Find research datasets worth reusing

opencc-by-4.0Apr 2023View details →

Selected simulation input and output for patchoulol synthase and germacradien-11-ol synthase

Files related to the biomolecular simulations in the following publication: Active site loop engineering abolishes water capture in hydroxylating sesquiterpene synthases Prabhakar L. Srivastava,[a] Sam T. Johns,[b] Rebecca Walters,[b] David J. Miller,[a] Rudolf K. Allemann*[a] and Marc W. Van der Kamp*[b]   [a]School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom [b]School of Biochemistry, University of Bristol, University Walk, Bristol BS8 1TD, United Kingdom See the README.docx document for details.

opencc-by-4.0Mar 2022View details →

Inputs and Outputs for paper The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations

Contains the DESC and VMEC input and output files used in the paper, as well as the plotting scripts used to create the figures in the paper (Current with the <a href="https://arxiv.org/abs/2203.17173">arxiv Mar 31 2023 version</a>):   3D equilibrium codes are vital for stellarator design and operation, and high-accuracy equilibria are also necessary for stability studies. This paper details comparisons of two 3D equilibrium codes, VMEC, which uses a steepest-descent algorithm to reach a minimum-energy plasma state, and DESC, which minimizes the MHD force error in real space directly. Accuracy as measured by final plasma energy and satisfaction of MHD force balance, as well as other metrics, will be presented for each code, along with the computation time. It is shown that DESC is able to achieve more accurate solutions, especially near-axis. DESC's global Fourier-Zernike basis also yields the solution everywhere in the plasma volume, not just on discrete flux surfaces. Further, DESC can compute the same accuracy solution as VMEC in an order of magnitude less time.   Updated dataset for better readability 5-5-23

opencc-by-4.0Mar 2023View details →

Model inputs and outputs for: Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)

Files used in initial submission of manuscript to Geoscientific Model Development. Files with names beginning sdates and gdds were used as model inputs for some experiments. File with name beginning hdates was used in postprocessing. ZIP archives are model experimental outputs.

Emission input data for WRF-CHMIERE

Emission input data for WRF-CHIMERE in eastern China during 2017.

Example input files for Wolf-Rayet star models in paper "Tidal Spin-up of Black Hole Progenitor Stars"

The files here show example input files for the models in the paper "Tidal Spin-up of Black Hole Progenitor Stars" by Ma & Fuller (2023). File "inlist_MS" and "inlist_WR" show MESA inlists to set up a Wolf-Rayet star model of 10 solar-masses (model 3 in Ma & Fuller 2023, Table 1). We used MESA version r12778 for our calculation. Specifically, "inlist_MS" starts a stellar model at zero-age main-sequence (ZAMS), and evolves it to the end of core hydrogen depletion. After resuming the model from a photo file, "inlist_WR" turns on artificial mass-loss ("relax_mass" and "new_mass") to remove its hydrogen envelope, and then evolves the model throughout the helium burning Wolf-Rayet phase, until the end of core helium depletion. During this phase, pulsation data are also created by MESA ("write_pulse_data_with_profile = .true."). File "gyre.in" is an example GYRE input file to solve for oscillation modes in the established Wolf-Rayet star models. We used GYRE version 6.0.1 for our calculation.

Raw Counts: A protocol for low-input RNA-sequencing of patients with febrile neutropenia captures relevant immunological information

Raw counts for scientific article:  "A protocol for low-input RNA-sequencing of patients with febrile neutropenia captures relevant immunological information" Victoria Probst*1, Lotte Møller Smedegaard*2, Arman Simonyan1, Yuliu Guo1, Olga Østrup1, Kia Hee Schultz Dungu2, Nadja Hawwa Vissing2, Ulrikka Nygaard2 and Frederik Otzen Bagger1 *Shared first authorship Data description:  The raw counts are from 88 samples of 22 patients with leukaemia and suspected infection sequenced by a low-input protocol (Takara SMART-seq HT) (96% succeeded) and 15 of these were also processed by the standard protocol (Truseq).   CLI.CSV: Raw counts of control samples processed using a low input RNA sequencing protocol. 15 samples processed by the low-input protocol.   CRNA.CSV: Raw counts of control samples processed using a standard RNA sequencing protocol. 15 samples processed by the standard protocol.  FEB.CSV: Raw gene counts from patients. 88 samples processed by the low input protocol. 4 samples failed sequencing.

opencc-byMay 2023View details →

Datalog subsetting input files (Wikidata 2015 NTriple-to-CSV dump)

This is a Wikidata 2015 NTriple dump in which the delimiter is changed to ','. The file is used in subsetting experiment via <a href="https://github.com/seyedahbr/radlog">Radlog</a>.

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

Input data for the case study reported in "DREAM: an R package for druggability evaluation of human complex diseases".

The data included in this record constituted the input for the case study reported in the manuscript "DREAM: an R package for druggability evaluation of human complex diseases", by Antonio Federico, Michele Fratello, Alisa Pavel, Lena Möbus, Giusy del Giudice, Angela Serra, Dario Greco. The data derive from transcriptomics experiments executed on lesional skin from atopic dermatitis patients and unaffected skin counterparts. The data consists of two files in ".txt" format reporting gene expression data in tabular format, where on the rows are reported genes and on the columns are reported samples. The data is an aggregated and batch-corrected collection of datasets originally downloaded by Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The file "GE_Mic_AD_Pamr_MAARS.txt" reports gene expression estimates of lesional skin of atopic dermatitis patients, while the file "GE_Mic_AD_Pamr_nl_MAARS.txt" reports gene expression estimates of non-lesional skin of atopic dermatitis patients.

opencc-by-4.0Sep 2022View details →

Input data, species level results and code accompanying paper: Drivers of the changing abundance of European birds at two spatial scales

This repository contains the input data, species level results and code associated with the paper: Drivers of the changing abundance of European birds at two spatial scales.

opencc-by-4.0Jun 2022View details →

Input data for Atollgen pipeline

Input data for Atollgen pipeline Contains: <ul> <li>Frozen island raw sources (atollgen database inputs)</li> <li>hmm database (integrase and mobility signatures coming from ConjScan and Pfam-A)</li> <li>categorisation metadata for each signature contained in the integrase and mobility database</li> <li>Frozen genomes sequences from the NCBI</li> <li>Frozen list of actinobacteria taxonomy ids</li> <li>Frozen defense-finder database</li> <li>Frozen cards</li> </ul> Frozen data ensure reproducibility for the pipeline, but up-to-date data should give similar (yet not identical) results.

opencc-by-4.0Dec 2022View details →

Dataset for Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands

Soil organic carbon dynamics for Ecuadorian croplands on the period 2020-2070, under RCP4.5, for various carbon sequestration alternatives, relative to a business as usual situation. Results obtained from adapted version of RothC for carbon sequestration technologies.

opencc-by-4.0Feb 2023View details →

Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme

The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the  GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions  to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.

Input data for running a GWAS on penicillin resistance in Streptococcus pneumoniae

Results from running the pyseer tutorial at https://pyseer.readthedocs.io/en/master/tutorial.html

PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign

Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.

HeatResilientCity II - work package 2.3: Interactions between buildings and open space adaptation measures – Meteorological input data for building performance simulation

This repository contains meteorological data from urban climate simulations that were carried out in districts of the cities of Dresden and Erfurt as part of the <a href="http://heatresilientcity.de/">HeatResilientCity II</a> project. The data was extracted at specific points (receptors) of the urban climate model. In addition to the data, a script is attached that can be utilized to generate a time series for IDA ICE building performance simulations using IceWeather.exe. Therefore, a Microsoft Windows operating system is required. To create a time series, simply use the function createIdaIceInput() at the end of the script createTimeSeries.py. Further explanations can be found at the beginning of the script. Information about the ENVI-met data used to create the IDA ICE input can be found in README_RawENVImetOutput_DD.txt and README_RawENVImetOutput_EF.txt. Some input data files have already been generated and can be directly used for thermal building performance simulations with IDA ICE. These files can be found in the folder 0.3_Input_Timeseries (Climate) for IDA ICE. The naming convention of the final input data files for IDA ICE is as follows: <ul> <li>TOWN_SCENARIO_RECEPTOR_AVERAGING_INTERFACE_LATITUDE_LONGITUDE_VERSION</li> <li>TOWN: Choose between 'Erfurt' and 'Dresden'</li> <li>SCENARIO: See further information in README_RawENVImetOutput_DD.txt and README_RawENVImetOutput_EF.txt</li> <li>RECEPTOR: Location in the modelled area (ENVI-met simulation) where data was extracted.</li> <li>AVERAGING: Information about averaging the hourly values of the urban climate simulation (see createTimeSeries.py and READMEs)</li> <li>INTERFACE: Information on how single days were joined together (see createTimeSeries.py).</li> <li>LATITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function setIceWeatherParams() if you are using other cities/custom ENVI-met simulation data.</li> <li>LONGITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function setIceWeatherParams() if you are using other cities/custom ENVI-met simulation data.</li> <li>VERSION: The version number can be set in the script.</li> </ul> Example: Dresden_2y_A1_a_timeSeries_24-24_51.0468_13.6707_v11.prn Folder overview: <ul> <li>The ENVI-met raw data is stored in 0.1_Input_RawENVImetOutput.</li> <li>The script is stored in 0.2_Input_ScriptsToCreateTimeSeries.</li> <li>The final datasets ready for simulation with IDA ICE are stored in 0.3_Input_Timeseries(Climate)ForIDAICE. This folder also contains some weather data time series that have already been created and can be used for IDA ICE (subfolders Erfurt_v11 and Dresden_v11).</li> </ul>

Deep-SDMs in the open oceans - INPUT DATA

This repository contains input files to train the Deep-SDM model described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id) 2. Prediction data: 2.1. Global use case (solstices & equinoxes of 2021): CSV dataset + data folder 2.2. Western Indian Ocean use case: CSV dataset + data folder 3. species.csv contains the taxonomic name of each taxon, as well as its GBIF id. 4. stats.npy contains normalization factors for the data files <pre><code class="language-python">meds, perc1, perc99 = np.load("stats.npy") item = np.load(file)[:,:,:25] real_values = (perc99 - perc1) * item + perc1</code></pre>   Each of these elements can be downloaded separately by scrolling to the Files section.

opencc-by-4.0Aug 2023View details →

Input files for the MD simulations and free energy calculations for the article "Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions"

Article: <a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.1c04818">J. Phys. Chem. B. 125, 9357–9371 (2021) [DOI: 10.1021/acs.jpcb.1c04818]</a> The structures of the homopolymers and copolymers simulated are shown in Figures 1 and S1 and Tables 2 and 3. All-atom MD simulation was carried out using GROMACS, and this repository provides the input files with the GAFF/RESP force and initial coordinate files. The free energy of water dissolution was obtained with <a href="https://sourceforge.net/projects/ermod/">ERmod</a>, and the input files for the free-energy calculations are also contained. See the README files for details.

opencc-by-4.0Aug 2023View details →

Input Data for TEMIR v1.0

This dataset contains the necessary input data for the Terrestrial Ecosystem Model in R (TEMIR) version 1.0 (<a href="https://github.com/amospktai/TEMIR">https://github.com/amospktai/TEMIR</a>).