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12,237 results for “2019”
St Kitts Mosquito Survey and Model November 2017 to March 2019
<p>Background: High quality mosquito surveys that collect fine resolution local data on<br> mosquito species' abundances provide baseline data to help us understand potential<br> host-pathogen-mosquito relationships, accurately predict disease transmission, and<br> target mosquito control efforts in areas at risk of mosquito borne diseases.</p> <p>Methods: As part of an investigation into arboviral sylvatic cycles on the Caribbean<br> island of St. Kitts, we carried out an island wide mosquito survey from November 2017<br> to March 2019. Using Biogents Sentinel 2 and miniature CDC light traps that were set<br> monthly and run for 48 hour intervals, we collected mosquitoes from a total of 30 sites<br> distributed across the five common land covers on the island (agricultural, mangrove,<br> rainforest, scrub, and urban). We developed a mixed effects negative binomial<br> regression model to predict the effects of land cover, seasonality, and precipitation on<br> observed counts of the most abundant mosquito species we found.</p> <p>Results: We captured 10 of the 14 mosquito species reported on the island, the four<br> most abundant being Aedes taeniorhynchus , Culex quinquefasciatus , Aedes<br> aegpyti , and Deinocerites magnus. Sampling in the mangroves yielded the most<br> mosquitoes, with Ae. taeniorhynchus, Cx. quinquefasciatus, and De. magnus<br> predominating. Aedes aegypti was recovered primarily from urban and agricultural<br> habitats, but also at lower frequency in other land covers. Psorophora pygmaea and<br> Toxorhynchites guadeloupensis were only captured in scrub habitat. Capture rates in<br> rainforests were low. Our models indicated the relative abundance of the four most<br> common species varied seasonally and with land cover. They also suggested that the<br> extent to which monthly average precipitation influenced counts varied according to<br> species.</p> <p>Conclusions: This study demonstrates there is high seasonality in mosquito<br> abundances and that land cover influenced the distribution and abundance of mosquito<br> species on St. Kitts. Further, human-adapted mosquito species (e.g. Ae. aegypti and<br> Cx. quinquefasciatus ) that are known vectors for many human relevant pathogens are<br> the most wide-spread (across land covers) and the least responsive to seasonal<br> variation in precipitation.</p>
Evolución del precio del Bitocoin entre el 20 de marzo del año 2019 hasta el 27 de agosto del 2019
<p>El fichero está compuesto por <strong>20604</strong> registros que corresponden a los datos tomados desde el 20 de marzo del año 2019 hasta el 27 de agosto del 2019.</p> <p>Su composición esta explicada en la url: <a href="https://1938.com.es/bitcoin_prediction#sec_dataset_blockchain">https://1938.com.es/bitcoin_prediction#sec_dataset_blockchain</a></p> <p> </p>
Georeferenced drone thermal video and related data, recorded on 6 and 28 August 2019 in Siikaneva peatland
<p>Data files in Matlab .mat format.</p> <p>1) EC_and_timeaxes.mat: Eddy Covariance data covering the drone measurement periods, time axes for drone 1Hz sequences and EC data;</p> <p>2) T_fluctuation_corr.mat: Four georeferenced sequences of surface temperature fluctuation T'(x,y,z) derived from the four measured thermal videos;</p> <p>3) T_georef_orig.mat: Four georeferenced sequences of surface temperature T(x,y,z) corresponding to the four measured thermal videos;</p> <p>4) UTMgrid_anemometer_RGBpic.mat: UTM grid for the T'(x,y,z) and T(x,y,z) sequences, pixel coordinates of anemometer location, georeferenced UAV RGB image (with the same UTM grid as the thermal images).</p>
z4ch4ry/leadershipdata: Garfield, Hubbard, & Hagen, 2019, Evolutionary models of leadership: Tests and synthesis
<p>Public release of data associated with publication in Human Nature 30 (1) 2019 and Evolution and Human Behavior 31 (2) 2020.</p> <p>Updated to reflect additional data used in the second publication in Evolution and Human Behavior, <a href="https://doi.org/10.1016/j.evolhumbehav.2020.07.012">https://doi.org/10.1016/j.evolhumbehav.2020.07.012</a>.</p> <p> </p> <p> </p>
Level 2 spectra of the global solar irradiance in the wavelength range 290 - 500 nm measured at Aosta - Saint Christophe, Italy by the Bentham DTMc300 with serial number 5541 in 2006 - 2019
<p>The provided dataset includes the Level 2 <strong>spectral measurements</strong> (in Watt/m<sup>2</sup>/nm) of the solar irradiance in the range 290 - 500 nm, performed by the Bentham DTMc300 spectroradiometer with serial number 5541. The particular instrument performs automated continuous, high quality measurements at Aosta - Saint Christophe, Italy (45.7° N, 7.4° E, 570 m a.s.l.) since 2006. The Level 2 spectra are re-evaluated and homogenized and are currently available for the period 24 July 2006 - 8 July 2019. Each file contains the spectra for one day. The time (in UTC) for the measurements at 290, 400, and 500 nm is also provided for each spectral scan so that the user has also information for the duration of each scan.</p> <p>The daily <strong>noon UV index</strong> is also provided for the same period in the file "<strong>aao_noon_uvi_l2.dat</strong>". Erythemal doses have been calculated by weighting each spectrum with the CIE (1999) effective spectrum, and then integrating in the range 290 - 400 nm. Then the UV index has been calculated by dividing erythemal dose (in mWatt/m<sup>2</sup>) by 25. The noon UV index for each day has been calculated as the average of available measurements for ±15 minutes around the exact local noon.</p> <p>For further information for file format and contents please see the <strong>readme.txt</strong> file.</p> <p>Reference</p> <p>CIE: CIE S007/E-1998 Erythema reference action spectrum and standard erythema dose, Color Research & Application, 24, 158-158, 10.1002/(sici)1520-6378(199904)24:2<158::aid-col11>3.0.co;2-4, 1999.</p>
Data used for the paper Andres Parra-Puerto, Kai Ling NG, Kieran Fahy, Angela E Goode, Mary P. Ryan, and Anthony Kucernak Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER and Corrosion Resistance in Acid and Alkaline Electrolytes ACS Catalysis, 2019 DOI: 10.1021/acscatal.9b03359
<p>This is an updated version of the spreadsheet which corrects the first version which had the wrong datasets provided for figure 2 c and d (ORR) </p> <p> </p> <p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Andres Parra-Puerto, Kai Ling NG, Kieran Fahy, Angela E Goode, Mary P. Ryan, and Anthony Kucernak</p> <p>Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER and Corrosion Resistance in Acid and Alkaline Electrolytes</p> <p>ACS Catalysis, 2019</p> <p>DOI: 10.1021/acscatal.9b03359Please cite the above reference if you wish to use this data</p>
[2019 QSM Reconstruction Challenge] Metrics and Submission Information
<p>This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available <a href="http://dx.doi.org/10.5281/zenodo.3687342">here</a> and <a href="http://dx.doi.org/10.5281/zenodo.3688703">here</a>.</p> <p>The package contains seven Comma-Separated Values (CSV) files and two PDF files:</p> <ul> <li><em>master_stage1_anonymized.csv</em>: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr1_anonymized.csv</em>: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr2_anonymized.csv</em>: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>submission_form_stage1.pdf</em>: PDF export of the online form used in stage 1;</li> <li><em>submission_form_stage2.pdf</em>: PDF export of the online form used in stage 2.</li> </ul> <p>For the manuscript, we analyzed these CSV files with scripts reported <a href="https://doi.org/10.5281/zenodo.4559540">here</a>.</p> <p>Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names:</p> <ul> <li><em>normalized rmse</em>: Whole-brain root-mean-squared error relative to ground truth;</li> <li><em>rmse_detrend_tissue</em>: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask;</li> <li><em>rmse_detrend_blood</em>: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask;</li> <li><em>rmse_detrend_DGM</em>: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate);</li> <li><em>DeviationFromLinearSlope</em>: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0;</li> <li><em>CalcStreak</em>: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth;</li> <li><em>DeviationFromCalcMoment</em>: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model);</li> <li><em>Submission Identifier</em>: Self-chosen unique identifier of the submission;</li> <li><em>Submission Identifier of the corresponding Stage 1 submission</em>: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1;</li> <li><em>Changes with respect to Stage 1 submission</em>: Self-reported information about modifications made to the algorithm for Stage 2;</li> <li><em>Number of submissions in Stage 2</em>: The number of solutions that were submitted to Stage 2 with a similar algorithm;</li> <li><em>Sim1/Sim2</em>: Filename of the submitted solutions for Stage 1;</li> <li><em>File name of the zip-file you are going to upload</em>: Filename of the file uploaded to Stage 2;</li> <li><em>Full name of the algorithm</em>: Self-reported full name of the algorithm used;</li> <li><em>Preferred Acronym</em>: Self-reported acronym of the algorithm used;</li> <li><em>Algorithm-type</em>: Self-reported type of algorithm used;</li> <li><em>Does your algorithm incorporate information derived from magnitude images?</em>: Self-reported Yes/No;</li> <li><em>Regularization terms</em>: Self-reported types of regularization terms involved;</li> <li><em>Did your algorithm use the provided frequency map or the four individual echo phase images?</em>: Self-reported information about involved magnitude information;</li> <li><em>Publication-ready description of the reconstruction technique</em>: Self-reported description of the algorithm;</li> <li><em>Publications that describe the algorithm</em>: Self-reported literature reference;</li> <li><em>Algorithm publicly available?</em>: Self-reported public availability of the algorithm;</li> <li><em>If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?</em>: Self-reported willingness to share the algorithm code with the public;</li> <li><em>Specific information about this solution</em>: Self-reported detailed information about the solution;</li> <li><em>Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge</em>: Self reported agreement with publication of submitted solution;</li> <li><em>Ground truth was not explicitly or implicitly incorporated into your algorithm or solution</em>: Self-reported confirmation that the ground truth was not incorporated in the solution.</li> </ul>
Impact of coronavirus disease 2019 (COVID-19) outbreak on radiology research: an Italian survey
<p>This article reports the results of a national survey, which had the purpose of understanding how COVID-19 pandemic has changed the scientific activity of Italian radiology researchers. A total of 327 Italian radiologists took part in the survey (mean age: 49±12 years). The majority of participants (245/327, 74.9%) was not working for or in agreement with a University and most of them were hospital staff radiologists (222/327, 67.9%). More than two-thirds of surveyed radiologists (231/327, 71%) was working in a public institution, which mostly was a general hospital (188/327, 57.5%); 86/327 (26.3%) participants declared to work in a university hospital. After national lockdown, the working-flow came back to normal in the vast majority of cases (285/327, 87.2%). Participants reported that a total of 462 radiological trials were recruiting patients at their institutions prior to COVID-19 outbreak, of which 332 (71.9%) were stopped during the emergency. On the other hand, 252 radiological trials have been started during the pandemic, of which 156 were non-COVID-19 trials (61.9%) and 96 were focused on COVID-19 patients (38.2%). Participants reported a significant increase of the number of hours per week spent for research purposes during national lockdown (mean 4.5±8.9 hours during lockdown vs. 3.3±6.8 hours before lockdown; p=.046), followed by a significant drop after lockdown (3.2±6.5 hours per week, p=.035). Notably, 60% of participants reported that they do not spend any time on research. During national lockdown, 15.6% of participants started new review articles and completed old papers, 14.1% completed old works, and 8.9% started new review articles. Ninety-six surveyed radiologists (29.3%) declared to have submitted at least one article during COVID-19 emergency. This study confirms the need to be prepared to future challenging scenarios like COVID-19 emergency in order to support radiology researchers, thereby allowing them to push forward their activity.</p>
Higher Education Research: A Compilation of Journals and Abstracts 2019
<p>Dataset to original publication:</p> <p>Hertwig, Alexandra (2020): Higher Education Research. A Compilation of Journals and Abstracts 2019. Kassel: INCHER-Kassel. DOI: 10.17170/kobra-202010292027.</p> <p>The Research Information Service (RIS) of INCHER-Kassel, Germany provides annual compilation of academic journals since 2013. This useful information tool for researchers also provides as a “side effect” an overview of the current topics of higher education research. The datasets allow for further evaluation of single or multiple volumes. For more information on original publications and available datasets please visit INCHER’s RIS websites.</p> <p>http://www.uni-kassel.de/einrichtungen/en/incher/risspecial-research-library/ris-documents.html</p>
Exploits: All registered in exploit-db, from January 2019 to October 2020
<p>This dataset contains all exploits registered on the <a href="https://www.exploit-db.com/">exploit-db website</a>, from 02 January 2019 to 06 November 2020. 2,665 exploits were found in this time range, and stored in CSV file. The CSV fields are as follows:</p> <ul> <li><strong>Id:</strong> exploit-db identifier.</li> <li><strong>Link Exploit:</strong> Link to download exploit code source.</li> <li><strong>App: </strong>Link to download the application in case the exploit is an application, and if it isn't an app the field is empty.</li> <li><strong>Date: </strong>Publication date on the website.</li> <li><strong>Verification:</strong> Exploit verfication by website. </li> <li><strong>Title:</strong> Exploit title.</li> <li><strong>Type:</strong> Exploit type. Could be Local, Remote and Webapp.</li> <li><strong>Platform:</strong> Platform on which the exploit is based on.</li> <li><strong>Author:</strong> Name of exploit author.</li> </ul>
Interpreting Lee (2019)'s five dimensions through time and linking them to an 'ideal' PhD graduate
<p>Figure 1: Variation of student potential based on Lee (2019)’s five dimensions relative with time</p> <p>Figure 2: Linking Anne Lee’s approach to the ‘ideal’ profile after earning a PhD</p>
Number of bovine animals tested by age group, reporting country and target group, 2019
<p>The tables contain the number of bovine animals tested by age group, reporting country and target group, 2019</p>
Monthly Global NDVI at 5 km based on MODIS and AVHRR products - 1982 to 2019
<p>Long-term monthly NDVI calculated through daily images of AVHRR (<a href="https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5?hl=en">NOAA/CDR/AVHRR/NDVI/V5</a>) and MOD09GA.006 (<a href="https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GA">MODIS/006/MOD09GA</a>), which were cloud screened and aggregated by percentile 90th on <a href="https://earthengine.google.com/">Google Earth Engine</a>-GEE. All the images were exported from GEE to R environment (MOD09GA data were downsampled to 5km using the average), where the follow procedures were executed on a pixel-basis:</p> <ol> <li><strong>Outlier removal</strong> based on a temporal moving window, used to identify and remove NDVI outlier values (<em>i.e.</em> greater or less than 1.5 standard deviation calculated on 60-months time window) </li> <li><strong>Smoothing and gap filling</strong> approach implemented by the <a href="https://greenbrown.r-forge.r-project.org/">greenbrown package</a> (TSGFssa method)</li> <li><strong>Temporal harmonization</strong> using MOD09GA NDVI as target variable to train a model <em>[modis.ndvi ~ poly(avhrr.ndvi, 2) + max.temp + min.temp]</em> able to predict the MODIS NDVI before 2000</li> </ol> <p>The final product is composed by 456 NDVI global images without gaps, which were harmonized, from 1982 to 1999, to match with the expected MODIS NDVI values from 2000 onwards. For each month, an auxiliary image indicates which pixels were:</p> <ul> <li><strong>Disregarded</strong>: value 0 (nodata)</li> <li><strong>Gapfilled</strong>: value 1</li> <li><strong>smoothed</strong>: value 2</li> </ul> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>Publication in preparation.</p> <p>All files were internally compressed using "COMPRESS=DEFLATE" creation option in GDAL.<br> File naming convention <em>(veg_ndvi_avhrr.mod09ga_p90_5km_s0..0cm_20000101..20000131_v1.0.tif)</em>:</p> <ul> <li>veg = Vegetation theme</li> <li>ndvi = Normalized Difference Vegetation Index </li> <li>avhrr.mod09ga = NOAA CDR AVHRR V5 and MOD09GA version 6 products</li> <li>p90 = Percentile 90th to aggregate the daily images</li> <li>5km = 5 km of spatial resolution</li> <li>s0..0cm = land surface as vertical reference</li> <li>20000101..20000131 = from 2000-01-01 to 2000-01-31 (time reference)</li> <li>v1.0 = version number</li> </ul>
Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019 - monthly maps
<p>Here you can find monthly maps referred in publication 'Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019' by EFSA.</p> <p>Figure legend for all figures:<br> Monthly observations and samples from wild birds on the EFSA list of target species for 2019 by NUTS3 region. The green colour scale represents the number of wild bird observations from the target species, as per data provided by the EuroBirdPortal project. The black dots represent the number of wild bird samples from target species tested within the countries' AI passive surveillance programmes. Wild bird samples reported at NUTS2 level are not shown on these maps.</p>
RAMP data subset, January 1 through May 31, 2019
<p>The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://ramp.montana.edu/</a>), consisting of data from 35 (out of 50) participating institutional repositories (IR) from the period of January 1 through May 31, 2019. This subset represents data analyzed for a pending publication. For a description of the data collection, processing, and output methods, please see the "methods" section below.</p> <p>The 'RAMP Primer,' a Jupyter Notebook consisting of Python code for combining monthly data and generating some aggregate statistics is available from <a href="https://github.com/imls-measuring-up/ramp-documentation.git">https://github.com/imls-measuring-up/ramp-documentation.git</a>. The linked repository also includes data documentation similar to that provided in the "methods" section below, as well as a file of IR index names useful for subsetting and filtering the data.</p> <p><b>Update: </b>Version two of this dataset was uploaded on January 14, 2020. Thanks to RAMP participants, the RAMP administrators discovered an error in the daily data harvest that resulted in incomplete page-click data for roughly 15 RAMP participating repositories. Only page-click data as described below were affected, and the corrsponding CSV files have been replaced with corrected data. The country-device data were not affected and have not been changed from version 1.</p>
Anopheles stephensi mosquitoes as vectors of Plasmodium vivax and falciparum, Horn of Africa, 2019
<p>An. stephensi, an efficient malaria vector in parts of Asia and Africa, was observed in 75.3% of water sources surveyed, and contributed to 80.9% of wild-caught Anopheles mosquitoes in Awash Sebat Kilo, Ethiopia. High susceptibility of these mosquitoes to Plasmodium falciparum and vivax infection presents a challenge for malaria control in the Horn of Africa. This study was conducted in Awash Sebat Kilo, Ethiopia, an area of perennial malaria transmission, from April to September 2019. We examined aquatic habitats for immature-stage <i>Anopheles </i>mosquitoes by standard dipping (10x/site) for 5 consecutive days. We assessed mosquito resting, feeding, and host-seeking behavior.</p>
FIGURE 8. Smithophis bicolor. A. holotype ZSIK 7030 in Description of a new species of Smithophis Giri et al. 2019 (Serpentes: Colubridae Natricinae) from Arunachal Pradesh, India
FIGURE 8. Smithophis bicolor. A. holotype ZSIK 7030, male, B. ZSI/ERS 2592, male, Shillong, Meghalaya and C. ZSI/ERS 105, male, South Garo hills, Meghalaya.
FIGURE 7 in Description of a new species of Smithophis Giri et al. 2019 (Serpentes: Colubridae Natricinae) from Arunachal Pradesh, India
FIGURE 7. Habitat of Smithophis arunachalensis sp. nov. at the type locality Koronu, Lower Dibang Valley District, Arunachal Pradesh, India.
FIGURE 6 in Description of a new species of Smithophis Giri et al. 2019 (Serpentes: Colubridae Natricinae) from Arunachal Pradesh, India
FIGURE 6. Smithophis arunachalensis sp. nov., A. unnumbered referred specimen in Roing Forest Department collection. Total length 613 mm. B. unnumbered (2) referred specimen in Miao museum collection.
FIGURE 4 in Description of a new species of Smithophis Giri et al. 2019 (Serpentes: Colubridae Natricinae) from Arunachal Pradesh, India
FIGURE 4. Smithophis arunachalensis sp. nov., paratypes in dorsal views A. BMNH 1935.10.12.10 (insert image shows the detached anterior part of the head) and B. (ZSIK 23875). Scale bar 10mm.
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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)
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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.