Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Global forest cover loss tipping points leading to changing hydrologic responses

This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses. EVENTS_List_45.docx is a table describing each deforestation event used for the study MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one. MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx

opencc-by-4.0Sep 2018View details →

Experimental data of: When higher carrying capacities lead to faster propagation

These data sets correspond to the evolution of the number of patches colonized in the experimental landscapes of Trichogramma chilonis. Data_xp contains the data set used in the article When higher carrying capacities lead to faster propagation, and Data_sup_xp is an additional data set whose results are visible in the supplementary material of the article. Data_main_xp: We tested 2 carrying capacity modalities ("Modalite"), one of about 200 individuals, Small K, noted "4" in the file, and one of about 500 individuals, Large K, noted "10" in the file. The "Bloc" column corresponds to the experimental block that the landscape belongs to (from 1 to 4). "Replicat" is the replicates identifier of the landscape for one modality (from 1 to 40). For each landscape, we introduced the individuals in the middle of the patches, so the expansion occurred on both sides of the patch of introduction. Each side of the expansion is called a front, "Front" in the file, (from 1 to 80). "Front" summarizes the affiliation to a replicate and the considered side of the expansion. Thus, each landscape has a replicate identifier and two front identifiers.   "Generation" is the generation time at which the data was collected (from 0 to 10). "Npatch" is the number of patches colonized on a front from the patch of introduction.   Data_sup_xp: "Modality" is equivalent to "Modality" in the Data_main_xp file, here it is "2" which is equivalent to a carrying capacity of nearly 90 individuals. "Bloc" (from 1 to 4), "Replicat" (from 1 to 16), "Generation" (from 0 to 9) and "Npatch" are identical to those described in Data_main_xp . Here we do not find a "Front" column because this experiment was conducted with only one side of expansion .

opencc-by-sa-4.0Oct 2018View details →

Resource heterogeneity leads to unjust effort distribution in climate change mitigation

Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences. Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369

opencc-by-4.0Sep 2019View details →

Unbalanced species losses and gains lead to non-linear trajectories as grasslands become forests

Datasets for the article "Unbalanced species losses and gains lead to non-linear trajectories as grasslands become forests" in Journal of Vegetation Science. Plot data contains information on grassland sites in the archipelago, including how long they have been abandoned for and the surrounding landscape composition. Species occurrence matrix contains data on the plant communities found in vegetation sampling plots at respective sites.

opencc-by-4.0Oct 2024View details →

Data and Code: Decreased spinal inhibition leads to undiversified locomotor patterns

Data and Code belonging to the article 'Increased spinal excitation causes decreased locomotor complexity'. Available as preprint at: <a href="https://www.biorxiv.org/content/10.1101/2022.04.21.489087v2">https://www.biorxiv.org/content/10.1101/2022.04.21.489087v2</a> Included files CODE:  -        runExampleNet.m: this trains and tests an example network and plots the network outputs. The settings for the signal and the network can all be changed in this file. -        runMFT.m: this runs the meanfield theory analysis for various levels of Imbalance and g_tot -        createSignal.m: function to create the test and train signals, called from runExampleNet.m -        ESN_EI.m: function for the echo state network with distinct excitatory and inhibitory populations, called from runExampleNet.m -        getNPG.m: function to get parameter values for the desired imbalances and overall parameter levels DATA: Myonardo (a 3D musculoskeletal model) output for walking slowly (XXX=WalkingI), walking fast (XXX=WalkingII) and running (XXX=Running), used in createSignal. Each folder contains: -        XXX.qtm: File with the labelled qualisys data  -        XXX.mat: File with the recorded kinetic ('force') and kinematic ('trajectories') data  -        modelOutput.mat: the relevant outputs of the myonardo simulation, used to create the signals in createSignal:  -        includes: time, right heel marker, muscle length, muscle velocity and muscle activation -        musclenames.mat: file indicating which column corresponds to what muscle, necessary for signal creation in createSignal.   Upon request, the model simulations results can be shared (several GB). Contact: mdegraaf@uni-muenster.de

opencc-by-4.0Dec 2022View details →

Increased egg shell temperature during incubation leads to changes in transcriptional and epigenetic profiles in chicken lungs

These RDS files contain DESeqDataSet objects subsets per broiler age and treatment. These objects are the result of DESeq2::DESeq( … ,betaPrior=FALSE).The .txt-objects contain the normalized sequencing counts per broiler age and treatment group. These objects are the result of DESeq2::counts( … , normalized=TRUE). Data was generated using STAR v2.7.10a and DESeq2 v1.36. Metadata is included as Excel file. Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA949139.        Study abstract D. Schokker, J. de Vos, P.B. Stege, O. Madsen, H.J. Wijnen, S.K. Kar, and J.M.J. Rebel Health and resilience against respiratory diseases are important features for broiler chicken. In this study, epigenetic and transcriptomic changes in the lungs of broiler chickens of different ages during rearing that were either exposed to elevated egg shell temperature (HIGH) of 38.9°C during mid-incubation or normal egg shell temperature (control; CON). The objective was to better understand how environmental challenges, such as heat stress during egg incubation, affect the development of the immune system and health of broiler chicken at later age. To this end we generated both epigenetic and transcriptomic data of lung tissue of elevated HIGH and CON chicken, furthermore these chicken were challenged by introducing either an infectious E. coli or an IBV vaccination to monitor the respiratory response. Thousands of differential methylated sites were observed at days 15 and 33, when comparing HIGH vs. CON. Pathway enrichment analysis of HIGH vs. CON showed that differentially expressed genes were mainly involved in cilium, cytoskeleton, and immune processes. These findings provide insight into the underlying biological mechanisms of early life conditions, like elevated EST, and their potential role in health of broilers.

opencc-by-4.0Apr 2023View details →

Nonlinear THz Control of the Lead Halide Perovskite Lattice - Experimental data

Experimental data for the paper "Nonlinear THz Control of the Lead Halide Perovskite Lattice", published with open-access in Science Advances under <a href="https://doi.org/10.1126/sciadv.adg3856">https://doi.org/10.1126/sciadv.adg3856</a> The data was measured at the Department of Physical Chemistry, Fritz Haber Institute of the Max Planck Society in Berlin. Contents: <ul> <li>THz E-field data from Fig. 1</li> <li>THz-induced Kerr effect time domain data, fluence dependence, azimuthal angle dependence, and corresponding THz fields for MAPbBr3 and CsPbBr3 at room temperature from Fig. 2.</li> <li>THz-induced Kerr effect time domain data for MAPbBr3 single crystals and thin films for room temperature, 180K and 80K from Fig. 3.</li> <li>THz-induced Kerr effect experimental data and simulated Kerr signals from Fig. 4.</li> <li>THz-induced Kerr effect time domain data for MAPbBr3 single crystal at different THz fluences from Fig. 5a.</li> </ul> Raw data and data of the Supplementary Materials (SM) will be provided upon request. Please contact Maximilian Frenzel (frenzel@fhi-berlin.mpg.de) and Sebastian F. Maehrlein (maehrlein@fhi-berlin.mpg.de) for such a request or for general questions.

opencc-by-4.0Jul 2023View details →

DATASET: Protein Binding Leads to Reduced Stability and Solvated Disorder in the Polystyrene Nanoparticle Corona

This dataset contains the DLS, CD, fluorescence, ITC, TEM, and ANS raw data used for the manuscript.

edi44/100

Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness, Berchtesgaden National Park, Bavaria, Germany

Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall AUC = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 2

openCC (other)Dec 2023View details →

opencc-by-4.0Jan 2021View details →

Data from Potential source areas for atmospheric lead reaching Ny-Ålesund from 2010 to 2018

date reports the sampling data in YYYY-MM-DD format and volume the sampling volume in m3. pb_sign is = for Pb concentrarion data above limit of quantification (LoQ) and < for data below LoQ. pb_val is numeric and it is the measured Pb concentration or LoQ in pg/m3. pb is text and it is the measured Pb concentration or <LoQ in pg/m3. al_ef is the enrichment factor (EF) EF(Pb/Al)c in comparison to the upper continental crust (UCC, Wedepohl 1995). pb20x20y is the value measured for 20xPb / 20yPb isotope ratio. u20x20y is the 95-confidence level uncertainty for the measured 20xPb / 20yPb isotope ratio value. Missing values are reported as NA. Wedepohl 1995: Wedepohl, K.H., 1995. The composition of the continental crust. Geochim. Cosmochim. Acta 58A, 959–960. https://doi.org/10.1180/minmag.1994.58A.2.234

opencc-zeroFeb 2020View details →

Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system

In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays.

opencc-by-4.0Jun 2021View details →

GFDL CM2.1 Partially-Coupled Simulations Data for "Understanding Lead Times of Warm-Water-Volumes to ENSO Sea Surface Temperature Anomalies"

GFDL CM2.1 partially-coupled idealized simulations: Two sets of idealized experiments with prescribed EP and CP ENSO SST anomaly patterns. Each set of experiments has a prescribed idealized sinusoidal ENSO oscillation with periodicities of 48, 36, and 24 months, respectively. For the details please refer to our paper; Zhao, S., Jin, F.-F., & Stuecker, M. F. (2021). Understanding Lead Times of Warm Water Volumes to ENSO Sea Surface Temperature Anomalies. Geophysical Research Letters, 48(19), e2021GL094366. <a href="https://doi.org/10.1029/2021GL094366">https://doi.org/10.1029/2021GL094366</a>

opencc-zeroDec 2019View details →

Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila

Abiotic stress is a major force of selection that organisms are constantly facing. While the evolutionary effects of various stressors have been broadly studied, it is only more recently that the relevance of interactions between evolution and underlying ecological conditions, that is, eco-evolutionary feedbacks, have been highlighted. Here, we experimentally investigated how populations adapt to pH-stress under high population densities. Using the protist species Tetrahymena thermophila, we studied how four different genotypes evolved in response to stressfully low pH conditions and high population densities. We found that genotypes underwent evolutionary changes, some shifting up and others shifting down their intrinsic rates of increase (r0). Overall, evolution at low pH led to the convergence of r0 and intraspecific competitive ability (α) across the four genotypes. Given the strong correlation between r0 and α, we argue that this convergence was a consequence of selection for increased density-dependent fitness at low pH under the experienced high density conditions. Increased density-dependent fitness was either attained through increase in r0 , or decrease of α, depending on the genetic background. In conclusion, we show that demography can influence the direction of evolution under abiotic stress.

opencc-by-4.0Oct 2023View details →

W4RES Case studies of women leading RHC market uptake

23 interviews were conducted with women (co-)leading or initiating RHC concepts in 8 different countries (IT, DK, EL, SK, AT, DE, BE, BG) that served as the background material for this dataset. Before conducting the interviews, the W4RES partners were asked to identify case studies based on: • Structure of the organization• Geographical outreach• Visibility and interaction on social media• Financial power• Source of Renewable Energy• Renewable Heating and Cooling conceptsIt moreover entailed a scoring section in which the case studies were ranked according to:• Technical innovation• Social innovation• Transferability• Societal impact• Political impact• Market potential• External communication• Female leadership and influence• Internal support measures for womenW4RES partners interviewed the female leaders and stakeholders in the organizations that ranked highest in the case study identification tables. These women were asked to reflect on the impact of their RHC solutions, the role of women in the organization, the support measures targeting women, as well as potential barriers and needs. The interview material was coded manually and collected in a synthesis matrix. This overview facilitates cross-case comparison and allows to see patterns for barriers and success factors.

opencc-by-4.0Dec 2023View details →

HPF data for "A Large and Variable Leading Tail of Helium in a Hot Saturn Undergoing Runaway Inflation"

Data from the Habitable Zone Planet Finder (HPF) Spectrograph at McDonald Observatory, in the form of high resolution infrared echelle spectra.  The target is HAT-P-67, a planet host star.  The spectra were acquired by Queue observations with the Hobby Eberly Telescope in the period 2020-2022.  The data were reduced with the "Goldilocks" pipeline.  The full dataset is described in detail in the paper "A Large and Variable Leading Tail of Helium in a Hot Saturn Undergoing Runaway Inflation".   The abstract for that paper is reproduced below: <div> <div>Atmospheric escape shapes the fate of exoplanets, with statistical evidence for transformative mass loss imprinted across the mass-radius-insolation distribution. Here we present transit spectroscopy of the highly irradiated, low-gravity, inflated hot Saturn HAT-P-67 b. The Habitable Zone Planet Finder (HPF) spectra show a detection of up to 10% absorption depth of the 10833 Angstrom Helium triplet. The 13.8 hours of on-sky integration time over 39 nights sample the entire planet orbit, uncovering excess Helium absorption preceding the transit by up to 130 planetary radii in a large leading tail. This configuration can be understood as the escaping material overflowing its small Roche lobe and advecting most of the gas into the stellar---and not planetary---rest frame, consistent with the Doppler velocity structure seen in the Helium line profiles. The prominent leading tail serves as direct evidence for dayside mass loss with a strong day-/night- side asymmetry. We see some transit-to-transit variability in the line profile, consistent with the interplay of stellar and planetary winds. We employ 1D Parker wind models to estimate the mass loss rate, finding values on the order of 2x10^13 g/s, with large uncertainties owing to the unknown XUV flux of the F host star. The large mass loss in HAT-P-67 b represents a valuable example of an inflated hot Saturn, a class of planets recently identified to be rare as their atmospheres are predicted to evaporate quickly. We contrast two physical mechanisms for runaway evaporation: Ohmic dissipation and XUV irradiation, slightly favoring the latter.</div> </div>

opencc-by-4.0Dec 2023View details →

Data from: Soil incubation methods lead to large differences in inferred methane production temperature sensitivity

Quantifying the temperature sensitivity of methane (CH4) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q10 values, with a large portion of this variation remaining unexplained. Here we applied observations in Stordalen Mire, a thawing permafrost peatland, and a well-tested process-rich model, ecosys, to interpret incubation observations and investigate controls on inferred CH4 production temperature sensitivity. We developed a Field-Storage-Incubation (FSI) modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season,refrigerated storage, and the laboratory incubation process, followed by model evaluation. We found that CH4 production rates during incubation are regulated by seasonally-dependent substrate availability and active microbial biomass of key microbial functional groups. Applying a model sensitivity analysis, we found that storage duration, storage temperature, and field sampling time significantly affect CH4 production during incubation. Shorter storage duration and lower storage temperature led to larger CH4 production during incubation. Our findings revealed a wide range of inferred Q10 values (1.2 to 3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. Q10 of CH4 production is controlled by many interacting biological, biochemical, and physical processes, which cause the aggregated Q10 values to differ from those of the component processes. Terrestrial ecosystem models that use a constant Q10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios. This dataset includes all the data used to plot figures in the manuscript, including Fig.2-6 and Fig.S2-S11. Each sheet in the aggregated spreadsheet corresponds to one figure in the manuscript. The simulation experiment setup and analyses are thoroughly described in the manuscript. Here we provide a brief summary. The data includes field greenhouse gas observations and laboratory incubation measurements of CH4 production in Stordalen Mire. These datasets were already published and references were provided in the manuscript and spreadsheet. The data also includes simulation data, including modeled cumulative CH4 production, CH4 production rates, substrate concentrations, and active microbial biomass under different incubation temperature, sampling time and storage conditions. This data also includes inferred temperature sensitivity of CH4 production as Q10 values under different scenarios. Please refer to the manuscript for more detailed information. Please see "Related works" at the bottom of this page and the "References" tab in the spreadsheet for a full list of source datasets and associated publications.   FUNDING: This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). Incubation and field observation data were collected under the IsoGenie Project, which was funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.

opencc-zeroJan 2024View details →

Data for: Microbe-induced plant resistance alters aphid inter-genotypic competition leading to rapid evolution with consequences for plant growth and aphid abundance

Plants and insect herbivores are two of the most diverse multicellular groups in the world, and both are strongly influenced by interactions with the belowground soil microbiome. Effects of reciprocal rapid evolution on ecological interactions between herbivores and plants have been repeatedly demonstrated, but it is unknown if (and how) the soil microbiome could mediate these eco-evolutionary processes on a shared host plant. We tested the role of a plant-beneficial soil bacterium (Acidovorax radicis) in altering eco-evolutionary interactions between different aphid genotypes (Sitobion avenae; genotypes Sickte and Fescue) feeding on barley (Hordeum vulgare). We measured fecundity, longevity and population growth of two aphid genotypes reared separately or together (population mixture) on three different barley varieties that were inoculated with or without A. radicis. Results showed that across all plant varieties A. radicis increased plant growth and suppressed aphid populations via reduced longevity and fecundity. The strength of effect was dependent on aphid genotype and barley variety, while the direction of effect was altered by aphid population mixture. Using Lotka-Volterra modelling, we demonstrated that while A. radicis inoculation decreased growth rates for both aphid genotypes it increased the competitiveness of one genotype against the other. In general, in the presence of A. radicis, the Fescue aphid genotype became more inhibitory of Sickte aphids, while Sickte aphids facilitated the growth of Fescue aphids. Our work demonstrates that plant rhizosphere microbiomes exert community-level influences by mediating eco-evolutionary interactions between herbivores and host plants. By altering competitive interaction outcomes among aphids and thus impacting processes such as rapid evolution, soil microbes contribute to the short- and long-term structure and functioning of terrestrial habitats.

opencc-zeroFeb 2024View details →

Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection

Phylogenetic reconstruction and introgression detection rely on an assumption about the probability distribution of gene tree topologies. Recently, evidence has emerged that population size differences can affect the probability distribution of gene tree topologies in the presence of purifying selection. Here, using the population genetic simulator SLiM, we provide evidence that in the presence of purifying selection, population size differences can lead to biases in phylogenetic inference. We also provide evidence that in the presence of purifying selection, population size differences can cause statistics used for introgression detection to exhibit patterns resembling those caused by introgression. In addition, we present a theoretical analysis showing that the occurrence of population size–dependent gene tree distributions is an inherent consequence of purifying selection. Our work underscores the importance of considering the potential confounding effect of purifying selection on phylogenetic inference and introgression detection.

opencc-by-4.0Apr 2024View details →

Data supplementing Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858.

These files supplement the publication  Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858. https://doi.org/10.1038/s41598-024-58953-4 The files data_expX.mat, where X is the experiment number (1-4), contain the data as described below.  The files dataTraining_expX.mat contain the data of the first (training) block of each experiment. They are needed only for the supplemental material.  To exemplify the usage, the functions figure2and3.m, figure4.m, figure5.m, figure6.m and Table1.m output the paper's figures and the data of Table 1, respectively; figureS2.m, figureS3.m, figureS4.m and figureS5.m output the figures of the supplemental material (figure S1 needs substantial amounts of external source code to compute the salience maps and is therefore not included). data_exp1.mat contains the following variables For alert trials, variable of dimensions subjects x blocks x alert trials (20x10x64); note that only used participants and blocks with alert trials (2 through 11) are included in the data set: alert_aud - the salience level of the alert tone (1-8, corresponding to 54dB(A) through 89 dB(A)) alert_vis - the salience level of the alert frame (1-8, corresponding to 0.10 to 8.50 Weber contrasts in logarithmic steps) alert_side - the side on which the alert frame and the tone were presented (1-left, 2-right) alert_fixOk - derived from eye movement data, was the first fixation closer to the alert square than to the center? alert_primaryRT - primary-task reaction time (for alert trials) alert_alertRT - alert-task reaction time  alert_correctAlert - was the response (up/down) to the alert correct? alert_intrusionAlert - was there an intrusion (left/right pressed before up or down)? alert_correctPrimary - was the primary task conducted correctly? alert_intrusionPrimary - was there an intrusion for the primary task? alert_timeToFixation - time to first fixation on alert square  alert_fixationToResp - time from beginning of fixation to response to the alert  alert_fixDur - duration of first fixation after trial onset For no-alert trials, variable of dimensions subjects x blocks x no-alert trials (20x10x448): noalert_correctPrimary - was the primary task conducted correctly? noalert_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)? For all trials, variable of dimensions subjects x blocks x no-alert trials (20x10x512): all_correctPrimary - was the primary task conducted correctly? all_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)? all_RT - reaction time in the primary task all_isAlertTrial - was the trial an alert trial? (useful to map no-alert trials and alert trials on all trials) In addition, there are some raw eye movement data for the alert blocks: alert_eyeX, alert_eyeY - dimension 20 x 10 x 64 x 6000; x and y position in pixel coordinates relative to trial (and alert) onset, 1ms/sample, ends at conclusion of trials, filled up with NaN if duration was less than 6000ms  alert_eyeFixX, alert_eyeFixY, alert_eyeFixTon, alert_eyeFixDur - 20 x 10 x 64 x 15; x and y position, onset (in ms relative to trial onset) and duration of fixations during the trial (from onset to primary-task response), filled with NaN when less than 15 fixations were made. Note that the first entry of alert_eyeFixDur along the forth dimension will usually equal the alert_fixDur data_exp2.mat contains the same variables as data_exp1.mat with the following exceptions: alert_vis - contains only two levels (1 and 2) corresponding to Weber contrasts of 0.10 and 2.39, respectively alert_dur - the level of duration of the alert frame (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms) alert_aud is not included (all tones were at 54 dB(A)) there are only 19 participants; hence the variables are of size 19 x ... note: block 8 for subject 6 contains only 450 trials (57 alert trials), the remainder is filled with NaN. data_exp3.mat contains the same variables as data_exp1.mat with the following exceptions: alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively alert_dur - the level of duration of the alert tone (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms) alert_vis is not included (all alert frames were at 0.10 contrast)   data_exp4.mat contains the same variables as data_exp1.mat with the following exceptions: alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively alert_vis is not included and replaced by alert_condBefore - alert frame contrast level before the saccade (1 - 0.10 contrast, 2 - 2.39 contrast) alert_condAfter - alert frame contrast level after the saccade (1 - 0.10 contrast, 2 - 2.39 contrast) dataTraining_expX.mat contains for the first (training) block of experiment X (X being 1, 2, 3 or 4) the following variables of size 20x512 (participant x trial) [19x512 in case of Experiment 2]: all_correctPrimary - was the primary task conducted correctly? all_RT - reaction time in the primary task [Note that there are no alert trials in this block and these data are only used in the supplementary material (part 4)]