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All-optical quality-control of indenene intercalation into graphene/SiC
No full textRaw data used for the manuscript: 'All-optical quality-control of indenene intercalation into graphene/SiC' published in Applied Physics Letter 2024. Please read the 'README' file.We are grateful for funding support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy through the Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter ct.qmat (EXC 2147, Project ID 390858490) as well as through the Collaborative Research Center SFB 1170 ToCoTronics (Project ID 258499086). e acknowledge financial support from the PNRR MUR Project PE0000023-NQSTI
Cold Nights Percent (tn10p)
No full textcdo -eca_tn10p -ydrunpctl,10,5 TNref.nc -ydrunmin,5 TNref.nc -ydrunmax,5 TNref.nc out.ncPercentage of cold nights: Let TNt be the daily minimum temperature on day t and let TNin10 be the calendar day 10th percentile centred on a 5-day window for the base period 1981-2010. The percentage of time for the base period is determined where: TNt < TNin10
LANDSURF - Climate Indices for Africa
No full textThe dataset contains a wide range of climatological and agrometeorological indices of Africa for 1981-2100. The index calculation is based on global and regional climate models from CMIP5 and CORDEX-CORE, respectively, and covers the period 1981-2100. For the future, a low (RCP2.6) and a high (RCP8.5) greenhouse gas emission scenario are used. A total of 25 indices can be divided into five groups with a focus (1) temperature, (2) precipitation, (3) rainy season, (4) agriculture, and (5) drought. An overview of all indices can be found in the readme.txt. Temperature indices contain threshold- and percentile-based indices from the ETCCDI as well as heatwave indices. Precipitation indices are taken from the ETCCDI as well. The rainy season is determined based on Liebmann et al. (2016), enabling the identification of a first and a second rainy season. The resulting rainy season mask was used for some of the ETCCDI indices and for calculating the respective onset and cessation days. The agricultural indices depend on the rainy season onset, plant specific crop parameters, and related temperatures and precipitation. In this dataset, three indices of four different plant phases for twelve different crops (Barley_Oats_Wheat_S, Barley_Oats_Wheat_L, Maize_grain_S, Maize_grain_L, Maize_sweet_S, Maize_sweet_L, Millet_S, Millet_L, Sorghum_S, Sorghum_L, Soybean_S, Soybean_L) are available. Drought indices contain SPI and SPEI for four different accumulation time periods. A validation of the climate models in representing the selected precipitation, rainy season, and agricultural indices during 1981-2010 is available by Abel et al. (2023). This reference also describes the calculation of the rainy season mask and the agricultural indices in more detail. The data was developed in the frame of the WASCAL WRAP2.0 project LANDSURF. We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups (list see readme.txt) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure
Climate Indicators: Cold Nights Percent (tn10p)
No full textcdo -eca_tn10p -ydrunpctl,10,5 TNref.nc -ydrunmin,5 TNref.nc -ydrunmax,5 TNref.nc out.ncPercentage of cold nights: Let TNt be the daily minimum temperature on day t and let TNin10 be the calendar day 10th percentile centred on a 5-day window for the base period 1981-2010. The percentage of time for the base period is determined where: TNt < TNin10
Climate Indicators: First Rainy Season (rs1)
No full textRainy season: The onset is set as the cumulative minimum of daily precipitation anomalies over a year, the cessation is defined by the cumulative maximum. If a grid point is marked by two local minima and maxima, a second rainy season is depicted. The cumulative curves are filtered by the 15 day running mean (for more detailed information see Abel et al. 2024)
Climate Indicators: Extremely Wet Days in Rainy Season (r99prs)
No full textcdo -yearpctl,99 RRmask.nc out.ncAnnual threshold for extreme heavy precipitation in rainy season: Let RRt be the daily precipitation amount on day t. For R99prs list all RRt of one year, but only in rainy season (which means rs1_ons≤t≤rs1_ces or rs2_ons≤t≤rs2_ces, for climatological rs1_ons, rs1_ces, rs2_ons, rs2_ces) and take the 99th percentile value
Climate Indicators: Warm Nights Percent (tn90p)
No full textcdo -eca_tn90p -ydrunpctl,90,5 TNref.nc -ydrunmin,5 TNref.nc -ydrunmax,5 TNref.nc out.ncPercentage of warm nights: Let TNt be the daily minimum temperature on day t and let TNin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1981-2010. The percentage of time for the base period is determined where TNt > TNin90
Crop Indicators: Sorghum_L
No full textCrop water need: Let ET0i be the daily potential evapotranspiration [mm] for day i, and Kc(pl,ph) the crop factor per plant pl and phase ph, and s(pl,ph) and e(pl,ph) the corresponding start and end day of pl and ph then CWN(pl,ph) is the daily average of the product of Kc(pl,ph) and ET0i, for s(pl,ph)≤ i < e(pl,ph) and s(pl,IS) = climatological ons (of rs1). Water deficit: Let CWN(pl,ph)i be the daily crop water need and efftpi the daily effective precipitation (which is 0mm for daily precipitation < 6.5mm, is 75mm for daily precipitation ≥ 75mm, and else the daily precipitation RRi), then Ir(pl,ph) is the daily average of the difference of cwn and efftp per plant and phase. Water balance: Let ETi be the daily actual evapotranspiration [mm] (calculated by the daily surface latent heat flux) then WA is the daily average of the difference of the daily precipitation and ETi per plant and phase
Remote Sensing Indicators: Plant Functional Types
No full textMonthly prediction of 14 Plant Funtional Types (PFTs) in a spatial resolution of 1 x 1 km based on MODIS, AVHRR and GLC FCS30D datasets. For predicting the retrospective PFT values the STARFM algorithm was utilized in a Python environment. All available months are packed into one .zip file which can be (i) downloaded and (ii) extracted using free and open standard software (e.g. 7-zip)
Donor Acceptor Triads with Triptycene Bridges, ns and fs laser raw data
No full textThis dataset contains the raw datasets measured on a Helios pump-probe spectrometer (Ultrafast Systems) and an inhouse built transient absorption spectrometer based on a LP920 setup (Edinburgh Instruments) which in an extended version allows for magnetic field dependent measurements. The LP920 data was deconvoluted using the L900 software (Edinburgh Instruments)