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opencc-by-4.0Jun 2023View details →

CONCEPT-DIABETES DATA MODEL TO ANALYSE HEALTHCARE PATHWAYS OF TYPE 2 DIABETES

Technical notes and documentation on the common data model of the project CONCEPT-DM2.  This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes, version v0.2.0. Aims of the CONCEPT-DM2 project: General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems. Main specific aims: <ul> <li>To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations</li> <li>To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines</li> <li>To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.</li> <li>To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.</li> </ul> Study Design: It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register. Cohort definition: All patients with code of Type 2 Diabetes in the clinical health records <ul> <li>Inclusion criteria: patients that, at 2017-01-01 or during the follow-up from 2017-01-01 to 2022-12-31 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)</li> <li>Exclusion criteria: <ul> <li>patients with no contact with the health system from 2017-01-01 to 2022-12-31</li> <li>patients that had a T1D (DM1) code opened after the T2D code during the follow-up.</li> </ul> </li> <li>Study period. From 2017-01-01 to 2022-12-31</li> </ul> Files included in this publication: <ul> <li>Datamodel_CONCEPT_DM2_diagram_v0.2.0.jpg</li> <li>Common data model specification (Datamodel_CONCEPT_DM2_v.0.2.0.xlsx)</li> <li>Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data_v0.2.0) <ul> <li>sample_data1_dm_patient.csv</li> <li>sample_data2_dm_param.csv</li> <li>sample_data3_dm_patient.csv</li> <li>sample_data4_dm_param.csv</li> <li>sample_data5_dm_patient.csv</li> <li>sample_data6_dm_param.csv</li> <li>sample_data7_dm_param.csv</li> <li>sample_data8_dm_param.csv</li> </ul> </li> <li>Datamodel_CONCEPT_DM2_explanation_v0.2.0.pptx</li> </ul> CHANGE-LOG from version v0.1.0 to v0.2.0. The main changes are the following: <ul> <li>Missing data is now identified leaving the field empty</li> <li> All ICD diagnosis given in the Datamodel refer to the root code, so that all codes and subcodes that start with the given codes need to be considered. For instance, if in the data model appears I21, then all codes I21.x should be included. </li> <li> All admissions registered in the CMBD will be included to facilitate the extraction procedure (annex 8 disappears). </li> <li> Three diagnosis codes and three procedure codes are now included in table dm_cmbd. </li> </ul> CHANGE-LOG from version v0.2.0 to v0.3.0. The main changes are the following: <ul> <li>Variable 'copayment' (annex 2) change: cod 002.01 =>0; cod 002.02 =>1</li> <li>Variable 'visit_service' (annex 8) change: APR refers to Primary Care and APA refers to Pathological Anatomy</li> <li>Variable 'filglom' (annex 4) change:  non numerical values compatible with '> 60' => 999</li> </ul>

3D models and raw data for the "Photogrammetric 3D modelling and experimental archaeology reveals new technological insights into engraved soapstone sinker production in Western Norway (6400-3300 cal. BC)" paper, Radchenko et al. in prep.

3D models and raw data for the "Photogrammetric 3D modelling and experimental archaeology reveals new technological insights into engraved soapstone sinker production in Western Norway (6400-3300 cal. BC)" paper, Radchenko et al. in prep. 5 models of soapstone sinkers and 5 models of experimentally produced objects.

Data from: A framework for modeling the impacts of searcher behavior on the efficiency of abundance surveys

When planning abundance surveys, the impact of search effort on the quality of the density estimates is rarely considered. We constructed a time-budget modeling framework for abundance surveys using principles from optimal foraging theory. We link search effort to the number of sample units surveyed, searcher detection probability, the number of detections made, and the precision of the estimated resource density. This framework allowed us to determine how a surveyor should behave to produce optimal density estimates. Using data collected from quadrat and removal surveys of zebra mussels (Dreissena polymorpha) in central Minnesota, we applied this framework to evaluate potential improvements. By tuning searcher behavior, we find that density estimates from removal surveys of zebra mussels could be improved by up to 60% in some cases, without changing the overall survey effort. Our framework also predicts a critical population density where the best survey method switches from removal surveys at low densities to quadrat surveys at high densities, consistent with past empirical work. Our results provide insights into how to improve the performance of many survey methods in high-density environments by either tuning searcher behavior or decoupling the estimation of resource density and detection probability.

Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.

Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data

This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil.  <div>Not all variables were used in the final analysis <table> <tbody> <tr> <td>               NetCDF File</td> <td>     Variable </td> <td> Used/not Used </td> <td> Source/Reference </td> </tr> <tr> <td> burned_area.nc </td> <td> Burned area </td> <td> As training data </td> <td>MCD64A1/ Giglio et al. (2018)</td> </tr> <tr> <td> burned_area_nat_veg.nc </td> <td> Burned area in natural vegetation </td> <td> As training data </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> burned_area_non_nat_veg.nc </td> <td> Burned area in non natural vegetation </td> <td> As training data </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td>  tas_max.nc </td> <td>  Maximum Temperature </td> <td> Used </td> <td> ISIMIP3a FRIELER et al. (2023) </td> </tr> <tr> <td> precip.nc </td> <td> Precipitation </td> <td> Used </td> </tr> <tr> <td> vpd.nc </td> <td> Vapor pressure deficit </td> <td> Not Used </td> </tr> <tr> <td> rhumid.nc </td> <td>  Relative Humidity  </td> <td> Not Used </td> </tr> <tr> <td> consec_dry_days.nc</td> <td> Consecutive number of dry days  </td> <td>Not Used</td> </tr> <tr> <td> soilM.nc </td> <td> Soil  Moisture </td> <td> Not Used </td> <td> JULES-ES </td> </tr> <tr> <td> lightn.nc    </td> <td>  Lightning </td> <td> Not Used </td> <td>  ISIMIP3a FRIELER et al. (2023) </td> </tr> <tr> <td> popDen.nc </td> <td>  Population density </td> <td> Not Used </td> </tr> <tr> <td> road_density.nc </td> <td> Road density </td> <td>Used</td> <td>  GRIP global (MEIJER et al., 2018) </td> </tr> <tr> <td> cveg.nc </td> <td> Vegetation carbon </td> <td> Not Used </td> <td> JULES-ES </td> </tr> <tr> <td> csoil.nc </td> <td> Carbon in dead vegetation </td> <td>Used</td> <td> JULES-ES </td> </tr> <tr> <td> forest.nc </td> <td>   Forest </td> <td> Used </td> <td>  MAPBIOMAS, 2022 </td> </tr> <tr> <td> grassland.nc </td> <td>   Grassland </td> <td> Not Used </td> </tr> <tr> <td> savanna.nc </td> <td>   Savanna </td> <td> Not Used </td> </tr> <tr> <td> cropland.nc </td> <td>   Cropland </td> <td> Not Used </td> </tr> <tr> <td> pasture.nc </td> <td>   Pasture </td> <td> Used </td> </tr> <tr> <td> np.nc </td> <td> Number of patches  </td> <td> Not Used </td> <td> Calculated from MAPBIOMAS, 2022 </td> </tr> <tr> <td> ed.nc  </td> <td>Edge density</td> <td>Used</td> </tr> </tbody> </table> </div>

Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data

We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial species and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single bacteria species to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different bacteria species in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift. We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight bacterial species that are particularly influential in driving our model's predictions for IBD.

FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"

This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.

opencc-by-4.0May 2024View details →

Supporting data for "Granularity of model input data impacts estimates of carbon storage in soils"

The exchange of carbon between the soil and the atmosphere is an important factor in climate change.  Soil organic carbon (SOC) storage is sensitive to land management, soil properties, and climatic conditions, and these data serve as key inputs to computer models projecting SOC change.  Farmland has been identified as a sink for atmospheric carbon, and we have previously estimated the potential for SOC sequestration in agricultural soils in Vermont, USA using the Rothamsted Carbon Model.  However, fine spatial-scale (high granularity) input data are not always available, which can limit the skill of SOC projections.  For example, climate projections are often only available at scales of 10s to 100s of km2.  To overcome this, we use a climate projection dataset downscaled to <1 km2  (~18,000 cells).  We compare SOC from runs forced by high granularity input data to runs forced by aggregated data averaged over the 11,690 km2 study region.  We spin up and run the model individually for each cell in the fine-scale runs and for the region in the aggregated runs factorially over three agricultural land uses and four Global Climate Models.   In this repository are the downscaled climate input data that drive the RothC model, as well as the model outputs for each GCM.

Medical interview score data from PostCC-OSCE and programs for an extended many-facet IRT model

Objective structured clinical examinations (OSCEs) are widely used performance assessments for medical and dental students. A common limitation of OSCEs is that the evaluation results depend on the characteristics of raters and the scoring rubric. To overcome this limitation, item response theory (IRT) models such as the many-facet models have been proposed to estimate examinee abilities while accounting for the characteristics of raters and evaluation items in a rubric. However, conventional IRT models have two impractical assumptions: constant rater severity across all evaluation items in a rubric and an equal interval rating scale among evaluation items, which can decrease model fitting and ability measurement accuracy. To resolve this problem, we propose a new IRT model that relaxes these assumptions. We demonstrate the effectiveness of the proposed model by applying it to actual data collected from a medical interview test conducted at Tokyo Medical and Dental University as part of a post-clinical clerkship (PostCC) OSCE. The experimental results showed that the proposed model fit our OSCE data well and measured ability accurately. Furthermore, it provided abundant information on rater and item characteristics that conventional models cannot, helping us to better understand rater and item properties. This dataset includes the actual score data collected from the above-mentioned medical interview test in a PostCC OSCE, as well as the program for estimating the parameters of the proposed IRT model.

TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats

TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.

opencc-by-4.0Mar 2024View details →

Probability distributed equivalent circuit model - Data

<h2>General information</h2> Measurement data, parameter and analytical functions for the simulation of the results described in the publication "A physically motivated hysteresis model for lithium-ion batteries using a probability distributed equivalent circuit" published in Nature Communications Engineering in 2024 by Jahn et al.  Matlab Code for the simulation can be found under: <a href="https://www.doi.org/10.5281/zenodo.10852695">www.doi.org/10.5281/zenodo.10852695</a> <h2>File description</h2> <h3>_incrOCV.csv</h3> Measurement: Full cycle with current interupts at specific states of charge.  Data: nx2 vector [Q U] with Q being the currently stored amount of charge in the cell and U being the voltage measured  <h3>_pOCV.csv</h3> Measurement: C/20 constant current full cycle starting at fully discharge state. Data: nx3 vector [t Q U] with t being the recorded time, Q the amount of charge stored in the cell, and U the measured terminal voltage. <h3>HysPowerTest_ ... _SOC50.csv / HysPowerTest_ ... _SOC100.csv</h3> Measurement:  SOC50 - starting at 0 % SOC with C/2 constant current charge to 50 % SOC followed by a 4C constant current discharge to the lower cut-off voltage.  SOC100 - starting at 100 % SOC with C/2 constant current discharge to 50 % SOC followed by a 4C constant current discharge to the lower cut-off voltage.  Data: nx4 vector [t Q I U] with t being the recorded time, Q the amount of charge stored in the cell, I the current flowing, and U the measured terminal voltage. <h3>Hysteresis_SOC50Loops_ ... </h3> Measurement: Partial cycles with widening SOC window in 5 % steps in higher and lower SOC direction. The first hysteresis loop is measured from 45 % SOC to 55 % SOC while the final loop is measured from 0 % SOC to 100 % SOC. Data: 9x5 Matlab struct with each row being a partial cycle. Each column corresponds to [t SOC U I Q] of this partial cycle. t being the recorded time, SOC the amount of stored charge referenced to the previously determined cell capacity, Q the amount of charge stored in the cell, I the current flowing, and U the measured terminal voltage. Parameter sets Parameter sets for the Matlab code for the simulation of the probability distributed equivalent circuit model, available under Zenodo repository given in the Related Works section. param_ocpn_graphite.mat - OCP function parameter for the graphite anode half-cell 20230221_graphite_opt_parameter_man - optimized model parameter for the graphite half-cell param_ocpn_lfp.mat - OCP function parameter for the LFP cathode half-cell 20230221_lfp_opt_parameter_man.mat - optimized model parameter for the LFP half-cell A123_parameter_posPulse/A123_parameter_negPulse - parameter of the R-RC ECM extracted using positive or negative pulses respectively

Switchable Contact Model (SCM) Research Data

This is the research data of the article describing the implementation of the Switchable Contact Model (SCM), link: https://github.com/DamlaSerper/Switchable_Contact_Model-SCM.

Spectroscopic data for the compounds and reactions published in "Nucleophiles Target the Tungsten Center Over Acetylene in Biomimetic Models"

Here, the uploaded data are associated with the manuscript "Nucleophiles Target the Tungsten Center Over Acetylene in Biomimetic Models" published in Inorg. Chem. under the following https://doi.org/10.1021/acs.inorgchem.4c00286 The .dpt files represent IR spectra of the compounds published in the manuscript. Gas IR spectrum is reported as .scv file. The .scv files represent NMR spectra of the compounds and reactions published in the manuscript. Line Shape Analysis calculation is presented in the excel file. The labeling of the compounds and reactions follows the one in the published manuscript.

Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment

Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. In vitro human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing in vitro and in vivo models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted in vitro to in vivo therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (i.e., action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (i.e., quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable in vitro proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.

opencc-by-4.0Dec 2023View details →

Data & code repository for the article "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"

This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes".  In detail the following data sources have been included: <ul> <li>the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures.zip)</li> </ul> </li> </ul>

Data for "The very-high resolution configuration of the EC-Earth global model for HighResMIP"

Model data and plot scripts to reproduce the figures of the manuscript "The very-high resolution configuration of the EC-Earth global model for HighResMIP". Authors Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, Sergi Palomas Abstract We here present the very-high resolution version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ~16 km and an oceanic resolution of 1/12° (~8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ~100-km-grid EC-Earth3P-LR, and the ~25-km-grid EC-Earth3P-HR. The models' biases are evaluated against observations over the period 1980–2014. Compared to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic  orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, with improved stormtrack over the North Atlantic, yet worsened stormtrack over the Southern Ocean compared to the lower resolution model versions. Other biases persist with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, and the precipitation excess over the tropical South Atlantic and North Pacific. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at mid-latitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional/local spatial scales, in line with regional climate models.