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12,237 results for “2019”
2019-Xunyu Zhang-Mafic minerals in the South Pole-Aitken basin
<p>It is the dataset of extracted spectra and their location used in the paper "Mafic minerals in the South Pole-Aitken basin".</p>
Data used in "Seasonality of Intraseasonal Variability in CMIP5 and Nonhydrostatic Atmospheric Global Models" by Nakano and Kikuchi (2019) submitted to GRL
<p>PCs time series and NICAM-AMIP 2.5 degree gridded data used in Nakano and Kikuchi (2019) submitted to GRL.</p>
European Robotics League Consumer - Lisbon 2019 contest - SocRob team's files for task benchmarks
<p>Internal log files of the robot used by SocRob's team (from Instituto Superior Técnico) for the 4 Task Benchmarks performed at the ERL Consumer Lisbon 2019 contest.</p>
Raw data presented in Hayes at al Current Biology 2019
<p>Raw data presented in Hayes et al. Current Biology 2019</p> <p>Data used to prepare each figure can be found under separate tabs. Tabs are labelled with their corresponding figure in the manuscript.</p> <p>---------------------------------------------------------------------------------------------------------------------------------</p> <p>N.B. The raw data for Fig S3D in this file was posted incorrectly. The correct file can be found at https://zenodo.org/record/2592526#.XIk36yhKg2w</p> <p>---------------------------------------------------------------------------------------------------------------------------------</p>
Supplementary material 2 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180
Supplementary material 2 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180
Supplementary material 1 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180
Supplementary material 1 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180
TAU Urban Acoustic Scenes 2019 Mobile, Development dataset
<p>TUT Urban Acoustic Scenes 2019 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Recordings were made with three devices that captured audio simultaneously. Each acoustic scene has 1440 segments (240 minutes of audio) recorded with device A (main device) and 108 segments of parallel audio (18 minutes) each recorded with devices B and C. The dataset contains in total 46 hours of audio.</p>
TAU Urban Acoustic Scenes 2019, Development dataset
<p>TAU Urban Acoustic Scenes 2019 development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Each acoustic scene has 1440 segments (240 minutes of audio). The dataset contains in total 40 hours of audio.</p>
TAU Urban Acoustic Scenes 2019 Openset, Development dataset
<p>TAU Urban Acoustic Scenes 2019 Openset development dataset is designed for researching open set classification problem in acoustic scene classification. In this problem, the test recording may be from a different environment than the 10 target classes. The dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>In addition, material for Unknown scene `unknown` is provided from 4 scenes:</p> <ul> <li>Beach - <em>beach</em>, extracted from TUT Acoustic scenes 2017 dataset</li> <li>Library - <em>library</em>, extracted from TUT Acoustic scenes 2017 dataset</li> <li>Office - <em>office</em>, extracted from TUT Acoustic scenes 2017 dataset</li> <li>Public event - <em>public_event</em></li> </ul>
2019_Earths_Future_Zhan_et_al_Data
<p>The data are rice yield simulations for "Impacts of sulfate geoengineering on rice yield in China: results from a multi-model ensemble" </p>
supplement_ahrens_et_al_mar_2019
<p>Data include all force-distance-raw-data of the article <em>A Combined AFM and Lateral Stretch Device Enables Microindentation Analyses of Living Cells at High Strains. </em>Furthermore, images used for local strain calibration are included.</p>
Berends_etal_2019_GMD_supplement
<p>Selected results from the Pliocene simulations by Berends et al., 2019: the reconstructed records of pCO<sub>2</sub>, global mean sea level and benthic d<sup>18</sup>O, as well as NetCDF files containing ice thickness, bedrock topography and annual mean surface temperature and precipitation for all four ice-sheet model regions during MIS M2, MIS KM5c and MIS KM3.</p>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Development Datasets
<p>This package consists of two development datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These development datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>.</p> <p>Both the development set consists of 400, one minute long recordings sampled at 48000 Hz, and divided into four cross-validation splits of 100 recordings each. These recordings were synthesized using spatial room impulse response (IRs) collected from five indoor locations, at 504 unique combinations of azimuth-elevation-distance. Furthermore, in order to synthesize the recordings, the collected IRs were convolved with <a href="http://www.cs.tut.fi/sgn/arg/dcase2016/task-sound-event-detection-in-synthetic-audio#audio-dataset">isolated sound events dataset from DCASE 2016 task 2</a>. Finally, to create a realistic sound scene recording, natural ambient noise collected in the IR recording locations was added to the synthesized recordings such that the average SNR of the sound events was 30 dB.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>foa_dev.z01</em></strong>,<strong><em> foa_dev.z02</em></strong> and <strong><em>foa_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> development dataset.<br> The two files, <strong><em>mic_dev.z01</em></strong> and, <strong><em>mic_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development dataset.<br> The <strong><em>metadata_dev.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development datasets.</p> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p>
data of JGR 2019
<p>Data and IDL scripts used to make the plots in my JGR 2019 paper are organized in a self-explanatory fashion.</p>
Dataset underpinning the paper "Terahertz plasmon resonances in two-dimensional electron systems: Modeling approaches" by S. Siaber et al 2019 Phys. Rev. Appl.
<p>Dataset underpinning the paper "Terahertz plasmon resonances in two-dimensional electron systems: Modeling approaches" by S. Siaber et al 2019 Phys. Rev. Appl.</p>
FIGURE 1. Alburnus carianorum, FFR 04677 in Comments on the holotype of Alburnus kurui Mangit & Yerli 2018 and re-description of A. carianorum Freyhof, Kaya, Bayçelebi, Geiger & Turan, 2019 (Teleostei: Leuciscidae)
FIGURE 1. Alburnus carianorum, FFR 04677, holotype, 95.2 mm SL, Turkey: Muğla prov.: Dalaman River north of Dalaman.
The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Replication
<p>This archive contains detailed results from QComp 2019 as well as the necessary scripts and data to replicate them.</p> <p>Visit http://qcomp.org for more information for QComp.</p> <p>Overview of Contents</p> <p>- `qcomp.org/` contains the state of our website from the timepoint of the competition. This includes:<br> - All benchmark files, browsable at `qcomp.org/benchmarks/index.html`<br> - Detailed competition results in a human-readable format, browsable at `qcomp.org/competition/2019/results/index.html`<br> - `logs/` contains the raw logfiles and data gathered by our scripts<br> - `scripts/` contains scripts to replicate the whole competition<br> - `toolpackages/` contains a package for each participating tool which includes<br> - Instructions for obtaining and installing the tool<br> - a file `invocations.json` listing the commandlines used in QComp 2019<br> - a file `tool.py` providing functionalities to obtain the result from the tool output.</p>
TAU Moving Sound Events 2019 - Ambisonic, Anechoic, Synthetic IR and Moving Source Dataset
<p><strong>Tampere University (TAU) Moving Sound Events 2019 - Ambisonic, Anechoic and Synthetic Impulse Response (IR) and Moving Source Dataset</strong></p> <p>This dataset consists of simulated anechoic first order Ambisonic (FOA) format recordings with moving point sources each in 2D spherical space represented with azimuth and elevation angles. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), starting spatial location and directional spatial location in azimuth and elevation angles (in degrees), angular velocity of motion, and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the DCASE 2016 task 2 dataset. This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. Every event is assigned a spatial trajectory on an arc with a constant distance from the microphone (in the range 1-10 m) and moving with a constant angular velocity for its duration. Due to the choice of the ambisonic spatial recording format, the steering vectors for a plane wave source or point source in the far field are frequency-independent. Hence, there is no need for a time-variant convolution or impulse response interpolation scheme as the source is moving; the spatial encoding of the monophonic signal was done sample-by-sample using instantaneous ambisonic encoding vectors for the respective DOA of the moving source. The synthesized trajectories in the dataset vary in both azimuth and elevation and are simulated to have a constant angular velocity in the range [-90, 90]/s with 10-degree/s steps.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Localization, Detection and Tracking of Multiple Moving Sound Sources with Convolutional Recurrent Neural Networks'</a> work.</p>
Supplementary material 2 from: Kirichenko N, Triberti P, Lopez-Vaamonde C (2019) New species of leaf-mining Phyllonorycter (Lepidoptera Gracillariidae) from Siberia feeding on Caragana (Fabaceae). ZooKeys 835: 17-41. https://doi.org/10.3897/zookeys.835.33166
: Explanation note: Fabaceae-feeding Phyllonorycter species involved in the study. Where relevent, genitalia preparation number is given in square brackets in the Life stage column. Both the Process ID and Sample ID codes link the record in the BOLD database and the voucher specimen from which the sequence is derived.
Chicago Crime Data 2001 to Present (April 2019)
<p>City of Chicago Crime Data from 2001 to April 2019.</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.