Find research datasets worth reusing

opencc-by-4.0Feb 2020View details →

GAIA model simulate data of doubled CO2

This dataset contains Temperature, wind, and density output from the GAIA model, that are related to the Figures in the paper

opencc-by-4.0May 2020View details →

Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"

Gzipped CSV files containing convection scheme inputs and outputs used for training. Column format of each file: THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28 where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg]. Key: "llcs" are simulations with Lambert-Lewis. "gr" are simulations with Gregory-Rowntree. "4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.) "rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively. All are 30 day simulations either for January "jan" or July "jul".

opencc-by-4.0Jun 2020View details →

SCINDA GPS and UHF data supporting analysis in "On the assessment of daily Equatorial Plasma Bubble occurrence modeling and forecasting"

This dataset consists of ionospheric scintillation data collected from August 1, 2013 until July 25, 2014 by a collection of GPS and UHF receiver stations in the Scintillation Network Decision Aid (SCINDA) network (Groves et al., 1997). This particular dataset supports the analysis conducted in Carter et al. (2020). Carter, B.A., J.L. Currie, T. Dao, E. Yizengaw, J.M. Retterer, M. Terkildsen, K. Groves and R. Caton (2020), On the assessment of daily Equatorial Plasma Bubble occurrence modeling and forecasting, Submitted to Space Weather, Jun 2020. Groves, K.M., S. Basu, E. J. Weber, M. Smitham, H. Kuenzler, C.E. Valladares, R. Sheehan, E. MacKenzie, J.A. Secan, P. Ning, W.J. McNeill, D.W. Moonan, and M.J. Kendra (1997), Equatorial scintillation and systems support, Radio Science, 32, 2047-2064, doi:10.1029/97RS00836.

opencc-by-4.0May 2020View details →

Data supplement for "Thin-Film Modeling of Resting and Moving Active Droplets"

This dataset contains the data and source files for figures 4-10, 12, and 14-20 in the following publication:  S. Trinschek, F. Stegemerten, K. John and U. Thiele "Thin-Film Modeling of Resting and Moving Active Droplets" published in 2020 in Physical Review E. Please follow the instructions given in 'Readme.txt'.

opencc-by-4.0Jun 2020View details →

Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths

<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>

Sample of initial condition data for the ABC simplified atmospheric model and data assimilation system (vn1.4da)

The file contains a link to a sample of initial condition data for use with the ABC simplified atmospheric model and data assimilation system (vn1.4da).

Data and code for publication "The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"

Data and code underlying the publication <a href="https://tc.copernicus.org/preprints/tc-2019-330/">"The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"</a>. Journal: The Cryosphere Authors: Ronja Reese1*, Anders Levermann1,2,3, Torsten Albrecht1, Hélène Seroussi4, Ricarda Winkelmann1,2 (1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany (2) Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany (3) LDEO, Columbia University, New York, USA (4) Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA (*) email ronja.reese@pik-potsdam.de Abstract: Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams is the major reason for currently observed mass loss from Antarctica and is expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in the ISMIP6 simulations where the sub-shelf melt sensitivity is comparably low, opposed to a likely range of 9.2 to 35.9 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity based on oceanographic studies. Furthermore, using two initial states, one with and one without a previous historic simulation from 1850 to 2014, we show that while differences between the ice sheet configurations in 2015 are marginal, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by about 50 %. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections. Large zip files contain data, small zip file python notebooks for data analysis and PISM code. Please contact ronja.reese@pik-potsdam.de if you have any further questions.

Data from: Evolutionary constraint on low elevation range expansion: defense-abiotic stress tolerance tradeoff in crosses of the ecological model Boechera stricta

Most transplant experiments across species geographic range boundaries indicate that adaptation to stressful environments outside the range is often constrained. However, the mechanisms of these constraints remain poorly understood. We used extended generation crosses from diverged high and low elevation populations. In experiments across low elevation range boundaries, there was selection on the parental lines for abiotic stress tolerance and resistance to herbivores. However, in support of a defense-tolerance tradeoff, extended generation crosses showed non-independent segregation of these traits in the lab across a drought-stress gradient and in the field across the low elevation range boundary. Genotypic variation in a marker from a region of the genome containing a candidate gene (MYC2) was associated with change in the genetic tradeoff. Thus, using crosses and forward genetics, we found experimental genetic and molecular evidence for a pleiotropic tradeoff that could constrain the evolution of range expansion.

Data from: Exposure effects beyond the epithelial barrier: trans-epithelial induction of oxidative stress by diesel exhaust particulates in lung fibroblasts in an organotypic human airway model

In vitro bronchial epithelial monoculture models have been pivotal in defining the adverse effects of inhaled toxicant exposures; however, they are only representative of one cellular compartment and may not accurately reflect the effects of exposures on other cell types. Lung fibroblasts exist immediately beneath the bronchial epithelial barrier and play a central role in lung structure and function, as well as disease development and progression. We tested the hypothesis that in vitro exposure of a human bronchial epithelial cell barrier to the model oxidant diesel exhaust particulates caused trans-epithelial oxidative stress in the underlying lung fibroblasts using a human bronchial epithelial cell and lung fibroblast co-culture model. We observed that diesel exhaust particulates caused trans-epithelial oxidative stress in underlying lung fibroblasts as indicated by intracellular accumulation of the reactive oxygen species hydrogen peroxide, oxidation of the cellular antioxidant glutathione, activation of NRF2, and induction of oxidative stress responsive genes. Further, targeted antioxidant treatment of lung fibroblasts partially mitigated the oxidative stress response gene expression in adjacent human bronchial epithelial cells during diesel exhaust particulate exposure. This indicates that exposure induced oxidative stress in the airway extends beyond the bronchial epithelial barrier and that lung fibroblasts are both a target and a mediator of the adverse effects of inhaled chemical exposures despite a lack of direct exposure to the inhaled material. These findings illustrate the value of co-culture models and suggest that trans-epithelial exposure effects should be considered in inhalation toxicology research and testing.

Data from: Evaluating the potential for bird-habitat models to support biodiversity-friendly urban planning

<ol> <li>Urban expansion poses a major threat to wildlife populations. Biodiversity-friendly urban landscapes could deliver benefits for both wildlife and people, by incorporating conservation and ecosystem services objectives. Well-designed urban developments could also soften the ecological impacts of urbanisation. However, delivering urban landscapes that integrate biodiversity requirements effectively remains challenging.</li> <li>Ecological models, designed to predict wildlife population responses to alternative urban designs, could prove effective in supporting the creation of biodiversity-friendly urban landscapes. Here, we combine national-scale bird abundance data with high resolution, spatially explicit habitat data to characterise relationships between bird densities and urban landscape form in Britain. From these analyses and cross-validation, we evaluate the potential for well-parameterised, species-specific models to be used to predict bird densities in novel or modified urban areas.</li> <li>Our analyses indicate that responses of bird abundance to urban habitat are species-specific and complex, with few variables consistently affecting a large proportion of species. However, contiguous areas of greenspace within urban sites are preferential for accommodating breeding birds, compared to a more fragmented arrangement of multiple, small greenspace patches. In combination, the bird-habitat relationships identified could successfully predict observed variation in abundance for most bird species considered.</li> <li>Further evaluation of habitat descriptor variables, spatial scales of species' habitat use and analytical modelling approaches may be needed to improve the predictive ability of bird-habitat models for certain species, particularly waterbirds and those observed less frequently in urban areas.</li> <li> Synthesis and applications. We modelled breeding bird abundance in built-up areas with respect to the characteristics and contexts of urban environments. While most variables were important for multiple species, responses overall were species-specific, so simple assemblage metrics, like diversity, will not describe the variation in bird communities well. However, the results illustrate the potential of an evidence-based, spatially explicit evaluation of urban development impacts on biodiversity, by predicting the consequences for bird numbers. Subject to verification of predictive ability, practitioners can apply the models to compare, for example, land-sparing and sharing within developments, or to quantify the biodiversity requirements for effective offsetting. This would be facilitated by incorporation into an online tool allowing user-determined input scenarios.</li> </ol>

opencc-by-nc-sa-3.0Jul 2020View details →

Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"

This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"

Data release for 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models'

This is a release of the data that were used for validation in the paper 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models' by J. A. Guerra, S. A. Murray, D. S. Bloomfield, and P. T. Gallagher, that has been submitted to the Journal of Space Weather and Space Climate.   <ul> </ul> The naming scheme for the files is in the format: <pre><code>class_type_metric.dat</code></pre> <ul> <li>'class' denotes whether the forecast is for M- or X- class flares.</li> <li>'type' is what kind of forecast, i.e., the original ensemble members, an ensemble created from probabilistic validation metrics, or an ensemble created from categorical validation metrics.</li> <li>'metric' specifies the metric used to create the ensemble in the case of 'probabilistic' or 'categorical' types as above (see paper for further details), or in the case of the original ensemble members the name of the operational forecasting method.</li> </ul>   The data files are in the format: <pre><code>obs,prob</code></pre> <ul> <li>'obs' denotes whether or not a flare was observed within 24 hours of the forecast issue time (1 for yes and 0 for no).</li> <li>'prob' gives the probabilistic forecast value (between 0.0 and 1.0).</li> </ul>   These data files can easily be read into the <a href="https://cran.r-project.org/web/packages/verification/verification.pdf">R verification package</a> to replicate the results presented in the paper.

A calibrated groundwater model (Modflow-NWT) data repository in the Koga Irrigation Project area, Ethiopia

The repository includes the research data pertaining to the Modflow-NWT based groundwater model developed for the Koga irrigation project area, Ethiopia. The database constitutes three archived data folders namely, 1. MainData (mostly excel files which include model forcings, data used in model calibration, citizen science data, etc.), 2. GIS (mostly geospatial files to assist readers with the spatial locations of the irrigation project structures, as well as the important data and administrative locations), 3. ModelFiles (mostly text files which include model inputs and outputs). The data has been used in preparation of the manuscript titled, "A numerical framework to advance agricultural water management under hydrological stress conditions in a data scarce environment", published in the Agricultural Water Management journal (<a href="http://dx.doi.org/10.1016/j.agwat.2021.106947">10.1016/j.agwat.2021.106947</a>). Readers are requested to go through this article to find more details on the data. The model simulations ranged from 1st January 2008 to 15th August 2019.

Robot action execution model learning data

Short summary This dataset accompanies our paper <code>A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Representation and Experience-Based Learning of Explainable Models for Robot Action Execution," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.</code>   Contents There are three zip archives included, each of them a dump of a MongoDB database corresponding to one of the three experiments in the paper: <ul> <li>Grasping a drawer handle (handle_drawer_logs.zip)</li> <li>Grasping a fridge handle (handle_fridge_logs.zip)</li> <li>Pulling an object (pull_logs.zip)</li> </ul> All three experiments were performed with a Toyota HSR. Only the data necessary for learning the models used in our experiments are included here.   Usage After unzipping the archives, each database can be restored with the command <blockquote> mongorestore [directory_name] </blockquote> This will create a MongoDB database with the name of the directory (handle_drawer_logs, handle_fridge_logs, and pull_logs). Code for processing the data and model learning can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository</a>.

Data from: Mapping Tasmania's cultural landscapes: using habitat suitability modelling of archaeological sites as a landscape history tool

Aim: Understanding past distributions of people across the landscape is key to understanding how people used, affected and related to the natural environment. Here we use habitat suitability modelling to represent the landscape distribution of Tasmanian Aboriginal archaeological sites and assess the implications for patterns of past human activity. Location: Tasmania, Australia Methods: We developed a RandomForest 'habitat suitability' model of site records in the Tasmanian Aboriginal Heritage Register. We applied a best-effort bias correction, considered 31 predictor variables relating to climate, topography and resource proximity, and used a variable selection procedure to optimise the final model. Model uncertainty was assessed via bootstrapping and we ran an analogous MAXENT model as a cross-validation exercise. Results: The results from the RandomForest and MAXENT models are highly congruent. The strongest environmental predictors of site occurrence include distance to coast, elevation, soil clay content, topographic roughness and distance to inland water. The highest habitat suitability scores are distributed across a wide range of environments in central, northern and eastern Tasmania, including coastal areas, inland water body margins, and forests and savannas in the drier parts of Tasmania. With the exception of coastal areas much of western Tasmania has low habitat suitability scores, consistent with theories of low-density Holocene Tasmanian Aboriginal settlement in this region. Main conclusions: Our modelling suggests Tasmanian Aboriginal people occupied a heterogeneity of habitats but targeted coastal areas around the whole island, and drier, less steep, and/or open forest and savanna environments in the central lowlands. The western interior was identified as being rarely used by Aboriginal people in the Holocene, with the exception of isolated pockets of habitat; yet whether this is a true reflection of Aboriginal resource use demands increased archaeological surveys, particularly in the Tasmanian Wilderness World Heritage Area.

opencc-zeroAug 2020View details →

Supplementary material 1 from: Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299

Variable selection using cluster analsys based on Spearman's rank corellation and UPGMA method for agglomeration

opencc-by-4.0Aug 2020View details →

groundwater model and data of SWB aquifer

groundwater model and observed groundwater levels of "Südliches Wiener Becken" aquifer

opencc-by-4.0Aug 2020View details →

Data for paper "Micromechanical modeling of MXene-polymer composites"

Data for paper “Micromechanical modeling of MXene-polymer composites” <a href="https://doi.org/10.1016/j.carbon.2020.02.070">https://doi.org/10.1016/j.carbon.2020.02.070</a> M_M_MX_P_C_data.xlsx is the data represented in the paper. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 777810.

opencc-zeroAug 2020View details →

Data from: Postglacial recolonization of North America by spadefoot toads: integrating niche and corridor modeling to study species' range dynamics over geologic time

Understanding the factors that shape species' distributions is a key topic in biogeography. As climates change, species can either cope with these changes through evolution, plasticity or by shifting their ranges to track the optimal climatic conditions. Ecological niche modeling (ENM) is a widespread technique in biogeography that estimates the niche of the organism by using occurrences and environmental data to estimate species' potential distributions. ENMs are often criticized for failing to take species' dispersal abilities into consideration. Here, we attempt to fill this gap by combining ENMs with dispersal and corridor modeling to study the range dynamics of North American spadefoot toads (Scaphiopodidae) over the Holocene. We first estimated the current and past distributions of spadefoot toads and then estimated their past distributions from the Last Glacial Maximum (LGM) to the present day. Then, we estimated how each taxon recolonized North American by using dispersal and corridor modeling. By combining these two modeling approaches we were able to 1) estimate the LGM refugia used by the North American spadefoot toads, 2) further refine these projections by estimating which of the putative LGM refugia contributed to the recolonization of North America via dispersal, and 3) estimate the relative influence of each LGM refugium to the current species' distributions. The models were tested using previously published phylogeographic data, revealing a high degree of congruence between our models and the genetic data. These results suggest that combining ENMs and dispersal modeling over time is a promising approach to investigate both historical and future species' range dynamics.