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849 results for “linear”
Raw data and results for the paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>*Data* (comets_condBoot_data.zip)</p> <p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p> <p>*Results:* (comets_scenarioXXX_results.zip, XXX=1,.. 6)</p> <p>6 simulation scenarios were assessed in the paper. Each file corresponds to 1 of 6 folders, one for each scenario:<br> - scenario 1 - pdemax.rich/results<br> - scenario 2 - pdemax.sparse/results<br> - scenario 3 - pdhillhigh.rich/results<br> - scenario 4 - pdhillhigh.sparse/results<br> - scenario 5 - pdhillhigh.n20rich/results<br> - scenario 6 - pdhillhigh.n20sparse/results</p> <p>In each "results" subfolder, the results for each bootstrap method and each dataset are written to a separate file, eg for simulation 1 in the first scenario:<br> - case bootstrap: scenarioHill1_bootstrapCase_sim1.res <br> - non-parametric bootstrap: scenarioHill1_bootstrapNP_sim1.res<br> - conditional non-parametric bootstrap: scenarioHill1_bootstrapNPc_sim1.res<br> - parametric bootstrap: scenarioHill1_bootstrapPar_sim1.res<br> The folder also contains:<br> - the saemix estimates for the 200 simulations: scenarioHill1_fitOrig.res<br> - tables with the bias and SE for the different bootstraps over the set of simulations, used to evaluate the methods: rbiasSEboot200.res, rbiasSEboot.res, rbiasWRsampleEstimates.res</p> <p> </p>
Spatially anonymized data from: Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds
<p>This dataset consists of spatially anonymized movement data as well as environmental covariate data to estimate energy landscape step selection selections for migratory golden eagles that summer in Alaska.</p> <ol> <li>Human modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality.</li> <li>Here, we used satellite telemetry combined with step selection functions designed to explicitly incorporate the energy landscape (el‐SSFs) to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagle <em>Aquila chrysaetos</em>, during migration. Our sample consisted of 32 adult eagles tracked for 45 spring and 39 fall migrations from 2014 to 2017.</li> <li>Fitted el‐SSFs indicated eagles had a strong general preference for south‐facing slopes, where thermal uplift develops predictably, and that these areas are likely important aspects of migratory pathways. el‐SSFs also provided evidence that roads and railroads affected movement during both spring and fall migrations, but eagles selected areas near roads to a greater degree in spring compared to fall and at higher latitudes compared to lower latitudes. During spring, time spent near linear features often occurred during slower‐paced or stopover movements, perhaps in part to access carrion produced by vehicle collisions.</li> <li>Regardless of the behavioural mechanism of selection, use of these features could expose eagles and other soaring species to elevated risk via collision with vehicles and/or transmission lines. Linear features have previously been documented to affect the ecology of terrestrial species (e.g. large mammals) by modifying individuals' movement patterns; our work shows that these effects on movement extend to avian taxa.</li> </ol>
Dataset accompanying article: On Linear and Circular Approach to GPS Data Processing: Analyses of the Horizontal Positioning Deviations Based on the Adriatic Region IGS Observables
<p>The dataset accompanies journal article: On Linear and Circular Approach to GPS Data Processing: Analyses of the Horizontal Positioning Deviations Based on the Adriatic Region IGS Observables published on 21 January 2021 in Data. </p> <p>SINEX, RINEX, position solution files, logs used for positional accuracy and deviation distribution of IGS observables and additional figures with results are included. Dataset covers period from 10th to 20th of March 2017. </p> <p>IGS SINEX combined weekly station position/velocity solutions were retrieved from the online archives of the Crustal Dynamics Data Information System (CDDIS), NASA Goddard Space Flight Center, Greenbelt, MD, USA, available at: ftp://cddis.nasa.gov/gnss/products/. Please note that from from October 31, 2020 anonymous ftp service has been discontinued. Access with examples is available via HTTPS or ftp-ssl from <a href="https://cddis.nasa.gov/Data_and_Derived_Products/CDDIS_Archive_Access.html">CDDIS Archive</a></p> <p>RINEX observation and navigation files were retrieved from IGS repository available at: ftp://igs.ign.fr/pub/igs/data/</p> <p>Position solution files were determined with <a href="https://github.com/tomojitakasu/RTKLIB_bin/">RTKLIB</a>: An Open Source Program Package for GNSS Positioning, version 2.4.3 b33 available</p> <p>Please cite respective sources accordingly.</p> <p> </p>
Description of the thermodynamic properties and fluid-phase behaviour of aqueous solutions of linear, branched, and cyclic amines. AIChE J 2021
<p>All computational data for figures presented in the publication. </p>
Data from: A national-scale model of linear features improves predictions of farmland biodiversity
1. Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically-relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies. 2. We assessed whether a novel spatial dataset mapping linear and woody linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites respectively. 3. Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. 4. Synthesis and applications. This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems, and also can potentially be used to assess habitat connectivity. 10-Mar-2017
Data from: Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits
The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F2 (IF2) to the two parents, from random-cross of IF2 or from selfing of IF2 population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF2 population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.
Data from: Non-linear effects of phylogenetic distance on early-stage establishment of experimentally introduced plants in grassland communities
1. The phylogenetic distance of an introduced plant species to a resident native community may play a role in determining its establishment success. While Darwin's naturalization hypothesis predicts a positive relationship, the preadaptation hypothesis predicts a negative relationship. Rigorous tests of this now so-called Darwin's naturalization conundrum require not only information on establishment successes but also of failures, which is frequently not available. Such essential information, however, can be provided by experimental introductions. 2. Here, we analysed three datasets from two field experiments in Germany and Switzerland. In the Swiss experiment, alien and native grassland species were introduced as seeds only with and without disturbance (tilling). In the German experiment, alien and native grassland species were introduced both as seeds and as seedlings with and without disturbance (tilling), and with and without fungicide application. For the seedling introduction experiment, there was an additional herbivore-exclusion treatment. 3. Phylogenetic distance affected establishment in the three datasets differently, with success peaking at intermediate distances for the seed datasets, but decreasing with increasing distances in the seedling dataset. Disturbance favored seedling survival, most likely by weakening the resident community. 4. Synthesis: By analyzing experimental introductions, we show that the relationship between phylogenetic distance and establishment, at least for seedling emergence, may actually be non-linear with an optimum at intermediate distances. Therefore, Darwin´s naturalization hypothesis and the preadaptation hypothesis need not be in conflict. Rather, the mechanisms underlying them can operate simultaneously or alternately depending on the life stage and on the environmental conditions of the resident community.
Data from: Probability matching in perceptrons: effects of conditional dependence and linear nonseparability
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.
Data from: Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays
We have recently developed a high-density photolithographic, peptide array technology with a theoretical upper limit of 2 million different peptides per array of 2 cm2. Here, we have used this to perform complete and exhaustive analyses of linear B cell epitopes of a medium sized protein target using human serum albumin (HSA) as an example. All possible overlapping 15-mers from HSA were synthesized and probed with a commercially available polyclonal rabbit anti-HSA antibody preparation. To allow for identification of even the weakest epitopes and at the same time perform a detailed characterization of key residues involved in antibody binding, the array also included complete single substitution scans (i.e. including each of the 20 common amino acids) at each position of each 15-mer peptide. As specificity controls, all possible 15-mer peptides from bovine serum albumin (BSA) and from rabbit serum albumin (RSA) were included as well. The resulting layout contained more than 200.000 peptide fields and could be synthesized in a single array on a microscope slide. More than 20 linear epitope candidates were identified and characterized at high resolution i.e. identifying which amino acids in which positions were needed, or not needed, for antibody interaction. As expected, moderate cross-reaction with some peptides in BSA was identified whereas no cross-reaction was observed with peptides from RSA. We conclude that high-density peptide microarrays are a very powerful methodology to identify and characterize linear antibody epitopes, and should advance detailed description of individual specificities at the single antibody level as well as serologic analysis at the proteome-wide level.
Dataset: Modelling Seepage Erosion in Porous Media with a Linear Decay Function
Open the record for dataset details and reuse information.
Supplementary material 1 from: Pashova-Dimova S, Petrov P, Karachanak-Yankova S, Pashov A (2023) Neurodegenerative diseases associated antibody repertoire signatures in mimotope arrays based on cyclic versus linear peptides. Pharmacia 70(4): 1439-1447. https://doi.org/10.3897/pharmacia.70.e115179
Supplementary method
Dataset for "Linear theory analysis and one-dimensional hybrid simulations of high-frequency EMIC waves in a dipole magnetic field"
<p>Processed dataset to produce the figures in the manuscript</p>
Supplementary material 3 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
LSE and the threat to erosion by water
Supplementary material 1 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
Summary statistics
Supplementary material 4 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
LSE and hydrologic soil groups
Supplementary material 2 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
LSE and landscape's slope
Supplementary material 3 from: Paquet J-Y, Swinnen K, Derouaux A, Devos K, Verbelen D (2022) Sensitivity mapping informs mitigation of bird mortality by collision with high-voltage power lines. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 215-233. https://doi.org/10.3897/natureconservation.47.73710
Table S4
Supplementary material 2 from: Paquet J-Y, Swinnen K, Derouaux A, Devos K, Verbelen D (2022) Sensitivity mapping informs mitigation of bird mortality by collision with high-voltage power lines. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 215-233. https://doi.org/10.3897/natureconservation.47.73710
Table S2, S3
Linear Bases for use in MEDEA
<p>These files include horizon profiles in Healpix maps and linear basis functions produced using Cryofunk for the following experiments: HERA, PRIZM, SARAS, EDGES, REACH, and CTP. We also include a flat horizon, which assumes that the horizon begins at 90 degrees theta for all degrees phi. For each experiment, we include the basis functions in both an hdf5 file format and the original cfb that Cryofunk uses. </p> <p>The horizon files for all experiments are stored in "horizon_files.hdf5"</p> <p>The linear bases are stored in files denoted "cryo_basis_{experiment}_nside32.hdf5" and "cryo_basis_{experiment}_nside32.cfb" depending on if the user wants to open the file in hdf5 format or directly into Cryofunk using CryoFaBs.</p> <p>Lastly, we include example beam maps (in Healpix) for an analytical horizontal small dipole over an infinite perfect electrical conductor in the frequency range 50 - 100 MHz with 1 MHz spacing between beams in the file denoted "horizontal_dipole_PEC_beam_maps.hdf5."</p> <p> </p> <p>For Julia users, to open the basis functions in CryoFaBs:</p> <p>'''</p> <p>using CryoFaBs</p> <p>cfb = AngularCryoFaB(basis_filepath_in_cfb)</p> <p>cfb_beam_to_kl = cfb.TransInv</p> <p>'''</p> <p>Or to open them in HDF5:</p> <p>'''</p> <p>using HDF5</p> <p>cfb_file = h5read(basis_filepath_in_hdf5, "/Basis")</p> <p>cfb_beam_to_kl = cfb_file["Transform_to_kl"]</p> <p>'''</p> <p>Python users can open the HDF5 files in the usual manner:</p> <p>'''</p> <p>import h5py</p> <p>with open(basis_filepath_in_hdf5, 'r') as hd:</p> <p> cfb_file = hd['Basis/Transform_to_kl'][()]</p> <p>'''</p> <p> </p>
Structure prediction of linear and cyclic peptides using CABS-flex
<p>PDB models from: Structure prediction of linear and cyclic peptides using CABS-flex.</p>
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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.