Find research datasets worth reusing

Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'

GENERAL INFORMATION 1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?' 2. Author Information     A. Investigator Contact Information         Name: Philip G. Madgwick         Institution: Syngenta          Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK         Email: philip.madgwick@syngenta.com     B. Investigator Contact Information         Name: Ricardo Kanitz         Institution: Syngenta          Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland         Email: ricardo.kanitz@syngenta.com 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01  4. Geographic location of data collection: UK  5. Information about funding sources that supported the collection of the data:  This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC). SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: NA 2. Links to publications that cite or use the data: [UPDATE] 3. Links to other publicly accessible locations of the data: NA 4. Links/relationships to ancillary data sets: NA 5. Was data derived from another source? No 6. Recommended citation for this dataset: [UPDATE] DATA & FILE OVERVIEW 1. File List:  PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION) Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   2. Relationship between files, if important:  Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'. 3. Additional related data collected that was not included in the current data package: NA 4. Are there multiple versions of the dataset? No METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  4. Standards and calibration information, if appropriate: NA 5. Environmental/experimental conditions: NA 6. Describe any quality-assurance procedures performed on the data: NA 7. People involved with sample collection, processing, analysis and/or submission: NA  DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv 1. Number of variables:  17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript):  Population Size    = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9 Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1 Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1 Female Exposure    = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1 Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1 Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size Effectiveness 1    = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1 Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1 Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1 Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5 Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1 Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size Effectiveness 2    = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1 Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1 Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1 Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5 Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1 2. Number of cases/rows:  10^6, corresponding to the number of random samples  3. Variable List: NA  4. Missing data codes: NA 5. Specialized formats or other abbreviations used: NA DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv)  1. Number of variables:  10 variables with column names that have the following meanings: A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency   A_f_250 = the frequency of resistance allele A at the 250th generation  A_f_bar = the mean frequency of resistance allele A over the first 250 generations   B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency   B_f_250 = the frequency of resistance allele B at the 250th generation  B_f_bar = the mean frequency of resistance allele B over the first 250 generations  nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation nf_250 = the female population size at the 250th generation nf_bar = the mean female population size over the first 250 generations  nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1 2. Number of cases/rows:  10^6, corresponding to the number of random samples  3. Variable List: NA 4. Missing data codes: all missing data is recorded as 0  5. Specialized formats or other abbreviations used: NA

Data for "Deep learning-based model for diagnosing Alzheimer's disease and tauopathies"

Image datasets and tuned models used in the paper (Koga et al., 2021). Data.zip contains image and text files for training models. Test.zip contains 12 images from 4 patients, which are a part of the hold-out dataset images used in the paper. There are 9 CSV files, which contain the results of tau burden quantification. Python code is available at GitHub (<a href="https://github.com/Koga-MD/DL-Tauopathies">https://github.com/Koga-MD/DL-Tauopathies</a>).

opencc-zeroJul 2021View details →

dryad40/100

Physiological data and R script for running physiology combined model for Drosophila suzukii

This is the dataset that accompanies an article entitled "The use of insect life tables in optimizing invasive pest distributional models" that would be published in Ecography. The dataset include two R script that used to generate physical model and the physiology combined model respectively. Our paper shows that the physiology combined model show good performance when applying ecological niche model in risk assessment. We addressed this by determining whether incorporating physiological data from life table analyses of an invasive insect, Drosophila suzukii, improved predictions of ecological niche models. The dataset also include the physiology data D. suzukii that we assembled for running our physiology combined model.

opencc-by-4.0Oct 2020View details →

A new lepto-hadronic model applied to the first simultaneous multiwavelength data set for Cygnus X–1

This is a basic reproduction package for the paper "A new lepto-hadronic model applied to the first simultaneous multiwavelength data set for Cygnus X-1" by [Kantzas et al. (2020)](<a href="https://doi.org/10.1093/mnras/staa3349">https://doi.org/10.1093/mnras/staa3349</a>).

Data for publication of "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning"

The data are made available as part of the paper "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning", submitted to Geoscientific Model Development. This data set incorporates selected post-processed files needed to reproduce the results presented in the paper. The data contains six zip files, that are: <ul> <li>Namelist.input files for the WRF model simulations of ten tropical cyclones</li> <li>WRF model simulation outputs using the default parameter values</li> <li>WRF model simulation outputs using the optimal parameter values (which give minimum RMSE value)</li> <li>IMDAA surface observations and IMERG precipitation data</li> <li>IMD observed tracks of ten tropical cyclones</li> <li>Ipython notebooks of sensitivity analysis and machine learning codes</li> </ul> The remaining files are the ncl scripts that were used to obtain the figures. The ncl scripts used the data in the zip files.

opencc-by-4.0Jan 2021View details →

Urheberrechtsgesetz (UrhG): software and data related definitions depicted using UML class modeling — Release 10

Diagram depicting definitions and conceptual relationships within the German Copyright and Related Rights Act or Urheberrechtsgesetz (UrhG) up to and including the amendments of 28 November 2018.  The diagram focuses on software and data related definitions and presents these using Unified Modeling Language (UML) class modeling.  Please contact the author is you require the underlying Inkscape SVG vector art.

Research project on field data collection for honey bee colony model evaluation - datasets

Description of the datasets The file 00_MUSTB_field_data_model.docx contains the data model according to which the data collected in the context of the MUSTB field data collection were reported to EFSA. The current data model description includes some modifications with respect to the specifications published before the beginning of the project (EFSA, 2017, https://doi.org/10.2903/sp.efsa.2017.EN-1234). All the tables included in the data model are published here in csv format. The underlying schemas are also published in xsd format. Sites: General information about the sites where the data collection took place; Polygons: General information about the polygons where the botanical survey took place. Table I: Pesticide application, reporting data on experimental spraying events; Table II: Resource providing unit and landscape fitness, reporting data on abundance of flowering plants in polygons mostly within 1.5 km, but in some cases up to 3 km of the experimental colony; Table III: Master list of all hives included in the study; Table IV: Colony management, reporting the log of the beekeeper regarding input (if material was added to the hive: e.g. empty frames, chemicals for varroa treatment, sugar), output (if material was removed from the hive, e.g. honey combs, supers), queen loss, swarming, or clinical signs observed in the experimental hives; Table V: Hive inspection, reporting data on in-hive measurements in the experimental colonies. This table contained several types of data, including: <ul> <li>Data on brood development and food provision (“cell utilization”) obtained from image analysis of combs;</li> <li>Data on forager activity obtained from automatic video recordings and image analysis by a bee counter;</li> <li>Data on hive weight obtained from automatic logging by a hive scale;</li> <li>Data on adult bee strength, obtained by weight assessment of combs with and without adult bees (“bees per comb data”);</li> </ul> Table VI: SSD2, reporting data on results of laboratory analyses of pollen, pesticide residues and parasites/pathogens. These four types of laboratory analyses involved different methods, and were reported according to different standards. Therefore, a number of the fields in the technical specifications for the SSD2 table (EFSA, 2017) were not applicable for records reporting results of some analyses, in particular palynological, parasite and pathogen analyses. These fields were left empty; Table VII: Colony observation, reporting observations of honey bee waggle dances from observation hives. Orientation denotes the angle of the waggling phase relative to the vertical axis on the comb. Direction denotes the actual direction in the landscape, as calculated from the orientation of the waggle dance. In all the csv files, columns with the suffix "_desc" have been included, where relevant, to include the name corresponding to the EFSA controlled terminology used in the previous column (e.g. resUnit contains EFSA term codes while resUnit_desc contains the term names). Data storage All data collected during the project was stored in a relational database. The database was developed in .NET Entity Framework Core, ran on a PostgreSQL, and was hosted by Amazon Web Service during the whole duration of the project development. Data could be imported or entered manually in the database through a web form. Administrators could create new users and administrators, new sites, and new colonies, i.e., administrators were allowed to enter or change data of all tables. Users were allowed to enter data, and could view, retrieve, and modify their own data of all tables, except for Table III (description of experimental colonies). Administrators could view and retrieve all data. Data was retrieved in CSV and XML formats, and were structured to secure a smooth transmission of data to the Data Collection Framework of EFSA. Furthermore, data flow from the field data collection to the development of ApisRAM was secured by direct communication between the field and modelling teams.   Version 2 contains the UTM coordinates in tables Sites, Polygons and Resource providing unit.

opencc-by-4.0Aug 2021View details →

Data input for the RegMex model experiment on the power system and flexible sector coupling

This file provides the input data used in the power system flexibility model experiment performed within the RegMex project. Comprehensive information about the project can be found in the project report [Lechtenböhmer2018] (in German, see link in the file). In the experiment performed with the data documented here, three scenarios were considered, labelled "Import", "Decentralized" and "Offshore". This file contains the input for all scenarios. All further information on the model and scenario configuration is available from the project report. Many technology parameter have been derived as own assumptions within previous projects, relying on different sources. Details can be found in the cited PhD and masters theses. In the experiment, Germany was modelled with 18 regions reflecting the transmission grid operator zones (see map in the file).

opencc-by-4.0May 2023View details →

Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets

This dataset accompanies the paper titled Data Models for Dataset Drift Controls in Machine Learning with Images that appeared in the Transactions on Machine Learning Research <a href="https://openreview.net/forum?id=I4IkGmgFJz">https://openreview.net/forum?id=I4IkGmgFJz</a>   <pre><code>@article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} }</code></pre> We make available two datasets. Raw-Microscopy: <ul> <li>940 raw bright-field microscopy images of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels).</li> <li>5,640 variations measured at six additional different intensities (microscopy/images/raw_scale001-raw_scale0075)</li> <li>11,280 images of the raw sensor data processed through twelve different pipelines (microscopy/images/processed_views)</li> </ul> Raw-Drone: <ul> <li>548 raw drone camera images for car segmentation (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472.</li> <li>3,288 variations measured at six additional different intensities (drone/images_tiles_256/raw_scale001-raw_scale075).</li> <li>6,576 images of the raw sensor data processed through twelve different pipelines (drone/images_tiles_256/processed_views).</li> </ul> Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper. The code repository for this project can be found at <a href="https://github.com/aiaudit-org/raw2logit">https://github.com/aiaudit-org/raw2logit</a>

opencc-by-4.0Nov 2020View details →

Text-fig. 5. Macroevolutionary trends related to the IC model in the first three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dashed line (- -) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle). in Unexpected Inhibitory Cascade In The Molariforms Of Sloths (Folivora, Xenarthra): A Case Study In Xenarthrans Honouring Gerhard Storch'S Open-Mindedness

Text-fig. 5. Macroevolutionary trends related to the IC model in the first three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dashed line (- -) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle).

opencc-by-4.0Nov 2020View details →

Text-fig. 4. Macroevolutionary trends related to the IC model in the last three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dash-dot line (-.-) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle). in Unexpected Inhibitory Cascade In The Molariforms Of Sloths (Folivora, Xenarthra): A Case Study In Xenarthrans Honouring Gerhard Storch'S Open-Mindedness

Text-fig. 4. Macroevolutionary trends related to the IC model in the last three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dash-dot line (-.-) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle).

Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1: Modeling Data

This archive contains the modeling data for the study <a href="https://www.frontiersin.org/articles/10.3389/fchem.2021.736457/full">"Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1"</a> (doi: 10.3389/fchem.2021.736457). The following data sets are available: <ul> <li>Induced fit docking results of all mentioned compounds in the study:  ifd_hBGT1_occ_clustering_all_compounds.zip  </li> <li>MD simulations of bicyclo-GABA and compound 1 (100ns, 3 replica): MD_simulation_bicyclo-GABA_run1.zip MD_simulation_bicyclo-GABA_run2.zip MD_simulation_bicyclo-GABA_run3.zip MD_simulation_cmd1_run1.zip MD_simulation_cmd1_run2.zip MD_simulation_cmd1_run3.zip</li> </ul> A detailed description of the methods is available in the aforementioned publication.   The compound numbering in the uploaded files differs from the compound numbering in the mentioned study:   study / upload bicyclo-GABA / cmd4 1 / IIa 2 /8-2 3 / 8-3 4a / 7-1 4b / 7-2 4c / 7-3

opencc-by-4.0Apr 2021View details →

Proccessed data for Trend Validation of Metabolic Models Against Measurements Using Indirect Calorimetry

A cleaned data set used to validate metabolism models in a muscuskeletal modeling software. The dataset contains 240 rows and 18 columns.  Labels: <ul> <li>AnyMet = Metabolic output by the modelling software. Calculated as the mean energy cost per repetition  [J] .</li> <li>VynMet = Metabolic output by the indirect calorimetry system (Vyntus CPX). Calculated as the mean energy cost per repetition  [J].</li> <li>rest_energy = total energy cost during rest [J]. Measured with Indirect caliometry</li> <li>rest_time = total time of rest [min]</li> <li>Work = Energy cost times the displacement per rep [J].</li> <li>watt = Work divided by total duration of a repetition [J/s]</li> <li>extension time = duration of the extension part of the movement [s]</li> <li>flexion time = duration of the flexion part of the movement [s]</li> <li>bw = bodyweight [kg]</li> <li>height [m]</li> <li>CV = coefficient of variation for the measured rest_energy. </li> <li>model = model type used for AnyMet. </li> <li>Subject </li> <li>Contraction = Contraction type performed</li> <li>intensity = Intensity to overcome created by the dynamometer. </li> <li>mech_watt_kg = mechcanical watt, watt divided by bodyweight</li> <li>any_met_watt_kg = watt pr kg: (AnyMet / bw) / (extension time + flexion time)</li> <li>vyn_met_watt_kg = watt pr kg: (VynMet / bw) / (extension time + flexion time) There is also a zip file containing the raw data from the dynanometer and the Vyntus PGE system.</li> </ul>

Data for the paper: Earth System Model Parameter Adjustment Using a Green's Functions Approach

This dataset contains model codes and scripts used to generate the results of the paper submitted to Geoscientific Model Development journal

opencc-by-4.0Mar 2021View details →

CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"

Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript: Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"

Code and data archive to accompany "A derivative-free optimisation method for global ocean biogeochemical models", Oliver et. al. 2021

This archive is to accompany the article: A derivative-free optimisation method for global ocean biogeochemical models, Sophy Oliver, Coralia Cartis, Iris Kriest, Simon Tett, and Samar Khatiwala. The optimisation framework used in this study can be found here: https://doi.org/10.5281/zenodo.5517610 The original source code of MOPS were from the Supplement of Kriest et al. (2017). The most recent TMM source code is available at https://github.com/samarkhatiwala/tmm. In this archive: Supplement/Configurations/OxfordMOPS_Configs contains: - ReadOnlyFiles (Files and Code specifically used to run the global ocean biogeochemical model MOPS model with   the Transport Matrix Method, which have been edited to differ from the versions downloaded from the sources above.) - RunCode (runscripts to run the MOPS model with the TMM) - TWIN_Configs (JSON files required by each optimisation experiment carried out). Supplement/OxfordMOPS_EXP contains data for each iteration of all optimisation experiments carried out. Supplement/OPTCLIMSO_PlottingScripts contains MATLAB plotting scripts used to create results figures of these experiments.

opencc-by-4.0Oct 2021View details →

Upslope migration of snow avalanches in a warming climate: data and model source files

Complete data and model source files corresponding to: Giacona, F., Eckert, N., Corona, C., Mainieri, R., Morin, S., Stoffel, M., Martin, B., Naaim, M. (2021). Upslope migration of snow avalanches in a warming climate. Proceedings of the National Academy of Sciences America, Nov 2021, 118 (44) e2107306118; DOI: 10.1073/pnas.2107306118

Scientific data: Laboratory modelling of urban flooding

These datasets include experimental data obtained in the hydraulic laboratory. A detailed description of the datasets is available in the article.