Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,267
datasets available to search
ShareScore release 0.9.0
Dataset results
1,267 results for “data paper”
Data and code to accompany oyster pound biofouling paper
<p>The fouling_for_zenodo folder contains the data and code needed to reproduce the analyses and figures in Krasnow et al. (2025). The other folders contain images of the experimental oysters taken during the measurement process that may be useful in the future.</p> <p>Krasnow, R., Kiffney, T., Coleman, S., Cuddy, R., & Brady, D. (2025). Interacting effects of environment and cultivation method on biofouling of farmed oysters (<em>Crassostrea virginica</em>). <em>Journal of the World Aquaculture Society</em>, <em>56</em>(3), e70012. <a href="https://ruby.science/pubs.html#0">https://doi.org/10.1111/jwas.70012</a></p>
Data for paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints"
<p>This is the data set (models, sources and results) for the paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints" published in Informatica Fundamentae in 2021.</p>
Data for paper "Parametric analyses of attack-fault trees"
<p>This is the dataset for paper "Parametric analyses of attack-fault trees" published in the proceedings of the 19th International Conference on Application of Concurrency to System Design (ACSD 2019).</p>
The datasets for "Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data" paper
<p>Paper title: Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data</p> <p>Conference: ICSME 2021</p>
Data sets of research paper "Towards SHM of medium-rise buildings in non-seismic areas" (2021)
<p>Accompanying data sets to the research article:</p> <p>Gaile L., Sliseris J., Ratnika L. Towards SHM of medium-rise buildings in non-seismic areas (2021) International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII, 2021-June, pp. 1023 - 1030.</p> <p>https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130738547&partnerID=40&md5=d1aaeceae696dadebb7862d4e556abd2</p>
Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"
<p>LM is the linear inversion seafloor topography model</p> <p>NLM is the nonlinear inversion seafloor topography model</p> <p>PM is the prior seafloor topography model</p>
Data used in the paper: Heatwaves, droughts, and fires: Exploring compound and cascading dry T hazards at the pan-European scale
<p>These datasets were used to analyze European compound and cascading dry hazards. The scripts are publicly available on GitHub: https://github.com/sjsutanto/Dryhazards.git.</p> <p>File Daily_SM_drought_WB_Converted.nc is for soil moisture drought, fwi_1990_2016_binary_95th_lowThreshold.nc is for wildfires, and datacube_2mtpp_19902016_HW.nc is for heatwaves.</p> <p> </p>
Data for paper on the evolution of Chinese characters
<p>This dataset contains all image, complexity and distinctiveness data that was used for:</p> <p>Han, S. J, Kelly, P., Winters, J., & Kemp, C. (2022). Simplification is not dominant in the evolution of Chinese characters. <em>Open Mind</em>.</p> <p>The code for this project can be found <a href="https://github.com/cskemp/chinesecharacters">here</a>. The file uploaded here is intended to replace the sample data folder that is available in the code repository.</p> <p>Our dataset includes data scraped from hanziyuan.net, as well as data from the following sources:</p> <p>Sun, C. C., Hendrix, P., Ma, J., & Baayen, R. H. (2018). Chinese lexical database (CLD): A large-scale lexical database for simplified Mandarin Chinese. Behavior Research Methods, 50(6), 2606–2629.</p> <p>Wikimedia Commons. (2021). Chinese characters decomposition. <a href="https://commons.wikimedia.org/wiki/Commons:Chinese_characters_decomposition">https://commons.wikimedia.org/wiki/Commons:Chinese_characters_decomposition</a></p> <p>Liu, C.-L., Yin, F., Wang, D.-H., & Wang, Q.-F. (2011). CASIA online and offline Chinese handwriting databases. In 2011 international conference on document analysis and recognition (pp. 37–41). <a href="https://doi.org/10.1109/ICDAR.2011.17">https://doi.org/10.1109/ICDAR.2011.17</a></p> <p>Chen, P.-C. (2020). Traditional Chinese handwriting dataset. GitHub. <a href="https://github.com/AI-FREE-Team/Traditional-Chinese-Handwriting-Dataset">https://github.com/AI-FREE-Team/Traditional-Chinese-Handwriting-Dataset</a></p>
Data for figures in the global streamflow change paper
<p>This is the dataset used for generating figures 1-4 in the manuscript of "<strong>Future global streamflow declines likely more severe than previously estimated</strong>"</p>
Data for GECCO2023 Paper "Many-objective (Combinatorial) Optimization is Easy"
<p><strong>Data for Paper "Many-objective (Combinatorial) Optimization is Easy"</strong></p> <ul> <li><strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances</li> <li><strong>metrics.csv</strong> contains the metric-values based on full enumeration</li> <li><strong>performance.csv</strong> contains the Pareto resolution and the hypervolume of the different algorithms one each instance</li> <li><strong>performance_neval.csv</strong> contains the number of evaluations performed by PLS</li> <li><strong>script.R</strong> is the R script for producing the figures</li> </ul> <p><strong>Reference</strong></p> <p>Arnaud Liefooghe and Manuel López-Ibáñez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15–19, 2023, Lisbon, Portugal. <a href="https://doi.org/10.1145/3583131.3590475">https://doi.org/10.1145/3583131.3590475</a></p> <p><strong>Abstract</strong></p> <p>It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.</p>
Raw data files associated with the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees"
<p>These are the raw data CSV files associated with the results described in the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees".</p> <p>By Sara Hellström, Verena Strobl, Lars Straub, Wilhelm H. A. Osterman, Robert J. Paxton, Julia Osterman</p>
IRIS preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Prprocessed data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of observations performed by IRIS, a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre.</p> <p>Multiple Time Series of IRIS level 2 data are available <a href="https://iris.lmsal.com/search/">here</a></p> <p>The selected data was labeled using these definitions:</p> <p>QS: Quiet Sun<br> AR: Active Regions of the Sun<br> FL: Flare</p> <p>A time series is labeled QS when every single time step refer to a quiet sun activity.<br> When a given time series is partially composed of flaring events, the global time series is labeled as FL.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=240 representing a range between 2793.8401Å and 2806.02Å).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :</p> <p>Type: .npz<br> Size: 11.89GB</p> <p>*** Key: 'data_TR_QS'<br> ndarray data of length 2467<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR_AR'<br> ndarray data of length 1042<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR_FL'<br> ndarray data of length 1055<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_QS'<br> ndarray data of length 325<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_AR'<br> ndarray data of length 1042<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_FL'<br> ndarray data of length 714<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_QS'<br> ndarray data of length 1428<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_AR'<br> ndarray data of length 792<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_FL'<br> ndarray data of length 356<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR'<br> ndarray data of length 4564<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL'<br> ndarray data of length 2081<br> containing np.ndarray of shapes ['various', 240]</p> <p><br> *** Key: 'data_TE'<br> ndarray data of length 2576<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'position_TR_QS'<br> ndarray data of length 2467<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR_AR'<br> ndarray data of length 1042<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR_FL'<br> ndarray data of length 1055<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_QS'<br> ndarray data of length 325<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_AR'<br> ndarray data of length 1042<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_FL'<br> ndarray data of length 714<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_QS'<br> ndarray data of length 1428<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_AR'<br> ndarray data of length 792<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_FL'<br> ndarray data of length 356<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR'<br> ndarray data of length 4564<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL'<br> ndarray data of length 2081<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE'<br> ndarray data of length 2576<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p>
Supplementary data for the paper "Visual integration of omics data to improve 3D models of fungal chromosomes"
<ul> <li>13 parameter files (*.YML) used by the 3DGB workflow to produce models of 3D genomes.</li> <li>13 3D genomes structures (*.PDB).</li> <li>4 animated GIF of representative structures.</li> <li>1 XLSX file that lists raw (Hi-C and ChIP-seq) data used in this study and the associated analysis.</li> </ul>
Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"
<p>Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".</p>
Network data and script accompanying the paper "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"
<p>This repository accompanies the paper<a href="https://rdcu.be/dbuhi"> "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"</a>, by Cottica et al. It contains:</p> <ol> <li>A data file, containing networks of co-occurrence of ethnographic codes from three ethnographies. Data are pseudonymized (see the paper for details).</li> <li>A script that, when run on the data, produces simplified versions of each network. Simplifications follow four different techniques, described in the paper. Each technique relies on a tuning parameter, so that, for each network and each techniques, the script produces several simplified networks, each one associated with a unique value of the tuning parameter.</li> </ol> <p>The data file format is that of a Tulip perspective. To open, download Tulip (https://tulip.labri.fr), launch it and open the file from within the Tulip GUI.</p> <p>The script file is in Python. To run, open it from within the Tulip IDE first.</p> <p> </p>
Data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is the data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on https://arxiv.org/abs/2306.12908.</p> <p>These results are derived from the gravitational-wave parameter-estimation results by the LIGO-Virgo-KAGRA Collaboration, released with the GWTC-1, GWTC-2, GWTC-2.1, and GWTC-3 catalogs under the following links:</p> <ul> <li> https://dcc.ligo.org/P1800370-v5/public</li> <li> https://dcc.ligo.org/P2000223-v7/public</li> <li> https://doi.org/10.5281/zenodo.6513631</li> <li> https://doi.org/10.5281/zenodo.5546663</li> </ul> <p>For the lensed-unlensed hypothesis test posterior overlap Bayes factors, we provide the following files for event pairs from within each observing run:</p> <ul> <li> blu_all_pairs_O1.txt</li> <li> blu_all_pairs_O2.txt</li> <li> blu_all_pairs_O3.txt</li> </ul> <p>In each file, the column "event_pair" contains the names of the two events from the pair sorted chronologically, the column "data_releases" contains the names of the data releases from which the posterior samples of each event were taken, the column "waveform" contains the name of the waveform model used in the parameter estimation for both sets of posteriors, and the column "log10blu" contains the log10 of the Bayes factors.</p> <p>The differences between runs for the same event pair, only including O1-O1, O2-O2, O3-O3 pairs, where at least one run gave log10blu>0, are also given in the file "blu_differences_pairs_with_log10blu_pos.txt". The column "event_pair" contains the event pairs, the columns "waveform_{1,2}" contain the names of the waveform models used in the parameter estimation for both sets of posteriors, the columns "data_releases_{1,2}" contain the the data releases from which the posterior samples of each event were taken, the columns "log10blu_{1,2}" contain the log10 Bayes factors, and the column "difference" contains the difference between "log10blu_1" and "log10blu_2".</p> <p>We also provide the following files corresponding to the appendix of the paper, analyzing overlaps between posterior samples for individual events:</p> <ul> <li> overlap_different_runs.txt</li> <li> overlap_same_run.txt</li> <li> rescaled_difference_single_event.txt</li> </ul> <p>The file "overlap_different_runs.txt" contains Bayes factors for a single event, but comparing the posteriors from different runs. The file "overlap_same_run.txt" contains Bayes factors for the overlap of a single run on a single event with itself. The file "rescaled_difference_single_event.txt" contains the difference between the results contained in the file overlap_different_runs.txt and the results in overlap_same_run.txt, taking the ones that produce the biggest difference, as per equation (A.1) in the paper.</p> <p>In these files, the column "event_name" is the name of the event, the column "data_release" or "data_releases" contains the name(s) of the data release(s) from which the posterior samples of each run were taken, the column "waveform" or "waveform_pair" contains the name(s) of the waveform model(s) used, and the column "log10blu" is the log10 Bayes factor obtained. In the file "rescaled_difference_single_event.txt", the columns "max_run_waveform" and "max_run_data_release" identify an entry from the "overlap_same_run.txt" file from which we use the "log10blu" to compute the value listed in the "difference" column using equation (A.1).<br> </p>
Data and Scripts for the paper "Evidence on the Impact of the Prudential Center on Crime in Downtown Newark"
<p>This repository contains the data and R scripts utilized to perform the analyses in the paper entitled "Evidence on the Impact of the Prudential Center on crime in downtown Newark", co-authored by Gian Maria Campedelli, Eric Piza, Alex Piquero, and Justin Kurland. The article has been published in the Journal of Experimental Criminology and can be read at: https://link.springer.com/article/10.1007/s11292-023-09576-8 (open access).</p> <p>The newer version of the current repository (v2) includes the appendix file to the main text (named supplementary.docx) which was excluded by the online publication due to errors made by the Springer editorial office. We have uploaded the appendix here to avoid issuing an erratum to the article. </p>
Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes
<p>Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles:</p> <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> <p>The spatial reference system is EPSG 25830.</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>
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>
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.