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405 results for “EA”
Gedminiene_ea_Dukstelis_paleolake_climate reconstructions
<p>Supplementary dataset related to the Gedminiene et al. manuscript, describing multiproxy analyses from a Late Glacial- Holocene core sequence from Dukštelis paleolake, SE Lithuania. This file includes:</p> <ul> <li>Palynological data (pollen, using calibration data taxonomy, in percentages, for Ensemble of six calibrations models and for calibration with Modern analogue technique (MAT)</li> <li>Pollen-based reconstructions (temperatures and precipitation)</li> </ul>
EA-MD-QD: Large Euro Area and Euro Member Countries Datasets for Macroeconomic Research
<p>EA-MD-QD is a collection of large monthly and quarterly EA and EA member countries datasets for macroeconomic analysis.<br>The EA member countries covered are: AT, BE, DE, EL, ES, FR, IE, IT, NL, PT.</p> <p>The formal reference to this dataset is: </p> <p><strong>Barigozzi, M. and Lissona, C. (2024) "EA-MD-QD: Large Euro Area and Euro Member Countries Datasets for Macroeconomic Research". Zenodo.</strong></p> <p>Please refer to it when using the data.</p> <p>Each zip file contains:<br><br>- Excel files for the EA and the countries covered, each containing an unbalanced panel of raw de-seasonalized data.<br><br>- A Matlab code taking as input the raw data and allowing to perform various operations such as:<br>choose the frequency, fill-in missing values, transform data to stationarity, and control for covid outliers.<br><br>- A pdf file with all informations about the series names, sources, and transformation codes.</p> <p><strong>This version (10.2025):</strong></p> <p>Updated data as of 31-October-2025. </p>
Bayesian evaluation of the mass calibration example from EA 4/02
<p>The example describes how the measurement uncertainty of the calibration of a 10 kg weight can be performed using Bayes’ rule and a measurement model. The purpose of the example is to demonstrate how Bayes’ rule can be implemented in the context of the Guide to the expression of Uncertainty in Measurement and the uncertainty can be propagated. Special attention is paid to the assignment of probability density functions of type A and type B evaluations of standard uncertainty.</p> <p>The files contained in the dataset are:</p> <ul> <li>Readme.txt: instructions on how to install and run the project</li> <li>Mass_example_EA_4_02.pdf: Report "Bayesian evaluation of the mass calibration example from EA 4/02"</li> <li>Compendium.bib: bibliography file</li> <li>Mass_example_EA_4_02_Article.Rproj: RStudio project file</li> <li>Mass_example_EA_4_02.Rnw: source file to generate Mass_example_EA_4_02.tex in RStudio</li> <li>Mass_example_EA_4_02.bbl: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.log: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.synctex.gz: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.tex: LaTeX file to be compiled in order to produce Mass_example_EA_4_02.pdf</li> <li>Mass_example_EA_4_02-concordance.tex: file generated by RStudio when compiling Mass_example_EA_4_02.Rnw.</li> </ul>
Dataset: Electronic Arts Inc. (EA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: EA Bridgeway Omni Small-Cap Value ETF (BSVO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Place-based Mapping in EAS Listeners
ClinicalTrials.gov study NCT04722042. IPD Sharing: YES. Countries: 1. Publications: 3.
Ea T'Ling
Núi lửa hoạt động cách ngày nay khoảng 5,33 – 0,78 triệu năm, ứng với phụ giai đoạn thứ 2 (Pliocene – Pleistocene) thuộc giai đoạn thứ 3 của lịch sử phát triển địa chất Công viên địa chất Đắk Nông. Núi lửa có hình nón cụt trũng giữa, gờ miệng hình oval, trong miệng hình phễu và có khe thoát dòng là đặc trưng cho kiểu phun trào trung tâm, mang tính giáo khoa điển hình; có thể lấy làm ví dụ tiêu biểu trong nghiên cứu, giảng dạy, học tập về cơ chế phun trào ở các trường học và phổ biển kiến thức tới cộng đồng. Source: Objaverse 1.0 / Sketchfab
Experiments with Frequency Fitness Assignment in a (1+1) EA on the Traveling Salesperson Problem
<p><strong>1. Introduction</strong></p> <p>The implementation and experimental results of the (1+1) EA with and without Frequency Fitness Assignment (FFA) to solve the <code>EUC_2D</code> Traveling Salesperson Problem (TSP) instances from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p> <p>A TSP is defined by a fully-connected weighted graph of <code>n</code> cities. The goal is to find the overall shortest tour that visits each cities exactly once and returns to its starting point. The TSP is NP-hard. We consider 18 symmetric Euclidean instances from the well-known <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p> <p>Solutions in our work are stored in the path representation, where such a tour is encoded as a permutation <code>x</code> of the numbers <code>1</code> to <code>n</code>, each identifying a city. If a city appears at index <code>j</code> in the permutation <code>x</code>, then it will be the <code>j</code><sup>th</sup> city to be visited. This means that a tour <code>x</code> will pass the following edges: <code>(x[1], x[2])</code>, <code>(x[2], x[3])</code>, <code>(x[3], x[4])</code>, … <code>(x[n-1], x[n])</code>, <code>(x[n], x[1])</code>.</p> <p>The (1+1) EA is the most basic evolutionary algorithm and also be considered as a randomized local search. It starts with one random solution/permutation <code>xc</code> and computes its length <code>yc=f(xc)</code>. In each iteration, it applies a unary search operator <code>op</code> to obtain a new tour <code>xn=op(xc)</code> and computes its length <code>yn=f(xn)</code>. If <code>yn<=yc</code>, then it will accept the new tour and set <code>xn=xn</code> and <code>yc=yn</code>.</p> <p>FFA is a fitness assignment process that takes place before this last step in the EA. We integrate FFA into the (1+1) EA and obtain the (1+1) FEA. This algorithm uses an additional table <code>H</code> which counts, for any tour length <code>y</code>, how often it has been seen during the search so far. After the new tour <code>xn</code> is created and its objective value <code>yn</code> is computed, the (1+1) FEA sets <code>H[yc] = H[yc] + 1</code> and <code>H[yn] = H[yn] + 1</code>. It will accept <code>xn</code> if and only if <code>H[yn] <= H[yc]</code> and, only in this case, set <code>xn=xn</code> and <code>yc=yn</code>.</p> <p>We apply both EAs with two operators. <code>swap</code> exchanges two randomly chosen cities in the permutation. <code>reverse</code> reverses a randomly chosen subsequence of the tour.</p> <p><strong>2. Directory Structure</strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>results_and_evaluation</code> contain the results of two experiments as well as their evaluation. <ul> <li><code>performance</code> contains the results and evaluation of the main experiment, namely the 21 runs on 18 <code>EUC_2D</code> Traveling Salesperson Problem (TSP) instances from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>. <ul> <li><code>results</code> is the directory with the log files</li> <li><code>evaluation</code> is a folder with the extracted evaluation and figures</li> <li><code>evaluation.py</code> is a Python script that generates all the files in <code>evaluation</code> from the data it finds in <code>results</code>. It requires the <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a> package being installed for running.</li> </ul> </li> <li><code>H</code> contains the results of the experiment conducting single runs on the instances and gathering the data of the frequency table <code>H</code> at different objective function evaluations in the log files. <ul> <li><code>results</code> is the directory with the log files</li> <li><code>evaluation</code> is a folder with the extracted evaluation and figures</li> <li><code>evaluation.py</code> is a Python script that generates all the files in <code>evaluation</code> from the data it finds in <code>results</code>. It requires the <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a> package being installed for running.</li> </ul> </li> </ul> </li> <li><code>source</code> contains the Python source codes needed to run the <code>performance</code> experiment. <ul> <li><code>moptipy</code> is a local copy of the <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a> package used for our experiment.</li> <li><code>tsplib</code> contains the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> data. This includes the instances used in our experiments as files in text format with suffix <code>.tsp</code>. If an optimal tour is given, it is stored in a text format file with suffix <code>.opt.tour</code> and name prefix identical to the instance file. In other words, the file <code>eil51.tsp</code> contains the TSP instance <code>eil51</code> and the file <code>eil51.opt.tour</code> contains the corresponding optimal tour. Both the TSP instances and optimal tours can be downloaded from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/</a>. We also include the documentation of TSPLIB in file <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf"><code>tsp95.pdf</code></a> documenting them. We further include the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/TSPFAQ.html">TSPLIB FAQ</a> both as HTML and PDF file (<code>tsplib_faq.html</code> and <code>tsplib_faq.pdf</code>) and the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html">list of known optimal tour lengths</a> as HTML and PDF file (<code>optimal_tour_lengths_of_symmetric_tsps.html</code>, <code>optimal_tour_lengths_of_symmetric_tsps.pdf</code>). Notice that, while the TSP instances we used are Euclidean, all distances are converted to integers as prescribed by the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">documentation</a>.</li> </ul> </li> </ul> <p><strong>3. What algorithms are included in this experiment?</strong></p> <p>We implement the simple local search with (1+1) EA with and without Frequency Fitness Assignment. We use the common path representation for the TSP with <code>n</code> cities, which encodes each solution as a permutation of the numbers <code>1..n</code>. The value <code>i</code> at position <code>j</code>, i.e., <code>x[j] = i</code>, in such a permutation indicates that the <code>j</code><sup>th</sup> city to be visited by <code>i</code>. We implement two search operators, namely <code>reverse</code>, which reverses a subsequence of the tour, and <code>swap</code> which swaps two cities.</p> <ul> <li>(1+1) EA <ul> <li>with <code>swap</code> operator</li> <li>with <code>reverse</code> operator</li> </ul> </li> <li>(1+1) FEA, i.e., the (1+1) EA with Frequency Fitness Assignment (FFA) <ul> <li>with <code>swap</code> operator</li> <li>with <code>reverse</code> operator</li> </ul> </li> </ul> <p><strong>4. How to Run the Experiment</strong></p> <p>First, you must make sure to have all the dependencies installed that this program requires. You can do this by executing the following command in the terminal:</p> <pre><code>pip install matplotlib numba numpy pandas psutil scikit-learn</code></pre> <p>Now enter the <code>source</code> directory, i.e., the directory containing the <code>run.py</code> file, in your terminal. Depending on your system configuration and whether you run Windows or Linux, you can start the program with <em>one</em> of the commands below. (If running the first command returns with an error, just try the next one in the list.)</p> <ul> <li><code>python3 -m run</code></li> <li><code>python -m run</code></li> <li><code>python run.py</code></li> <li><code>python3 run.py</code></li> </ul> <p>Then the experiment will run. It will automatically create a sub-folder <code>results</code> in <code>source</code> and place all log files that are generated into it. Be careful: The experiment will take a long time. However, if you have multiple CPUs, you can simply start several instances of this program in independent terminals. Each instance will then conduct different runs. This also works if this folder is shared over the network, in which case you can run multiple processes on multiple PCs.</p> <p>Side note: This experiment uses the <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a> package for implementing its algorithms, running the experiments, and gathering their results. If you want to install <code>moptipy</code> on your system instead of using the version supplied here, you can install it via <code>pip install moptipy</code>.</p> <p><strong>5. Literature</strong></p> <ul> <li>Frequency Fitness Assignment (FFA): <ol> <li>Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value. <em>IEEE Transactions on Evolutionary Computation</em> 25(2):307–319. April 2021. Preprint available at <a href="http://arxiv.org/abs/2001.01416">arXiv:2001.01416v5</a> [cs.NE] 15 Oct 2020. doi:<a href="http://dx.doi.org/10.1109/TEVC.2020.3032090">10.1109/TEVC.2020.3032090</a>. Experimental results and source code are available at doi:<a href="http://doi.org/10.5281/zenodo.3899474">10.5281/zenodo.3899474</a>.</li> <li>Thomas Weise, Zhize Wu, Xinlu Li, Yan Chen, and Jörg Lässig. Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient. <a href="https://arxiv.org/abs/2112.00229v4">arXiv:2112.00229v4</a> [cs.NE] 25 May 2022.</li> <li>Thomas Weise, Mingxu Wan, Ke Tang, Pu Wang, Alexandre Devert, and Xin Yao. Frequency Fitness Assignment. <em>IEEE Transactions on Evolutionary Computation (IEEE-EC)</em> 18(2):226-243, April 2014. doi:<a href="http://dx.doi.org/10.1109/TEVC.2013.2251885">10.1109/TEVC.2013.2251885</a>.</li> <li>Thomas Weise, Xinlu Li, Yan Chen, and Zhize Wu. Solving Job Shop Scheduling Problems Without Using a Bias for Good Solutions. In <em>Genetic and Evolutionary Computation Conference Companion (GECCO’21 Companion),</em> July 10-14, 2021, Lille, France. ACM, New York, NY, USA. ISBN 978-1-4503-8351-6. doi:<a href="http://doi.org/10.1145/3449726.3463124">10.1145/3449726.3463124</a>.</li> <li>Thomas Weise, Yan Chen, Xinlu Li, and Zhize Wu. Selecting a diverse set of benchmark instances from a tunable model problem for black-box discrete optimization algorithms. <em>Applied Soft Computing Journal (ASOC)</em>, 92:106269, June 2020. doi:<a href="http://dx.doi.org/10.1016/j.asoc.2020.106269">10.1016/j.asoc.2020.106269</a>.</li> <li>Thomas Weise, Mingxu Wan, Ke Tang, and Xin Yao. Evolving Exact Integer Algorithms with Genetic Programming. In <em>Proceedings of the IEEE Congress on Evolutionary Computation (CEC’14), Proceedings of the 2014 World Congress on Computational Intelligence (WCCI’14)</em>, pages 1816-1823, Beijing, China, July 6-11, 2014. Los Alamitos, CA, USA: IEEE Computer Society Press. ISBN: 978-1-4799-1488-3. doi:<a href="http://dx.doi.org/10.1109/CEC.2014.6900292">10.1109/CEC.2014.6900292</a>.</li> </ol> </li> <li>Traveling Salesperson Problem (TSP): <ol> <li>Pedro Larrañaga, Cindy M. H. Kuijpers, Roberto H. Murga, I. Inza, and S. Dizdarevic. Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. <em>Artificial Intelligence Review,</em> 13(2):129–170, April 1999. Kluwer Academic Publishers, The Netherlands. doi:<a href="https://doi.org/10.1023/A:1006529012972">10.1023/A:1006529012972</a>.</li> <li>Gerhard Reinelt. TSPLIB — A Traveling Salesman Problem Library. <em>ORSA Journal on Computing</em> 3(4):376-384. 1991. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/</a>.</li> <li>Gerhard Reinelt. TSPLIB95. 1995. Heidelberg, Germany: Universität Heidelberg, Institut für Angewandte Mathematik. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf</a>.</li> <li>Thomas Weise, Raymond Chiong, Ke Tang, Jörg Lässig, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, and Xin Yao. Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. <em>IEEE Computational Intelligence Magazine (CIM)</em> 9(3):40-52, August 2014. doi:<a href="http://dx.doi.org/10.1109/MCI.2014.2326101">10.1109/MCI.2014.2326101</a>.</li> <li>Eugene Leighton Lawler, Jan Karel Lenstra, Alexander Hendrik George Rinnooy Kan, and David B. Shmoys. <em>The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization.</em> Wiley Interscience. 1985.</li> <li>David Lee Applegate, Robert E. Bixby, Vasek Chvatal, and William John Cook. <em>The Traveling Salesman Problem: A Computational Study.</em> Princeton University Press. 2007.</li> <li>Gregory Z. Gutin and Abraham P. Punnen, editors. <em>The Traveling Salesman Problem and its Variations.</em> Volume 12 of Combinatorial Optimization. Kluwer Academic Publishers. 2002. doi:<a href="https://dx.doi.org/10.1007/b101971">10.1007/b101971</a>.</li> </ol> </li> <li>Software: <ol> <li>The Metaheuristic Optimization in Python Package <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a></li> </ol> </li> </ul> <p><strong>6. License</strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> in directory <code>source/tsplib</code>, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE</code> in this archive.</p>
Different embedding initializations for EA datasets
<p>Embedding initializations used in https://arxiv.org/abs/2010.16314</p> <p>See also: https://github.com/mberr/ea-sota-comparison</p>
Efficacy of Electroacupuncture (EA) for Women With Pure Stress Urinary Incontinence (SUI)
ClinicalTrials.gov study NCT02445573. IPD Sharing: Not stated. Countries: 1. Publications: 1.
PTBi EA simulation and team training – knowledge and skills assessment
Open the record for dataset details and reuse information.
Improving early estimates of large earthquake's final fault lengths and magnitudes leveraging source fault structural maturity information - supplementary data
<p>Supplementary dataset for <em>Improving early estimates of large ea­­rthquake’s final fault lengths and magnitudes leveraging source fault structural maturity information. </em>This contains individual performance test results for the algorithm discussed in this publication for each earthquake included in the study</p>
Supplementary material 1 from: Shepherd KA, Lepschi BJ, Johnson EA, Gardner AG, Sessa EB, S. Jabaily R (2020) The concluding chapter: recircumscription of Goodenia (Goodeniaceae) to include four allied genera with an updated infrageneric classification. PhytoKeys 152: 27-104. https://doi.org/10.3897/phytokeys.152.49604
Summary of GenBank accession numbers, Taxon names, Project numbers, Herbarium Accession numbers, voucher collectors and collection numbers, phylogenetic position and taxonomy and classification according to Shepherd et al.
FIGURES 5–9. Tsudaea kitayamana. 5 in Tsu da ea, a new genus of Brachycentridae (Trichoptera) from Japan
FIGURES 5–9. Tsudaea kitayamana. 5: Map of Honshu, showing collection sites with arrow pointing to the type locality. 6: Habitat at headwater of the Tama River, Enzan, Yamanashi. 7: Larval microhabitat, mossy boulders in marginal area. 8: Adult female, scale: 1 mm. 9: Larva, scale: 1 mm.
FIGURE 2. Tsudaea kitayamana. Male genitalia A–F in Tsu da ea, a new genus of Brachycentridae (Trichoptera) from Japan
FIGURE 2. Tsudaea kitayamana. Male genitalia A–F: A, lateral; B, dorsal; C, ventral; D, inferior appendage, mesal; E, phallus, dorsal; F, phallus, lateral. Female genitalia G–I: G, lateral; H, dorsal; I, ventral. Abbreviations: IX, X, abdominal segments IX and X; ae., aedeagus; b.s., basal segment of inferior appendage; d.s., distal segment of inferior appendage; int., intermediate appendage of segment X; pr. preanal appendage.
FIGURE 1. Tsudaea kitayamana. A in Tsu da ea, a new genus of Brachycentridae (Trichoptera) from Japan
FIGURE 1. Tsudaea kitayamana. A: male adult, head and pro- and mesonota, dorsal; B: Male adult, head, frontal; C, male, head, lateral; D, male, maxillary palp, lateral (D1: Yamanashi, D2: Hyogo); E, female, maxillary palp, lateral; F, male, wings; G: female, wings.
FIGURE 3. Tsudaea kitayamana. Larva A–J in Tsu da ea, a new genus of Brachycentridae (Trichoptera) from Japan
FIGURE 3. Tsudaea kitayamana. Larva A–J: A, lateral; B, head, dorsal, primary setae numbered; C, head, ventral, primary setae numbered; D, left mandible, dorsal; E, right mandible, dorsal; F, head, thorax and abdominal segment I, dorsal; G, foreleg, lateral, ventral margin of femur enlarged; H, midleg, lateral; I, hindleg, lateral; J, abdominal segment IX, dorsal.
FIGURE 4. Tsudaea kitayamana. Pupa A–B in Tsu da ea, a new genus of Brachycentridae (Trichoptera) from Japan
FIGURE 4. Tsudaea kitayamana. Pupa A–B: A, dorsal, row of spines of abdominal segment V, anterior hook plate of abdominal segment VI and right anal process enlarged; B, head, frontal, apex of right mandible enlarged. Case C–E: C, lateral; D, caudal; E, anterior closure of pupal case, frontal.
LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS
<p>Estimated LD block matrices and other LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS for approximately 2.0 million SNPs in HapMap 3 plus MEGA. Given the limited storage space on Zenodo, we have deposited the LD information generated based on 1000 Genomes LD reference panel and provided the link to download the LD files below using:</p> <p>(1) 1000 Genomes LD reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0">https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0</a> (~6.73G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0">https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0</a> (~7.69G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0">https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0</a> (~8.80G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0">https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0</a> (~5.63G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0">https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0</a> (~2.60G, unzip by: tar -zxvf SAS.tar.gz)</p> <p> </p> <p>(2) UK Biobank reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0">https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0</a> (~13.15G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0">https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0</a> (~11.59G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0">https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0</a> (~4.88G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0">https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0</a> (~4.27G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0">https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0</a> (~11.44G, unzip by: tar -zxvf SAS.tar.gz)</p>
EAS-1000G
<p>These data are prepared in the BALDER project funded by the ODIN platform. ODIN is sponsored by the Novo Nordisk Foundation (grant number NNF20SA0061466)</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.
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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.