cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3547721
title: Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge

description: These are the official datasets for the LHC Olympics 2020 Anomaly Detection Challenge. Each "black box" contains 1M events meant to be representative of actual LHC data. These events may include signal(s) and the challenge consists of finding these signals using the method of your choice. <strong>We have uploaded a total of THREE black boxes to be used for the challenge. </strong> In addition, we include a background sample of 1M events meant to aid in the challenge. The background sample consists of QCD dijet events simulated using Pythia8 and Delphes 3.4.1. Be warned that both the physics and the detector modeling for this simulation may not exactly reflect the "data" in the black boxes. For both background and black box data, events are selected using a single fat-jet (R=1) trigger with pT threshold of 1.2 TeV. These events are stored as pandas dataframes saved to compressed h5 format. For each event, all reconstructed particles are assumed to be massless and are recorded in detector coordinates (pT, eta, phi). More detailed information such as particle charge is not included. Events are zero padded to constant size arrays of 700 particles. The array format is therefore (Nevents=1M, 2100). For more information, including a complete description of the challenge and an example Jupyter notebook illustrating how to read and process the events, see the official LHC Olympics 2020 webpage here. <strong>UPDATE: November 23, 2020</strong> Now that the challenge is over, we have uploaded the solutions to Black Boxes 1 and 3. They are simple ASCII files (events_LHCO2020_BlackBox1.masterkey and events_LHCO2020_BlackBox3.masterkey) where each line is the truth label -- 0 for background and 1 (and 2 in the case of BB3) for signal -- of each event in the corresponding h5 files (same ordering). For more information about the solutions, please visit the LHCO2020 webpage. <strong>UPDATE: February 11, 2021</strong> We have uploaded the Delphes detector cards and Pythia command files used to produce the Black Box datasets.
DOI: 10.5281/zenodo.4536617
title: Heang60/PBPK-of-Magic-Mushroom: Coding for PBPK of Magic Mushroom

description: No description provided.
DOI: 10.5281/zenodo.4536616
title: Heang60/PBPK-of-Magic-Mushroom: Coding for PBPK of Magic Mushroom

description: No description provided.
DOI: 10.5519/qd.hurm9rnv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.4536627
title: Replication Package for "Global Imbalances and Policy Wars at the Zero Lower Bound"

description: The package contains the data to reproduce Figures 1-2, and the code to reproduce Figures 3-4 and A.1 in Caballero, Farhi and Gourinchas, "Global Imbalances and Policy Wars at the Zero Lower Bound" (forthcoming), Review of Economic Studies. Detailed instructions on data sources are also provided.
DOI: 10.7931/gj1a-f647
title: Black swan egg harvest experiment data
DOI: 10.25952/7e2e-5158
title: Designing and Evaluating an Online Intervention for Australian Consumers

description: The dataset is drawn from an online experiment conducted with 628 Australians, who were randomly assigned to one of three conditions. Two of these conditions aimed at increasing sustainable palm oil-related purchases, while one condition served as an attentional control. Follow-up data after two weeks (n=403) are also included. The SPSS data file includes all the data. SPSS output files specify the various analyses that were run, which include descriptive statistics, multiple analysis of variance and chi-square analysis (Descriptives & Preliminary Analysis; Outcome Measures), mediation analysis, and subsequent analyses after the latent profile analysis. MPlus input and output files for the latent profile analysis are included for a range of two to six groups.
description: The dataset is drawn from an online experiment conducted with 628 Australians, who were randomly assigned to one of three conditions. Two of these conditions aimed at increasing sustainable palm oil-related purchases, while one condition served as an attentional control. Follow-up data after two weeks (n=403) are also included. The SPSS data file includes all the data. SPSS output files specify the various analyses that were run, which include descriptive statistics, multiple analysis of variance and chi-square analysis (Descriptives & Preliminary Analysis; Outcome Measures), mediation analysis, and subsequent analyses after the latent profile analysis. MPlus input and output files for the latent profile analysis are included for a range of two to six groups.
DOI: 10.25345/c50n5z
title: MassIVE MSV000086852 - 021121_Osmundea_autoMS/MS_newdata
title: Osmundea new auto ms/ms data data exclude 718, see if can get a better network
DOI: 10.5281/zenodo.1935381
title: FIFA World Cups 2010, 2014 and 2018 matches' information datasets

description: Data from FIFA World Cups 2010 (South Africa), 2014 (Brazil) and 2018 (Russia) collected from the FIFA website from the section of each match's facts. The original data were pdf files, which were cleaned and organized. The data was collected for the analysis of competition networks in soccer matches, where the high and low performance teams were studied. The zip file contains 3 folders of each world cup. Each folder contains the following files: - Players performance measures: input_worldcup.csv. This file contains information from 'fulltime statistics' and 'teams statistics' original files, complete for each match played in the world cup. More detailed player statistics is only complete for the teams that were champion, runner-up, and the teams that accumulated the less points and goals-against in their respective world cup. - The description of each column can be found in data_dictionary_worldcup.csv. - The list of countries that participated in the world cup with its abbreviation is found in countries_abbreviations_worldcup.csv. - The result of each match is found in tags_worldcup.csv - The players position information is the tactical line-up for World Cup 2010 and initial actual formation for World Cups 2014 and 2018. This information was manually set as a best approximation of position for the starting 11 players and a receiving shots nodes 'XS' - special for the visualization developed. The x-axis is between 0.5 and 6 and y-axis is flipped and between 0 and 6. - Folder of ball passing distributions for each match and team. The name of each file contains the match number and the team's ball passing distribution. The column names are the players numbers who received the ball. Rows represent 'from' and the columns 'to'. Each value are the number of passes between players.
DOI: 10.25345/c5vz2p
title: MassIVE MSV000086853 - 021021_Plocamiumpacificum_autoMS_newdata
title: new auto ms-ms data, exclude 718, see if can get a better network
DOI: 10.5439/1737636
title: SGP Precipitation 2017-2019 Best Estimate

description: The Atmospheric Radiation Measurement user facility (ARM) Southern Great Plains observatory (SGP) houses over 50 precipitation instruments, using approximately 10 different methods for measurement. Nine different types of sensors are deployed at the SGP Central Facility with duplicates of the video disdrometer, impact disdrometer, and laser disdrometer. These instruments are deployed relatively close to one another in the “Central Cluster” of the SGP Central Facility. This effort employed simple clustering techniques on these data to calculate a precipitation best estimate, which in turn was used to develop a weighting scheme for each instrument type across rain rates that can be reused in the future for different configurations of instrument deployments. Product should be considered as an evaluation data set.
DOI: 10.5439/1755091
title: COMBLE Ice Nucleating Particle Measurements

description: These data list ice nucleating particle (INP) number concentrations active in the immersion freezing mode per large volumes of air collected using filters, sampled atop the AOS trailer, part of AMF1, sited at Nordmela Harbor, Andøya Island, Norway, from 01 December 2019 to 26 May 2020, as part of the Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE). Collections were made over periods of 6 to 74 hours (average 34 h and 42 m3 filtered), with periods tailored to integrate cold-air outbreaks or flanking periods of non-cold-air outbreak air. We used open-faced polycarbonate filters that were sampled at a height of 4 m AGL on the AOS trailer (6 m below its inlet), 6-8 m ASL and 30 m inland from the harbor wall, and near the other AMF measurements. Filters were stored and returned frozen to Colorado State University (CSU), where immersion freezing measurements over a range from 0 to typically -28°C were made using the CSU Ice Spectrometer (IS). This first entailed the removal of particles (agitation of filter in 0.1 µm filtered ultrapure water) from filters to create aliquot suspensions that were subdivided (and frozen) for subsequent analyses. One portion was used to obtain a basic temperature spectrum of INP number concentrations on the basis of the freezing of an array of smaller liquid aliquots during cooling, and relation of the frozen fraction to the total air volume sampled. A subset of other portions of the original suspension aliquots were heat treated (95°C for 20 min) to denature biological INPs (a reduction in concentrations reveals the proportion of all INPs that were likely of biological origin, such as microbial or proteinaceous) and digested in 10% H2O2 at 95°C under UV-B illumination to remove all organic carbon INPs; the residual is presumed to be inorganic (likely mineral) INPs. Other samples have been archived frozen for future potential testing of INP source compositions and aerosol chemical and biological source attribution.
DOI: 10.25346/s6/qsiog1/fsmupv
title: snap_v_000000glo
DOI: 10.25346/s6/qsiog1/nqbkrg
title: tremor_3d_noEQ_linear_new.m
DOI: 10.25346/s6/qsiog1/h6hrkg
title: stations.dat
DOI: 10.25346/s6/qsiog1/0qqdpr
title: test_3D_loop_w_3Dwm_fft.m
DOI: 10.25346/s6/qsiog1/5bkjju
title: tremor_3d_noEQ_bi.m
DOI: 10.25346/s6/qsiog1/mzmetv
title: test_3D_loop_w_3Dwm_fft.m

description: Script to run 3D simulation
DOI: 10.25346/s6/qsiog1/3xexav
title: qdyn.in
DOI: 10.25346/s6/qsiog1/npamhi
title: tremor_3d_noEQ_linear.m
DOI: 10.25346/s6/qsiog1/rliwl8
title: plot3d_custom_ox_new_WX_combined_select.m
DOI: 10.25346/s6/qsiog1/chw7ov
title: stations.dat
DOI: 10.25346/s6/qsiog1/mc6k63
title: tremor_3d_noEQ_linear.m
DOI: 10.25346/s6/qsiog1/zooz7l
title: tremor_3d_noEQ_bi.m
DOI: 10.25346/s6/qsiog1/4zaesy
title: snap_v_000000glo
DOI: 10.25346/s6/qsiog1/f7tokx
title: qdyn.in
DOI: 10.25346/s6/qsiog1/mmg4xi
title: Vmax_rnd.eps
DOI: 10.25346/s6/qsiog1/4a8cqn
title: tremor_3d_noEQ_linear_new.m
DOI: 10.25346/s6/qsiog1/a31iez
title: tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.5sigmaR20Dc0.8Dc20.4.mat

description: Random pattern asperity distribution result Matlab data
DOI: 10.25346/s6/qsiog1/ydfrc5
title: snap_v_000000glo
DOI: 10.25346/s6/qsiog1/fkhcze
title: Vmax_tri.eps

description: Hexagon Pattern result plot
DOI: 10.25346/s6/qsiog1/xtf5p1
title: test_3D_loop_w_3Dwm_fft.m
DOI: 10.25346/s6/qsiog1/ycctyv
title: tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.5sigmaR20Dc0.8Dc20.4.mat

description: Hexagon pattern asperity distribution result Matlab data
DOI: 10.25346/s6/qsiog1/nnkj7t
title: tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.5sigmaR20Dc0.8Dc20.4.mat

description: Square pattern asperity distribution result Matlab data
DOI: 10.25346/s6/qsiog1/etxdn3
title: stations.dat
DOI: 10.25346/s6/qsiog1/8mz7jx
title: qdyn.in
DOI: 10.25346/s6/qsiog1/hsgp5u
title: Vmax_sqr.eps
DOI: 10.25346/s6/qsiog1/vjcq6f
title: tremor_3d_noEQ_bi.m
DOI: 10.25346/s6/qsiog1/8ctjbe
title: tremor_3d_noEQ_bi_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat

description: Binary Model result Matlab data
DOI: 10.25346/s6/qsiog1/zadipu
title: Plot_Seism_3D_dot_combined_tricolor.m
DOI: 10.25346/s6/qsiog1/aiqbev
title: tremor_3d_noEQ_linear.m
DOI: 10.25346/s6/qsiog1/uitdhc
title: tremor_3d_noEQ.m
DOI: 10.25346/s6/qsiog1/9dofns
title: tremor_3d_noEQ.m
DOI: 10.25346/s6/qsiog1/phftj0
title: tremor_3d_noEQ.m
DOI: 10.25346/s6/qsiog1/szc9uz
title: tremor_3d_noEQ_linear_new.m
DOI: 10.25346/s6/qsiog1/o74a88
title: Run3D_loop_long_5t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/zwuyp9
title: Run3D_loop_long_8t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/lzyptl
title: Run3D_loop_long_1t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/pschva
title: Run3D_loop_long_9t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/tqse20
title: Run3D_loop_long_10t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/ilml6n
title: Run3D_loop_long_2t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/cna9tq
title: Run3D_loop_long_3t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/nfv4ar
title: Run3D_loop_long_4t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/8xa0xh
title: Run3D_loop_long_7t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/7dd7hd
title: Run3D_loop_long_6t1nt1000tremor_3d_noEQ_dx500twm4sigma01sigmaR20.mat
DOI: 10.25346/s6/qsiog1/gtvp0z
title: Run3D_loop21t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/zxhtiz
title: Run3D_loop19t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/q6xbyh
title: Run3D_loop16t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/rfxoo1
title: Run3D_loop26t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/sguiea
title: Run3D_loop24t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/frgkz0
title: Run3D_loop13t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/mmunfi
title: Run3D_loop14t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/b0hg3q
title: Run3D_loop6t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/gunk3g
title: Run3D_loop5t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/rtkp6q
title: Run3D_loop38t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/jfjxro
title: Run3D_loop34t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/6clocp
title: Run3D_loop1t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/wmbsfz
title: Run3D_loop23t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/spumjw
title: Run3D_loop3t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/nvqx9u
title: Run3D_loop11t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/qm1dvk
title: Run3D_loop29t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/vqzs1i
title: Run3D_loop31t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/fudntw
title: warmup_tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/641lio
title: Run3D_loop33t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/rzzhah
title: Run3D_loop36t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/0acggk
title: Run3D_loop8t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/nt396g
title: Run3D_loop2t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/ihoo6o
title: Run3D_loop10t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/ogahoc
title: Run3D_loop7t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/m5oaut
title: Run3D_loop9t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/tf50wu
title: Run3D_loop4t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/e1dsv9
title: Run3D_loop15t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/zydshs
title: Run3D_loop20t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/iecvd7
title: Run3D_loop18t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/ag15oy
title: Run3D_loop12t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/pldxxc
title: Run3D_loop17t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/sejzkx
title: Run3D_loop27t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/3r4lvc
title: Run3D_loop28t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/9j7uzh
title: Run3D_loop30t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/ki8mre
title: Run3D_loop22t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/osd8j7
title: Run3D_loop25t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/qhw09b
title: Run3D_loop32t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/ameait
title: Run3D_loop37t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/zsioqa
title: Run3D_loop35t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/5pxvcg
title: Run3D_loop39t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1/kfsfbb
title: Run3D_loop40t0.5nt2000tremor_3d_noEQ_linear_dx500twm4sigma01sigma020.1sigmaR20Dc0.8Dc20.08.mat
DOI: 10.25346/s6/qsiog1
title: Simulation results of 3D ETS (Episodic Tremor and Slow-slip) asperity-in-matrix model using QDYN earthquake simulator

description: This data set contains simulation results of 3D ETS asperity-in-matrix model of Matlab .mat files for Luo, Y., and Liu, Z. (2021) Fault Zone Heterogeneities Explain Depth-dependent Pattern and Evolution of Slow Earthquakes in Cascadia, Nature Communications. It is generated by QDYN earthquake simulator (https://github.com/ydluo/qdyn Luo, Y., Ampuero, J. P., Galvez, P., Van den Ende, M., and Idini, B. (2017). QDYN: a Quasi-DYNamic earthquake simulator (v1. 1). Zenodo.(doi: 10.5281/zenodo. 322459).) 1. Uniform Model (10 years, dense output, Uniform_model/) 2. Binary (Bi-modular) Model (4 years, Binary_model/) 3. Linear Model (40 years, dense output, Linear_model/) 4. Comparison of asperity distributions (asp_patttern/): 4.1 (asp_patttern/tri/) - hexagon pattern asperity distribution 4.2 (asp_patttern/sqr/) - rectangular pattern asperity distribution 4.2 (asp_patttern/rnd/) - random asperity distribution File format: (see details in documents of QDYN) p - model settings ot0, ot3d: timeseries outputs ox0, ox3d: snapshot outputs
DOI: 10.25346/s6/qsiog1/l0y5p7
title: read.me.txt

description: Original file description and information. Text format.
DOI: 10.5281/zenodo.3988573
title: Collection of in situ monitoring data in the Southern California Bight 1950-2017 for model validation

description: This set of oceanic monitoring surveys represent 1950-2017 monitoring data in the Southern California Bight used for model validation in (Kessouri et al 2020, and Deutsch et al, 2020). The data come from (1) the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program, initiated in the 1950s, samples the SCB quarterly each year, collecting hydrographic and biogeochemical measurements in coordination with the Southern California Coastal Ocean Observing System (SCCOOS). These observations are augmented nearshore by (2) quarterly surveys of nearshore water column and benthic parameters conducted collaboratively since 1990 by POTW agencies as a part of their regulatory monitoring requirements. These programs provide good temporal and geographical coverage of both the offshore (CalCOFI) and nearshore (POTW) areas, coinciding with the model period, and include publicly available water quality data for targeted sites measured quarterly. The collection includes High Frequency Radar data from the database of the University of California, San Diego (https://hfrnet-tds.ucsd.edu/thredds/catalog.html). We validated model output against observed temperature, dissolved oxygen, nitrate, ammonium, chlorophyll, carbon-system parameters (pH and aragonite saturation state), primary production, and nitrification (McLaughlin et al, 2020).
DOI: 10.5517/ccdc.csd.cc241v8b
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DOI: 10.5517/ccdc.csd.cc241vcf
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