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12,237 results for “2019”
New Zealand native forest plant cover data for Popovic et al. MEE (2019), Untangling direct species associations from indirect mediator species effects with graphical models.
<p>Forest cover measurements were collected at 1246 native forest sites that form part of a network of permanent 20 x 20 m plots spread throughout New Zealand. A total of 1831 plant species were present in these plots, with the most common being herbs, graminoids, ferns, shrubs and trees. Plant cover (in ordinal categories) was assessed for each species in several tiers at different heights. The cover data we analysed (<em>NZ_native_forest_cover.csv) </em>were the maximum cover recorded over all the tiers at the 964 sites identified as native forests, containing 1311 species with at least one presence. <em>NZ_native_forest_species.csv</em> contains species data including species name, exotic/native, and plant type (tree, shrub, etc.), corresponding to the plant species in the columns of <em>NZ_native_forest_cover.csv</em>.</p> <p>We acknowledge the use of data drawn from the Natural Forest plot data collected between January 2002 and March 2007 by the LUCAS programme for the Ministry for the Environment, New Zealand.</p> <p> </p>
6th International Conference "Fracture Mechanics of Materials and Structural Integrity" (June 3–6, 2019, Lviv, Ukraine)
<p><strong>12.06.2019</strong></p> <p>03–06 червня 2019 року у м. Львові відбулася 6-та Міжнародна конференція з механіки руйнування матеріалів і цілісності конструкцій (“Fracture Mechanics of Materials and Structural Integrity”, FMSI 2019). Співорганізаторами конференції виступили Європейське товариство з цілісності конструкцій (European Structural Integrity Society, ESIS), Українське товариство з механіки руйнування матеріалів, Фізико-механічний інститут імені Г.В. Карпенка НАН України та Національний університет “Львівська політехніка”.</p> <p><em>Матеріали прес-служби НАН України</em></p>
Cities for IDL 2019
<p>This map shows the set of cities considered in IDL 2019. More about: https://idl.institutomongeralaegon.org/</p>
Binary black-hole simulation SXS:BBH:2019
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Courage registry - open dataset 1.1 (July 2019)
<p>The package contains the Linked Data of the COURAGE registry (http://cultural-opposition.eu/registry/) built by the COURAGE H2020 project. The dataset contains collections, featured items, groups, organizations and persons for the better understanding of the cultural heritage of dissent in the former socialist countries.</p>
Photos from DH2019 DH & Lib SIG Preconference workshop on 8 July 2019 in KB National Library of the Netherlands
<p>A selection of photos taken at the DH2019 DH & Lib SIG Preconference workshop on 8 July 2019 in KB National Library of the Netherlands</p>
Excel mapping tool for 2019 avian influenza data reporting
<p>Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.</p> <p>Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.</p> <p>The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.</p>
Data set supporting Journal article: Siebicke, L., and Emad, A., "True eddy accumulation trace gas flux measurements: proof of concept", Atmos. Meas. Tech., 12, 1–28, 2019.
<p>This data set contains carbon dioxide trace gas flux measurements obtained by the true eddy accumulation technique as described in the research article:</p> <p>Siebicke, L., and Emad, A., "True eddy accumulation trace gas flux measurements: proof of concept", Atmos. Meas. Tech., 12, 1–28, 2019.</p> <p>It contains meteorological measurements (file "meteo.csv"), turbulent energy fluxes (file "energy_fluxes.csv"), three-diomensional wind vector measurements ("file wind.csv"), and carbon dioxide trace gas fluxes obtained by true eddy accumulation (file "co2_flux_TEA.csv") and eddy covariance using data from two separate anomemoters (files "co2_flux_EC_TEA_sonic.csv" and "co2_flux_EC_EC_sonic.csv").</p>
Ghosh and Rihel 2019
<p>Data from Ghosh and Rihel 2019:</p> <p>https://doi.org/10.1101/694471</p>
Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes
<p>Post-processed and real-time GNSS displacement waveforms for the 2019 Ridgecrest earthquakes. Waveforms are for the M6.4 and M7.1 events recorded at 1 and 5Hz sample rates. Data are in miniSEED format, channel codes LYE, LYN, LYZ correspond to east, north, and up respectively. Displacement units are meters and time is UTC. The data are trimmed 60s before the USGS origin times for<br> the earthquakes. No filtering has been applied.</p> <p>A paper describing the data has been submitted to SRL. In the meantime if you use the data please cite our <a href="https://eartharxiv.org/pdxqw/">preprint on the EarthArXiv</a> as:</p> <p>Melgar, D., TI Melbourne, BW Crowell, J Geng, W Szeliga, C Scrivner, M Santillan, DER Goldberg. 2019. Real-time High-rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes. EarthArXiv, doi:10.31223/osf.io/pdxqw.</p>
Data and R code used in Delory et al (2019) When history matters: the overlooked role of priority effects in grassland overyielding
<p>This repository contains the raw data and R code used for the following paper: Delory et al (2019) When history matters: the overlooked role of priority effects in grassland overyielding.</p>
Dataset from 'Klever, L., Voudouris, D., Fiehler, K., & Billino, J. (2019). Age effects on sensorimotor predictions: What drives increased tactile suppression during reaching? Journal of Vision, 19(9):9, 1-17. doi:10.1167/19.9.9
<p>Dataset associated with the following publication:</p> <p>Klever, L., Voudouris, D., Fiehler, K., & Billino, J. (2019). Age effects on sensorimotor predictions: What drives increased tactile suppression during reaching? Journal of Vision.</p> <p>--------------------------------------------------------------</p> <p>We provide two files; one data file contains data on which analyses are based, the other one gives the column labels for the data file.</p> <p>-------------------------------------------------------------</p> <p>For further questions, please contact:<br> lena.klever[at]psychol.uni-giessen.de</p> <p> </p>
Parametric bump noise data (from Varnet et al. 2019, Trends in Hearing)
<p>Data collected by Léo Varnet and Chloé Langlet<br> Parametric bump noise experiment (main experiment)</p> <p>See description of the methods and results in 'High-frequency sensorineural hearing loss alters cue-weighting strategies when discriminating stop consonants in noise' (Varnet et al., 2019, Trends in Hearing)</p>
Bump noise ACI data (from Varnet et al. 2019, Trends in Hearing)
<p>Data collected by Leo Varnet<br> Bump noise ACI experiment (pilot experiment)</p> <p>See description of the methods and results in 'High-frequency sensorineural hearing loss alters cue-weighting strategies when discriminating stop consonants in noise' (Varnet et al., 2019, Trends in Hearing)</p>
GCTB SBayesR shrunk sparse linkage disequilibrium matrices for HM3 variants, summary statistics and predictors generated from "Improved polygenic prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al. 2019.
<p>GCTB LD matrices and results for HapMap 3 variants and 2.8M variants, which were used for</p> <p>simulation, cross-validation and across biobank analyses in the manuscript "Improved polygenic</p> <p>prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al.</p> <p>2019.</p> <p>Unzip and see README for further details.</p>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Evaluation Datasets
<p>This package consists of two evaluation 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 evaluation datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>. The corresponding development datasets can be downloaded <a href="https://doi.org/10.5281/zenodo.2599196">here</a>.</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> <ul> <li>The <strong>foa_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> evaluation dataset.</li> <li>The <strong>mic_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation dataset.</li> </ul> <p>-- Version 2 updates --</p> <p>The<a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection-results"> DCASE 2019 sound event localization and detection task has now ended</a>. Hence we are releasing the reference labels for the evaluation dataset in this version.</p> <ul> <li>The <strong><em>metadata_eval.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> evaluation datasets. </li> <li>The <strong>short2longnames.txt</strong> file consists of the corresponding names for each recording in the dataset in the <a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection#development-dataset">development-set format</a>, i.e., including the information of the impulse response location and the maximum number of overlapping sound events in the recording.</li> </ul> <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> <p> </p> <p> </p>
Prioriteringsscore op basis van weging aantal stemmen - bijlage bij: Landelijk Coördinatiepunt Research Data Management (LCRDM) Positioning paper voor 2019 en verder
<p>Bijlage bij Positinioningpaper (juni 2019) https://doi.org/10.5281/zenodo.3266833</p>
NGS data produced in 'Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries'; Nature Communications (2019)
<p>Sharma, G et al. Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries. <em>Nature Communications</em>. Accepted (August 2019)</p> <p><strong>Abstract:</strong></p> <p>Cytotoxic CD8+ T-cells recognize and eliminate infected or malignant cells that present, at their cell surfaces, short peptide epitopes derived from intracellularly processed antigens. However, broadly searching for specific major histocompatibility complex (MHC)-bound peptide epitopes that are naturally processed and capable of eliciting a functional T-cell response has been challenging. Here, we report a method for deep and unbiased T-cell epitope profiling, which is done by using <em>in vitro</em> co-culture of CD8+ T-cells and target cells transduced with high-complexity epitope-encoding minigene libraries. Target cells that are subject to cytotoxic attack from T-cells in co-culture are isolated, before they are lost to apoptosis, by fluorescence-activated cell sorting and characterized by sequencing the minigenes encoded within. In the present study, we validate this highly parallelized method using known murine T-cell receptor/peptide-MHC pairs and diverse minigene-encoded epitope libraries to identify naturally processed and MHC-presented peptide epitopes unambiguously and with high sensitivity.</p>
FeRh on I16 2019 September
<p>Datasets collected during beamtime MM21966-1 (I16)</p>
Underwater-sound records in glacier fjords (Inglefield Bredning, Baffin Bay, NW Greenland, Denmark), 19-28 July 2019
<p>Acoustic data (.wav) recorded by 2 hydrophones suspended from boats in Inglefield Bredning and Bowdoin fjords (Baffin Bay, NW Greenland, Denmark) in July 2019 for underwater soundscape documentation (narwhal vocalizations and environmental sources). </p> <p>*******************</p> <p>First set-up had a hydrophone AQH-020 by AquaSound Inc. (20Hz – 20kHz) connected to Amplifier Aquafeeler III (SQE-1001B, 50dB gain) by AquaSound Inc. and a recorder PCM-M10 by Sony (44.1 kHz, 16 bit, auto-mode). Recording depth was about 6.6 m, except a record collected on July 27, 2019 at 15:56:33 (depth was about 0.5 m).</p> <p>Channels: 2 (but records are only at “Left”/1 channel; the “Right”/2 is electric noise).</p> <p>File name: sony.YYMMDDhhmmss.wav</p> <p>Note that the strongest regularly-spaced impulsive sounds in two files (sony.190719130533.WAV, sony.190719132214.WAV) are seemingly not due to a whale nearby, but due to repetitive impacts of the hydrophone with a ballast-rope in strong current. This issue was fixed by adjusting the rope length, after which the sound was gone.</p> <p>*******************</p> <p>Second set-up had a hydrophone SoundTrap SD3000 by Ocean Instruments NZ (20Hz – 60kHz), integrated with amplifier and recorder, sampling at 96 kHz, 16 bit. Signal-to-pressure conversion constant was 176.2 dB for this particular device (ID number 5146, at High-Gain mode). Recording depth was about 10.8 m, except records collected on July 20, 2019 between 00:44:47 and 08:44:47 (depth was about 1 m). </p> <p>Channels: 1</p> <p>File name format: 5146.YYMMDDhhmmss.wav</p> <p>*******************</p> <p>Coordinates for each record by each set-up are shown below.</p> <p> </p> <p>Geographic position of each measurement with <strong>SoundTrap</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 12:55:17, 77.474752, -68.660610 </p> <p>19 July 2019, 13:19:53, 77.488215, -68.597227 </p> <p>19 July 2019, 14:19:53, 77.485103, -68.574985 </p> <p>19 July 2019, 16:19:41, 77.523033, -68.403958 </p> <p>19 July 2019, 19:06:57, 77.618543, -68.564536 </p> <p> </p> <p>20 July 2019, 00:44:47, 77.548649, -68.550752 </p> <p>20 July 2019, 13:02:59, 77.527026, -68.534285</p> <p>20 July 2019, 14:30:26, 77.487211, -68.483834</p> <p>20 July 2019, 15:10:13, 77.495954, -68.658084 </p> <p> </p> <p>*******************</p> <p>Geographic position of each measurement with <strong>Sony-AquaSound</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 13:05:33, 77.474752, -68.660610 </p> <p>19 July 2019, 13:22:14, 77.488215, -68.597227 </p> <p>19 July 2019, 16:11:54, 77.523033, -68.403958 </p> <p>19 July 2019, 19:09:11, 77.618543, -68.564536</p> <p> </p> <p>20 July 2019, 13:04:42, 77.527026, -68.534285</p> <p>20 July 2019, 14:01:42, 77.505033, -68.553648</p> <p>20 July 2019, 15:11:10, 77.495954, -68.658084 </p> <p> </p> <p>21 July 2019, 22:17:04, 77.675334, -68.636040 </p> <p>21 July 2019, 22:19:53, 77.671904, -68.639090</p> <p> </p> <p>22 July 2019, 00:12:34, 77.525553, -68.442136 </p> <p> </p> <p>27 July 2019, 13:28:58, 77.617588, -68.597946</p> <p>27 July 2019, 14:13:59, 77.667788, -68.643976 </p> <p>27 July 2019, 15:56:33, 77.665487, -68.778195 </p> <p>27 July 2019, 17:03:04, 77.672426, -68.658150 </p> <p>27 July 2019, 17:22:16, 77.669874, -68.657587 </p> <p>27 July 2019, 17:59:39, 77.676941, -68.664817 </p> <p>27 July 2019, 19:44:32, 77.668669, -68.656365 </p> <p>27 July 2019, 21:57:07, 77.628218, -68.637347</p> <p>27 July 2019, 23:07:12, 77.625571, -68.616875</p> <p> </p> <p>28 July 2019, 00:02:41, 77.619299, -68.595757</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.