Find research datasets worth reusing

COSMOS Model Data

The archive contains climate model data using the Earth System Model COSMOS in the study of Knorr et al (2021). COSMOS is a fully coupled general circulation model with atmosphere, ocean–sea ice and vegetation components (Roeckner et al. 2003, Marsland et al. 2003, Brovkin et al. 2009). The notation of the individual files in the archive corresponds to the figure numbers in Knorr et al. (2021). The respective file content is described by the corresponding figure caption in the manuscript. File names for the Extended Data Figure data are indicated by the prefix ‘ED’. For the data of Extended Data Figures 5 and 10c please see the main figure data of Fig. 4b-d and Fig. 2b, respectively. For details regarding the model configuration and the experimental design we would like to refer to the Methods in Knorr et al. (2021) and the references therein.   References: Brovkin, V., Raddatz, T. Reick, C. H., Claussen, M. & V. Gayler: Global biogeophysical interactions between forest and climate. Geophys. Res. Lett. 36, 1–5 (2009). Knorr, G., Barker, S., Zhang, X., Lohmann, G., Gong, X., P. Gierz, C. Stepanek, L. Stap: A salty deep ocean as a prerequisite for glacial termination, Nature Geoscience (2021). doi: 10.1038/s41561-021-00857-3 Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M. & F. Röske: The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5, 91–127 (2003). Roeckner, E. et al.: The atmospheric general circulation model ECHAM5 Part 1: Model description. Report, Max-Planck-Institut für Meteorologie 349, 1–127 (2003).

opencc-zeroDec 2016View details →

Data from: Towards robust evolutionary inference with integral projection models

Integral projection models (IPMs) are extremely flexible tools for ecological and evolutionary inference. IPMs track the distribution of phenotype in populations through time, using functions describing phenotype-dependent development, inheritance, survival and fecundity. For evolutionary inference, two important features of any model are the ability to (i) characterize relationships among traits (including values of the same traits across ages) within individuals, and (ii) characterize similarity between individuals and their descendants. In IPM analyses, the former depends on regressions of observed trait values at each age on values at the previous age (development functions), and the latter on regressions of offspring values at birth on parent values as adults (inheritance functions). We show analytically that development functions, characterized this way, will typically underestimate covariances of trait values across ages, due to compounding of regression to the mean across projection steps. Similarly, we show that inheritance, characterized this way, is inconsistent with a modern understanding of inheritance, and underestimates the degree to which relatives are phenotypically similar. Additionally, we show that the use of a constant biometric inheritance function, particularly with a constant intercept, is incompatible with evolution. Consequently, current implementations of IPMs will predict little or no phenotypic evolution, purely as artefacts of their construction. We present alternative approaches to constructing development and inheritance functions, based on a quantitative genetic approach, and show analytically and through an empirical example on a population of bighorn sheep how they can potentially recover patterns that are critical to evolutionary inference.

Simulation data for the model of the Cetus Stream

This archive contains an N-body simulation model of the formation of Cetus Stream.

Finite Mixture Models for Clustering Sales Series Data in the Presence of Promotions

<pre> 131 sales data where promotion causes volatility over the entire time series</pre>

Master Thesis- Modeling of Electric Vehicle Charging Infrastructure and Comparison of Electric Vehicle Load Simulation with Empirical Charging Data

All the data behind relevant plots in the thesis report are stored  here

data for modeling scale of representation of heterogeneity on simulated salinity and saltwater circulation in coastal aquifers

This is the modeling data for modeling scale of representation of heterogeneity on simulated salinity and saltwater circulation in coastal aquifers. We setup a series of SEAWAT models to see the scale-dependent heterogeneity in simulations of saltwater circulation and cautions

Data for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa"

This is the data used for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model". The data includes two folders: fireon and fireoff representing the scenarios with the fire model turned on and off. Each folder includes 14 years of data from 2000-2013.

Scripts and data for the PARASO Geoscientific Model Development paper figures

Scripts and data for reproducing all figures from the PARASO model description paper. <ul> <li> <code>*.tex</code> sources generate Tikz pdf figures calling <code>pdflatex <input.tex></code>. they require a working LaTeX installation with some standard libraries (e.g. Tikz and others). </li> <li> <code>*.py</code> are python scripts generating figures. They require standard python libraires such as matplotlib, numpy, cartopy... </li> </ul> Model description: Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022. Source code (no COSMO): Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. <a href="https://doi.org/10.5281/zenodo.5576201">10.5281/zenodo.5576201</a> Input data: Pelletier, Charles, Klein, François, Zipf, Lars, Vanden Broucke, Sam, Haubner, Konstanze, & Helsen, Samuel. (2021). Input data for PARASO, a circum-Antarctic fully-coupled 5-component model (v1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5588468">10.5281/zenodo.5588468</a> Forcings: Pelletier, Charles, & Helsen, Samuel. (2021). PARASO ERA5 forcings (1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5590053">10.5281/zenodo.5590053</a> Acknowledgements <ol> <li> The ERA5 data (Hersbach, 2019) was downloaded on 01-09-2019 from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Reference: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 01-SEP-2019), <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>. </li> <li> The sea-ice index (NSIDC-G02315) was downloaded on 01-09-2019 from the National Snow & Ice Data Center. Reference: Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. Daily Antarctic. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. <a href="https://doi.org/10.7265/N5K072F8">10.7265/N5K072F8</a>. Accessed on 01-SEP-2019. </li> <li> The World Ocean Atlas 2018 (WOA18): Reference: Boyer, Tim P.; Garcia, Hernan E.; Locarnini, Ricardo A.; Zweng, Melissa M.; Mishonov, Alexey V.; Reagan, James R.; Weathers, Katharine A.; Baranova, Olga K.; Seidov, Dan; Smolyar, Igor V. (2018). World Ocean Atlas 2018. Statistical means of temperature and salinity on $0.25^\circ$ grid.. NOAA National Centers for Environmental Information. Dataset. <a href="https://accession.nodc.noaa.gov/NCEI-WOA18">https://accession.nodc.noaa.gov/NCEI-WOA18</a>. Accessed 01-SEP-2020. </li> <li> Ice-shelf melt rates observations: Reference: Rignot, E., Jacobs, S., Mouginot, J., & Scheuchl, B. (2013). Ice-shelf melting around Antarctica (Supplementary Material) Science, 341(6143), 266-270. <a href="https://doi.org/10.1126/science.1235798">10.1126/science.1235798</a>. Reference: Adusumilli, Susheel; Fricker, Helen A.; Medley, Brooke C.; Padman, Laurie; Siegfried, Matthew R. (2020). Data from: Interannual variations in meltwater input to the Southern Ocean from Antarctic ice shelves. UC San Diego Library Digital Collections. <a href="https://doi.org/10.6075/J04Q7SHT">10.6075/J04Q7SHT</a> </li> <li> JRA-55 reanalysis: Reference: Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi , 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5-48, <a href="https://doi.org/10.2151/jmsj.2015-001">10.2151/jmsj.2015-001</a> </li> <li> BedMachine Antarctic topography: Reference: Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nature Geoscience 13, 132–137 (2020). <a href="https://doi.org/10.1038/s41561-019-0510-8">10.1038/s41561-019-0510-8</a> Reference: Morlighem, M. 2020. MEaSUREs BedMachine Antarctica, Version 2. Ice-sheet thickness, surface elevation and mask. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/E1QL9HFQ7A8M">10.5067/E1QL9HFQ7A8M </a>(accessed 01-FEB-2020). </li> </ol>

Hyper and viscoelastic single point ball impact FEM model Abaqus data

Finite element models of a rubber ball  impact on a steel or polyurethane target published in connection with the journal paper: Jespersen, K. M., Eftekhar, M., Johansen, N. F.-J., Bech, J. I., Mishnaesvky Jr., L., Mikkelsen, L. P. (2022), High rate response of elastomeric coatings for wind turbine blade erosion protection evaluated through impact tests and numerical models (Reference to be updated!) Videos with reference to the Fig. 7  in the paper: Nitrile_Impacting_PU_U2_video.avi: <a href="https://video.dtu.dk/media/Nitrile_Impacting_PU_U2_video/0_v62cln3h">https://video.dtu.dk/media/Nitrile_Impacting_PU_U2_video/0_v62cln3h</a> Nitrile_Impacting_Steel_U2_video.avi: <a href="https://video.dtu.dk/media/Nitrile_Impacting_Steel_U2_video/0_q3twt90u">https://video.dtu.dk/media/Nitrile_Impacting_Steel_U2_video/0_q3twt90u</a> Nitrile_Impacting_Steel_HighspeedVideo.avi: <a href="https://video.dtu.dk/media/Rubber_Impacting_Steel_HighspeedVideo/0_ns450rev">https://video.dtu.dk/media/Rubber_Impacting_Steel_HighspeedVideo/0_ns450rev</a> Combined video: <a href="https://video.dtu.dk/media/Combined-PU-Steel-HighSpeed/0_u3y3228x">https://video.dtu.dk/media/Combined-PU-Steel-HighSpeed/0_u3y3228x</a>

Data sets used in "Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model"

The training, validation, and testing data sets for toluene, dodecane, and alpha-pinene models described in the manuscript. A link to the manuscript will be added here when it becomes available. All trajectories in the data sets were generated using GECKO-A. The source code for using the data sets can be found at https://github.com/NCAR/gecko-ml

opencc-zeroDec 2021View details →

Data from: Numerical biomechanics modelling of indirect mitral annuloplasty treatments for functional mitral regurgitation

Mitral valve regurgitation (MR) is a common valvular heart disease where an improper closure leads to leakage from the left ventricle into the left atrium. There is a need for less-invasive treatments such as percutaneous repairs for a large inoperable patient population. The aim of this study is to compare several indirect mitral annuloplasty (IMA) percutaneous repair techniques by finite element analyses. Two types of generic IMA devices were considered, based on coronary sinus vein shortening (IMA-CS) to reduce the annulus perimeter and based on shortening of the anterior-posterior diameter (IMA-AP). The disease, its treatments, and the heart function post-repair were modelled by modifying the living heart human model (Dassault Systèmes). A functional MR pathology that represents ischemic MR was generated and the IMA treatments were simulated in it, followed by heart function simulations with the devices and leakage quantification from blood flow simulations. All treatments were able to reduce leakage and the IMA-AP device achieved better sealing and there was a correlation between the IMA-CS device length and the reduction in leakage. The results of this study can help in bringing IMA-AP to market, expand the use of IMA devices, and help optimize future designs of such devices.

Data files for the article "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region"

This repository contains R data files required for reproducing the results from the article by Räty et al (2021) "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region", submitted to Nat. Hazards Earth Syst. Sci. See the README file for more details on the content of the files.

opencc-by-4.0Feb 2021View details →

Data and Codes of the Research 《An Analytical Spectral Model for Infragravity Waves over Topography in Intermediate and Shallow Water》

This is the dataset and codes prepared for the submission of the research in the title. Please run the script Main.m directly after unzipping the package. For a detailed guidance of how to use it, please see the Guide.html in the UserGuide folder.

Modeling Data from "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. Insights from Radiative Transfer Modeling"

This dataset includes the results from the radiative transfer modeling done in the paper "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars: Insights from Radiative Transfer Modeling" by Sheehan et al. Included are the full posteriors from the model fitting for each source as a Python pickle file that contains a dictionary with keys given by the source names, e.g. "HOPS-2", that point to numpy arrays containing the posterior distributions. The information can be loaded like so: <pre><code class="language-python">import pickle data, keys = pickle.load(open("posteriors.p","rb"))</code></pre> Here "keys" is a list containing the names of the parameters from the model fit that are a part of the posterior distribution for each source. Also included are the configuration files and datasets used in the modeling for each source, as well as the results from the fit so that anyone can work with these models for their own purposes. An example script that shows how to use these files is included, and further information can be found at <a href="http://pdspy.readthedocs.io">http://pdspy.readthedocs.io</a>.

Data and scripts for manuscript "Full-field modeling of heat transfer in asteroid regolith 2: Effects of porosity"

Includes summary spreadsheet, solution files (zipped) in vtk format, and scripts. Vtk filenames are the same as those listed in the tables in the supplement to the JGR:Planets paper.

openother-openJan 2022View details →

Piecewise Lognormal Approximation Aerosol Model (PAM) data files

<table> <tbody> <tr> <td>File</td> <td>Format</td> <td>Description</td> </tr> <tr> <td>ACTDAT1</td> <td>ASCII</td> <td>Lookup table for aerosol activation</td> </tr> <tr> <td>COAGDAT</td> <td>ASCII</td> <td>Lookup table for aerosol coagulation</td> </tr> <tr> <td>SSDAT</td> <td>ASCII</td> <td>Lookup table for sea salt aerosol optical properties</td> </tr> </tbody> </table> License: https://open.canada.ca/en/open-government-licence-canada

Data set for "Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit"

This data set is for the paper "Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit". It contains the raw DCA HD5 and DQMC plain text output files, as well as the scripts and final processed data used to generate figures 1-5 of the main text and supplementary figures 1-12. Copies of the figures and latex files are also included for completeness.

Supplementary material for "Spatio-temporal modelling of abundance from multiple data sources in an integrated spatial distribution model"

Abstract Aim: In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous data sets are often deemed to be incompatible. Consequently, many available data sets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends. Location: Switzerland (Europe) Taxon: Birds Methods: We developed an integrated, hierarchical species distribution model with a joint likelihood for all data sets using a shared state process (e.g., latent species abundance or occurrence), but distinct observation process for each data set. We show how the abundance submodel of a binomial N-mixture model can fuse four different data types (count, detection/non-detection, presence-only, and absence-only data) and enable improved inferences about spatio-temporal patterns in abundance. As case studies, we use data from multiple avian biodiversity monitoring schemes. In the first, the goal is estimating abundance-based species distribution maps. In the second, we infer trends in population abundance across time. Results: Accuracy and precision of abundance estimates increased when combining data from different sources compared to using a single data source alone. This is particularly valuable when data from each single data source is too sparse for reliable parameter estimation. Main conclusions: We show that exploiting the complementary nature of "cheap", but abundant, citizen-science data and less abundant, but more information-rich, data from structured monitoring programs might be ideal to estimate distribution and population trends more accurately, especially for rare species. Joint likelihoods allow to include a wide variety of different data sets to (1) combine all the available information and to (2) mitigate weaknesses of one by the strength of another.

opencc-zeroFeb 2022View details →

Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data

Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities.