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Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
PREPCLIM sample data set for publication in "Geoscientific Model Development" 2024
<p>Data set illustrating the functionality of software developed in the PREPCLIM project and used in the proposed paper:</p> <p>A Modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen<br>fertilization under climate change – PREPCLIM-v1</p> <p>https://doi.org/10.5194/gmd-2024-105<br>Preprint. Discussion started: 11 July 2024<br>c Author(s) 2024. CC BY 4.0 License.</p>
CICE6 simulations: Quality Control for Community Based Sea Ice Model Development
<p>CICE6 simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the CICE6 simulations by Elizabeth C. Hunke at Los Alamos National Laboratory described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using the CICE sea ice model version 6.0.0.alpha with the baseline namelist:</p> <p>&setup_nml<br> days_per_year = 365<br> use_leap_years = .false.<br> year_init = 1990<br> istep0 = 0<br> dt = 3600.0<br> npt = 43800<br> ndtd = 1<br> runtype = 'continue'<br> ice_ic = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/ic/gx1/iced_gx1_v5.nc'<br> restart = .true.<br> restart_ext = .false.<br> use_restart_time = .true.<br> restart_format = 'nc'<br> lcdf64 = .false.<br> restart_dir = './restart/'<br> restart_file = 'iced'<br> pointer_file = './ice.restart_file'<br> dumpfreq = 'm'<br> dumpfreq_n = 1<br> dump_last = .false.<br> bfbflag = .false.<br> diagfreq = 24<br> diag_type = 'stdout'<br> diag_file = 'ice_diag.d'<br> print_global = .true.<br> print_points = .true.<br> latpnt(1) = 90.<br> lonpnt(1) = 0.<br> latpnt(2) = -65.<br> lonpnt(2) = -45.<br> dbug = .false.<br> histfreq = 'm','d','x','x','x'<br> histfreq_n = 1 , 1 , 1 , 1 , 1<br> hist_avg = .true.<br> history_dir = './history/'<br> history_file = 'iceh'<br> write_ic = .true.<br> incond_dir = './history/'<br> incond_file = 'iceh_ic'<br> /</p> <p>&grid_nml<br> grid_format = 'bin'<br> grid_type = 'displaced_pole'<br> grid_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/grid_gx1.bin'<br> kmt_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/kmt_gx1.bin'<br> gridcpl_file = 'unknown_gridcpl_file'<br> kcatbound = 0<br> /</p> <p>&domain_nml<br> nprocs = 32<br> processor_shape = 'slenderX2'<br> distribution_type = 'cartesian'<br> distribution_wght = 'latitude'<br> ew_boundary_type = 'cyclic'<br> ns_boundary_type = 'open'<br> maskhalo_dyn = .false.<br> maskhalo_remap = .false.<br> maskhalo_bound = .false.<br> /</p> <p>&tracer_nml<br> tr_iage = .true.<br> restart_age = .false.<br> tr_FY = .true.<br> restart_FY = .false.<br> tr_lvl = .true.<br> restart_lvl = .false.<br> tr_pond_cesm = .false.<br> restart_pond_cesm = .false.<br> tr_pond_topo = .false.<br> restart_pond_topo = .false.<br> tr_pond_lvl = .true.<br> restart_pond_lvl = .false.<br> tr_aero = .false.<br> restart_aero = .false.<br> /</p> <p>&thermo_nml<br> kitd = 1<br> ktherm = 2<br> conduct = 'bubbly'<br> a_rapid_mode = 0.5e-3<br> Rac_rapid_mode = 10.0<br> aspect_rapid_mode = 1.0<br> dSdt_slow_mode = -5.0e-8<br> phi_c_slow_mode = 0.05<br> phi_i_mushy = 0.85<br> /</p> <p>&dynamics_nml<br> kdyn = 1<br> ndte = 120<br> revised_evp = .false.<br> advection = 'remap'<br> kstrength = 1<br> krdg_partic = 1<br> krdg_redist = 1<br> mu_rdg = 3<br> Cf = 17.<br> Ktens = 0.<br> e_ratio = 2.<br> basalstress = .false.<br> /</p> <p>&shortwave_nml<br> shortwave = 'dEdd'<br> albedo_type = 'default'<br> albicev = 0.78<br> albicei = 0.36<br> albsnowv = 0.98<br> albsnowi = 0.70 <br> ahmax = 0.3<br> R_ice = 0.<br> R_pnd = 0.<br> R_snw = 1.5<br> dT_mlt = 1.5<br> rsnw_mlt = 1500.<br> kalg = 0.6<br> /</p> <p>&ponds_nml<br> hp1 = 0.01<br> hs0 = 0.<br> hs1 = 0.03<br> dpscale = 1.e-3<br> frzpnd = 'hlid'<br> rfracmin = 0.15<br> rfracmax = 1.<br> pndaspect = 0.8<br> /</p> <p>&zbgc_nml<br> tr_brine = .false.<br> restart_hbrine = .false.<br> tr_zaero = .false.<br> modal_aero = .false.<br> skl_bgc = .false.<br> z_tracers = .false.<br> dEdd_algae = .false.<br> solve_zbgc = .false.<br> bgc_flux_type = 'Jin2006'<br> restore_bgc = .false.<br> restart_bgc = .false.<br> scale_bgc = .false.<br> solve_zsal = .false.<br> restart_zsal = .false.<br> bgc_data_dir = '/uknown_bgc_data_dir'<br> sil_data_type = 'default'<br> nit_data_type = 'default'<br> fe_data_type = 'default'<br> tr_bgc_Nit = .true.<br> tr_bgc_C = .true.<br> tr_bgc_chl = .false.<br> tr_bgc_Am = .true.<br> tr_bgc_Sil = .true.<br> tr_bgc_DMS = .false.<br> tr_bgc_PON = .true.<br> tr_bgc_hum = .true.<br> tr_bgc_DON = .false.<br> tr_bgc_Fe = .true. <br> grid_o = 0.006<br> grid_o_t = 0.006<br> l_sk = 0.024<br> grid_oS = 0.0<br> l_skS = 0.028<br> phi_snow = -0.3<br> initbio_frac = 0.8<br> frazil_scav = 0.8 <br> ratio_Si2N_diatoms = 1.8 <br> ratio_Si2N_sp = 0.0<br> ratio_Si2N_phaeo = 0.0<br> ratio_S2N_diatoms = 0.03 <br> ratio_S2N_sp = 0.03 <br> ratio_S2N_phaeo = 0.03<br> ratio_Fe2C_diatoms = 0.0033<br> ratio_Fe2C_sp = 0.0033<br> ratio_Fe2C_phaeo = 0.1<br> ratio_Fe2N_diatoms = 0.023 <br> ratio_Fe2N_sp = 0.023<br> ratio_Fe2N_phaeo = 0.7<br> ratio_Fe2DON = 0.023<br> ratio_Fe2DOC_s = 0.1<br> ratio_Fe2DOC_l = 0.033<br> fr_resp = 0.05<br> tau_min = 5200.0<br> tau_max = 173000.0<br> algal_vel = 0.0000000111<br> R_dFe2dust = 0.035<br> dustFe_sol = 0.005<br> chlabs_diatoms = 0.03<br> chlabs_sp = 0.01<br> chlabs_phaeo = 0.05<br> alpha2max_low_diatoms = 0.8<br> alpha2max_low_sp = 0.67<br> alpha2max_low_phaeo = 0.67<br> beta2max_diatoms = 0.018<br> beta2max_sp = 0.0025<br> beta2max_phaeo = 0.01<br> mu_max_diatoms = 1.44<br> mu_max_sp = 0.851<br> mu_max_phaeo = 0.851<br> grow_Tdep_diatoms = 0.06<br> grow_Tdep_sp = 0.06<br> grow_Tdep_phaeo = 0.06<br> fr_graze_diatoms = 0.0<br> fr_graze_sp = 0.1<br> fr_graze_phaeo = 0.1<br> mort_pre_diatoms = 0.007<br> mort_pre_sp = 0.007<br> mort_pre_phaeo = 0.007<br> mort_Tdep_diatoms = 0.03<br> mort_Tdep_sp = 0.03<br> mort_Tdep_phaeo = 0.03<br> k_exude_diatoms = 0.0<br> k_exude_sp = 0.0<br> k_exude_phaeo = 0.0<br> K_Nit_diatoms = 1.0<br> K_Nit_sp = 1.0<br> K_Nit_phaeo = 1.0<br> K_Am_diatoms = 0.3<br> K_Am_sp = 0.3<br> K_Am_phaeo = 0.3<br> K_Sil_diatoms = 4.0<br> K_Sil_sp = 0.0<br> K_Sil_phaeo = 0.0<br> K_Fe_diatoms = 1.0<br> K_Fe_sp = 0.2<br> K_Fe_phaeo = 0.1<br> f_don_protein = 0.6<br> kn_bac_protein = 0.03<br> f_don_Am_protein = 0.25<br> f_doc_s = 0.4<br> f_doc_l = 0.4<br> f_exude_s = 1.0<br> f_exude_l = 1.0<br> k_bac_s = 0.03<br> k_bac_l = 0.03<br> T_max = 0.0<br> fsal = 1.0<br> op_dep_min = 0.1<br> fr_graze_s = 0.5<br> fr_graze_e = 0.5<br> fr_mort2min = 0.5<br> fr_dFe = 0.3<br> k_nitrif = 0.0<br> t_iron_conv = 3065.0<br> max_loss = 0.9<br> max_dfe_doc1 = 0.2<br> fr_resp_s = 0.75<br> y_sk_DMS = 0.5<br> t_sk_conv = 3.0<br> t_sk_ox = 10.0<br> algaltype_diatoms = 0.0<br> algaltype_sp = 0.5<br> algaltype_phaeo = 0.5<br> nitratetype = -1.0<br> ammoniumtype = 1.0<br> silicatetype = -1.0<br> dmspptype = 0.5<br> dmspdtype = -1.0<br> humtype = 1.0<br> doctype_s = 0.5<br> doctype_l = 0.5<br> dontype_protein = 0.5<br> fedtype_1 = 0.5<br> feptype_1 = 0.5<br> zaerotype_bc1 = 1.0<br> zaerotype_bc2 = 1.0<br> zaerotype_dust1 = 1.0<br> zaerotype_dust2 = 1.0<br> zaerotype_dust3 = 1.0<br> zaerotype_dust4 = 1.0<br> ratio_C2N_diatoms = 7.0<br> ratio_C2N_sp = 7.0<br> ratio_C2N_phaeo = 7.0<br> ratio_chl2N_diatoms= 2.1<br> ratio_chl2N_sp = 1.1<br> ratio_chl2N_phaeo = 0.84<br> F_abs_chl_diatoms = 2.0<br> F_abs_chl_sp = 4.0<br> F_abs_chl_phaeo = 5.0<br> ratio_C2N_proteins = 7.0<br> /</p> <p>&forcing_nml<br> formdrag = .false.<br> atmbndy = 'default'<br> fyear_init = 1990<br> ycycle = 20<br> atm_data_format = 'bin'<br> atm_data_type = 'LYq'<br> atm_data_dir = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/forcing/gx1/COREII'<br> calc_strair = .true.<br> highfreq = .false.<br> natmiter = 5<br> calc_Tsfc = .true.<br> precip_units = 'mm_per_sec'<br> ustar_min = 0.0005<br> fbot_xfer_type = 'constant'<br> update_ocn_f = .false.<br> l_mpond_fresh = .false.<br> tfrz_option = 'mushy'<br> oceanmixed_ice = .true.<br> ocn_data_format = 'nc'<br> sss_data_type = 'ncar'<br> sst_data_type = 'ncar'<br> ocn_data_dir = '/usr/projects/climate/eclare/DATA/gx1v3/gx1v3/forcing/'<br> oceanmixed_file = 'oceanmixed_ice_depth.nc'<br> restore_sst = .false.<br> trestore = 90<br> restore_ice = .false.<br> /</p> <p>&icefields_nml<br> f_tmask = .true.<br> f_blkmask = .true.<br> f_tarea = .true.<br> f_uarea = .true.<br> f_dxt = .false.<br> f_dyt = .false.<br> f_dxu = .false.<br> f_dyu = .false.<br> f_HTN = .false.<br> f_HTE = .false.<br> f_ANGLE = .true.<br> f_ANGLET = .true.<br> f_NCAT = .true.<br> f_VGRDi = .false.<br> f_VGRDs = .false.<br> f_VGRDb = .false.<br> f_VGRDa = .true.<br> f_bounds = .false.<br> f_aice = 'm' <br> f_hi = 'm'<br> f_hs = 'm' <br> f_Tsfc = 'm' <br> f_sice = 'm' <br> f_uvel = 'm' <br> f_vvel = 'm' <br> f_uatm = 'm' <br> f_vatm = 'm' <br> f_fswdn = 'm' <br> f_flwdn = 'm'<br> f_snowfrac = 'x'<br> f_snow = 'x' <br> f_snow_ai = 'm' <br> f_rain = 'x' <br> f_rain_ai = 'm' <br> f_sst = 'm' <br> f_sss = 'm' <br> f_uocn = 'm' <br> f_vocn = 'm' <br> f_frzmlt = 'm'<br> f_fswfac = 'm'<br> f_fswint_ai = 'm'<br> f_fswabs = 'x' <br> f_fswabs_ai = 'm' <br> f_albsni = 'm' <br> f_alvdr = 'x'<br> f_alidr = 'x'<br> f_alvdf = 'x'<br> f_alidf = 'x'<br> f_alvdr_ai = 'x'<br> f_alidr_ai = 'x'<br> f_alvdf_ai = 'x'<br> f_alidf_ai = 'x'<br> f_albice = 'x'<br> f_albsno = 'x'<br> f_albpnd = 'x'<br> f_coszen = 'x'<br> f_flat = 'x' <br> f_flat_ai = 'm' <br> f_fsens = 'x' <br> f_fsens_ai = 'm' <br> f_fswup = 'x' <br> f_flwup = 'x' <br> f_flwup_ai = 'm' <br> f_evap = 'x' <br> f_evap_ai = 'm' <br> f_Tair = 'm' <br> f_Tref = 'x' <br> f_Qref = 'x'<br> f_congel = 'm' <br> f_frazil = 'm' <br> f_snoice = 'm' <br> f_dsnow = 'x' <br> f_melts = 'm'<br> f_meltt = 'm'<br> f_meltb = 'm'<br> f_meltl = 'm'<br> f_fresh = 'x'<br> f_fresh_ai = 'm'<br> f_fsalt = 'x'<br> f_fsalt_ai = 'm'<br> f_fbot = 'm'<br> f_fhocn = 'x' <br> f_fhocn_ai = 'm' <br> f_fswthru = 'x' <br> f_fswthru_ai = 'm' <br> f_fsurf_ai = 'x'<br> f_fcondtop_ai = 'x'<br> f_fmeltt_ai = 'x' <br> f_strairx = 'm' <br> f_strairy = 'm' <br> f_strtltx = 'x' <br> f_strtlty = 'x' <br> f_strcorx = 'x' <br> f_strcory = 'x' <br> f_strocnx = 'x' <br> f_strocny = 'x' <br> f_strintx = 'x' <br> f_strinty = 'x'<br> f_taubx = 'x'<br> f_tauby = 'x'<br> f_strength = 'm'<br> f_divu = 'm'<br> f_shear = 'm'<br> f_sig1 = 'm' <br> f_sig2 = 'm' <br> f_dvidtt = 'm' <br> f_dvidtd = 'm' <br> f_daidtt = 'm'<br> f_daidtd = 'm' <br> f_dagedtt = 'm'<br> f_dagedtd = 'm' <br> f_mlt_onset = 'm'<br> f_frz_onset = 'm'<br> f_hisnap = 'd'<br> f_aisnap = 'd'<br> f_trsig = 'm'<br> f_icepresent = 'm'<br> f_iage = 'm'<br> f_FY = 'x'<br> f_aicen = 'x'<br> f_vicen = 'x'<br> f_vsnon = 'x'<br> f_snowfracn = 'x'<br> f_keffn_top = 'x'<br> f_Tinz = 'x'<br> f_Sinz = 'x'<br> f_Tsnz = 'x'<br> f_fsurfn_ai = 'x'<br> f_fcondtopn_ai = 'x'<br> f_fmelttn_ai = 'x'<br> f_flatn_ai = 'x'<br> f_fsensn_ai = 'x'<br> /</p> <p>&icefields_mechred_nml<br> f_alvl = 'm'<br> f_vlvl = 'm'<br> f_ardg = 'm'<br> f_vrdg = 'm'<br> f_dardg1dt = 'x'<br> f_dardg2dt = 'x'<br> f_dvirdgdt = 'x'<br> f_opening = 'x'<br> f_ardgn = 'x'<br> f_vrdgn = 'x'<br> f_dardg1ndt = 'x'<br> f_dardg2ndt = 'x'<br> f_dvirdgndt = 'x'<br> f_krdgn = 'x'<br> f_aparticn = 'x'<br> f_aredistn = 'x'<br> f_vredistn = 'x'<br> f_araftn = 'x'<br> f_vraftn = 'x'<br> /</p> <p>&icefields_pond_nml<br> f_apondn = 'x'<br> f_apeffn = 'x'<br> f_hpondn = 'x'<br> f_apond = 'm'<br> f_hpond = 'm'<br> f_ipond = 'm'<br> f_apeff = 'm'<br> f_apond_ai = 'm'<br> f_hpond_ai = 'm'<br> f_ipond_ai = 'm'<br> f_apeff_ai = 'm'<br> /</p> <p>&icefields_bgc_nml<br> f_faero_atm = 'x'<br> f_faero_ocn = 'x'<br> f_aero = 'x' <br> f_fbio = 'm'<br> f_fbio_ai = 'm'<br> f_zaero = 'x'<br> f_bgc_S = 'm'<br> f_bgc_N = 'm'<br> f_bgc_C = 'x'<br> f_bgc_DOC = 'm'<br> f_bgc_DIC = 'x'<br> f_bgc_chl = 'x'<br> f_bgc_Nit = 'm'<br> f_bgc_Am = 'm'<br> f_bgc_Sil = 'm'<br> f_bgc_DMSPp = 'x'<br> f_bgc_DMSPd = 'x'<br> f_bgc_DMS = 'x'<br> f_bgc_DON = 'x' <br> f_bgc_Fe = 'm' <br> f_bgc_hum = 'm' <br> f_bgc_PON = 'm'<br> f_bgc_ml = 'm'<br> f_upNO = 'm'<br> f_upNH = 'm'<br> f_bTin = 'm'<br> f_bphi = 'm' <br> f_iDi = 'm'<br> f_iki = 'm'<br> f_fbri = 'm' <br> f_hbri = 'm'<br> f_zfswin = 'm'<br> f_bionet = 'm'<br> f_biosnow = 'm'<br> f_grownet = 'm'<br> f_PPnet = 'm'<br> f_algalpeak = 'm'<br> f_zbgc_frac = 'm'<br> /</p> <p>&icefields_drag_nml<br> f_drag = 'x'<br> f_Cdn_atm = 'x'<br> f_Cdn_ocn = 'x'<br> /</p>
RASM simulations: Quality Control for Community Based Sea Ice Model Development
<p>RASM simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using RASM with CICE sea ice model version 5.2 with the namelist:</p> <p>&setup_nml<br> bfbflag = .false. <br> diagfreq = 24 <br> hist_avg = .true. <br> histfreq = 'h','x','x','x','x'<br> histfreq_n = 1,0,0,0,0<br> ice_ic = '/work4/projects/apcraig_dir/RACM/inputdata/ccsm4_init/R1008Gcaaa01a_loop2/1979-09-01/R1008Gcaaa01a_loop2.cice.r.1979-09-01-00000.nc'<br> latpnt = 90.0,-65.0 <br> lcdf64 = .true. <br> lonpnt = 0.0,-45.0 <br> ndtd = 1 <br> pointer_file = './rpointer.ice'<br> print_global = .true. <br> print_points = .false. <br> restart_ext = .false. <br> restart_format = 'pio'<br> year_init = 1 <br> /<br> &grid_nml<br> grid_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/ar9v3_20101118.grid'<br> grid_format = 'bin'<br> grid_type = 'regional'<br> gridcpl_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/domain.ocn.ar9v4.100920.nc'<br> kcatbound = 0 <br> kmt_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/ar9v3_20110106.kmt'<br> /<br> &tracer_nml<br> restart_aero = .false. <br> restart_age = .false. <br> restart_fy = .false. <br> restart_lvl = .false. <br> restart_pond_cesm = .false. <br> restart_pond_lvl = .false. <br> restart_pond_topo = .false. <br> tr_aero = .false.<br> tr_fy = .true. <br> tr_iage = .true. <br> tr_lvl = .true. <br> tr_pond_cesm = .false. <br> tr_pond_lvl = .true. <br> tr_pond_topo = .false. <br> /<br> &thermo_nml<br> a_rapid_mode = 0.5e-03 <br> aspect_rapid_mode = 1.0 <br> conduct = 'bubbly'<br> dsdt_slow_mode = -1.5e-07 <br> kitd = 1 <br> ktherm = 2 <br> phi_c_slow_mode = 0.05 <br> phi_i_mushy = 0.85 <br> rac_rapid_mode = 10 <br> /<br> &dynamics_nml<br> advection = 'remap'<br> cf = 21.3<br> kdyn = 2 (EAP) or kdyn = 2 (EVP)<br> krdg_partic = 1<br> krdg_redist = 1<br> kstrength = 1 <br> mu_rdg = 3.0<br> ndte = 600 <br> revised_evp = .false. <br> /<br> &shortwave_nml<br> ahmax = 0.3 <br> albedo_type = 'default'<br> albicei = 0.45<br> albicev = 0.75<br> albsnowi = 0.73<br> albsnowv = 0.98<br> dt_mlt = 1.00 <br> kalg = 0.0 <br> r_ice = 0.0 <br> r_pnd = 0.0 <br> r_snw = 0.50 <br> rsnw_mlt = 1000. <br> shortwave = 'dEdd'<br> /<br> &ponds_nml<br> dpscale = 1.0e-3 <br> frzpnd = 'hlid'<br> hp1 = 0.01 <br> hs0 = 0.03 <br> hs1 = 0.03 <br> pndaspect = 0.8 <br> rfracmax = 0.85 <br> rfracmin = 0.15 <br> /<br> &forcing_nml<br> fbot_xfer_type = 'constant'<br> formdrag = .false.<br> highfreq = .true.<br> l_mpond_fresh = .false.<br> natmiter = 25 <br> tfrz_option = 'mushy'<br> /<br> &domain_nml<br> distribution_type = 'roundrobin'<br> ew_boundary_type = 'cyclic'<br> maskhalo_bound = .true.<br> maskhalo_dyn = .true.<br> maskhalo_remap = .true.<br> ns_boundary_type = 'open'<br> processor_shape = 'null'<br> /<br> &zbgc_nml<br> bgc_data_dir = 'unknown_bgc_data_dir'<br> bgc_flux_type = 'Jin2006'<br> nit_data_type = 'unknown'<br> phi_snow = 0.5 <br> restart_bgc = .false. <br> restart_hbrine = .false. <br> restore_bgc = .false. <br> sil_data_type = 'unknown'<br> skl_bgc = .false. <br> tr_bgc_am_sk = .false. <br> tr_bgc_c_sk = .false. <br> tr_bgc_chl_sk = .false. <br> tr_bgc_dms_sk = .false. <br> tr_bgc_dmspd_sk = .false. <br> tr_bgc_dmspp_sk = .false. <br> tr_bgc_sil_sk = .false. <br> tr_brine = .false. <br> /<br> &icefields_bgc_nml<br> f_aero = 'mxxxx'<br> f_aeron = 'xxxxx'<br> f_bgc_am_ml = 'xxxxx'<br> f_bgc_am_sk = 'xxxxx'<br> f_bgc_c = 'xxxxx'<br> f_bgc_c_sk = 'xxxxx'<br> f_bgc_chl = 'xxxxx'<br> f_bgc_chl_sk = 'xxxxx'<br> f_bgc_dms = 'xxxxx'<br> f_bgc_dms_ml = 'xxxxx'<br> f_bgc_dms_sk = 'xxxxx'<br> f_bgc_dmsp_ml = 'xxxxx'<br> f_bgc_dmspd = 'xxxxx'<br> f_bgc_dmspd_sk = 'xxxxx'<br> f_bgc_dmspp = 'xxxxx'<br> f_bgc_dmspp_sk = 'xxxxx'<br> f_bgc_n = 'xxxxx'<br> f_bgc_n_sk = 'xxxxx'<br> f_bgc_nh = 'xxxxx'<br> f_bgc_nit_ml = 'xxxxx'<br> f_bgc_nit_sk = 'xxxxx'<br> f_bgc_no = 'xxxxx'<br> f_bgc_s = 'xxxxx'<br> f_bgc_sil = 'xxxxx'<br> f_bgc_sil_ml = 'xxxxx'<br> f_bgc_sil_sk = 'xxxxx'<br> f_bphi = 'xxxxx'<br> f_btin = 'xxxxx'<br> f_faero_atm = 'mxxxx'<br> f_faero_ocn = 'mxxxx'<br> f_fbri = 'xxxxx'<br> f_fn = 'xxxxx'<br> f_fn_ai = 'xxxxx'<br> f_fnh = 'xxxxx'<br> f_fnh_ai = 'xxxxx'<br> f_fno = 'xxxxx'<br> f_fno_ai = 'xxxxx'<br> f_fsil = 'xxxxx'<br> f_fsil_ai = 'xxxxx'<br> f_grownet = 'xxxxx'<br> f_hbri = 'xxxxx'<br> f_ppnet = 'xxxxx'<br> /<br> &icefields_drag_nml<br> f_cdn_atm = 'hxxxx'<br> f_cdn_ocn = 'hxxxx'<br> f_drag = 'hxxxx'<br> /<br> &icefields_mechred_nml<br> f_alvl = 'hxxxx'<br> f_aparticn = 'xxxxx'<br> f_araftn = 'xxxxx'<br> f_ardg = 'hxxxx'<br> f_ardgn = 'hxxxx'<br> f_aredistn = 'xxxxx'<br> f_dardg1dt = 'xxxxx'<br> f_dardg1ndt = 'xxxxx'<br> f_dardg2dt = 'xxxxx'<br> f_dardg2ndt = 'xxxxx'<br> f_dvirdgdt = 'xxxxx'<br> f_dvirdgndt = 'xxxxx'<br> f_krdgn = 'xxxxx'<br> f_opening = 'hxxxx'<br> f_vlvl = 'hxxxx'<br> f_vraftn = 'xxxxx'<br> f_vrdg = 'hxxxx'<br> f_vrdgn = 'hxxxx'<br> f_vredistn = 'xxxxx'<br> /<br> &icefields_pond_nml<br> f_apeff = 'hxxxx'<br> f_apeff_ai = 'hxxxx'<br> f_apeffn = 'hxxxx'<br> f_apond = 'hxxxx'<br> f_apond_ai = 'hxxxx'<br> f_apondn = 'hxxxx'<br> f_hpond = 'hxxxx'<br> f_hpond_ai = 'hxxxx'<br> f_hpondn = 'hxxxx'<br> f_ipond = 'hxxxx'<br> f_ipond_ai = 'hxxxx'<br> /<br> &icefields_nml<br> f_a11 = 'hxxxx'<br> f_a12 = 'hxxxx'<br> f_aice = 'hxxxx'<br> f_aicen = 'hxxxx'<br> f_aisnap = 'hxxxx'<br> f_albice = 'hxxxx'<br> f_albpnd = 'hxxxx'<br> f_albsni = 'hxxxx'<br> f_albsno = 'hxxxx'<br> f_alidf = 'hxxxx'<br> f_alidr = 'hxxxx'<br> f_alvdf = 'hxxxx'<br> f_alvdr = 'hxxxx'<br> f_angle = .true.<br> f_anglet = .true.<br> f_blkmask = .true.<br> f_bounds = .false.<br> f_congel = 'hxxxx'<br> f_coszen = 'hxxxx'<br> f_daidtd = 'hxxxx'<br> f_daidtt = 'hxxxx'<br> f_divu = 'hxxxx'<br> f_dsnow = 'hxxxx'<br> f_dvidtd = 'hxxxx'<br> f_dvidtt = 'hxxxx'<br> f_dxt = .false.<br> f_dxu = .false.<br> f_dyt = .false.<br> f_dyu = .false.<br> f_e11 = 'xxxxx'<br> f_e12 = 'xxxxx'<br> f_e22 = 'xxxxx'<br> f_evap = 'hxxxx'<br> f_evap_ai = 'hxxxx'<br> f_fcondtop_ai = 'hxxxx'<br> f_fcondtopn_ai = 'hxxxx'<br> f_fhocn = 'hxxxx'<br> f_fhocn_ai = 'hxxxx'<br> f_flat = 'hxxxx'<br> f_flat_ai = 'hxxxx'<br> f_flatn_ai = 'hxxxx'<br> f_flwdn = 'hxxxx'<br> f_flwup = 'hxxxx'<br> f_flwup_ai = 'hxxxx'<br> f_fmeltt_ai = 'hxxxx'<br> f_fmelttn_ai = 'hxxxx'<br> f_frazil = 'hxxxx'<br> f_fresh = 'hxxxx'<br> f_fresh_ai = 'hxxxx'<br> f_frz_onset = 'hxxxx'<br> f_frzmlt = 'hxxxx'<br> f_fsalt = 'hxxxx'<br> f_fsalt_ai = 'hxxxx'<br> f_fsens = 'hxxxx'<br> f_fsens_ai = 'hxxxx'<br> f_fsensn_ai = 'hxxxx'<br> f_fsurf_ai = 'hxxxx'<br> f_fsurfn_ai = 'hxxxx'<br> f_fswabs = 'hxxxx'<br> f_fswabs_ai = 'hxxxx'<br> f_fswdn = 'hxxxx'<br> f_fswfac = 'hxxxx'<br> f_fswint_ai = 'xxxxx'<br> f_fswthru = 'hxxxx'<br> f_fswthru_ai = 'hxxxx'<br> f_fy = 'hxxxx'<br> f_hi = 'hxxxx'<br> f_hisnap = 'xxxxx'<br> f_hs = 'hxxxx'<br> f_hte = .false.<br> f_htn = .false.<br> f_iage = 'hxxxx'<br> f_icepresent = 'hxxxx'<br> f_keffn_top = 'xxxxx'<br> f_meltb = 'hxxxx'<br> f_meltl = 'hxxxx'<br> f_melts = 'hxxxx'<br> f_meltt = 'hxxxx'<br> f_mlt_onset = 'hxxxx'<br> f_ncat = .true.<br> f_qref = 'hxxxx'<br> f_rain = 'hxxxx'<br> f_rain_ai = 'hxxxx'<br> f_s11 = 'hxxxx'<br> f_s12 = 'hxxxx'<br> f_s22 = 'hxxxx'<br> f_shear = 'hxxxx'<br> f_sice = 'hxxxx'<br> f_sig1 = 'hxxxx'<br> f_sig2 = 'hxxxx'<br> f_sinz = 'xxxxx'<br> f_snoice = 'hxxxx'<br> f_snow = 'hxxxx'<br> f_snow_ai = 'hxxxx'<br> f_sss = 'hxxxx'<br> f_sst = 'hxxxx'<br> f_strairx = 'hxxxx'<br> f_strairy = 'hxxxx'<br> f_strcorx = 'hxxxx'<br> f_strcory = 'hxxxx'<br> f_strength = 'hxxxx'<br> f_strintx = 'hxxxx'<br> f_strinty = 'hxxxx'<br> f_strocnx = 'hxxxx'<br> f_strocny = 'hxxxx'<br> f_strtltx = 'hxxxx'<br> f_strtlty = 'hxxxx'<br> f_tair = 'hxxxx'<br> f_tarea = .true.<br> f_tinz = 'xxxxx'<br> f_tmask = .true.<br> f_tref = 'hxxxx'<br> f_trsig = 'hxxxx'<br> f_tsfc = 'hxxxx'<br> f_tsnz = 'xxxxx'<br> f_uarea = .true.<br> f_uatm = 'hxxxx'<br> f_uocn = 'hxxxx'<br> f_uvel = 'hxxxx'<br> f_vatm = 'hxxxx'<br> f_vgrdb = .true.<br> f_vgrdi = .true.<br> f_vgrds = .true.<br> f_vicen = 'hxxxx'<br> f_vocn = 'hxxxx'<br> f_vsnon = 'hxxxx'<br> f_vvel = 'hxxxx'<br> f_yieldstress11 = 'hxxxx'<br> f_yieldstress12 = 'hxxxx'<br> f_yieldstress22 = 'hxxxx'<br> /<br> &ice_prescribed_nml<br> prescribed_ice = .false.<br> /</p>
GOFS simulations: Quality Control for Community Based Sea Ice Model Development
<p>GOFS simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Global Ocean Forecast System (GOFS 3.1) from the Naval Research Laboratory, curated by Richard Allard, and described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using GOFS with CICE sea ice model version 4.0 with the namelist:</p> <p>&setup_nml<br> days_per_year= 366<br> , year_init = 1901<br> , istep0 = 0<br> , dt = 225.0<br> , npt = -1 <br> , ndyn_dt = 1<br> , inserthr = 0<br> , runtype = 'continue'<br> , ice_ic = 'default'<br> , restart = .true.<br> , restart_dir = './'<br> , restart_file = 'cice.restart'<br> , pointer_file = './cice.restart_file'<br> , histfreq = 'h'<br> , histfreq_n = 12<br> , dumpfreq = '5'<br> , dumpfreq_n = 1<br> , diagfreq = 8<br> , diag_type = 'stdout'<br> , diag_file = 'ice.diag.d'<br> , print_global = .true.<br> , print_points = .true.<br> , latpnt(1) = 72.011<br> , lonpnt(1) = -149.987<br> , latpnt(2) = 90.<br> , lonpnt(2) = 0.<br> , dbug = .false.<br> , incond_dir = <br> , incond_file = './cice'<br> /<br> &grid_nml<br> grid_format = 'bin'<br> , grid_type = 'hycom'<br> , grid_file = 'regional.cice.r'<br> , kmt_file = 'kmt'<br> , kcatbound = 0 <br> /<br> &domain_nml<br> nprocs = 1800<br> , processor_shape = 'slenderX4'<br> , distribution_type = 'cartesian'<br> , distribution_wght = 'latitude'<br> , ew_boundary_type = 'cyclic'<br> , ns_boundary_type = 'tripole'<br> /<br> &tracer_nml<br> tr_iage = .false.<br> , restart_age = .false.<br> , tr_pond = .false.<br> , restart_pond = .false.<br> /<br> &ice_nml<br> kitd = 1<br> , kdyn = 1<br> , ndte = 120<br> , kstrength = 1<br> , krdg_partic = 1<br> , krdg_redist = 1<br> , advection = 'remap'<br> , heat_capacity = .true.<br> , shortwave = 'default'<br> , albedo_type = 'default'<br> , albicev = 0.78<br> , albicei = 0.36<br> , albsnowv = 0.98<br> , albsnowi = 0.70 <br> , R_ice = 0.0<br> , R_pnd = 0.0<br> , R_snw = 0.0<br> , atmbndy = 'default'<br> , fyear_init = 1911 <br> , ycycle = 1 <br> , atm_data_format = 'bin'<br> , atm_data_type= 'cfsr'<br> , atm_data_dir = './cice.'<br> , calc_strair = .true.<br> , calc_Tsfc = .true.<br> , precip_units = 'mm_per_month'<br> , Tfrzpt = 'linear_S'<br> , update_ocn_f = .false.<br> , oceanmixed_ice = .true.<br> , ocn_data_format = 'bin'<br> , sss_data_type= 'cfsr'<br> , sst_data_type= 'cfsr'<br> , ocn_data_dir = 'COUPLED'<br> , oceanmixed_file= 'pop_frc.gx1v3.051202.nc'<br> , restore_sst = .true.<br> , insert_ssmi = .true.<br> , insert_sih = .false.<br> , trestore = 1 <br> , atm_netrad = .false.<br> , albnetrad = 0.00<br> /<br> &icefields_nml<br> f_tmask = .false.<br> , f_tarea = .false.<br> , f_uarea = .false.<br> , f_dxt = .false.<br> , f_dyt = .false.<br> , f_dxu = .false.<br> , f_dyu = .false.<br> , f_HTN = .false.<br> , f_HTE = .false.<br> , f_ANGLE = .false.<br> , f_ANGLET = .false.<br> , f_bounds = .false.<br> , f_hi = .true.<br> , f_hs = .true. <br> , f_Tsfc = .true. <br> , f_aice = .true. <br> , f_uvel = .true. <br> , f_vvel = .true. <br> , f_fswdn = .true. <br> , f_flwdn = .true.<br> , f_snow = .true. <br> , f_snow_ai = .true. <br> , f_rain = .false. <br> , f_rain_ai = .true. <br> , f_sst = .true. <br> , f_sss = .true. <br> , f_uocn = .true. <br> , f_vocn = .true. <br> , f_frzmlt = .true.<br> , f_fswabs = .true. <br> , f_fswabs_ai = .true. <br> , f_albsni = .true. <br> , f_alvdr = .false.<br> , f_alidr = .false.<br> , f_flat = .false. <br> , f_flat_ai = .true. <br> , f_fsens = .false. <br> , f_fsens_ai = .true. <br> , f_flwup = .false. <br> , f_flwup_ai = .true. <br> , f_evap = .false. <br> , f_evap_ai = .true. <br> , f_Tair = .true. <br> , f_Tref = .false. <br> , f_Qref = .false.<br> , f_congel = .true. <br> , f_frazil = .true. <br> , f_snoice = .true. <br> , f_meltt = .true.<br> , f_meltb = .true.<br> , f_meltl = .true.<br> , f_fresh = .false.<br> , f_fresh_ai = .true.<br> , f_fsalt = .false.<br> , f_fsalt_ai = .true.<br> , f_fhocn = .false. <br> , f_fhocn_ai = .true. <br> , f_fswthru = .false. <br> , f_fswthru_ai= .true. <br> , f_fsurf_ai = .false.<br> , f_fcondtop_ai= .false.<br> , f_fmeltt_ai = .false. <br> , f_strairx = .true. <br> , f_strairy = .true. <br> , f_strtltx = .false. <br> , f_strtlty = .false. <br> , f_strcorx = .true. <br> , f_strcory = .true. <br> , f_strocnx = .true. <br> , f_strocny = .true. <br> , f_strintx = .false. <br> , f_strinty = .false.<br> , f_strength = .true.<br> , f_divu = .true.<br> , f_shear = .false.<br> , f_sig1 = .false. <br> , f_sig2 = .false. <br> , f_dvidtt = .false. <br> , f_dvidtd = .false. <br> , f_daidtt = .false.<br> , f_daidtd = .false. <br> , f_mlt_onset = .false.<br> , f_frz_onset = .false.<br> , f_dardg1dt = .false.<br> , f_dardg2dt = .false.<br> , f_dvirdgdt = .false.<br> , f_opening = .true.<br> , f_hisnap = .false.<br> , f_aisnap = .false.<br> , f_trsig = .false.<br> , f_icepresent= .false.<br> , f_iage = .false.<br> , f_aicen = .false.<br> , f_vicen = .false.<br> , f_fsurfn_ai = .false.<br> , f_fcondtopn_ai = .false.<br> , f_fmelttn_ai = .false.<br> , f_flatn_ai = .false.<br> /</p> <p> </p> <p> </p>
ECCC simulations: Quality Control for Community Based Sea Ice Model Development
<p>ECCC simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Environment and Climate Change Canada (ECCC) simulation by Jean-François Lemieux at Environment and Climate Change Canada, described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using ECCC with CICE sea ice model version 4.0 with the namelist:</p> <p>&setup_nml<br> days_per_year = 365<br> , year_init = 2001100100<br> , istep0 = 0<br> , dt = 600<br> , npt = 2880<br> , ndyn_dt = 1<br> , runtype = 'continue'<br> , ice_ic = 'default'<br> , restart = .true.<br> , restart_dir = 'not_used_at_cmc'<br> , restart_file = 'not_used_at_cmc'<br> , pointer_file = './ice.restart_file'<br> , dumpfreq = 's'<br> , dumpfreq_n = 2880<br> , diagfreq = 144<br> , diag_type = 'file'<br> , diag_file = 'ice_stdout'<br> , print_global = .false.<br> , print_points = .false.<br> , latpnt(1) = 90.<br> , lonpnt(1) = 0.<br> , latpnt(2) = -65.<br> , lonpnt(2) = -45.<br> , dbug = .false.<br> , histfreq = 's' <br> , histfreq_n = 144<br> , hist_avg = .false.<br> , history_dir = './history/'<br> , history_file = 'iceh'<br> , history_format = 'nc'<br> , write_ic = .false.<br> , incond_dir = './history/'<br> , incond_file = 'iceh_ic'<br> /</p> <p>&grid_nml<br> grid_format = 'cmc'<br> , grid_type = 'cmc'<br> , grid_file = 'not_used_at_cmc'<br> , kmt_file = 'not_used_at_cmc'<br> , kcatbound = 2<br> /</p> <p>&domain_nml<br> nprocs = 72<br> , processor_shape = 'square-pop'<br> , distribution_type = 'cartesian'<br> , distribution_wght = 'block'<br> , ew_boundary_type = 'closed'<br> , ns_boundary_type = 'closed'<br> /</p> <p>&tracer_nml<br> tr_iage = .false.<br> , restart_age = .false.<br> , tr_pond = .false.<br> , restart_pond = .false.<br> /</p> <p>&ice_nml<br> kitd = 1<br> , kdyn = 1<br> , ndte = 900<br> , kstrength = 0<br> , Pstar = 27.5e3<br> , floediam = 300.0<br> , hfrazilmin = 0.05 <br> , iceruf = 0.0004<br> , krdg_partic = 1<br> , krdg_redist = 1<br> , advection = 'remap'<br> , heat_capacity = .true.<br> , shortwave = 'default'<br> , albedo_type = 'default'<br> , albicev = 0.85<br> , albicei = 0.40<br> , albsnowv = 0.98<br> , albsnowi = 0.70 <br> , R_ice = 0.<br> , R_pnd = 0.<br> , R_snw = 0.<br> , atmbndy = 'default'<br> , fyear_init = 1000<br> , ycycle = 1<br> , atm_data_format = 'not_used_at_cmc'<br> , atm_data_type = 'cmc'<br> , atm_data_dir = 'not_used_at_cmc'<br> , calc_strair = .true.<br> , calc_Tsfc = .true.<br> , precip_units = 'mks'<br> , Tfrzpt = 'nonlin'<br> , update_ocn_f = .true.<br> , oceanmixed_ice = .false.<br> , oceanmixed_frc = .false.<br> , ocn_data_format = 'not_used_at_cmc'<br> , sss_data_type = 'cmcint'<br> , sst_data_type = 'cmcint'<br> , ocn_data_dir = 'not_used_at_cmc'<br> , oceanmixed_file = 'not_used_at_cmc'<br> , restore_sst = .false.<br> , trestore = 0<br> , dirassm_ice = .false.<br> , restore_ice = .false.<br> , restore_nul = .false.<br> , l_thermo = .true.<br> , l_dynmcs = .true.<br> , z0io = 0.0165<br> , l_dragio_r = .false.<br> , l_basalstress = .true.<br> , k1 = 8.0<br> , k2 = 15.0<br> , u0 = 5e-5<br> , CC = 20.0<br> , Ktens = 0.05<br> , e_ratio = 1.4<br> /</p> <p>&namcmc_nml<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! CMC grid parameters (cmc_ln_gemgrid = T ==> grtyp=Z, else grtyp=X)<br> ! The buffer zone is not output <br> ! cmc_ln_bdyopn=T ==> open boundaries including buffer zone<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> cmc_ln_gemgrid = .false.<br> , cmc_ln_nemogrid = .true.<br> , cmc_nbuff_zone = 0<br> , cmc_ln_bdyopn = .false.<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! CMC atmospheric forcing parameters<br> cmc_dt_frcd = -9<br> , cmc_dt_prec = -9<br> , cmc_ln_intrp = .true.<br> , cmc_dateo = '20011001.000000' <br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Input varables ! Long name (doc only) ! nomvar ! Constant (C) or Field (F) !<br> ! ...! Value (if C) ! Binary (B) or Integer (I) (if F) !<br> ! ...! ip1_1 ! ip1_2 ! scaling ! offset !<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Grid ...<br> cmc_ulat = 'u-Lat(deg)','ULAT','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_ulon = 'u-Lon(deg)','ULON','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_tlat = 't-Lat(deg)','TLAT','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_tlon = 't-Lon(deg)','TLON','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_kmt = 'Mask (0-1)','KMT', 'F',-999.0,'I',-1 ,-1 , 1., 0.<br> cmc_hte = 'HTE (m) ','HTE' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_htn = 'HTN (m) ','HTN' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> ! Ice-ocean state ...<br> cmc_ln_gslcat = .false.<br> cmc_ln_wmocat = .false.<br> cmc_ln_avgcat = .true.<br> cmc_ln_itdspr = .true.<br> cmc_gl = 'GL de CMC ','GL' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_i8 = 'I8 de CMC ','I8' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_uice0 = 'U ice Vel.','null','C', 0.0,' ',-999999999,-999999999, -999., -999.<br> cmc_vice0 = 'V ice Vel.','null','C', 0.0,' ',-999999999,-999999999, -999., -999.<br> ! Ocean forcing ...<br> cmc_ln_frcsss = .false.<br> cmc_ln_frcsst = .false.<br> cmc_ln_frcuvo = .false.<br> ! Atm forcing ... (for ice I. C. internal temp override by restart)<br> cmc_tair = 'Air temp ','TT', 'C', 273.15,' ',999999999,999999999, 999., 999.<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Mixed layer parameters<br> cmc_frzeps = 0.0005<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Output parameters (cmc_ofmt='F' or 'H', Forecast or Hindcast style)<br> cmc_ofmt = 'H'<br> cmc_npako = -32<br> cmc_ln_stdrsti = .true.<br> cmc_ln_stdrsto = .true.<br> cmc_npakri = -64<br> cmc_npakro = -64<br> cmc_etiket = 'CREG_HCST'<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> /</p> <p>&icefields_nml<br> f_tmask = .false.<br> , f_tarea = .false.<br> , f_uarea = .false.<br> , f_dxt = .false.<br> , f_dyt = .false.<br> , f_dxu = .false.<br> , f_dyu = .false.<br> , f_HTN = .false.<br> , f_HTE = .false.<br> , f_ANGLE = .false.<br> , f_ANGLET = .false.<br> , f_bounds = .false.<br> , f_hi = .true.<br> , f_hs = .false. <br> , f_Tsfc = .false. <br> , f_aice = .true. <br> , f_uvel = .false. <br> , f_vvel = .false. <br> , f_fswdn = .false. <br> , f_flwdn = .false.<br> , f_snow = .false. <br> , f_snow_ai = .false. <br> , f_rain = .false. <br> , f_rain_ai = .false. <br> , f_sst = .false. <br> , f_sss = .false. <br> , f_uocn = .false. <br> , f_vocn = .false. <br> , f_frzmlt = .false.<br> , f_fswfac = .false.<br> , f_fswabs = .false. <br> , f_fswabs_ai = .false. <br> , f_albsni = .false. <br> , f_alvdr = .false.<br> , f_alidr = .false.<br> , f_albice = .false.<br> , f_albsno = .false.<br> , f_albpnd = .false.<br> , f_coszen = .false.<br> , f_flat = .false. <br> , f_flat_ai = .false. <br> , f_fsens = .false. <br> , f_fsens_ai = .false. <br> , f_flwup = .false. <br> , f_flwup_ai = .false. <br> , f_evap = .false. <br> , f_evap_ai = .false. <br> , f_Tair = .false. <br> , f_Tref = .false. <br> , f_Qref = .false.<br> , f_congel = .false. <br> , f_frazil = .false. <br> , f_snoice = .false. <br> , f_meltt = .false.<br> , f_meltb = .false.<br> , f_meltl = .false.<br> , f_fresh = .false.<br> , f_fresh_ai = .false.<br> , f_fsalt = .false.<br> , f_fsalt_ai = .false.<br> , f_fhocn = .false. <br> , f_fhocn_ai = .false. <br> , f_fswthru = .false. <br> , f_fswthru_ai = .false. <br> , f_fsurf_ai = .false.<br> , f_fcondtop_ai = .false.<br> , f_fmeltt_ai = .false. <br> , f_strairx = .false. <br> , f_strairy = .false. <br> , f_strtltx = .false. <br> , f_strtlty = .false. <br> , f_strcorx = .false. <br> , f_strcory = .false. <br> , f_strocnx = .false. <br> , f_strocny = .false. <br> , f_strintx = .false. <br> , f_strinty = .false.<br> , f_strength = .false.<br> , f_divu = .false.<br> , f_shear = .false.<br> , f_sig1 = .false. <br> , f_sig2 = .false. <br> , f_sigI = .false. <br> , f_dvidtt = .false. <br> , f_dvidtd = .false. <br> , f_daidtt = .false.<br> , f_daidtd = .false. <br> , f_mlt_onset = .false.<br> , f_frz_onset = .false.<br> , f_dardg1dt = .false.<br> , f_dardg2dt = .false.<br> , f_dvirdgdt = .false.<br> , f_opening = .false.<br> , f_hisnap = .false.<br> , f_aisnap = .false.<br> , f_trsig = .false.<br> , f_icepresent = .false.<br> , f_iage = .false.<br> , f_aicen = .false.<br> , f_vicen = .false.<br> , f_fsurfn_ai = .false.<br> , f_fcondtopn_ai = .false.<br> , f_fmelttn_ai = .false.<br> , f_flatn_ai = .false.<br> , f_apondn = .false.<br> , f_tau_bu = .false.<br> , f_tau_bv = .false.<br> /</p> <p> </p>
2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development
<p>2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development</p> <p>------------------------------------------------------------------------------------</p> <p>This dataset comes from Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described and used in Figure 3 in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>Files for the series in Figure 3 as they appear in that paper to demonstrate the Two-Stage Paired Thickness Test:</p> <p>Figure 3a:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRcevp01a_2.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3b:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3c:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p> </p> <p>Where the name "evp" or "eap" in the file name corresponds to the EVP and EAP simulations as described in the manuscript. These are selected timeseries from 1996 to 2000 from the RASM simasimulations described in section 2 of the above manuscript. CICE namelist settings are identical to the description for the dataset:</p> <p>Roberts, A. (2018). RASM simulations: Quality Control for Community Based Sea Ice Model Development [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1308236</p> <p> </p>
Development of an imaging toolbox to assess the therapeutic potential and biodistribution of macrophages in a mouse model of multiple organ dysfunction
<p>Data set to accompany manuscript entitled "Development of an imaging toolbox to assess the therapeutic potential and biodistribution of regenerative therapies in a mouse model of multiple organ dysfunction " which can be found on BioRxiv.</p>
Physiological parameters for four fish species (rainbow trout, zebra fish, fathead minnow and three-spined stickleback) as the basis for the development of generic physiologically-based kinetic models
<p>This excel file (DOI: 10.5281/zenodo.1414332) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for four fish species: rainbow trout (<em>Onchorhynchus mykiss</em>), zebrafish (<em>Danio rerio</em>), fathead minnow (<em>Pimephales promelas</em>), and three-spined stickleback (<em>Gasterosteus aculeatus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Grech et al., (2018). </p> <p>This file is associated with R codes (DOI: 10.5281/zenodo.1414332) for generic PB-K models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each fish species and parameterisation of model for males and females of each species separately.</p> <p>The full data collection and implementation of the models using case studies are described in Grech et al., 2018 (<a href="https://doi.org/10.1016/j.scitotenv.2018.09.163">https://doi.org/10.1016/j.scitotenv.2018.09.163</a>)</p>
Data and Source Codes used in "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"
<p>Data and Source Codes used in the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>Advection Test (ADV): East-West A_TST (100km, Cube), C_TST (25km, Cube), E_TST (5km, Cube),</p> <p> North-South K_TST (100km, Cube), M_TST (25km, Cube), O_TST (5km, Cube) </p> <p>Barotropic Test (BAR): A_TST5 (100km, Cube), Y_TST4 (100km, RLL), C_TST3 (5km, Cube), C_TST1 (5km, RLL)</p> <p>Baroclinic Test (BCL): J_TST30 (100km, Cube), J_TST20 (100km, RLL)</p>
Scripts developed to modelling the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery.
<p>The zipfile contains the scripts developed in the paper on the modelling of the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery, as published in Remote Sensing Applications: Society and Environment journal (RSASE - Elsevier). </p> <ul> <li>Scripts developed for the extraction of light sources from night orthophotography ; [SAFE Software Inc. (2017). FME Desktop Esri Edition.Version 2017.1.]</li> <li>ModelBuilder developed for the visibility modelling of light sources ; [ESRI (2017). ArcGIS Desktop Pro.Version 2.1.]</li> </ul>
Modeling Data vs Modeling Processes: How Digital Humanities developed in Russia
<p>2nd lecture</p>
Data set related to the manuscript "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors"
<p>Graphical files in the agr format for all the figures in the main text of the manuscript entitled "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors" (<a href="https://doi.org/10.1016/j.electacta.2019.135022">10.1016/j.electacta.2019.135022</a>).</p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
A machine learning model reveals expansive downregulation of ligand-receptor interactions enhancing lymphocyte infiltration in melanoma with developed resistance to Immune Checkpoint Blockade
<p>Data repository containing the data to reproduce the results and findings that are published in:</p> <p>Sahni, S., Wang, B., Wu, D. <em>et al.</em> A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. <em>Nat Commun</em> <strong>15</strong>, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4</p>
Developing and Benchmarking Sulfate and Sulfamate Force Field Parameters via Ab Initio Molecular Dynamics Simulations to Accurately Model Glycosaminoglycan Electrostatic Interactions
<p>To cite and for more details: Riopedre-Fernandez et al. <em>J. Chem. Inf. Model.</em> <strong>2024</strong>, 64 (18), 7122–7134. DOI: <a href="https://doi.org/10.1021/acs.jcim.4c00981">https://doi.org/10.1021/acs.jcim.4c00981</a></p> <p>The dataset includes molecular dynamics simulations of sulfated saccharides and their sulfated analogs in the presence of calcium cations in aqueous solution. Several force field parameter sets were compared (CHARMM36, GLYCAM06, AMOEBA, Drude) and new have been developed (prosECCo75 and GLYCAM-ECC75).</p> <p>The uploaded files contain the following simulation input files or/and simulation trajectories:</p> <p>1) Sulfated_Molecules_Umbrella_Sampling_AIMD: Umbrella sampling ab initio molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water.</p> <p>2) Sulfated_Molecules_Umbrella_Sampling_FFMD: Umbrella sampling force field molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water.</p> <p>3) Sulfated_Molecules_AWH_FFMD: Accelerated weight histogram force field molecular dynamics simulations of calcium interacting with both methylsufate and N-methylsulfamate in water.</p> <p>4) Disaccharides_FFMD: Unbiased force field molecular dynamics simulations of calcium-sulfated disaccharide aqueous solutions.</p> <p>UPD. Version 2.0 has updated one of the disaccharide-containing simulations (GLYCAM06, N-sulfation) due to incorrect calcium LJ parameters in the original upload.</p>
Veillon et al. (2020), Geoscientific Model Development : data and development
<p>F.Veillon, M.Dumont, C.Amory, M.Fructus : A versatile method for computing optimized snow albedo from spectrally fixed radiative variables : VALHALLA v1.0, Geoscientific Model Development, in review, 2020. </p> <p>See README for a full description of the dataset content</p> <p>Please contact me at marie.dumont@meteo.fr if you need more details on the dataset</p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
Development and validation of the Health Belief Model questionnaire to promote smoking cessation for nasopharyngeal cancer prevention: a cross-sectional study
<p>Objective: Nasopharyngeal cancer (NPC) risk factors caused by lifestyle choices are substantial yet avoidable. Using the Health Belief Model as a conceptual framework, this study develops and validates a questionnaire to predict smokers' intentions to quit in Sarawak, Malaysia (HBM).</p> <p>Design: A cross sectional study.</p> <p>Setting: Urban and suburban areas in Sarawak, Malaysia.</p> <p>Participants: Following a thorough literature study, the preliminary items for the instrument were created. Prior to beginning the content validity by a panel of 10 experts, the instrument was translated into the Malay language utilising the forward-backwards approach. 10 smokers carried out face validity in both quantitative and qualitative ways. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to assess the instrument's concept validity. Phase 1 of the EFA involved 100 smokers, whereas phase 2 of the CFA involved 171 smokers. Cronbach's alpha coefficients were used to measure internal consistency and assess reliability.</p> <p>Results: The factor loading of each item remained within the acceptable threshold in the exploratory stage. The final revised CFA yielded the following model fit indices that suitably fitted the seven-factor model: Chi Square: 641.705; df= 500; P< 0.001; CFI = 0.953; TLI: 0.948; RMSEA= 0.041. With the exception of one pairwise construct, satisfactory convergent validity and divergent validity were demonstrated. Phases 1 and 2 both have Cronbach's alpha values over 0.7, suggesting acceptable internal reliability.</p> <p>Conclusions: The study confirmed the validity and reliability of the instrument in predicting smokers' tendency to adopt healthy behaviour of smoking cessation to lower risk of developing cancer. The instrument consists of 34 items, dividing into two sections: 6 HBM components and health behavioural intention. The instrument is regarded as innovation and can be applicable in other smoking-related malignancies in different susceptible populations and geographical locations.</p>
Data for the Eastern African power pool's energy systems model, developed in OSeMOSYS
<p>This repository consists of the following datasets</p> <p>1. EAPP_reference scenario_datafile.DD- This dataset is a model file that needs to be used with the code available in this <a href="https://github.com/KTH-dESA/OSeMOSYS/blob/master/OSeMOSYS_GNU_MathProg/osemosys_short.txt">GitHub</a> link. This data file (in concurrence with the OSeMOSYS code) can be used to create a linear programming file (LP file) to be solved using any mathematical optimisation solver like GLPSOL/C-PLEX/GUROBI/CBC.</p> <p>2. Main article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the main article.</p> <p>3. Supplementary article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the supplementary article.</p>
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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)
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DANDI Archive for NWB datasets
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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.