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1,163 results for “demonstrators”
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
Demo bids for to demonstration areas in OneNet project of the Hungarian demonstration
<p>Bid auction data of a DSO flexibility market simulation data in two demo areas based on past real measurement, power gas exchange data.</p> <p>The two .csv files contain the bid data for all FSP assets in two demonstration areas, both are a given snippet of DSO networks used for congestion simulations, Demo Area 1 and Demo Area 2, respectively. The bids are simulated and based on post hoc day-ahead market data and measurements. All FSP assets are photovoltaic generators. For a given day every asset submits stepwise hourly bids for every hour of the day.</p> <p>The two .CSV files consist of the following columns:</p> <p>Date: the date that the bid is submitted to (YYYY-MM-DD)</p> <p>Time: the hour that the bid is submitted to (HH)</p> <p>AssetId: ID of the bidding asset (photovoltaic generator)</p> <p>quantity: quantity of a bid step for an hour of a day in MW</p> <p>price: the price of the bid step for an hour of a day in EUR</p> <p> </p> <p>More information on the demo areas can be found in the <a href="https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D10.4_V1.0.pdf">D10.4 Report on demonstration</a> deliverable of the OneNet project.</p> <p>public_demo_area_1.csv file represents the bids in the E.On demo area and the public_demo_area_2.csv file in the MVM demo area, respectively. </p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Measurement Data
<p>This dataset contains measurement datas collected from mesurements devices in the field (substation, battery storage, etc) during the application of Use Case 1 (Islanding/Maximization of local self-consumption).</p> <p> </p>
CROSSBOW TC_01.05.01 final demonstration results
<p>This data set represents final demonstration results for the Test Case 5.1 from the High Level Use case 1 of the CROSSBOW project. It contains lists of Critical Network Element & Contingency (CNEC) pairs for 17 SEE borders (AL-GR, AL-RS, BA-HR, BA-ME, BA-RS, BG-GR, BG-MK, BG-RO, BG-RS, BG-TR, GR-ALMKBGTR, ME-AL, ME-RS, RS-BAHR, RS-BGRO, RS-HU, RS-MEMKAL) and 4 seasons (spring, summer, autumn and winter), which are used as input data for Net Transmission Capacity (NTC) calculation. For each CNEC pair in the list, Outage Transmission Distribution Factor (OTDF) is presented. Selection of CNEC pairs is determined based on predefined criteria (OTDF>20%) , using algorithm presented in CROSSBOW deliverable: D4.2 CROSSBOW Regional Operation Centre Balancing Cockpit (ROC-BC).</p>
H2020 Platone Greek Demonstrator PV_generation_20190227_20200506
<p>This dataset contains PV generation (kWh) of 7 producers (connected to the medium voltage (MV) level, 20kV) from 27/02/2019 to 06/05/2020 in 15 min intervals and contains the following fields:</p> <p>Customer_id</p> <p>Value_KWh</p> <p>Timestamp</p>
CROSSBOW HLU2-UC4-TC6 Day ahead energy Price for the demonstration period in Greece
<p>For the evaluation of the curtailment distribution algorithm, an experiment was made with the forecast generation or RES assets in the area of Crete, Greece and the simulation of 30 limitations applied on random days. This dataset contains the DA energy prices in Greece at the time the demonstration was held</p>
CROSSBOW HLU2-UC4-TC4 Day ahead energy Price for the demonstration period in Croatia
<p>For the evaluation of the curtailment distribution algorithm, an experiment was made with the forecast generation from TS Konjsko and the simulation of 30 limitations applied on random days. This dataset contains the DA energy prices in Croatia at the time the demonstration was held</p>
CROSSBOW HLU2-UC4-TC5 Day ahead energy Price for the demonstration period in Romania
<p>For the evaluation of the curtailment distribution algorithm, an experiment was made with the forecast generation or RES assets in the area of Tariverde and the simulation of 30 limitations applied on random days. This dataset contains the DA energy prices in Croatia at the time the demonstration was held</p>
BIM4EEB Demonstration building BIM Model
<p>BIM4EEB Demonstration building BIM Model. The model contains only external perimeter walls, structural geometry, and common public spaces, The internal layout of the apartments is undesclosed for privacy reasons.</p>
Demonstration Cases - Simulation data of energy consumption of residential building typologies
<p>The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki).<br> The dataset includes:<br> (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios:<br> - Locating external new insulation in walls and roof.<br> - Replacing Windows.<br> - Combination strategies: locating new insulation layers and replacing the existing windows.<br> - Installing solar protection devices.</p>
Supplementary data for the time-bidirectional states tomography demonstration
<p>Supplementary data for the time-bidirectional tomography demonstration realized on ibm_oslo seven-qubit superconducting quantum processor.</p> <ul> <li>ibm_oslo_calibrations_data.csv contains calibration data at the time of the demonstration realized.</li> <li>input.json contains three input quantum circuits in qiskit format.</li> <li>output.json contains corresponding measurement counts in qiskit format.</li> </ul> <p> </p>
Demonstration case study reports
<p>As part of the PLAID project an in-depth assessments of the processes involved in achieving efficient and effective on-farm demonstration activities was investigated. 24 year-long studies of demonstration events and activities were selected and followed 2 case studies in each partner country were selected, which followed demonstration from initial commissioning through to impact assessment. The specific objectives were</p> <ul> <li>Improved understanding of the key elements that contribute to successful and effective demonstration activities on commercial farms, looking specifically at: commissioning and financing, topic selection, accessibility, mediation techniques, and embeddedness. Gender will be specifically addressed.</li> <li>Improved understanding of the effectiveness of demonstration activities in terms of learning by participants (at the individual level and the network level: what is learned, by whom, how and why?) and the subsequent impact (i.e. actual implementation of what farmers have learned on their own farm, new network development, innovation inception).</li> <li>Assess the role of demonstration within the wider AKIS systems and what the impact of demonstration is on learning and technology adoption</li> <li>Develop insights into critical success factors, best (and poor) practices, and indicators for effective and successful demonstration activities.</li> </ul>
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
<p>Version 4 of the dataset is available (Sep 19 2019)!</p> <p>Note this version has significantly more data than Version 2. </p> <p>Dataset description paper (full version) is available!</p> <p>https://arxiv.org/pdf/1903.06754.pdf (updated Sep 7 2019)</p> <p>Tools for visualizing the data is available!</p> <p>https://github.com/corgiTrax/Gaze-Data-Processor</p> <p> </p> <p><strong>=========================== Dataset Description ===========================</strong></p> <p>We provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded.</p> <p> </p> <p>Q & A: Why frame-by-frame game mode?</p> <p><strong>Resolving state-action mismatch</strong>: Closed-loop human visuomotor reaction time is around 250-300 milliseconds. Therefore, during gameplay, state (image) and action that are simultaneously recorded at time step t could be mismatched. Action at time t could be intended for a state 250-300ms ago. This effect causes a serious issue for supervised learning algorithms, since label at and input st are no longer matched. Frame-by-frame game play ensures states and actions are matched at every timestep.</p> <p><strong>Maximizing human performance</strong>: Frame-by-frame mode makes gameplay more relaxing and reduces fatigue, which could normally result in blinking and would corrupt eye-tracking data. More importantly, this design reduces sub-optimal decisions caused by inattentive blindness.</p> <p><strong>Highlighting critical states that require multiple eye movements</strong>: Human decision time and all eye movements were recorded at every frame. The states that could lead to a large reward or penalty, or the ones that require sophisticated planning, will take longer and require multiple eye movements for the player to make a decision. Stopping gameplay means that the observer can use eye-movements to resolve complex situations. This is important because if the algorithm is going to learn from eye-movements it must contain all “relevant” eye-movements.</p> <p> </p> <p><strong>============================ Readme ============================</strong></p> <p>1. meta_data.csv: meta data for the dataset., including:</p> <ul> <li> <p>GameName: String. Game name. e.g., “alien” indicates the trial is collected for game Alien (15 min time limit). “alien_highscore” is the trajectory collected from the best player’s highest score (2 hour limit). See dataset description paper for details.</p> </li> </ul> <ul> <li> <p>trial_id: Integer. One can use this number to locate the associated .tar.bz2 file and label file.</p> </li> <li> <p>subject_id: Char. Human subject identifiers.</p> </li> <li> <p>load_trial: Integer. 0 indicates that the game starts from scratch. If this field is non-zero, it means that the current trial continues from a saved trial. The number indicates the trial number to look for.</p> </li> <li> <p>highest_score: Integer. The highest game score obtained from this trial.</p> </li> <li> <p>total_frame: Number of image frames in the .tar.bz2 repository.</p> </li> <li> <p>total_game_play_time: Integer. game time in ms. </p> </li> <li> <p>total_episode: Integer. number of episodes in the current trial. An episode terminates when all lives are consumed.</p> </li> <li> <p>avg_error: Float. Average eye-tracking validation error at the end of each trial in visual degree (1 visual degree = 1.44 cm in our experiment). See our paper for the calibration/validation process.</p> </li> <li> <p>max_error: Float. Max eye-tracking validation error. </p> </li> <li> <p>low_sample_rate: Percentage. Percentage of frames with less than 10 gaze samples. The most common reason for this is blinking.</p> </li> <li> <p>frame_averaging: Boolean. The game engine allows one to turn this on or off. When turning on (TRUE), two consecutive frames are averaged, this alleviates screen flickering in some games.</p> </li> <li> <p>fps: Integer. Frame per second when an action key is held down.</p> </li> </ul> <p> </p> <p>2. [game_name].zip files: these include data for each game, including:</p> <p>*.tar.bz2 files: contains game image frames. The filename indicates its trial number.</p> <p>*.txt files: label file for each trial, including:</p> <ul> <li> <p>frame_id: String. The ID of a frame, can be used to locate the corresponding image frame in .tar.bz2 file.</p> </li> <li> <p>episode_id: Integer (not available for some trials). Episode number, starting from 0 for each trial. A trial could contain a single trial or multiple trials.</p> </li> <li> <p>score: Integer (not available for some trials). Current game score for that frame.</p> </li> <li> <p>duration(ms): Integer. Time elapsed until the human player made a decision. </p> </li> <li> <p>unclipped_reward: Integer. Immediate reward returned by the game engine.</p> </li> <li> <p>action: Integer. See action_enums.txt for the mapping. This is consistent with the Arcade Learning Environment setup.</p> </li> <li> <p>gaze_positions: Null/A list of integers: x0,y0,x1,y1,...,xn,yn. Gaze positions for the current frame. Could be null if no gaze. (0,0) is the top-left corner. x: horizontal axis. y: vertical.</p> </li> </ul> <p> </p> <p>3. action_enums.txt: contains integer to action mapping defined by the Arcade Learning Environment. </p> <p> </p> <p><strong>============================ Citation ============================</strong></p> <p>If you use the Atari-HEAD in your research, we ask that you please cite the following:</p> <p>@misc{zhang2019atarihead,</p> <p> title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},</p> <p> author={Ruohan Zhang and Calen Walshe and Zhuode Liu and Lin Guan and Karl S. Muller and Jake A. Whritner and Luxin Zhang and Mary M. Hayhoe and Dana H. Ballard},</p> <p> year={2019},</p> <p> eprint={1903.06754},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={cs.LG}</p> <p>}</p> <p>Zhang, Ruohan, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. "AGIL: Learning attention from human for visuomotor tasks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 663-679. 2018.</p> <p>@inproceedings{zhang2018agil,</p> <p> title={AGIL: Learning attention from human for visuomotor tasks},</p> <p> author={Zhang, Ruohan and Liu, Zhuode and Zhang, Luxin and Whritner, Jake A and Muller, Karl S and Hayhoe, Mary M and Ballard, Dana H},</p> <p> booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},</p> <p> pages={663--679},</p> <p> year={2018}</p> <p>}</p> <p><br> <br> </p>
On the order of demonstrative, numeral, adjective, and noun
<p>Cite the source of the dataset as:</p> <blockquote> <p>Dryer, M.S. (2018). On the order of demonstrative, numeral, adjective, and noun. Language 94(4), 798-833. doi:10.1353/lan.2018.0054.</p> </blockquote>
The DARRL dataset: Demonstrations for Action Recognition and Robot Learning
<p>The DARRL dataset (Demonstrations for Action Recognition and Robot Learning) is a collection of 760 RGB-D videos of humans performing various manipulation tasks. It is provided with object and action annotations (in the COCO format) for 30 of those videos; segmentation masks are also provided.</p> <p>It can also be used as a basis for learning from demonstrations for a robotic arm, for instance.</p> <p> </p> <p>This work is supported by Région Pays de la Loire.</p>
Demonstration record for discoverable IPCC WGIII data
<p>This is a record used to demonstrate the concept of discoverable data for IPCC AR7 WGIII.</p>
TraceVis: Visualization for DSMC: tool, demonstration video, data
<p>Tool demonstration video and source code of <em>TraceVis</em>, the visualization tool for Deep Statistical Model Checking, presented in the paper <em>TraceVis: Towards Visualization for Deep Statistical Model Checking</em>, published at ISoLA 2020 (9th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation).</p>
Capacitance clamp demonstration in rat dentate gyrus granule cells
<p>Dataset for a demonstration of the capacitance clamp, a variant of the dynamic clamp technique, which allows to virtually modify the capacitance of a biological neuron (or any electrically excitable cell).</p> <p>Code for analysis and further explanations will be made available. </p>
Atom probe tomography nomad-FAIR demonstrator dataset R76-20231-v01.epos.apth5
<p>This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).</p> <p><strong>This repository serves three aims:</strong></p> <p>1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment detailed exemplarily in DOI 10.1017/S1431927616012654 Fig. 1d by Zirong Peng and coworkers.</p> <p>2. The dataset contributes to tests of an extension to "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of an automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.</p> <p>The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br> The APT domain-specific parser is developed by Markus Kühbach.</p> <p>3. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.</p> <p><br> <strong>The organization of this repository with respect to above aims is as follows:</strong></p> <p>-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.</p> <p>-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.</p> <p>-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.</p> <p>-Other files constitute logs of the transcoding process.</p> <p><strong>Funding:</strong><br> The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.</p>
Dataset for publication "Cell design strategies for sodium-zinc chloride (Na-ZnCl2) batteries, and first demonstration of tubular cells with 38 Ah capacity"
<p><span lang="EN-US">stationary energy storage; ZEBRA battery; high-temperature metal chloride battery; molten-salt battery; molten sodium anode.</span></p> <p>Measured data to recreate Figures 1-8 in the above manuscript.</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.