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12,237 results for “2019”
Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science
<p>Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science</p> <p> </p> <ul> <li>info.txt contains a general description of how the data was processed and the main results.</li> <li>pipeline.tar contains the data pipeline used to process the e-MERLIN observations</li> <li>imaging.py is the script used to produce the final images</li> <li>CY6213_images.tar contain the final images of the target source (not corrected by calibration factor, described in the imaging script).</li> </ul> <p> </p>
Supplementary data and scripts for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes"
<p>Supplementary data for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes". The data set consists of two folders: “Bali-Phy” and “RandomPermutationTests”. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</p>
2019 Ziraat Bankasının Konut Kredisi Faiz Oranı
<p>Ziraat Bankası, 2019 yılı içerisinde büyük atılımlar meydana getirdi. Konut kredisi konusunda daha önce görülmemiş faiz oranlarına imza atan Ziraat Bankası, %0,98 faiz oranı ile birçok kişiyi ev sahibi yaptı. Kredi faiz oranlarında meydana gelen değişiklik, Ziraat Bankası kampanyalarına da yansımız durumda. Aşağıda yer alan yazımız içerisinde Ziraat Bankası Konut Kredisi Faiz ve vade oranları hakkında bilgiler vermeye çalıştık.</p> <p><a href="https://birkredi.net/"><strong>BirKredi.net</strong></a></p>
Ziraat Bankası 2019 Kredi Fırsatları
<p>2019 yılını rahat bir şekilde geçirmek isteyen kişiler için güzel bir haberimiz var. Ziraat Bankası, güncel faiz oranlarına sahip yeni ihtiyaç kredisi, konut kredisi ve taşıt kredisi hakkında duyurular paylaştı. Birçok kişi, özellikle konut kredisi ve ihtiyaç kredisi almak istemeleri durumunda uygun faiz oranları ile karşılaşamamaklarından şikayet etmektedir. Ziraat Bankası Kredi Fırsatları, bu bağlamda tüketicilerin ihtiyacını karşılamak için oldukça birebirdir. Bu konu hakkındaki detayları aşağıda yer alan internet sitemiz üzerinden dile getirdik.</p> <p><a href="https://birkredi.net/"><strong>BirKredi.net</strong></a></p>
Supplementary data and scripts for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and de novo emergence of E5 oncogenes"
<p>Supplementary data for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and <em>de novo</em> emergence of E5 oncogenes". The data set consists of three folders: “Alignments”, “Bali-Phy” and “RandomPermutationTests”. The “Alignments” folder contains the different alignments used for phylogenetic tree construction and comparison. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</p>
Dataset associated with Schyns & Vanham (2019) "The water footprint of wood for energy consumed in the European Union"
<p>Input and output datasets related to the paper Schyns & Vanham (2019) The water footprint of wood for energy consumed in the European Union. <em>Water</em>, 11(2): 206.</p>
AOSLO Single Cell Blood Flow - Raw Data (eLife paper: Joseph et al. 2019)
<p>Raw AOSLO videos of eLife paper Joseph et al. 2019 - 'Imaging single-cell blood flow in the smallest to largest vessels in the living retina'. <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.7554_eLife.45077&d=DwMFaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=EdsTL7DuEvOHun7eVBmBd9sxUuPhDEmdFDf0tlkKUO4&m=56MyNrE_d-v6PSU7Go9NePVOrIpAvWHBaC8wVvgs3_k&s=G7SkWT-fSV9d3SJmpWEUUGp2a6mGotbG-uAwNZftpIo&e=">https://doi.org/10.7554/eLife.45077</a> . For additional data or questions, please contact author Aby Joseph (aby.joseph@rochester.edu, dreamworks1991@gmail.com)</p>
Landscape and habitat data for Tetramorium ant species from Cordonnier et al. 2019 Landscape Ecology
<p>This README accompanies the file "data_Cordonnier_LandEcol.txt"</p> <p>Associated publication : </p> <p>Multi-scale impacts of urbanization on species distribution within the genus <br> <em>Tetramorium </em>- Landscape Ecology<br> M. Cordonnier, C. Gibert, A. Bellec, B. Kaufmann, G. Escarguel</p> <p> <br> ********************************** CONTENTS ***********************************<br> The data are in table form with TABs as variables field delimiters so they can <br> be readily imported in any statistical package or spreadsheet program. Please, <br> contact me if you need the file formatted otherwise. </p> <p>This file includes a description of the variables.</p> <p>The individuals described in this file were identified to species and analyzed for climate variables in</p> <p>Cordonnier, M., Bellec, A., Dumet, A., Escarguel, G., & Kaufmann, B. (2019). <br> Range limits in sympatric cryptic species: a case study in Tetramorium pavement <br> ants (Hymenoptera: Formicidae) across a biogeographical boundary. Insect <br> Conservation and Diversity, 12(2), 109-120.<br> </p> <p>*******************************************************************************<br> Variable names and descriptions</p> <p>ID Sample name<br> X Longitude in WGS 84 (World Geodetic System 1984) decimal degrees rounded to 5 decimal places<br> Y Latitude in WGS 84 (World Geodetic System 1984) decimal degrees rounded to 5 decimal places<br> SZ Name of the sampling area sensu Cordonnier et al. (2019)<br> SP Species identification based on mtDNA COI gene<br> PI10 Percentage of impervious surfaces within a 10 m buffer around the sample<br> PI30 Percentage of impervious surfaces within a 30 m buffer around the sample<br> PI500 Percentage of impervious surfaces within a 500 m buffer around the sample<br> MH1 Presence / absence of full soil with vegetation <br> MH2 Presence / absence of pavement <br> MH3 Presence / absence of unstabilized material (sand. gravel. compacted soil <br> with pebbles or small rocks) <br> MH4 Presence / absence of wood or root <br> MH5 Presence / absence of litter (woodchips or dead leaves) <br> MH6 Presence / absence of curb <br> MH7 Presence / absence of building <br> MH8 Presence / absence of feature (p.ex. lamp post. elec. pole. large rock) <br> MH9 Presence / absence of ditch or strong slope</p> <p>********************************* CONTACT **********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com</p> <p>*******************************************************************************<br> </p>
Data from: Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: A case study with the bat Plecotus auritus. PLoS ONE 14(7)
<p><strong>Supporting data for: </strong>Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: a case study with the bat <em>Plecotus auritus</em>. PLoS ONE 14(7): e0219135. https://doi.org/10.1371/journal.pone.0219135</p> <p>Raw DNA sequences of prey of <em>Plecotus auritus</em>. Sampling information separated by semicolums as folows:</p> <p>>Sequence number; Colony; Date; Sample name; Dataset; Is the sequence attributable to the diet or not (Diet); Read numbers (Size); DNA sequence</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with a prototype based on Arduino UNO. Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Aula I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a prototype based on Arduino UNO, have been investigated:</p> <ul> <li>Temperature and Relative Humidity of the internal environment and Temperature and Relative Humidity of contact on the masonry (with two sensors DHT22 AM2302);</li> <li>Acceleration along the three spatial axes (X, Y and Z), Pitch Index and Roll Index (with Gy 521 sensor);</li> <li>Surface pressure for the movement of a lesion (with FlexiForxe sensor).</li> </ul> <p>The data produced by the sensors were acquired and sent to the computer which automatically saved them every 10 seconds. The dataset is composed of the data obtained from 2017 to 2019 and separated by month in 11 sheets. In total about 40 million values were recorded, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Labsphere SRS-99-020 - Labsphere (2019)
<p><strong>Description of Calibrated Items</strong></p> <p>One white diffuse reflectance sample, model SRS-99-020 Reflectance Standard, serial number 99AA04-0119-1879</p> <p><strong>Calibration</strong></p> <p>8<strong>°</strong>/Hemispherical (8/h) Spectral Reflectance Calibration over the range 250 – 2500nm, reported at 50nm intervals</p> <p><strong>Description of Calibration</strong></p> <p>The calibration items are measured using a Perkin Elmer Lambda 900 or Lambda 950 dual beam spectrophotometer with LambdaX 150mm InGaAs RSA integrating sphere reflectance accessories which perform the 8/h reflectance measurements.</p>
Project STORM: monitoring the masonry of Michelangelo's Cloister, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2018 - 2019
<p>This dataset for monitoring the masonry of Michelangelo's Cloister at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0 and S3);</li> <li>Temperature of masonry (sensors S1, S2 and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2018 to May 2019 and separated by month in 8 sheets. In total more than 2 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
STORM Project: monitoring environment conditions at Baths of Diocletian site (Rome, Italy). Dataset 2018 - 2019
<p>This dataset was created by the Engineering Ingegneria Informatica S.p.A. through a set of prototypes based on Libelium Waspmote for collecting the following parameters: </p> <ul> <li>Climate parameters (Temperature, Relative Humidity, Barometric Pressure, Luminosity, Wind direction/speed and Rainfull) using a Libelim PlugAndSense Agricolture Pro;</li> <li>Environmental Parameters (Monoxide Carbon, Oxigen, Air Polluction, Volatile Organic Compounds VOC, Carbon Dioxide, Nitric Dioxide , Hydrogen Sulfide, Sulfure Dioxide and Particle Matter PM 1, 2.5 and 10) using two nodes: PlugAndSense Smart Cities Pro and Waspmote with gases sensor board;</li> <li>Acoustic Noise Sensor and Vibrations with accelerometer, using a prototype based on Libelium Waspmote.</li> </ul> <p>The data produced by the sensors were acquired and sent to the Meshlium (mini-pc linux based) which automatically saved and sent to the STORM Platform. The dataset is composed of the data obtained from February 2018 to March 2019.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Hall I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0, S2 and S3);</li> <li>Temperature of masonry (sensors S1, and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2017 to May 2019 and separated by month in 14 sheets. In total more than 4 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Artifact for the ESEC/FSE 2019 Paper: Effects of Explicit Feature Traceability on Program Comprehension
<p>This is the artifact for our ESEC/FSE 2019 paper "Effects of Explicit Feature Traceability on Program Comprehension", providing 1) the experiment as reusable source code and 2) the anonymized results of our experiment. For more details on how to use the source code and interpret the data, please refer to the readme file.</p>
Radiocarbon Palaeolithic Europe Database v25 June 2019 extract
<p>The database, version 25 (first version was available in2002), contains now <strong>13093</strong> site forms, (most of them with their geographical coordinates), comprising <strong>15500 </strong>radiometric data: Conv. <sup>14</sup>C and AMS <sup>14</sup>C (<strong>12500 </strong>items), TL (<strong>1023</strong> items), OSL (<strong>627</strong> items), ESR, Th/U and AAR (<strong>2015</strong> items) from the European (Russian Siberia included) Lower, Middle and Upper Palaeolithic. All <sup>14</sup>C dates are conventional dates BP. 263 new sites are incorporated and 447 sites have a corrected or an updated content. The latest version is available at https://ees.kuleuven.be/en/geography/projects/14c-palaeolithic/.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
MA Digital Cultures Dissertation Dataset 2019
<p>This project contains the data, code, and files for my Master dissertation for the MA Digital Cultures at UCC 2018/19. I complied a data set from the Union List of Artist Names (ULAN) online repository (http://www.getty.edu/research/tools/vocabularies/ulan/) with SSMS and created a data visualization with Gephi. The thesis can be found at https://ckdigitalarts.com/dissertation-documentation/.</p>
Programas académicos en el Ranking de Shanghai 2019 de Universidades Colombianas
<p>Ranking de Shanghai 2019</p> <p>Programas académicos en el Ranking de Shanghai 2019 de Universidades Colombianas</p> <p>Fuente: http://www.shanghairanking.com/es/</p>
Child mortality dataset (from the UN Inter-agency Group for Child Mortality Estimation database). June 2019
<p>This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (<a href="https://childmortality.org/data">https://childmortality.org/data</a>, downloaded June 2019).</p> <p>It includes:</p> <p><strong>Reference area: </strong>name of the country</p> <p><strong>Indicator:</strong> child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)</p> <p><strong>Sex: </strong>sex of the child (male, female and total)</p> <p><strong>Series name:</strong> name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]</p> <p><strong>Series year: </strong>year of survey/census/VR series</p> <p><strong>Observation value: </strong>value of indicator from survey/census/VR</p> <p><strong>Observation status:</strong> indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]</p> <p><strong>Series Category:</strong> category of survey/census/VR, and can be:</p> <ul> <li>DHS [Demographic and Health Survey]</li> <li>MIS [Malaria Indicator Survey]</li> <li>AIS [AIDS Indicator Survey]</li> <li>Interim DHS</li> <li>Special DHS</li> <li>NDHS [National DHS]</li> <li>WFS [World Fertility Survey]</li> <li>MICS [Multiple Indicator Cluster Survey]</li> <li>NMICS [National MICS]</li> <li>RHS [Reproductive Health Survey]</li> <li>PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]</li> <li>LSMS [Living Standard Measurement Survey]</li> <li>Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]</li> <li>Census</li> <li>VR [Vital Registration]</li> <li>SVR [Sample Vital Registration]</li> <li>Others [e.g. Life Tables]</li> </ul> <p><strong>Series type: </strong>the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)</p> <p><strong>Standard error: </strong>sampling standard error of the observation value</p> <p><strong>Series method: </strong>data collection method, and can be:</p> <ul> <li>Survey/census with Full Birth Histories</li> <li>Survey/census with Summary Birth Histories</li> <li>Survey/census with Household death</li> <li>Vital Registration</li> <li>Other</li> </ul> <p><strong>Lower and upper bound:</strong> the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).</p> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)</em></p>
ScienceDex guides
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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)
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.