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Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model
<p>This repository contains supplementary data for the journal paper:</p> <blockquote> <p>Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi: 10.3389/fnbot.2023.1179224</p> </blockquote> <p>It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive <strong>EMG_model_configs.zip</strong>.</p> <p><br> The files <strong>IP_tracking_opt_res.json</strong><a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and <strong>IP_tracking_opt_res.pkl</strong> contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored.</p> <p>For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.</p> <p> </p> <p>[1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152</p>
Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"
<p>Supplementary datasets accompanying the manuscript "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds" published in the Metabolic Engineering Journal (<a href="https://doi.org/10.1016/j.ymben.2022.03.013">https://doi.org/10.1016/j.ymben.2022.03.013). </a>In line with the standards of open science, the ARBRE toolbox is freely available to the scientific community on gitHub (<a href="https://github.com/EPFL-LCSB/ARBRE">https://github.com/EPFL-LCSB/ARBRE</a>) and we also provide the web-version at <a href="http://lcsb-databases.epfl.ch/arbre/">http://lcsb-databases.epfl.ch/arbre/</a></p> <p>ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer is a new computational resource consisting of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 33′000 known and 390′000 novel reactions predicted with generalized enzymatic reactions rules and over 74′000 compounds, of which 19′000 are known to biochemical databases and 55′000 only to PubChem. Over 1′000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included in the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways and products of lignin degradation to predict valuable compound derivations.</p> <p>Supplementary files are organized as follows:</p> <p>- 1-s2.0-S1096717622000490-mmc4.docx contains Supplementary Figures 1-4 and Tables 1, 2, and 4.</p> <p>- 1-s2.0-S1096717622000490-mmc2.xlsx contains Supplementary Table 3.</p> <p>- 1-s2.0-S1096717622000490-mmc1.xlsx contains Supplementary Table 5</p> <p>- 1-s2.0-S1096717622000490-mmc3.xlsx contains Supplementary Table 6</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset for the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects"
<p>Datasets to accompany the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects". Written by J. Elgy, P. D. Ledger, J. L. Davidson, T. Özdeğer and A. J. Peyton. The article has been submitted to "Engineering Computations" (2023).</p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch).</p> <p>The datasets also include measurement data for real world objects courtesy of The University of Manchester.</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.<br> J. L. Davidson and A. J. Peyton are grateful for the financial support received from an Innovate UK Grant (reference number 39814).<br> T. Özdeğer and A. J. Peyton are grateful for the financial support received from EPSRC, U.K. through the research grant EP/R002177/1.</p>
A Computational Analysis of Telegram's Narrative Affordances
<p><strong>Overview</strong></p> <p>Anonymized message classification data and actantial analyses of public Telegram channels pertaining to the paper "A Computational Analysis of Telegram's Narrative Affordances". </p> <p><strong>Message classification data</strong></p> <p>All files are included in the zipped folder 'narrative_affordances_data.zip'</p> <p>Each file contains the message classification data for a single Telegram channel. Numbered files are included for each of the six datasets (1 combined, 5 thematic) discussed in the paper. </p> <p><strong>Actantial analysis</strong></p> <p>Frequency lists of retrieved actants are included in the zipped folder 'overview_of_actants.zip'</p> <p> </p>
Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.1
<p>DFT optimised Structures of Silver NHC benzoates, and other files related to the effects of the benzoate anion in the cycloisomerisation of propargyl amides to 5-alkylidene oxazoles, including intermediates, NBO analysis and predicted NMR data, completed as a part of AMS MRes Research Module at Imperial College London, used in the MRes Thesis of Philip David Krause, under the supervision of Prof King Kuok (Mimi) Hii, and Dr Ben Deadman.</p> <p>Thesis: SUBSTITUENT EFFECTS OF BENZOATES IN SILVER N-HETEROCYCLIC CARBENE BENZOATE COMPLEXES AND THEIR CATALYSIS</p>
Data of the INFORMS Journal on Computing paper: Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses
<p>In what follows, you will find data of the paper:<br> "Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses" published in INFORMS Journal on Computing</p> <p>List of files:<br> - Computational_results_BB_NN_RW_CPLEX.xlsx: Excel file that gives all results<br> - instance_gen.cc: Instance generator<br> - instances.zip: compressed file of all instances that are sorted by Sections. It additionally includes the generator<br> - Makefile: Makefile for compiling/debugging, i.e., "make all" or "make debug" do the jobs<br> - MersenneTwister.h: needed by schedule_finder.cc<br> - results_Section_5_1.zip: compressed file of all output files of Section 5.1<br> - results_Section_5_2.zip: compressed file of all output files of Section 5.2<br> - results_Section_5_3.zip: compressed file of all output files of Section 5.3<br> - schedule_finder.cc: Main program containing the B&B, the S-shape, and Nearest Neighbor procedure (see details for customizing the parameters at the top of this file)<br> - valgrind_debug.txt: Only contains the used debug command</p> <p>instances/instance_gen.cc generates a problem instance in file problems.txt<br> The structure of the these problem files is the following:<br> /*<br> NE Total number of experiments given by the currently considered file<br> -2 Separator<br> EXPGRP Index of the current experiment group the current experiment belong to<br> N Number of vacant positions in the warehouse<br> M Number of requests to be stored by the tour<br> P Number of pickers to be scheduled in the warehouse<br> A Number of vertical aisles<br> B Number of horizontal (cross) aisles<br> L_A Length of each vertical aisle<br> L_B Length of each cross aisle<br> UF_VA Up-factor of each vertical aisle (A values)<br> DF_VA Down- factor of each vertical aisle (A values)<br> UF_CA Up-factor of each cross aisle (B values)<br> DF_CA Down- factor of each cross aisle (B values)<br> x_pos_vertical_aisle x-position of vertical aisle (A values)<br> y_pos_cross_aisle y-position of cross aisle (B values)<br> warehouse_graph values For each node of the warehouse graph all entries (15 each) are given (total_number_of_warehouse_graph_nodes*15)<br> FS << warehouse_graph[curr_node].free_position << " " << endl;<br> FS << warehouse_graph[curr_node].depot_node << " " << endl;<br> FS << warehouse_graph[curr_node].vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].region << " " << endl;<br> FS << warehouse_graph[curr_node].x_position << " " << endl;<br> FS << warehouse_graph[curr_node].y_position << " " << endl;<br> shortest_path_distance For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the distance<br> shortest_path_length_including_start_and_end For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the number of visited nodes<br> shortest_path_visited_nodes For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the detailed path (length is respectively given by shortest_path_length_including_start_and_end)<br> dd_free_position For each free position and the depot (here with index N) the due date is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> weight_of_free_position For each free position and the depot (here with index N) the weight is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> capacity_of_free_position For each free position the storage capacity transferred (N values)<br> -2 Separator indicating the end of an instances<br> -3 Separator indicating the end of all experiments (i.e., indicating the end of the file)<br> */</p> <p>output files (results_Section_5_1.zip/results_Section_5_2.zip/results_Section_5_3.zip):<br> results_BB_NXXX_MYYY_A10_B05: Output file of applying B&B<br> results_RW_NXXX_MYYY_A10_B05: Output file of applying s-shape random walk<br> results_NN_NXXX_MYYY_A10_B05: Output file of applying nearest neighbor</p> <p>In these files you find all outputs of schedule_finder.cc. <br> Among others, you will find the generated tour schedules (for Experiment with index I) in the output files by searching the phrase: "Experiment I completed with result="<br> or for the next Experiment " completed with result="</p> <p>Example (results_BB_N030_M150_A10_B05.txt, experiment 0, the tardiness values are to be ignored, see comments in schedule_finder.cc)<br> Pos 0 depot node with index 80 Number of stored items 0 CT 0 No tardiness<br> Pos 1 position 27 Number of stored items 4 Current accumulated number of stored items 4 CT 163 DD 5629 No additional tardiness<br> Pos 2 position 28 Number of stored items 5 Current accumulated number of stored items 9 CT 399 DD 2962 No additional tardiness<br> Pos 3 position 29 Number of stored items 4 Current accumulated number of stored items 13 CT 670 DD 12631 No additional tardiness<br> Pos 4 position 26 Number of stored items 5 Current accumulated number of stored items 18 CT 840 DD 10142 No additional tardiness<br> Pos 5 position 23 Number of stored items 7 Current accumulated number of stored items 25 CT 1134 DD 6000 No additional tardiness<br> Pos 6 position 22 Number of stored items 4 Current accumulated number of stored items 29 CT 1170 DD 6396 No additional tardiness<br> Pos 7 position 16 Number of stored items 5 Current accumulated number of stored items 34 CT 1448 DD 8962 No additional tardiness<br> Pos 8 position 11 Number of stored items 10 Current accumulated number of stored items 44 CT 1674 DD 6336 No additional tardiness<br> Pos 9 position 0 Number of stored items 10 Current accumulated number of stored items 54 CT 2062 DD 1141 Additional tardiness 921<br> Pos 10 position 2 Number of stored items 9 Current accumulated number of stored items 63 CT 2201 DD 7742 No additional tardiness<br> Pos 11 position 4 Number of stored items 5 Current accumulated number of stored items 68 CT 2407 DD 4846 No additional tardiness<br> Pos 12 position 3 Number of stored items 5 Current accumulated number of stored items 73 CT 2717 DD 2316 Additional tardiness 401<br> Pos 13 position 1 Number of stored items 8 Current accumulated number of stored items 81 CT 2876 DD 9917 No additional tardiness<br> Pos 14 position 6 Number of stored items 3 Current accumulated number of stored items 84 CT 3073 DD 7299 No additional tardiness<br> Pos 15 position 5 Number of stored items 8 Current accumulated number of stored items 92 CT 3144 DD 6152 No additional tardiness<br> Pos 16 position 8 Number of stored items 6 Current accumulated number of stored items 98 CT 3337 DD 3705 No additional tardiness<br> Pos 17 position 12 Number of stored items 9 Current accumulated number of stored items 107 CT 3452 DD 7622 No additional tardiness<br> Pos 18 position 13 Number of stored items 4 Current accumulated number of stored items 111 CT 3522 DD 7833 No additional tardiness<br> Pos 19 position 14 Number of stored items 5 Current accumulated number of stored items 116 CT 3647 DD 9877 No additional tardiness<br> Pos 20 position 19 Number of stored items 1 Current accumulated number of stored items 117 CT 3922 DD 2905 Additional tardiness 1017<br> Pos 21 position 20 Number of stored items 5 Current accumulated number of stored items 122 CT 3923 DD 2538 Additional tardiness 1385<br> Pos 22 position 21 Number of stored items 7 Current accumulated number of stored items 129 CT 3976 DD 2769 Additional tardiness 1207<br> Pos 23 position 18 Number of stored items 10 Current accumulated number of stored items 139 CT 4173 DD 3552 Additional tardiness 621<br> Pos 24 position 25 Number of stored items 4 Current accumulated number of stored items 143 CT 4482 DD 11710 No additional tardiness<br> Pos 25 position 24 Number of stored items 7 Current accumulated number of stored items 150 CT 4509 DD 10599 No additional tardiness<br> Pos 26 visiting the node with index 80 Number of stored items 0 CT 4710 DD 10893 No additional tardiness<br> opt_makespan=4710 opt_total_tardiness=5552<br> TSP_procedure returned value 4710<br> Experiment 0 completed with result=3<br> BFS Branch&Bound report: Consumed time: 1</p> <p>Copied from schedule_finder.cc:<br> Note that the procedure used as a solution procedure in the paper is int TSP_procedure(struct bb_node *curr_bb_node, int version)</p> <p>It is called by BB_procedure() as a subroutine for computing a lower bound value of an extended problem<br> (for instance, this extended problem additionally covers due dates. Therefore, due dates are also part of the problem instances, but can be ignored)<br> Specifically, TSP_procedure(struct bb_node *curr_bb_node, int version) is called once by lb_computation()</p>
Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.4
<p>DFT optimised Structures of Silver NHC benzoates, and other files related to the effects of the benzoate anion in the cycloisomerisation of propargyl amides to 5-alkylidene oxazoles, including intermediates, NBO analysis and predicted NMR data, completed as a part of AMS MRes Research Module at Imperial College London, used in the MRes Thesis of Philip David Krause, under the supervision of Prof King Kuok (Mimi) Hii, and Dr Ben Deadman.</p> <p>Thesis: SUBSTITUENT EFFECTS OF BENZOATES IN SILVER N-HETEROCYCLIC CARBENE BENZOATE COMPLEXES AND THEIR CATALYSIS</p>
Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.3
<p>DFT optimised Structures of Silver NHC benzoates, and other files related to the effects of the benzoate anion in the cycloisomerisation of propargyl amides to 5-alkylidene oxazoles, including intermediates, NBO analysis and predicted NMR data, completed as a part of AMS MRes Research Module at Imperial College London, used in the MRes Thesis of Philip David Krause, under the supervision of Prof King Kuok (Mimi) Hii, and Dr Ben Deadman.</p> <p>Thesis: SUBSTITUENT EFFECTS OF BENZOATES IN SILVER N-HETEROCYCLIC CARBENE BENZOATE COMPLEXES AND THEIR CATALYSIS</p>
Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.2
<p>DFT optimised Structures of Silver NHC benzoates, and other files related to the effects of the benzoate anion in the cycloisomerisation of propargyl amides to 5-alkylidene oxazoles, including intermediates, NBO analysis and predicted NMR data, completed as a part of AMS MRes Research Module at Imperial College London, used in the MRes Thesis of Philip David Krause, under the supervision of Prof King Kuok (Mimi) Hii, and Dr Ben Deadman.</p> <p>Thesis: SUBSTITUENT EFFECTS OF BENZOATES IN SILVER N-HETEROCYCLIC CARBENE BENZOATE COMPLEXES AND THEIR CATALYSIS</p>
Raw data for the article: High-throughput computational solvent screening for lignocellulosic biomass processing
<p>This data set contains the raw data for the article "High-throughput computational solvent screening for lignocellulosic biomass processing" published in <em>Chemical Engineering Journal</em>, DOI: <a href="https://doi.org/10.1016/j.cej.2022.139476">https://doi.org/10.1016/j.cej.2022.139476</a></p>
Data base of the complexity Indexes to compute MFA and MCI from the paper "Unleashing The Potential Of Artificial Reefs Design"
<p>This data base compiles the Complexity indexes used to compute the MFA and extract the MCI from the paper "Unleashing The Potential Of Artificial Reefs Design: A Purpose-Driven Evaluation Of Structural Complexity" (https://doi.org/10.32942/X2G300)</p>
MCR LTER: Coral Reef: Computer Vision: Multi-annotator Comparison of Coral Photo Quadrat Analysis
This repository contains the Moorea portion of a larger data package published in conjuncture with: "Towards automated annotation of benthic survey images: variability of human experts and operational modes of automation", Beijbom et al. PLOS One, 2015. The rest of the data package is hosted at the Dryad data repository (doi:10.5061/dryad.m5pr3). The larger data package is an aggregate dataset from four Pacific coral reef monitoring projects in: Moorea (French Polynesia), the northern Line Islands, Nanwan Bay (Taiwan) and Heron Reef (Australia). It contains 5090 coral reef survey images, and 251,988 random-point annotations by coral ecology experts. The point-annotations indicate the dominant benthic substrate at 10 to 200 random point locations per image, using a label-set of 20 categories. In addition, 200 images from each location have been cross-annotated by 6 experts, for a total of 7 sets of annotations for each image. This set of cross-annotations can be used to contextualize the performance of automated annotation methods for coral reef ecology. The full data package can also be used by computer-vision and machine learning researchers to develop object classification, image segmentation, and domain transfer learning methods. These data contain a subset of the raw data from which dataset knb-lter-mcr.4 is derived.
De computer de wet laten herschrijven…?!
<p>De computer de wet laten herschrijven…?!</p> <p> </p> <p><strong>Als Researcher-in-Residence houd ik mij bezig met wetteksten uit de periode ± 1500-1800. De boeken waarin deze teksten zijn verzameld zijn niet goed leesbaar gedigitaliseerd en dus is het systematisch doorzoeken lastig. Daarom trainen we binnen het <em>‘Entangled Histories’</em>-project de computer om de teksten beter te herkennen én we gebruiken <em>machine learning</em> om de teksten in categorieën te plaatsen. Hoe we dit doen, leg ik je in deze lunchlezing van de Weetfabriek uit.</strong></p> <p> </p> <p>Annemieke Romein houdt zich als historica bezig met vroegmoderne regelgeving. Waar zij voorheen alles met de hand moest categoriseren, is het <em>Entangled Histories</em>-project op zoek naar mogelijkheden om dit door de computer te laten overnemen. In deze lunchlezing zal Annemieke laten zien met wat voor soort teksten het projectteam (Sara Veldhoen en Michel de Gruijter en zijzelf) aan de slag is gegaan en wat het doel van het onderzoek is.</p> <p> </p> <p>KB Onderzoeksweek: Weetfabrieksessie.</p>
Supplementary data: Computational analysis of mechanical stress in colonic diverticulosis
<p>The data set contains code, source data, and derivatives data for the results presented in our research paper titled "Computational analysis of mechanical stress in colonic diverticulosis".</p> <p>The "code" contains Abaqus (SIMULIA, Providence, RI) files for the simulations presented in the paper (tested with Abaqus version 6.13) and jupyter notebooks developed to analyze simulation results.</p> <p>The "sourcedata" folder contains Excel files with parameters used for the simulations as well as data directly extracted from the simulation results.</p> <p>The "derivatives" folder contains secondary data calculated based on the files from "sourcedata".</p> <p>The README document included in the dataset contains a more detailed description of the files and folders.</p>
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
<p>The file <strong>Corpora.txt </strong>keeps the corpus used to train the model and the different instances of the classifier. It is basically a text file with one sentence per line from the original corpus called <strong>test.tsv</strong> available at <a href="https://github.com/google-research-datasets/wiki-split.git">https://github.com/google-research-datasets/wiki-split.git</a>. We eliminated punctuation marks and special characters from the original file putting each sentence per line.</p> <p><strong>Enju_Output.txt </strong>holds the outputs generated by Enju in -so mode (Output in stand-off format) using Corpora.txt as input. This file has basically a natural language English per-sentence parse with a wide-coverage probabilistic for HPSG grammar.</p> <p>The file <strong>Supervision.txt </strong>keeps the grammatical tags of the corpus. This file holds a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file<strong> Word_Category.txt</strong> carries the coarse-grained word category information needed by the model and introduced in it by apical dendrites. Each word in the corpus has a word-category tag which provides additional constraints to those provided by lateral dendrites. This file contains a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file <strong>SynSemTests.xlsx</strong> keeps all the grammar classification results as well as the statistical analysis in the classification tests.</p>
Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers
<p>Nine animation videos used in the questionnaire described in the articles "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome</strong>" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers</strong>" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). <em>Video animations were designed and produced by Merel Horsmeier.</em></p>
Automatically computed correspondence patterns among six Burmish languages (based on 500 concepts in Huang 1992)
<p>This document is a printout of automatically computed correspondence patterns amount six Burmish languages (Old Burmese, Longchuan Achang, Xiandao, Atsi, Bola, and Maru). It uses as input 500 concepts taken from Huang 1992.</p>
The Resolution of Keller's Conjecture - Computation Logs
<p>Logs of the computations performed to settle Keller's conjecture on cube tilings. For every value of s∊{3,4,6} there are files:</p> <ul> <li>s?.cnf encoding the problem for that value of s as explained in the paper, with additional symmetry breaking clauses.</li> <li>s?.dnf which is a tautology of assignments that need to be verified by SAT solvers.</li> </ul> <p>Inside the Keller-logs.zip archive, for every value of s∊{3,4,6} you will find:</p> <ul> <li>A folder sym-s? containing all the verification logs of the symmetry breaking clauses added to the original encoding of the problem for that value of s as a SAT instance in order to obtain s?.cnf.</li> <li>A folder unsat-s? containing one log file for reach assignment in s?.dnf.</li> </ul>
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack: Experimental Data
<p>Full experimental dataset for the publication "Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack". The archive `application_motivated_benchmarks.zip` contains the following files and directories:</p> <p>- uncompiled_log.csv</p> <p>Gives IDs for the uncompiled circuits initially generated for use in our<br> experiments, along with the properties of the circuits.</p> <p>- properties_log.csv</p> <p>Gives IDs for device property files, along with the device and the time at which<br> they were collected.</p> <p>- compiled_log.csv</p> <p>Gives the calculated figures of merits for the compiled and run circuits.<br> Compiled circuits are identified by the ID of the uncompiled circuit, the<br> compilation strategy used, and the device compiled onto. Device property IDs at<br> the time of compilation and run are given.</p> <p>- circuits/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 2 forms.<br> <br> - uncompiled.qasm is the uncompiled circuit.<br> - files of the form 'strategy'_'device'.qasm are the compiled circuits.</p> <p>- data/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 3 forms.</p> <p> - prob_vector.csv contains the ideal output probability distribution.<br> - files of the form 'strategy'_'device'.csv contain the shot counts for<br> each compiled circuit when run on the real device.<br> - files of the form 'strategy'_'device'_simulated.csv contain the shot<br> counts for each compiled circuit when run using a classical simulator<br> with noise model build from device properties at the time of the real<br> run.</p> <p>- device_properties/</p> <p>Contains json files detailing device properties for each device property ID.</p> <p> </p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></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.