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Phases and exotic phase transitions of a two-dimensional Su-Schrieffer-Heeger model
No full textData to reproduce the figures of the publication: A. Götz, M. Hohenadler, and F. F. Assaad, Phases and exotic phase transitions of a two-dimensional Su-Schrieffer-Heeger model, Phys. Rev. B 109, 195154 (2024). Please read the 'README' file.We thank the Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter ct.qmat (EXC 2147, Project No. 390858490) as well as the DFG under the grant AS 120/16- 1 (Project No. 493886309) that is part of the collaborative research project SFB Q-M&S funded by the Austrian Science Fund (FWF) F 86. We are grateful for funding support from the DFG funded SFB 1170 on Topological and Correlated Electronics at Surfaces and Interfaces under the Grant No. C01. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. for funding this project by providing computing time on the GCS Supercomputer SuperMUC-NG at the Leibniz Supercomputing Centre. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under NHR Project No. 80069. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) through Grant No. 440719683
Crop Water Need (cwn)
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)
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
Proteomic data _ Identification of neutral sphingomyelinase-2 (NSM2) proximal proteins by APEX2-mediated proximity labeling in Jurkat cells
No full textProteomic mass spectrometry data supplementing the data published in the thesis: Identification of NSM2 proximal proteins by APEX2-mediated proximity labeling in Jurkat cells.The csv files contain tables that list proteins identified by label free LC-MS and their Log2 Fold Changes (Log2FC) and p values. Information about the individual csv files can be found in the "readme.txt”.A proximity labeling strategy was used based on the engineered ascorbate peroxidase 2 (APEX2) to explore the neutral sphingomyelinase 2 (NSM2) proximitome, specifically in Jurkat cells. For this purpose, cell lines stably expressing NSM2 fused to APEX2 at the C-terminus were generated. NSM2-APEX2 proximal proteins covalently labeled with biotin were purified using streptavidin-coated beads and identified by mass spectrometry (MS). The first analysis of NSM2-APEX2 labeling by MS accurately identified proteins under steady-state conditions (published in 10.3390/ijms25063247). Further, I applied the proximity labeling protocol to elucidate TNFα-induced alterations in the NSM2 proximitome within the first 5 minutes of stimulation (published in 10.3389/fimmu.2024.1435701). The NSM2 proximal network and its TNFα-induced changes provide a valuable resource for further investigations into the involvement of NSM2 in the early signaling pathways triggered by TNFα
Climate Indicators: Standardized Precipitation Evapotranspiration Index (spei)
No full textStandardized Precipitation Evapotranspiration Index: A precipitation and evapotranspiration(Hargreaves) anomaly is considered relativ to the mean of a reference period (1981-2010) and based on the underlying statistical distribution (Gamma). The anomlies are considered over different months (3, 6, 9, 12). (More information under: https://climate-indices.readthedocs.io/en/latest/
Crop Indicators: Barley_Oats_Wheat_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
Crop Indicators: Maize_sweet_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: Normalized Difference Vegetation Index (NDVI)
No full textPredicted monthly Normalized Difference Vegetation Index (NDVI) dataset in a spatial resolution of 1 x 1 km based on MODIS and AVHRR datasets. For predicting the restrospective NDVI vaules 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)
Landsurf _DSS_Data
No full textThe dataset contains a wide range of climatological, agrometeorological, and remote sensing indices of West Africa for 1981-2100. The climatological and agrometeorlogical 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 documentation. 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. (2024). This reference also describes the calculation of the rainy season mask and the agricultural indices in more detail. The presented remote sensing indicators are based on MODIS and AVHRR data. 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 for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure
Heat Wave Duration Index (hwdi)
No full textcdo -eca_hwdi TX.nc -ydrunmean,5 TXref.nc out.ncDuration of Heat Wave: Let TXt be the daily maximum temperature on day t and TX’t be the climatological average of a running 5 day mean (1981-2010), then hwdi is the longest period of consecutive days (≥ 6 days) in one year, where TXt > TX’t +5°C