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657 results for “July”
Extreme precipitation event over Henan in July 2021
<p>HYSPLIT-generated trajectories associated with extreme precipitation evnets over Henan in July 2021 and August 1975.</p>
Forecasting the July Precipitation over the middle-lower reaches of the Yangtze River with a flexible statistical model
<p>The archive.zip contains the MLYR forecast system codes, data, results and all the figures which used in the paper“Forecasting the July Precipitation over the middle-lower reaches of the Yangtze River with a flexible statistical model.”</p> <ul> <li>The MLYR forecast system codes of each experiment used in this study are under the directory of RegMLYR.</li> <li>The observed data of sea surface temperature and precipitation used in the experiments are under the directory of ObsData.</li> <li>The NCL and Python scripts used for figures in the paper are under the directory of Figuers.</li> <li>The forecasting results of each experiment in this paper are under the directory of Results.</li> </ul>
Mean wind and stability time series at FINO1 between June and July 2015 (from OBLEX-F1)
<p>Data collected from a sonic anemometer mounted at 15m above the mean sea level at the geographical location of FINO1 offshore met-mast. Time series cover yeardays between 160 and 210, 2015.</p> <p>% % Datasets are as follows:</p> <p>% % time15: mean time at each 30-min burst of 15m sonic data.</p> <p>% % umean15: mean wind speed at 15m height from average of each sonic 30-min segment at 15m sonic.</p> <p>% % UU10: wind at 10m heigh from umean15 using logarithmic wind profile.</p> <p>% % ti_ref: reference time - first of January 2015.</p> <p>% % wdir: wind direction at 15m measured from high freq. sonic data.</p> <p>% % wa: wave age data estimated from sonic data and wave phase speed (see OBLEX-F1 data).</p> <p>% % L15: Moninnon-Obukhov length scale measured by 25Hz sonic data at 15m height.</p> <p>% %</p> <p>% % Mostafa Bakhoday Paskyabi, 10 Dec. 2022</p> <p>% % Mostafa.Bakhoday-Paskyabi@uib.no</p> <p> </p> <p> </p>
TROPOMI SIF data in July, 2019
<p>TROPOMI SIF global data in July, 2019 (Floating type-binary data. Geographic projection at a spatial resolution of 0.05 degree. Samples = 7200, Lines = 3600) </p>
Continuous data of unconfined groundwater level recorded at Isawa Fan (Iwate Prefecture, northern Japan) from January 2008 to July 2021
<p>We present the continuous data of unconfined groundwater level recorded at Mizusawa Campus, National Astronomical Observatory of Japan (hereafter referred to as NAOJ Mizusawa), located in the central part of Isawa Fan, Iwate Prefecture, northern Japan. In 2008, we installed a water pressure gauge (Mini-Diver DI501, Van Essen Instruments B.V.) at each of four shallow wells located around the gravity observation building of NAOJ Mizusawa, to monitor temporal variations in the water level at NAOJ Mizusawa. We also installed a barometer (Baro-Diver DI500, Van Essen Instruments B.V.) above the water level at the northern well, to subtract the effect of air pressure changes from the water pressure changes observed in the groundwater. The unconfined groundwater levels have been continuously measured with these instruments at 5-minute intervals, and the water level data have been used in some studies (Kazama et al., 2012; Tamura et al., 2023) to model gravity changes due to spatiotemporal variations in underground water such as soil water and groundwater.</p> <p>PNG files in map.zip shows the location of NAOJ Mizusawa with the aerial photographs obtained from Google Earth Pro. The red pin denoted by SG indicates the gravity observation building, in which a superconducting gravimeter have been recording gravity continuously (Kazama et al., 2012; Tamura et al., 2023). Yellow pins around the gravity observation building indicate the shallow wells to monitor unconfined groundwater levels.</p> <p>JPG files in photo.zip are the photographs taken on October 3, 2008, when the water pressure gauges were installed at the four wells in NAOJ Mizusawa. A water pressure gauge was connected to the top of a well by a metal wire of about 5 m in length, and installed into the groundwater in the well.</p> <p>data.zip contains the time series of unconfined groundwater level recorded at NAOJ Mizusawa from January 2008 to July 2021. The time series from 2008 to 2010 indicates the average water level of the four water wells (see Kazama et al., 2012 in detail). In and after 2011, however, we were unable to obtain the average water level, because some of the four water gauges failed. Alternatively, we here used the water level data observed at the eastern well from 2011 to 2013, assuming that the variation of the water level at each well is consistent with that of the average water level. We also used the water level data observed at the northern well from 2014 to 2021. Note that we corrected for artificial steps in the water level caused by the switching of the water gauges to be used.</p> <p>Each file of 20xx.txt in data.zip stores the times series of water level in the year of 20xx (xx = 08, 09, ..., 21). The water level was recorded every 5 minutes and time-stamped in Japan Standard Time (JST). Each column of 20xx.txt means as follows: ($1) year, ($2) month, ($3) day, ($4) day of year, ($5) hour, ($6) minute, ($7) time [day] from 00:00 in January 1, ($8) water level [cm] of unconfined groundwater relative to ground surface. For example, the value of -51.88 for the eighth column means that the water table is located at a depth of 51.88 cm below the ground surface.</p> <p>Note that the file of 2011.txt stores incorrect water level values from March 28 to April 25, 2011, because we were unable to collect the data during this period due to the 2011 Tohoku earthquake. Therefore, we additionally provide a file of 2011_modified.txt, in which synthetic water level values are stored for the period from March 28 to April 25. The synthetic water level was calculated by assuming a linear response to precipitation of 16.5 cm/cm and a constant decrease in water level of -3 cm/day. These parameters were determined from the water level and precipitation data observed at NAOJ Mizusawa in March 2011.</p> <p>Each file of 20xx.png in figure.zip shows the time variation of unconfined groundwater level at NAOJ Mizusawa in the year of 20xx. The water level rises rapidly due to rainfall and decreases exponentially after rainfall. The water level typically changes in the range of 1-3 m below the ground surface, but it becomes shallower than 1 m during heavy rainfall events. It also becomes deeper than 3 m below the ground surface during extreme dry periods such as in August 2011 and September 2012.</p>
Ozone concentrations and meteorological factors in coastal areas of China in July of 2015-2022
<p>Ozone concentrations and meteorological factors in four coastal areas of China (the coast of Fujian Province (c-FJ), the coast of Yangtze River Delta region (c-YRD), the coast of Shandong Peninsula (c-SD) and Beijing-Tianjin-Hebei (c-BTH) in July of 2015-2022. Totally 25 coastal cities were picked as typical cities representative of the studied regions (Ningde, Fuzhou, Putian, Quanzhou, Xiamen and Zhangzhou in c-FJ; Wenzhou, Taizhou, Ningbo, Zhoushan, Jiaxing, Shanghai, Nantong, Yancheng and Lianyungang in c-YRD; Rizhao, Qingdao, Weifang, Weihai, Yantai and Dongying in c-SD; Cangzhou, Tianjin, Tangshan and Qinhuangdao in c-BTH) and the data was averaged to obtain the O<sub>3</sub> concentrations and meteorological conditions regionally.</p>
Pedopsychiatric and Multidisciplinary Research Devoted to Children Exposed to the Attack in Nice on July 14, 2016
ClinicalTrials.gov study NCT03356028. IPD Sharing: NO. Countries: 1. Publications: 3.
Hospital Cohort Survey of Public Health Post-attack of July 14, 2016
ClinicalTrials.gov study NCT03353805. IPD Sharing: NO. Countries: 1. Publications: 0.
The JULI Registry--Hemp and Cannabis Observational Registry
ClinicalTrials.gov study NCT04726254. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Data from: "White-tailed deer (Odocoileus virginianus) transcriptome assembly and SNP discovery" in Genomic Resources Notes accepted 1 June 2013-31 July 2013
Open the record for dataset details and reuse information.
Data from: "De novo assembled transcriptome of organs involved in reproduction in an endangered endemic Iberian cyprinid fish (Squalius pyrenaicus)" in Genomic Resources Notes Accepted 1 June 2015 to 31 July 2015
Open the record for dataset details and reuse information.
Symbiodiniaceae cell densities in feces of coral reef fish, sediments and seawater in Mo'orea, French Polynesia, July-August 2019
Open the record for dataset details and reuse information.
Data pertaining to lobster phyllosoma, Panulirus interruptus, collection methods, locations, identification and staging (1951-2008, months of July and August)
The very large collection of plankton hauls taken in connection with the California Cooperative Oceanic Fisheries Investigations (CalCOFI) has afforded a unique opportunity to study the occurrence and distribution of the phyllosoma larvae of the spiny lobster. JOHNSON, MARTIN W. 1960. The Distribution of Larvae of the Spiny Lobster, Panulirus interruptus (Randall) with Records on P. Gracilis Streets. California Cooperative Fisheries Investigation 7: 413-46.
Measurements of dissolved inorganic concentrations of nutrient iron and of iron limitation at selected stations and depths from CalCOFI cruises in the California Current System, Nov. 2002 - July 2004 (completed)
Measurements were made of iron (Fe) and the potential for phytoplankton iron limitation on CalCOFI survey cruises (2002-2004) in coastal transition zones of the southern California Current System. This is a weak upwelling regime that is relatively low in nutrients and chlorophyll. The majority of seawater samples from the mixed layer were collected using a pole sampler, others were collected using a trace metal clean Teflon pump system. Profiles for dissolved Fe analysis were collected using GO Flo bottles attached to a synthetic line and the seawater samples were filtered under ultra-pure conditions. Dissolved Fe was measured using an FeLume flow injection analysis system. Changes in phytoplankton (Chla response to Fe+) and nutrient parameters upon iron addition were also investigated.
Global ECMWF Fire Forecasting system - sample data for wildfires in Siberia on 23-25 July 2019
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#%21/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides FWI sample datasets for the assessment of the Siberian wildfires occurred on 23-25 July 2019:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 46r1 of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> <li> <p>Geographical bounding box: lon_min = 40, lon_max = 180, lat_min = -10, lat_max = 90</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Anhang IV: Rohdaten aus der JIPS-Datenbank (Stand Juli 2016 )
<p>Anhang IV: Rohdaten aus der JIPS-Datenbank ( Stand Juli 2016 )</p> <p><strong>Quantitative Skizzierung eines Forschungsfeldes am Beispiel der Journal of Interdisciplinary Placebo Studies Database</strong></p> <p><strong>Quantitative delineation of a research field using the example of the Journal of Interdisciplinary Placebo Studies Database </strong></p>
Figure 1 from: Lindon H, Hartley H, Knapp S, M. Monro A, Turland N (2020) XIX International Botanical Congress, Shenzhen: report of the Nomenclature Section, 17th to 21st July 2017. PhytoKeys 150: 1-276. https://doi.org/10.3897/phytokeys.150.50687
Figure 1 The Nomenclature Section of the XIX International Botanical Congress, Shenzhen, China, 20 July 2017. The Bureau of Nomenclature is seated on the front row (left to right): Li Zhang, Yun-Fei Deng (Recorders), Anna M. Monro (Recorders' Assistant), John H. Wiersema (Vice-rapporteur), Nicholas J. Turland (Rapporteur-général), Sandra Knapp (President), Renée H. Fortunato, John McNeill, Werner Greuter, Gideon F. Smith, and Karen L. Wilson (Vice-presidents). – Reproduced by permission of the photographer, Li Zhang, Fairy Lake Botanical Garden.
CTD dataset of the Po river (Italy) during one ebb and flood phase on 26 July 2017
<p>CTD dataset: temperature and salinity profiles acquired in the water<br> column with CTD probes (Ocean Seven 316Plus and 304, Idronaut srl, 20 Hz<br> and 8 Hz sampling rate, respectively), starting from the Po river mouth<br> and navigating upstream along the river talweg 10-18 km depending on the<br> river branch and in any case detecting a complete freshwater column.<br> Two sets of samplings were conducted on the 26th July 2017 during a spring<br> tide cycle, both starting at the slack tide and following the ebb and<br> flood tidal phase for almost two hours. The CTD casts location was decided<br> a priori based on the experience of previous campaigns, trying to<br> match the largest number of casts within an appropriate time interval to<br> detect the freshwater. During the ebb tide, when the salt wedge was<br> shorter, some additional casts where conducted to increase the<br> longitudinal resolution of the campaign. The CTD raw data were extracted<br> along the water column every 10 cm. Part of this dataset is used for<br> validation purposed in the paper Bellafiore et al., submitted to Journal<br> of Geophysical Research - Oceans, title "Saltwater intrusion in a<br> Mediterranean delta under a changing climate".</p>
Data from: "You are not what you eat: massive parallel sequencing reveals that gut microbiome is not diet-related in larval Dilophus febrilis (Diptera: Bibionidae)" in Genomic Resources Notes Accepted 1 June 2015 to 31 July 2015
This article documents the public availability of metagenome sequence data from 454 amplicon sequencing of larval dipteran gut (Dilophus febrilis) and their potential food sources dwarf shrub litter (Vaccinium gaultheroides), grass litter (Dactylis glomerata), and cow dung (Bos primigenius taurus).
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWLNETS (v2.1.0 - July 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>July 06, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/July-06%2C-2021">here</a>.</li></ul>
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.