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Low-dose antibiotic prophylaxis has no significant impact on the stability of the intestinal microbiome in children with urogenital tract malformations under 1 year of age.
<p>Raw data for the evaluation of the stool microbiome in children under antibiotic prophylaxis vs. controls.</p>
GPS dataset for 'Global View of Ionospheric Disturbance Impacts on Kinematic GPS Positioning Solutions during the 2015 St Patrick's Day Storm' by Zhe Yang, Y. Jade Morton, Irina Zakharenkova, Iurii Cherniak, Shuli Song, Wei Li
<p>This dataset contains observations of ionospheric plasma irregularities and GPS positioning errors for ~5500 stations reported in the paper 'Global View of Ionospheric Disturbance Impacts on Kinematic GPS Positioning Solutions during the 2015 St Patrick’s Day Storm' by Zhe Yang, Y. Jade Morton, Irina Zakharenkova, Iurii Cherniak, Shuli Song, Wei Li</p>
Massive donors in interacting binaries: impact of metallicity
<p>Input files needed to reproduce the simulations as well as output MESA history files (i.e. stellar tracks) to paper "Massive donors in interacting binaries: impact of metallicity". MESA version r11554.</p> <p>Output history files are provided for the models with the chosen reference set of input physical assumptions. History files from other models discussed in the paper can be provided upon request. </p> <p> </p>
Supplementary material 1 from: Dickey JWE, Cuthbert RN, South J, Britton JR, Caffrey J, Chang X, Crane K, Coughlan NE, Fadaei E, Farnsworth KD, Ismar-Rebitz SMH, Joyce PWS, Julius M, Laverty C, Lucy FE, MacIsaac HJ, McCard M, McGlade CLO, Reid N, Ricciardi A, Wasserman RJ, Weyl OLF, Dick JTA (2020) On the RIP: using Relative Impact Potential to assess the ecological impacts of invasive alien species. NeoBiota 55: 27-60. https://doi.org/10.3897/neobiota.55.49547
Table S1. Outline of different numerical response proxies available, guidance for their use and the advantages and disadvantages of each
Data from: Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations
<p>In this archive we share the data and R code used for the construction of population models for seven bird species (Common Starling, Black-tailed Godwit<strong>,</strong> Marsh Harrier, Eurasian Spoonbill, White Stork, Common Tern and White-tailed Eagle) for our assessment of the effects of wind farms (Schippers et al. 2020). In most cases we parameterized our population models based on species-specific survival and reproduction rates from scientific articles and reports, but in the case of the Western Marsh Harrier we analyzed previously unpublished nest success and capture-mark-resighting data. Below we first describe per species which data we used for model parameterization, and then describe per data file what each variable represents.</p> <p>We selected populations of seven species based on the availability of data, considerable likelihood to collide with wind turbines and contrasting ages of first reproduction. For species for which long time series of demographic data were available with population trends clearly changing over time, we separately assessed periods with contrasting population trends, as detailed in the species descriptions below. Mean survival and reproduction rates, standard deviations and additional information like the age of first reproduction can be found in the accompanying paper by Schippers et al. (2020). </p> <p> </p> <p><strong>Common Starling</strong></p> <p>On the fast-slow continuum of reproductive capacity, the common starling is the fastest of the seven species we selected: it starts reproducing at an age of one year. We used the mean survival and reproductive rates for the whole Dutch breeding population (Versluijs et al. 2016), distinguishing three separate periods: 1960-1978, 1978-1990 and 1990-2012. In the first period (1960-1978) the population grew at 10% per year. This was followed by a period where the population was relatively stable (1978-1990). During the last period (1990-2012) the population declined strongly.</p> <p> </p> <p><strong>Black tailed Godwit</strong></p> <p>Kentie et al. (2017) studied two Dutch populations of the Black-tailed Godwit in southwestern Fryslân (Skriezekrite and Kuststrook) over four to five annual transitions (Kentie et al. 2017). Godwits started reproducing at age two, but only had 0.5-0.6 fledglings per breeding pair per year. The adults are rather long-lived with an 86% annual survival rate. We construct separate matrix models for the two populations.</p> <p> </p> <p><strong>Marsh Harrier</strong></p> <p>Mean vital rates of the Dutch breeding population of Marsh Harriers were estimated for 1997-2015 using respectively ring recoveries available at the Dutch Centre for Avian Migration and Demography NIOO-KNAW and reproduction data from the Dutch Raptor Working Group. Annual survival of Marsh Harriers was analyzed using live re-sightings and dead recoveries of 12,059 birds ringed as nestling between 1991 and 2016 and 74 birds ringed as ‘adult’ in the same period (due to low sample sizes, birds ringed in their first and second calendar year were lumped with older birds in the ‘adult’ category; see ‘marshHarrierSurvival.csv’ below). Nest success was estimated using data of 1914 nests, which were followed from the beginning to the end of the nest cycle, in the Netherlands between 1997 and 2015 (see ‘marshHarrierReproduction.csv’ below; we thank Rob G. Bijlsma for making the data available). </p> <p> </p> <p><strong>Spoonbill</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates were derived for the Dutch Spoonbill population from van der Jeugd et al. (2014). Participation in the breeding population was 0% in the first three years and went up from 63% at age four to 95% at age 6 and older.</p> <p> </p> <p><strong>White Stork</strong></p> <p>Schaub et al. (2004) analyzed demographic data on White Storks in Switzerland from 1977 till 2000. Here we extracted annual survival and reproduction rates from the COMADRE Animal Matrix Database (version 2.0.1; Salguero-Gómez et al., 2016). Storks start reproducing at age 3, with breeding participation increasing with age from 48% to 100%. </p> <p> </p> <p><strong>Common Tern</strong></p> <p>For the Common Tern we used mean vital rate estimates published by van der Jeugd et al. (2014) for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010 (van der Jeugd et al. 2014). The total Waddenzee and IJsselmeer population is estimated at 7,630 pairs (average population 2010-2014), constituting approximately 40% of the Dutch breeding population of about 20,000 pairs (Sovon 2016). </p> <p> </p> <p><strong>White-tailed Eagle</strong></p> <p>Krüger et al. (2010) published demographic data on White-tailed Eagles in Schleswig-Holstein, Germany, over the period 1947 till 2008. Following these authors, and based on the two matrices in COMADRE v.2.0.1 (Salguero-Gómez et al., 2016), we used separate matrix models for the early period (stable population dynamics) and from 1975 onwards (population growth). These eagles start reproducing at age five. </p> <p> </p> <p>Here we describe the archived files:</p> <p> </p> <p><strong>matrices.R</strong></p> <p>This annotated R file details how the vital rate estimates are used to construct age-structured, post-breeding-census, one-year-timestep population matrix models. In these so-called post-breeding census models the birds in the first class were 0 years old (Caswell 2001).</p> <p> </p> <p><strong>commonstarling19602012.csv</strong></p> <p>Mean survival and reproductive rates for the whole Dutch breeding population of Common Starlings for the time period 1960-2012. </p> <p>year = start year</p> <p>juvSurv = first-year survival of fledgelings</p> <p>adultSurv = annual survival of older birds</p> <p>fec = number of fledgelings per pair (which have a 1:1 sex ratio)</p> <p> </p> <p><strong>blacktailedgodwit20112016.csv</strong></p> <p>Mean survival and reproduction rates of the Black-tailed Godwit in southwestern Fryslân (populations Skriezekrite and Kuststrook) over four to five annual transitions in the period 2011-2016. </p> <p>pop = population</p> <p>startYear = start year</p> <p>adultSurv = annual survival of older birds</p> <p>chickSurv = first-year survival of chicks</p> <p>nestSuc = probability that a nest is successful</p> <p> </p> <p><strong>marshharrier19972015.csv</strong></p> <p>Mean vital rates of the Dutch breeding population of Western Marsh Harriers for 1997-2015.</p> <p>year = start year</p> <p>r = number of fledgelings per pair</p> <p>s1 = first-year survival of fledgelings</p> <p>s2 = annual survival of older birds</p> <p> </p> <p><strong>marshharrierreproduction.csv</strong></p> <p>Western Marsh Harrier nest record data of in the Netherlands.</p> <p>year = year</p> <p>clutchSize = number of eggs</p> <p>young = number of chicks (if known)</p> <p>fledgelings = number of fledgelings</p> <p> </p> <p> </p> <p> </p> <p><strong>marshharriersurvival.csv</strong></p> <p>Ringing and resighting data (using EURING coding) on Western Marsh Harriers in the Netherlands. </p> <p>ringID = ring identifier</p> <p>date = observation date</p> <p>metalRingInformation</p> <p>1 = Metal ring added (where no metal ring was present), position (on tarsus or above) unknown or unrecorded.</p> <p>2 = Metal ring added (where no metal ring was present), definitely on tarsus.</p> <p>3 = Metal ring added (where no metal ring was present), definitely above tarsus.</p> <p>4 = Metal ring is already present.</p> <p>condition </p> <p>0 = Condition completely unknown.</p> <p>1 = Dead but no information on how recently the bird had died (or been killed).</p> <p>2 = Freshly dead – within about a week.</p> <p>3 = Not freshly dead – information available that it had been dead for more than about a week.</p> <p>4 = Found sick, wounded, unhealthy etc. and known to have been released (including ring or other mark identified on a bird in poor condition without the bird having being caught).</p> <p>5 = Found sick, wounded, unhealthy etc. and not released or not known if released.</p> <p>6 = Alive and probably healthy but taken into captivity.</p> <p>7 = Alive and probably healthy and certainly released (including ring or other mark identified on a healthy bird without the bird having being caught).</p> <p>8 = Alive and probably healthy and released by a ringer (including ring or other mark identified on the bird by a ringer without the bird having being caught). </p> <p>ageReported </p> <p>0 = Age unknown, i.e. not recorded.</p> <p>1 = Pullus: nestling or chick, unable to fly freely, still able to be caught by hand.</p> <p>2 = Full-grown: able to fly freely but age otherwise unknown.</p> <p>3 = First-year: full-grown bird hatched in the breeding season of this calendar year.</p> <p>4 = Afer first-year: full-grown bird hatched before this calendar year; year of hatching otherwise unknown.</p> <p>5 = 2<sup>nd</sup> year: a bird hatched last calendar year and now in its second calendar year.</p> <p>6 = Afer 2<sup>nd</sup> year: full-grown bird hatched before last calendar year; year of hatching otherwise unknown.</p> <p>7 = 3<sup>rd</sup> year: a bird hatched two calendar years before, and now in its third calendar year.</p> <p>8 = Afer 3<sup>rd</sup> year: a full-grown bird hatched more than three calendar years ago (including present year as one); year if bird otherwise unknown.</p> <p>9 = 4<sup>th</sup> year: a bird hatched three calendar years before, and now in its fourth calendar year.</p> <p>A = Afer 4<sup>th</sup> year: a bird older than category 9 – age otherwise unknown.</p> <p>sexReported</p> <p>U = Unknown</p> <p>M = Male</p> <p>F = Female</p> <p> </p> <p><strong>eurasianspoonbill19942008.csv</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates are given for the Dutch Spoonbill population.</p> <p>year = start year</p> <p>fled = number of fledgelings per breeding pair</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>s3 = third-year survival rate</p> <p>s4 = older birds' annual survival rate</p> <p> </p> <p><strong>whitestork19772000.csv</strong></p> <p>Demographic data on White Storks in Switzerland from 1977 till 2000.</p> <p>year = start year</p> <p>fled = number of fledgelings per pair</p> <p>sj = first-year survival of fledgelings</p> <p>sa = annual survival of older birds</p> <p> </p> <p><strong>commontern19942009.csv</strong></p> <p>Mean vital rate estimates for the Common Tern for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010.</p> <p>year = start year</p> <p>r = number of daughter fledgelings per adult female</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>sA = older birds' annual survival rate</p> <p> </p> <p><strong>whitetailedeaglepmat1.csv</strong></p> <p><strong>whitetailedeaglepmat2.csv</strong></p> <p><strong>whitetailedeaglefmat1.csv</strong></p> <p><strong>whitetailedeaglefmat2.csv</strong></p> <p>White-Tailed Eagle age-specific survival (Pmat) and reproduction (Fmat) matrices as found in COMADRE v.2.0.1, for Schleswig-Holstein, Germany, studied over the period 1947-2008. Period 1 lasts upto 1975, period 2 from 1975. </p>
COVID-19 impact on the concentration and composition of submicron particulate matter in a typical city of Northwest China
<p>This dataset include the data used in the study of "COVID-19 impact on the concentration and composition of submicron particulate matter in a typical city of Northwest China" submitted to GRL.</p>
Covid-19 Seafood Impacts
<p>Reported impacts and responses on the seafood sector were compiled from April 3 to May 30, 2020, which includes (n = 175) articles published from January 28 to May 27, 2020. News articles were collected by monitoring Google News alerts for ("seafood" OR "fish") AND ("coronavirus" OR "covid"), daily data scraping of Twitter posts for "seafood" and "coronavirus" OR "covid," website searches of primary seafood industry news outlets (e.g., SeafoodSource,Undercurrent, and IntraFish), and compiling information shared through the authors’ professional networks. All news articles containing information about an impact on or response to the COVID-19 pandemic relating to any stage of the seafood supply chain were considered relevant. For each article, we extracted information the: article title, article date, article link, date of the impact, the type of impact, and the countries, sector(s), supply chain stage(s), species, and product form(s) involved, as well as whether the production is recreational, small-scale, and/or industrial.</p>
Behavioural and neurophysiological data from experiments on the impact of spatial frequency information to action comprehension
<p>Understanding object-directed actions performed by others is central to everyday life. This ability is thought to rely on the interaction between the dorsal action observation network (AON) and a ventral object recognition pathway. On this view, the AON would encode action kinematics, and the ventral pathway, the most likely intention afforded by the objects. However, experimental evidence supporting this model is still scarce. Here, we aimed to disentangle the contribution of dorsal vs. ventral pathways to action comprehension by exploiting their differential tuning to lowspatial frequencies (LSFs) and high-spatial frequencies (HSFs). We filtered naturalistic action images to contain only LSF or HSF and measured behavioral performance and corticospinal excitability (CSE) using transcranial magnetic stimulation (TMS). Actions were embedded in congruent or incongruent scenarios as defined by the compatibility between grips and intentions afforded by the contextual objects. Behaviorally, participants were better at discriminating congruent actions in intact than LSF images. This effect was reversed for incongruent actions, with better performance for LSF than intact and HSF. These modulations were mirrored at the neurophysiological level, with greater CSE facilitation for congruent than incongruent actions for HSF and the opposite pattern for LSF images. Finally, only for LSF did we observe CSE modulations according to grip kinematics. While results point to differential dorsal (LSF) and ventral (HSF) contributions to action comprehension for grip and context encoding, respectively, the negative congruency effect for LSF images suggests that object processing may influence action perception not only through ventral-to-dorsal connections, but also through a dorsal-to-dorsal route involved in predictive processing.</p>
Characterization matrix linking ecoinvent3.5 to IMPACT World+
<p>Characterization matrix linking the elementary flows of ecoinvent3.5 to 36 impact categories of IMPACT World+.</p> <p>The dataset includes the characterization matrix itself and another matrix linking UUIDs of the elementary flows to their metadata (i.e., units, compartments, sub-compartments).</p> <p>Version of IW+ used: 10.5281/zenodo.3521034</p>
Hydraulic and geochemical impact of occasional saltwater intrusions through a submarine spring in a karst and thermal aquifer (Balaruc peninsula near Montpellier, France)
<p>Contains geochemical and isotopic data presented in the scientific paper "Hydraulic and geochemical impact of occasional saltwater intrusions through a submarine spring in a karst and thermal aquifer (Balaruc peninsula near Montpellier, France)".</p> <p>Files contain physico-chemical parameters, major ions, rare earth elements and isotopic data (Sr, B) for karst and thermal groundwater samples collected between 2010 and 2018 in the Balaruc-les-Bains (France) area.</p>
Starlink satellite constellation: simulating its impact on optical observatories
<p>SEA Icosaedro simulations</p> <p>May and June 2020</p> <p>For the moment, all of them are based on the constellation profile starlink.dat, that considers some 12 thousand satellites.</p> <p>File: bv2020_microsatelites.pdf, report published in the Butlletin of the Spanish Astronomical Society (in Spanish language)</p> <p><br> All these tests include a photometric model of intermediate complexity, that takes into account distance to the observatory, phase angle, extinction (0.12 mag/airmass was selected for this bunch of simulations) and geometry of the shadow cone of the Earth. This model is very similar to that of Hainaut & Williams (2020) and our results may be directly compared to theirs.</p> <p>Affectation depends on the observatory latitude, but not on longitude. However, the main factors are the width of the field of view (FOV) and integration time (T). We performed tests for several different observatories available to the SEA community and this certainly illustrates latitude effects, but our results underline the strong importance of FOV and T and they are useful mainly to analyse these factors.</p> <p>Observatories and latitudes (degrees):<br> Calar Alto +037<br> Javalambre +040<br> La Palma +029<br> Montsec +042<br> Paranal -024</p> <p>For each observatory (i.e., each latitude) we perform simulations of two kinds:</p> <p>1) All-sky simulations:</p> <p>Counting the number of satellites visible as a function of time along one complete night</p> <p>--> For five different Sun declinations: +023, +012, +000, -012, -023 degrees</p> <p>--> For two different elevations over the horizon: satelites visible above +000 deg, and above +030 degrees</p> <p>So, in total, ten simulations are done at each location.</p> <p>File naming conventions:</p> <p>S_+LLL_+DDD_+HHH_NNN.xxx</p> <p>S: means "Starlink"<br> +LLL: observatory latitude, degrees<br> +DDD: Sun declination, degrees<br> +HHH: Elevation over which satellites are counted, degrees<br> NNN: Number of individual interations that are averaged out (050 in all cases)<br> xxx: Type of file:</p> <p>xxx = jpg, ps, pdf, graphs with number of visible satellites as a function of time, time is measured from the previous noon and is given in mintutes, vertical lines indicate the instants of beginning and end of civil, nautical and astronomical twilights, and midnight.</p> <p>xxx = mp4, animation with the apparent magnitude histogram of visible satellites at one minute steps; background colour means: white in daylight, grey in civil twilight, light blue in nautical twilighg, deep blue in astronomical twilight, black during astronomical night</p> <p>xxx = dat, files with very detailed information about the simulation, not included here, but may be provided, with indications about their contents</p> <p>Example:</p> <p>S_+037_+012_+030_050.mp4</p> <p>S: Starlink<br> +037: Calar Alto latitude<br> +012: Solar declination intermediate north +12 degrees<br> +030: Counting satellites at 30 deg or more above horizon<br> 050: 50 simulations were averaged<br> mp4: Movie with the histogram of apparent magnitudes</p> <p><br> 2) Pointing-oriented simulations:</p> <p>We select a FOV in arcminutes and an integration time T in seconds. Then, five observing directions are predefined:</p> <p>N, S, E, W at 45 deg elevation, and zenith</p> <p>For the given latitude we select Solar declination (0, +23, -23) and, both fixed, we study the five fields of view for three different solar elevations: -12, -25, -37, both PM and AM.</p> <p>This means that for one given observatory (latitude, FOV, T), 5 x 3 x 3 x 2 = 90 configurations, see:</p> <p>5 pointing directions<br> 3 solar declinations<br> 3 solar elevations<br> 2 for am/pm conditions</p> <p>This produces quite a large amount of information that is organised in form of detailed files and summary tables.</p> <p>Detailed files are:</p> <p>P_+LLL_+DDD_+HHH_pm_+aaa_+hhh_FOVi_iTim_NCRO.dat<br> p_+LLL_+DDD_+HHH_pm_+aaa_+hhh_FOVi_iTim_NCRO.dat</p> <p>P: very detailed output, p: less detailed output<br> +LLL: observatory latitude<br> +DDD: Sun declination<br> +HHH: Sun elevation<br> pm = "pm" or "am"<br> +aaa: observation azimuth from the South (O = S; 90 = W, 180 = N, 270 = E)<br> +hhh: observation elevation (45 deg for NSEW, 90 deg for Z)<br> FOVi: field of view in arcminutes<br> iTim: integration time in seconds<br> NCRO: number of crossing (multiple shots are simulated until NCRO sat crossings are registered, or until 1000 shots have been simulated)</p> <p>Detailed files are probably intersting only for very technical analysis, so they are not included here, but they may be provided upon request.</p> <p>The main results are contained in pdf tables that are, hopefully, self-explaining.</p>
Supplementary material 3 from: Bieberich J, Feldhaar H, Lauerer M (2020) Micro-habitat and season dependent impact of the invasive Impatiens glandulifera on native vegetation. NeoBiota 57: 109-131. https://doi.org/10.3897/neobiota.57.51331
Figure S1. Initial model of the piecewise structural equation modeling (SEM) for summer (A) and spring (B)
Supplementary material 4 from: Bieberich J, Feldhaar H, Lauerer M (2020) Micro-habitat and season dependent impact of the invasive Impatiens glandulifera on native vegetation. NeoBiota 57: 109-131. https://doi.org/10.3897/neobiota.57.51331
Table S1. Result of the automated model selection approach identifying environmental variables that affected the cover of Impatiens glandulifera in summer 2016 and spring 2017
Supplementary material 7 from: Bieberich J, Feldhaar H, Lauerer M (2020) Micro-habitat and season dependent impact of the invasive Impatiens glandulifera on native vegetation. NeoBiota 57: 109-131. https://doi.org/10.3897/neobiota.57.51331
Figure S3. Micro-habitat specific impact of I. glandulifera on the resident plant species composition
Extended data: Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx
<p>eQTL summary statistics and GWAS colocalization posterior probabilities from eQTL calling in an admixed subcohort of GTEx v8 with local and global ancestry adjustments. For the original, non-peer-reviewed preprint, see <a href="https://www.biorxiv.org/content/10.1101/836825v1">https://www.biorxiv.org/content/10.1101/836825v1</a>. </p> <p>For the related source code, see <a href="https://doi.org/10.5281/zenodo.3924788">https://doi.org/10.5281/zenodo.3924788</a> or <a href="https://github.com/nicolerg/gtex-admixture-la">https://github.com/nicolerg/gtex-admixture-la</a>. </p>
Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)
<p>Datasets for the Geoscientific Model Development publication: "Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)"</p> <p>Abstract: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958-2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~1º) to eddy-resolving (~0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the 20 extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are 25 significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve’’ most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.</p>
Benefit of Modified Atmosphere Packaging on the overall environmental impact of the supply chain of packed strawberries
<p>This dataset gives the raw results of Life Cycle Analysis of fresh strawberries packed in Modified Atmosphere Packaging (MAP) or conventionnel packaging (macroperfoated) stored either at ambiant temperature or refrigerated at household.</p> <p>These raw data were used to draw Figures 3, 4, 5 and Figure-S1 in the associated paper "Benefit of Modified Atmosphere Packaging on the overall environmental impact of the supply chain of packed strawberries".</p> <p>The dataset contains also results of the sensitivity analysis povided at:</p> <p>- consumer level</p> <p>- supermarket.</p> <p> </p>
The impact of stellar rotation on the black hole mass-gap from pair-instability supernovae
<p>Necessary files to reproduce the simulations of the paper "The impact of stellar rotation on the black hole mass-gap from pair-instability supernovae", as well as simulation output.</p> <p>These simulations were performed using MESA version 13311, with a small bugfix that can be applied with the provided <a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/hydro_riemann.f90?versionId=36bcfc7a-4535-4494-ba2b-c7ea69b9b362">hydro_riemann.f90</a> file.</p> <p>A description of the included files is as follows:</p> <ul> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/final_profiles.tar.gz?versionId=b85755ce-cd64-4e6b-ac14-d9e5364ee86b">final_profiles.tar.gz</a>: Individual profiles for all models at iron-core collapse (or last profile for PISN models). Files are separated into three folders, 'non_rot', 'rot_ST', 'rot_no_ST' corresponding to non-rotating models, rotating models with ST and rotating models without ST. Names of individual files contain the initial mass and the initial ratio between surface omega and its critical value. So for example, a file named "50.00_0.90_profile.data" represents an initial mass of 50 Msun and an initial rotation rate of 0.9 times critical. This work only contains non-rotating models and models at 0.9 critical though.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/histories.tar.gz?versionId=d3876d5e-c95e-4945-8715-9da428837afa">histories.tar.gz</a>: MESA history files containing various stellar properties describable by a single number at each timestep of the simulation (for instance, mass, effective temperature, etc...). Files follow the same naming scheme as those in final_profiles.tar.gz. For each simulation there is an additional file with a name ending in "pulse_indexes" that contains various lines with two integers per line. These specify for PPISN models the simulation step (or model_number in MESA-speak) at which a pulsation begins, and the step at which it ends.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/tables.tar.gz?versionId=69974ece-20e5-4509-a1a4-7dad7a92f4b6">tables.tar.gz</a>: Machine readable tables summarizing our simulations, these correspond to the data shown in the tables in the paper. The names of the tables "non_rot.txt", "rot_ST.txt" and "rot_no_ST.txt" correspond to non-rotating models, rotating models with ST and rotation models without ST respectively. The meaning of each column is: <ul> <li>M_i[Msun]: Initial mass of the helium star</li> <li>(om/omc)_i: Ratio between omega and its critical value at the star of core helium burning.</li> <li>M_Hedep[Msun]: Mass of the star at core helium depletion</li> <li>M_CO[Msun]: Carbon oxygen core mass at core helium depletion. Defined as the innermost mass coordinate at which Y<0.01.</li> <li>M_preSN[Msun]: Mass at the onset of PPISN/PISN. For models that do not undergo pair-instability, it corresponds to the mass at iron-core collapse.</li> <li>M_He,preSN[Msun]: Total mass in helium of the star at the onset of PPISN/PISN, or at iron-core collapse for non pair-unstable models. This does not represent the mass of the helium-rich envelope, but the total baryonic mass in helium of the star.</li> <li>M_ejecta[Msun]: Mass ejected by pulsations.</li> <li>M_final[Msun]: Final mass of the star at iron-core collapse.</li> <li>number_of_pulses: Number of pulsations.</li> <li>time_to_coll[yrs]: Time between the first pulse and iron-core collapse.</li> <li>max_KE[foe]: Maximum kinetic energy achieved by any pulse, measured in 10^51 erg/s=1 foe.</li> <li>a_i: Dimensionless spin of the star (a=jc/Gm) at the beginning of core helium burning.</li> <li>a_he_dep: Dimensionless spin at core helium depletion</li> <li>a_preSN: Dimensionless spin at the onset of PPISN/PISN.</li> <li>a_f: Final dimensionless spin from our simulations at iron-core collapse.</li> <li>BH_mass[Msun]: Final BH mass predicted from the properties of our model at iron-core collapse following Batta & Ramirez-Ruiz (2019).</li> <li>BH_spin: Final BH spin predicted from the properties of our model at iron-core collapse following Batta & Ramirez-Ruiz (2019).</li> </ul> </li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/pulses.tar.gz?versionId=5afe548e-3f3f-429a-98f6-b3cb14c77cc6">pulses.tar.gz</a>: Information on individual pulses for each PPISN simulation in our grid. Files are contained in three separate folders "non_rot", "rot_ST" and "rot_no_ST" for models that are non-rotating, rotating with ST and rotating without ST respectively. Naming of individual files is the same as those in final_profiles.tar.gz. Information contained in these tables is: <ul> <li>pulse_num: Integer identifying the pulse.</li> <li>ejecta[Msun]: Total mass ejected by the pulse.</li> <li>time_to_coll[yrs]: Time between the onset of this pulse and iron-core collapse.</li> <li>KE[foe]: Maximum kinetic energy reached during the pulse.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/template_ST.tar.gz?versionId=ac1563c9-5b0c-422a-8739-2fbc82ff5788">template_ST.tar.gz</a>: Template used for simulations. This template is setup for the simulations that include the ST dynamo and have an initial rotation rate of 90% critical. To modify the rotation rate and the initial mass, modify new_omega_div_omega_crit and initial_mass in the inlist_extra file. To turn of ST, set am_nu_ST_factor=0 in inlist_ppisn. Only one model required additional tuning to comple, the 60 Msun rotating model with ST. For this one, the model was restarted at step 98000 with the value of the option x_ctrl(18) switched from 0.025d0 to 0.25d0. This particular simulation did not reach the automated terminating condition and was stopped manually, but at the end it does have a collapsing iron-core with an infall velocity ~5000 km/s.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/notebook.tar.gz?versionId=d59d2404-1517-4c0a-910e-4adbc14511f8">notebook.tar.gz</a>: jupyter notebook used to process the data from our simulations and produce tables and figures (except for the information on the spin of the primary BH of GW170729).</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/GW170729_spin.tar.gz?versionId=47f46740-3c0d-4355-b2a2-9d7a454f388b">GW170729_spin.tar.gz</a>: jupyter notebook used to compute the spin posterior of GW170729 using the data from the first catalogue of gravitational wave transients.</li> </ul>
Dataset for Supersaturation in the Wake of a Precipitating Hydrometeor and its Impact on Aerosol Activation
<p>******************Dataset Details******************</p> <p>1. Article Title: Supersaturation in the Wake of a Precipitating Hydrometeor and its Impact on Aerosol Activation<br> 2. Journal: Geophysical Research Letters, 47(22), e2020GL091179, <a href="https://doi.org/10.1029/2020GL091179">https://doi.org/10.1029/2020GL091179</a>, 2020<br> 3. Name and contact information:<br> Eberhard Bodenschatz,<br> Laboratory for Fluid Physics, Pattern Formation and Biocomplexity,<br> Max Planck Institute for Dynamics and Self-Organization,<br> Am Faßberg 17,<br> 37077 Göttingen, Germany<br> 4. Email: eberhard.bodenschatz@ds.mpg.de</p> <p>5. Funding: This research was funded by the Marie - Sk lodowska Curie Actions (MSCA) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 675675), and an extension to programme COMPLETE by Department of Applied Science and Technology, Politecnico di Torino.</p> <p>6. Abstract: The activation of aerosols impacts the life cycle of a cloud. A detailed understanding is necessary for reliable climate prediction. Recent laboratory experiments demonstrate that aerosols can be activated in the wake of precipitating hydrometeors. However, many quantitative aspects of this wake‐induced activation of aerosols remain unclear. Here, we report a detailed numerical investigation of the activation potential of wake‐induced supersaturation. By Lagrangian tracking of aerosols, we show that a significant fraction of aerosols are activated in the supersaturated wake. These “lucky aerosols” are entrained in the wake's vortices and reside in the supersaturated environment sufficiently long to be activated. Our analyses show that this wake‐induced activation of aerosols can contribute to the life cycle of the clouds.</p> <p>******************Dataset Organization******************</p> <p>1. Datasets are organized in folders which correspond to Figures in the Letter or in the Supporting Information (e.g., Figure 1 or Figure S2).<br> 2. Details of the entries can be found in the header of each dataset.<br> 3. Details of the simulation setup can be found in the text of each Figure.</p> <p>******************Software Details******************</p> <p>1. Open-source LBM library Palabos (Latt et al., 2020). Link: https://palabos.unige.ch/<br> 2. Data visualization is achieved using open-source library ParaView version 5.7 and Gnuplot Version 5.2.</p>
The impact of learning modality on team-based learning (TBL) outcomes in anatomical sciences education
<p>Team-based learning (TBL) is an instructional methodology that has been increasingly used in anatomy and physiology education in recent years. The appropriateness of TBL methods for students with diverse preferred sensory learning modalities has not been adequately examined. This study aimed to show the influence of students preferred sensory modality for learning on TBL and traditional academic outcome measures (tests, assignments, etc.). 157 American undergraduate Communication Sciences and Disorders students taking anatomy and physiology courses took the VARK, a questionnaire quantifying their preferred learning modality. These students traditional and TBL measures of academic success were compared by preferred learning modality. A one-way MANOVA failed to find any difference in TBL or other academic outcomes by preferred learning modality (<em>p</em> = .252). The results of this study support the effectiveness of TBL methods for undergraduate students regardless of what sensory mode they prefer to receive information</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.