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opencc-by-4.0May 2019View details →

Case Studies analysis of prospects for different CSP technology concepts - Input data

Description of the dataset This dataset contains the input data (.inc files) for Balmorel, used for the Case Studies analysis of prospects for different CSP technology concepts conducted within Deliverable 8.1 in the MUSTEC project. For description of the modelled scenarios, results and findings, see: Schöniger, F., Resch, G. (2019): Case Studies analysis of prospects for different CSP technology concepts. Deliverable 8.1 MUSTEC project, TU Wien, Wien. For information on the project see: https://www.mustec.eu/ Data format We provide the data in form of the data folders holding the .inc files for the scenarios described in the report above. The original Balmorel source code is available under https://github.com/balmorelcommunity/Balmorel under the ISC license. It was adapted in order to include a new technology generating electricity from heat (GETOH). The inputs for this development were kindly supported by DTU and Ea Energy Analyses with previously done works.

opencc-by-4.0Sep 2020View details →

Sample data for analysis of period/frequency gradient and phase gradient in spreadouts, ex vivo models of somitogenesis

Here are timelapse imaging (as .tif) of a dynamic Notch signaling reporter (i.e. LuVeLu) in spreadouts, ex vivo models of somitogenesis. Also here are the corresponding period and phase wavelet movies, generated using a wavelet analysis workflow developed by Gregor Mönke. These sample data are used to run an accompanying Python script, available at: <a href="https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis">https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis</a>

openmit-licenseSep 2020View details →

Analysis of migration patterns of western marsh harriers using GPS tracking data

This repository contains analysis code for Vansteelant et al. (2020, <a href="https://doi.org/10.1007/s10336-020-01785-6">https://doi.org/10.1007/s10336-020-01785-6</a>). See the <code>README.md</code> for more information.

Raw data and analysis outputs for accompanying doi: 10.5281/zenodo.4064305

These are the raw data and analysis outputs accompanying the GitHub repository archived here: 10.5281/zenodo.4064304

Key input and output data for the multi-model analysis "Open Source Energiewende"

This repository contains key input and output data of the multi-model analysis carried out in the project "Open Source Energiewende", financed by the German Federal Ministry for Economic Affairs and Energy. The results are presented and discussed in the paper "Power sector effects of cheaper stationary batteries: insights from an open multi-model analysis". The model codes are availabe in individual repositories, which are provided in the paper.

opencc-zeroJul 2021View details →

Data from: Beta diversity patterns of bats in the Atlantic Forest: how does the scale of analysis affect the importance of spatial and environmental factors?

Aim: Environmental and spatial factors are broadly recognized as important predictors of beta diversity patterns. However, the scale at which beta diversity patterns are evaluated will affect the outcoming results. For example, studies at larger scales will usually find spatial processes as the main predictor of beta diversity patterns. In this study we evaluate how beta diversity patterns change when analyses are conducted at different scales by reducing the scale of analysis in a hierarchical manner. Taxon: Chiroptera. Location: Atlantic Forest biome. Methods: Information on the occurrence of 59 bat species were obtained from the Atlantic Bats and Species Link database. We partitioned beta diversity into its two components (nestedness and turnover), and calculated these indexes hierarchically: the biome in its entirety (all ecoregions); between larger regions (north, central and south); and between ecoregions within each region. We performed a Generalized Dissimilarity Model (GDM) to identify and predict the turnover of bat species in the Atlantic Forest based on geo-climatic predictors. We obtained 19 geo-climatic data from AMBDATA, an environmental dataset based on different data sources commonly used in species distribution modeling. Results: We found that turnover was the main component influencing a latitudinal gradient when the biome was analysed in its entirety. However, when the scale of the analysis was reduced, we found that species loss (nestedness component) had a large effect in determining beta diversity dissimilarity. We also found that nestedness was the main pattern explaining beta diversity dissimilarity along a longitudinal gradient. Main conclusions: Beta diversity patterns changed with the scale of analysis, which indicates that bat species composition does not follow the same pattern throughout the Atlantic Forest. This corroborates the importance of analysing beta diversity patterns at different scales in order to understand how environmental dissimilarity across geographic space can influence species distribution patterns.

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning: W+jet large test dataset

W+jet events at generator and reconstruction level, used to train analysis-specific generative models. Events are represented as an array of relevant high-level features. Reco objects are matched to Gen objects and a minimal selection is applied to define the generator support in the N-dim space identified by the input features. About 2M events, used for large-scale testing Details in https://arxiv.org/abs/2010.01835

opencc-zeroDec 2018View details →

Data from: A meta-analysis of plant interaction networks reveals competitive hierarchies as well as facilitation and intransitivity

The extent to which competitive interactions and niche differentiation structure communities has been highly controversial. To quantify evidence for key features of plant community structure, I recharacterized published data from interaction experiments as networks of competitive and facilitative interactions. I measured the network structure of 31 woody and herbaceous communities, including the intensity, distribution, and diversity of interactions at the species-pair and community level to determine the generality of competition, winner-loser relationships, and unequal interaction allocation. I developed novel methodology using meta-analysis to incorporate interaction uncertainty into estimates of structural metrics among independent networks. Plant communities were competitive, but intraspecific interactions were sometimes more intense than interspecific interactions. On the whole, interactions were imbalanced and communities were transitive. However, facilitation, balanced interactions, and intransitivity were common in individual communities. Synthesizing network metrics using meta-analysis is an original approach with which to generalize community structure in a systematic way.

opencc-by-4.0Aug 2020View details →

Supplemtary Data 2 - Panga ya Saidi Averaged Spectra for Zooarchaeology by Mass Spectrometry Analysis (ZooMS)

Averaged ZooMS spectra from Iron Age deposits at Panga ya Saidi, Kenya 2020.

opencc-zeroNov 2020View details →

Data from: Dietary constraints of phytosaurian reptiles revealed by dental microwear textural analysis

Phytosaurs are a group of large, semi-aquatic archosaurian reptiles from the Middle–Late Triassic. They have often been interpreted as carnivorous or piscivorous due to their large size, morphological similarity to extant crocodilians and preservation in fluvial, lacustrine and coastal deposits. However, these dietary hypotheses are difficult to test, meaning that phytosaur ecologies and their roles in Triassic food webs remain incompletely constrained. Here, we apply dental microwear textural analysis to the three-dimensional sub-micrometre scale tooth surface textures that form during food consumption to provide the first quantitative dietary constraints for five species of phytosaur. We furthermore explore the impacts of tooth position and cranial robusticity on phytosaur microwear textures. We find subtle systematic texture differences between teeth from different positions along phytosaur tooth rows, which we interpret to be the result of different loading pressures experienced during food consumption, rather than functional partitioning of food processing along tooth rows. We find rougher microwear textures in morphologically robust taxa. This may be the result of seizing and processing larger prey items compared to those captured by gracile taxa, rather than dietary differences per se. We reveal relatively low dietary diversity between our study phytosaurs and that individual species show a lack of dietary specialisation. Species are predominantly carnivorous and/or piscivorous, with two taxa exhibiting slight preferences for 'harder' invertebrates. Our results provide strong evidence for higher degrees of ecological convergence between phytosaurs and extant crocodilians than previously appreciated, furthering our understanding of the functioning and evolution of Triassic ecosystems.

opencc-by-4.0Dec 2019View details →

VOC analysis data set for polyfilament analysis of antibacterial PE, PA and PLA fibre samples with natural additive rosin and silver

This data is a detail report related to the VOC measurements described and analyzed in the work Weathering and safety of antibacterial polymer-rosin polyfilaments. The detailed description of the method, samples preparations and main outcomes are given in the work. Here, Tables 1-7 indicate the average and (range) concentration of emitted compounds from each polyfilament fibre sample analyzed at different temperatures for their VOC emissions.

Dataset and Data analysis "Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?"

Data Analysis for the publication 10.1002/jbio.202000264. It is divided in three different folders describing three different part of the data analysis: A. DATA TREATMENT RAMAN (Folder 1) 1. Import data using the Import_Raman script. 2. Plot Spectra  and integrate DOX band     Figure 1A     Figure 1B 3. PCA     Figure 1D     Figure 1C     SM 1 4. PLS     Figure 1F     Figure 1E B. ANALYSIS OF IR DATA AND MULTIMODAL IR-RAMAN OF DOX UPTAKE (Folder 2) 1 Load Data IR 2 Exploratory Analysis IR     Figure 2A 3 PCA         SM 2 4. Partial Least Squares vs time     Figure 2C     Figure 2B 5. Partial Least Squares vs Raman Signal     Figure 2E     Figure 2D 6. Make Averages and clean up Data for DATA Fusion     IR     Raman 7. 2DCORR     Figure 3B 8. MCR_ALS WITH DATA FUSION     Fitting of the concentration of Raman using the method in [9].     MCR-ALS     Figures 4 A, B and C   C. SIMULATION (Folder 3) 1. Load Raman DATA 2. Simulate Raman DAta 3. Load and simulate IR Data 4. 2D corr     Figure 3A   . Each folder contains a .mlx with the data analysis performed. Figures numbering corresponds to the one found in the article.

Raw data and analysis results in paper by Cho and Iwata published in JGR

Thirty-two microtremor arrays were analyzed in Cho and Iwata (2021) (Cho, I., & Iwata, T., 2021, Limits and benefits of the spatial autocorrelation microtremor array method due to the incoherent noise, with special reference to the analysis of long wavelength ranges. Journal of Geophysical Research: Solid Earth, 126, e2020JB019850. https://doi.org/10.1029/2020JB019850). This archive includes the raw digital data of microtremors and analysis results, plots of the processing results of individual arrays, and script files to plot some graphs.

The TIEGCM simulation data, simulation codes, and analysis routines for NO cooling paper (2020JA027992 )

The TIEGCM simulation data, simulation codes, and analysis routines for the JGR paper (2020JA027992): Comments on "Poststorm Thermospheric NO Overcooling?" by Mikhailov and Perrone (2020)

opencc-zeroNov 2020View details →

Data from: Statistical analysis of the presidential elections in Belarus in 2020

Elections in Belarus attract much attention around the world. The election result is declared as a victory of Mr. Lukashenko with 80% votes. It is interesting to give the simplest statistical analysis of this victory. According to Belarus law, protocols of precinct election commissions (PECs) must be posted up just after the election procedure, so that everybody could take a photograph of the protocols. Currently, 1527 of the 5767 protocols of PECs are available in the open access at <a href="https://docs.google.com/spreadsheets/d/17aK3JxBTGtzULB0-YZGOF0hJwhuViHO3/edit#gid=84585767">https://docs.google.com/spreadsheets/d/17aK3JxBTGtzULB0-YZGOF0hJwhuViHO3/edit#gid=84585767</a>. We focus an attention on two arrays of numbers taken from these photographs. Namely, the number Ni of voters at some polling station and the number Mi of voters for Mr. Lukashenko at the same polling station. These numbers give a possibility to calculate the average percentage of those who voted for Mr. Lukashenko, which turns out to be about 60%. That is, a random sample approximately of ¼ of total number of protocol gives a value that differs at about 20% from the declared total value 80%. Using Monte-Carlo simulation we have calculated a probability of this event and obtain less than one part in million. Next we have considered Ni and Mi as the random variables and calculate probability distribution functions for Mi/Ni and Mi/<Ni> quantities. First function f(x) is of non Gaussian form and has a maximum at x≈0.6, and, an additional maximum at x≈0.8. Second function f(y) has only one maximum at y≈0.6. One the possible explanations is that the correlation (i.e. maximum) in distribution f(x) at x≈0.8 arises due to artificial trimming of the percentage of those who voted for Lukashenko to 80% in some polling stations.

Snapshots, frequency contact maps analysis, Poisson Boltzmann calculations, and data scripts for characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein

Molecular dynamics simulation trajectories, which have been performed using the Amber ff14SB force field running with the Amber18 package at the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Simulation methodology and further details are described in [1] and [2]. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu). The Poisson Boltzmann energy calculations have been achieved by using the input_files.tar.xz found here and solving the Poisson Boltzmann equation with pygbe. A more detailed example and tutorial can be found at [4]. For further details contact Horacio V Guzman. The dataset contains <ul> <li>A total of 30 snapshots of the three trajectories (10 snapshots each system = two per replica x 5 replicas/system):</li> </ul> <ol> <li>SARS-CoV-2002 spike protein with three RBD in the down positions: "COV2-DDD/PDB/" .</li> <li>SARS-CoV-2002 spike protein with one RBD in the up and two RBD in the down positions: "COV2-UDD/PDB/".</li> <li>SARS-CoV-2002 spike protein with two RBD in the up and one RBD in the down positions: "COV2-DUU/PDB/".</li> </ol> <ul> <li>Input files for Poisson-Boltzmann analysis:</li> </ul> <ol> <li>PoissonBoltzmann/input_files.tar.xz</li> </ol> <ul> <li>Data for the frequency contact map and processing scripts:</li> </ul> <ol> <li>cov2-ddd.pdb, cov2-udd.pdb, cov2-duu.pdb reference PDB files.</li> <li>Contact maps [3] at  "COV2-DDD/CONTACT_MAP/",  "COV2-UDD/CONTACT_MAP/",  "COV2-DUU/CONTACT_MAP/".</li> <li>frequency.lua: get frequency of contacts from a set of contacts map files.</li> <li>diff_frequency.lua: get differential frequency of contacts from a set of frequency files.</li> <li>Frequency of contacts listed in frequency.data files at "COV2-DDD/", "COV2-UDD/" and "COV2-DUU/" directories.</li> </ol> Read the "INFO" files for further informations. This dataset and the code is part of a 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> <li>School of engineering in bioinformatics, University of Talca, Talca, Chile.</li> </ul> [1] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. (2020). All-atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme (Version 0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3817447 [2] Chad W. Hopkins, Scott Le Grand, Ross C. Walker, and Adrian E. Roitberg. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. Journal of Chemical Theory and Computation 2015 11 (4), 1864-1874. http://doi.org/10.1021/ct5010406 [3] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. Quantitative determination of mechanical stability in the novel coronavirus spike protein. Nanoscale, 2020,12, 16409-16413. <a href="https://doi.org/10.1039/D0NR03969A">https://doi.org/10.1039/D0NR03969A</a> [4] https://github.com/pyF4all

Raw data for the research article "Rasch analysis of the Listening Effort Questionnaire - Cochlear Implant (LEQ-CI)"

These are the raw data for the paper entitled "Rasch analysis of the Listening Effort Questionnaire - Cochlear Implant (LEQ-CI)" that is currently under revision in Ear and Hearing.

opencc-by-4.0Dec 2020View details →

Token-based data sets for the analysis of the academic language of literary studies and linguistics

These are the token-based data sets used for my PhD thesis ("Potentiale syntaktischer Annotationen für die datengeleitete Sprachbeschreibung am Beispiel der Wissenschaftssprachen der Germanistik", publication in progress). For python scripts and further data see https://github.com/melandresen/dissertation. Due to copyright law, the annotated texts of the corpus could only be published without the token layer. The files provided here include the token-based frequency data that have been derived from the origial texts and can be used as input to the analysis scripts in the GitHub-Repository.

opencc-by-4.0Dec 2020View details →

VOC chromatogram data set for polyfilament analysis of antibacterial polyfilament fibre samples with natural additive rosin and silver

This data is a detail report related to the VOC measurements described and analyzed in the paper Weathering of antibacterial melt-spun polyfilaments modified by pine rosin. The detailed description of the method, sample codes, samples preparations and main outcomes are given in the manuscript. Figures (1-7) are the direct graphs from the device software as ‘chromatograms’ (TIC, total ion chromatograms) for each polyfilament fibre sample analyzed at different temperatures. The graphs indicate the measured (computed) ion count (abundance, arbitrary units) as a function of time (minutes).