Find research datasets worth reusing

opencc-zeroNov 2022View details →

Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization

Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A. that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas or GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hotspots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially-explicit predictions of abundance and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated high representation of this bird assemblage within the entire conservation network, but with a bias towards birds associated with moderately tall grasses relative to those associated with short or tall grasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serve as a complementary tool for conservation planning efforts globally.

opencc-zeroNov 2022View details →

Adaptive, randomized, non-inferiority trial to evaluate the efficacy of monoclonal antibodies in outpatients with mild or moderate COVID-19

The dataset is based on the results of a trial called MANTICO (Clinical trial number NCT05205759). The study is a non-inferiority randomised controlled trial comparing the clinical efficacy of bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab in outpatients aged 50 or older with early COVID-19. The primary outcome was COVID-19 progression (hospitalisation, need of supplemental oxygen therapy, or death through day 14).

opencc-by-4.0Nov 2022View details →

Data for Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science

The data are used in 'Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science' and are described in the ReadMe file and in the manuscript and Supporting information.

opencc-zeroNov 2022View details →

Data and code from: Experimental evolution of environmental tolerance, acclimation, and physiological plasticity in a randomly fluctuating environment

Environmental tolerance curves, representing absolute fitness against the environment, are an empirical assessment of the fundamental niche, and emerge from the phenotypic plasticity of underlying phenotypic traits. Dynamic plastic responses of these traits can lead to acclimation effects, whereby recent past environments impact current fitness. Theory predicts that higher levels of phenotypic plasticity should evolve in environments that fluctuate more predictably, but there have been few experimental tests of these predictions. Specifically, will still lack experimental evidence for evolution of acclimation effects in response to environmental predictability. Here, we exposed 25 genetically diverse populations of the halotolerant microalgae Dunaliella salina to different constant salinities, or to randomly fluctuating salinities, for over 200 generations. The fluctuating treatments differed in their autocorrelation, which determines the similarity of subsequent values, and thus environmental predictability. We then measured acclimated tolerance surfaces, mapping population growth rate against past (acclimation) and current (assay) environments. We found that experimental mean and variance in salinity caused the evolution of niche position (optimal salinity) and breadth, with respect to not only current but also past (acclimation) salinity. We also detected weak but significant evidence for evolutionary changes in response to environmental predictability, with higher predictability leading notably to an upwards shift in optimal salinities and stronger acclimation effect of past environment on current fitness. We further showed that these responses are related to the evolution of plasticity for intracellular glycerol, the major osmoregulatory mechanism in this species. However the direction of plasticity evolution did not match simple theoretical predictions. Our results underline the need for a more explicit consideration of the dynamics of environmental tolerance and its underlying plastic traits to reach a better understanding of ecology and evolution in fluctuating environments.

opencc-by-4.0Nov 2022View details →

Internet-based interdisciplinary therapeutic group for perinatal anxiety and depression - a randomized pilot study during COVID-19

Abstract: Background: Recent years have seen growing interest in the impact that stress and depressive/anxiety symptomatology have on mothers, fetal development, the mother-baby relationship and infant development. Depressed mothers spend less time making eye-contact with their babies, and caressing and talking to them, which can cause delays in cognitive and emotional development. Early interventions promote reductions in mothers’ stress and anxiety symptoms along with improvements in their ability to read infant cues appropriately. Group perinatal care  and internet-based therapeutic interventions were essential during the Covid period and telemedicine ensures continuity of contact with high-risk populations. The aim of this novel study was to assess the effects of interdisciplinary online therapeutic group in at-risk mothers and babies during the Covid-19 pandemic in a disadvantaged neighborhood in Barcelona (Spain). Method: An interdisciplinary team  offered gestating women and mothers comprehensive care in a protective environment in a Primary Health Care Center. Pregnant women and new mothers were screened to detect depressive-anxiety symptomatology through EPDS/STAI questionnaires. A total of 135 babies were born from March 2020 to June 2021, during the pandemic, and 72 women met high-risk criteria for anxiety or depression. Some 37 pregnant women and mother-baby dyads were treated in the therapeutic group and compared with 27 women randomly selected from the control group, who received treatment as usual. A blinded pediatric follow-up was established when the babies were 6 months old to monitor their development through the Llevant Scale. The course of the mothers’ symptomatology was also assessed. Results: We obtained a significant decrease in depressive-anxiety symptomatology (EPDS p=0.000; STAI state p=0.015 and STAI trait p=0.001 at 6 months of life) in the therapeutic group compared with controls. Follow-up at 6 months demonstrated significant differences between groups in assessment of the babies’ development (manipulation p=0.003; language p=0.000; sociability p=0.000). The therapeutic group showed no difficulties in any areas of development. Conclusions: Interdisciplinary online therapeutic group helps to ensure healthy development of the baby and reduction of the mothers’ depressive-anxiety symptomatology. Future research should involve larger samples to explore the impact of online group interventions in both mothers and babies. Trial registration: The study is registered at Jordi Gol i Gurina Foundation number 21/276-P.

opencc-zeroDec 2022View details →

Data for: A new paradigm for medium-range severe weather forecasts: Probabilistic random forest-based predictions

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ~9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

opencc-zeroFeb 2023View details →

DMID14-0100: A randomized, controlled Phase 1b trial of the Sm-TSP-2 vaccine for intestinal schistosomiasis ELISA results

Recombinant Schistosoma mansoni Tetraspanin-2 formulated on Alhydrogel (Sm-TSP-2/Alhydrogel) is being developed to prevent intestinal and hepatic disease caused by S. mansoni. The tegumentary Sm-TSP-2 antigen was selected based on its unique recognition by cytophilic antibodies in putatively immune individuals living in areas of ongoing S. mansoni transmission in Brazil, and preclinical studies in which vaccination with Sm-TSP-2 protected mice following infection challenge.

openother-closedFeb 2023View details →

Video supplement to "A randomized, controlled, crossover trial of median nerve stimulation for treatment of Tourette syndrome"

Video from the last 60 s of Block 5 (stimulation off) and the first 60 s of Block 6 (stimulation on) from the rhythmic \(\pm \) arrhythmic visits of three study participants who reported substantial improvement in tic severity on at least one visit.

opencc-zeroMar 2023View details →

The effectiveness and safety of intensive lipid-lowering with different Rosuvastatin-based regimens in patients at high risk: A nonblind, randomized, controlled trial

Background: Statin alone or nonstatins as add-ons have been introduced to intensive low-density lipoprotein cholesterol (LDL-C) lowering therapy in patients at high cardiovascular disease (CVD) risk. The purpose of this study was to evaluate the effectiveness and safety of different statin-based regimens for patients at high risk. Methods: Three hundred patients at high CVD risk were randomly assigned to the statin group (Rosuvastatin 20mg/d), statin_EZ group (Rosuvastatin 10mg/d + ezetimibe 10mg/d), statin_pcsk group (Rosuvastatin 10mg/d + alirocumab 75mg/2weeks) or combine3 group (Rosuvastatin 10mg/d + ezetimibe 10mg/d + alirocumab 75mg/2weeks). The primary outcome measure was cholesterol levels (LDL-C, total cholesterol (TC) triglycerides (TGs), and high-density lipoprotein cholesterol (HDL-C)) after 24 weeks of follow-up. The secondary outcome measures were safety markers and the proportion of patients who achieved the < 70 mg/dL (1.8 mmol/L) LDL-C target. A logistic regression model was performed to explore the factors affecting lipid target achievement. Results: The TC and LDL-C levels after treatment were significantly different among the four groups (p < 0.05). The levels in both the combine3 group and the statin_pcsk9 group were significantly lower than those in the statin group and the statin_EZ group (p < 0.05), but there was no significant difference between the combine3 group and the statin_pcsk9 group. The incidence of adverse events in the four groups was low. Body mass index (BMI) and hypertensive status were related to target achievement. Conclusion: The combination of a statin and a PCSK9 inhibitor was safe and more effective for the treatment of high-risk CVD patients, while the addition of ezetimibe was unable to significantly lower lipid levels any further. The rate of achieving the target was higher in patients with hypertension and a low BMI.

opencc-by-4.0May 2022View details →

Weighted Link Schedules in sub-100 node Random Topology Wireless Networks

This is a data repo for the data samples used for learning the link scheduling in a randomly placed networks. This data set contains samples for sub 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.

opencc-zeroMar 2023View details →

Automated, medication-targeted alerts for Acute Kidney Injury – A randomized trial

Acute kidney injury is common among hospitalized individuals, particularly those exposed to certain medications, and is associated with substantial morbidity and mortality. In a pragmatic, open-label, parallel group randomized controlled trial (clinicaltrials.gov NCT02771977), we investigate whether an automated clinical decision support system affects discontinuation rates of potentially nephrotoxic medications and improves outcomes in patients with AKI. Participants included 5,060 hospitalized adults with AKI and an active order for any of three classes of medications of interest: non-steroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system inhibitors, or proton pump inhibitors. Within 24 hours of randomization, a medication of interest was discontinued in 61.1% of the alert group versus 55.9% of the usual care group (relative risk 1.08, 1.04–1.14, p=0.0003). The primary outcome – a composite of progression of acute kidney injury, dialysis, or death – occurred in 585 (23.1%) individuals in the alert group and 639 (25.3%) patients in the usual care group (RR 0.92, 0.83–1.01, p=0.09). The pre-specified subgroup analysis found a significant benefit of alerting among those exposed to proton pump inhibitors but not non-steroidal anti-inflammatory drug or renin-angiotensin system inhibitor.

opencc-by-4.0Apr 2023View details →

Glass-Like Random Catalogues for Two-Point Estimates on the Light Cone (Data Repository)

This is the data repository for the article Glass-Like Random Catalogues for Two-Point Estimates on the Light Cone. arxiv link: https://arxiv.org/abs/2304.02040 DOI:   The file grlic_data.tar.gz contains three directories: /data, /correlations, and /randoms. Inside the /data folder, the data catalogues used in the article are stored: in "cat_part_1162568" the three columns correspond to the redshift, the cosine of the polar angle, and the azimuthal angle, respectively. The first three columns of the "cat_high_1164853" and "cat_low_1164853" catalogues correspond to the same properties for the high-mass and low-mass halos, respectively. In addition to that, these files also contain additional information about the halos: the number of particles (4th column), their M_200b mass (5th column) and their parent ID (6th colum), which is -1 if the halo is not a subhalo. Inside the /randoms folder, the random catalogues for each of the data catalogues in /data are stored. Files beginning with "part" refer to randoms based on the particle catalogue, in a similar fashion the files beginning with "high" correspond to randoms based on the high-mass halo catalogue and those beginning with "low" refer to the randoms based on the low-mass halo catalogue. Files with "..._glass<x>..." correspond to the glass-like random catalogues, and files with "..._rand<x>..." correspond to the Poisson-sampled randoms, where <x> is the value of \(\alpha\) used. \(\alpha\) is the factor by which the number of objects in the data catalogue is multiplied to get the number of objects in the random catalogues, \(N_R = \alpha N_D\). For the glass-like randoms based on the high-mass halo catalogue, the additional suffix, "..._deltagrid<y>...", refers to the number of grid-cells used in the Zeldovich approximation, where <y> is the number of grid cells, and "..._Niter<z>..." refers to the number of Zeldovich iterations perfomed, which is given by <z>. Similarly to the data catalogues, the three columns represent the redshift, cosine of the polar angle, and the azimuthal angle of each object in the catalogue. Inside the /correlations folder, there are two subdirectories: /correlations/full and /correlations/multipoles. The /correlations/full directory contains the raw output from CUTE for each correlated pair of catalogues. For example, the subdirectory /correlations/full/low_low contains the outputs for the low-mass halo autocorrelation, or the subdirectory /correlations/full/high_part contains the outputs for the cross-correlation between the high-mass halo catalogue and the particle catalogue. The naming convention of these files is "<type><alpha>_<catalogues>_", where <type> can either be "rand" or "glass", for either the Poisson-sampled or glass-like random catalogues, <alpha> is the factor \(\alpha\) already introduced above, <catalogues> again describes which two data catalogues have been cross- (or auto-) correlated, e.g. "highlow" refers to a cross-correlation between the high-mass halo catalogue and the low-mass halo catalogue, and finally is a number between 0 and 19 for the 20 independent measurements of the correlations. For the high-mass halo catalogue autocorrelations involving glass-like randoms, the additional suffix, "..._deltagrid<y>...", refers to the number of grid-cells used in the Zeldovich approximation, where <y> is the number of grid cells, and "..._Niter<z>..." refers to the number of Zeldovich iterations perfomed, which is given by <z>. The format of the files is the standard CUTE format for the 3D correlation using binning in (mu,r), i.e. see the readme of https://github.com/damonge/CUTE/tree/master/CUTE, section 5. It reads: <pre>For the 3-D correlation functions the output file has 7 columns with x1 x2 xi(x1,x2) D1D2(x1,x2) D1R2(x1,x2) R1D2(x1,x2) R1R2(x1,x2) where (x1,x2) is either (pi,sigma) or (mu,r). </pre> The /correlations/multipoles subdirectory contains the estimated mean multipoles and their variance derived from the 20 individual correlation measurements for each data catalogue and type of random catalogue. Similarly to the /correlations/full subdirectory, it contains a separate directory for each pair of data catalogue for which the correlation was estimated. The files are named according to "<multipole>_<type><alpha>_<catalogues>_mean_var", where <multipole> is either l0 for the monopole, l1 for the dipole, or l2 for the quadrupole. The <type>, <alpha> and <catalogues> are identical to what was described above for the /correlations/full subdirectory.  Again, for the high-mass halo catalogue autocorrelations involving glass-like randoms, the additional suffix, "..._deltagrid<y>...", refers to the number of grid-cells used in the Zeldovich approximation, where <y> is the number of grid cells, and "..._Niter<z>..." refers to the number of Zeldovich iterations perfomed, which is given by <z>. The columns in each of these files are the comoving separation d, the mean multipole \(\xi_l\), and its variance \(\sigma^2\)of each bin.

opencc-zeroApr 2023View details →

Data from: Evaluation of a community health worker home visit intervention to improve child development in South Africa: A cluster-randomized controlled trial

This dataset was collected as part of a cluster-randomized controlled trial that evaluated the impact of a home visit intervention on child development in Limpopo Province, South Africa. Household survey data were collected at baseline and endline. In a subsample of children, neural function was assessed at a lab at endline and at two interim time points. Primary outcomes were: height-for-age z-scores (HAZ) and stunting; child development scores measured using the Malawi Developmental Assessment Tool (MDAT); absolute electroencephalography (EEG) gamma and total power; relative EEG gamma power; and saccadic reaction time (SRT)—an eye-tracking measure of visual processing speed.

Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber

The files with simulation results for ECOC 20223 submission "Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber"."4CCF_eigenvectorsPol" file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 4-coupled-core fiber (4CCF). These results are uploaded to the python notebook "4CCF_modelingECOC" in order to plot them to get Fig. 2 in the paper. "TransferMatrix" is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook "4CCF_modelingECOC", where all the calculations for figures in the paper are presented. ! UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in  "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [A. Ankiewicz, A. Snyder, and X.-H. Zheng, "Coupling between parallel optical fiber cores–critical examination", Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986]:(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.! UPD 9.12.2023: I have noticed that in the published version of the code I forgot to change the wavelength range for impulse response calculation. So instead of seeing the nice shape as in the paper you will see resolution limited shape. To solve that just change the range of wavelengths, you can add "wl = [1545e-9, 1548e-9]" in the first cell after "Total power impulse response".P.s. In case of any questions or suggestions you are welcome to write me an email ekader@chalmers.se

Mbit/s-range alkali vapour spin noise quantum random number generators - DATASETS

Two example datasets. Each dataset consists of 2 files; one, where spin noise is present in the spectrum (noise_on.npy) and one, where spin noise is absent (noise_off.npy). The former is used generate random numbers, while the latter is used to 1. find a baseline threshold \Sigma, as described in the paper, and 2. to find the spin noise spectrum.  The spin noise spectrum can be found by subtracting the spectra of the first and second file, as it is only present in the first file. Every dataset should be filtered around the Larmor frequency prior to any bit generation. The width of the band-pass filter should be such that the entirety of the spin noise spectrum is contained. Bit generation is to be done by protocols described in the paper "Mbit/s-range alkali vapour spin noise quantum random number generators". All bitrates are calculated at \Sigma = 5 \sigma, where \sigma is the standard deviation of noise_off.npy for the given experiment. Cs12: Sample rate 50 MHz.  Measured at a temperature of approximately 140 degrees C.  The Larmor frequency is approximately 670 kHz. There is a large induced gradient present in the system, so the spin noise spectrum is not a Lorentzian shape, but is spread with a full-width half max of 500 kHz . Protocol 1 raw bitrates are approximately 40 kb/s, whereas protocol 2 raw bitrates are approximately 2.40 Mb/s. Cs14: Sample rate 100 MHz. Spin noise measurement (noise_on.npy) has 1G samples (16 bit signed). Background measurement (noise_off.npy) has 10M samples (16 bit signed). Measured at a temperature of approximately 140 degrees C. The Larmor frequency is approximately 440kHz  ,T2 is approximately 2e-6 s. This is the dataset presented in the paper. Protocol 1 raw bitrates are approximately 40 kb/s, protocol 2 raw bitrates are approximately 1.97 Mb/s.

Underlying qualitative data for: "What went wrong? A qualitative exploration of the determinants of poor antiretroviral therapy adherence and retention in care among pregnant and postpartum women in the Uganda WiseMama randomized controlled trial"

This dataset is the underlying qualitative data for the paper entitled: "What went wrong? A qualitative exploration of the determinants of poor antiretroviral therapy adherence and retention in care among pregnant and postpartum women in the Uganda WiseMama randomized controlled trial"

opencc-by-4.0Nov 2022View details →

Randomly Distributed Crab Burrows Enhance Groundwater Flow and Salt Transport in Creek-Marsh Systems

This dataset contains SUTRA input file and training image that used in our research paper "Randomly Distributed Crab Burrows Enhance Groundwater Flow and Salt Transport in Creek-Marsh Systems".

opencc-by-4.0Jun 2023View details →

Pharmacokinetic dataset of a randomized controlled trial investigating the efficacy and safety of intravenous imatinib mesylate (Impentri®) in subjects with acute respiratory distress syndrome induced by COVID-19

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has led to a disruptive increase in the number of intensive care unit (ICU) admissions with acute respiratory distress syndrome (ARDS). ARDS is a severe, life-threatening medical condition characterized by widespread inflammation and vascular leak in the lungs. Although there is no proven therapy to reduce pulmonary vascular leak in ARDS, recent studies demonstrated that the tyrosine kinase inhibitor imatinib reinforces the endothelial barrier and prevents vascular leak in inflammatory conditions, while leaving the immune response intact. Methods This is a randomized, double-blind, parallel-group, placebo-controlled, multicenter clinical trial of intravenous (IV) imatinib mesylate in 90 mechanically ventilated subjects with COVID-19-induced ARDS. Subjects are 18 years or older, admitted to the ICU for mechanical ventilation, meeting the Berlin criteria for moderate-severe ARDS with a positive polymerase chain reaction test for SARS-CoV2. Participants will be randomized in a 1:1 ratio to either imatinib (as mesylate) 200 mg bis in die (b.i.d.) or placebo IV infusion for 7 days, or until ICU discharge or death. The primary study outcome is the change in Extravascular Lung Water Index (EVLWi) between day 1 and day 4. Secondary outcome parameters include changes in oxygenation and ventilation parameters, duration of invasive mechanical ventilation, number of ventilator-free days during the 28-day study period, length of ICU stay, and mortality during 28 days after randomization. Additional secondary parameters include safety, tolerability, and pharmacokinetics. Discussion The current study aims to investigate the efficacy and safety of IV imatinib in mechanically ventilated subjects with COVID-19-related ARDS. We hypothesize that imatinib decreases pulmonary edema, as measured by extravascular lung water using a PiCCO catheter. The reduction in pulmonary edema may reverse hypoxemic respiratory failure and hasten recovery. As pulmonary edema is an important contributor to ARDS, we further hypothesize that imatinib reduces disease severity, reflected by a reduction in 28-day mortality, duration of mechanical ventilation, and ICU length of stay.

opencc-by-4.0Jun 2023View details →

A Pilot Randomized Controlled Trial of Distance Laughter Therapy for Mothers' Level of Depression, Anxiety, and Parental Stress during the COVID-19 Pandemic

Presented here are both quantitative and qualitative datasets from a study titled 'A Pilot Randomized Controlled Trial of Distance Laughter Therapy for Mothers’ Level of Depression, Anxiety, and Parental Stress during the COVID-19 Pandemic.' The COVID-19 pandemic has necessitated significant lifestyle modifications globally, leading to heightened psychological stressors, notably depression and anxiety. Concurrently, the exigencies of parental care have escalated, thereby amplifying the risk of caregiver exhaustion and potential child maltreatment. This study sought to assess the efficacy and feasibility of utilizing distance laughter therapy for mothers who are looking after young children during these challenging times. The primary objective was to alleviate symptoms of depression, anxiety, and parental stress.

Notebooks and calculation files for: Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Models

The files with simulation results for JLT submission "Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Modelsr". "3CCF_supermodes" file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 3-coupled-core fiber (4CCF). These results are uploaded to the python notebook "3CCF_modelingJLTPaper" in order to plot them to get Fig. 3 in the paper. "TransferMatrix" is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook "3CCF_modelingJLTPaper", where all the calculations for figures in the paper are presented.     ! UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in  "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [A. Ankiewicz, A. Snyder, and X.-H. Zheng, “Coupling between parallel optical fiber cores–critical examination”, Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986]: (4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W))) The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.   P.s. In case of any questions or suggestions or if you need more explanations, you are welcome to write me an email ekader@chalmers.se. If it seems like the code does not work or mistakes in simulations are found, I also appreciate letting me know.