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477 results for “input data”
Emergence and radiation of distemper viruses in terrestrial and marine mammals - Input files, bash and R codes for analysing PDV and CDV sequence data
<p><span>Canine distemper virus (CDV) and phocine distemper virus (PDV) are major pathogens to terrestrial and marine mammals. Yet little is known about the timing and geographical origin of distemper viruses and to what extent it was influenced by environmental change and human activities. To address this, we i) performed the first comprehensive time-calibrated phylogenetic analysis of the two distemper viruses; ii) mapped distemper antibody and virus detection data from marine mammals collected between 1972-2018; iii) and compiled historical reports on distemper dating back to the 18<sup>th</sup> century. We find that CDV and PDV diverged in the early 17<sup>th</sup> century. Modern CDV strains last shared a common ancestor in the 19<sup>th</sup> century with a marked radiation during the 1930s-50s. Modern PDV strains are of more recent origin, diverging in the 1970s-80s. Based on the compiled information on distemper distribution, the diverse host range of CDV and basal phylogenetic placement of terrestrial morbilliviruses, we hypothesize a terrestrial CDV-like ancestor giving rise to PDV in the North Atlantic. Moreover, given the estimated timing of distemper origin and radiation, we hypothesize a prominent role of environmental change such as the Little Ice Age, and human activities like globalisation and war in distemper virus evolution. </span></p>
input data for topo_drag.f90 using GFDL dynamical cores
<p> Use XXX=150 for T85, and XXX=300 for T42. Remember to change the file names in the source code or rename these input files accordingly.</p>
Input data for oktoberfest
<p>Example input data for Oktoberfest <a href="https://github.com/wilhelm-lab/oktoberfest">https://github.com/wilhelm-lab/oktoberfest</a> </p>
Data from: No evidence for sex differences in the electrophysiological properties and excitatory synaptic input onto nucleus accumbens shell medium spiny neurons
<p>Sex differences exist in how the brain regulates motivated behavior and reward, both in normal and pathological contexts. Investigations into the underlying neural mechanisms have targeted the striatal brain regions, including the dorsal striatum and nucleus accumbens core and shell. These investigations yield accumulating evidence of sexually different electrophysiological properties, excitatory synaptic input, and sensitivity to neuromodulator/hormone action in select striatal regions both before and after puberty. It is unknown whether the electrical properties of neurons in the nucleus accumbens shell differ by sex and whether sex differences in excitatory synaptic input are present before puberty. To test the hypothesis that these properties differ by sex, we performed whole-cell patch-clamp recordings on male and female medium spiny neurons (MSNs) in acute brain slices obtained from prepubertal rat nucleus accumbens shell. We analyzed passive and active electrophysiological properties, and miniature EPSCs (mEPSCs). No sex differences were detected; this includes those properties, such as intrinsic excitability, action potential after hyperpolarization, threshold, and mEPSC frequency, that have been found to differ by sex in other striatal regions and/or developmental periods. These findings indicate that, unlike other striatal brain regions, the electrophysiological properties of nucleus accumbens shell MSNs do not differ by sex. Overall, it appears that sex differences in striatal function, including motivated behavior and reward, are likely mediated by other factors and striatal regions.</p>
Input data for Reversible Unwrapping Algorithm for Constant-Pressure Molecular Dynamics Simulations
<p>As described in the main text, here is the input data used for simulation, as well as analysis directories. The archive was generated in my project folder with "tar --exclude=*trr --exclude=pbctools --exclude=qtwrap --exclude=old* --exclude=*npz --exclude=*pdf --exclude=*png --exclude=*ppm --exclude=*dcd* --exclude=*xtc --exclude=*slurm* --exclude=core* --exclude=*sh --exclude=*xvg --exclude=*out --exclude=*git* --exclude=*edr --exclude=*log --dereference -zcvf kulke-$(date +"%F").tar.gz data figures scripts Simulations". Big data and trajectory files were excluded to keep the archive size small. The archive includes all necessary files to reproduce the simulations, analysis and figures for the publication.</p>
Data set for the article "One-time freeze-thawing or carbon input events have long-term legacies in soil microbial communities"
<p>The following are data and code used for statistical analysis and figure plotting in the manuscript</p> <p>Gorka et al. (2023) "One-time freeze-thawing or carbon input events have long-term legacies in soil microbial communities", Geoderma, <a href="https://doi.org/10.1016/j.geoderma.2023.116399">https://doi.org/10.1016/j.geoderma.2023.116399</a></p> <p>It contains the following files:</p> <ol> <li>Microbial PLFA and NLFA analysis files (contained in <em>Fatty_acid_analysis.zip</em>) <ul> <li>PLFA and NLFA abundance data, in nmol C g<sup>-1</sup> dw (in <em>fatty_acid_data.csv</em>)</li> <li>Taxonomic specificities of fatty acids, needed for R-script to run (in <em>phylum.csv</em>)</li> <li>An R Script reproducing the statistical analysis, figure plotting and output for tables, as used in the manuscript (<em>Fatty_acid_analysis.R</em>)</li> </ul> </li> <li>Soil C and N stoichiometry analysis files (contained in <em>TOC_TN_analysis.zip</em>) <ul> <li>Dissolved organic C (DOC), total dissolved N (TN), and C and N in microbial biomass (Cmic, Nmic) abundance data, in mg g<sup>-1</sup> dw (in <em>toc_tn_data.csv</em>)</li> <li>An R Script reproducing the statistical analysis, figure plotting and output for tables, as used in the manuscript (<em>TOC_TN_analysis.R</em>)</li> </ul> </li> </ol>
Evaluation Input Data For The Combined Approach to Query Answering in Horn-ALCHOIQ
<p>This repository contains the evaluation input data for the following publication:</p> <blockquote> <p>David Carral, Irina Dragoste, Markus Krötzsch: <strong>The Combined Approach to Query Answering in Horn-ALCHOIQ.</strong> Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018).</p> </blockquote> <p>Further details are described therein.</p> <p><strong>Contents</strong><br> The repository contains the following directories:<br> - <strong>input-files</strong>: The input files used in the evaluation, both for our method (using RDFox) and for Konclude reasoner.<br> These are partitions of LUMB, Reactome, Uniprot and UOBM ontologies.<br> - <strong>RDFox_dependency</strong>: The RDFox reasoner used in our prototype implementation</p>
Input data and model implementation from: How do terrestrial wildlife communities respond to small-scale Acacia plantations embedded in harvested tropical forest?
<p class="MsoNormal"><span>To offset the declining timber supply from shifting towards more sustainable forestry practices, industrial tree plantations are expanding in tropical production forests. The conversion of natural forest to tree plantation is generally associated with loss of biodiversity and shifts toward more generalist and disturbance tolerant communities; but effects of mixed-landuse landscapes integrating natural and plantation forest remain little understood. Using camera traps, we surveyed the medium-to-large bodied terrestrial wildlife community across two mixed-land-use forest management areas in Sarawak, Malaysia Borneo which include areas dedicated for logging of natural forest and adjacent planted <em>Acacia</em> forests. We analysed data from a 25-wildlife species community using a Bayesian community occupancy model to assess species richness and species-specific occurrence responses to <em>Acacia</em> plantations at a broad scale, and to remote-sensed local habitat conditions within the different forest land-use types. All species were estimated to occur in both land-use types, but species-level percent area occupied and predicted average local species richness were slightly higher in the natural forest management areas compared to licensed planted forest. Similarly, occupancy-based species diversity profiles and defaunation indices for both a full community and only threatened and endemic species suggested the diversity and occurrence were slightly higher in the natural forest management areas. At the local scale, forest quality was the most prominent predictor of species occurrence. These associations with forest quality varied among species but were predominantly positive. Our results highlight the ability of a mixed-land-use landscape with small-scale<em> Acacia</em> plantations embedded in natural forest to retain terrestrial wildlife communities while providing an alternate source of timber. Nonetheless, there was a tendency towards reduced biodiversity in planted forests, which would likely be more pronounced in plantations that are larger or embedded in a less natural matrix.</span></p>
Input Data for the Simulation of Water Supply System of Athens
<p>Input Data for the Simulation of Water Supply System of Athens:</p> <p>Stochastic time-series of water demands, Stochastic time-series of rainfall, evaporation runoff, Stochastic time-series of electricity price, Characteristics of the system, Operational Rules of the system</p>
Bacteriorhodopsin TR-SFX input data for LPSA.
<p>Input matrices for the low-pass spectral analysis of a time-resolved serial femtosecond crystallography data set from bacteriorhodopsin.</p> <p>T_sel_sparse_light.jbl: bR TR-SFX intensities (m Bragg points x S time points)</p> <p>ts_sel_light.jbl: associated timestamps in femtoseconds (S values)</p> <p>miller_h_light.jbl, miller_k_light.jbl and miller_l_light.jbl: m-element vectors with Miller indices.</p> <p>dT_bst_sparse_light_mirrored.jbl: intensity deviations derived from T_sel_sparse_light.jbl, by subtracting reflection-dependent time averages and applying a scale factor to account for the different redundancy of observations. To overcome the LPSA cyclic boundary conditions, 2S frames are generated, with frames S+1, ..., 2S corresponding to frames 1, ..., S in reversed order.</p> <p>ts_sel_light_mirrored: 2S timestamps associated to the frames in dT_bst_sparse_light_mirrored.jbl. Values 1, ..., S are the experimental timestamps in ts_sel_ligth.jbl, and values S+1, ..., 2S are fictitious.</p> <p>I_dark_avg.mtz: merged resting state data for map calculation.</p>
Data and Code for "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application" authored by Juan José Price and Arne Henningsen and published in the Journal of Productivity Analysis (DOI: <a href="https://doi.org/10.1007/s11123-023-00684-1">10.1007/s11123-023-00684-1</a>).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.0) using the add-on packages "combinat" (version 0.0.8), "miscTools" (version 0.6.28), "quadprog" (version 1.5.8), sfaR (version 1.0.0), stargazer (version 5.2.3), and "xtable" (version 1.8.4) that are available at CRAN. We created the R package "micEconDistRay" that provides the functions for empirical analyses with ray-based input distance functions that we developed for the above-mentioned paper. Also this R package is available at CRAN (https://cran.r-project.org/package=micEconDistRay).</p> <p>This replication package contains the following files and folders:</p> <ul> <li><strong>README</strong><br> This file</li> <li><strong>MuseumsDk.csv</strong><br> The original data obtained from the Danish Ministry of Culture and from Statistics Denmark. It includes the following variables: <ul> <li><em>museum</em>: Name of the museum. </li> <li><em>type</em>: Type of museum (Kulturhistorisk museum = cultural history museum; Kunstmuseer = arts museum; Naturhistorisk museum = natural history museum; Blandet museum = mixed museum).</li> <li><em>munic</em>: Municipality, in which the museum is located.</li> <li><em>yr</em>: Year of the observation.</li> <li><em>units</em>: Number of visit sites.</li> <li><em>resp</em>: Whether or not the museum has special responsibilities (0 = no special responsibilities; 1 = at least one special responsibility).</li> <li><em>vis</em>: Number of (physical) visitors.</li> <li><em>aarc</em>: Number of articles published (archeology).</li> <li><em>ach</em>: Number of articles published (cultural history).</li> <li><em>aah</em>: Number of articles published (art history).</li> <li><em>anh</em>: Number of articles published (natural history).</li> <li><em>exh</em>: Number of temporary exhibitions.</li> <li><em>edu</em>: Number of primary school classes on educational visits to the museum.</li> <li><em>ev</em>: Number of events other than exhibitions.</li> <li><em>ftesc</em>: Scientific labor (full-time equivalents).</li> <li><em>ftensc</em>: Non-scientific labor (full-time equivalents).</li> <li><em>expProperty</em>: Running and maintenance costs [1,000 DKK].</li> <li><em>expCons</em>: Conservation expenditure [1,000 DKK]. </li> <li><em>ipc</em>: Consumer Price Index in Denmark (the value for year 2014 is set to 1).</li> </ul> </li> <li><strong>prepare_data.R</strong><br> This R script imports the data set MuseumsDk.csv, prepares it for the empirical analysis (e.g., removing unsuitable observations, preparing variables), and saves the resulting data set as DataPrepared.csv.</li> <li><strong>DataPrepared.csv</strong><br> This data set is prepared and saved by the R script prepare_data.R. It is used for the empirical analysis.</li> <li><strong>make_table_descriptive.R</strong><br> This R script imports the data set DataPrepared.csv and creates the LaTeX table /tables/table_descriptive.tex, which provides summary statistics of the variables that are used in the empirical analysis.</li> <li><strong>IO_Ray.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions with the 'optimal' ordering of outputs, imposes monotonicity on this distance function, creates the LaTeX table /tables/idfRes.tex that presents the estimated parameters of this function, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>IO_Ray_ordering_outputs.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions, imposes monotonicity for each of the 720 possible orderings of the outputs, and saves all the estimation results as (a huge) R object allOrderings.rds.</li> <li><strong>allOrderings.rds</strong> (not included in the ZIP file, uploaded separately)<br> This is a saved R object created by the R script IO_Ray_ordering_outputs.R that contains the estimated ray-based Translog input distance functions (with and without monotonicity imposed) for each of the 720 possible orderings.</li> <li><strong>IO_Ray_model_averaging.R</strong><br> This R script loads the R object allOrderings.rds that contains the estimated ray-based Translog input distance functions for each of the 720 possible orderings, does model averaging, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>/tables/</strong><br> This folder contains the two LaTeX tables table_descriptive.tex and idfRes.tex (created by R scripts make_table_descriptive.R and IO_Ray.R, respectively) that provide summary statistics of the data set and the estimated parameters (without and with monotonicity imposed) for the 'optimal' ordering of outputs.</li> <li><strong>/figures/</strong><br> This folder contains 48 figures (created by the R scripts IO_Ray.R and IO_Ray_model_averaging.R) that illustrate the results obtained with the 'optimal' ordering of outputs and the model-averaged results and that compare these two sets of results.</li> </ul>
Emission input data for WRF-CMAQ
<p>Emission input data for WRF-CHIMERE in eastern China during 2017.</p>
Emission input data for WRF-Chem
<p>Emission input data for WRF-Chem in eastern China during 2017.</p>
Input and output data for the paper "Evaluating the German PV auction program: The secrets of individual bids revealed"
<p>Batz Liñeiro, T., Müsgens, F., (2021). Energy Policy</p> <p><a href="http://doi.org/10.1016/j.enpol.2021.112618">doi.org/10.1016/j.enpol.2021.112618</a></p> <p>ABSTRACT</p> <p>Auctions have become the primary instrument for promoting renewable energy around the world. However, the data published on such auctions are typically limited to aggregated information (e.g., total awarded capacity, average payments). These data constraints hinder the evaluation of realisation rates and other relevant auction dynamics. In this study, we present an algorithm to overcome these data limitations in German renewable energy auction programme by combining publicly available information from four different databases. We apply it to the German solar auction programme and evaluate auctions using quantitative methods. We calculate realisation rates and—using correlation and regression analysis—explore the impact of PV module prices, competition, and project and developer characteristics on project realisation and bid values. Our results confirm that the German auctions were effective. We also found that project realisation took, on average, 1.5 years (with 28% of projects finished late and incurring a financial penalty), nearly half of projects changed location before completion (again, incurring a financial penalty) and small and inexperienced developers could successfully participate in auctions.</p> <p>Description</p> <p>The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "2 Auction Realizations Solar-2" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values. However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process.</p> <p>For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.</p>
Input and output data for the paper "Evaluating the German onshore wind auction programme: An analysis based on individual bids"
<p>Batz Liñeiro, T., Müsgens, F., (2023). Energy Policy</p> <p><a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a></p> <p>ABSTRACT</p> <p>Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals but create incentives for large actors to game the system.</p> <p>Description</p> <p>The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "3 Auction Realizations Onshore Wind" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values (BV). However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process.</p> <p>For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.<br> </p>
Results and input data related to the case study 3 of the openENTRANCE project - version 20062023
<p>This dataset comprises the main results of case study 3 of the openENTRANCE project. Additionally, input data related to the power generation system and renewables profiles are included in the dataset.</p>
coSIF Input Data
<p>This compressed file contains all input data needed to reproduce the results in Jacobson et al. (2023) Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data. In particular, the following are included:</p> <ul> <li> <p>The directory `OCO2_L2_Lite_SIF_10r` is a subset of daily OCO-2 SIF Lite files (version 10r) for February, April, July, and October 2021. The full dataset is available at https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_SIF_10r/summary.</p> </li> <li> <p>The directory `OCO2_L2_Lite_FP_10r` is a subset of daily OCO-2 XCO2 Lite files (version 10r) for March, May, August, and November 2021. The full dataset is available at https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary.</p> </li> <li> <p>The directory `MCD12C1v061` contains the MODIS Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6.1 annual data product for 2021 only. The full dataset is available at https://lpdaac.usgs.gov/products/mcd12c1v061/.</p> </li> </ul>
GRASS Input Data
<p>This record contains the dataset that is used as input to GRASS v1.1.x. See <a href="https://github.com/palumbom/GRASS">https://github.com/palumbom/GRASS</a> for details and instructions. </p>
CSPG GridPath Model Input and Output Data
<p>This data repository holds input and output data for the paper Jin XY., Chowdhury, A.K., Cheng CT., and Galelli, S. “The unintended consequences of decarbonizing the China Southern Power Grid”. See Readme for more details. </p>
Input data and modelling results of GXAJ, GIE and GXAJ-IE model
<p>This dataset archived the underlying surface data and hydrometeorological data of the four typical watersheds in different hydrometeorological zones of China that used as input for GXAJ, GIE and GXAJ-IE model, and the simulation results produced by the three models.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.