aisle number and a position number within the aisle.</p> </li> <li> <p><strong><code>warehouse_map.ods</code></strong>:<br>This file provides a schematic layout of the warehouse, illustrating the arrangement of aisles and positions.</p> </li> <li> <p><strong><code>reassignment.csv</code></strong>:<br>This file contains the results of an optimization algorithm, showing the new assignment of addresses in the warehouse.</p> </li> </ol>
A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection - Online Appendix
<p>QAOA-TCS - Quantum Regression Test Case Selection <br>This repository contains all the necessary resources to reproduce the results of the QAOA-TCS method. </p> <p>Dataset Files <br>The "datasets" folder contains: <br>- "sir_programs" </p> <p>SIR Programs <br>The "datasets/sir_programs" folder contains, for each SIR program considered by this project, all the files needed to gather statement coverage, execution costs, and past fault coverage information. </p> <p>For example, in the "flex" program folder: <br>- The file "fault-matrix.txt" contains rows representing flex's test cases. Each row has columns representing different versions of the program. Each cell (i,j) contains a binary value (0 or 1) indicating whether the i-th test case detects a fault in the j-th version. This configuration is called the fault matrix and provides historical fault coverage information. <br>- The folder "json_flex" contains a folder for each test case, with files like "flexi.gcov.json" to recover statement coverage and execution costs. These files detail which basic blocks were executed and how many times, enabling the calculation of total statement coverage and execution costs for each test case. </p> <p>Source Code Files </p> <p>DIVGA.m <br>The "MATLAB/DIVGA.m" file contains the pipeline for the DIVGA algorithm. For simplicity, the statement coverage, execution costs, and fault coverage data already gathered by "Notebook.ipynb" are written into text files, which DIVGA.m reads to bypass the actual datasets. </p> <p>DIVGA.m must be reconfigured for each target program. Update parameters like M, N, and gamultiobj routine settings. Ensure H_size in line 104 is less than max{N, M} + 1. Update the denominator in line 53 based on the total number of code lines in the target program. Adjust the result reporting target files as well. </p> <p>Notebook.ipynb <br>This file includes pipelines for dataset analysis, algorithm execution, and empirical comparisons. </p> <p>It has two main sections: </p> <p>1. QAOA-TCS vs SelectQA and Classical Algorithms <br> - Pipelines analyze SIR programs and compare QAOA-TCS, SelectQA, and classical algorithms. Manual configuration is needed when changing the target program, including updating file paths for Pareto fronts and configuring frontiers to build the reference. <br> - Statistical analysis requires populating the variables "algorithm_nondom_sirprogram" with the number of non-dominated solutions found during each of the 10 runs. </p> <p>Results Files <br>The "results" folder contains the outcomes of QAOA-TCS, DIV-GA, Additional Greedy, and SelectQA after experiment execution. These files enable empirical evaluations and comparisons between the methods. </p>
Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture
<p>Dataset associated with the publication "Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture".</p> <p> </p> <p>Changelog:</p> <p>- Include sample input files for GCMC using RASPA2 and geometry optimization using LAMMPS.</p>
Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity: Raw Data
<p>This dataset contains all GenX model input and results data relevant to the working paper ‘Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity.’ Data for each modeled scenario is contained within a folder in the main directory ('Final_Outputs'), using the naming convention p1_2030_case[number]. Scenarios correspond to the following table:</p> <table> <tbody> <tr> <td><strong>Case Number</strong></td> <td><strong>Scenario</strong></td> <td><strong>VRE Cost</strong></td> <td><strong>Forced Battery Capacity (GW)</strong></td> </tr> <tr> <td>1</td> <td>Fixed Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>2</td> <td>Fixed Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>3</td> <td>Fixed Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>4</td> <td>Fixed Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>5</td> <td>Optimized Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>6</td> <td>Optimized Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>7</td> <td>Optimized Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>8</td> <td>Optimized Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>9</td> <td>Co-Located Storage</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>10</td> <td>Co-Located Storage</td> <td>Low</td> <td>5</td> </tr> <tr> <td>11</td> <td>Co-Located Storage</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>12</td> <td>Co-Located Storage</td> <td>Low</td> <td>15</td> </tr> <tr> <td>13</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>14</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>15</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>16</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>17</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>18</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>19</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>20</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>21</td> <td>Co-Located Storage</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>22</td> <td>Co-Located Storage</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>23</td> <td>Co-Located Storage</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>24</td> <td>Co-Located Storage</td> <td>Mid</td> <td>15</td> </tr> </tbody> </table> <p>The 'fixed interconnection' scenario describes the scenario where the capacity of interconnection for each solar photovoltaic (PV) or wind site is fixed to assumed values. The 'optimized interconnection' scenario enables the model to independently size the renewable energy to interconnection and grid connection capacity. The 'co-located storage' scenario enables any solar PV or wind resource and storage resource to be sited behind a grid connection point while optimizing the interconnection buildout for each site. These scenarios are further explained in the respective working paper. Renewable energy cost sensitivity tags include ‘low’ for assumed low projected VRE and battery costs in 2030 and ‘mid’ for assumed mid projected VRE and battery costs in 2030. Various storage discharge capacities are forced into the system as a percentage of peak demand and range from 3.75-15 GW. Within each case folder, all of the input files (.csv), result files directly outputted by the model (in the 'Results' folder), and setting files (GenX and solver settings in the 'Settings' folder) can be found. All model outputs are described in detail in the GenX documentation. The code can be found on the GenX GitHub repository: https://github.com/GenXProject/GenX.jl. This work has not yet been peer-reviewed.</p>
Replication Package for "Impact of ML Optimization Tactics on Greener Pre-Trained ML Models"
<p>This repository contains the replication package for the paper titled "Impact of ML Optimization Tactics on Greener Pre-Trained ML Models". The README file and accompanying scripts provide detailed instructions to facilitate the replication of the analysis.</p>
Wing Microstructure - Optimizing micro-tiles in micro-structures as a design paradigm.
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific wing is a functional composition of trivariate spline tiles inside a macro trivariate shape of a wing.</p> <p>Model is provided in IGES format, as B-spline surfaces.</p>
Iterative cell optimization in refinement of small molecule electron diffraction data.
<p>Electron diffraction data for the MOF Vie-1 and for Oseltamivir. Associated with the manuscript "Iterative cell optimization in refinement of small molecule electron diffraction data." in submission process.</p>
A statistical method to optimize the chemical etching process of Zinc Oxide thin films
<p>Zinc Oxide (ZnO) is an attractive material for micro and nanoscale devices. Its desirable semiconductor, piezoelectric, and optical properties <span>make</span> it useful in applications ranging from microphones to missile warning systems to biometric sensors. This work introduces a demonstration of blending statistics and chemical etching of thin films to identify the dominant factors, and interaction between factors, and <span>develop</span> statistically enhanced models on etch rate and selectivity of ZnO thin films. Over other mineral acids, ammonium chloride (NH<sub>4</sub>Cl) solutions <span>have</span> commonly been used to <span>wet</span> etch microscale ZnO devices because of their controllable etch rate and near-linear behavior. <span>Etchant</span> concentration and temperature were found to <span>have</span> a significant effect on etch rate. Moreover, this is the first demonstration that has identified multifactor interactions between temperature and concentration and between temperature and agitation. A linear model <span>was</span> developed relating etch rate and its variance against these significant factors and multifactor interactions. An average selectivity of 73:1 <span>was </span>measured with none of the experimental factors having a significant effect on the <span>selectivity. </span>This statistical study captures the significant variance observed <span>by</span> other researchers. <span>Furthermore,</span> it enables statistically enhanced microfabrication processes for other materials.</p>
FIGURE 2. Optimal phylogenetic tree from Maximum Likelihood analyses for the nrITS1–5.8S–ITS2 in Morphology and molecular data of the species of Suillus (Suillaceae, Boletales) associated with Pinus sibirica at the European northeast of Russia
FIGURE 2. Optimal phylogenetic tree from Maximum Likelihood analyses for the nrITS1–5.8S–ITS2 Suillus data. Bootstrap values (BS ≥ 70%) is added to the left of a node as follows: nearest neighbour method / maximum likelihood method. Scale bar indicates expected changes per site. New sequences from the Komi Republic are marked with red blocks. The specimen GenBank accession numbers in parentheses follows the names.
FIGURE 22 in Corrections to "Padial et al. (2014) Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of alignment and optimality criteria"
FIGURE 22. Schematic representation of the new family- and genus-level taxonomy of Brachycephaloidea presented in this study as derived from the results of tree-alignment + parsimony phylogenetic analyses of nucleotide sequences. The placement and affinities of Dischidodactylus and Niceforonia are based on morphological synapomorphies (see text).
Optimizing Within-Distance Queries by Approximating Shapes with Maximal Bounded Boxes - Datasets
<p>Csv and sql files for the underlying data.<br> Figure 9: Calculating the distance between various number and type of shapes, i.e. from polygons, rotated and axis aligned rectangles, to points, polygons, rotated and axis aligned rectangles.<br> Table 1: Calculating distance between shapes of various complexity, i.e. points, lines, quadrilaterals, hexagons, dodecagons, icosagons, pentacontagons.<br> Table 2: Comparing the ordering of the shapes when distance operation is calculated, (a) from polygons to rectangles, (b) from rectangles to polygons.<br> Table 5 & 6: Within-distance and distance queries between (a) actual polygons, (b) their bounded rectangles, (c) actual polygons and points, (d) their bounded rectangles and points.</p>
Supporting Information: Environmental benefits of large-scale second-generation bioethanol production in the EU: An integrated supply chain network optimization and Life Cycle Assessment approache
<p>This supporting information provides all input data of the model, the assumptions made and the literature and database references for the publication<em> Environmental benefits of large-scale second-generation bioethanol production in the EU: An integrated supply chain network optimization and Life Cycle Assessment approache</em>. The environmental data is based on life cycle assessments, with full information on the life cycle inventory and the results of the life cycle impact assessment based on the ReCiPe method. It also includes detailed results for all objective functions in all scenarios (optimization of 18 midpoints, 3 endpoints, and economic optimization in 5 tax scenarios and 2 feedstock scenarios), and detailed results of the sensitivity analysis and Pareto optimization.</p>
Data from: Fusobacterium nucleatum and Bacteroides fragilis detection in colorectal tumours: optimal target site and correlation with total bacterial load
<p>These data were generated to investigate detection of <em>Fusobacterium nucleatum </em>(<em>F. nucleatum</em>) and <em>Bacteroides fragilis</em> (<em>B. fragili</em>s) across different regions of human colorectal tumours. Relative abundance of each species in DNA extracted for clinical molecular mutation testing from formalin-fixed, paraffin-embedded (FFPE) tumour samples from 42 patients was assessed using targeted real-time PCR quantitative (qPCR) (the screening cohort). DNA was then freshly extracted from specific regions of tumours testing positive for one or both species (n = 20) and from 31 additional patients, and relative abundance of each species assessed using qPCR (site investigation cohort). Total bacterial load at the tumour luminal surface (where <em>F. nucleatum</em> and <em>B. fragilis</em> were most frequently detected) was also assessed by qPCR using primers targeting amplification of 16S rRNA. 16S sequencing was performed on tumour luminal surface DNA samples from five patients as an orthogonal method to confirm the ability to detect the targeted species by qPCR.</p>
Increasing morphological disparity and decreasing optimality for jaw speed and strength during the radiation of jawed vertebrates
<p>The Siluro-Devonian adaptive radiation of jawed vertebrates, which underpins almost all living vertebrate biodiversity, is characterised by the evolutionary innovation of the lower jaw. Multiple lines of evidence have suggested that the jaw evolved from a rostral gill arch, but when the jaw took on a feeding function remains unclear. We quantified the variety of form in the earliest jaws in the fossil record and , from which we generated a range of theoretical morphospacelogies within this morphological range, which that we then tested for their functional optimality. By drawing comparisons with the real jaw data and reconstructed ancestral forms, our results show that the earliest jaw shapes were optimised for fast closure and stress resistance, inferring a feeding rather than solely ventilation function. Jaw shapes then became less optimal for these functions during the later radiation of jawed vertebrates. Thus, the evolution of jaw morphology has continually explored new morphospace and accumulated disparity through time, laying the foundation for diverse feeding strategies and the success of jawed vertebrates.</p>
Enhanced Weighted Quantum Particles Swarm Optimization in Electromagnetic Contenst
<p>This article describes how the EWQPSO (Enhanced Weigthed Quantum Particle Swarm Optimization) optimization algorithm can also be used in an electromagnetic context for the circuit synthesis of an antenna (in this case of a sinuous antenna) and how, this version of the algorithm , is better than its previous versions (WQPSO, QPSO and PSO).</p>
Raw data for: Captivating color: evidence for optimal stimulus design in a polymorphic prey lure
<p><span>Many species – humans included – employ color as an instrument of deception. One intriguing example of this resides in the conspicuous abstract color patterns displayed on the bodies of female orb weaving spiders. These displays increase prey interception rates and thereby function at least as visual lures. Their chromatic properties however vary extensively, both across and within species, with discrete forms often co-existing in the manner of a stable polymorphism. Variation is principally expressed in terms of signal hue (color <em>per se</em>), but it is unclear how attractiveness scales with this property and if extant morphs are maximally attractive relative to a graded range of potential alternatives. We examined these questions by assessing catch rates among color-manipulated females of the dimorphic jeweled spider <em>Gasteracantha fornicata</em> in their natural webs. The manipulation altered dorsal appearance in a manner akin to adding six new variants of their existing white/yellow phenotypes. This magnified the natural variation in stimulus hue independently of chroma (saturation) across a range spanning most of the color spectrum. Catch rate varied across treatments in simple accordance with how greatly stimulus hue deviated from either of the two extant spider phenotypes. Predictions based upon fly-perceived chromatic and achromatic background contrast were clearly unsupported despite dipterans constituting ~60 % of identifiable prey. This study supports the importance of signal coloration <em>per se</em> in </span><em><span>G. fornicata</span></em><span> and suggests that </span><span>extant lure phenotypes reside in a broadly optimal spectral range for stimulating their aggregate prey community.</span></p>
Artifact for an optimized polite combination method
<p>The artifact contains the binary, challenge benchmark and results of running with and without the optimization.</p> <p>It also includes instructions for reproduction.</p>
Dataset: Architectural Optimization for Confidentiality under Structural Uncertainty
<p>Dataset for the publication Architectural Optimization for Confidentiality under Structural Uncertainty. The Zip file contains eclipse products containing our used models for the evaluation.</p> <p> </p> <p> </p>
Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction"
<p><strong>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</strong></p> <p>This publication contains the research data and example scripts for the paper “Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction” [1]. The research data is found in the directory <code>research_data</code>, the example scripts are found in the directory <code>example_scripts</code>.</p> <p>The research data contains all necessary information to be able to reconstruct the figures and values given in the paper, as well as all result figures shown. Where possible, the directories contain the necessary scripts to recreate the results themselves, up to stochastic variations.</p> <p>The example scripts are intended to show how one can (i), perform a least-square type optimization of a model function (here we focus our efforts on the analytical model functions MGH17 and Gauss3, as described in the paper) using various methods (BTVO, LM, BO, L-BFGS-B, NM, including using derivative information when applicable), and (ii), perform Markov chain Monte Carlo (MCMC) sampling around the found maximum likelihood estimate (MLE) to estimate the uncertainties of the MLE parameter (both using a surrogate model of the actual model function, as well as using the actual model function directly).</p> <p> </p> <p><strong>Research data</strong></p> <p>Contained are directories for the experimental problem GIXRF, and the two analytical model functions MGH17 and Gauss3. What follows is a listing of directories and the contents:</p> <ul> <li><code>gauss3_optimization</code>: Optimization logs for the Gauss3 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_optimization</code>: Optimization logs for the MGH17 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_mcmc_analytical</code>: Scripts for the creation of the plots (does not use an optimization log), as well as plots shown in the paper. This uses the model function directly to perform the MCMC sampling.</li> <li><code>mgh17_mcmc_surrogate</code>: Optimization log of the MGH17 function used for the creation of the MCMC plots, scripts for the creation of the plots (use the optimization log), as well as plots shown in the paper. This uses a surrogate model to perform the MCMC sampling.</li> <li><code>gixrf_optimization</code>: <code>benchmark.py</code> file to perform the optimization, the optimization logs for the various methods (BTVO, LM, BO, L-BFGS-B, NM), .npy files and scripts used for the creation of the plots, and the plots shown in the paper.</li> <li><code>gixrf_mcmc_supplement</code>: optimization log used for the creation of the plot, pickle file used for the creation of the plot, script to create the MCMC plot.</li> <li><code>gixrf_optimum_difference_supplement</code>: optimization logs of BTVO optimization of the GIXRF problem, scripts to create the difference/error plots shown for the GIXRF problem in the supplement, and the plots themselves.</li> </ul> <p><strong>Employed software for creating the research data</strong></p> <p>The software used in the creation is:</p> <ul> <li>JCMsuite Analysis and Optimization toolkit, development version, commit d55e99b (the closest commercial release is found in JCMsuite version 5.0.2)</li> <li>A list of Python packages installed (excerpt from <code>conda list</code>, name and version) <ul> <li>corner 2.1.0</li> <li>emcee 3.0.2</li> <li>jax 0.2.22</li> <li>jaxlib 0.1.72</li> <li>matplotlib 3.2.1</li> <li>numba 0.40.1</li> <li>numpy 1.18.1</li> <li>pandas 0.24.1</li> <li>python 3.7.11</li> <li>scikit-optimize 0.7.4</li> <li>scipy 1.7.1</li> <li>tikzplotlib 0.9.9</li> </ul> </li> <li>JCMsuite 4.6.3 for the evaluation of the experimental model</li> </ul> <p> </p> <p><strong>Example scripts</strong></p> <p>This directory contains a few sample files that show how parameter reconstructions can be performed using the JCMsuite analysis and optimization toolbox, with a particular focus on the Bayesian target-vector optimization method shown in the paper.</p> <p>It also contains example files that show how an uncertainty quantification can be performed using MCMC, both directly using a model function, as well as using a surrogate model of the model function.</p> <p>What follows is a listing of the contents of the directory:</p> <ul> <li><code>mcmc_mgh17_analytical.py</code>: performs a MCMC analysis of the MGH17 model function directly, without constructing a surrogate model. Uses <code>emcee</code>.</li> <li><code>mcmc_mgh17_surrogate.py</code>: performs a MCMC analysis of the MGH17 model function by constructing a surrogate model of the model function. Uses the JCMsuite analysis and optimization toolbox.</li> <li><code>opt_gauss3.py</code>: performs a parameter reconstruction of the Gauss3 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>opt_mgh17.py</code>: performs a parameter reconstruction of the MGH17 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>util/model_functions.py</code>: contains the MGH17 and Gauss3 model functions, their (automatic) derivatives, and objective functions used in the optimizations.</li> </ul> <p><strong>Requirements to execute the example scripts</strong></p> <p>These scripts have been developed and tested under Linux, Debian 10. We have tried to make sure that they would also work in a Windows environment, but can unfortunately give no guarantees for that.</p> <p>We mainly use Python to run the reconstructions. To execute the files, a few Python packages have to be installed. In addition to the usual scientific Python stack (NumPy, SciPy, matplotlib, pandas, etc.), the packages <code>jax</code> and <code>jaxlib</code> (for automatic differentiation of Python/NumPy functions), <code>emcee</code> and <code>corner</code> (for MCMC sampling and subsequent plotting of the results) have to be installed.</p> <p>This can be achieved for example using pip, e.g.</p> <pre><code>pip install -r requirements.txt</code></pre> <p>Additionally, JCMsuite has to be installed. For this you can visit [2] and download a free trial version.</p> <p>On Linux, the installation has to be added to the PATH, e.g. by adding the following to your <code>.bashrc</code> file:</p> <pre><code>export JCMROOT=/FULL/PATH/TO/BASE/DIRECTORY export PATH=$JCMROOT/bin:$PATH export PYTHONPATH=$JCMROOT/ThirdPartySupport/Python:$PYTHONPATH</code></pre> <p> </p> <p><strong>Bibliography</strong></p> <p>[1] <span>M. Plock</span>, <span> K. Andrle</span>, <span> S. Burger</span>, <span> P.-I. Schneider</span>, <span>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</span>. <em>Adv. Theory Simul.</em> <strong><span>5</span></strong>, 2200112 (2022).</p> <p>[2] https://jcmwave.com/</p>
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.