Find research datasets worth reusing

Companion data for RAT 3.0: Global Database, Test data, Parameter files and Routing Script

<a href="https://depts.washington.edu/saswe/rat/">Reservoir Assessment Tool version 3.0</a> is a scalable and user-friendly software platform to mobilize the global water management community. RAT uses satellite remote sensing data to monitor water surface area and water level changes in artificial reservoirs. It uses this information, along with topographical information (either derived from satellite data, or in-situ topo maps) to estimate the Storage Change (∆S) in the reservoirs. Additionally, RAT models the Inflow (I) and the Evaporation (E) of each reservoir. Finally, RAT uses the modeled I, and E, and estimated ∆S, to estimate the Outflow (O) from reservoirs. The datasets and files provided here are used by RAT 3.0 as default inputs to make it easy to set up and execute RAT for first-time users. global_data.zip - It includes <a href="https://rat-satellitedams.readthedocs.io/en/latest/RAT_Data/GlobalDatabase/">Global Database</a> encompassing global elevation data, global reservoir and dam data, major river basins in the world and the river networks, flow direction file, and geoid model. It is used by RAT 3.0 as default input for easy execution for first-time users. global_vic_params.zip - It contains <a href="https://zenodo.org/record/3475602">global VIC soil and domain parameters</a> for executing the hydrological model within RAT 3.0. It is considered a part of the Global Database but is packaged separately. params.zip - It consists of all the default parameter files used by RAT 3.0 to execute the hydrological model within it and to execute RAT itself.  routing.zip - It consists of the Fortran code for <a href="https://vic.readthedocs.io/en/vic.4.2.d/Documentation/Routing/RunRouting/">the Routing model</a> for easy installation for users.  test_data.zip - It consists of data used by RAT 3.0 to test whether it has been installed and initialized properly in a user's system.

Dataset and scripts for "Non-zero temperature study of spin 1/2 charmed baryons using lattice gauge theory"

charmJ12Scripts A set of scripts and folders to reproduce the analysis and plots in the spin 1/2 charm baryon paper which can be found at <a href="https://doi.org/10.1140/epja/s10050-024-01261-2">EPJA</a>   This repository includes the raw correlator data, the scripts and software used to analyse them as well as a script which can be run in order to reproduce the entire analysis, and particularly the figures in the manuscript.   correlators Correlators from openqcd-fastsum-hadspec are zipped in the correlators folder. These are unzipped automatically by the script. The correlators are plain text files.   output Analysis output is placed here. You do not need to look here in order to see the figures in the paper   code The python code and scripts that do the analysis. There is some modularity here with the libraries in the lib folder   paperPlots The plots from the paper will be generated here. They are not supplied with this repo as they can be found in the paper   plotXYData The x-y and y-error data of each plot in the paper. Only 'scatter' style data is included. This is generated by the run script, but also supplied herein. It will be overwritten by the runscript   run The folder where the main script needed to run all the analysis is.   Conda Notes Install your favourite conda solution, such as <a href="https://docs.conda.io/en/latest/miniconda.html">https://docs.conda.io/en/latest/miniconda.html</a>   Switch to a faster environment solver This is optional, but likely will solve the dependencies much much faster. See <a href="https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community">https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community</a> <code>conda update -n base conda</code> <code>conda install -n base conda-libmamba-solver</code> <code>conda config --set solver libmamba</code>   Install Environment <code>conda env create -f environment.yml</code>   Activate/Use <code>conda activate charm</code>   Update (w. new packages) <ol> <li>Edit <code>environment.yml</code></li> <li>Deactivate conda environment with <code>conda deactivate</code></li> <li>Update conda environment with <code>conda env update -f=environment.yml</code></li> </ol>

Data and scripts for collective intelligence research (arXiv:2204.13424)

This is the data and scripts for the study From Prediction Markets to Interpretable Collective Intelligence by Alexey V. Osipov and Nikolay N. Osipov (<a href="http://doi.org/10.48550/arXiv.2204.13424">arXiv:2204.13424</a> [cs.GT])

Dataset and Experiment Scripts for "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance"

This repository contains the Experiment Results and Collection scripts for our ESEC/FSE 2023 submission, "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance" Our runtime experiment data is located in the Experiment Results directory. This directory contains two subdirectories, All Experiment Results and Counterintuitive Results Only. In All Experiment Results, we present the runtime results from our Experiments 1-5 and the Libsodium.js case study as CSV files. For the Chromium and Firefox results, each CSV file list the sample names and multiple columns for each of the four optimization levels, O0-O3. Under each optimization level, we list the runtime (in milliseconds) with inlining enabled (from the Baseline experiment), the runtime with inlining enabled (from one of Experiment #1-5), and percent change in runtime after disabling inlining. The Counterintuitive Results Only directory contains CSV files presenting only the samples from each Experiment #1-5 that meet our threshold of at least a 5% decrease in runtime after disabling inlining. The Excel file, Wasm Function Inlining Experiment Data.xlsx, contains all of these results in a single workbook, as well as formatting applied to highlight the counterintuitive runtime values presented in our paper. The raw data collected from our experiments, including the generated WebAssembly, HTML, and JS files to run the samples, is found under the RawCollectedData directory. This folder contains a zipped file that, when extracted, contains subdirectories for each sample's collected data. The CollectionScripts directory contains the scripts necessary to run our experiments. The PatchFiles directory contains the files from the Binaryen and LLVM infrastructures with the changes that we introduced to enable and disable select optimization passes through environment variables. The Scripts directory contains the scripts we used to run our experiments. The main file within this directory that serves as the entry point is opt_level_inlining.py. This file uses the other scripts to run Experiments 1-5 in our study. The script libsodiumjs_script.py is used to run our Libsodium.js case study. The scripts are written in Python and Node.js, and the dependencies to run the scripts are Node.js, Python, and MySQL. To download the necessary dependencies for the scripts, run the command `pip install -r requirements.txt` in Scripts folder, and run `npm install` in the Scripts/performance-measurement-tool folder. Then, import  the included MySQL schemas under the Scripts folder into a MySQL database. Update the MySQL connection details in the Scripts\__db_utils.py and Scripts\performance-measurement-tool\src\MySQLConnector.ts files, and then run `npm run build` in the Scripts\performance-measurement-tool folder.

opencc-by-4.0Jan 2023View details →

Data and analysis scripts for: Recent acceleration in global ocean heat accumulation by mode and intermediate waters

The folder contains the MATLAB code and data to re-create Figures 1-9 and S1-3 within the publication by Li, Z., England, M. H., & Groeskamp, S. Recent acceleration in global ocean heat accumulation by mode and intermediate waters, Nature Communications, 2023.

openmit-licenseMay 2019View details →

Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"

This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media, submitted to JGR: Solid Earth. If you find this data useful in your own research, please cite this manuscript.

edi44/100

Data and R scripts for analyses of declines in invertebrate species from the Gulf of Maine, USA, 1997 - 2018

Data files and R scripts are for the analyses that are presented in an article that is under revision for Biology Communications. The title of the article is: Declines over the last two decades of five key invertebrate species found on rocky intertidal shores throughout the western North Atlantic. The three data files contain the abundances of four gastropod species and the recruitment of barnacles and mussels from 1997 to 2018, monthly temperature data from three buoys from 2001 to 2018, and pH and aragonite saturation state from 1997 to 2014. R scripts include details of Bayesian estimates for Poisson regressions of species over time, clean-up of environmental data, imputation of missing environmental data and analyses of species versus environmental parameters.

openCC (other)Apr 2020View details →

opencc-by-4.0Jul 2019View details →

Transgenerational plasticity of inducible defenses: combined effects of grand-parental, parental and current environments - PDF, datasets and R script

PDF, dataset and R script for the paper: Tariel J, Plénet S, & Luquet E. Transgenerational plasticity of inducible defenses: combined effects of grand-parental, parental and current environments.  Two data sets are provided: one for the analysis of the behavior (dataBehaviour.csv) and one for the analysis of other variables (weight, shell thickness and morphology; dataMorphology.csv). There are analyzed in the same R script (Script_TARIEL-Juliette.R) Signification of variables names: G1: treatment/environment of the grand-parental generation (control C or with predator-cue P) G2: treatment/environment of the parental generation (control C or with predator-cue P) G3: treatment/environment of the offspring generation (control C or with predator-cue P) Family: unique ID for each family Individual: number to identify siblings within a family  ID: unique ID for each individual  W: snail total weight (g) Th: shell thickness (mm) L: shell length (mm) l: shell width (mm) Lo: aperture length (mm) lo: aperture width (mm) crawlout : position of the snail (0:  below the water surface; 1: above/on the water surface) Day : day of measurement (crawling-out behaviour was measured three times through three consecutives days d2, d3 and d4)

opencc-by-4.0Dec 2019View details →

Supplementary data and scripts for "An Atlas of Human Metabolism"

This repository contains the models, data, and scripts associated with the publication "An Atlas of Human Metabolism". The content is divided into three main directories, each containing their own README file containing instructions. <ol> <li>tINIT_GEMs - Contains the genome-scale metabolic models (GEMs) generated in the study using the tINIT algorithm, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>ec_GEMs - Contains the enzyme-constrained models (ecGEMs) generated in the study using the GECKO framework, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>GEM_PRO - Contains the GEM-PRO dataframe with the protein structure information associated with the Human1 model.</li> </ol>

opencc-by-4.0Nov 2019View details →

Simulation data and software scripts used in calculus of ∆36 signature from EMAC clumped O2 isotope-inclusive model

This publication contains simulation data and software scripts for calculating quantities related to clumped oxygen isotope signature (∆36) derivation, as described in the "static" framework of Yeung&zwj; et&zwj; al. (2016), hereinafter "Y16") and subsequently used in Yeung&zwj; et&zwj; al.&zwj; (2019) analysis. We provide the output of the 1950–2011 transient simulation with EMAC model with explicit "dynamic" simulation of ∆36 (i.e. 18O18O isotopologues undergoing transport, mixing and O(3P)-mediated isotope equilibration) to demonstrate the importance of several assumptions/simplifications involved in the static calculus.   Please refer to .README.pdf for details.

opencc-by-4.0Feb 2020View details →

Simulation data and scripts for CFD-DEM simulation of saturated bi-disperse granular flows

<ul> <li>Data set 1 - contains the raw data required to replicate and validate all plots in the main article. </li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>

opencc-by-4.0Feb 2020View details →

Ebutius's Dilemma story / character narratives script

The EMOTIVE experience "Ebutius's Dilemma" script. The experience was created for the Antonine Wall Gallery of the Hunterian Museum. More information can be found here: https://emotiveproject.eu/index.php/what-we-do/experiences/

opencc-by-4.0Mar 2020View details →

Datos ambientales, tamaños cerebros, script Proyecto FONDECYT 1170815

Bases de datos ambientales, poligonos de distribución, tamaños cerebrales recopilados durante el proyecto Fondecyt 1170815

opencc-by-4.0May 2020View details →

All atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme

The dataset contains a total of 40 snapshots of the four trajectories (10 snapshots each system = two per replica x 5 replicas/system): <ol> <li>SARS-CoV-2002 spike protein without ACE2</li> <li>SARS-CoV-2 spike protein without ACE2</li> <li>SARS-CoV-2002 spike protein with ACE2</li> <li>SARS-CoV-2 spike protein with ACE2</li> </ol> Molecular dynamics simulation trajectories (320ns each) have been performed using the Amber ff14SB force field running with the Amber18 package at the the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Under the following simulation methodology: All-atom simulations were carried out with Amber18 (<a href="https://slack-redir.net/link?url=http%3A%2F%2Fambermd.org">ambermd.org</a>), and system components (protein, ions, water) were modeled with the included FF14SB and TIP3P parameter sets. Energy minimization used CPU pmemd, while later simulation stages used GPU pmemd. CoV2 and CoV1 systems with one RBD up (with/without ACE2) were solvated in 12 angstrom water shells. Cysteine residues identified in the initial models as having a disulfide bond (DB) were bonded using tLeap. All simulations used 0.150 M NaCl. Hydrogen mass repartitioning was applied only to the protein to enable a 4 fs timestep (<a href="https://slack-redir.net/link?url=https%3A%2F%2Fpubs.acs.org%2Fdoi%2Fabs%2F10.1021%2Fct5010406">https://pubs.acs.org/doi/abs/10.1021/ct5010406</a>). The SHAKE algorithm was applied to hydrogens, and a real-space cutoff of 8 angstroms was used. Periodic boundary conditions were applied and PME was used for long-range electrostatics. Minimization was by steepest descent (2000 steps) followed by conjugate gradient (3000 steps). Heating used two stages: (1) NVT heating from 0 K to 100 K (50 ps), and (2) NPT heating from 100 K to 300 K (100 ps). Restraints of 10 kcal mol-1 angstrom-2 were applied during minimization and heating to C-alpha atoms. During 6 ns of equilibration at 300 K C-alpha restraints were gradually reduced from 10 kcal mol-1 angstrom-2 to 0.1 kcal mol-1 angstrom-2. Finally, restraints were released and 320 ns unrestrained production simulations were carried out for CoV2 and CoV1 systems. Production simulations began from the final equilibrated snapshots, and five copies of each system were simulated. As unrestrained systems can freely rotate we monitored simulations for any close contacts and found that in one copy of the CoV1 simulation without ACE2 and one RBD up that a few contacts close to 8 angstrom occur near the end of the 320 ns between the RBD and a different subdomain of the spike complex in a periodic image. However this did not influence analyzed structural properties which is verified by comparing results across simulations. The Monte Carlo barostat was used to maintain pressure (1 atm), and the Langevin thermostat was used to maintain 300 K temperature (collision frequency 1 ps-1), as implemented in Amber18. In aggregate, nearly 7 microseconds of simulation of systems ranging from 396,147 to 879,100 atoms was carried out for this work. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu). Regarding the contact map analysis scripts (contactMaps_Analysis.tar.gz), they contain the following workflow: contactmap      --> source files from contact_map executable process_nc.sh   --> convert raw data from all-atom simulation to numbered PDB files and get the contact maps frequency.lua   --> read a set of PDB files and output the frequency count for each contact consensus.fasta --> align sequence of Covid19 and SARS from Chimera consensus.lua   --> read data previously generated and compute the frequency per residue, among other things. consensus.sh    --> input information to consensus.lua consensus.gp    --> gnuplot script to plot figures This dataset and the code is part of tripartite collaboration between: <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> </ul>

opencc-by-4.0Apr 2020View details →

Dataset and R-script for simple mechanistic model of Heracleum sosnowskyi seed dispersal by wind

The dataset contains: - primary data about Heracleum sosnowskyi seeds traits  (terminal velocity, mass, area, wing loading) and release heights  for  H. sosnowskyi populations from two geographically distant Russia regions; - results of experiments of model seeds launches under different wind speeds; - R script for exploratory statistical analysis, linear regressions and mechanistc models testing. The anemochorous seed dispersal was generalized with a number of empirical and mechanistic models of varying complexity. The aim of this work was to develop the simplest possible mechanistic model of Heracleum sosnowskyi that allows to determine the distance of seed dispersal by wind with an accuracy comparable to that of empirical measurements. We measured and compared the characteristics of the seeds (terminal velocity, mass, area, wing loading) as well as the release height for H. sosnowskyi populations from two geographically distant Russia regions. We tested two simplest mechanistic models: a ballistic model and a wind gradient model using identical artificial seeds with characteristics similar to those of real H. sosnowskyi seeds. The wind gradient model gave the best results, despite the fact that uniform in shape, weight and size artificial H. sosnowskyi seeds, when dropped simultaneously from the same height, fly off at different distances. This model provides an estimate of dispersal distances with an accuracy comparable to that of empirical measurements. We plan to use the presented model to develop an individual-based model that will allow us to calculate the flight distances of H. sosnowskyi propagules, taking into account real weather conditions in different years and in different parts of its invasion range. All primary data and R-scripts used are freely available at the Zenodo repository (https://doi.org/10.5281/zenodo.3766035).

opencc-by-4.0Jul 2020View details →

Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures

This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)   CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.

dryad40/100

Scripts from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms

<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various symptoms (macroinvertebrate metrics) and probabilistically link them to various potential causes of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> Synthesis and applications. Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28 July 2020</li> </ol>

opencc-zeroAug 2020View details →

opencc-by-4.0Sep 2020View details →

Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data.

Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data. The tutorial is published in a blog for The Node (https://thenode.biologists.com/converting-excellent-spreadsheets-tidy-data/education/)

opencc-by-4.0Oct 2020View details →

Data, chains and plotting scripts for arXiv: 2008.00487

chains are: page_bao:  BAO only, flat page_sltd: SLTD only, flat page_cc: CC only, flat page_nosn: combined, flat kpage_bao:  BAO only, nonflat kpage_sltd: SLTD only, nonflat kpage_cc: CC only, nonflat kpage_nosn: combined, nonflat