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564 results for “June”
Solar and meteorological data collected from the Cilaos Piscine station (La Réunion) by the ENERGY-lab at the University of La Reunion between June 2013 and November 2016
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Cilaos Thermes station (La Réunion) by the ENERGY-lab at the University of La Reunion between December 2012 and June 2013
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Saint Joseph Marie station (La Réunion) by the ENERGY-lab at the University of La Reunion between September 2013 and June 2015
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
LPL2 - June 2021
<p>Measurements of LPL2 (PN Caldera de Taburiente) in June 2021</p> <p>El Paso, La Palma</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></p>
UEX1 - June 2021
<p>Measurements of UEX1 (Universidad Extremadura) in June 2021</p> <p>Universidad Extremadura</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/UEX1">info</a></p>
Microstructure and velocity data collected near Velasco Reef during the June 2016 FLEAT field program
<p>Data used in Wynne-Cattanach et al paper, "Measurements of turbulence generated by wake eddies near a steep headland"</p>
Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019
<p>This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.</p>
The creative, collaborative lab. A round table (June 01, 2022. 1400-1600 UTC)
<p>The purpose of this roundtable is to gain an interdisciplinary understanding of the lab as a collaborative and creative space. Our panel are distinguished scientists at the University of Lethbridge who have experience running and/or working in a variety of lab contexts. In this session they will discuss their experiences, provide tips, and generally share their knowledge and experience with lab science.</p>
Data complementing the Avian influenza overview March – June 2022
<p>Annex A – Characteristics of the HPAI A(H5Nx)-positive poultry establishments</p> <p>The Annex contains table with the characteristics of the HPAI A(H5Nx)-positive poultry establishments by affected EU Member State from 4 March to 1 June 2022.</p> <p>Annex B – Applied prevention and control measures on avian influenza</p> <p>The Annex contains an overview of specific prevention and control measures applied in Albania, Belgium, Bulgaria, Czechia, France, Hungary, Iceland, Italy, Moldova, Kosovo, The Netherlands, Poland, Romania, Slovakia, Spain from 1 December 2021 to 4 March 2022 in relation to HPAI outbreaks in poultry and in wild birds.</p> <p>Annex C – Data on wild birds</p> <p>The Annex contains tables and plots on HPAI notifications in wild birds in Europe.</p>
LPL7 - June 2022
<p>Measurements of LPL7 (Archipielago Chinijo) in June 2022</p> <p>Archipielago Chinijo</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL7">info</a></p>
LPL2 - June 2022
<p>Measurements of LPL2 (Caldera de Taburiente NP) in June 2022</p> <p>La Palma</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></p>
CloudButton Serverless Benchmarks results (June 2022)
<p>Results of the <strong>CloudButton Serverless Benchmark</strong>. It includes plots with results of the <strong>Flops benchmark</strong> and the <strong>Storage benchmark</strong>. The first one evaluates compute power and scalability of cloud providers by running multiple compute intensive tasks concurrently. The second one evaluates the throughput of read and write operations to the object storage services of different cloud providers by running such operations in many tasks and concurrently.</p> <p>The benchmark has been run on 6 FaaS services (IBM Cloud Functions, AWS Lambda, Microsoft Azure Functions, Google Cloud Functions, Google Cloud Run, and Alibaba Aliyun Function Compute) and 5 Object Storage services (IBM Cloud Object Storage, AWS S3, Microsoft Azure Blob, Google Storage, Alibaba Aliyun Object Storage Service).</p>
Data complementing the Avian influenza overview June – September 2022
<p>Annex A – Characteristics of the HPAI A(H5Nx)-positive poultry establishments</p> <p>The Annex contains table with the characteristics of the HPAI A(H5Nx)-positive poultry establishments by affected EU Member State from 2 June to 2 September 2022.</p> <p>Annex B – Applied prevention and control measures on avian influenza</p> <p>The Annex contains an overview of specific prevention and control measures applied in Bulgaria, Croatia, France, Hungary, Germany, Moldova, the Netherlands, Poland, Portugal, Spain from 2 June to 2 September 2022 in relation to HPAI outbreaks in poultry and in wild birds.</p> <p>Annex C – Data on wild birds</p> <p>The Annex contains tables and plots on HPAI virus notifications in wild birds in Europe.</p>
Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.
<p>Dataset of measurements of the soil CO<sub>2</sub> flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The dataset is structured as follows:</p> <p>Column A is the progressive number of the point (#);</p> <p>Column B is the Longitude of the point, datum WGS 1984;</p> <p>Column C is the Latitude of the point, datum WGS 1984;</p> <p>Column D is the Universal Transverse Mercator (UTM) Longitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column E is the Universal Transverse Mercator (UTM) Latitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column F is the soil brightness temperature, in °C;</p> <p>Column G is the soil CO<sub>2</sub> flux in grams of CO<sub>2</sub> per square meter, per day (g m<sup>-2</sup> day<sup>-1</sup>)</p>
Dataset of structural measurements at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.
<p>The dataset contains measurements of fractures and bedding at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The term fractures in this dataset indicate a break in a rock where the orthogonal opening is predominant; when clear lateral displacement by shearing is observed, then we adopt the term fault accordingly to the definition by National Research Council (1996). The topological analysis has been conducted using the methods described by Sanderson and Nixon (2015; 2018). This dataset consists of two text files described below.</p> <p><strong>structural-dataset.txt:</strong></p> <p>this file contains the measured fractures and bedding planes, it is structured as follow:</p> <p>Column A is the Longitude of the point, datum WGS 1984;</p> <p>Column B is the Latitude of the point, datum WGS 1984;</p> <p>Column C is the dip direction of the measured structure;</p> <p>Column D is the dip of the measured structure;</p> <p>Column E is the type of the measured structure (fracture, fault, bedding)</p> <p> </p> <p><strong>topological-analysis.txt:</strong></p> <p>this file contains the measurement done for the topological analysis on nine sites at Le Biancane area, it is structured as follow:</p> <p>Column A is the code of the site;</p> <p>Column B is the Longitude of the point, datum WGS 1984;</p> <p>Column C is the Latitude of the point, datum WGS 1984;</p> <p>Column D is the number of nodes I (NI);</p> <p>Column E is the number of nodes Y (NY);</p> <p>Column F is the number of nodes X (NX);</p> <p>Column G is the percent of nodes I (%NI);</p> <p>Column H is the percent of nodes Y (%NY);</p> <p>Column I is the percent of nodes X (%NX);</p> <p>Column J is the probability of connection of nodes I-I (PII’);</p> <p>Column K is the probability of connection of nodes I-C (PIC’);</p> <p>Column L is the probability of connection of nodes C-C (PCC’);</p> <p>Column M is the radius in meter (r) of the circle used for the topological analysis;</p> <p>Column N is the value of the parameter CL;</p> <p>Column O is the value of the parameter CB;</p> <p>Column P is the area (m^2) of the circle;</p> <p>Column Q is the fracture intensity;</p> <p> </p> <p> </p> <p> </p>
Рис. 2. Размерная структура G. lacustris в ΛитораΛьной зоне озера АрахΛей: 1 — июнь; 2 — август; 3 — октябрь Fig. 2. G. lacustris population size structure in the Lake Arakhley littoral zone: 1 — June, 2 — August, 3 — October in The life cycle of Gmelinoides fasciatus (Stebbing, 1899) and Gammarus lacustris (Sars, 1863) amphipods in the lake Arakhley littoral during the extreme low-water phase of the hydrological cycle
Рис. 2. Размерная структура G. lacustris в ΛитораΛьной зоне озера АрахΛей: 1 — июнь; 2 — август; 3 — октябрь Fig. 2. G. lacustris population size structure in the Lake Arakhley littoral zone: 1 — June, 2 — August, 3 — October
Рис. 1. Размерная структура Gm. fasciatus в ΛитораΛьной зоне озера АрахΛей: 1 — в июне; 2 — в августе; 3 — в октябре; 4 — в Αекабре 2017 г. и июне 2018 г. Fig. 1. Gm. fasciatus population size structure in the Lake Arakhley littoral zone: 1 — June; 2 — August; 3 — October; 4 — December, 2017 and June, 2018 in The life cycle of Gmelinoides fasciatus (Stebbing, 1899) and Gammarus lacustris (Sars, 1863) amphipods in the lake Arakhley littoral during the extreme low-water phase of the hydrological cycle
Рис. 1. Размерная структура Gm. fasciatus в ΛитораΛьной зоне озера АрахΛей: 1 — в июне; 2 — в августе; 3 — в октябре; 4 — в Αекабре 2017 г. и июне 2018 г. Fig. 1. Gm. fasciatus population size structure in the Lake Arakhley littoral zone: 1 — June; 2 — August; 3 — October; 4 — December, 2017 and June, 2018
Рис. 15. ПляЖи и пляЖевые танатоценоЗы б. Теляковского (июнь 2015 г.): А – северная часть бухты: пляЖ и валунно-глыбовая литораль; В – срединная часть бухты; С – массовые выбросы устриц (Crassostrea gigas), мидий (Crenomytilus grayanus) и модиолусов (Modiolus kurilensis) в северной части бухты; D – выбросы спиЗулы (Spisula sachalinensis) в срединной части бухты. Fig. 15. Beaches and beach thanatocoenoses of Telyakovskogo Bay (June 2015): A – northern part of the bay: a beach and rocky intertidal zone; B – middle part of the bay; C – abundant strandings of oysters (Crassostrea gigas), mussels (Crenomytilus grayanus and Modiolus kurilensis) in the northern part; D – strandings of Spisula sachalinensis in the middle part. in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 15. ПляЖи и пляЖевые танатоценоЗы б. Теляковского (июнь 2015 г.): А – северная часть бухты: пляЖ и валунно-глыбовая литораль; В – срединная часть бухты; С – массовые выбросы устриц (Crassostrea gigas), мидий (Crenomytilus grayanus) и модиолусов (Modiolus kurilensis) в северной части бухты; D – выбросы спиЗулы (Spisula sachalinensis) в срединной части бухты. Fig. 15. Beaches and beach thanatocoenoses of Telyakovskogo Bay (June 2015): A – northern part of the bay: a beach and rocky intertidal zone; B – middle part of the bay; C – abundant strandings of oysters (Crassostrea gigas), mussels (Crenomytilus grayanus and Modiolus kurilensis) in the northern part; D – strandings of Spisula sachalinensis in the middle part.
Dataset 2 for UV Plasmon-Enhanced Chiroptical Spectroscopy of Membrane-Binding Proteins, June 2024
<p>Scanning electron microscopy images of Al nanostructures</p>
Reading Information Technology and Libraries (volume 41, number 2, June 2022)
Today, for a good time, I applied my Reader Toolbox to the latest issue of ITAL for the two-fold purposes of: 1) just seing whether the Toolbox could function, and 2) determine the degree I could extract meaningful themes from the issue. Well, the Toolbox functioned, in that it did not crash nor output invalid data, and I do believe I could pull out themes, in that each issue's authors wrote about something distinctive, and I could identify those things. Below describes my process.
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.