Find research datasets worth reusing

opencc-zeroJun 2022View details →

Data from: A stochastic generative model for citation networks among academic papers

We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.

opencc-by-4.0Jun 2022View details →

Data & parsing software for CMB Topographic Model

Datasets and associated code for reading the data (and generating tomographic models) for the CMB topography / lowermost mantle tomographic model of Muir et al.

opencc-zeroJun 2022View details →

Data from: Compensatory adaptation and diversification subsequent to evolutionary rescue in a model adaptive radiation

Biological populations may survive lethal environmental stress through evolutionary rescue. The rescued populations typically suffer a reduction in growth performance and harbour very low genetic diversity compared with their parental populations. The present study addresses how population size and within-population diversity may recover through compensatory evolution, using the experimental adaptive radiation of bacterium Pseudomonas fluorescens. We exposed bacterial populations to an antibiotic treatment; and then imposed a one-individual-size population bottleneck on those surviving the antibiotic stress. During the subsequent compensatory evolution, population size increased and leveled off very rapidly. The increase of diversity was of slower paces and persisted longer. In the very early stage of compensatory evolution, populations of large sizes had a greater chance to diversify; however, this productivity-diversification relationship was not observed in later stages. Population size and diversity from the end of the compensatory evolution was not contingent on initial population growth performance. We discussed the possibility that our results be explained by the emergence of a "holey" fitness landscape under the antibiotic stress.

openother-openJun 2021View details →

Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling

egon-data provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project eGon. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGon <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a> egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data. The following data sets are part of the available data bundle: <ol> <li>climate_zones_germany <ul> <li>Climate zones in Germany</li> <li>source: Own representation based on DWD TRY climate zones</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>emobility <ul> <li>Data on eMobility mit_trip_data: motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</li> <li>Reiner Lemoine Institut, June 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>geothermal_potential <ul> <li>Spatial distribution of deep geothermal potentials in Germany</li> <li>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>household_electricity_demand_profiles <ul> <li>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children. The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012. The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh. A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>household_heat_demand_profiles <ul> <li>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>hydrogen_storage_potential_saltstructures <ul> <li>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</li> <li>Source: Flach lagernde Salze, (c) BGR Hannover, 2021. Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. & Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., & Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen, Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) – Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht. Hannover: BGR.</li> <li>License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</li> </ul> </li> <li>industrial_sites <ul> <li>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</li> <li>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>nep2035_version2021 <ul> <li>Data extracted from the German grid development plan - power</li> <li>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>pipeline_classification_gas <ul> <li>Parameters for the classification of gas pipelines</li> <li>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>pypsa_eur_sec <ul> <li>Preliminary results from scenario generator pypsa-eur-sec</li> <li>source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>regions_dynamic_line_rating <ul> <li>German regions suitable to model dynamic line rating</li> <li>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>re_potential_areas <ul> <li>Eligible areas for wind turbines and ground-mounted PV systems.</li> <li>Reiner Lemoine Institut, January 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>WZ_definition <ul> <li>Definitions of industrial and commercial branches</li> <li>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></li> <li>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</li> </ul> </li> <li>zensus_households <ul> <li>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</li> <li>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps: <ul> <li>Search for: "1000A-2029"</li> <li>or choose topic: "Bevölkerung kompakt"</li> <li>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</li> <li>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</li> </ul> </li> <li>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</li> </ul> </li> </ol>

opencc-by-4.0Jun 2022View details →

Tool and python programs for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model"

Here is the content:    * file dir_list which contains information about each file's content    * the tool is used to alter a data file by keeping a specific number of significant digits for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model'    * pythons program and its associated data to create each figure and table in the paper (data for Table 07 is not included due to size is larger than 50GB)

opencc-by-4.0Jun 2022View details →

Data Set of Publication on Accurate Performance Predictions with Component-based Models of Data Streaming Applications

This is the data set for the article "Accurate Performance Predictions with Component-based Models of Data Streaming Applications" by Dominik Werle, Stephan Seifermann and Anne Koziolek which appears in the proceedings of the 16th European Conference on Software Architecture (ECSA). The data set contains measurements of the evaluation system, models of the system, simulation results, derived analysis results and code for running the simulation. This work was supported by KASTEL Security Research Labs and by the German Research Foundation (DFG) under project number 432576552, HE8596/1-1 (FluidTrust).

opencc-zeroNov 2015View details →

Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small datasets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multi-model inference as an improvement over single-model based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling Antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation by resistance (IBR) model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small datasets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical.

opencc-zeroDec 2015View details →

Data from: Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail

Stable hydrogen isotope (δD) methods for tracking animal movement are widely used yet often produce low resolution assignments. Incorporating prior knowledge of abundance, distribution or movement patterns can ameliorate this limitation, but data are lacking for most species. We demonstrate how observations reported by citizen scientists can be used to develop robust estimates of species distributions and to constrain δD assignments. We developed a Bayesian framework to refine isotopic estimates of migrant animal origins conditional on species distribution models constructed from citizen scientist observations. To illustrate this approach, we analysed the migratory connectivity of the Virginia rail Rallus limicola, a secretive and declining migratory game bird in North America. Citizen science observations enabled both estimation of sampling bias and construction of bias-corrected species distribution models. Conditioning δD assignments on these species distribution models yielded comparably high-resolution assignments. Most Virginia rails wintering across five Gulf Coast sites spent the previous summer near the Great Lakes, although a considerable minority originated from the Chesapeake Bay watershed or Prairie Pothole region of North Dakota. Conversely, the majority of migrating Virginia rails from a site in the Great Lakes most likely spent the previous winter on the Gulf Coast between Texas and Louisiana. Synthesis and applications. In this analysis, Virginia rail migratory connectivity does not fully correspond to the administrative flyways used to manage migratory birds. This example demonstrates that with the increasing availability of citizen science data to create species distribution models, our framework can produce high-resolution estimates of migratory connectivity for many animals, including cryptic species. Empirical evidence of links between seasonal habitats will help enable effective habitat management, hunting quotas and population monitoring and also highlight critical knowledge gaps.

opencc-zeroDec 2013View details →

Data from: Phylogenetic signal detection from an ancient rapid radiation: effects of noise reduction, long-branch attraction, and model selection in crown clade Apocynaceae

Crown clade Apocynaceae comprise seven primary lineages of lianas, shrubs, and herbs with a diversity of pollen aggregation morphologies including monads, tetrads, and pollinia, making them an ideal group for investigating the evolution and function of pollen packaging. Traditional molecular systematic approaches utilizing small amounts of sequence data have failed to resolve relationships along the spine of the crown clade, a likely ancient rapid radiation. The previous best estimate of the phylogeny was a five-way polytomy, leaving ambiguous the homology of aggregated pollen in two major lineages, the Periplocoideae, which possess pollen tetrads, and the milkweeds (Secamonoideae plus Asclepiadoideae), which possess pollinia. To assess whether greatly increased character sampling would resolve these relationships, a plastome sequence data matrix was assembled for 13 taxa of Apocynaceae, including nine newly generated complete plastomes, one partial new plastome, and three previously reported plastomes, collectively representing all primary crown clade lineages and outgroups. The effects of phylogenetic noise, long-branch attraction, and model selection (linked versus unlinked branch lengths among data partitions) were evaluated in a hypothesis-testing framework based on Shimodaira–Hasegawa tests. Discrimination among alternative crown clade resolutions was affected by all three factors. Exclusion of the noisiest alignment positions and topologies influenced by long-branch attraction resulted in a trichotomy along the spine of the crown clade consisting of Rhabdadenia + the Asian clade, Baisseeae + milkweeds, and Periplocoideae + the New World clade. Parsimony reconstruction on all optimal topologies after noise exclusion unambiguously supports parallel evolution of aggregated pollen in Periplocoideae (tetrads) and milkweeds (pollinia). Our phylogenomic approach has greatly advanced the resolution of one of the most perplexing radiations in Apocynaceae, providing the basis for study of convergent floral morphologies and their adaptive value.

opencc-zeroDec 2015View details →

Data from: Human judgment vs. quantitative models for the management of ecological resources

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1–3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time.

openapache2.0Feb 2022View details →

Casanovo data set and model weights

Benchmark multi-species data set with pre-processing and the pre-trained model weights used to generate results in "De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model" Source code is available <a href="https://github.com/Noble-Lab/casanovo">here</a>.

Base Data for port-Hamiltonian Poro-Elasticity Models

Base Data (.mat-Files) for models created in Altmann et al. "Port-Hamiltonian formulations of poroelastic network models" Data taken from https://zenodo.org/record/4632901#.YZI9XLso8UE

opencc-by-4.0Dec 2021View details →

Forcing data, evaluation data, model output and analysis scripts used in LPJ-GUESS/LSM description paper

This archive contains: - model_output.tar.gz: Model output - extracted_fluxes.tar.gz: Sensible heat, latent heat and CO2 fluxes extracted from the FLUXNET2015 dataset, used to evaluate the model output - extracted_climate.tar.gz: Climate data extracted from the FLUXNET2015 dataset, used to force the simulations - scripts.tar.gz: Python scripts used to analyze the data and produce the results reported in the model description paper The climate forcing data has been extracted from the FLUXNET2015 dataset [1]. Input and output data is stored in netCDF files. Evaluation data is stored in python serialized files (pickle). [1] Pastorello, G., Trotta, C., Canfora, E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020). https://doi.org/10.1038/s41597-020-0534-3

Data for "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation"

The datasets for the manuscript "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation". model: WRF3.1.1 location: Southern Great Plains time: from 2100 UTC 23 May to 0600 UTC 24 May time interval: 6 minutes domain size: 512km x 512km vertical layer: 500hpa variables: P, PB, PH, PHB, U, V, W, T, QCLOUD, QICE, QVAPOR calculated data: mse, up_only(only consider updraft), up_down(consider both updrafts and downdrafts)

Input data and model output for study about wind changes and impact on the Subtropical Front

This dataset contains: Model data for the CONTROL simulation (CONTROL.gz) Model data for the SHIFT simulation (SHIFT.gz) where the westerly winds have been shifted by 1degree per decade Model data for the INCREASE simulation (INCREASE.gz) where the westerly winds have been incresaed by 1 percent per decade Reference dataset are provided (Argo.gz and Modiz.gz)

On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Article Data

NetCDF datatset of presented results from the publication titled "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model" in the Journal of Geophysical Research - Atmospheres, Paper #2021JD036214R.

Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean'

Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean', submitted to the Journal for Advances in Modeling Earth Systems (JAMES). The repository consists of three main parts: <ol> <li>The `run` directory contains a pre-built run directory as an example.</li> <li>The `scripts` directory contains the scripts that were used to generate important plots.</li> <li>The `data` directory contains model output from performance and accuracy experiments.</li> </ol>

opencc-by-4.0Aug 2022View details →

Data for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"

Data for the main figures for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"

opencc-by-4.0Aug 2022View details →

Supplementary Data for the Project Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome

Supplementary Tables for the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome" Supplementary Table 1. List of 18 genome-sequenced human gut bacteria with metabolic characteristics that were used for community assembly. Supplementary Table 2. Diet composition. Supplementary Table 3. Species relative abundance. Supplementary Table 4. Gene abundance and expression changes. Supplementary Table 5. Gene pathway enrichment results. Supplementary Table 6. Metabolomics data. Supplementary Table 7. Metabolite fold changes, clustering and model parameters. Supplementary Table 8. Description of the intestinal flux model. Supplementary Table 9. Enzymatic paths between substrates and products. Supplementary Table 10. Pearson's correlation coefficients between potential substrates and products, and metagenomics and metatranscriptomic measurements.