cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7281/t1/1ecvb0/98yo6u
title: DustOff-monthly-average_wrfout_d01_2013-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/adthsn
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-03_18_std.nc
DOI: 10.7281/t1/1ecvb0/dl9ntk
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-10_18_std.nc
DOI: 10.7281/t1/1ecvb0/eodvtv
title: DustOff-monthly-average_wrfout_d01_2008-07_18_mean.nc
DOI: 10.7281/t1/1ecvb0/iemre6
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-08_18_std.nc
DOI: 10.7281/t1/1ecvb0/lz4ttr
title: DustOff-monthly-average_wrfout_d01_2009-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/mfvfru
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-04_18_std.nc
DOI: 10.7281/t1/1ecvb0/p8alm5
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-10_06_std.nc
DOI: 10.7281/t1/1ecvb0/ruk5er
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-08_12_std.nc
DOI: 10.7281/t1/1ecvb0/sa7ew8
title: DustOff-monthly-average_wrfout_d01_2013-05_12_mean.nc
DOI: 10.7281/t1/1ecvb0/uozbdv
title: DustOff-monthly-average_wrfout_d01_2013-07_18_mean.nc
DOI: 10.7281/t1/1ecvb0/uxaqol
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-04_06_std.nc
DOI: 10.7281/t1/1ecvb0/vzdngu
title: DustOff-monthly-average_wrfout_d01_2008-06_06_mean.nc
DOI: 10.7281/t1/1ecvb0/x8r3ht
title: DustOff-monthly-average_wrfout_d01_2013-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/zdkfnj
title: DustOff-monthly-average_wrfout_d01_2008-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/zobkcy
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-08_00_std.nc
DOI: 10.17632/hjh2wm3r5k.1
title: Apollo 12 landing site photogrammetry data

description: Photogrammetric data for Apollo 12 landing site: coordinates of camera stations, selected objects on the surface, map, *.rzi files with raw data for ImageModeler. It is based on Hasselblad photographs made by the crew during their EVAs. Positions of camera stations, artifacts (Lunar Module, Surveyor 3 space probe, ALSEP instruments ets) and landscape features were determined.
DOI: 10.17632/sg5twgw2mk.1
title: A simple mixing method for polyamide 12/attapulgite nanocomposites: structural and mechanical characterization

description: In this work, the attapulgite (ATP) was used as a promising mineral clay to prepare polyamide 12 (PA12) matrix polymer nanocomposites. ATP has a relatively low cost compared to other nanoclays and is a very abundant raw material in the northeast region of Brazil. The ATP was characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy and field emission gun scanning electron microscopy (FEG-SEM). The PA12/ATP nanocomposites with 0, 1, 2.5, 5, 7.5 and 10 wt% of ATP were prepared using a simple blending method in a high-speed thermokinetic homogenizer (3000 rpm) in which the melting of the PA12 and the mixture with ATP occurred by friction, followed by hot pressing and stamping of the specimens. The nanocomposites were characterized by mechanical properties, the degree of crystallinity and crystallite size were calculated by XRD, and the morphological characteristics were observed by SEM. The addition of ATP in the PA12 matrix increased the modulus of elasticity, hardness, degree of crystallinity and the apparent crystallite size of the nanocomposites. The addition of up to 5 wt% of ATP increased tensile strength and deformation at break; for higher concentrations, the dispersion was not efficient. A major advantage of using ATP as a reinforcement agent for PA12 is the low cost of this material plus the great interaction with PA12 which can dispense the use of compatibilizer agents and/or surface modification in the ATP, making it a potential material to extend PA12’s range of applications.
DOI: 10.17632/v955jxxrsc.1
title: DateSnO2

description: Article date
DOI: 10.7281/t1/1ecvb0/3rvlrk
title: DustOff-monthly-average_wrfout_d01_2009-09_06_mean.nc
DOI: 10.7281/t1/1ecvb0/6tykl9
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-05_06_std.nc
DOI: 10.7281/t1/1ecvb0/8h6qtj
title: DustOff-monthly-average_wrfout_d01_2013-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/gb1hge
title: DustOff-monthly-average_wrfout_d01_2009-08_00_mean.nc
DOI: 10.7281/t1/1ecvb0/hzvcsa
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-04_00_std.nc
DOI: 10.7281/t1/1ecvb0/i2zdag
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-10_00_std.nc
DOI: 10.7281/t1/1ecvb0/kkqqws
title: DustOff-monthly-average_wrfout_d01_2013-09_00_mean.nc
DOI: 10.7281/t1/1ecvb0/ln86ux
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-10_12_std.nc
DOI: 10.7281/t1/1ecvb0/mfvthg
title: DustOff-monthly-average_wrfout_d01_2009-05_12_mean.nc
DOI: 10.7281/t1/1ecvb0/nc1uly
title: DustOff-monthly-average_wrfout_d01_2009-04_06_mean.nc
DOI: 10.7281/t1/1ecvb0/oreivr
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-09_00_std.nc
DOI: 10.7281/t1/1ecvb0/qvxero
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-08_06_std.nc
DOI: 10.7281/t1/1ecvb0/rmwfdw
title: DustOff-monthly-average_wrfout_d01_2013-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/u8phlf
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-05_18_std.nc
DOI: 10.7281/t1/1ecvb0/wdqxix
title: DustOff-monthly-average_wrfout_d01_2009-08_18_mean.nc
DOI: 10.7281/t1/1ecvb0/wriums
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-04_12_std.nc
DOI: 10.7281/t1/1ecvb0/yad1zx
title: DustOff-monthly-average_wrfout_d01_2009-05_00_mean.nc
DOI: 10.7281/t1/1ecvb0/yei00x
title: DustOff-monthly-average_wrfout_d01_2013-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/znjgzv
title: DustOff-monthly-average_wrfout_d01_2009-07_18_mean.nc
DOI: 10.17632/sdctdf3yby.1
title: Tilting table method for snow adhesion measurements - Analysis of the method

description: A method for measuring snow adhesion has been developed and tested. The present dataset represent the resulting data from this testing. Testing was performed in a controlled environment and using synthetic snow, in order to make the results as reproducible as possible. The data is structured so as to be self-explanatory given a context is provided as above, and include time and date of each sample run. This allows for analysis of the temperature and humidity stability.
DOI: 10.7281/t1/1ecvb0/1fpsdy
title: DustOff-monthly-average_wrfout_d01_2009-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/2kmyo0
title: DustOff-monthly-average_wrfout_d01_2009-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/3zh5eo
title: DustOff-monthly-average_wrfout_d01_2013-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/a3pcfx
title: DustOff-monthly-average_wrfout_d01_2013-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/f5rtbg
title: DustOff-monthly-average_wrfout_d01_2009-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/g3am7h
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-06_18_std.nc
DOI: 10.7281/t1/1ecvb0/hlkiie
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-07_00_std.nc
DOI: 10.7281/t1/1ecvb0/i79lp6
title: DustOff-monthly-average_wrfout_d01_2009-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/j7n3ic
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-07_06_std.nc
DOI: 10.7281/t1/1ecvb0/jckbjg
title: DustOff-monthly-average_wrfout_d01_2009-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/mmyyhy
title: DustOff-monthly-average_wrfout_d01_2009-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/o56jpy
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-09_12_std.nc
DOI: 10.7281/t1/1ecvb0/qupwnr
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-06_06_std.nc
DOI: 10.7281/t1/1ecvb0/sjsv8n
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-07_12_std.nc
DOI: 10.7281/t1/1ecvb0/t7ubwr
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-06_06_std.nc
DOI: 10.7281/t1/1ecvb0/vqa28s
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-06_12_std.nc
DOI: 10.7281/t1/1ecvb0/wtaqxt
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-06_00_std.nc
DOI: 10.7281/t1/1ecvb0/xz4vi2
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-07_18_std.nc
DOI: 10.7281/t1/1ecvb0/y3yclw
title: DustOff-monthly-average_wrfout_d01_2013-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/yez3gd
title: DustOff-monthly-average_wrfout_d01_2009-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/ywgtuz
title: DustOff-monthly-average_wrfout_d01_2013-05_06_mean.nc
DOI: 10.7281/t1/1ecvb0/zgo2uf
title: DustOff-monthly-average_wrfout_d01_2009-10_06_mean.nc
DOI: 10.17632/vrsh2fdngw.1
title: UNSM Residual and Hardness Data of Nickel-Based Alloys

description: This dataset is collection of hardness and residual stress measurement result of UNSM treated various nickel based alloys
DOI: 10.7281/t1/1ecvb0/3b0mtz
title: DustOff-monthly-average_wrfout_d01_2013-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/41tx3c
title: DustOff-monthly-average_wrfout_d01_2009-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/7jkxok
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-07_00_std.nc
DOI: 10.7281/t1/1ecvb0/8ypkzx
title: DustOff-monthly-average_wrfout_d01_2009-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/9k4yqh
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-06_18_std.nc
DOI: 10.7281/t1/1ecvb0/buekzo
title: DustOff-monthly-average_wrfout_d01_2013-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/efhgzs
title: DustOff-monthly-average_wrfout_d01_2009-07_06_mean.nc
DOI: 10.7281/t1/1ecvb0/el5boq
title: DustOff-monthly-average_wrfout_d01_2009-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/fg9jc9
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-07_06_std.nc
DOI: 10.7281/t1/1ecvb0/gtxwmy
title: DustOff-monthly-average_wrfout_d01_2009-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/ltgcmd
title: DustOff-monthly-average_wrfout_d01_2013-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/miu7xv
title: DustOff-monthly-average_wrfout_d01_2009-09_00_mean.nc
DOI: 10.7281/t1/1ecvb0/oqqdd9
title: DustOff-monthly-average_wrfout_d01_2009-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/pltcal
title: DustOff-monthly-average_wrfout_d01_2009-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/r5lfoj
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-07_12_std.nc
DOI: 10.7281/t1/1ecvb0/rztbwk
title: DustOff-monthly-average_wrfout_d01_2013-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/xmagqz
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-07_18_std.nc
DOI: 10.7281/t1/1ecvb0/yfn8xj
title: DustOff-monthly-average_wrfout_d01_2009-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/zubsin
title: DustOff-monthly-average_wrfout_d01_2013-09_12_mean.nc
DOI: 10.17632/59ktwdby4r.1
title: PittPack: An open-source Poisson’s equation solver for extreme-scale computing with accelerators

description: We present a parallel implementation of a direct solver for Poisson’s equation on extreme-scale supercomputers with accelerators. We introduce a chunked-pencil decomposition as the domain-decomposition strategy to distribute work among processing elements to achieve improved scalability at high counts of accelerators. Chunked-pencil decomposition enables overlapping MPI communication and data transfer between the central processing units (CPUs) and the graphics processing units (GPUs). It enables contiguous message transfer among the nodes and improves data locality by keeping neighboring elements in adjacent memory locations while permitting the use of shared memory for certain segments of the algorithm when possible. We study two different communication patterns within the chunked-pencil decomposition. The first pattern fully overlaps the communication with data transfer and aims to speedup the overall turnaround time. The second pattern concentrates on low memory usage and is more network friendly than the first pattern for computations at extreme scale. In our parallel implementation, we interleave OpenACC with MPI to support computations on the GPU or the CPU. The numerical solution and its formal second order of accuracy is verified using the method of manufactured solutions for various combinations of boundary conditions. Additionally, we used PittPack within an incompressible flow solver to further validate its accuracy and as well as demonstrate its versatility as a software package. We performed weak scaling analysis with up to 1.1 trillion Cartesian mesh points distributed over 16384 GPUs on a petascale leadership class supercomputer.
DOI: 10.17632/9zsxjtzfr7.1
title: HTR solver: An open-source exascale-oriented task-based multi-GPU high-order code for hypersonic aerothermodynamics

description: In this study, the open-source Hypersonics Task-based Research (HTR) solver for hypersonic aerothermodynamics is described. The physical formulation of the code includes thermochemical effects induced by high temperatures (vibrational excitation and chemical dissociation). The HTR solver uses high-order TENO-based spatial discretization on structured grids and efficient time integrators for stiff systems, is highly scalable in GPU-based supercomputers as a result of its implementation in the Regent/Legion stack, and is designed for direct numerical simulations of canonical hypersonic flows at high Reynolds numbers. The performance of the HTR solver is tested with benchmark cases including inviscid vortex advection, low- and high-speed laminar boundary layers, inviscid one-dimensional compressible flows in shock tubes, supersonic turbulent channel flows, and hypersonic transitional boundary layers of both calorically perfect gases and dissociating air.
DOI: 10.17632/fj57tvsr6p.1
title: Structure, characterization of chitosan derived from T.telescopium data set

description: FTIR: The chitosan derived from T. telescopium was characterized using a Fourier Transform Infra-Red Spectrophotometer (FTIR). The spectrum showed peak at 873.75 cm-1, which could be assigned C-N stretching. The absorption peak at 1454.33 cm-1is assigned to C-H bending of the side chain –CH2OH. 1654.92 cm-1is a characteristic of bending vibration of N-H while 3637.75 cm-1 is characteristic of free O-H groups. The band at 1103.28 cm−1 is attributed to the C O C stretching. The spectrum is relatively similar to that of commercial chitosan and all these absorption peaks are typical of chitosan molecule. SEM: The structure and morphology of the chitosan was studied by SEM analysis. The SEM micrograph shows that there is a difference in the surface morphology between the gastropod and the commercial chitosan. For T. telescopium chitosan the micrograph shows a smooth and homogenous surface with clustering of uneven particles. At high magnification, the particles were observed to be irregular in shape and dispersed as flakes. XRD: The XRD pattern of chitosan exhib¬ited broad diffraction peaks at 2θ = 18º and 35º which are typical finger prints of semi-crys¬talline chitosan. The gastropod chitosan was found to have few different peaks compared to commercial grade chitosan, which may be attributed to the presence of minerals in the prepared chitosan; but the major XRD peaks recorded are the characteristics of formation of chitosan compound.
DOI: 10.17632/fj57tvsr6p.2
title: Structure, characterization of chitosan derived from T.telescopium data set

description: FTIR: The chitosan derived from T. telescopium was characterized using a Fourier Transform Infra-Red Spectrophotometer (FTIR). The spectrum showed peak at 873.75 cm-1, which could be assigned C-N stretching. The absorption peak at 1454.33 cm-1is assigned to C-H bending of the side chain –CH2OH. 1654.92 cm-1is a characteristic of bending vibration of N-H while 3637.75 cm-1 is characteristic of free O-H groups. The band at 1103.28 cm−1 is attributed to the C O C stretching. The spectrum is relatively similar to that of commercial chitosan and all these absorption peaks are typical of chitosan molecule. SEM: The structure and morphology of the chitosan was studied by SEM analysis. The SEM micrograph shows that there is a difference in the surface morphology between the gastropod and the commercial chitosan. For T. telescopium chitosan the micrograph shows a smooth and homogenous surface with clustering of uneven particles. At high magnification, the particles were observed to be irregular in shape and dispersed as flakes. XRD: The XRD pattern of chitosan exhib¬ited broad diffraction peaks at 2θ = 18º and 35º which are typical finger prints of semi-crys¬talline chitosan. The gastropod chitosan was found to have few different peaks compared to commercial grade chitosan, which may be attributed to the presence of minerals in the prepared chitosan; but the major XRD peaks recorded are the characteristics of formation of chitosan compound.
DOI: 10.7281/t1/1ecvb0/0vbcte
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-03_12_std.nc
DOI: 10.7281/t1/1ecvb0/8xwe49
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-06_12_std.nc
DOI: 10.7281/t1/1ecvb0/c2vm4h
title: DustOff-monthly-average_wrfout_d01_2009-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/cqyvaf
title: DustOff-monthly-average_wrfout_d01_2013-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/d7xip8
title: DustOff-monthly-average_wrfout_d01_2014-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/eyhmsl
title: DustOff-monthly-average_wrfout_d01_2009-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/hdzi8r
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-08_00_std.nc
DOI: 10.7281/t1/1ecvb0/hg8oqh
title: DustOff-monthly-average_wrfout_d01_2010-10_06_mean.nc
DOI: 10.7281/t1/1ecvb0/jgtjyl
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-10_06_std.nc
DOI: 10.7281/t1/1ecvb0/odwtig
title: DustOff-monthly-average_wrfout_d01_2010-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/pmnptj
title: DustOff-monthly-average_wrfout_d01_2009-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/pnklbu
title: DustOff-monthly-average_wrfout_d01_2010-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/pocswa
title: DustOff-monthly-average_wrfout_d01_2013-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/q0lozh
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-06_00_std.nc
DOI: 10.7281/t1/1ecvb0/q4hlxl
title: DustOff-monthly-average_wrfout_d01_2013-07_06_mean.nc
DOI: 10.7281/t1/1ecvb0/qujpmd
title: DustOff-monthly-average_wrfout_d01_2009-05_06_mean.nc
DOI: 10.7281/t1/1ecvb0/ray6m3
title: DustOff-monthly-average_wrfout_d01_2014-10_06_mean.nc
DOI: 10.7281/t1/1ecvb0/rpkpms
title: DustOff-monthly-average_wrfout_d01_2010-08_18_mean.nc
DOI: 10.7281/t1/1ecvb0/vkd45x
title: DustOff-monthly-average_wrfout_d01_2013-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/xw87pj
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-10_18_std.nc
DOI: 10.7281/t1/1ecvb0/ykfcid
title: DustOff-monthly-average_wrfout_d01_2014-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/zti2uv
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-03_00_std.nc
DOI: 10.7281/t1/1ecvb0/zwocxf
title: DustOff-monthly-standard-deviation_wrfout_d01_2008-08_12_std.nc
DOI: 10.17632/d3bgrrfz7k.1
title: Evapotranspiration and crop coefficients of potato in a semi-arid climate (South Africa)

description: Correct estimation of crop coefficients is essential to accurately assess water requirements of crops and thereby optimize irrigation management. The objectives of this study were (i) to quantify evapotranspiration and water use efficiency of potato crops grown in different locations and seasons in a semi-arid climate under irrigation, (ii) to estimate Kc values for these crops at different growth stages, and (iii) to assess the usefulness of the Penman-Monteith equation to estimate evapotranspiration and irrigation requirements. An eddy covariance system (ECV) was used in potato fields in three production regions of South Africa: the western Free State (summer crop), North West (spring crop) and Limpopo (winter crop). An IRGASON integrated open-path CO2/H2O gas analyzer - the ECV system (Campbell Scientific) was used to measure the H2O vapour fluxes above the crop canopy. It was integrated with a sonic anemometer, which measures the three-dimensional wind speed. Additional supporting sensors (fine wire thermocouple, NR-Lite net radiometer, silicon pyranometer, krypton hygrometer, CS616 reflectometer, Hukseflux heat flux plates) and a tipping bucket rain gauge were added to the system. Data were sampled at a frequency of 10 Hz, processed using EasyFlux DL software (Campbell Scientific), and recorded using a CR3000 datalogger. Seasonal mean crop evapotranspiration (ETc) was 4.6, 4.4 and 2.8 mm/day, respectively, for the summer, spring and winter crops. The reference evapotranspiration (ETo) correlated well with the daily patterns in ETc (r = 0.89, 0.56 and 0.76 for winter, spring and summer potato crops, respectively) and can be used for irrigation scheduling of potato. Seasonal mean Kc values varied considerably between the winter (1.00) and the spring (0.69) / summer (0.79) crops. This range is within the range reported from other parts of the world. The winter crop had the highest WUE of 3.2 kg dry potato tuber m3 of water evapotranspired, whilst WUE for the spring and summer crops was 2.9 and 2.2 kg/m3. The corresponding dry matter tuber yields were 12.0 t/ha (winter crop), 14.1 t/ha (spring crop) and 9.1 t ha/1 (summer crop). Our results suggest that ETo, which can be relatively easily estimated based on data from a weather station, can be used for irrigation scheduling of potato, but Kc values used to estimate crop evapotranspiration and irrigation requirements may need to be adjusted depending on the cropping season. Water use efficiency of potato was higher for the crop growing in the cooler winter compared to the crops growing in spring / summer. To optimize water use efficiency of potato in water-scarce areas that rely mostly on irrigation water for potato production, it is advisable to grow potato crops in the cooler season, outside the frost-prone period if possible.