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47,155 results for “evaluations”
Dataset on an Evaluation of MCQs from MOOCs for Common Item Writing Flaws
<p>This dataset is comprised of data collected from questions in 18 MOOCs in the areas of medical health education, life sciences and computer science. 204 questions were evaluated by two evaluators using an evaluation protocol from the literature. Over 50% of the MCQs (112) were found to have one or more item writing flaw; 57 of the MCQs contained two or more flaws. A considerable proportion of the MCQs were found to violate item-writing guidelines, which confirms findings of previous research in this area that examined rates of flaws in MCQs in traditional formal educational contexts. The issue of flawed MCQs can be addressed by training faculty in how to conduct quality checks before before and after their deployment. The data consists of an Excel file with several workbooks </p>
Investigating Attitudinal Ambivalence via Sequential Priming: Evidence for the Simultaneous and Unintentional Activation of Opposite Evaluations
<p>This research tested a central assumption of attitudinal ambivalence research: ambivalent attitude objects simultaneously trigger positive and negative evaluations. It further specifies at which stage this activation is likely to produce an evaluative conflict. Experiments 1 to 3 involved two evaluative priming paradigms, in which ambivalent stimuli served either as primes or as targets. The <em>Ambivalent Primes Paradigm</em> tested the degree to which the concurrent and unintentional activation of positivity and negativity influences responding to univalent targets. The <em>Ambivalent Targets Paradigm</em> tested the degree to which ambivalent targets entail an evaluative response conflict irrespective of prime valence. Experiments 1 and 2 revealed slower responses for ambivalent trials compared to congruent trials in both paradigms. Employing a longer stimulus onset asynchrony, Experiment 3 attested to the short-lived nature of the joint activation of opposite evaluations triggered by ambivalent primes. In contrast, the deliberate categorization of ambivalent targets was not affected by this procedural variation. Finally, by relying on a valent/neutral categorization task, Experiments 4 and 5 indicate that conflicts triggered by ambivalent stimuli occur at the response selection rather than the exposure stage. Our findings lend original empirical support to the assumption that positivity and negativity are activated simultaneously and unintentionally in ambivalent attitude objects. Moreover, the present research suggests that ambivalence generates a conflict only if the task at hand requires a univalent categorization. We discuss the extent to which the activation of ambivalent attitudes may be automatic and the implications of our findings for dual-process models of attitudes.</p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
DS6. UNDERSTANDING PEDAGOGICAL DESIGN REQUIREMENTS AND ITERATIVE EVALUATION (TEACHERS)
<p>Data driven design framework, outlining key considerations for game design form a pedagogical perspective </p>
NNSeval: Evaluating Lexical Simplification for Non-Natives
<p>We have conducted a user study to learn more about word complexity for non-native speakers. 400 non-native speakers participated in the experiment, all university students or staff. They were asked to judge whether or not they could understand the meaning of each content word (nouns, verbs, adjectives and adverbs, as tagged by Freeling (Padr and Stanilovsky (2012)) in a set of sentences, each of which was judged independently. Volunteers were instructed to annotate all words that they could not understand individually, even if they could comprehend the meaning of the sentence as a whole.</p> <p>All sentences used were taken from Wikipedia, LSeval and LexMTurk. A total of 35,958 distinct words from 9,200 sentences were annotated (232,481 total), of which 3,854 distinct words (6,388 total) were deemed as complex by at least one annotator.</p> <p>Using the data produced in the user study, we first assessed reliability of the LSeval and LexMTurk datasets in evaluating LS systems for non-native speakers. We found that the proportion of target words deemed complex by at least one annotator was only 30.8% for LexMTurk, and 15% for LSe- val. As for the candidate substitutions, 21.7% of the ones in LSeval and 13.4% in LexMTurk were deemed complex by at least one annotator.</p> <p>These results show that, although they may not be used in their entirety, both datasets contain instances that are suit- able for our purposes. To create our dataset, we first used the Text Adorning module of LEXenstein (Paetzold and Specia 2015; Burns 2013) to inflect all candidate verbs and nouns in both datasets to the same tense as the target word. We then used the Spelling Correction module of LEXenstein to correct any misspelled words among the candidates of both datasets. Next, we removed all candidate substitutes which were deemed complex by at least one annotator in our user</p> <p>study. Finally, we discarded all instances in which the target word was not deemed complex by any of our annotators. The resulting dataset, which we refer to as NNSeval, contains 239 instances.</p>
Timbral Characterisation Tool v0.1 Evaluation Dataset
<p>This dataset contains data generated as part of the AudioCommons project (DS 5.3.1). Data relate to the Deliverable D5.3 which was the evaluation of the first prototype of a set of timbral characterisation tools for semantically annotating audio content.</p> <p>Data comprise Max-based listening test interfaces, Python code to facilitate the evaluation, audio files, test results in the form of csv files, and documentation.</p> <p><strong>References</strong></p> <p>D5.3 (2017): A.Pearce, T.Brookes, R.Mason, "Evaluation report on the first prototypes of the timbral characterisation tools", AudioCommons Deliverable Report<br> - http://www.audiocommons.org/materials/</p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 0
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images </strong>of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. <strong>This data set</strong> (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. The second part (part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 1
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images</strong> of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. The first part (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. <strong>This data set </strong>(part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Does evaluative learning depend on the statistical relationship between stimuli? On the sensitivity of evaluative cue conditioning to ecological contingencies
<p>Evaluative conditioning (EC) is concerned with the transfer of valence from an unconditioned stimulus (US) to a conditioned stimulus (CS). Only recently the notion of EC was extended from individual CSs to categories of CSs that share certain cues. That research shows that a contingency implemented between a cue dimension and US valence has a direct effect on the evaluation of stimuli carrying values of this cue dimension. This phenomenon was coined “evaluative cue conditioning” (ECC). The present research tests whether ECC is sensitive to the contingency between a cue dimension and US valence. The present work thereby investigates the impact of ecological contingencies that define contingency by reference to all other CS-US pairings in the learning environment. Two experiments demonstrate that ECC effects are sensitive to the strength of the ecological contingency, suggesting that ECC is a relative phenomenon that depends on the valence of the CS-US pairings in the reference set. Moreover, these findings call for more research on the contingency sensitivity of standard EC effects using this revised definition of contingency.</p>
Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite"
<p>Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite", reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100013). See preprint and supplementary materials.</p>
Replication data for Evaluation of movements with greater trunk acceleration and their properties during volleyball games
<p>The present study aimed to elucidate the movements that require greater trunk acceleration and their frequency during volleyball matches and compare their acceleration components. A triaxial accelerometer was used to measure trunk acceleration during volleyball matches. The moments that generated resultant accelerations >4G, >5G and >6G were extracted, and movements that coincided with the extracted moments were identified. The ratios and frequencies of the extracted movements were calculated, and the resultant, mediolateral, vertical and anteroposterior accelerations among the top seven volleyball-specific extracted movements were compared. For attackers, 361, 185 and 97 movements were extracted with acceleration thresholds of 4G, 5G and 6G, respectively. At 6G, landing (77%) was the most frequently observed movement. For receivers, 297, 115 and 38 movements were extracted with acceleration thresholds of 4G, 5G and 6G, respectively. At 4G, running (49%) and steps (31%) were the most frequently observed movements. Resultant and vertical accelerations of spike landing were significantly greater than those of the other six movements (p < 0.001, respectively). Stationary or directional steps observed in receivers showed greater anteroposterior acceleration. Frequency or magnitude of trunk acceleration during these movements might determine the risk of injury during volleyball.</p>
Evaluation of disjunctive programs in WASP
<p>Benchmark set for the paper "Evaluation of disjunctive programs in WASP" accepted for the International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2019).</p>
Expertise-Driven Semi-Quantitative Analysis for Policy Evaluation tool
<p>This dataset contains the underlying data for the following publication: <strong>A semi-quantitative modelling application for assessing energy efficiency strategies, Applied Soft Computing. </strong></p>
Diagnostic Evaluation of Large-domain Hydrologic Models calibrated across the Contiguous United States
<p>Data repository for: Rakovec, O., Mizukami, N., Kumar, R., Newman, A., Thober, S., Wood, A. W., et al. ( 2019). Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States. <em>Journal of Geophysical Research: Atmospheres</em>, 2019; 124: 13991–14007. <a href="https://doi.org/10.1029/2019JD030767">https://doi.org/10.1029/2019JD030767</a></p> <p>If you use this dataset in scientific publication, the aforementioned publication needs to be acknowledged.</p> <p><strong>rakovec_JGRA_2019.tar.gz </strong>refers to the mHM model simulations</p> <p><strong>Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</strong> refers to a dataset published earlier in Mizukami et al. (2017, doi: 10.1002/2017WR020401)</p> <p>#########################################################################</p> <p>## BASIN-WISE DISCHARGE SIMULATIONS:</p> <p>#########################################################################</p> <p><strong>(1) VIC model: CONUS-wide runs based on the Mizukami et al. 2017 WRR paper</strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04_rgn0.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04_rgn0.txt</li> </ul> <p>Note the time stamp is missing in the VIC files, and should be following for</p> <ul> <li>calibration period: init_date="1999-10-01"</li> <li>validation periods: init_date="1989-10-01"</li> </ul> <p>Finally, headers are missing for the VIC files, should be:</p> <ul> <li>qsim is first column</li> <li>qobs is second column</li> </ul> <p><strong>(2) VIC model: onsite calibrations </strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04.txt</li> </ul> <p><strong>(3) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output_calibMB_eval/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_validMB_eval/daily_discharge.out</li> </ul> <p><strong>(4) mHM model: onsite calibrations </strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_valid/daily_discharge.out</li> </ul> <p><strong>(5) mHM model: default parameter set from EU/Germany</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>the prior parameter set taken from the develop git branch of mhm</p> <p>Note that the calibration and validation periods are in one file:</p> <ul> <li>./mHM_basins/$BASIN_ID/def_000/output/daily_discharge.out </li> </ul> <p>#########################################################################</p> <p>## CONUS-WISE FLUXES,STATES,PARAMETERS</p> <p>#########################################################################</p> <p><strong>(1) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <ul> <li>Fluxes/states: mHM_entire_domain/calib_001/output/mHM_Fluxes_States.nc (monthly time step: 1950-2010)</li> <li>Model parameters (from the restart file): mHM_entire_domain/calib_001/output/mHM_restart_001.nc</li> </ul> <p> </p>
CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms
<p>The CID2013 Camera Image Database consists of real images taken by consumer cameras and mobile phones. It is developed to provide useful tool to allow researchers target more commercially relevant distortions when developing processes of objective image quality assessment algorithms.</p> <p>The CID2013 database consists of 480 evaluated images captured by 79 imaging devices (mobile phones, DSC, DSLR) in six Image Sets. Note that the actual number of images in the database is 474. In Image Set II, Device 6 is evaluated twice as we wanted to test inter-observer reliablity. The scores are later combined into a single MOS value as the two evaluations correlated strongly.</p> <p>If you use this database in your research, we kindly ask that you follow the copyright notice bellow and cite the following paper:</p> <p>Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 390-402, Jan. 2015. <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6975172">[pdf]</a></p> <p><strong>Method</strong></p> <p>The images are evaluated by 188 observers using Dynamic Reference (DR-ACR) method (explained below). A separate scale realignment ACR data consisting evaluations from 34 observers is also included that allows to combine the data from the six image sets</p> <p>In other respects the DR-ACR method resembles very much a basic Absolute Category Rating (ACR) method (ITU-R 500-11), except the observers saw a slideshow of all the other images in the test depicting the same scene before every evaluation (See DR_demo.mp4). By seeing the other images in the test setup as reference the observers were more aware of the total variation of quality represented within a single image set. This improved their evaluation as they didn’t need to save the far ends of the scale in case there would be even more better or worse image later on the experiment. The DR-ACR method is explained in detail in:</p> <p>Mikko Nuutinen, Toni Virtanen, Tuomas Leisti, Terhi Mustonen, Jenni Radun, Jukka Häkkinen (2014) A new method for evaluating the subjective image quality of photographs : dynamic reference Multimedia Tools and Applications 75: 4. 2367-2391 Dec.</p> <p>Database contains consumer camera images and their subjective evaluations in mean opinion score (MOS), sharpness, graininess, lightness and color saturation scales. It includes the complete raw data and background information from the naïve observers used to evaluate the images. Subjects’ vision was controlled for the near visual acuity, near contrast vision (near F.A.C.T.) and color vision (Farnsworth D15) before the participation. They received movie tickets as a reward. Outlier removal is made for mean opinion score (MOS) evaluations using ITU-R 500-11 recommendations to ease out the implementation of the database.</p> <p><strong>Material</strong></p> <p>The images in CID2013 are intended to represent typical photographs that consumers might capture with their cameras. The photographed scenes were based partly on the Photospace approach described by I3A (CPIQ Initiative Phase 1 White Paper: Fundamentals and review of considered test methods, I3A, 2007) The I3A CPIQ project has migrated under IEEE.</p> <p><strong>The test environment</strong></p> <p>The room has been covered with medium gray curtains to diffuse the ambient illumination. Fluorescent lights (5800K) were positioned behind the monitors and reflected from the back wall covered with grey curtain to create dim and uniform ambient illumination in the room. The light hitting the monitors measured below 20 lx. The subject’s viewing distance (approximately 80 cm) was controlled by a line hanging from the ceiling, and they were instructed to keep their forehead steady next to the line. Because of the display size, images were scaled to a size of 1600 x 1200 pixels using the bicubic interpolation method. Eizo ColorEdge CG241W, with 1920x1200 pixel resolution, monitors in was calibrated to sRGB having target values of: 80 cd/m2, 6500K and gamma 2.2 using EyeOne Pro calibrator (X-rite co.).</p> <p> </p> <p>-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------</p> <p>Copyright (c) 2014 The University of Helsinki<br> All rights reserved.</p> <p>Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirely appear in all copies of this database, and the original source of this database,Visual Cognition research group (www.helsinki.fi/psychology/groups/visualcognition/index.htm) and the Institute of Behavioral Science (www.helsinki.fi/ibs/index.html) at the University Helsinki (www.helsinki.fi/university/), is acknowledged in any publication that reports research using this database. Individual videos and images may not be used outside the scope of this database (e.g. in marketing purposes) without prior permission.</p> <p>The database and our paper are to be cited in the bibliography as:</p> <p>-----------------------------------------------------------------------------<br> Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, 2014, In press.<br> -----------------------------------------------------------------------------</p> <p>LIMITATION OF LIABILITY</p> <p>UNIVERSITY OF HELSINKI SHALL IN NO CASE BE LIABLE IN CONTRACT, TORT OR OTHERWISE FOR ANY LOSS OF REVENUE, PROFIT, BUSINESS OR GOODWILL OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL OR PUNITIVE COST, DAMAGES OR EXPENSE OF ANY KIND HOWEVER CAUSED OR HOWEVER ARISING UNDER OR IN CONNECTION WITH THE USE OF THIS DATABASE.</p> <p>THE UNIVERSITY OF HELSINKI SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF HELSINKI HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.</p> <p>THIS AGREEMENT SHALL BE CONSTRUED AND INTERPRETED IN ACCORDANCE WITH THE LAWS OF FINLAND, EXCLUDING ITS RULES FOR CHOICE OF LAW.</p> <p>-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------</p> <p> </p>
Are there two independent evaluative conditioning effects in relational paradigms? Dissociating the effects of CS-US pairings and their meaning
<p>Recent research into evaluative conditioning (EC) shows that information about the relationship between the conditioned and unconditioned stimuli can exert strong effects on the size and direction of the EC effect. Additionally, the co-occurrence of these stimuli seems to exert an orthogonal effect on evaluations. This finding has been interpreted as support for two independent types of EC effects. However, previous research devoted to this question relied on aggregated evaluative measures, allowing for alternative interpretations. In four experiments, we developed and validated a multinomial processing tree model that distinguishes effects of the pairings from effects of the meaning of the pairings. Our findings suggest that two independent EC effects contribute to overall evaluative change in a relational EC paradigm. The model that we developed offers a helpful method for future research in that it allows for an assessment of the effects of manipulations on processes rather than overall performance on an evaluative measure.</p>
Evaluation of tracking devices and elicitation of wearability requirements for animal-centred biotelemetry in cats
<p>Thirteen cat participants wearing GPS devices were monitored through ethologically-informed observations, designed specifically to measure the behaviour of the animals with the biotelemetry tags. Here, findings from the behavioural analysis are presented.</p>
R code and simulation output for Efford, M. G. & Boulanger, J. 2019. Fast evaluation of study designs for spatially explicit capture-recapture. Methods in Ecology and Evolution
<p>R code and simulation output for Efford, M. G. & Boulanger,<br> J. 2019. Fast evaluation of study designs for spatially explicit<br> capture-recapture. Methods in Ecology and Evolution In press.</p> <p>R code draws on previously published R packages 'secr' and 'secrdesign' available from CRAN:</p> <p><a href="https://CRAN.R-project.org/package=secr">https://CRAN.R-project.org/package=secr</a></p> <p><a href="https://CRAN.R-project.org/package=secrdesign">https://CRAN.R-project.org/package=secrdesign</a></p>
Dataset for Evaluating Pedalling Techniques Recognition Using Gesture Data
<p>With the help of a dedicated measurement system (see reference for the details of the system), the pedalling gestures and the piano sound can be synchronously recorded at an audio sampling rate and a high resolution. The measurement system was deployed on the sustain pedal of a Yamaha baby grand piano situated in the studios at Queen Mary University of London. Ten well known passages of Chopin's piano music were selected to form this dataset. Therefore the dataset consists of:</p> <p>- <strong>audio-data.zip</strong>: piano sound of the ten passages recorded at 44.1kHz, each saved as "<strong>PASSAGE.wav</strong>".</p> <p>- <strong>pedal-data.zip</strong>: associated gesture data recorded at 22.05kHz, each saved as "<strong>PASSAGE.npy</strong>". The gesture data correspond to the movement trajectory of the sustain pedal.</p> <p>- <strong>pedal-label.zip</strong>: label the "continuous" gesture data by "discrete" pedalling techniques at every 0.02 second. Label 0-4 represents none, 1/4, 1/2, 3/4 and full pedalling technique, respectively. Labels for gesture data "<strong>PASSAGE.npy</strong>" are saved in "<strong>PASSAGE-label.npy</strong>".</p> <p>- <strong>passage.zip</strong>: music scores of the ten passages, each saved as "<strong>PASSAGE.pdf</strong>". They were annotated with pedalling techniques by the experimenter in advance and then performed by a pianist, who was asked to follow the annotated scores. The resulting audio recording and gesture data formed the above "<strong>PASSAGE.wav</strong>" and the "<strong>PASSAGE.npy</strong>". The annotated score guided the labelling process and formed the "<strong>PASSAGE-label.npy</strong>".</p>
Experimental Evaluation of DimDraw
<p>Experimental Evaluation of DimDraw: A document containing all 44994 drawings of lattices on 11 vertices or less.</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.