cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/k7gc59d86x.1
title: PDA_MR

description: We aimed to predict residual tumor (R) classification and overall survival (OS) in patients who underwent first-line surgery for pancreatic ductal adenocarcinoma (PDA) using preoperative magnetic resonance imaging (MRI). 210 patients with PDA who underwent MRI and first-line surgery were randomly categorized into a test group (n=150) and a validation group (n=60). The R classification was divided into R0 (no residual tumor) and R1/R2 (microscopic/macroscopic residual tumor). Preoperative MRI findings associated with R classification and OS were assessed by using logistic regression and Cox proportional hazard models. In addition, the prediction models for the R classification and OS were validated using calibration plots and C statistics. On preoperative MRI, portal vein encasement (odds ratio 4.755) was an independent predictor for R1/R2 resection (P=0.040). Tumor size measured on MRI (hazard ratio [HR] per centimeter 1.539) was a predictor of OS, along with pathologic N1 and N2 stage (HR 1.944 and 3.243, respectively), R1/R2 resection (HR 3.273), and adjuvant chemoradiation therapy (HR 0.250) (Ps&lt;0.050). These results have been validated. In conclusion, preoperative MRI was valuable for predicting R1/R2 resection using portal vein encasement, and was useful for predicting OS after surgery for PDA using tumor size.
DOI: 10.17632/jhwfgxycyp.1
title: Data for: Minimizing bio-signal recording sites for hybrid noninvasive brain/neural system control

description: This dataset is associated with the article "Minimizing bio-signal recording sites for hybrid noninvasive brain/neural system control" submitted to the special issue of the IEEE Systems Journal on “Translational Issues in Clinical Brain Machine Interface Systems for Assistance, Diagnostic and Restorative Applications” (under review). The EOG/EOG signals provided in this dataset were recorded with an active wet electrode system and processed using BCI2000. Please see the paper for more information about this dataset.
DOI: 10.34848/fk2/8ipig6/wqs0wf
title: peru.tab
DOI: 10.34848/fk2/8ipig6/sloav2
title: cuba.csv
DOI: 10.34848/fk2/8ipig6/ztycc9
title: ecuador.csv
DOI: 10.34848/fk2/8ipig6/loikce
title: uruguay.tab
DOI: 10.34848/fk2/8ipig6/xg5ipf
title: chile.tab
DOI: 10.34848/fk2/8ipig6/juhett
title: honduras.csv
DOI: 10.34848/fk2/8ipig6/nlfm3h
title: brasil.tab
DOI: 10.34848/fk2/8ipig6/4sal0l
title: colombia.csv
DOI: 10.34848/fk2/8ipig6/sxx5bw
title: republica dominicana.tab
DOI: 10.34848/fk2/8ipig6/ruiht7
title: nicaragua.csv
DOI: 10.34848/fk2/8ipig6/z0jeit
title: venezuela.tab
DOI: 10.34848/fk2/8ipig6/rgmw7q
title: latam.csv
DOI: 10.34848/fk2/8ipig6/czfhu5
title: puerto rico.tab
DOI: 10.34848/fk2/8ipig6/6xvbiq
title: bolivia.csv
DOI: 10.34848/fk2/8ipig6/q1epxk
title: guatemala.tab
DOI: 10.34848/fk2/8ipig6/tpmszk
title: argentina.csv
DOI: 10.34848/fk2/8ipig6/sstczh
title: mexico.tab
DOI: 10.34848/fk2/8ipig6/ncy0wg
title: costa rica.csv
DOI: 10.34848/fk2/8ipig6/ilohfp
title: panama.tab
DOI: 10.34848/fk2/3alhlf/wtczeu
title: latam.svg
DOI: 10.34848/fk2/3alhlf/7lkowc
title: peru.svg
DOI: 10.34848/fk2/3alhlf/tbyal0
title: cuba.svg
DOI: 10.34848/fk2/3alhlf/hbkzuh
title: nicaragua.svg
DOI: 10.34848/fk2/3alhlf/it9efj
title: colombia.svg
DOI: 10.34848/fk2/3alhlf/cr7uwb
title: venezuela.svg
DOI: 10.34848/fk2/3alhlf/kchr0o
title: uruguay.svg
DOI: 10.34848/fk2/3alhlf/lx4j3w
title: honduras.svg
DOI: 10.34848/fk2/3alhlf/eirxhf
title: puerto rico.svg
DOI: 10.34848/fk2/3alhlf/hr14ro
title: costa rica.svg
DOI: 10.34848/fk2/3alhlf/tka0qo
title: ecuador.svg
DOI: 10.34848/fk2/3alhlf/iaense
title: guatemala.svg
DOI: 10.34848/fk2/3alhlf/5pccsw
title: mexico.svg
DOI: 10.34848/fk2/3alhlf/fxc5fp
title: bolivia.svg
DOI: 10.34848/fk2/3alhlf/glmudw
title: chile.svg
DOI: 10.34848/fk2/3alhlf/shtx5v
title: brasil.svg
DOI: 10.34848/fk2/3alhlf/2fhawj
title: panama.svg
DOI: 10.34848/fk2/3alhlf/itdte3
title: argentina.svg
DOI: 10.34848/fk2/3alhlf/f0dvsq
title: republica dominicana.svg
DOI: 10.34848/fk2/3alhlf/sh0clt
title: america.latina.svg

description: Mapa de América Latina con las redes de acoplamiento bibliográfico en el lugar de las divisiones geopolíticas
DOI: 10.34848/fk2/e3alsq/mv7vsm
title: mexico.label.svg
DOI: 10.34848/fk2/e3alsq/2udn1a
title: paraguay.label.svg
DOI: 10.34848/fk2/e3alsq/hsbhze
title: uruguay.label.svg
DOI: 10.34848/fk2/e3alsq/5wlxck
title: ecuador.label.svg
DOI: 10.34848/fk2/e3alsq/ahibol
title: argentina.label.svg
DOI: 10.34848/fk2/e3alsq/hwmmp3
title: chile.label.svg
DOI: 10.34848/fk2/e3alsq/q9epew
title: panama.label.svg
DOI: 10.34848/fk2/e3alsq/gcz6rg
title: bolivia.label.svg
DOI: 10.34848/fk2/e3alsq/hlln8n
title: el salvador.label.svg
DOI: 10.34848/fk2/e3alsq/b1eiqp
title: costa rica.label.svg
DOI: 10.5281/zenodo.3898524
title: MWR/UVS Aurorae

description: Repository for Juno MWR and UVS data from Hodges et al. 2020
DOI: 10.34848/fk2/e3alsq/phnepm
title: guatemala.label.svg
DOI: 10.34848/fk2/e3alsq/kfbnji
title: rep dominicana.label.svg
DOI: 10.34848/fk2/e3alsq/t3bnvu
title: honduras.label.svg
DOI: 10.34848/fk2/e3alsq/vtagz9
title: nicaragua.label.svg
DOI: 10.34848/fk2/e3alsq/wintay
title: colombia.label.svg
DOI: 10.34848/fk2/e3alsq/m6r2ss
title: puerto rico.label.svg
DOI: 10.34848/fk2/e3alsq/jmkoou
title: peru.label.svg
DOI: 10.34848/fk2/e3alsq/dnat7e
title: brasil.label.svg
DOI: 10.34848/fk2/e3alsq/ua6prj
title: cuba.label.svg
DOI: 10.5281/zenodo.3630583
title: Electroencephalography of completely locked-in state patients with amyotrophic lateral sclerosis

description: The data set includes raw EEG data for 10 completely locked-in state (CLIS) patients with amyotrophic lateral sclerosis (ALS) and 6 healthy participants. The EEG data were recorded during resting state and the reference was right mastoid. This data was used in the publication https://doi.org/10.1016/j.neures.2020.01.013.
DOI: 10.5281/zenodo.3630582
title: Electroencephalography of completely locked-in state patients with amyotrophic lateral sclerosis

description: The data set includes raw EEG data for 10 completely locked-in state (CLIS) patients with amyotrophic lateral sclerosis (ALS) and 6 healthy participants. The EEG data were recorded during resting state and the reference was right mastoid. This data was used in the publication https://doi.org/10.1016/j.neures.2020.01.013.
DOI: 10.5281/zenodo.3898530
title: Towards a decade of solar irradiation data (La Réunion Island, SW Indian Ocean)

description: Years of solar irradiation data together with meteorological data acquisition localized in the French region of La Réunion Island (South West Indian Ocean).
DOI: 10.5281/zenodo.3898529
title: Towards a decade of solar irradiation data (La Réunion Island, SW Indian Ocean)

description: Years of solar irradiation data together with meteorological data acquisition localized in the French region of La Réunion Island (South West Indian Ocean).
DOI: 10.17632/szb5x6xw53.1
title: 2E-TVRP instances

description: We use 140 randomly-generated instances to verify the effectiveness of the models and the heuristic. The number (denoted as n) of CDCs (including the central depot) was 3, 4, 5 or 10. In an instance, suppose the numbers of satellites served by each CDC (denoted as num_C) are the same, and cargo sources (i.e., CDCs) needed by any satellite served by a CDC (denoted as SL_type_num) are the same. A satellite demand may include cargoes that are provided by more than one CDC. Time windows of satellites served by a CDC are generated by the method provided by Solomon (1987). All instances are classified by n, num_C and SL_type_num. There are 5 different instances for the same n, num_C and SL_type_num. All instances (in Table 2) can be obtained by emailing the corresponding author.
DOI: 10.17632/szb5x6xw53.2
title: 2E-TVRP instances

description: We use 140 randomly-generated instances to verify the effectiveness of the models and the heuristic. The number (denoted as n) of CDCs (including the central depot) was 3, 4, 5 or 10. In an instance, suppose the numbers of satellites served by each CDC (denoted as num_C) are the same, and cargo sources (i.e., CDCs) needed by any satellite served by a CDC (denoted as SL_type_num) are the same. A satellite demand may include cargoes that are provided by more than one CDC. Time windows of satellites served by a CDC are generated by the method provided by Solomon (1987). All instances are classified by n, num_C and SL_type_num. There are 5 different instances for the same n, num_C and SL_type_num. All instances (in Table 2) can be obtained by emailing the corresponding author.
DOI: 10.17632/szb5x6xw53.3
title: 2E-TVRP instances

description: We use 140 randomly-generated instances to verify the effectiveness of the models and the heuristic. The number (denoted as n) of CDCs (including the central depot) was 3, 4, 5 or 10. In an instance, suppose the numbers of satellites served by each CDC (denoted as num_C) are the same, and cargo sources (i.e., CDCs) needed by any satellite served by a CDC (denoted as SL_type_num) are the same. A satellite demand may include cargoes that are provided by more than one CDC. Time windows of satellites served by a CDC are generated by the method provided by Solomon (1987). All instances are classified by n, num_C and SL_type_num. There are 5 different instances for the same n, num_C and SL_type_num. All instances (in Table 2) can be obtained by emailing the corresponding author.
DOI: 10.17632/v54fds6p7f.5
title: Impact of hybrid FACTS devices on Kenya's power transmission system data

description: This data was obtained on the use of paired (hybrid) Flexible Alternating Current Transmission System (FACTS) devices and studying their impact on voltage, small signal and transient stability simultaneously under various system disturbances on Kenya's transmission network.
DOI: 10.17632/v54fds6p7f.6
title: Impact of hybrid FACTS devices on Kenya's power transmission system data

description: This data was obtained on the use of paired (hybrid) Flexible Alternating Current Transmission System (FACTS) devices and studying their impact on voltage, small signal and transient stability simultaneously under various system disturbances on Kenya's transmission network.
DOI: 10.17632/zchwgnd777.1
title: AST utility in 3 Ugandan Hospitals

description: Antimicrobial resistance (AMR) is a global health threat responsible for increased hospital costs and mortality. The WHO global action plan on AMR recommends antimicrobial sensitivity testing (AST) and surveillance of antibiotic use to address this threat. We aimed to determine the utility of AST in three Ugandan hospitals. We conducted a cross-sectional study was conducted in three major referral hospitals in Uganda. We used abstraction checklists to collect data on AST requests, AST performed, AST turnaround time, and dispensed antibiotics from in-patient files, lab, and pharmacy records. Antibiotic data were summarized using proportions. The dispensed antibiotics and AST antibiotic-disks were compared using spearman’s rank correlation test. Of the 607 in-patient files reviewed, AST was requested in 24 (4.0%), and done in 13 (2.1%). All three hospitals used the Kirby-Bauer disk diffusion method for AST, with a median turnaround time of 5 days (IQR 4-8). While the frequently used AST antibiotic-disks were cotrimoxazole (13.0%), ampicillin (11.7%), and ceftriaxone (10.5%), the most dispensed antibiotics were metronidazole (30.3%), amoxicillin (19.6%) and ceftriaxone (14.8%). There was a weak correlation (r=0.313, p=0.120) between dispensed antibiotics and AST antibiotic-disks. We report an underutilization of AST, and the few tests that were performed were discordant with the most dispensed antibiotics. The commonly used disk diffusion method in hospitals of resource-limited settings is inexpensive but has a long turnaround time. We recommend alternative faster and cost-effective AST methods.
DOI: 10.17632/zchwgnd777.2
title: Antimicrobial sensitivity testing in 3 Ugandan Hospitals

description: Antimicrobial resistance (AMR) is a global health threat responsible for increased hospital costs and mortality. The WHO global action plan on AMR recommends antimicrobial sensitivity testing (AST) and surveillance of antibiotic use to address this threat. We aimed to determine the utility of AST in three Ugandan hospitals. We conducted a cross-sectional study was conducted in three major referral hospitals in Uganda. We used abstraction checklists to collect data on AST requests, AST performed, AST turnaround time, and dispensed antibiotics from in-patient files, lab, and pharmacy records. Antibiotic data were summarized using proportions. The dispensed antibiotics and AST antibiotic-disks were compared using spearman’s rank correlation test. Of the 607 in-patient files reviewed, AST was requested in 24 (4.0%), and done in 13 (2.1%). All three hospitals used the Kirby-Bauer disk diffusion method for AST, with a median turnaround time of 5 days (IQR 4-8). While the frequently used AST antibiotic-disks were cotrimoxazole (13.0%), ampicillin (11.7%), and ceftriaxone (10.5%), the most dispensed antibiotics were metronidazole (30.3%), amoxicillin (19.6%) and ceftriaxone (14.8%). There was a weak correlation (r=0.313, p=0.120) between dispensed antibiotics and AST antibiotic-disks. We report an underutilization of AST, and the few tests that were performed were discordant with the most dispensed antibiotics. The commonly used disk diffusion method in hospitals of resource-limited settings is inexpensive but has a long turnaround time. We recommend alternative faster and cost-effective AST methods.
DOI: 10.5281/zenodo.3648650
title: Sub-picosecond thermalization dynamics in condensation of strongly coupled lattice plasmons

description: Raw data used in manuscript Figures 1-7 of the publication. The data are in the form of .csv as read from a spectrometer.
description: {"references": ["V\u00e4kev\u00e4inen, et al., Sub-picosecond thermalization dynamics in condensation of strongly coupled lattice plasmons"]}
DOI: 10.5281/zenodo.3648649
title: Sub-picosecond thermalization dynamics in condensation of strongly coupled lattice plasmons

description: Raw data used in manuscript Figures 1-7 of the publication. The data are in the form of .csv as read from a spectrometer.
description: {"references": ["V\u00e4kev\u00e4inen, et al., Sub-picosecond thermalization dynamics in condensation of strongly coupled lattice plasmons"]}
DOI: 10.21227/zyq0-5j64
title: Indian Cautionary Traffic Sign data-set

description: In this the Indian Cautionary Traffic sign data-set has been proposed for classifying the cautionary traffic signs. It is composed of more than 9900 images clustered in 17 different classes. The dataset is trained with different convolutional neural networks and the performance of the classification has been compared and analyzed and achieved high performance when compared with state - of- the - art methodologies
DOI: 10.5281/zenodo.3898556
title: SailBuoy weather measurements during NORSE19 experiment

description: Weather measurements from SailBuoy weather station during NORSE2019 experiment. The data and experiment is described in Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020.
description: The relevant parameters can be read with the following Python code: csv1 = np.genfromtxt ('GPRMC2.txt', delimiter=",") time = csv1[:,1] date = csv1[:,9] csv2 = np.genfromtxt ('WIMDA2.txt', delimiter=",") V_sailbuoy = csv2[:,19] Dir_sailbuoy = csv2[:,15] Temp_sailbuoy = csv2[:,5] hh=np.floor(time/10000) mm=np.floor((time-hh*10000)/100) ss = np.floor(time -hh*10000 - mm*100) ff = ((time -hh*10000 - mm*100) - ss)*100 day=np.floor(date/10000) mon=np.floor((date-day*10000)/100) HH=str(hh) MM=str(mm) dates = np.zeros((len(hh),7),dtype=np.int16) dates[:,1]=mon dates[:,2]=day dates[:,0]=2019 dates[:,3]=hh dates[:,4]=mm dates[:,5]=ss+(ff/100) dates[:,6]=ff
description: {"references": ["Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020."]}
DOI: 10.5281/zenodo.3898555
title: SailBuoy weather measurements during NORSE19 experiment

description: Weather measurements from SailBuoy weather station during NORSE2019 experiment. The data and experiment is described in Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020.
description: The relevant parameters can be read with the following Python code: csv1 = np.genfromtxt ('GPRMC2.txt', delimiter=",") time = csv1[:,1] date = csv1[:,9] csv2 = np.genfromtxt ('WIMDA2.txt', delimiter=",") V_sailbuoy = csv2[:,19] Dir_sailbuoy = csv2[:,15] Temp_sailbuoy = csv2[:,5] hh=np.floor(time/10000) mm=np.floor((time-hh*10000)/100) ss = np.floor(time -hh*10000 - mm*100) ff = ((time -hh*10000 - mm*100) - ss)*100 day=np.floor(date/10000) mon=np.floor((date-day*10000)/100) HH=str(hh) MM=str(mm) dates = np.zeros((len(hh),7),dtype=np.int16) dates[:,1]=mon dates[:,2]=day dates[:,0]=2019 dates[:,3]=hh dates[:,4]=mm dates[:,5]=ss+(ff/100) dates[:,6]=ff
description: {"references": ["Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020."]}
DOI: 10.5281/zenodo.3898474
title: Kaggle Wikipedia Web Traffic Daily Dataset (with Missing Values)

description: This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.
description: {"references": ["Google, 2017. Web traffic time series forecasting. URL https://www.kaggle.com/c/web-traffic-time-series-forecasting"]}
DOI: 10.18112/openneuro.ds002898.v1.0.0
title: Monash rsPET-MR
DOI: 10.5281/zenodo.3898561
title: The_Global_Distribution_of_Ultra-Low-Frequency_Waves_in_Jupiter's_Magnetosphere_MannersH_ds01

description: A supporting information dataset for the article "The Global Distribution of Ultra-Low-Frequency Waves in Jupiter's Magnetosphere", submitted by H. Manners and A. Masters. The file contains an Excel spreadsheet detailing the "datetime" intervals containing the events used in the survey described by the article. Additional details are listed alongside the datetimes: the average positions of the spacecraft during each event in radius from the planet, latitude and local time, expressed in Sysem III Jovian coordinates.
DOI: 10.5281/zenodo.3898560
title: The_Global_Distribution_of_Ultra-Low-Frequency_Waves_in_Jupiter's_Magnetosphere_MannersH_ds01

description: A supporting information dataset for the article "The Global Distribution of Ultra-Low-Frequency Waves in Jupiter's Magnetosphere", submitted by H. Manners and A. Masters. The file contains an Excel spreadsheet detailing the "datetime" intervals containing the events used in the survey described by the article. Additional details are listed alongside the datetimes: the average positions of the spacecraft during each event in radius from the planet, latitude and local time, expressed in Sysem III Jovian coordinates.
DOI: 10.5281/zenodo.3898473
title: Kaggle Wikipedia Web Traffic Daily Dataset (without Missing Values)

description: This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10. The original dataset contains missing values. They have been simply replaced by zeros.
description: {"references": ["Google, 2017. Web traffic time series forecasting. URL https://www.kaggle.com/c/web-traffic-time-series-forecasting"]}
DOI: 10.5281/zenodo.3898470
title: London Smart Meters Dataset (with Missing Values)

description: Kaggle London Smart Meters dataset contains 5560 half hourly time series that represent the energy consumption readings of London households in kilowatt hour (kWh) from November 2011 to February 2014.<br>
description: {"references": ["Kaggle, 2019. Smart meter data from london area. URL https://www.kaggle.com/jeanmidev/smart-meters-in-london"]}
DOI: 10.7281/t1/fis2qt/5uu2k0
title: 01. sst_sss_400.nc

description: Surface hydrography file for AREDI400 simulation.
DOI: 10.7281/t1/fis2qt/onyhyu
title: 02. sst_sss_2400.nc

description: Same as File 1, but for AREDI2400 simulation.
DOI: 10.7281/t1/fis2qt/dhjebj
title: 03. sst_sss_gm600.nc

description: Same as File 1 but for AGMmin600 simulation.
DOI: 10.7281/t1/fis2qt/596res
title: 06. aredi400_taux.nc

description: Gridded monthly mean file for the eastward wind stress at the ocean surface. Same as File 1 except that sst and sss are replaced by taux (Pa).
DOI: 10.7281/t1/fis2qt/j4a8zn
title: 05. aredi400_mixsalt60.nc

description: Gridded monthly mean file for the vertical transport of salt due to turbulent mixing across a depth of 60m. Same as File 1 except that sst and sss are replaced by mixsalt60 (kg/m2/yr).
DOI: 10.7281/t1/fis2qt/edciyi
title: 07. salt_balance_400_ew.nc

description: Salt balance for top 60m of Eastern Weddell region.
DOI: 10.7281/t1/fis2qt/ysv0ep
title: 08. salt_balance_400_ww.nc

description: Salt balance for top 60m of Western Weddell region.
DOI: 10.7281/t1/fis2qt/mhcp1p
title: 09. aredi400_temp_balance_ew.nc

description: Heat balance for top 60m of Eastern Weddell Sea.
DOI: 10.7281/t1/fis2qt/v919kr
title: 10. aredi400_temp_balance_ww.nc

description: Heat balance for top 60m of Western Weddell Sea.
DOI: 10.7281/t1/fis2qt/a2gije
title: 11. aredi400_temp_balance_ew_deep.nc

description: Heat balance in ocean between 60 and 1000m in the Eastern Weddell Sea.
DOI: 10.7281/t1/fis2qt/v0gn2y
title: 12. aredi400_temp_balance_ww_deep.nc

description: Heat balance in ocean between 60 and 1000m in the Western Weddell Sea.
DOI: 10.7281/t1/fis2qt/uxtytj
title: 13. mk_eofs_aredi_so_400_rotate.m

description: Matlab script that makes and saves out rotated EOFs patterns, time-varying coefficients, and the correlation matrix used to calculate the Principal Oscillation patterns from the AREDI400 simulation.
DOI: 10.7281/t1/fis2qt/hppua8
title: 14. mk_eofs_aredi_so_2400_rotate.m

description: Same as File 12 but for the AREDI2400 simulation.
DOI: 10.7281/t1/fis2qt/2ag2po
title: 19. analyze_salt_heat_400_ww.m

description: Script to create Figures 5 and 7 Must be run after Files 12 and 16
DOI: 10.7281/t1/fis2qt/bxmqhu
title: 20. analyze_heat_400_deep.m

description: Script to create Supplemental Fig. 2. Must be run after Files 12, 16 and 17.
DOI: 10.7281/t1/fis2qt/dooeyk
title: 18. analyze_salt_heat_400_ew.m

description: Script to create Figure 6. Must be run after File 12.
DOI: 10.7281/t1/fis2qt/n8gpfq
title: 15. mk_eofs_aredi_so_gmin600_rotate.m

description: Same as File 12 but for the AGMmin600 simulation.
DOI: 10.7281/t1/fis2qt/pjl9xv
title: 22. sw_dens.m

description: Matlab routine used by 19
DOI: 10.7281/t1/fis2qt/xvurdr
title: 23. sw_dens0.m

description: Matlab routine used by 19
DOI: 10.7281/t1/fis2qt/kck3ni
title: 16. plot_factors_400_a.m

description: Script to create Figure 3- must be run after mk_eofs_aredi_so_400_rotate.m (File 12)
DOI: 10.7281/t1/fis2qt/0qo6nw
title: 24. sw_seck.m

description: Matlab routine used by 19
DOI: 10.7281/t1/fis2qt/40fuf5
title: 26. tsplot.m

description: Matlab routine used by 19
DOI: 10.7281/t1/fis2qt/pxjbrs
title: 25. sw_smow.m

description: Matlab routine used by 19
DOI: 10.7281/t1/fis2qt/xfmbeh
title: 21. mk_tsplot_wedd_400_2400_gm600.m

description: Script to create Figures 11b and 12. Must be run after Files 12-14.
DOI: 10.7281/t1/fis2qt/00alg3
title: 17. plot_modal_amplitudes_a.m

description: Script to create Figure 4- must be run after File 12.
DOI: 10.7281/t1/fis2qt/uuyebe
title: 27. newCO2_control_400

description: Runscript for AREDI400 model.
DOI: 10.7281/t1/fis2qt/x2l6bd
title: 04. aredi400_hblt.nc

description: Gridded monthly mean file for boundary layer depth calculated by K-profile mixed layer scheme. Same as File 1 except that sst and sss are replaced by hblt in m.