Find research datasets worth reusing

A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 03

We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container. Data set 03 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C05, which was acquired by bright-field light microscopy.

A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 18

We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container. Data set 18 contains stitched image montages of a thin section through the lung of patient C08 which were acquired by scanning electron microscopy. The file “Data_set_18.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.

A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 17

We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container. Data set 17 contains stitched image montages of a thin section through the lung of patient C07 which were acquired by scanning electron microscopy. The file “Data_set_17.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.

Data set of a survey of people 6 months before reaching their regular retirement age

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.

Raw data set used for a paper by Adachi et al

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.

opencc-by-4.0Dec 2020View details →

Data set - Photosynthetic light harvesting and thylakoid organization in a CRISPR/Cas9 Arabidopsis thaliana LHCB1 knockout mutant.

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." The report presents the difference in terms of protein accumulation, light harvesting, electron transport and thylakoid structure between the WT line (Col0) of Arabidopsis thaliana and a newly generated line that contains multiple mutations in all the genes coding for the light harvesting protein LHCB1, named L1ko. Paper_raw_data.xlsx contains the data utilised to produce the figures presented in the paper. 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 Chloroplasts_TEM_WT_L1KO.zip contains all the electron microscopy pictures, at full resolution analysed for the report.

dryad36/100

Data sets and analyses for genomics of cryptic speciation in Catharus thrushes

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 (Catharus bicknelli) and its sister species, the Gray-cheeked Thrush (Catharus minimus), 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 C. bicknelli and C. minimus, we sequenced and assembled high-coverage reference genomes of both species and re-sequenced genomes from population samples of C. bicknelli, C. minimus, and two individuals of the Swainson's Thrush (C. ustulatus). The genome of C. bicknelli exhibits markedly higher abundances of transposable elements compared to other Catharus and chicken. Demographic and admixture analyses confirm moderate genome-wide differentiation (Fst ≈ 0.10) and limited gene flow between C. bicknelli and C. minimus, 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 C. bicknelli. These genomes are a useful resource for future investigations of speciation, migration, and adaptation in Catharus thrushes.

opencc-zeroNov 2021View details →

rime fraction training data set extracted from BAECC

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. rime mass fraction retrieved from PIP data cloud radar observations at Ka- and W-band (ARM KAZR and MWACR) attenuation estimated using the Passive and Active Microwave Remote Sensing Tool (PAMTRA)

COBALTO data set at large at CIEM large scale wave flume

The experiments carried out are framed within the COBLTO project (CTM2017-88036-R) and the doctoral thesis of Carlos Astudillo. A surrogate Posidonia meadow has been used to study the wave attenuation, velocity affections and changes in sediment transport due to the comparison of experiments with the meadow and the benchmark cases where the meadow was absent on the flume experiments. Two different wave conditions, high energy waves and low energy waves, have been considered. The case with higher energy was also repeated with a third configuration in which a meadow length of 5 m was also studied.

DDoS Attacks Data Set - Consolidated from CICDDOS2019 and CICIDS2017

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. 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).

Data set used for the article "Atmospheric triggers of the Brunt Ice Shelf calving in February 2021"

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.

Data Set of Publication - Outcome of Scleral Rupture Primary Management Without Vitrectomy Jakarta Eye Trauma Study

This dataset was collected for research purposes. The dataset was about the scleral rupture we found in our teritories

MUESLI Hyperspectral & LiDar Data Set

The data set contain the hyperspectral images and the corresponding LiDar data from the MUESLI project. The meta data is included in the tif files. For the spectral bands, a copy of the original hdr file is below: 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} 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}

data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease

data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease

Year-round observations of stable carbon isotopic composition of diacids and related compounds in fine aerosols at Tianjin, North China – Data set

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 (δ13C) of diacids, oxoacids, α-dicarbonyls, citric acid and fatty acids in fine aerosols (PM2.5) collected at an urban (Nankai District, ND; n = 121) and a suburban (Haihe Education Park, HEP; n = 40) sites in Tianjin from 5 July 2018 to 4 July 2019. The δ13C 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 δ13C 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.

opencc-by-4.0May 2021View details →

CONUS NG-IDF Data Sets

The NG-IDF datasets, 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 (NG-IDF).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. Abbreviations:<blockquote>P = PrecipitationR = RainfallROS = Rain-on-snowM = Snowmelt W = Water reaching the land surface from rain, snowmelt, and ROS events</blockquote> Classification of Driving Mechanism for Extreme W Events:For each location, the driving mechanism was determined for extreme W events with different durations and return periods, following the classification criteria as follows:<blockquote>Rainfall (R): precipitation on snow-free ground; Snowmelt (M): decreasing SWE, daily rainfall < 10 mm, and the sum of rainfall and snowmelt has < 20% contribution from rainfall.Rain-on-snow (ROS): 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</blockquote> The datasets include:Note: In data format description, C = column, R = row, C[i] indicates the ith column of a data file. All data files are prepared in comma-separated value (.csv) format.<ul><li>list.csv<ul><li>Description: The geographic coordinates and cluster ID (used for snow parameterization) of 207,173 locations over the CONUS</li><li>Data Dimension: 207,173 (R) x 3 (C)</li><li>Format: C1: Latitude; C2: Longitude; C3: cluster ID (ranging from 1‒5)</li></ul></li></ul> <ul><li>AMF_WY/<ul><li>Description: 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>Subfolder/File Naming Convention: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li>Data Dimension: 207,173 (R) x 65 (C)</li><li>Unit: mm</li><li>Format: C1: Latitude; C2: Longitude; C3‒C65: maximum value for each year from 1951-2013</li></ul></li></ul> <ul><li>AMF_CY/<ul><li>Description: 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>Subfolder/File Naming Convention: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li>Data Dimension: 207,173 (R) x 65 (C)</li><li>Unit: mm</li><li>Format: C1: Latitude; C2: Longitude; C3‒C65: maximum value for each year from 1950-2012</li></ul></li></ul> <ul><li>IDF/<ul><li>Description: 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>Subfolder/File Naming Convention: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li>Data Dimension: 207,173 (R) x 9 (C)</li><li>Unit: mm</li><li>Format: 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> <ul><li>IDF_90CI/<ul><li>Description: 90% C.I. for IDF values in the IDF/ folder described above</li><li>Subfolder/File Naming Convention: [duration]/[mechanism]_H.csv, e.g., 24h/W_H.csv<ul><li>Data Dimension: 207,173 (R) x 9 (C)</li><li>Unit: mm</li><li>Format: C1: Latitude; C2: Longitude; C3‒C9: 95% quantile for IDF values with the return period of 2, 5, 10, 25, 50, 100, and 500 years. NaN indicates no runoff event caused by a given mechanism.</li></ul></li><li>Subfolder/File Naming Convention: [duration]/[mechanism]_L.csv, e.g., 24h/W_L.csv<ul><li>Data Dimension: 207,173 (R) x 9 (C)</li><li>Unit: mm</li><li>Format: C1: Latitude; C2: Longitude; C3‒C9: 5% quantile for IDF values with the return period of 2, 5, 10, 25, 50, 100, and 500 years. NaN indicates no runoff event caused by a given mechanism.</li></ul></li></ul></li></ul> <ul><li>trend/<ul><li>Description: 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>Subfolder/File Naming Convention: [duration]/[mechanism].csv, e.g., 24h/W.csv</li><li>Data Dimension: 207,173 (R) x 3 (C)</li><li>Unit: mm/year</li><li>Format: 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> <ul><li>Driver/<ul><li>Description: Dominant driving mechanism of extreme W events with different durations and return periods</li><li>Subfolder/File Naming Convention: [duration]/[return period].csv, e.g., 24h/50y.csv</li><li>Data Dimension: 207,173 (R) x 3 (C)</li><li>Format: C1: Latitude; C2: Longitude; C3: Dominant driver IDs (1=R, 2=ROS, 3=M).</li></ul></li></ul> <ul><li>risk/<ul><li>Description: Design risk associated with PREC-IDF estimated 100-year extreme events</li><li>Subfolder/File Naming Convention: [duration]/100y.csv, e.g., 24h/100y.csv</li><li>Data Dimension: 207,173 (R) x 3 (C)</li><li>Unit: %</li><li>Format: C1: Latitude; C2: Longitude; C3: Bias in the 100-year event based on PREC-IDF vs. NG-IDF</li></ul></li></ul> <ul><li>SI/<ul><li>Description: Seasonality of annual maximum W events with 24-, 48-, and 72-h durations over water years 1951‒2013</li><li>Subfolder/File Naming Convention: [duration]/W.csv, e.g., 24h/W.csv</li><li>Data Dimension: 207,173 (R) x 4 (C)</li><li>Format: 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

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

Data set of eradication project on Tsuken Island

Host plant survey.csv: Result of host plant surveys SIT.csv: Date and the number of sterile insects released Sweet potato.csv: Result of host plant survey on sweet potato Trap.csv: Result of trap surveys

Good/Bad data set

The Good/Bad data set is used for the image-quality research, containing unsuccessfully and successfully synthetic samples.

Modelling and Enforcing Access Control Requirements for Smart Contracts - Data Set

This data set contains all artifacts for the master thesis of Jan-Philipp Töberg at the Karlsruher Institute of Technology. This includes the Eclipse project for the metamodel and the generator, the extension of the Slither framework and the use case instances employed during the evaluation. Additionally, extensive instructions regarding the installation and usage are provided.