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12,237
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Dataset results
12,237 results for “2019”
Camera trap image of Turdus iliacus (2019-02-12T12:19:17Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Corvus corone (2019-02-13T11:48:05Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Turdus iliacus (2019-01-28T13:49:19Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Sciurus vulgaris (2019-02-20T14:45:38Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Lepus europaeus (2019-02-06T09:42:34Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Parus major (2019-02-03T12:09:06Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Parus major (2019-02-04T10:28:40Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Parus major (2019-02-12T11:49:40Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Capreolus capreolus (2019-01-22T14:35:33Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Apodemus sylvaticus (2019-01-11T18:53:54Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Sciurus vulgaris (2019-02-07T15:28:29Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Data from: Are drivers of microbial diatom distributions context dependent in human impacted and pristine environments? in Ecological Applicatios (2019)
<p>Species occurrence (0/1) and environmental data from research article "Are drivers of microbial diatom distributions context dependent in human impacted and pristine environments?" in Ecological Applications (2019). </p> <p>Please see more details in the readme-file and the original article. </p>
dataset for SMFR article for ISCRAM 2019
<p>This dataset contains data generated during experiments for the article</p> <p><strong>Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach </strong></p> <p>For ISCRAM 2019 conference</p>
Data from isotopic measurements for: Hirave et al., 2019
<p>Data from isotopic and molecular measurements for: Understanding the effects of early degradation on isotopic tracers: implications for sediment source attribution using compound-specific isotope analysis (CSIA)</p>
SeisSol dynamic rupture model setup of the Mw 7.5 Palu earthquake scenario published in Ulrich et al. (2019)
<p>All data required to run the dynamic rupture model of the Palu earthquake presented in:</p> <p>Ulrich, T., Vater, S., Madden, E. H., Behrens, J., van Dinther, Y., van Zelst, I., Fielding, E. J., Liang, C. & Gabriel, A. A. (2019). Coupled, Physics-based Modeling Reveals Earthquake Displacements are Critical to the 2018 Palu, Sulawesi Tsunami. doi: 10.31223/osf.io/3bwqa.</p> <p>A detailed readme file summarizing the data and data formats is also provided.</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>
ICDAR 2019 Competition on Table Detection and Recognition (cTDaR)
<p>The aim of this competition is to evaluate the performance of state of the art methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done. The Ground Truth is provided in a similar format as for the ICDAR 2013 competition (see [2]):</p> <p><?xml version="1.0" encoding="UTF-8"?></p> <p><<strong>document</strong> filename='filename.jpg'></p> <p> <<strong>table</strong> id='Table_1540517170416_3'></p> <p><strong> <Coords points="180,160 4354,160 4354,3287 180,3287"/></strong></p> <p> <<strong>cell</strong> id='TableCell_1540517477147_58' <strong>start-row</strong>='0' <strong>start-col</strong>='0' <strong>end-row</strong>='1' <strong>end-col</strong>='2'></p> <p> <<strong>Coords</strong> <strong>points</strong>="180,160 177,456 614,456 615,163"/></p> <p> </cell></p> <p> ...</p> <p> </table></p> <p> ...</p> <p></document></p> <p> </p> <p>The difference to Gobel et al. [2] is the Coords tag which defines a table/cell as a polygon specified by a list of coordinates. For B.1 the table and its coordinates is given together with the input image.</p> <p>Important Note:</p> <p>For the modern dataset, the convex hull of the content describes a cell region. For the historical dataset, it is requested that the output region of a cell is the cell boundary. This is necessary due to the characteristics of handwritten text, which is often overlapping with different cells.</p> <p>See also: http://sac.founderit.com/tasks.html</p> <p>The evaluation tool is available at github: https://github.com/cndplab-founder/ctdar_measurement_tool</p>
Renaud et al. 2019 data
<p>Four .xlsx files and one .accdb file containing results and underlying data from Renaud et al., (2019):</p> <p>SINMODub_species_listc.xlsx --- Benthic fauna occurrence data with matched bias-corrected SINMOD bottom water output</p> <p>SINMODub_species_gridcells_1979_2008.xlsx --- Bias-corrected SINMOD bottom water time averages from hindcast (1979-2008)</p> <p>SINMODub_species_gridcells_2090s_A1B.xlsx --- Bias-corrected SINMOD bottom water time averages from SRES A1B projection (2090-2099)</p>
Australasia Preserves June 2019 Monthly Meetup
<p>Since the beginning of the Australasia Preserves digital preservation community of practice in February 2018, the Digital Scholarship team at the University of Melbourne has been running monthly meetups for the community, featuring practitioners and academics working in the field of digital preservation in the Australasian region. This June monthly meetup for 2019 was organised in collaboration with the National and State Libraries Australia (NSLA) digital preservation community of practice. This meetup was held on Friday 7 June, 9.30am-12pm (AEST), and the community welcomed guest speakers Micky Lindlar and Melanie Swalwell. This dataset contains a video recording of Micky Lindlar's presentation (introduced by Jaye Weatherburn), Micky Lindlar's slides, community collaborative notes, and a collection of Tweets from the event. </p>
Energy Consumption of IO APIS (ESEM'2019 paper)
<p>## About the experiments</p> <p>### Acronyms used in the figures</p> <p>- BufferedReader (BR).<br> - LineNumberReader (LNR).<br> - CharArrayReader (CAR).<br> - PushbackReader (PBR).<br> - FileReader (FR).<br> - FileInputStream (FIS).<br> - BufferedInputStream (BIS).<br> - StringReader (SR).<br> - PushbackInputStream (PBIS).<br> - StringBufferInputStream (SBIS).<br> - ByteArrayInputStream (BAIS).<br> - LineNumberInputStream (LNIS).<br> - Scanner (SCN).<br> - O método readAllLines da classe Files, e um Stream de String (RFAL).<br> - O método lines da classe Files, e um Stream de String (RFL).<br> - O método newBufferedReader da classe Files, e um Stream de String (BRFL)<br> - BufferedWriter (BW).<br> - FileWriter (FW).<br> - StringWriter (SW).<br> - PrintWriter (PW).<br> - CharArrayWriter (CAW).<br> - FileOutputStream (FOS).<br> - ByteArrayOutputStream (BAOS).<br> - BufferedOutputStream (BOS).<br> - PrintStream (POS).</p> <p>### Settings</p> <p>For each setting, we experimented with three files of different sizes: 1 Mb, 10 Mb e 20 Mb.</p> <p>The experiments were ran in the following machine</p> <p>- Hardware: Intel® Core™ i7-2670QM CPU @ 2.20GHz, with 4 processors, 16GB DDR3 1600MHz, Ubuntu 16.04 LTS (kernel 4.4.0-112-generic).<br> - Java: Java(TM) SE Runtime Environment, version 1.8.0-151</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.