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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Du, Andrew; Zipkin, Andrew M.; Hatala, Kevin G.; Renner, Elizabeth; +4 Authors

    A large brain is a defining feature of modern humans, yet there is no consensus regarding the patterns, rates, and processes involved in hominin brain size evolution. We use a reliable proxy for brain size in fossils, endocranial volume (ECV), to better understand how brain size evolved at both clade- and lineage-level scales. For the hominin clade overall, the dominant signal is consistent with a gradual increase in brain size. This gradual trend appears to have been generated primarily by processes operating within hypothesized lineages – 64% or 88% depending on whether one uses a more or less speciose taxonomy, respectively. These processes were supplemented by the appearance in the fossil record of larger-brained Homo species and the subsequent disappearance of smaller-brained Australopithecus and Paranthropus taxa. When the estimated rate of within-lineage ECV increase is compared to an exponential model that operationalizes generation-scale evolutionary processes, it suggests that the observed data were the result of episodes of directional selection interspersed with periods of stasis and/or drift; all of this occurs on too fine a time scale to be resolved by the current human fossil record, thus producing apparent gradual trends within lineages. Our findings provide a quantitative basis for developing and testing scale-explicit hypotheses about the factors that led brain size to increase during hominin evolution. Appendix S1Supplementary methods, results, figures, and tables for the analysis.Du et al 2018 revised Appendix S1 R2_ESM_FINAL changes accepted.docxTable S1Excel spreadsheet with the raw data used for all analyses. Each row is a separate specimen along with its ID. Columns include the “lumper’s” and “splitter’s” taxonomy used in the random effects ANOVA to get inter-observer error (“lump.taxon” and “split.taxon”), the less and more speciose lineages used in the lower-taxonomic additive partitioning analyses (“lump.part” and “split.part”), region where each specimen comes from which aided in the allocation of specimens to lineages (“region”), grade for coding points in fig. 2 (“grade”), which specimens were excluded for the damaged specimens sensitivity analysis (“reliab.sens”), ECV replicate measurements from different researchers (“ecv1” to “ecv6”), and the various dates for each specimen (“min.date”, “max.date”, “mean.date”, and “sd.date”) and their respective age error distribution (“date.dist”).ProcB SI ECV dataset FINAL.xlsxR scriptR script for (1) running variance partitioning analyses to get inter-observer endocranial volume (ECV) error, (2) fitting evolutionary mode models to the hominin clade-level ECV data using the R package "paleoTS", (3) calculating R2 and model parameters for the gradualism model, and (4) running the lower taxonomic level additive partitioning analyses.Du et al R script_ESM.txt

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NARCIS; ZENODO; DRYAD
    Dataset . 2018
    License: CC 0
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NARCIS; ZENODO; DRYAD
      Dataset . 2018
      License: CC 0
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ikkai, Akiko; Dandekar, Sangita; Curtis, Clayton E.;

    Attending to a task-relevant location changes how neural activity oscillates in the alpha band (8–13Hz) in posterior visual cortical areas. However, a clear understanding of the relationships between top-down attention, changes in alpha oscillations in visual cortex, and attention performance are still poorly understood. Here, we tested the degree to which the posterior alpha power tracked the locus of attention, the distribution of attention, and how well the topography of alpha could predict the locus of attention. We recorded magnetoencephalographic (MEG) data while subjects performed an attention demanding visual discrimination task that dissociated the direction of attention from the direction of a saccade to indicate choice. On some trials, an endogenous cue predicted the target’s location, while on others it contained no spatial information. When the target’s location was cued, alpha power decreased in sensors over occipital cortex contralateral to the attended visual field. When the cue did not predict the target’s location, alpha power again decreased in sensors over occipital cortex, but bilaterally, and increased in sensors over frontal cortex. Thus, the distribution and the topography of alpha reliably indicated the locus of covert attention. Together, these results suggest that alpha synchronization reflects changes in the excitability of populations of neurons whose receptive fields match the locus of attention. This is consistent with the hypothesis that alpha oscillations reflect the neural mechanisms by which top-down control of attention biases information processing and modulate the activity of neurons in visual cortex. IkkaiDataUpload

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NARCIS; ZENODO; DRYA...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NARCIS; ZENODO; DRYAD
    Dataset . 2017
    License: CC 0
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2016
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NARCIS; ZENODO; DRYA...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NARCIS; ZENODO; DRYAD
      Dataset . 2017
      License: CC 0
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2016
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PANGAEAarrow_drop_down
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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PANGAEAarrow_drop_down
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Schutte, Iris; Slagter, Heleen A.; Collins, Anne G.E.; Frank, Michael J.; +2 Authors

    Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning. Probabilistic selection task data_ Exp1A_Fig2a_Fig3aData corresponds to experiment 1A and Figure 2A/3A.Probabilistic selection task data_ Exp1B_Fig2b_Fig3b_Schutte_etalData corresponds to Experiment 1B and Figure 2B/3B.Reaction time data_ Exp2_Fig5_Schutte_etalData corresponds to Experiment 2 and Figure 5Probabilistic selection task data_Exp3_Fig6_Fig7Data corresponds to experiment 3 and Figure 6 and 7.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
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    DANS-EASY
    Dataset . 2017
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2017
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PANGAEAarrow_drop_down
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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Semple, Bridgette D.; Noble-Haeusslein, Linda J.; Kwon, Yong Jun; Sam, Pingdewinde N.; +12 Authors

    Despite the life-long implications of social and communication dysfunction after pediatric traumatic brain injury, there is a poor understanding of these deficits in terms of their developmental trajectory and underlying mechanisms. In a well-characterized murine model of pediatric brain injury, we recently demonstrated that pronounced deficits in social interactions emerge across maturation to adulthood after injury at postnatal day (p) 21, approximating a toddler-aged child. Extending these findings, we here hypothesized that these social deficits are dependent upon brain maturation at the time of injury, and coincide with abnormal sociosexual behaviors and communication. Age-dependent vulnerability of the developing brain to social deficits was addressed by comparing behavioral and neuroanatomical outcomes in mice injured at either a pediatric age (p21) or during adolescence (p35). Sociosexual behaviors including social investigation and mounting were evaluated in a resident-intruder paradigm at adulthood. These outcomes were complemented by assays of urine scent marking and ultrasonic vocalizations as indices of social communication. We provide evidence of sociosexual deficits after brain injury at p21, which manifest as reduced mounting behavior and scent marking towards an unfamiliar female at adulthood. In contrast, with the exception of the loss of social recognition in a three-chamber social approach task, mice that received TBI at adolescence were remarkably resilient to social deficits at adulthood. Increased emission of ultrasonic vocalizations (USVs) as well as preferential emission of high frequency USVs after injury was dependent upon both the stimulus and prior social experience. Contrary to the hypothesis that changes in white matter volume may underlie social dysfunction, injury at both p21 and p35 resulted in a similar degree of atrophy of the corpus callosum by adulthood. However, loss of hippocampal tissue was greater after p21 compared to p35 injury, suggesting that a longer period of lesion progression or differences in the kinetics of secondary pathogenesis after p21 injury may contribute to observed behavioral differences. Together, these findings indicate vulnerability of the developing brain to social dysfunction, and suggest that a younger age-at-insult results in poorer social and sociosexual outcomes. BodyWeights_dataBody weights of mice used in this study, across time post-injury3chamber_test_dataMice injured at adolescence (postnatal day 35) and tested in the three-chamber social approach task at adulthood. Percentage time spent in each chamber was quantified.CC_volume_dataStereological analyses were performed using StereoInvestigator to determine the volume loss of the corpus callosum at adulthood after injury at either p21 or p35.Res-Intr_test_dataSocial investigation of a novel (male or female) stimulus mouse was quantified as duration spent investigating different regions (e.g. anogenital), during the resident-intruder task.Scent_mark_test_dataGrid squares containing scent marks were quantified during the scent marking test towards a novel female mouse, of sham or TBI (traumatic brain injury) mice at adulthood after injury at p21 or p35.USV_dataUltrasonic vocalizations (USVs) were recorded during encounters of the test mouse (sham or brain-injured) with (1) a novel male, (2) a novel female, or (3) soiled bedding from a novel female. Several parameters including total number of calls, high frequency calls (> 75 kHz) and # calls in bursts were quantified.

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    DANS-EASY
    Dataset . 2014
    Data sources: B2FIND
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    Dataset . 2015
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      DANS-EASY
      Dataset . 2014
      Data sources: B2FIND
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      NARCIS; DRYAD; ZENODO
      Dataset . 2015
      License: CC 0
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    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Bell, Clive; van Dillen, Susanne;

    This paper analyzes the effects of all-weather rural roads on households' net output prices, education and health in a poor, drought-prone region of India. Of 30 villages originally surveyed in 2001-02, when two had such roads, a further nine received them between January 2007 and December 2009 under the program Pradhan Mantri Gram Sadak Yojana. Cross-section comparisons involving all villages and 'before and after' comparisons in the nine yielded these findings: (i) net output prices were 5 per cent or more higher; (ii) substantially fewer days of schooling were lost due to bad weather, largely because teachers had fewer absences; (iii) the acutely sick received more timely treatment and were more likely to be treated in a hospital than in the nearest primary health clinic; and (iv) the respondents ranked the resulting benefits in the domains of health and education at least as highly as the 'commercial' ones.

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    Authors: Benjamini, Yuval;

    This dissertation discusses how predictive models are being used for scientific inquiry. Statistical and computational advances have given rise to high-dimensional models that can be fit on relatively small samples but still predict well the behavior of complex systems. Scientists try to use such models to learn about complex biological systems; but it is not always clear how prediction accuracy translates to understanding the underlying system. In the chapters below, I present different approaches to learn from predictive models in bioinformatics and neuroscience. In each of these collaborative works, we tailor models that would both fit well and be interpretable in the context of the scientific questions.In the first chapter, we fit and compare predictive models for the GC-content bias, an important confounder in DNA-sequencing. We develop a high-resolution model that treats each base-pair in the genome as a separate example; this allows us to compare many representations of GC-content, identifying which representation best predicts the variation in the coverage. To deal with the huge volumes of data, we develop a new conditional dependence measure that efficiently compares different models. Selection of the model that maximizes this dependence reveals a recurring association with an experimental parameter: the selected model in each sample corresponds to a window size almost identical to the average size of DNA fragments in the sample. This recurring result can be used both for correcting the bias and for learning about the causes for the bias. In the next chapter, we propose a new estimator for interpreting prediction-accuracy results of models for neural activity in the visual cortex. Our shuffle estimator targets the explainable variance - the proportion of signal in the measured response - while accounting for auto-correlation in the noise. Re-analyzing models of functional MRI voxels within visual area V1, we observe a strong linear correlation between the signal-to-noise and prediction accuracy.In the final chapter we analyze neurophysiology data recorded from visual area V4, and present a full cycle of scientific investigation using prediction models in neuroscience. Whereas the previous chapters developed metrics for evaluating feature sets and prediction models, this chapter takes an extra leap: we use optimization algorithms together with prior scientific knowledge to propose a new feature-set. We then fit regularized linear models based on this representation that generalize well to a validation data set. Finally, novel visualization and model-summary techniques help interpret the resulting prediction models, revealing rich patterns of activity in the different neurons and unexpected categories of neurons.

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    Authors: Azevedo, Kathleen Cora Walker;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Du, Andrew; Zipkin, Andrew M.; Hatala, Kevin G.; Renner, Elizabeth; +4 Authors

    A large brain is a defining feature of modern humans, yet there is no consensus regarding the patterns, rates, and processes involved in hominin brain size evolution. We use a reliable proxy for brain size in fossils, endocranial volume (ECV), to better understand how brain size evolved at both clade- and lineage-level scales. For the hominin clade overall, the dominant signal is consistent with a gradual increase in brain size. This gradual trend appears to have been generated primarily by processes operating within hypothesized lineages – 64% or 88% depending on whether one uses a more or less speciose taxonomy, respectively. These processes were supplemented by the appearance in the fossil record of larger-brained Homo species and the subsequent disappearance of smaller-brained Australopithecus and Paranthropus taxa. When the estimated rate of within-lineage ECV increase is compared to an exponential model that operationalizes generation-scale evolutionary processes, it suggests that the observed data were the result of episodes of directional selection interspersed with periods of stasis and/or drift; all of this occurs on too fine a time scale to be resolved by the current human fossil record, thus producing apparent gradual trends within lineages. Our findings provide a quantitative basis for developing and testing scale-explicit hypotheses about the factors that led brain size to increase during hominin evolution. Appendix S1Supplementary methods, results, figures, and tables for the analysis.Du et al 2018 revised Appendix S1 R2_ESM_FINAL changes accepted.docxTable S1Excel spreadsheet with the raw data used for all analyses. Each row is a separate specimen along with its ID. Columns include the “lumper’s” and “splitter’s” taxonomy used in the random effects ANOVA to get inter-observer error (“lump.taxon” and “split.taxon”), the less and more speciose lineages used in the lower-taxonomic additive partitioning analyses (“lump.part” and “split.part”), region where each specimen comes from which aided in the allocation of specimens to lineages (“region”), grade for coding points in fig. 2 (“grade”), which specimens were excluded for the damaged specimens sensitivity analysis (“reliab.sens”), ECV replicate measurements from different researchers (“ecv1” to “ecv6”), and the various dates for each specimen (“min.date”, “max.date”, “mean.date”, and “sd.date”) and their respective age error distribution (“date.dist”).ProcB SI ECV dataset FINAL.xlsxR scriptR script for (1) running variance partitioning analyses to get inter-observer endocranial volume (ECV) error, (2) fitting evolutionary mode models to the hominin clade-level ECV data using the R package "paleoTS", (3) calculating R2 and model parameters for the gradualism model, and (4) running the lower taxonomic level additive partitioning analyses.Du et al R script_ESM.txt

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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    NARCIS; ZENODO; DRYAD
    Dataset . 2018
    License: CC 0
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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      NARCIS; ZENODO; DRYAD
      Dataset . 2018
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ikkai, Akiko; Dandekar, Sangita; Curtis, Clayton E.;

    Attending to a task-relevant location changes how neural activity oscillates in the alpha band (8–13Hz) in posterior visual cortical areas. However, a clear understanding of the relationships between top-down attention, changes in alpha oscillations in visual cortex, and attention performance are still poorly understood. Here, we tested the degree to which the posterior alpha power tracked the locus of attention, the distribution of attention, and how well the topography of alpha could predict the locus of attention. We recorded magnetoencephalographic (MEG) data while subjects performed an attention demanding visual discrimination task that dissociated the direction of attention from the direction of a saccade to indicate choice. On some trials, an endogenous cue predicted the target’s location, while on others it contained no spatial information. When the target’s location was cued, alpha power decreased in sensors over occipital cortex contralateral to the attended visual field. When the cue did not predict the target’s location, alpha power again decreased in sensors over occipital cortex, but bilaterally, and increased in sensors over frontal cortex. Thus, the distribution and the topography of alpha reliably indicated the locus of covert attention. Together, these results suggest that alpha synchronization reflects changes in the excitability of populations of neurons whose receptive fields match the locus of attention. This is consistent with the hypothesis that alpha oscillations reflect the neural mechanisms by which top-down control of attention biases information processing and modulate the activity of neurons in visual cortex. IkkaiDataUpload

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    NARCIS; ZENODO; DRYAD
    Dataset . 2017
    License: CC 0
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    DANS-EASY
    Dataset . 2016
    Data sources: B2FIND
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      NARCIS; ZENODO; DRYAD
      Dataset . 2017
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      DANS-EASY
      Dataset . 2016
      Data sources: B2FIND
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    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Schutte, Iris; Slagter, Heleen A.; Collins, Anne G.E.; Frank, Michael J.; +2 Authors

    Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning. Probabilistic selection task data_ Exp1A_Fig2a_Fig3aData corresponds to experiment 1A and Figure 2A/3A.Probabilistic selection task data_ Exp1B_Fig2b_Fig3b_Schutte_etalData corresponds to Experiment 1B and Figure 2B/3B.Reaction time data_ Exp2_Fig5_Schutte_etalData corresponds to Experiment 2 and Figure 5Probabilistic selection task data_Exp3_Fig6_Fig7Data corresponds to experiment 3 and Figure 6 and 7.

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    DANS-EASY
    Dataset . 2017
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2017
      Data sources: B2FIND
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    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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