Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
Deliverable D4.3: Andreev reflection and Coulomb blockade in a SSN SET observed
<p>Deliverable D4.3 and raw data included in it.</p>
Data set of a survey of people 6 months before reaching their regular retirement age
<p>This is an anonymized data set of a standardized survey of people about six months (+/- 3 months) before reaching their regular retirement age (n = 400). The survey is representative for the German-speaking part of Switzerland. Data was gained by telephone interviews, conducted by the market research institute DemoSCOPE in September 2019. The survey was part of the project “Identity Constructions for Retirement”, funded by the Swiss National Science Foundation (SNSF). Personal details that could lead back to the identity of the participants (among others postal code, profession, responses to open-ended questions) were removed from the data set.</p>
Raw data set used for a paper by Adachi et al
<p>Data set of aerosol particle compositions measured using scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy (STEM-EDS), sampling information, and model results used in a paper by Adachi et al. TEM samples were collected during the Aerosol Radiative Forcing in East Asia 2013 Summer (A-FORCE-2013S) campaign.</p>
Data set - Photosynthetic light harvesting and thylakoid organization in a CRISPR/Cas9 Arabidopsis thaliana LHCB1 knockout mutant.
<p>This data set was produced at the University of Neuchâtel, it cointains the raw data relative to the manuscript entitled "Photosynthetic light harvesting and thylakoid organization in a CRISPR/Cas9 Arabidopsis thaliana LHCB1 knockout mutant."</p> <p>The report presents the difference in terms of protein accumulation, light harvesting, electron transport and thylakoid structure between the WT line (Col0) of <em>Arabidopsis thaliana </em>and a newly generated line that contains multiple mutations in all the genes coding for the light harvesting protein LHCB1, named L1ko.</p> <p>Paper_raw_data.xlsx contains the data utilised to produce the figures presented in the paper.</p> <p>Fluorescence_data.xlsx contains the raw fluorescence traces in excel format for three experiments. The data of the measurments performed with the multispeq are also available here: https://photosynq.org/projects/etr_in_lab_test_protocols</p> <p>Chloroplasts_TEM_WT_L1KO.zip contains all the electron microscopy pictures, at full resolution analysed for the report.</p> <p> </p> <p> </p>
Data sets and analyses for genomics of cryptic speciation in Catharus thrushes
<p>Cryptic speciation may occur when reproductive isolation is recent or the accumulation of morphological differences between sister lineages is slowed by stabilizing selection preventing phenotypic differentiation. In North America, Bicknell's Thrush (<i>Catharus bicknelli</i>) and its sister species, the Gray-cheeked Thrush (<i>Catharus minimus</i>), are parapatrically breeding migratory songbirds, distinguishable in nature only by subtle differences in song and coloration, and were recognized as distinct species only in the 1990s. Previous molecular studies have estimated that the species diverged ~120 - 420 thousand YBP and found very low levels of introgression despite their similarity and sympatry in the spring (prebreeding) migration. To further clarify the history, genetic divergence, genomic structure and adaptive processes in <i>C. bicknelli</i> and <i>C</i>. <i>minimus</i>, we sequenced and assembled high-coverage reference genomes of both species and re-sequenced genomes from population samples of <i>C. bicknelli</i>, <i>C. minimus</i>, and two individuals of the Swainson's Thrush (<i>C. ustulatus</i>). The genome of <i>C. bicknelli</i> exhibits markedly higher abundances of transposable elements compared to other <i>Catharus</i> and chicken. Demographic and admixture analyses confirm moderate genome-wide differentiation (<i>F</i><sub>st</sub> <i>≈</i> 0.10) and limited gene flow between <i>C. bicknelli</i> and C. <i>minimus,</i> but suggest a more recent divergence than estimates based on mtDNA. We find evidence of rapid evolution of the Z-chromosome and elevated divergence consistent with natural selection on genomic regions near genes involved with neuronal processes in <i>C. bicknelli.</i> These genomes are a useful resource for future investigations of speciation, migration, and adaptation in <i>Catharus </i>thrushes.</p>
rime fraction training data set extracted from BAECC
<p>training data set used in Vogl et al. (https://amt.copernicus.org/preprints/amt-2021-137/) to derive rime mass fraction from Doppler cloud radar observations. Extracted from the BAECC data set.</p> <p>rime mass fraction retrieved from PIP data</p> <p>cloud radar observations at Ka- and W-band (ARM KAZR and MWACR)</p> <p>attenuation estimated using the Passive and Active Microwave Remote Sensing Tool (PAMTRA)</p>
caneill et al. The polar transition from alpha to beta regions set by a surface buoyancy flux inversion.
<p>NEMO outputs used for the paper Caneill, R., Roquet, F., Madec, G., and Nycander, J. (2022). The polar transition from alpha to beta regions set by a surface buoyancy flux inversion. Journal of Physical Oceanography.</p> <p><a href="https://doi.org/10.1175/JPO-D-21-0295.1">https://doi.org/10.1175/JPO-D-21-0295.1</a></p> <p>Abstract:</p> <p>The stratification is primarily controlled by temperature in subtropical regions (alpha-ocean), and by salinity in subpolar regions (beta-ocean). Between these two regions lies a transition zone, often characterized by deep mixed layers in winter and responsible for the ventilation of intermediate or deep layers. While of primary interest, no consensus on what controls its position exists yet. Amongst the potential candidates, we find the wind distribution, air-sea fluxes or the nonlinear cabbeling effect. Using an ocean general circulation model in an idealized basin configuration, a sensitivity analysis is performed testing different equations of state. More precisely, the thermal expansion coefficient (TEC) temperature dependence is explored, changing the impact of heat fluxes on buoyancy fluxes in a series of experiments. The transition zone is found to be located at the position where the sign of the surface buoyancy flux reverses to become positive, in the subpolar region, while wind or cabbeling are found of secondary importance. This inversion becomes possible because the TEC is reducing at low temperature, enhancing in return the relative impact of freshwater fluxes on the buoyancy forcing at high latitudes. When the TEC is made artificially larger at low temperature, the freshwater flux required to produce a positive buoyancy flux increases and the transition zone moves poleward. These experiments demonstrate the important role of competing heat and freshwater fluxes in setting the position of the transition zone. This competition is primarily influenced by the spatial variations of the TEC linked to meridional variations of the surface temperature.</p> <p> </p> <p>Download data:</p> <p>Due to their large size, the data have been split into multiple zip files. To extract them, follow the following commands:</p> <pre><code class="language-bash">zip -s 0 EXP_main.zip --out out.zip unzip out.zip</code></pre> <p> </p>
COBALTO data set at large at CIEM large scale wave flume
<p>The experiments carried out are framed within the COBLTO project (CTM2017-88036-R) and<br> the doctoral thesis of Carlos Astudillo. A surrogate Posidonia meadow has been used to study<br> the wave attenuation, velocity affections and changes in sediment transport due to the<br> comparison of experiments with the meadow and the benchmark cases where the meadow<br> was absent on the flume experiments. Two different wave conditions, high energy waves and<br> low energy waves, have been considered. The case with higher energy was also repeated with<br> a third configuration in which a meadow length of 5 m was also studied.</p>
DDoS Attacks Data Set - Consolidated from CICDDOS2019 and CICIDS2017
<p>This dataset is a transformation of the <a href="https://www.unb.ca/cic/datasets/ddos-2019.html">CICDDOS2019</a> collection of datasets by <a href="https://ieeexplore.ieee.org/abstract/document/8888419">Iman Sharafaldin et al. (2019)</a>. All datasets have been combined and have had their labels standardised. Infinity values have been removed. A supplement of Benign tuples has been introduced from <a href="https://www.unb.ca/cic/datasets/ids-2017.html">CICIDS2017</a>, another collection of datasets by <a href="https://fardapaper.ir/mohavaha/uploads/2018/07/Fardapaper-Toward-Generating-a-New-Intrusion-Detection-Dataset-and-Intrusion-Traffic-Characterization.pdf">Iman Sharafaldin et al. (2018)</a>, to increase the proportion of Benign tuples within the dataset.</p> <p>Our paper: <a href="https://doi.org/10.1063/5.0133063">source1</a>, <a href="https://www.researchgate.net/publication/370975966_Evaluating_classifiers'_performance_on_a_consolidated_DDoS_data_set">source 2 </a>(preprint).</p>
Data set used for the article "Atmospheric triggers of the Brunt Ice Shelf calving in February 2021"
<p>Data set used for the manuscript "Atmospheric triggers of the Brunt Ice Shelf calving in February 2021". Which is under review in "Geophysical Research Letters" Journal. </p>
Data Set of Publication - Outcome of Scleral Rupture Primary Management Without Vitrectomy Jakarta Eye Trauma Study
<p>This dataset was collected for research purposes. The dataset was about the scleral rupture we found in our teritories</p>
DepthTrack Test Set (ICCV2021)
<p>DepthTrack test set, including 50 RGBD sequences, their groundtruth bounding boxes, and visual attributes annotation.</p> <p>If you use it, please cite:</p> <blockquote> <pre><code>@InProceedings{yan2021det, author = {Yan, Song and Yang, Jinyu and Kapyla, Jani and Zheng, Feng and Leonardis, Ales and Kamarainen, Joni-Kristian}, title = {DepthTrack: Unveiling the Power of RGBD Tracking}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {10725-10733} }</code></pre> </blockquote>
MUESLI Hyperspectral & LiDar Data Set
<p>The data set contain the hyperspectral images and the corresponding LiDar data from the MUESLI project.</p> <p>The meta data is included in the tif files. For the spectral bands, a copy of the original hdr file is below:</p> <p>fwhm = {4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25}<br> wavelength = {414.681622,418.320307,421.958992,425.597676,429.236361,432.875046,436.513731,440.152415,443.7911,447.429785,451.06847,454.707155,458.345839,461.984524,465.623209,469.261894,472.900578,476.539263,480.177948,483.816633,487.455317,491.094002,494.732687,498.371372,502.010056,505.648741,509.287426,512.926111,516.564796,520.20348,523.842165,527.48085,531.119535,534.758219,538.396904,542.035589,545.674274,549.312958,552.951643,556.590328,560.229013,563.867697,567.506382,571.145067,574.783752,578.422437,582.061121,585.699806,589.338491,592.977176,596.61586,600.254545,603.89323,607.531915,611.170599,614.809284,618.447969,622.086654,625.725338,629.364023,633.002708,636.641393,640.280078,643.918762,647.557447,651.196132,654.834817,658.473501,662.112186,665.750871,669.389556,673.02824,676.666925,680.30561,683.944295,687.582979,691.221664,694.860349,698.499034,702.137719,705.776403,709.415088,713.053773,716.692458,720.331142,723.969827,727.608512,731.247197,734.885881,738.524566,742.163251,745.801936,749.44062,753.079305,756.71799,760.356675,763.99536,767.634044,771.272729,774.911414,778.550099,782.188783,785.827468,789.466153,793.104838,796.743522,800.382207,804.020892,807.659577,811.298261,814.936946,818.575631,822.214316,825.853,829.491685,833.13037,836.769055,840.40774,844.046424,847.685109,851.323794,854.962479,858.601163,862.239848,865.878533,869.517218,873.155902,876.794587,880.433272,884.071957,887.710641,891.349326,894.988011,898.626696,902.265381,905.904065,909.54275,913.181435,916.82012,920.458804,924.097489,927.736174,931.374859,935.013543,938.652228,942.290913,945.929598,949.568282,953.206967,956.845652,960.484337,964.123022,967.761706,971.400391,977.281135,982.74497,988.208806,993.672641,999.136476,1004.600311,1010.064146,1015.527981,1020.991816,1026.455651,1031.919486,1037.383321,1042.847156,1048.310991,1053.774826,1059.238662,1064.702497,1070.166332,1075.630167,1081.094002,1086.557837,1092.021672,1097.485507,1102.949342,1108.413177,1113.877012,1119.340847,1124.804682,1130.268518,1135.732353,1141.196188,1146.660023,1152.123858,1157.587693,1163.051528,1168.515363,1173.979198,1179.443033,1184.906868,1190.370703,1195.834538,1201.298374,1206.762209,1212.226044,1217.689879,1223.153714,1228.617549,1234.081384,1239.545219,1245.009054,1250.472889,1255.936724,1261.400559,1266.864394,1272.32823,1277.792065,1283.2559,1288.719735,1294.18357,1299.647405,1305.11124,1310.575075,1316.03891,1321.502745,1326.96658,1332.430415,1337.89425,1343.358086,1348.821921,1354.285756,1359.749591,1365.213426,1370.677261,1376.141096,1381.604931,1387.068766,1392.532601,1397.996436,1403.460271,1408.924106,1414.387942,1419.851777,1425.315612,1430.779447,1436.243282,1441.707117,1447.170952,1452.634787,1458.098622,1463.562457,1469.026292,1474.490127,1479.953962,1485.417798,1490.881633,1496.345468,1501.809303,1507.273138,1512.736973,1518.200808,1523.664643,1529.128478,1534.592313,1540.056148,1545.519983,1550.983818,1556.447654,1561.911489,1567.375324,1572.839159,1578.302994,1583.766829,1589.230664,1594.694499,1600.158334,1605.622169,1611.086004,1616.549839,1622.013674,1627.477509,1632.941345,1638.40518,1643.869015,1649.33285,1654.796685,1660.26052,1665.724355,1671.18819,1676.652025,1682.11586,1687.579695,1693.04353,1698.507365,1703.971201,1709.435036,1714.898871,1720.362706,1725.826541,1731.290376,1736.754211,1742.218046,1747.681881,1753.145716,1758.609551,1764.073386,1769.537221,1775.001057,1780.464892,1785.928727,1791.392562,1796.856397,1802.320232,1807.784067,1813.247902,1818.711737,1824.175572,1829.639407,1835.103242,1840.567077,1846.030913,1851.494748,1856.958583,1862.422418,1867.886253,1873.350088,1878.813923,1884.277758,1889.741593,1895.205428,1900.669263,1906.133098,1911.596933,1917.060769,1922.524604,1927.988439,1933.452274,1938.916109,1944.379944,1949.843779,1955.307614,1960.771449,1966.235284,1971.699119,1977.162954,1982.626789,1988.090625,1993.55446,1999.018295,2004.48213,2009.945965,2015.4098,2020.873635,2026.33747,2031.801305,2037.26514,2042.728975,2048.19281,2053.656645,2059.120481,2064.584316,2070.048151,2075.511986,2080.975821,2086.439656,2091.903491,2097.367326,2102.831161,2108.294996,2113.758831,2119.222666,2124.686501,2130.150337,2135.614172,2141.078007,2146.541842,2152.005677,2157.469512,2162.933347,2168.397182,2173.861017,2179.324852,2184.788687,2190.252522,2195.716357,2201.180193,2206.644028,2212.107863,2217.571698,2223.035533,2228.499368,2233.963203,2239.427038,2244.890873,2250.354708,2255.818543,2261.282378,2266.746213,2272.210049,2277.673884,2283.137719,2288.601554,2294.065389,2299.529224,2304.993059,2310.456894,2315.920729,2321.384564,2326.848399,2332.312234,2337.776069,2343.239905,2348.70374,2354.167575,2359.63141,2365.095245,2370.55908,2376.022915,2381.48675,2386.950585,2392.41442,2397.878255,2403.34209,2408.805925,2414.269761,2419.733596,2425.197431,2430.661266,2436.125101,2441.588936,2447.052771,2452.516606,2457.980441,2463.444276,2468.908111,2474.371946,2479.835781,2485.299617,2490.763452,2496.227287,2501.691122,2507.154957,2512.618792,2518.082627,2523.546462}</p> <p> </p> <p> </p> <p> </p>
Adult male baseball pitchers anthropometric measurements and pitching velocity in tryout settings
<p>In this study, we adopted field tests executed using affordable equipment in a tryout event for a professional baseball team in Taiwan, 2019. We have only half day to test 64 players, and the result of measurement are used to develop a model for predicting pitching velocity of amateur adult pitchers (age: 23.9 ± 2.8 years; height: 180.3 ± 5.9 cm; weight: 81.4 ± 10.9 kg) . The measurements and tests in tryout settings should be easy to implement, take short time, do not need high skill levels, and correlate to the pitching velocity. The outcome measures included maximum external shoulder rotation, maximum internal shoulder rotation, countermovement jump (CMJ) height, 20-kg loaded CMJ height, 30-m sprint time, height, age, and weight tests. </p>
data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease
<p>data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease</p>
A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network
<p>The file uploaded provides input data for the paper "A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network".</p>
Numerical discrimination of sets of elements by Archerfish
<p>Debates have arisen as to whether non-human animals actually can learn abstract non-symbolic numerousness or whether they always rely on some continuous physical aspect of the stimuli, covarying with number. Here we investigated archerfish (<em>Toxotes jaculatrix</em>) non-symbolic numerical discrimination with accurate control for co-varying continuous physical stimulus attributes. Archerfish were trained to select one of two groups of black dots (Exp. 1: 3 <em>vs. </em>6 elements; Exp. 2: 2 <em>vs.</em> 3 elements); these were controlled for several combinations of physical variables (elements' size, overall area, overall perimeter, density and sparsity), ensuring that only numerical information was available. Generalization tests with novel numerical comparisons (2 <em>vs.</em> 3, 5 <em>vs.</em> 8 and 6 <em>vs.</em> 9 in Exp. 1; 3 <em>vs.</em> 4, 3 <em>vs.</em> 6 in Exp. 2) revealed choice for the largest or smallest numerical group according to the relative number that was rewarded at training. None of the continuous physical variables, including spatial frequency, were affecting archerfish performance. Results provide evidence that archerfish spontaneously use abstract relative numerical information for both small and large numbers when only numerical cues are available.</p>
Year-round observations of stable carbon isotopic composition of diacids and related compounds in fine aerosols at Tianjin, North China – Data set
<p>To better understand the origins and photochemical processing of water-soluble organic aerosols in the Tianjin region, North China, we studied the stable isotopic composition (δ<sup>13</sup>C) of diacids, oxoacids, α-dicarbonyls, citric acid and fatty acids in fine aerosols (PM<sub>2.5</sub>) collected at an urban (Nankai District, ND; <em>n</em> = 121) and a suburban (Haihe Education Park, HEP; <em>n</em> = 40) sites in Tianjin from 5 July 2018 to 4 July 2019. The δ<sup>13</sup>C of diacids and related compounds were measured using a capillary gas chromatography (Agilent 7890B) coupled with isotope ratio mass spectrometry (GC-irMS) system. Based on seasonal variations in δ<sup>13</sup>C of selected species and linear relations with their concentrations and mass ratios, we found that the diacids and related compounds were mainly derived from fossil fuel including coal combustion and biomass burning and photochemical processing, preferably in gaseous- and aqueous-phases in warm (March-September) and cold (October-February) periods, respectively, in Tianjin, North China.</p>
CONUS NG-IDF Data Sets
<p>The <strong>NG-IDF datasets</strong>, spanning 1951‒2013, characterize the magnitude, trend, seasonality, and driving mechanism of extreme events relevant to hydrologic design for over 200,000 locations across the CONUS at a 1/16th-degree resolution. These datasets enhance traditional precipitation-based intensity-duration-frequency curves (PREC-IDF) by accounting for total water reaching the land surface from rainfall, snowmelt, and rain-on-snow (ROS), i.e., Next-Generation intensity-duration-frequency analysis (<strong>NG-IDF</strong>).</p><p>PREC-IDF's neglect of snow processes can lead to significant biases in extreme event analysis, especially in snowy regions. NG-IDF addresses this by including snow processes, providing a systematic and consistent technique for all environments from rain-dominated, transitional, to snow-dominated locations. The resulting datasets are spatially continuous, and readily usable for supporting site-specific infrastructure design and for assessing the potential biases and design risks related to the use of PREC-IDF.</p><p> </p><p><strong>Abbreviations:</strong></p><blockquote><p>P = Precipitation</p><p>R = Rainfall</p><p>ROS = Rain-on-snow</p><p>M = Snowmelt </p><p>W = Water reaching the land surface from rain, snowmelt, and ROS events</p></blockquote><p> </p><p><strong>Classification of Driving Mechanism for Extreme W Events:</strong></p><p>For each location, the driving mechanism was determined for extreme W events with different durations and return periods, following the classification criteria as follows:</p><blockquote><p><strong>Rainfall (R):</strong> precipitation on snow-free ground; </p><p><strong>Snowmelt (M): </strong>decreasing SWE, daily rainfall < 10 mm, and the sum of rainfall and snowmelt has < 20% contribution from rainfall.</p><p><strong>Rain-on-snow (ROS):</strong> decreasing SWE with at least 10 mm of daily rainfall falling on a snowpack with at least 10 mm daily SWE, and snowmelt contributes at least 20% of the total of rain and snowmelt</p></blockquote><p> </p><p><strong>The datasets include:</strong></p><p><i>Note: In data format description, <strong>C</strong> = column, <strong>R</strong> = row, <strong>C[i]</strong> indicates the ith column of a data file. All data files are prepared in comma-separated value (.csv) format.</i></p><ul><li><strong>list.csv</strong><ul><li><strong>Description:</strong> The geographic coordinates and cluster ID (used for snow parameterization) of 207,173 locations over the CONUS</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3: cluster ID (ranging from 1‒5)</li></ul></li></ul><p> </p><ul><li><strong>AMF_WY/</strong><ul><li><strong>Description:</strong> Annual maximum series with durations of 24h, 48h, and 72h, driven by different hydrometeorological mechanisms over water years 1951‒2013 (10/1/1950-09/30/2013). The mechanisms include W, P, R, ROS, and M.</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 65 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3‒C65: maximum value for each year from 1951-2013</li></ul></li></ul><p> </p><ul><li><strong>AMF_CY/</strong><ul><li><strong>Description:</strong> Annual maximum series with durations of 24h, 48h, and 72h, driven by different hydrometeorological mechanisms over calendar years 1950‒2012 (1/1/1950-12/30/2012). The mechanisms include W, P, R, ROS, and M.</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 65 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3‒C65: maximum value for each year from 1950-2012</li></ul></li></ul><p> </p><ul><li><strong>IDF/</strong><ul><li><strong>Description:</strong> Discrete IDF values, i.e., the magnitude of extreme events with durations of 24h, 48h, and 72h, driven by different hydrometeorological mechanisms. The mechanisms include W, P, R, ROS, and M.</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 9 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3‒C9: IDF values for the return period of 2, 5, 10, 25, 50, 100, and 500 years. NaN indicates no runoff caused by a given mechanism, such as ROS.</li></ul></li></ul><p> </p><ul><li><strong>IDF_90CI/</strong><ul><li><strong>Description:</strong> 90% C.I. for IDF values in the IDF/ folder described above</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism]_H.csv, e.g., 24h/W_H.csv<ul><li><strong>Data Dimension</strong>: 207,173 (R) x 9 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3‒C9: <i>95% quantile</i> for IDF values with the return period of 2, 5, 10, 25, 50, 100, and 500 years.<strong> </strong>NaN indicates no runoff event caused by a given mechanism.</li></ul></li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism]_L.csv, e.g., 24h/W_L.csv<ul><li><strong>Data Dimension</strong>: 207,173 (R) x 9 (C)</li><li><strong>Unit</strong>: mm</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3‒C9: <i>5% quantile</i> for IDF values with the return period of 2, 5, 10, 25, 50, 100, and 500 years.<strong> </strong>NaN indicates no runoff event caused by a given mechanism.</li></ul></li></ul></li></ul><p> </p><ul><li><strong>trend/</strong><ul><li><strong>Description</strong>: Sen's slope of Mann-Kendall trend in annual maximum series driven by different hydrometeorological mechanisms over water years 1951‒2013. The mechanisms include W, P, rainfall (R), ROS, and snowmelt (M).</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Unit:</strong> mm/year</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3: Trend indicated by Sen's slope. The value is zero if the trend is not statistically significant.</li></ul></li></ul><p> </p><ul><li><strong>Driver/</strong><ul><li><strong>Description</strong>: Dominant driving mechanism of extreme W events with different durations and return periods</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/[return period].csv, e.g., 24h/50y.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3: Dominant driver IDs (1=R, 2=ROS, 3=M).</li></ul></li></ul><p> </p><ul><li><strong>risk/</strong><ul><li><strong>Description</strong>: Design risk associated with PREC-IDF estimated 100-year extreme events</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/100y.csv, e.g., 24h/100y.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 3 (C)</li><li><strong>Unit</strong>: %</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3: Bias in the 100-year event based on PREC-IDF vs. NG-IDF</li></ul></li></ul><p> </p><ul><li><strong>SI/</strong><ul><li><strong>Description</strong>: Seasonality of annual maximum W events with 24-, 48-, and 72-h durations over water years 1951‒2013</li><li><strong>Subfolder/File Naming Convention</strong>: [duration]/W.csv, e.g., 24h/W.csv</li><li><strong>Data Dimension</strong>: 207,173 (R) x 4 (C)</li><li><strong>Format</strong>: C1: Latitude; C2: Longitude; C3: Mean data (=1 if Oct 1); C4: Seasonality index ranging from 0 to 1</li></ul></li></ul>
data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease – a comparison of three screening tools used in the GLIM diagnostic algorithm
<p>data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease – a comparison of three screening tools used in the GLIM diagnostic algorithm </p>
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.