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10,016 results for “Observation”
Supplementary data for the manuscript "Aqueous phase behavior of glyoxal and methylglyoxal observed with carbon and oxygen K-edge X-ray absorption spectroscopy".
<p>XAS carbon and oxygen K-edge measurements of aqueous solutions of glyoxal and methylglyoxal and their mixtures with sodium chloride and ammonium sulfate. XAS oxygen K-edge measurements of aqueous glycerol and mixtures with the above-mentioned inorganic salts.</p>
Simultaneous observations of EIP, TGF, Elve and optical lightning.
<p>Data used for the paper:</p> <p> </p> <p>Simultaneous observations of EIP, TGF, Elve and optical lightning.</p> <p>Østgaard et al.</p>
Data from: Morphological and anatomical observation during the formation of bulbils in Lilium lancifolium
Aerial bulbil, other than axillary branch and tendril, is a special propagative organ in Lilium lancifolium, but the development events occurring during bulbil formation of L. lancifolium have not been reported. In this study, we observed the changes of morphology and anatomical structure during the process of bulbil formation in L. lancifolium. The results indicated that bulbils of L. lancifolium formed in the joint region of leaf and stem and were derived from the division and diferentiation of axillary meristem cells. Bulbils frst generated on a certain leaf axil in the upper portion of stem, and then formed upward along the upper stem. The present study provides histological information for further studying the mechanism of bulbil formation.
Data from: Clinical factors associated with long-term complete remission versus poor response to chemotherapy in HIV-infected children and adolescents with Kaposi sarcoma receiving bleomycin and vincristine: a retrospective observational study
Kaposi sarcoma (KS) is the most common HIV-associated malignancy in children and adolescents in Africa. Pediatric KS is distinct from adult disease. We evaluated the clinical characteristics associated with long-term outcomes. We performed a retrospective observational analysis of 70 HIV-infected children and adolescents with KS less than 18 years of age diagnosed between 8/2010 and 6/2013 in Lilongwe, Malawi. Local first-line treatment included bleomycin and vincristine plus nevirapine-based highly active anti-retroviral therapy (HAART). Median age was 8.6 years (range 1.7–17.9); there were 35 females (50%). Most common sites of presentation were: lymph node (74%), skin (59%), subcutaneous nodules (33%), oral (27%), woody edema (24%), and visceral (16%). Eighteen (26%) presented with lymphadenopathy only. Severe CD4 suppression occurred in 28%. At time of KS diagnosis, 49% were already on HAART. Overall, 28% presented with a platelet count < 100 x 109/L and 37% with hemoglobin < 8 g/dL. The 2-year event-free (EFS) and overall survival (OS) were 46% and 58% respectively (median follow-up 29 months, range 15–50). Multivariable analysis of risk of death and failure to achieve EFS demonstrated that visceral disease (odds ratios [OR] 19.08 and 11.61, 95% CI 2.22–163.90 and 1.60–83.95 respectively) and presenting with more than 20 skin/oral lesions (OR 9.57 and 22.90, 95% CI 1.01–90.99 and 1.00–524.13 respectively) were independent risk factors for both. Woody edema was associated with failure to achieve EFS (OR 7.80, 95% CI 1.84–33.08) but not death. Univariable analysis revealed that lymph node involvement was favorable for EFS (OR 0.28, 95% CI 0.08–0.99), while T1 TIS staging criteria, presence of cytopenias, and severe immune suppression were not associated with increased mortality. Long-term complete remission is achievable in pediatric KS, however outcomes vary according to clinical presentation. Based on clinical heterogeneity, treatment according to risk-stratification is necessary to improve overall outcomes.
Field campaign observation data for 'Impacts of Heatwaves on Characteristics of Atmospheric Methylglyoxal in a Suburban Area in Eastern China'
Open the record for dataset details and reuse information.
Measuring exposure of agriculture to observed temperature change, 1961-2024
<p>The dataset disseminates regional indicators that link observed temperature change and agricultural statistics, connecting NASA-GISS and FAOSTAT data for mean annual temperature change on land with statistics of population, agricultural land use, crop and livestock production and gross economic value by country, over the time series 1961-2024.</p>
Global Observation-based LInear Vorticity Vertical Velocities (OLIV3) over vertical levels
<p>Observation-based Linear Vorticity Vertical Velocities (OLIV3) estimates from observation-based geostrophic velocities within the global themocline during the 1993-2019 period at annual frequency.</p> <p>The beta-plane geostrophic OLIV3 fields are computed following the indefinite depth-integrated geostrophic linear vorticity balance methodology described in <em>Cortés-Morales and Lazar, 2024, </em><em>Diego Cortés Morales, 2023 [thesis] and Cortés-Morales et al., (submitted) </em>applied to the ARMOR3D [<em>Mulet et al., 2013; https://data.marine.copernicus.eu/product/MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012</em>] geostrophic meridional velocities. The boundary condition used is the Ekman pumping vertical velocities computed from ERA5 wind stress [DOI: 10.24381/cds.f17050d7]. The velocity field is quality-flagged based on the relative error and interannual correlation coefficient between $w_g$ and $w_{tot}$ in an OGCM perfect model test (<em>Cortés-Morales et al., (submitted)</em>).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Global Ocean</p> <p>Grid and horizontal spatial resolution: Evenly spaced 0.25º grid</p> <p>Vertical levels: 50 levels from 0 to 5000 meters depth</p> <p>Temporal resolution: Annual (1993-2019)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (2D): Longitude</p> <p>lat (2D): Latitude</p> <p>verlev(1D): Vertical level</p> <p>time (1D): Year</p> <p>w_oliv3 (4D): Beta-plane geostrophic vertical velocities</p> <p>flag_time_var (3D): Flag based on correlation coeffcient between geostrophic and total vertical velocties from OGCM</p> <p>flag_time_mean (3D): flag based on relative error between geostrophic and total vertical velocties from OGCM</p>
The First Evaluation of Newly Calibrated MODIS Infrared Water Vapour Observations in the Iqaluit, Arctic
<p>This dataset has not been published yet. This dataset corresponds to the GRL submission. It will be updated based on the GRL publication</p>
Observation of U,V,T for data assimilation
Open the record for dataset details and reuse information.
All-sky Imager Observations from the Chinese Antarctic Zhongshan Station on April 23rd, 2019
<p>This dataset contains the all-sky imager observations from the Chinese Antarctic Zhongshan Station on April 23rd, 2019. The name of each compressed file corresponds to the observed waveband, i.e., 5577 Å and 6300 Å. The observation data files are in the png format.</p>
Time-domain observation of ballistic orbital-angular-momentum currents with giant relaxation length in Tungsten
<p>Data for the publication "Time-domain observation of ballistic orbital-angular-momentum currents with giant relaxation length in Tungsten" published in Nature Nanotechnology (2023). The datasets are provided for Figures 2- 5.</p>
Ice-penetrating radar data used in paper "Evaluating and locating a suitable bedrock drilling site near Zhongshan Station with airborne and ground-based observations"
<p>These are the ice-penetrating radar data used in Figure 4 of the paper "Evaluating and locating a suitable bedrock drilling site near Zhongshan Station with airborne and ground-based observations".</p> <p>The"read_image.m" file can be used to re-display Figure 4(a) of the paper, where the "x.mat", "y.mat", and "radar_profile_data.mat" are the x and y coordinates of the survey line, and the return power of the ice-penetrating radar data, respectively.</p> <p>The "Data_of_profile_A-A'(Figure_4b).xlsx" file includes two sheets. One is the "subglacial topography" sheet, which includes information about longitude, latitude, ice thickness, bed elevation, and hydraulic potential along the profile A-A'. The other sheet is the "BRP" sheet, which includes information about bed reflection power along the profile A-A'.</p>
LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)
<p>LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)</p>
• Bibliographic search on global warming effects and extracted metadata (pH, salinity, oxygen, temperatures, diseases observed in field/ in vitro/ in vivo, clinical signs, pathology, morbidity/mortality);
<p><a><span>B ibliographic search on global warming effects and extracted metadata </span></a><span><span><a href="#_msocom_1" name="_msoanchor_1">[MOU1]</a><span> </span></span></span><span>(pH, salinity, oxygen, temperatures, diseases observed in field/ <em>in</em> <em>vitro</em>/ <em>in</em> <em>vivo</em>, clinical signs, pathology, morbidity/mortality);</span><span></span></p>
Concurrent observations of plasma bubbles and field-aligned irregularities in low-latitudes of China
<p>These figures show the raw and processed data of the 630 nm all-sky airglow imager from Fuke Observatory in Hainan Province, China at 16:54-19:23 UT on September 23, 2014 and 15:00-16:33 UT on April 13, 2015, respectively, with a temporal resolution of 3 min.</p>
JGR-Oceans article 2024JC021722: High-Latitude Cabbeling Observations by the Air-Sea Interaction Profiler
<p>Data for JGR article 2024JC021722. Data is in matlab format with a set of structs where each struct is named: deployx_mmddHHMM, where x is the deployment number (4-6) and mmddHHMM is the time of the profile.</p> <p>Struct contains:</p> <p>temp: temperature in degC</p> <p>sal: salinity in PSU</p> <p>dens: density in kg/m3</p> <p>pres: pressure in dbar</p>
Increasing Contribution of Adolescent Type 1 Diabetes Drives Incidence Rates in Poland - a 40-year-long Observational Study
<div> <div> <p><span><span>Aims/hypothesis</span></span><span><span>: </span><span>40-year-long longitudinal observation</span> <span>of long-term trends of type 1 diabetes</span><span> incidence</span><span> and prevalence</span> <span>in children </span><span>in Central P</span><span>o</span><span>land </span></span><span> </span></p> </div> <div> <p><span><span>Methods</span></span><span><span>: This was a prospective observational study performed </span><span>by </span><span>a reference </span><span>regional </span><span>center</span><span> for pediatric diabetes care for Lodz Province (currently 2</span><span>·</span><span>4M </span><span>inhabitants</span><span>, 360K children). </span><span>We </span><span>registered </span><span>e</span><span>ach case of new-onset </span><span>type 1 </span><span>diabetes </span><span>admitted</span> <span>to regional pediatric diabetes centers </span><span>between </span><span>the </span><span>years 1983 and 2022 in children between 0 and 14 </span><span>y.o.</span> <span>The diagnosis</span><span> was based on </span><span>currently </span><span>available guidelines</span><span>.</span> <span>C</span><span>ases of other types of diabetes (e.g.</span><span>,</span><span> monogenic) were excluded</span><span> upon identification from incidence and prevalence rates</span><span>. Yearly data on the at</span><span>-</span><span>risk</span> <span>population were </span><span>acquired</span><span> from Poland`s General Statistical Office. Sex-specific data </span><span>on population structure </span><span>were available from 1989 onwards.</span><span> </span></span><span> </span></p> </div> <div> <p><span><span>Results</span></span><span><span>: In the </span><span>analyzed</span> <span>period, </span><span>the </span><span>incidence rate</span><span> of type 1 diabetes</span><span> increased </span><span>tenfold </span><span>from 3</span><span>·</span><span>29/100</span><span>,</span><span>000 (95%CI: </span><span>1</span><span>·</span><span>85</span><span>-</span><span>4</span><span>·</span><span>73</span><span>) in 1983 to 3</span><span>2.43</span><span> (2</span><span>6</span><span>·</span><span>42-38</span><span>·</span><span>44</span><span>) in 2022, with </span><span>an </span><span>average annual percentage change of 5</span><span>·</span><span>73</span><span>% (95%CI: 4</span><span>·</span><span>9</span><span>9</span><span>%-6</span><span>·</span><span>44</span><span>%). Joinpoint analysis detected two distinct periods of increase</span><span>:</span> <span>rapid in 1983-200</span><span>5</span><span> (annual percentage increase </span><span>of </span><span>7</span><span>·</span><span>38</span><span>%</span><span>, 95%CI: </span><span>6</span><span>·</span><span>30</span><span>-1</span><span>0</span><span>·</span><span>52</span><span>%</span><span>) and </span><span>a </span><span>slower </span><span>one</span><span> in </span><span>200</span><span>5</span><span>-2022 (3</span><span>·</span><span>65</span><span>%</span><span>, 95%CI: -0</span><span>·</span><span>86</span><span>-</span><span>5</span><span>·</span><span>13</span><span>%</span><span>). </span><span>I</span><span>ncidence rates among the youngest children (0-4</span> <span>y.o.</span><span>) were significantly lower than in 5-9</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>5</span><span>67</span><span>±</span><span>0</span><span>·</span><span>059</span><span>, p<0</span><span>·</span><span>0001</span><span>)</span><span> and 10-14</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>520</span><span>±</span><span>0</span><span>·</span><span>0</span><span>60</span><span>, p<0</span><span>·</span><span>0001)</span><span>. </span><span>The </span><span>incidence </span><span>growth dynamic </span><span>for</span> <span>the two older groups showed </span><span>a </span><span>consistent </span><span>increase</span><span>, </span><span>whereas</span><span> the incidence in </span><span>0-4 year-olds</span><span> plateaued after 2007.</span> <span>Incidence rates varied </span><span>seasonally,</span><span> with the </span><span>most cases diagnosed </span><span>during the </span><span>winter months (December, January, </span><span>and </span><span>February</span><span>;</span> <span>mean difference from remaining seasons of 29</span><span>±11</span><span>·</span><span>6 percentage points</span><span>, p</span><span><0</span><span>·</span><span>0001</span><span>). </span><span>Corresponding with increasing incidence rate, estimated prevalence of type 1 diabetes increased over the years and reached </span><span>177</span><span>·</span><span>21</span><span>/100</span><span>,</span><span>000 (95%CI: 163</span><span>·</span><span>18-191</span><span>·</span><span>24)</span><span> for children 0-14 </span><span>y.o.</span><span>,</span><span> and</span><span> </span> <span>1</span><span>7</span><span>·</span><span>11</span> <span>(95%CI:</span><span> 9</span><span>·</span><span>2</span><span>-</span><span>2</span><span>5</span><span>·</span><span>02</span><span>),</span><span> 1</span><span>90</span><span>·</span><span>54</span><span> (95%CI:</span> <span>16</span><span>5</span><span>·</span><span>03</span><span>-</span><span>21</span><span>5</span><span>·</span><span>75</span><span>),</span><span> 2</span><span>38</span><span>·</span><span>73</span><span> (95%CI: </span><span>21</span><span>1</span><span>·</span><span>7</span><span>-26</span><span>5</span><span>·</span><span>76</span><span>)</span><span> for 0-4</span><span>,</span><span> 5-9</span><span>, </span><span>and 10-14</span> <span>y.o.</span><span>, respectively.</span></span><span> </span></p> </div> <div> <p><span><span>Conclusion/i</span><span>nterpretation</span></span><span><span>: </span><span>Over the past 40 years, the incidence of </span><span>type 1 diabetes</span><span> in </span><span>children in </span><span>Central Poland </span><span>has increased significantly, but the rate of increase </span><span>appears to be</span><span> slowing.</span><span> As</span> <span>majority</span><span> of patients with type 1 diabetes are 10 years old or older, with the </span><span>most new</span><span> cases occurring in that age group</span><span> the healthcare systems should prepare for care of young adults who are extensive users of new diabetes technologies</span><span>.</span></span><span> </span></p> </div> </div>
Meteorology, MAIDENiso parameter files and observational data for Tungsten, Caniapiscau, and associated GPP stations
<p>This repository contains three datasets:</p> <p>1) Meteorology files needed to run the ecophysiological model of forest growth MAIDENiso at the dendrological sites of Tungsten and Caniapiscau and the eddy covariance flux stations of Uaf and Chibougamau.</p> <p>2) Parameter files needed to run the ecophysiological model of forest growth MAIDENiso at the dendrological sites of Tungsten and Caniapiscau and the eddy covariance flux stations of Uaf and Chibougamau.</p> <p>3) Observational dataset of snow, and d18O concentration at the dendrological sites, and GPP at the eddy covariance flux stations. In addition, observations of river discharge in the Caniapiscau basin (Quebec).</p>
Stellar specific intensity models used in "Hiding in plain sight: observing planet-starspot crossings with the James Webb Space Telescope"
<p>These stellar specific intensity models were used in the paper "Hiding in plain sight: observing planet-starspot crossings with the James Webb Space Telescope" to simulate starspot contrast spectra. See more details in the README file contained in the folder.</p>
Datalakes Observational model predictions (2019)
<p>Hydrodynamic model predictions for Lake Geneva generated using the SPUX Bayesian inference and uncertainty quantification package (https://doi.org/10.5281/zenodo.5638312) and the MITgcm hydrodynamics package (https://doi.org/10.5281/zenodo.5634041). The datasets contain the mean temperature/velocity predictions, as well as their respective spreads. The source repository for this dataset with predictions extended to September 2021 can be found at https://renkulab.io/gitlab/datalakes/hydrodynamic-model.</p> <p>The extended dataset is visualized on the Datalakes website: https://www.datalakes-eawag.ch/datadetail/17</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.