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Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms
<p>Dataset and script used for the publication <em>Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms, </em>doi (to be determined).</p>
Figure 2a - Rossi et al. Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells RRL Solar (2022)
<p>The Data set is related to the<strong> figure 2a</strong> of the paper </p> <p>Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells by Daniele Rossi,Karen Forberich,Fabio Matteocci,Matthias Auf der Maur,Hans-Joachim Egelhaaf,Christoph J. Brabec,Aldo Di Carlo, Rapid Research Letter (2022) https://doi.org/10.1002/solr.202200242</p>
Barn owl diet and prey fluctuations in the Jura mountains, eastern France
<p>Based on pellet collection, the diet of the Barn Owl (<em>Tyto alba</em>) was studied over a 8-year period in the Jura mountains, eastern France, during two population surges of its main prey (common vole, <em>Microtus arvalis </em>and montane water vole, <em>Arvicola amphibius</em>); Small mammals were sampled by trapping and index methods. Results have been published in the Canadian Journal of Zoology (<a href="http://doi.org/10.1139/z10-011">Bernard et al. 2010</a>). Dominique Michelat collected Barn Owl pellets and identified prey items, Pierre Delattre, Jean-Pierre Quéré Jean-Pierre Damange and Patrick Giraudoux sampled small mammals. Patrick Giraudoux managed the data.</p> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a>is the file of the raw trapping results for small mammals (instant abundance index i<sub>t</sub> in the article)</p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a> is a file with:</p> <ul> <li>the small mammal density computed by season (d<sub>t</sub> in the article, rough estimate of densities in number of individuals per ha for <em>Apodemus spp.</em>, <em>Myodes glareolus</em>, <em>Microtus arvalis</em>, or weighted interpolated i<sub>t</sub> for the other species)</li> <li>the ratios of each category of prey items on the total number of items collected in the church tower of three sites, <a href="https://www.openstreetmap.org/#map=16/46.9536/6.1189">Levier</a>, <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9321%2C6.1666#map=16/46.9321/6.1666">Chapelle d'Huin</a> and <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9382%2C6.1976#map=16/46.9382/6.1976">Le Souillot</a>.</li> </ul> <p>For details see the material and methods of the article.</p> <p><strong>FILE DESCRIPTION</strong></p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a></p> <ul> <li>date, year and season: year on two digits, then P, E, A, H respectively for<em> Printemps</em> (Spring), <em>Été</em> (Summer), <em>Automne</em> (Autumn), <em>Hiver</em> (Winter)</li> <li>at_t, abundance index of <em>Arvicola amphibius</em> (ex A.<em> terrestris</em>)</li> <li>ap_t, rough density estimate of <em>Apodemus sp.</em></li> <li>cg_t, rough density estimate of <em>Myodes glareolus</em></li> <li>ma_t, rough density estimate of <em>Microtus arvalis</em></li> <li>sa_t, relative abundance of <em>Sorex spp.</em></li> <li>n_l, number of prey items at Levier</li> <li>ma_p_l, ratio of <em>M. arvalis prey</em> items at Levier</li> <li>at_p_l, ratio of <em>A. amphibius</em> prey items at Levier</li> <li>apcg_p_l, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Levier</li> <li>sa_p_l, ratio of <em>Sorex spp.</em> prey items at Levier</li> <li>au_p_l, ratio of other prey items at Levier</li> <li>n_ch, number of prey items at Chapelle d'Huin</li> <li>map_ch, ratio of <em>M. arvalis prey</em> items at Chapelle d'Huin</li> <li>at_p_ch, ratio of <em>A. amphibius</em> prey items at Chapelle d'Huin</li> <li>apcg_p_ch, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Chapelle d'Huin</li> <li>sa_p_ch, ratio of <em>Sorex spp. </em>prey items at Chapelle d'Huin</li> <li>au_p_ch, ratio of other prey items at Chapelle d'Huin</li> <li>n_ls, number of prey items at Le Souillot</li> <li>ma_p_ls, ratio of <em>M. arvalis prey</em> items at Le Souillot</li> <li>at_p_ls, ratio of <em>A. amphibius</em> prey items at Le souillot</li> <li>apcg_p_ls, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Le Souillot</li> <li>sa_p_ls, ratio of <em>Sorex spp.</em> prey items at Le Souillot</li> <li>au_p_ls, ratio of other prey items at Le Souillot</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a></p> <ul> <li>date, trapping period: digit 1-2, year; digit 3-4, month. Example: 8704 = April 1987.</li> <li>n_traplines_f, number of traplines in forest</li> <li>ap_f, average number of <em>Apodemus spp</em>. captured in forest</li> <li>cg_f, average number of <em>Myodes glareolus</em> captured in forest</li> <li>sa_f, average number of <em>Sorex spp</em>. captured in forest</li> <li>n_traplines_hfb, number of traplines in hedges and forest borders</li> <li>ap_hfb, average number of <em>Apodemus spp</em>. captured in hedges and forest borders</li> <li>cg_hfb, average number of <em>Myodes glareolus</em> captured in hedges and forest borders</li> <li>sa_hfb, average number of <em>Sorex spp.</em> captured in hedges and forest borders</li> <li>n_traplines_g, number of traplines in grassland</li> <li>ma_g, average number of Microtus arvalis captured in grassland</li> <li>sa_g, average number of <em>Sorex spp.</em> captured in grassland</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/SmallMammalSamplingArea.kml?download=1">SmallMammalSamplingArea.kml</a> kml file locating the small mammal sampling area<br> </p>
Benchmarking on Microservices Configurations and the Impact on the Performance in Cloud Native Environments
<p><strong>The peer reviewed publication for this dataset has been published in LCN 2022, 47th Annual IEEE Conference on Local Computer Networks. Please cite this paper when referring to the dataset: https://www.eurecom.fr/publication/6971.</strong></p> <p>Cloud-native and containerization have changed the way to develop and deploy applications. Cloud-native rethinks the application architecture by embracing a microservice approach, where each microservice is packaged into containers to run in a centralized or an edge cloud. When deploying the container running the micro-service, the tenant has to specify the needed computing resources to run their workload in terms of the amount of CPU and memory limit. However, it is not straightforward for a tenant to know in advance the computing amount that allows running the microservice optimally. This will have an impact not only on the service performances but also on the infrastructure provider, particularly if the resource overprovisioning approach is used. To overcome this issue, we conduct an experimental study aiming to detect if a tenant's configuration allows running its service optimally. We run several experiments on a cloud-native platform, using different types of applications under different resource configurations. The obtained results are presented in the accepted IEEE LCN paper (https://www.eurecom.fr/publication/6971) and are shared in this dataset.</p> <p>The datasets are collected for 3 types of applications: Web servers written in python and Golang, RabbitMQ data broker and the OpenAirInterface 5G Core network function AMF (Access and Mobility Management Function).</p> <p><br> </p> <p><strong>Web Servers:</strong></p> <p><strong>files: </strong>golang-web-server-performance.csv, python-web-server-performance.csv</p> <p>We used Golang and Python-based web servers for the test. Each request to the web server returns a video of a size 43 MB. For testing we used ApacheBench, a command-line program used for benchmarking HTTP web servers. ApacheBench allows parallel requests from multiple clients. For each web server instance we send a number of requests ranging from 100 to 1000 and a concurrency level between 1 and 100, representing the number of parallel clients performing the requests.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of requests sent to the container.</p> <p><strong>c:</strong> the concurrency level in the requests.</p> <p><strong>lat50:</strong> the least response time for the best 50% requests in microseconds.</p> <p><strong>lat66:</strong> the least response time for the best 66% requests in microseconds.</p> <p><strong>lat75:</strong> the least response time for the best 75% requests in microseconds.</p> <p><strong>lat80:</strong> the least response time for the best 80% requests in microseconds.</p> <p><strong>lat90:</strong> the least response time for the best 90% requests in microseconds.</p> <p><strong>lat95:</strong> the least response time for the best 95% requests in microseconds.</p> <p><strong>lat98:</strong> the least response time for the best 98% requests in microseconds.</p> <p><strong>lat99:</strong> the least response time for the best 99% requests in microseconds.</p> <p><strong>lat100:</strong> the least response time in microseconds.</p> <p> </p> <p><strong>5G Core network’s AMF:</strong></p> <p><strong>file: </strong>amf-performance.csv</p> <p>For testing we use my5G-RANTester, a tool for emulating control and data planes of the UE and gNB (5G base station). The number of simultaneous registration requests that are sent to each instance of the AMF varies between 10 and 400.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of parallel registration requests sent to the AMF.</p> <p><strong>mean:</strong> the mean registration time for all the registration requests in microseconds.</p> <p><strong>lat50:</strong> the median registration time for registration requests in microseconds.</p> <p><strong>lat75: </strong>the least registration time for the best 75% registration requests in microseconds.</p> <p><strong>lat80:</strong> the least registration time for the best 80% registration requests in microseconds.</p> <p><strong>lat90:</strong> the least registration time for the best 90% registration requests in microseconds.</p> <p><strong>lat95:</strong> the least registration time for the best 95% registration requests in microseconds.</p> <p><strong>lat98:</strong> the least registration time for the best 98% registration requests in microseconds.</p> <p><strong>lat99:</strong> the least registration time for the best 99% registration requests in microseconds.</p> <p><strong>lat100:</strong> the least registration time in microseconds.</p> <p> </p> <p><strong>RabbitMQ data broker:</strong></p> <p><strong>file: </strong>rabbitmq-performance.csv</p> <p>For testing we used RabbitMQ PerfTest which is a throughput testing tool that simulates basic workloads and provides the throughput and the time that a message takes to be consumed by a consumer. For each deployed RabbitMQ server we used a number of producers and consumers that ranges from 50 to 500. Each producer sends messages to the broker with a rate of 100 messages per second for a period of time of 90 seconds.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of producers sending messages to the RabbitMQ server.</p> <p><strong>Min:</strong> the minimum consumption time for the producer messages.</p> <p><strong>lat50:</strong> the median consumption time for the producer messages.</p> <p><strong>lat75:</strong> the least consumption time for the best 75% messages in microseconds.</p> <p><strong>lat95:</strong> the least consumption time for the best 95% messages in microseconds.</p> <p><strong>lat99:</strong> the least consumption time for the best 99% messages in microseconds.</p>
On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data)
<p>This dataset includes the data to replicate the study for the paper <em>On-the-Fly Syntax Highlighting Using Neural Networks</em>. It can be reused for future research in the field. We also include the detailed results obtained by executing our approach.</p> <p>HLNN-Resources.zip includes the input data already formatted to be directly used with the shared source code.</p> <p>The paper is published in the proceeding of the <em>30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)</em>.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- DECEMBER 2020)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the sixth dataset of the project, corresponding to December 2020, collected nine months after the lockdown began in Mexico. Data collection was performed from November 27 to December 11, 2020.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19 - APRIL 2022)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the eleventh dataset of the project, corresponding to April 2022, collected 24 months after the lockdown began in Mexico. Data collection was performed from March 17 to May 2, 2022.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- MAY 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the ninth dataset of the project, corresponding to May 2021, collected thirteen months after the lockdown began in Mexico. Data collection was performed from May 21 to Jun 17, 2021.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- OCTOBER 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the tenth dataset of the project, corresponding to October 2021, collected 19 months after the lockdown began in Mexico. Data collection was performed from October 20 to November 13, 2021.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19 - MARCH 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the sixth dataset of the project, corresponding to March 2021, collected one year after the lockdown began in Mexico. Data collection was performed from February 26 to March 27, 2021.</p>
N Italy Zooarchaeological Dataset
<p>This dataset contains zooarchaeological data relevant to cattle, sheep/goat, and pigs from lowland northern Italy, and an associated R analysis and visualisation script. This dataset supports the journal article:<br>A. Trentacoste, A. Nieto-Espinet, S. Guimarães Chiarelli, and Valenzuela-Lamas. (2023). Systems change: Investigating climatic and environmental impacts on livestock production in lowland Italy between the Bronze Age and Late Antiquity (c. 1700 BC - AD 700). <em>Quaternary International </em>662–663:26-36.<em> </em><a href="https://doi.org/10.1016/j.quaint.2022.11.005">https://doi.org/10.1016/j.quaint.2022.11.005</a></p> <p>The majority of the data were collected under the auspices of the ERC-Starting Grant ZooMWest – Zooarchaeology and Mobility in the Western Mediterranean: Husbandry production from the Late Bronze Age to the Late Antiquity (award number 716298), funded by the European Research Council Agency (ERCEA) under the direction of Sílvia Valenzuela-Lamas (2017–2022). This work built on previous data collection undertaken for Trentacoste's (2014) PhD thesis. The dataset was also expanded with support from a Gerda Henkel Stifling Scholarship (AZ 44/F/20) awarded to A. Trentacoste.</p> <p>The chronological timespan of the dataset is between the Middle Bronze Age and Late Antiquity (c. 1700 BC - AD 700). For details on the methodology underlying the creation of the dataset see Trentacoste et al. (2018) and Trentacoste et al. (2021).</p> <p>Zooarchaeological data were collected from published sources (see references file), with the exception of some data for the sites of Spina, Vidulis and Aquileia. Metadata for these sites were available in the published literature, but individual data were collected from the archive papers of Italian zooarchaeologist Alfredo Reidel (1925–2014). We are grateful to Francesco Boschin (Università degli Studi di Siena) for access to the archive.</p> <p>The dataset includes:</p> <ul> <li>Raw biometric data for post-cranial bones for cattle, sheep/goat, pigs, and wild boar on a specimen level. Measurement abbreviations follow Von den Driesch (1976) and Davis (1996; only humerus HT and HTC). File: NItaly_Livestock_Metric_Data.csv</li> <li>NISP (Number of Identified Specimens) data for site phases with over 100 identified cattle/sheep/goat/pig specimens. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv</li> <li>Location coordinates and information on environmental context: mean, min, and/or max values for a 5km radius for sites with NISP data. Elevation information was taken from Shuttle Radar Topography Mission (SRTM) terrain data from the U.S. Geological Survey (90m resolution; Jarvis et al., 2008). Precipitation data were from World Clim 2.1 (average monthly climate data for 1970–2000, 30 arc-sec; Fick and Hijmans, 2017), and solar irradiance data were from Global Solar Atlas 2.0 (9 arc-sec; developed and operated by Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP); https://globalsolaratlas.info). Soil characteristics were derived from LUCAS topsoil data (500m; Ballabio et al., 2016): clay, silt, sand, and coarse fragments content (%), bulk density, and Available Water Capacity (AWC) for the topsoil fine earth fraction. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv</li> <li>Bibliographic information for each assemblage with indication of whether NISP and/or biometric data was used. File: NItaly_Livestock_References.csv</li> <li>R script file for the analyses and visualisations in the above journal article. File: NItaly_Livestock_SysChange_Script.R</li> </ul> <p>If you re-use this data, please cite this dataset and the associated journal articles as relevant.</p>
Land cover in the Purapel fluvial catchment
<p>The dataset contains 6 Land Cover maps at a 30m/pixel spatial resolution for the Purapel river catchment located in South-Central Chile. They were generated for the summer periods of 1986, 2000, 2005, 2010, 2015 and 2017.</p> <p>Maps of 1986-2015 were generated using atmospherically corrected Landsat CDR Scenes (<em>images courtesy of the U.S. Geological Survey</em>) including VNIR and SWIR bands from the TM5, ETM+ and OLI sensors and vegetation indices as auxiliary bands to highlight phenological differences among covers. Specifically the Normalized Difference Vegetation Index (NDVI) (Rouse et al,. 1974), the Green NDVI (Gitelson et al., 1996) and NDVI winter-summer Difference Index (ΔNDVI).</p> <p>Training and validation points were defined from field trips to the area in 2014-2015, various mid resolution satellite imagery sources and high-resolution Google Earth imagery (Map data ©2015 Google) when available. A topographic correction was applied using the C-Correction method (Teillet et al 1982), as proposed by Hantson and Chuvieco (2011), and the SRTM v3 DEM to account for the effect of local relief in the scene’s lighting.</p> <p>Accuracy assessment resulted in Overall Accuracy (OA), ranging from 82% to 92% (table 1).</p> <p>Table 1. Overall Accuracies for Land Cover maps from 1986 to 2017</p> <table> <tbody> <tr> <td> <p>Year</p> </td> <td> <p>OA</p> </td> </tr> <tr> <td> <p>1986</p> </td> <td> <p>89.7</p> </td> </tr> <tr> <td> <p>2000</p> </td> <td> <p>92.2</p> </td> </tr> <tr> <td> <p>2005</p> </td> <td> <p>91.5</p> </td> </tr> <tr> <td> <p>2010</p> </td> <td> <p>89.8</p> </td> </tr> <tr> <td> <p>2015</p> </td> <td> <p>82.7</p> </td> </tr> <tr> <td> <p>2017</p> </td> <td> <p>0.98</p> </td> </tr> </tbody> </table> <p> </p> <p>The 2017 map was generated using Random Forest classifier using several SI from Sentinel 2, Sentinel 1 C-band radar data (imagery from European Space Agency courtesy of the U.S. Geological Survey) and hydro-geomorphic indices obtained from 2009 LiDAR DTM data (Tolorza et al., 2022). Ninety polygons were used for training and thirty polygons and the classification of Zhao et al. (2016) were used for validation, obtaining an overall accuracy 0.98 (table 1).</p> <p> </p> <p>The 7 land cover classes defined following these codes and land use / covers:</p> <ul> <li>0 = Unclassified</li> <li>1 = Others (mainly crops and natural prairies in riverbeds)</li> <li>2 = Native Forest (mainly secondary-growth deciduous Nothofagus sp. Stands)</li> <li>3 = Shrubland (highly degraded formation of xerophytic and sclerophyllous shrubs such as <em>Acacia</em> <em>caven</em>, <em>Quillaja</em> <em>saponaria</em> and <em>Lithraea</em> <em>caustica</em>, among others).</li> <li>4 =Tree Plantations (industrial monocultures of <em>Pinus</em> <em>radiata</em> and <em>Eucalyptus</em> spp. of various age and development)</li> <li>5 = Seasonal grassland (annual pastures which wither in summer and urban areas)</li> <li>6 = Clear cuts (bare lands within industrial forestry surface)</li> </ul> <p>Codes 7 to 9 are specific to 2015 y 2017 because of the occurrence of two large (>5,000 hectares) fire events, and represent different Fire Severity levels based on the dNBR index (López and Caselles, 1991) according to Key and Benson (2006). They represent the following cases:</p> <ul> <li>7= Low Severity fire</li> <li>8 = Moderate severity fire</li> <li>9 = High severity fire</li> </ul> <p> </p> <p>Sources:</p> <p>Hantson, S. Chuvieco, E. 2011. Evaluation of different topographic correction methods for Landsat imagery. International Journal of Applied Earth Observation and Geoinformation 13:691-700.</p> <p>Rouse, J., R. Haas, J. Schell, and D. Deering. 1974. Monitoring vegetation systems in the Great Plains with erts. Third Earth Resources Technology Satellite-1 Symposium Volume I: Technical Presentations. NASA SP-351, compiled and edited by S.C. Freden, E.P. Mercanti, and M.A. Becker. Washington, DC: National Aeronautics and Space Administration</p> <p>Gitelson, A., Y. Kaufman, and M. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3):289-298.</p> <p>Teillet, P., B. Guindon, and D. Goodenough. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing 8:84–106.</p> <p>Key, C. Benson, N. 2006. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. Pp: 1-51.</p> <p>López, MJ. Caselles, V. 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International (1) 1991: 31- 37.</p> <p>Tolorza, V. Poblete-Caballero, D. Banda, D. Little, C. Galleguillos, M. 2022. An operational method for mapping the composition of post-fire litter. Remote Sensing letters (13) 2022: 511-521. 10.1080/2150704X.2022.2040752</p> <p> Zhao, Y. D. Feng, L. Yu, X. Wang, Y. Chen, Y. Bai, H. Hernández, et al. 2016. Detailed Dynamic Land Cover Mapping of Chile: Accuracy Improvement by Integrating Multi-temporal Data. Remote Sensing of Environment 183: 170–185. 10.1016/j.rse.2016.05.016.</p>
IMAJINE Survey
<p>IMAJINE’s survey explores citizens’ perceptions, attitudes, and policy preferences concerning spatial inequalities and the cohesion policies that can be adopted (at regional, national, and European level) to reduce such disparities; people’s support for territorial (regional) autonomy; public’s opinions about the relationship between migration flows and inequalities.</p> <p>The survey was conducted between 22 September and 15 October 2020 in eight European countries: France, Germany, Italy, the Netherlands, Poland, Romania, Spain, and the UK. The sample includes individuals aged 18 years and older, currently resident in the eight surveyed countries. In line with IMAJINE project’s research framework, the survey aimed at offering a view of the European opinions at regional levels. Accordingly, the sample size was defined at NUTS 2 (Italy, the Netherlands, Poland, Romania, and Spain) or NUTS 1 (France, Germany, and the UK) level, with about 170 respondents for each subnational unit, although the target could not be achieved in some smaller regions. The sample size was increased in Romania, due to the low number of NUTS 2 regions, to get a number of interviews at the national level comparable to the other countries.</p> <p> </p>
GIXD data of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), processed q-space maps
<p>This dataset contains grazing incidence x-ray diffraction (GIXD) maps projected in q-space and polar projection. The underlying raw data is published in <a href="https://doi.org/10.5281/zenodo.6683616">10.5281/zenodo.6683616</a> and processed with <a href="https://doi.org/10.5281/zenodo.6683658">10.5281/zenodo.6683658</a>. This data describes a time series of diffraction images acquired with 10 Hz.</p> <p> </p> <p>Parameters of the provided data:</p> <ul> <li> <p>Q-space-maps</p> </li> </ul> <p> </p> <ul> <li> <ul> <li> <p>Horizontal axis (Q<sub>xy</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (Q<sub>z</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Resolution: 1350x1350 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> <li> <p>Polar data</p> <ul> <li> <p>Horizontal axis (||<strong>q</strong>||) range: (0, 4.53) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (ф) range: (0, 90) deg</p> </li> <li> <p>Resolution: 512x1024 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> </ul>
In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data
<p>The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s.</p> <p> </p> <p> </p> <p>The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters:</p> <p> </p> <ul> <li> <p>X-ray wavelength: 0.6888 nm</p> </li> <li> <p>Sample detector distance: 809 mm</p> </li> <li> <p>Incidence angle: 0.5 deg.</p> </li> <li> <p>Detector model: XRD 1621 CN3 EHS</p> </li> <li> <p>Acquisition rate : 10 frames per second (10 Hz)</p> </li> <li> <p>Direct beam position (pixels): 545, 222</p> </li> </ul>
Updating Möller – Teil 2 (supplementary material)
<p>Supplementary material for blog post <a href="http://aku.hypotheses.org/412">Updating Möller – Teil 2</a>. It contains a CSV file with a concordance of sigla used in Möller, Georg. 1927. <em>Hieratische Paläographie. Die aegyptische Buchschrift in ihrer Entwicklung von der fünften Dynastie bis zur römischen Kaiserzeit: II. Von der Zeit Thutmosis’ III bis zum Ende der einundzwanzigsten Dynastie</em>. 2. Aufl. Leipzig: Hinrichs. The column names of the header are “Spalte”, “Sigle” and “Identifizierung“.</p>
[Dataset] Stroke Caregiver Burden in East Coast Peninsular Malaysia, A Short-term Longitudinal Study
<p>Raw dataset for study entitled "INFORMAL CAREGIVERS BURDEN AMONG STROKE PATIENTS IN EAST-COAST MALAYSIA: A SHORT-TERM LONGITUDINAL STUDY"</p> <p>This study is part of study funded by Newton Ungku Omar Fund (2020-2021) under grant for “A Scalable Solution for Supporting Informal Stroke Caregivers in Malaysia: Systematic Development and Feasibility Study” Malaysian Ministry of Education (203.PPSP.678003) and Medical Research Council, United Kingdom (MR/T018968/1).</p> <p>Please note that this data is in raw csv form, imported from REDCap. due to REDCap system, the raw file need to be relabel and relevel to reflect the original score or response.</p> <p>Data dictionary provided for data relabel and relevel purpose.</p> <p>R script also available to convert the raw csv into dataset with appropriate label and level</p> <p> </p> <p> </p>
DV-QKD in coexistence with classical channels in multicore fiber
<p>The simulation results demonstrate the possibility of simultaneous transmission of the classical and quantum channels in the same multicore fiber using space division multiplexing. Low crosstalk between the fiber cores helps to isolate the T12 DV-QKD channel (that propagates in one of the fiber cores) from the detrimental effect of the spontaneous Raman scattering of the classical channels propagating in both directions in each other core. The simulation illustrates how QBER and secret key rate degrade with an increase of the classical channel power for the specified crosstalk level between the fiber cores. The simulations examine the system over a wide range of classical channel powers (which may exceed typical values) to find an upper bound under which secure communication is still possible.</p>
Dataset: Strong isoprene emission response to temperature in tundra vegetation
<p>Dataset used in the article "<em>Strong isoprene emission response to temperature in tundra vegetation</em>" published in the journal <em><strong>Proceedings of the National Academy of Sciences of the USA </strong></em><strong>119: e2118014119</strong> <a href="https://doi.org/10.1073/pnas.2118014119">https://doi.org/10.1073/pnas.2118014119</a></p> <p>The tab-delimited file contains direct surface-atmosphere isoprene fluxes, measured every 30-minutes by Eddy Covariance with a Proton Transfer Reaction -Time of Flight- Mass Spectrometer (PTR-ToF-MS) during the whole growing season at two different tundra sites in Scandinavia (near Abisko, Sweden in 2018, and near Finse, Norway during 2019). It also contains the MEGANv2.1 biogenic model predicted isoprene emissions for the same periods and sites. In addition, air temperature, vegetation surface temperature, and photosynthetic photon flux density (PPFD) measured at the sites are also reported, together with their past 24h and 240h averages (needed to run the MEGAN simulation accounting for the recent past environmental conditions).</p>
DESIRA - inventory of digital tools for agriculture, forestry, and rural areas
<p>Inventory of digital tools for agriculture, forestry, and rural areas collected by the DESIRA consortium.</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.