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28 results for “benchmark data set”
Data set supplementing "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study"
<p>This is the de-identified data set used to conduct the analyses in the study published as Original Research in the JMIR under the title "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study" (https://doi.org/10.2196/24475)</p> <p>The data set contains the assessments of the urgency of symptoms to 45 fictitious clinical case vignettes by 91 US participants, and the participants' age, gender and level of education. Data for the symptom checker apps is needed to fully reproduce our study and can be found in the appendix of the paper "Evaluation of symptom checkers for self diagnosis and triage: audit study" by Semigran et al. (2015) (https://doi.org/10.1136/bmj.h3480).</p>
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena subject to imposed damage
<p>A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena (HBTA), a full-scale steel bridge subject to imposed damage, has been established. The data set includes organized dynamic response and load measurement data of the bridge under different structural state conditions, where the structural state conditions range from an undamaged (reference) state to known damage states. Furthermore, the data set includes acceleration and strain data from the response monitoring and acceleration data from the load monitoring, where a modal vibration shaker is used as an excitation source. The data is collected in one h5-file (hierarchical data format version 5) with a sampling rate of 100 Hz. Signal processing and resampling of the data has been performed according to the description provided in the references below. The data set is now published in this open-access data repository and can be accessed and downloaded freely. As such, the data set provides an important benchmark to the scientific community within bridge damage detection and SHM.</p>
Data set and benchmarks from Eriksson et al., ICAPS 2018
<p>These are the benchmarks and the experiment data used in the paper "A Proof System for Unsolvable Planning Tasks" by Eriksson et al. (ICAPS 2018). The raw data contains the logs from all runs, while the eval-directories contain a json file with all parsed attributes. The benchmarks directory contains all benchmarks used in the experiments. Finally, the file eriksson-et-al-icaps2018.html provides an overview over the most interesting attributes across all runs.</p>
Benchmark movement data set for trust assessment in human robot collaboration
<p>In the Drapebot project, a worker is supposed to collaborate with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from a standard Trust questionnaire (Trust perception scale - HRI, Schaefer 2016).</p> <p>Data has been collected in the transport and draping tasks (counterbalanced) from 20 participants, 7 female and 13 male, average age 25 (SD = 4.0). Average height was 1.74 meters (SD = 0.1). One session consists of 24 trials on average for the transport and draping task resulting in 951 trials across all conditions. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 20 files for 20 participants of each task accordingly (transport and draping). The name of the files is P01SD, where the number 01 is the participant the D stands for draping. Accordingly, P01ST stands for transport. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>For each procedure there is an annotation file called sorted_draping.xlsx and sorted_transport.xlsx. In these files the first column is the frame and from column 2 until column 21 are the annotations for each procedure for each participant. The annotations describe the different phases during the procedures for each data frame recorded by xsens:</p> <ul> <li>Transport phases: pick, transport, drop, return</li> <li>Draping phases: approach, draping, return</li> </ul> <p>The file trustscores.xlsx includes some demographic data as well as the results of the trust questionaire for each participant and each task, including the scores for the individual items as well as the calculated trust score. The different columns are:</p> <ul> <li>Subject: participant number for crossreferencing with annotation and movement data</li> <li>Transport.Speed: denoting the robot speed (fast or slow)</li> <li>Age: age of the participant</li> <li>Gender: gender of the participant</li> <li>DominantHand: dominant hand of the participant (left or right)</li> <li>Height: height of the participant</li> <li>Score for answers of the participant in related questions category.</li> </ul> <p>This is followed by the trust questionaire items:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>The last two columns are</p> <ul> <li>TrustScore – Final trust score calculated from all questions</li> <li>Task – Which task is being performed (Transport/Draping)</li> </ul>
Benchmark EEG data set for trust assessment for interactions with social robots
<p>The data collection consisted of a game interaction with a small humanoid EZ-robot. The robot explains a word to the participant either through movements depicting the concept or by verbal description. Depending on their performance, participants could "earn" or loose candy as remuneration for their participation.</p> <p>The dataset comprises EEG (Electroencephalography) recordings from 21 participants, gathered using Emotiv headsets. Each participant's EEG data includes timestamps and measurements from 14 sensors placed across different regions of the scalp. The sensor labels in the header are as follows: EEG.AF3, EEG.F7, EEG.F3, EEG.FC5, EEG.T7, EEG.P7, EEG.O1, EEG.O2, EEG.P8, EEG.T8, EEG.FC6, EEG.F4, EEG.F8, EEG.AF4, and Time.</p> <p>The EEG data provides insights into the electrical activity of the brain, offering a window into cognitive processes and emotional responses during various activities or stimuli in the form of microvolt and with a frame rate of 128 Hz. The whole data set consists of 3651124 data points for each sensor, i.e. 173863 on average for each participant (min. 128505, max. 249631). </p> <p>Files are named after participant numbers starting with ID01. The data has to be pre-processed making use of the information given in the details.xlsx file that contains annotations corresponding to the EEG recordings. These annotations denote the timing of different phases related to trust across the participants' interactions. Each phase is delineated by a start time and an end time, representing distinct stages of the trust-building process. All the other data (timestamps) which are outside the start and end of each phase should be considered as breaks, e.g. filling out the questionnaires. The last element is the trust score for the given phase, which is calculated on the answers in an MDMT questionnaire.</p> <p>The following phases have been annotated:</p> <ol> <li>Trust Building: This phase involves friendly initial interactions for establishing trust between participants and the robot.</li> <li>Situational Awareness: This phase continues to build up trust by showing situation awareness of the robot, e.g. by complimenting on the participant's fashion choice.</li> <li>Transparency: Trust is maintained by increased openness and clarity in communicating about the robot's abilities.</li> <li>Trust Violation: Trust is compromised during this phase by deliberately misleading the participant and making it impossible to answer correctly. </li> <li>Trust Repair: The robot shows efforts to repair trust by apologizing for the behavior in the previous stage.</li> </ol> <p>If you work with the data, please cite one of the article given below.</p>
Supporting Data for: The V30 Benchmark Set for Anharmonic Vibrational Frequencies of Molecular Dimers
<p>Intermolecular vibrations are extremely challenging to describe but are the most crucial part for determining entropy and hence free energies, and enable for instance the distinction between different crystal-packing arrangements of the same molecule via THz spectroscopy. Herein, we introduce a benchmark data set - V30 - containing 30 small molecular dimers with intermolecular interactions ranging from exclusively van-der-Waals dispersion to systems with hydrogen bonds. All calculations are performed with the gold standard of Quantum Chemistry CCSD(T). We discuss vibrational frequencies obtained via different models starting with the harmonic approximation over independent Morse oscillators up to second-order vibrational perturbation theory (VPT2), which allows a proper anharmonic treatment including coupling of vibrational modes. However, large amplitude motions present in many low-frequency intermolecular modes are problematic for VPT2. In analogy to the often used treatment for internal rotations, we replace such problematic modes by a simple one-dimensional hindered rotor model. We compare selected dimers with available experimental data or high-level calculations of potential energy surfaces and show that VPT2 in combination with hindered rotors can yield a very good description of fundamental frequencies for the discussed subset of dimers involving small and semi-rigid molecules.<br><br>This supporting dataset includes the calculated force constants, harmonic frequencies, Morse frequencies, VPT2 frequencies, and the optimized structures for the V30 dataset. See the included README.md file for more details. The related preprint can be found at <a href="https://doi.org/10.48550/arXiv.2209.04392">https://doi.org/10.48550/arXiv.2209.04392</a>.</p>
RSW gun fault prediction benchmark data set (demo)
<p>The resistance spot welding (RSW) welding gun fault prediction benchmark data set has 72 multivariate time series in the training set and 8 in the testing set. Each time series length 604800 sampled at 1 Hz with missing values and has 20 dimensions (c1-c19 and the error code). We retain the missing value and the outliers of the welding gun time series for the potential of imputation research in the future.<br> This data set supports an academic paper named 'benchmark study for welding gun fault prediction'.</p> <p><strong>Feature name and explanation:</strong></p> <p>c1 : Electrode cap offset;</p> <p>c2 : Electrode force;</p> <p>c3 : Electrode position;</p> <p>c4 : Force build-up;</p> <p>c5 : Balance pressure;</p> <p>c6 : Friction;</p> <p>c7 : Maximum aperture;</p> <p>c8 : Maximum electrode force;</p> <p>c9 : Mtart friction;</p> <p>c10 : US2;</p> <p>c11 : Welding point count;</p> <p>c12 : Position count;</p> <p>c13: Setpoints of counterbalance pressure;</p> <p>c14: Setpoints of electrode force;</p> <p>c15 : Setpoints of electrode position;</p> <p>c16: Setpoints of sheet thickness;</p> <p>c17 : Setpoints of velocity;<br> c18: Setpoints of force build-up;<br> c19 : Offset value in robot.</p> <p><strong>Machine Learning Task:</strong><br> This dataset is suitable for a time series forecasting task, where machine learning models can be trained to predict future welding parameters based on the provided welding parameters time series in history. </p> <p><strong>Code for quick start:</strong></p> <p><a href="https://zenodo.org/record/7655025">https://zenodo.org/record/7655025</a></p> <p>If you want to have an overview of the data before downloading all of it, you can download only the files with the word "Damo" in the file name.</p> <p>For any question, please contact 1910633@stu.neu.edu.cn</p>
Data set for the journal article: Social life cycle assessment of green methanol and benchmarking against conventional fossil methanol
<p>Single File containing:</p> <ul> <li>Green Methanol Inventories: numerical data as displayed in Figure 4, Main social life cycle inventory data of the green methanol system. </li> <li>Conventional Methanol Inventories: numerical data as displayed in Figure 5, Main social life cycle inventory data of the conventional methanol system. </li> <li>Supplementary information: Diagrams and tables describing teh flowsheet of the simulations used in this work: <ul> <li> <p>Green methanol production process (flowsheet and stream table)</p> </li> <li> <p>Syngas production through Steam Methane Reforming (flowsheet and stream table)</p> </li> <li> <p>Conventional methanol production process (flowsheet and stream table)</p> </li> </ul> </li> </ul>
Tassie BRUV: A benchmark data set for computer vision and movement quantification algorithms
Open the record for dataset details and reuse information.
Experimental Data Sets for the study "Benchmarking a $(\mu+\lambda)$ Genetic Algorithm with Configurable Crossover Probability"
<p>This is the experimental result of the study "Benchmarking a (μ+λ) Genetic Algorithm with Configurable Crossover Probability". A novel (μ+λ) GA is proposed and benchmarked, in which we stochastically determine whether to apply the crossover operator either for each individual or generation with a crossover probability <span class="math-tex">\(p_c\)</span>. This data set consists of two parts:</p> <ol> <li>The results of (μ+λ) GA on 25 pseudo-Boolean problems defined in <em>IOHprofiler </em>(<a href="https://iohprofiler.github.io/">https://iohprofiler.github.io/</a>) with the following setup: <span class="math-tex">\(\mu \in \{10, 50, 100\}, \lambda \in \{1, \lceil\mu/2\rceil, \mu\}, p_c\in\{0, 0.5\}.\)</span> <ul> <li>'IOHprofiler_Problems_standard_bit_mutation.csv' --> the (μ+λ) GA with standard bit mutation.</li> <li>'IOHprofiler_Problems_fast_mutation.csv' --> the (μ+λ) GA with fast mutation.</li> </ul> </li> <li>The results of (μ+λ) GA on OneMax and LeadingOnes problems with the following setup: <span class="math-tex">\(n \in \{64,100,150,200,250,500\}, \mu \in \{2,3,5,8,10,20,30,...,100\}, \\ \lambda \in \{1, \lceil \mu/2 \rceil, \mu\}, \text{and }p_c \in \{0.1 k \mid k \in [0..9]\}\cup\{0.95\}.\)</span> <ul> <li>'OneMax_raw.csv' --> the fixed-target running time/first hitting time from 100 independent runs for target values in <span class="math-tex">\([1..n]\)</span>.</li> <li>'OneMax_summary.csv' --> the mean, median, standard deviation, some quantiles, expected running time (ERT), the number of successful runs, and the success rate from 100 independent runs for target values in <span class="math-tex">\([1..n]\)</span>.</li> <li>'LeadingOnes_raw.csv' --> the same with 'OneMax_raw.csv' for LeadingOnes.</li> <li>'LeadingOnes_summary.csv' --> the same with 'OneMax_summary.csv' for LeadingOnes.</li> </ul> </li> </ol> <p><strong>Contact</strong>: if you have any questions or suggestions, please feel free to contact <a href="https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1">Furong Ye</a> or <a href="http://www-ia.lip6.fr/~doerr/">Carola Doerr</a>.</p>
CMOP 2012 AUV-WP Columbia River estuary benchmark data set
<p>Observational data set from the Columbia River estuary North Channel. The data were collected during two campaigns during May and October-November 2012.</p> <p>Included are Automated Underwater (AUV) mission, and ship-mounted whiched profiler (WP) data sets. Outputs from numerical circulation model are also included.</p> <p>See the README.txt in each zip file for further information.</p>
Data Set "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations"
<p>This data set accompanies the publication "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations" <br>by Anna M. van Bodegraven, Kevin Focke, Mario Wolter, and Christoph R. Jacob <br>(TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory '01_AIM':</p> <p> - input (structure.pdb, topol.top and *_input.txt) and results (*.log and<br> Hamiltonian/AtomPos/Dipole/Parameters.txt) from frequency calculations<br> with the Amide-I-maps (AIM) program for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip), <br> each in vacuo or water with three different maps (Jansen, Skinner, <br> Tokmakoff) for 11 snapshots based on a MD run.<br> To rerun the calculations, you will have to change the paths in <br> the *_input.txt files (topfile, trjfile, sourcedir) accordingly<br> <br>Directory '02_SNF':</p> <p> - results (*.dat, *.out, coord and control) from frequency calculations <br> using Turbomole, SNF, and LocVib for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in vacuo or water for snapshots based on an MD run.<br> <br>Directory '03_NMA':</p> <p> - coordinates and results from pyADF for NMA molecules each alligned <br> with a peptide bond from the six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in water for 11 snapshots based on a MD run and input <br> (*_input.txt) and results (*.log and Hamiltonian/AtomPos/Dipole/Parameters.txt) <br> from calculations with the Amide-I-maps (AIM) program<br> <br>Directory '04_Handling_Data':</p> <p> - contains all used notebooks to extract the data, plot the figures <br> and calculate the errors<br> - RMSD_Error_vacuo/water.ipynb is used to calculate the overall shifts <br> and generates a map-dependent mean value to shift the frequencies of AIM<br> - Frequencies.ipynb and Couplings.ipynb are used to plot the figures <br> - RMSD_values_vacuo/water.ipynb show the calculations for the statistical <br> analysis<br> To use the notebooks, start with the Dictionary_setup_for_data_for_paper.ipynb <br> to set up the main dictionary from the calculated data</p>
Input features and benchmark data sets for protein complex prediction and E. coli proteome application by AF2Complex
<p>Benchmark data sets of AF2Complex, input features for application to E. coli proteome, and predicted structural models of E. coli Ccm I as described in</p> <p><strong>Predicting direct physical interactions in multimeric proteins with deep learning</strong></p> <p><em>Mu Gao, Davi Nakajima An, Jerry M. Parks, Jeffrey Skolnick</em></p> <ol> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/af2complex_bench.tar.gz">af2complex_bench.tar.gz</a>: Benchmark data sets CP17, Dimer1193 and Oligomer562, including input features for AF2Complex/AF-Multimer, both paired and unpaired MSAs, as well as sequences, experimental structures, and results presented in the AF2Complex work (~90GB de-compressed size)</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_Ccm_I.tar.gz">ecoli_Ccm_I.tar.gz</a>: Computational models of the<em> E. coli</em> Ccm I system</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_set.tar.gz">ecoli_sets.tar.gz</a>: Lists of benchmark sets of positive and negative PPIs from <em>E. coli</em></li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_af_fea.tar.gz">ecoli_af_fea.tar.gz: </a>Pre-generated input features of <em>E. coli</em> proteome for protein complex prediction and modeling by AF2Complex (4,429 proteins, ~800 GB de-compressed size). This data set can be used with AF2Complex to probe the interactions of any combinations among the 4,429 proteins of E. coli.</li> </ol> <p> </p>
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
ArtTabGen Benchmarking Data Sets V1.0
<p>These datasets can be used to benchmark information extraction systems. To see an exemplary benchmarking script, please<br> refer to our PLIX publication. For more information regarding the generation of this dataset, please check out our<br> ArtTabGen Generation Code repository and our paper.</p>
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Oulu facility
<p>This data set covers core network and end-to-end measurements at the Oulu facility, part of the 6G-SANDBOX infrastructure.</p>
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Malaga facility
<p>This data set covers core network and end-to-end measurements at the Malaga facility, part of the 6G-SANDBOX infrastructure.</p>
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Berlin facility
<p>This data set covers core network and end-to-end measurements at the Berlin facility, part of the 6G-SANDBOX infrastructure.</p>
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Athens facility
<p>This data set covers core network and end-to-end measurements at the Athens facility, part of the 6G-SANDBOX infrastructure.</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.