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412 results for “September”
LPL5 - September 2021
<p>Measurements of LPL5 (Tejeda) in September 2021</p> <p>Tejeda, Gran Canaria</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
LPL1 - September 2020
<p>Measurements of LPL1 (Güímar) in September 2020</p> <p>Tenerife</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
LPL2 - September 2020
<p>Measurements of LPL2 (PN Caldera de Taburiente) in September 2020</p> <p>El Paso, La Palma</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></p>
Stable Isotopes of Water from the Glacier de Martinets, 14-September-2021
<p>Thirty samples from the Martinets Glacier in Vaud, Switzerland collected on single day in September 2021. Data set includes ice, water flowing on glacier, and old snow. All ice was sampled at a maximum of 10 cm under the surface. Some duplicate samples are included. Dataset includes deuterium, O-17 and O-18. All analyses were completed with a Picarro CRDS at the Geography Institute of the University of Bern. Metadata includes latitude, longitude, elevation, date, time, and notes. </p>
Model profiles at satellite locations, 09 - 21 UTC, 7th September, 2017
<p><strong>Summary</strong></p> <p>This dataset contains atmospheric profiles generated from an experimental run of the ECMWF integrated forecast system (IFS), interpolated to the observation locations and times of various satellite observations using the internal IFS observation operator (known as 'the GOM arrays'). The date and time range gives a snapshot of Hurricane Irma, 2017.</p> <p><strong>Satellite observations</strong></p> <p>Two satellite instruments are included:</p> <ul> <li>The Global Precipitation Mission (GPM) Microwave Imager (GMI): The profile locations are at the centres of boxes of roughly 40 km by 40 km. These boxes are used for superobbing the data, combining a minimum 15 raw GMI observations. The process of superobbing is used at ECMWF for a number of reasons including to colocate the various channels of GMI level 1b data, which are at different locations. Note that the 40 km superobbing resolution used here is different from the operational ECMWF superobbing resolution for this data (which is 80 km).</li> <li>The Advanced Technology Microwave Sounder (ATMS) on the Suomi NPP satellite. All locations in the raw level 1B dataset are included (note that the 3 x 3 averaging that is normally applied at ECMWF was switched off)</li> </ul> <p>The satellite observations themselves are not included in this dataset and must be obtained from the relevant data providers. The profiles are provided in a semi-random order and must be colocated to the relevant observations by the user.</p> <p><strong>Model details</strong></p> <p>IFS cycle 47r1 has been used; further information is at https://www.ecmwf.int/en/publications/ifs-documentation. The model profiles are generated from a run of the atmospheric forecast model that is initialised from the operational analysis at 00 UTC 7th September 2017. The profiles are hence based on a short forecast of 9 - 21 hours duration that is equivalent to the 'background forecast' in the data assimilation cycle. The model uses a horizontal resolution of T1279co (equivalent to 8 - 9 km) and 137 hybrid pressure levels in the vertical. Profiles represent slabs of atmosphere that are centred on the 'full' pressure levels and are bounded by 'half' pressure levels. Hence, half pressure level 1 is 0 hPa and half pressure at level 138 is the surface pressure. </p> <p>The dataset contains most of the necessary inputs for driving a radiative transfer model to simulate satellite-observed radiances. One important input that is not included is the surface emissivity, which may be estimated using a physical model integrated into the radiative transfer model, or it may be obtained from an emissivity atlas (in the context of the IFS data assimilation is in some cases estimated with a dynamic emissivity retrieval, which is not supplied in this dataset).</p> <p>There are 6 hydrometeors represented by the IFS. The large-scale condensation scheme represents cloud water, cloud ice, rain and snow as prognostic variables. Relating to these are a sub-grid cloud fraction which applies to cloud water and ice, and a precipitation fraction that applies to the large-scale rain and snow. Additionally, the mass flux convection scheme represents rain and snow generated by convection. The sub-grid fraction occupied by convection is not given in the file, since it is assumed to be a constant 0.05. The convection scheme does not represent convective cloud (e.g. non-precipitating particles) but it does represent the convective anvils via detrainment into the large-scale condensation scheme (hence convective anvils are represented in the large-scale cloud ice). Rain and snow were originally obtained as fluxes and converted to mixing ratios using assumptions of fall-speed and particle size distribution, as is standard within the all-sky satellite data processing at ECMWF.</p> <p>All mixing ratios [kg kg-1] are given relative to the moist atmosphere.</p> <p>The profile times are those of a model timestep close in time to when the observation was made. The model timestep is 7.5 minutes, but only every fourth timestep is available to the IFS observation operator, meaning that profile times are quantised at 0, 30 60 minutes (and so on) through the forecast range. Essentially, there should be no more than a 15 minute time mismatch between the validity time of the profile and the satellite observation time.</p> <p><strong>Licensing and copyright</strong></p> <ul> <li>Copyright statement: Copyright "© 2022 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source: www.ecmwf.int</li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/</li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Where applicable, when redistributing the data, give an indication if the material has been modified and an indication of previous modifications.</li> <li>Full ECMWF licence terms are given at https://apps.ecmwf.int/datasets/licences/general/ </li> </ul>
LEMORTHO datasets september 2022
<p>Datasets necessary to reproduce the LEMORTHO benchmark.</p> <p>The dataset used as the reference on LEMOrtho is a customized version of the Orthobench revisited study (<a href="https://dx.doi.org/10.1093/gbe/evaa211">https://dx.doi.org/10.1093/gbe/evaa211</a><a href="https://dx.doi.org/10.1093/gbe/evaa211"> </a>, <a href="https://dx.doi.org/10.1002/bies.201100062">https://dx.doi.org/10.1002/bies.201100062</a><a href="https://dx.doi.org/10.1002/bies.201100062"> </a>), from which we removed a few problematic genes:</p> <p><code>ENSCAFP00000022898,ENSCINP00000001707, ENSCINP00000002932, ENSCINP00000010295, ENSCINP00000018374, ENSCINP00000025546, ENSCINP00000025552, ENSCINP00000027090, ENSCINP00000035497, ENSDARP00000002330, ENSDARP00000076594, ENSDARP00000131597, ENSDARP00000156542, ENSMODP00000012647, ENSMUSP00000033502, ENSP00000316782, ENSP00000358380, ENSP00000359057, ENSP00000365858, ENSP00000371923, ENSP00000463957, ENSP00000474456, ENSP00000477509, ENSP00000477979, ENSP00000478609, ENSP00000478752, ENSP00000479545, ENSP00000479693, ENSP00000480818, ENSP00000481542, ENSP00000481835, ENSP00000498781, ENSPTRP00000001775, ENSPTRP00000040053, ENSPTRP00000056359, ENSPTRP00000058531, ENSPTRP00000086593, ENSPTRP00000092368, ENSPTRP00000092968, ENSRNOP00000038111, ENSRNOP00000067336, ENSTNIP00000010123, ENSTNIP00000017372, FBpp0073951, FBpp0075520, FBpp0082643, FBpp0082970, FBpp0291496, FBpp0291497, FBpp0309618, WBGene00001184.1, WBGene00001249.1, WBGene00006925.1, WBGene00006926.1, WBGene00006927.1, WBGene00006928.1, WBGene00006929.1, WBGene00006930.1, WBGene00010139.1, WBGene00015237.</code></p> <p>To use these datasets with the standalone LEMORTHO pipeline, place each dataset in the folder $LEM_ORTHO_ROOT/benchmark/datasets/</p>
LPL16 - September 2022
<p>Measurements of LPL16 (Caceres) in September 2022</p> <p>Provincia de Cáceres</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL16">info</a></p>
LPL15 - September 2022
<p>Measurements of LPL15 (Badajoz) in September 2022</p> <p>Provincia de Badajoz</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL15">info</a></p>
LPL3 - September 2022
<p>Measurements of LPL3 (Madeira East Coast) in September 2022</p> <p>Madeira</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL3">info</a></p>
LPL2 - September 2022
<p>Measurements of LPL2 (Caldera de Taburiente NP) in September 2022</p> <p>La Palma</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></p>
LPL7 - September 2022
<p>Measurements of LPL7 (Archipielago Chinijo) in September 2022</p> <p>Archipielago Chinijo</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL7">info</a></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>
TESS-September-2017
<p>Measurements of the STARS4ALL Photometers Network</p>
Рис. 1. 1 — берег искусственного воΑоема на окраине посеΛка Витязь; 2 — северный скΛон горы Туманная; 3 — Amphipyra tripartita Butler, 1878, северный скΛон горы Туманная; 4 — Amphipyra tripartita Butler, 1878, ♂, Japonia, Ashia, ex coll. Erschov, коΛΛекция ЗИН; 5 — Oligonyx vulnerata (Butler, 1878); 6 — Cryphia bryophasma (Boursin, 1951); 7 — Cryphia griseola (Nagano, 1918); 8 — Stenoloba assimilis (Warren, 1909); 9 — Hadena aberrans (Eversmann, 1856) Fig. 1. 1 — the shore of an artificial reservoir on the outskirts of the Vityaz village; 2 — the northern slope of mount Tumannaya; 3 — Amphipyra tripartita Butler, 1878, the northern slope of mount Tumannaya; 4 — Amphipyra tripartita Butler, 1878, ♂, Japonia, Ashia, ex coll. Erschov, collection of the Zoological Institute RAS; 5 — Oligonyx vulnerata (Butler, 1878); 6 — Cryphia bryophasma (Boursin, 1951), the outskirts of the Vityaz village; 7 — Cryphia griseola (Nagano, 1918); 8 — Stenoloba assimilis (Warren, 1909); 9 — Hadena aberrans (Eversmann, 1856) in Amphipyra tripartita Butler, 1878 - new species for the fauna of Russia and other Noctuidae (Insecta, Lepidoptera), found in Gamov peninsula in September 2022
Рис. 1. 1 — берег искусственного воΑоема на окраине посеΛка Витязь; 2 — северный скΛон горы Туманная; 3 — Amphipyra tripartita Butler, 1878, северный скΛон горы Туманная; 4 — Amphipyra tripartita Butler, 1878, ♂, Japonia, Ashia, ex coll. Erschov, коΛΛекция ЗИН; 5 — Oligonyx vulnerata (Butler, 1878); 6 — Cryphia bryophasma (Boursin, 1951); 7 — Cryphia griseola (Nagano, 1918); 8 — Stenoloba assimilis (Warren, 1909); 9 — Hadena aberrans (Eversmann, 1856) Fig. 1. 1 — the shore of an artificial reservoir on the outskirts of the Vityaz village; 2 — the northern slope of mount Tumannaya; 3 — Amphipyra tripartita Butler, 1878, the northern slope of mount Tumannaya; 4 — Amphipyra tripartita Butler, 1878, ♂, Japonia, Ashia, ex coll. Erschov, collection of the Zoological Institute RAS; 5 — Oligonyx vulnerata (Butler, 1878); 6 — Cryphia bryophasma (Boursin, 1951), the outskirts of the Vityaz village; 7 — Cryphia griseola (Nagano, 1918); 8 — Stenoloba assimilis (Warren, 1909); 9 — Hadena aberrans (Eversmann, 1856)
Рис. 3. ВывоΔок тигрицы в ΔоΛине руч. Соснинский, заповеΔник «БоΛьшехехцирский», 10 сентября 2021 г. Fig. 3. The tiger family in the Sosninsky Stream valley, Bolshekhekhtsirsky State Nature Reserve, 10 September, 2021 in New data on the mammalian fauna of the Bolshekhekhtsirsky Nature Reserve
Рис. 3. ВывоΔок тигрицы в ΔоΛине руч. Соснинский, заповеΔник «БоΛьшехехцирский», 10 сентября 2021 г. Fig. 3. The tiger family in the Sosninsky Stream valley, Bolshekhekhtsirsky State Nature Reserve, 10 September, 2021
Wideband VLF data observed in New Zealand during the period 07-08 September 2017
<p>Wideband VLF data observed in New Zealand during the period 07-08 September 2017.</p>
TESS-SEPTEMBER-2018
<p>Measurements taken by the European Photometer Network (Project STARS4ALL). September 2018</p>
Fig. 1. Location and vegetation types where small mammals were sampled between November 2012 and September 2013 in Small mammals from the lasting fragments of Araucaria Forest in southern Brazil: a study about richness and diversity
Fig. 1. Location and vegetation types where small mammals were sampled between November 2012 and September 2013, at Piraí do Sul National Forest, ParanÁ state, Brazil (A, Pine Plantation; B, Riparian Forest; C, Araucaria Plantation; D, Natural Regeneration forest; E, High Altitude forest). Original distribution of Atlantic Forest biome (light gray) and Araucaria forest (dark gray).
Video Documentation for the Giant Human Sorting Network event in Vienna, Austria, September 19, 2019
<p>This video is a supplementary material for the article "Large and Parallel Human Sorting Networks" by Stefan Szeider, to appear in the proceeedings of CMSC 2024, the 7th Conference on Creative Mathematical Sciences Communication, Springer Verlag, 2024.</p> <p> </p>
Covid-19 strains from December 2019 to September 2023
<p>This data is intended to be used by the programme file 'Polymorphism_CtoT.py' in the GitHub repository 'Nucleotide-Polymorphism'.</p>
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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.