Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

Data set for the journal article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion"

In the article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion" we demonstrated that Fe impurities in a hybrid CoPc@MWCNT catalyst lead to its performance deterioration during long-term CO2 electrolysis. Here we present the dataset the work was based on. The data are divided into four groups: (i) Current transients and gas chromatography data for short-term electrolysis at different potentials (in an excel file we give the numbers of chromatograms for each potential; current transients are given as an origin file with datasets and plots inside) (ii) Current transients and gas chromatography data for long-term electrolysis at different potentials and with different catalysts (in respective excel files we give the numbers of chromatograms; figure numbers are given in the folder names) (iii) Electron microscopy images and EDX datasets (the images and datasets are collected in the folders with respective figure numbers used in the paper) (iv) Calibration curves for ICP-MS

opencc-by-4.0Mar 2022View details →

Data sets and models for the Deep API Learning Revisited paper

Training and test data for the machine learning experiments described in the paper Deep API Learning Revisited paper.  Trained models are also included. Deep API Learning Revisited paper:  https://doi.org/10.1145/3524610.3527872 GitHub repository:  https://github.com/hapsby/deepAPIRevisited

opencc-byApr 2022View details →

Ecuador Real State data set

<pre>This set of data contains information on different characteristics of the properties for rent on the website https://arriendayvende.com, through which analyzes can be carried out on the distribution of prices according to location, area, among others.</pre>

Google Speech Commands-Musan test set

This noisy speech test set is created from the Google Speech Commands v2 [1] and the Musan dataset[2]. It is introduced in our ICASSP 2022 paper [3].  Specifically, we created this test set by mixing the speech in the Google Speech Commands v2 test set with random noise in the Musan dataset at different signal to noise ratio -12.5,-10,0,10,20,30 and 40 decibel (dB).  The Google Speech Commands v2 dataset is under the Creative Commons BY 4.0 license. It could be downloaded at: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz The Musan dataset is under Attribution 4.0 International (CC BY 4.0). It could be downlowned at https://www.openslr.org/17/ Citations: [1] Pete Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,” arXiv preprint arXiv:1804.03209, 2018. [2] David Snyder, Guoguo Chen, and Daniel Povey, “Musan: A music, speech, and noise corpus,” arXiv preprint arXiv:1510.08484, 2015. [3] V. A. Trinh, H. Salami Kavaki and M. I. Mandel, "Importantaug: A Data Augmentation Agent for Speech," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 8592-8596, doi: 10.1109/ICASSP43922.2022.9747003.

data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation

This is supporting data and software code for the following preprint in bioRxiv Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1

Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models

This data set contains the simulations and data analysis files used in the publication: "Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot. The data set includes: <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> A preprint of this work can be found in: David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models. DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a> The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files. To cite this dataset you can use the following bibtex entry: <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre>

Data Sets for SNR Estimation in Flexible Optical Networks: Lightpath, Link, and Span Levels

These data sets have been generated based on the analytic models [1,2] to estimate signal to the noise ratio (SNR) for spans, links, and lightpaths of a Flexible Optical Network (FON) over standard single-mode fiber (SSMF). For PM-BPSK and PM-QPSK modulation format levels, equation 41-43 [1], and for PM-8-64QAM modulation format levels, equation 7.32 [2], are applied. [1] P. Poggiolini, G. Bosco, A. Carena, V. Curri, Y. Jiang and F. Forghieri, "The GN-Model of Fiber Non-Linear Propagation and its Applications," in Journal of Lightwave Technology, vol. 32, no. 4, pp. 694-721, Feb.15, 2014, DOI:  10.1109/JLT.2013.2295208. [2]  P. Poggiolini, Y. Jiang, A. Carena and F. Forghieri, "Analytical modeling of the impact of fiber non-linear propagation on coherent systems and networks" in Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks, New York, NY, USA:Wiley, pp. 247-310, 2016.

Input data set for the statistical analsysis of rockfall reach probabilities

These files contain reach probability values extracted from 3D rockfall simulations for field-mapped block deposits as well as a series of attributes characterising the deposits. They served for the statistical analysis of the reach probability values as a function of site, forest and rockfall characteristics. The results of the analysis are published in Dorren et al. 2022: Delimiting rockfall runout zones using reach probability values simulated with a Monte-Carlo based 3D trajectory model. Natural Hazards and Earth System Scienses.

Ground-Truthed Data Set of Zenon Papyri for Handwritten Text Recognition

Diplomatic transcription of papyri found in the Zenon archive [see <a href="https://en.wikipedia.org/wiki/Zenon_of_Kaunos">en.wikipedia.org/wiki/Zenon_of_Kaunos</a>] Manually prepared as PageXML with Transkribus within <a href="http://d-scribes.philhist.unibas.ch/">D-Scribes</a> project.

MELA Dataset: A Benchmark for Mediastinal Lesion Analysis (Validation Set and Annotation)

MELA dataset is a benchmark for developing algorithms on mediastinal lesion analysis. We hope this large-scale dataset could facilitate the research and application of automatic mediastinal lesion detection and diagnosis.  MELA dataset contains 1100 CT scans collected from patients with one or more lesions in the mediastinum. The MELA dataset is split into a subset of 770 CT scans for training, a subset of 110 CT scans for validation, and a test set of 220 CT scans for evaluation. This is the Validation Set and Annotation of MELA dataset, including 110 CTs and the annotations of the whole training set and validation set. Files include: <ol> <li>Val.zip: 110 CTs in NII format (nii.gz).</li> <li>mela_train_val_annotations.csv: bounding box annotations in voxel coordinates for mediastinal lesions.</li> </ol>             `public_id: anonymous patient ID to match images and annotations.             `coordX, coordY, coordZ: coordinates of the center of annotated bounding box.             `x_length, y_length, z_length: the length of the bounding box in three dimensions.

opencc-by-4.0Mar 2022View details →

Data set for the manuscript 'Robustness Analysis of Metasurfaces: Perfect Structures are not always the Best'

In this data set, there are 1 PDF, 3 m-files, and 3 zip files. The manuscript (Readme document.pdf) contains three sections: Quasi-analytical model (analytical_model_EnergyConservation.m), post-processing full-wave simulations (post_processing_from_COMSOL.m), and post-processing experimental data (post_processing_from_experiment.m). Each Matlab code is explained in this manuscript. Corresponding raw data (COMSOL simulation data for reflective metallic metasurfaces.zip, COMSOL simulation data for transmitive dielectric metasurfaces.zip, and experimental data.zip) are attached. One can move the required m-file into the folder and run the m-file directly. In the COMSOL simulation data.zip file, one can find two COMSOL files, which retain the settings for simulation and extracting the required data.  The Matlab codes are implemented with version R2018b. The COMSOL files are created with version COMSOL Multiphysics 5.6.

opencc-by-4.0Jan 2022View details →

Evaluation Set - Contributions Similarity in the Open Research Knowledge Graph

This evaluation set has been created for evaluating a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts structured ORKG contribution as input and recommends existing contributions in the ORKG semantically relevant to the given one.   The evaluation set is manually annotated based on the <a href="https://www.orkg.org/orkg/featured-comparisons">featured comparisons</a> in the ORKG. In the course of this, it has been distinguished between homogeneous (those who are dissimilar in 2-3 properties) and heterogeneous (otherwise) instances. Multiple annotations have been obtained for the former and exactly one for the latter.   It has been also distinguished between "with_response" and "without_response" instances (50 instances for each). The former are those contributions for them the initial version of the contributions similarity service has found similarities and the latter are the opposite case.   This evaluation set has been created and applied on a modified version of the contributions similarity service in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>. The modified version of the service has simplified the document representation of contributions that are stored in an ElasticSearch index by omitting redundant terms. The evaluation set has the following schema: <pre><code class="language-json">{ "with_response": [ { "contribution_id": "some_id", "comparison_id": "some_id", "comparison_label": "some_label", "contribution_label": "some_label", "paper": "some_id", "research_field": "some_id", "research_problems": [ "some_id" ], "annotations": [ "some_id of a similar contribution", ... ] }, ... ], "without_response": [ ... ] }</code></pre>

opencc-by-4.0Sep 2019View details →

Neuromotor dynamics of human locomotion in challenging settings

Background Is the control of movement less stable when we walk or run in challenging settings? Intuitively, one might answer that it is, given that challenging locomotion externally (e.g. rough terrain) or internally (e.g. age-related impairments) makes our movements more unstable. Here, we investigated how young and old humans synergistically activate muscles during locomotion when different perturbation levels are introduced. Of these control signals, called muscle synergies, we analyzed the stability over time and the complexity (or irregularity). Surprisingly, we found that perturbations force the central nervous system to produce muscle activation patterns that are less unstable and less complex. These outcomes show that robust locomotion in challenging settings is achieved by producing less complex control signals which are more stable over time, whereas easier tasks allow for more unstable and irregular control. How to use the data set This supplementary data set contains: a) the metadata with anonymized participant information, b) the raw electromyographic (EMG) data acquired during locomotion, c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG, e) the muscle synergies extracted via non-negative matrix factorization and f) the code written in R (R Found. for Stat. Comp.) to process the data, including the scripts to calculate the short-term Maximum Lyapunov Exponents (sMLE) and Higuchi's fractal dimension (HFD) of motor primitives. In total, 476 trials from 86 participants are included in the supplementary data set. The file “participant_data.dat” is available in ASCII and RData (R Found. for Stat. Comp.) format and contains: <ul> <li>Code: the participant’s code</li> <li>Experiment: the experimental setup in which the participant was involved (E1 = walking and running, overground and treadmill; E2 = walking and running, even- and uneven-surface; E3 = unperturbed and perturbed walking, young and old)</li> <li>Group: the group to which the participant was assigned (see methods for the details)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (two values separated by a comma mean that recordings were done at two different speeds, i.e. walking and running)</li> <li>Age: the participant’s age in years (participants were considered old if older than 65 years, but younger than 80)</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001 and P0009 and the second uneven-surface running trial of participant P0048 consist of 22, 27 and 23 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0053_OW_02”, where the characters “P0053” indicate the participant number (in this example the 53rd), the characters “OW” indicate the locomotion type (E1: OW=overground walking, OR=overground running, TW=treadmill walking, TR=treadmill running; E2: EW=even-surface walking, ER=even-surface running, UW=uneven-surface walking, UR=uneven-surface running; E3: NW=normal walking, PW=perturbed walking), and the numbers “02” indicate the trial number (in this case the 2nd). The 10 trials per participant recorded for each overground session (i.e. 10 for walking and 10 for running) were concatenated into one. The filtered and time-normalized EMG data are named, following the same rules, like “FILT_EMG_P0053_OG_02”. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> The files containing the gait cycle breakdown are available in RData (R Found. for Stat. Comp.) format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times and the characters “P0020” indicate the participant number (in this example the 20th). Please note that the overground trials of participants P0001 and P0009 and the second uneven-surface running trial of participant P0048 only contain 22, 27 and 23 cycles, respectively. The files containing the raw, filtered and the normalized EMG data are available in RData (R Found. for Stat. Comp.) format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining thirteen columns contain the raw EMG data, named with muscle abbreviations that follow those reported in the methods section. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0053_OW_02”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0053” indicate the participant number (in this example the 53rd), the characters “OW” indicate the locomotion type (E1: OW=overground walking, OR=overground running, TW=treadmill walking, TR=treadmill running; E2: EW=even-surface walking, ER=even-surface running, UW=uneven-surface walking, UR=uneven-surface running; E3: NW=normal walking, PW=perturbed walking), and the numbers “02” indicate the trial number (in this case the 2nd). The 10 trials per participant recorded for each overground session (i.e. 10 for walking and 10 for running) were concatenated into one. The filtered and time-normalized EMG data is named, following the same rules, like “FILT_EMG_P0053_OG_02”. The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData (R Found. for Stat. Comp.) format, in the files named “SYNS_H.RData” and “SYNS_W.RData”. The muscle synergies files are divided in motor primitives and motor modules and are presented as direct output of the factorization and not in any functional order. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives), one column for each synergy plus the time points (columns are named e.g. “Time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed above to improve user readability. Each set of motor primitives is saved as an element of a single R list. Trials are named like “SYNS_H_P0012_PW_02”, where the characters “SYNS_H” indicate that the trial contains motor primitive data, the characters “P0012” indicate the participant number (in this example the 12th), ), the characters “PW” indicate the locomotion type (see above), and the numbers “02” indicate the trial number (in this case the 2nd). Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported in the methods section, contain the time-independent coefficients (motor modules), one for each synergy and for each muscle. Each set of motor modules relative to one synergy is saved as an element of a single R list. Trials are named like “SYNS_W_P0082_PW_02”, where the characters “SYNS_W” indicate that the trial contains motor module data, the characters “P0082” indicate the participant number (in this example the 82nd) ), the characters “PW” indicate the locomotion type (see above), and the numbers “02” indicate the trial number (in this case the 2nd). Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper. The files containing the sMLE calculated from motor primitives are available in RData (R Found. for Stat. Comp.) format, in the file named “sMLE.RData”. sMLE results are presented in a list of lists containing, for each trial, 1) the divergences, 2) the sMLE, and 3) the value of the R2 between the divergence curve and its linear interpolation made using the specified amount of points. The divergences are presented as a one-dimensional vector. sMLE are one number like the R2 value. Trials are named like “MLE_P0081_EW_01”, where the characters “sMLE” indicate that the trial containss sMLE data, the characters “P0081” indicate the participant number (in this example the 81st) ), the characters “EW” indicate the locomotion type (see above), and the numbers “01” indicate the trial number (in this case the 1st). The files containing the HFD calculated from motor primitives are available in RData (R Found. for Stat. Comp.) format, in the file named “HFD.RData”. HFD results are presented in a list of lists containing, for each trial, 1) the HFD, and 2) the interval time k used for the calculations. HFDs are presented as one number, as are the interval times k. Trials are named like “HFD_P0048_TR_01”, where the characters “HFD” indicate that the trial contains HFD data, the characters “P0048” indicate the participant number (in this example the 48th), the characters “TR” indicate the locomotion type (see above), and the numbers “01” indicate the trial number (in this case the 1st). All the code used for the preprocessing of EMG data, the extraction of muscle synergies, the calculation of sMLE and HFD is available in R (R Found. for Stat. Comp.) format. Explanatory comments are profusely present throughout the scripts (“SYNS.R”, which is the script to extract synergies, “fun_NMF.R”, which contains the NMF function, “sMLE.R”, which is the script to calculate the sMLE of motor primitives, “HFD.R”, which is the script to calculate the HFD of motor primitives, “fun_sMLE.R”, which contains the sMLE function and “fun_HFD.R”, which contains the HFD function).

opencc-by-4.0Jun 2018View details →

Modular control of human movement during running: an open access data set

The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the XX century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of five to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males, 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using non-negative matrix factorization. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing and late swing. We foresee many possible applications of our data, that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modelling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods. The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. The file "dataset.rar" contains data in older format, not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a>.

Urban Sound & Sight (Urbansas) - Labeled set

Urban Sound & Sight (Urbansas):  Version 1.0, May 2022 Created by Magdalena Fuentes (1, 2), Bea Steers (1, 2), Pablo Zinemanas (3), Martín Rocamora (4), Luca Bondi (5), Julia Wilkins (1, 2), Qianyi Shi (2), Yao Hou (2), Samarjit Das (5), Xavier Serra (3), Juan Pablo Bello (1, 2) 1. Music and Audio Research Lab, New York University 2. Center for Urban Science and Progress, New York University 3. Universitat Pompeu Fabra, Barcelona, Spain 4. Universidad de la República, Montevideo, Uruguay 5. Bosch Research, Pittsburgh, PA, USA Publication If using this data in academic work, please cite the following paper, which presented this dataset: M. Fuentes, B. Steers, P. Zinemanas, M. Rocamora, L. Bondi, J. Wilkins, Q. Shi, Y. Hou, S. Das, X. Serra, J. Bello. “Urban Sound & Sight: Dataset and Benchmark for Audio-Visual Urban Scene Understanding”. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022. Description Urbansas is a dataset for the development and evaluation of machine listening systems for audiovisual spatial urban understanding. One of the main challenges to this field of study is a lack of realistic, labeled data to train and evaluate models on their ability to localize using a combination of audio and video. We set four main goals for creating this dataset:  1. To compile a set of real-field audio-visual recordings; 2. The recordings should be stereo to allow exploring sound localization in the wild; 3. The compilation should be varied in terms of scenes and recording conditions to be meaningful for training and evaluation of machine learning models; 4. The labeled collection should be accompanied by a bigger unlabeled collection with similar characteristics to allow exploring self-supervised learning in urban contexts. Audiovisual data We have compiled and manually annotated Urbansas from two publicly available datasets, plus the addition of unreleased material. The public datasets are the TAU Urban Audio-Visual Scenes 2021 Development dataset (street-traffic subset) and the Montevideo Audio-Visual Dataset (MAVD): Wang, Shanshan, et al. "A curated dataset of urban scenes for audio-visual scene analysis." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. Zinemanas, Pablo, Pablo Cancela, and Martín Rocamora. "MAVD: A dataset for sound event detection in urban environments." Detection and Classification of Acoustic Scenes and Events, DCASE 2019, New York, NY, USA, 25–26 oct, page 263--267 (2019). The TAU dataset consists of 10-second segments of audio and video from different scenes across European cities, traffic being one of the scenes. Only the scenes labeled as traffic were included in Urbansas. MAVD is an audio-visual traffic dataset curated in different locations of Montevideo, Uruguay, with annotations of vehicles and vehicle components sounds (e.g. engine, brakes) for sound event detection. Besides the published datasets, we include a total of 9.5 hours of unpublished material recorded in Montevideo, with the same recording devices of MAVD but including new locations and scenes. Recordings for TAU were acquired using a GoPro Hero 5 (30fps, 1280x720) and a Soundman OKM II Klassik/studio A3 electret binaural in-ear microphone with a Zoom F8 audio recorder (48kHz, 24 bits, stereo). Recordings for MAVD were collected using a GoPro Hero 3 (24fps, 1920x1080) and a SONY PCM-D50 recorder (48kHz, 24 bits, stereo).  When compiled in Urbansas, it includes 15 hours of stereo audio and video, stored in separate 10 second MPEG4 (1280x720, 24fps) and WAV (48kHz, 24 bit, 2 channel) files. Both released video datasets are already anonymized to obscure people and license plates, the unpublished MAVD data was anonymized similarly using this anonymizer. We also distribute the 2fps video used for producing the annotations. The audio and video files both share the same filename stem, meaning that they can be associated after removing the parent directory and extension. MAVD: video/<location_id>_<mavd_clip_id>_<clip_split_id>.mp4 audio/<location_id>_<mavd_clip_id>_<clip_split_id>.wav TAU: video/<location_id>_<tau_clip_id>.mp4 audio/<location_id>_<tau_clip_id>.wav where location_id in both cases includes the city and an ID number.       city &  places &  clips &  mins &  frames &  labeled mins    \\ Montevideo &       8 &   4085 &   681 &  980400 &            92 \\  Stockholm &       3 &     91 &    15 &   21840 &             2 \\  Barcelona &       4 &    144 &    24 &   34560 &            24 \\   Helsinki &       4 &    144 &    24 &   34560 &            16 \\     Lisbon &       4 &    144 &    24 &   34560 &            19 \\       Lyon &       4 &    144 &    24 &   34560 &             6 \\      Paris &       4 &    144 &    24 &   34560 &             2 \\     Prague &       4 &    144 &    24 &   34560 &             2 \\     Vienna &       4 &    144 &    24 &   34560 &             6 \\     London &       5 &    144 &    24 &   34560 &             4 \\      Milan &       6 &    144 &    24 &   34560 &             6 \\ \midrule      Total &      50 &   5472 &   912 & 1.3M &           180 \\ Annotations Of the 15 hours of audio and video, 3 hours of data (1.5 hours TAU, 1.5 hours MAVD) are manually annotated by our team both in audio and image, along with 12 hours of unlabeled data (2.5 hours TAU, 9.5 hours of unpublished material) for the benefit of unsupervised models. The distribution of clips across locations was selected to maximize variance across different scenes. The annotations were collected at 2 frames per second (FPS) as it provided a balance between temporal granularity and clip coverage. The annotation data is contained in video_annotations.csv and audio_annotations.csv.  Video Annotations Each row in the video annotations represents a single object in a single frame of the video. The annotation schema is as follows: <ul> <li>frame_id: The index of the frame within the clip the annotation is associated with. This index is 0-based and goes up to 19 (assuming 10-second clips with annotations at 2 FPS)</li> <li>track_id: The ID of the detected instance that identifies the same object across different frames. These IDs are guaranteed to be unique within a clip.</li> <li>x, y, w, h: The top-left corner and width and height of the object’s bounding box in the video. The values are given in absolute coordinates with respect to the image size (1280x720). </li> <li>class_id: The index of the class corresponding to: [0, 1, 2, 3, -1] — see label for the index mapping. The -1 value corresponds to the case where there are no events, but still clip-level annotations, like night and city. When operating on bounding boxes, class_id of -1 should be filtered.</li> <li>label: The label text. This is equivalent to LABELS[class_id], where LABELS=[car, bus, motorbike, truck, -1]. The label -1 has the same role as above.</li> <li>visibility: The visibility of the object. This is 1 unless the object becomes obstructed, where it changes to 0.</li> <li>filename: The file ID of the associated file. This is the file’s path minus the parent directory and extension.</li> <li>city: The city where the clip was collected in.</li> <li>location_id: The specific name of the location. This may include an integer ID following the city name for cases where there are multiple collection points.</li> <li>time: The time (in seconds) of the annotation, relative to the start of the file. Equivalent to frame_id / fps .</li> <li>night: Whether the clip takes place during the day or at night. This value is singular per clip.</li> <li>subset: Which data source the data originally belongs to (TAU or MAVD).</li> </ul> Audio Annotations Each row represents a single object instance, along with the time range that it exists within the clip. The annotation schema is as follows: <ul> <li>filename: The file ID odd the associated audio file. See filename above. </li> <li>class_id, label: See above. Audio has an additional class_id of 4 (label=offscreen) which indicates an off-screen vehicle - meaning a vehicle that is heard but not seen. A class_id of -1 indicates a clip-level annotation for a clip that has no object annotations (an empty scene).</li> <li>non_identifiable_vehicle_sound: True if the region contains the sound of vehicles where individual instances cannot be uniquely identified. </li> <li>start, end: The start and end times (in seconds) of the annotation relative to the file. </li> </ul> Conditions of use Dataset created by Magdalena Fuentes, Bea Steers, Pablo Zinemanas, Martín Rocamora, Luca Bondi, Julia Wilkins, Qianyi Shi, Yao Hou, Samarjit Das, Xavier Serra, and Juan Pablo Bello. The Urbansas dataset is offered free of charge under the following terms: <ul> <li>Urbansas annotations are release under the CC BY 4.0 license</li> <li>Urbansas video and audio replicates the original sources licenses: <ul> <li>   MAVD subset is released under  CC BY 4.0 </li> <li>   TAU subset is released under a Non-Commercial license</li> </ul> </li> </ul> Feedback Please help us improve Urbansas by sending your feedback to: <ul> <li>Magdalena Fuentes: mfuentes@nyu.edu</li> <li>Bea Steers: bsteers@nyu.edu </li> </ul> In case of a problem, please include as many details as possible. Acknowledgments This work was partially supported by the National Science Foundation award 1955357 and Bosch RTC.

opencc-by-4.0Jun 2022View details →

Sp(2N) Yang-Mills theories on the lattice: scale setting and topology—data release

This release contains all data and metadata used to prepare the publications <a href="https://arxiv.org/abs/2205.09254">Topological susceptibility in Yang-Mills theories</a> and <a href="https://arxiv.org/abs/2205.09364">Sp(2N) Yang-Mills theories on the lattice: scale setting and topology</a>. Included are: <ul> <li>The raw log output from the Wilson flow computation, as well as metadata describing the ensembles used, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate `README.md` describing the layout of the data.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `datapackage.h5`.</li> <li>The data presented in all tables in both papers, in CSV format, as described in more detail below.</li> </ul> Further details are given in the file README.md.

opencc-by-4.0Jun 2022View details →

Optical Cluster set definitions associated with CERTO project deliverable 4.2

This set of files consists of pickle and csv files that describe the optical water class sets computed as part of the CERTO project ( https://certo-project.org ).  A written description and discussion of these clusters is provided in Deliverable 4.2 from the CERTO project.

opencc-by-4.0Jul 2022View details →

R-IBU: A basic ice breakup data set for the Aura and Kokemäki rivers

This data set is related to the article ‘Tricentennial trends in spring ice break-ups on three rivers in Northern Europe’ which was published in The Cryosphere in 2022. The data set includes the ice-off dates (the first day the river is ice-free) for Aura River in Turku (60°45’N, 22°27’E) over the period 1749–2020 and the break-up (the date when the ice started breaking up and/or moving) dates for Kokemäki River in Pori (61°48’N, 21°79’E) over the period 1793–2020. Both rivers are in Finland. The data set has been developed for climate research purposes. The aim was to create two series with homogenized break-up dates with regard to site and event. The series will be updated if new observations are obtained. See the Readme_txt for more information about the data sets.

opencc-by-4.0Jul 2022View details →

Distinguishing between canonical and non-canonical tRNA genes reveals that Thermococcaceae adhere to the standard archaeal tRNA gene set

Abstract Automated genome annotation is an essential tool for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed – for selected genomes – in the Genomic tRNA database (GtRNAdb). Given the central role of tRNA in molecular biology, the accuracy and proper application of tRNAscan-SE is important for both interpretation of the output, and continued improvement of the software. Here, we report a manual annotation of the predicted tRNA gene sets for 20 complete genomes from the archaeal taxon Thermococcaceae. According to GtRNAdb, these 20 genomes contain a number of putative deviations from the standard set of canonical tRNA genes in Archaea. However, manual annotation reveals that only one represents a true divergence; the other instances are either (i) non-canonical tRNA genes resulting from the integration of horizontally transferred genetic elements, or CRISPR-Cas activity, or (ii) attributable to errors in the input DNA sequence. To distinguish between canonical and non-canonical archaeal tRNA genes, we recommend using a combination of automated pseudogene detection by tRNAscan-SE and the tRNAscan-SE isotype score, greatly reducing manual annotation efforts and leading to improved predictions of tRNA gene sets in Archaea.   Repository contents 01_workflow_tRNAscanSE_predictions_210archaea.html contains the workflow and graphical output for tRNA gene set predictions in 20 Thermococcaceae genomes and 210 archaeal genomes. Files 03 to 06 below are the files quoted in this workflow. 02_workflow_tRNAscanSE_predictions_210archaea.Rmd contains the markdown file associated with 01_workflow_tRNAscanSE_predictions_210archaea.html above. 03_thermo_trnas_GtRNAdb.txt contains the predicted tRNA gene sets of 20 Thermococcaceae genomes as listed on GtRNAdb (Data Release 19 (June 2021)). 04_Archaea_genome_list.txt contains the details of all 217 archaeal genomes listed on GtRNAdb (Data Release 19 (June 2021)). The seven genomes for which the NCBI genome sequences were no longer available are indicated by #### preceding the name. 05_thermo_tRNAs_genome.txt contains the predicted tRNA gene sets of 20 Thermococcaceae genomes as predicted by locally run tRNAscan-SE (version 2.0.6), with standard settings for Archaea (option -A). To display the output, options -H and --detail were added. We note that pseudogene detection is active under these conditions. 06_Archaea_210_GtRNAdb_tRNAs.txt contains the predicted tRNA gene sets of the 210 archaeal genomes as listed on GtRNAdb (Data Release 19 (June 2021)). 07_Archaea_210genomes_tRNAs.txt contains the predicted tRNA gene sets of the 210 archaeal genomes as predicted by locally run tRNAscan-SE (version 2.0.6), with standard settings for Archaea (option -A). To display the output, options -H and --detail were added. We note that pseudogene detection is active under these conditions. 08_NCBI_genomes.zip contains the NCBI GenBank genome sequence files used in this study. These include the 20 Thermococcaceae genomes, the wider 210 archaeal genomes, and several others of interest.  09_phylogeny.tar.zip contains the data used to draw a phylogenetic tree for the 20 Thermococcaceae organisms. The folder includes a file listing the details of all data in the folder (Readme.md), a workflow file (workflow_UndinMarkers_v2.md), and data folders.   Notes The extended TIGRFAM database referred to in the phylogenetic tree construction process can be found at <a href="https://zenodo.org/record/3839790#.YjByaVzMI3g">https://zenodo.org/record/3839790#.YjByaVzMI3g</a> The perl script used during phylogenetic tree construction, catfasta2phyml.pl, is available in the GitHub repository <a href="https://github.com/nylander/catfasta2phyml">https://github.com/nylander/catfasta2phyml</a> tRNAscan-SE is a freely available resource available online (<a href="http://lowelab.ucsc.edu/tRNAscan-SE/">http://lowelab.ucsc.edu/tRNAscan-SE/</a>) GtRNAdb is a publicly accessible resource available online (<a href="http://gtrnadb.ucsc.edu/">http://gtrnadb.ucsc.edu/</a>) NCBI is a publicly accessible resource available online (<a href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov/</a>) rrnDB is a publicly accessible resource available online (<a href="https://rrndb.umms.med.umich.edu/">https://rrndb.umms.med.umich.edu/</a>) BLAST is a publicly accessible resource available online (<a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi">https://blast.ncbi.nlm.nih.gov/Blast.cgi</a>)