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355 results for “collapses”
Biomass, %N, and %C data for the BBC collapse scar for 2003 and 2004
This data set contains biomass measurements for a destructively harvested transect 30 m to the east of the permanent transect from the center of the BBC collapse scar (0m) into the surrounding fire scar (30m) of the Survey Line Fire (burned in June-July 2001). Biomass samples were collected on DOY 231 2004. Two 61 cm x 61 cm plots were harvested on the east and west side of every point along the transect (0, 6, 12, 18, 24, and 30 m). We sorted these above-ground biomass samples into plant type (Sphagnum spp., other mosses, Marchantia spp., Eriophorum vaginatum, Carex spp., Grasses, Betula spp., Salix spp., Potentilla palustris, Ledum groenlandicum, Vaccinium uliginosum, Vaccinium vitis-idaea, Chamaedaphne calyculata, other vascular plants, dead mosses, dead Carex spp., dead Graminoid, dead Potentilla palustris, dead Salix spp., and other litter). Photosynthetic green tissues were separated from the above-ground biomass samples. Samples were dried at 60degC to measure the dry mass. Samples were also analyzed for %C and %N. We oven-dried at 50 - 65degC and ground all samples before analysis. We analyzed samples for %C and %N using a Carlo Erba EA1108 CHNS analyzer (CE Instruments, Milan, Italy) and a COSTECH ECS 4010 CHNS-O analyzer (Costech Analytical Technologies Inc., Valencia, CA, USA). Sample standard errors were +/- 0.01% for nitrogen, +/- 0.45% for carbon. For the biomass transect samples, we analyzed for %C and %N when the samples were more than 10% of the plot biomass allowing for representative sampling of carbon and nitrogen from the dominant plant types. This data set was collected to monitor the change in biomass across the transect to relate this to disturbance, topography, soils, soil moisture and measured fluxes of CO2 and CH4 emissions.
Nutrient data from the Peat Collapse-Saltwater Intrusion Field Experiment from brackish and freshwater sites within Everglades National Park, Florida (FCE LTER), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly porewater nutrients were taken at 15 cm depth from a brackish and a freshwater marsh. Porewater physicochemistry was measured 24 hours after dosing. Collection occurred from Oct 2014 - Sep 2016. The collected water was then analyzed for temperature, conductivity, salinity, pH, alkalinity, chloride, DOC, NH4, SO4, TDN, SRP, TDP, and sulfide. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. 2018. Ecological Applications 28:2092-2018.
Leaf nutrient and root biomass data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Yearly sawgrass leaf carbon, nitrogen, and phosphorus concentrations and live root biomass measurements were measured from a brackish water and freshwater marsh. All measurements were taken every other month 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosystem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Biomass data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly biomass, aboveground net primary production, and culm density measurements were measured from a brackish water and freshwater marsh. All measurements were taken every other month 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Modeled flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly modeled ecosystem flux measurements were calculated from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park, collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly ecosystem flux measurements were taken from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: gravitational wave frequency fits
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes fit to the gravitational wave peak frequency versus time post-bounce, for a functional fit of the form f = A*sqrt(t) + B*t + C, where the frequency f is in Hz and the time t is in seconds. The columns are: progenitor mass [M_sun], fit coefficient A, fit coefficient B, fit coefficient C, and the R^2 of the fit.</p>
Collapse and Continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya Rapid Climate Change event (dataset and R scripts)
<p>The present digital archive is the outcome of the paper: <strong>Lawrence, D., Palmisano, A., and de Gruchy, M.W., 2021. <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244871">Collapse and Continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya Rapid Climate Change event</a></strong><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244871">.</a> <em><strong>PLoS ONE</strong></em><strong>,</strong> <strong><em>16</em></strong>(1).</p> <p>The dataset included here provides a collection of <strong>920 </strong>radiocarbon dates and <strong>1070</strong> sites from archaeological surveys. In addition, the digital archive related to this paper provides reproducible analyses in the form of three scripts written in R statistical computing language.</p>
Satellite images of the 17 July 2016 Aru Co glacier collapse
<p>These satellite images were made to visualize the Aru Co glacier avalanche. Some of them were used in these blog posts:</p> <ul> <li>Séries Temporelles (2016, August 25) Sentinel-2A captures a giant ice avalanche in Tibet. http://www.cesbio.ups-tlse.fr/multitemp/?p=8294</li> <li>Séries Temporelles (2016, August 25) Sentinel-2A (and Landsat-8) capture a giant ice avalanche in Tibet http://www.cesbio.ups-tlse.fr/multitemp/?p=8327</li> </ul> <p>Files description:</p> <ul> <li>File 2016-07-21_S2.tif: Sentinel-2A image of the Aru Co glacier avalanche acquired on 21-Jul-2016 (4 days after the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File 2016-06-24_L8mos.tif: Landsat-8 image of the Aru Co area acquired on 24-Jun-2016 (23 days before the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File anim.gif: animated sequence of both images using the lowest resolution image (Landsat-8)</li> <li>File diff_S2minusL8_band3.tif: difference between the band 3 of the 2016-07-21 Sentinel-2A image and the 2016-06-24 Landsat-8 image after a nearest neighbour resampling of the Sentinel-2 image to the same resolution as the Landsat-8 image (30 m).</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The images was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-01_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 07-Jul-2016 (10 days before the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-21-01_S1_diff_smoothed_Lee.tif : difference between both Sentinel-1 images after applying a refined Lee filter on the radar intensities</li> </ul> <p>Spatial extent of all the images in WGS 84 UTM 44N and lon/lat coordinates :</p> <p>Upper Left ( 602260.000, 3777670.000) ( 82d 6'32.64"E, 34d 8' 5.67"N)<br> Lower Left ( 602260.000, 3755030.000) ( 82d 6'23.08"E, 33d55'50.74"N)<br> Upper Right ( 640720.000, 3777670.000) ( 82d31'33.86"E, 34d 7'49.56"N)<br> Lower Right ( 640720.000, 3755030.000) ( 82d31'20.72"E, 33d55'34.75"N)</p>
Equation of State Effects on Gravitational Waves from Rotating Core Collapse
<p>Gravitational waveforms from 1824 fiducial and detailed electron capture simulations, sampled at 65535 Hz. The file is in HDF5 format, using the flags {dtype="f4",compression="gzip",shuffle=True,fletcher32=True}. Each group is contained in the "waveforms" top-level group and is named with the "A" and "omega_0" values from Equation 5 and the EOS. In each sub-group is a dataset containing timestamps in seconds (t=0 is core bounce) and a dataset containing the strain multiplied by the distance in centimeters. The values of A in kilometers, omega_0 in radians/s, and the EOS are stored as attributes of each group.</p> <p>In addition, the Ye(rho) profiles are stored in the "yeofrho" top-level group. Each sub-group is labeled by the EOS used to generate the profile.</p> <p>Finally, select reduced data is stored in the "reduced_data" top-level group. The following quantities are each stored as a 1824-element array, where elements of the same index from different datasets correspond to the same 2D simulation.</p> <p>A(km) -- differential rotation parameter in Equation 5<br> D*bounce_amplitude_1(cm) -- The minimum of the first (negative) GW strain peak, multiplied by distance.<br> D*bounce_amplitude_2(cm) -- The maximum of the second (positive) GW strain peak, multiplied by distance.<br> EOS -- the equation of state used in the simulation<br> MbarICgrav(Msun) -- gravitational mass of the inner core, averaged over time after core bounce<br> Mgrav1_IC_b(Msun) -- gravitational mass of the inner core at bounce<br> Mrest_IC_b(Msun) -- rest mass of the inner core at bounce<br> SNR(aLIGOfrom10kpc) -- signal to noise ratio of the GW signal, assuming a distance of 10kpc and aLIGO sensitivity<br> T_c_b(MeV) -- central temperature at bounce<br> Ye_c_b -- central electron fraction at bounce<br> alpha_c_b -- central lapse at bounce<br> beta1_IC_b -- ratio of rotational kinetic to gravitational potential energy of the inner core at bounce<br> fpeak(Hz) -- frequency of the post-bounce GW oscillations<br> j_IC_b() -- angular momentum of the inner core at bounce<br> omega_0(rad|s) -- initial (pre-collapse) rotation rate used in Equation 5<br> omega_max(rad|s) -- maximum rotation rate achieved outside of 5km<br> rPNSequator_b(km) -- radius of the rho=10^11 g/ccm contour along the equator at bounce<br> rPNSpole_b(km) -- radius of the rho=10^11 g/ccm contour along the pole at bounce<br> r_omega_max(km) -- radius where omega_max occurs<br> rho_c_b(g|ccm) -- central density at bounce (not time averaged)<br> rhobar_c_postbounce(g|ccm) -- central density time averaged after bounce<br> s_c_b(kB|baryon) -- central entropy at bounce<br> t_postbounce_end(s) -- time of the end of the postbounce signal (t=0 is core bounce)<br> tbounce(s) -- time of core bounce (t=0 is the beginning of the simulation)<br> </p>
Data from: Unexpected stability in faunal population abundances following an estuary-wide collapse of oysters
<p>Data describing the number and lengths of fish and macroinvertebrates sampled from long-term fisheries independent sampling in Florida’s coastal waters. Original data were collected by the Florida Fish and Wildlife Conservation Commission (FWC) Fish and Wildlife Research Institute (FWRI).</p>
Lacritin proteoforms prevent tear film collapse and maintain epithelial homeostasis
<p>All the datasets related to the article Lacritin proteoforms prevent tear film collapse and maintain epithelial homeostasis, Georgiev et al., J Biol Chem, Jan-Jun 2021;296:100070. doi: 10.1074/jbc.RA120.015833. Epub 2020 Nov 21. The diverse methods onvolved are described in the article which is available in gold open access. </p> <p>The dataset is available also at these links:</p> <p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7948570/</p> <p>https://www.sciencedirect.com/science/article/pii/S0021925820000575#appsec1</p> <p>https://www.jbc.org/article/S0021-9258(20)00057-5/fulltext#supplementaryMaterial</p>
Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw
<p>Data associated with publication "Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw". The data contained within this data set is arranged according to the main text and the supplementary information of this publication.</p> <p><strong>Background information</strong></p> <p>Field site: Stordalen mire, Abisko, Sweden (68 22ʹ N, 19 03ʹ E)</p> <p>Thaw stages: Palsa, bog and fen</p> <p>Type of samples: Gas samples, porewater samples, soil core samples</p>
Dataset for Bate (2022): Dust coagulation during the early stages of star formation: molecular cloud collapse and first hydrostatic core evolution
<p>This data set contains 12 smoothed particle hydrodynamics (SPH) dump files that were used to produce some of the figures in the journal paper:</p> <p>Bate, Matthew. R., 2022, Monthly Notices of the Royal Astronomical Society, accepted 13 May 2022</p> <p>Each of the SPH dump files is from a different calculation of the early stages of star formation: the gravitational collapse of a molecular cloud core, including dust coagulation. Each SPH dump file gives the state of the SPH calculation when the maximum temperature reached 1500 K, except for the beta=0.05 cases which give the state when the maximum hydrogen number density reaches 10^{14} cm^{-3}. The calculations were each performed using 3 million SPH particles and differed by their initial rotation rate, which was parameterised by beta=0, 0.0025, 0.005, 0.01, 0.02, and 0.05 (the magnitude of the ratio of the rotational and gravitational potential energies). Dump files from calculations that include and exclude envelope turbulence are provided (both are used for Figure B1). The dump files associated with each calculation are:</p> <p>beta=0: B1M0123 (does not include envelope turbulence)<br> beta=0.0025: B1M2123 (does not include envelope turbulence)<br> beta=0.005: B1M5123 (does not include envelope turbulence)<br> beta=0.01: B1M1128 (does not include envelope turbulence)<br> beta=0.02: B1M2126 (does not include envelope turbulence)<br> beta=0.05: B1M5109_b05_NoEnvTurb (does not include envelope turbulence)</p> <p>beta=0.0: B1M0123_b0_EnvTurb<br> beta=0.0025: B1M2177_b0025_EnvTurb<br> beta=0.005: B1M5209_b005_EnvTurb<br> beta=0.01: B1M1219_b01_EnvTurb<br> beta=0.02: B1M2221_b02_EnvTurb<br> beta=0.05: B1M5321_b05_EnvTurb</p> <p>The SPH dump files are Fortran binary files written in big endian format and generated by the sphNG code (Benz 1990; Bate 1995; Bate & Keto 2015). They can be read, visualised, and manipulated using the free, publicly available SPLASH visualisation code (which reads sphNG dump files), written by Daniel J. Price, that can be downloaded from: </p> <p>http://users.monash.edu.au/~dprice/splash/ </p> <p>The SPLASH configuration files used to produce Figs. 10,11,12,and B1 in Bate (2022) are included with this dataset in a gzipped tar file.</p> <p> </p>
On the impermanence of species: The collapse of genetic incompatibilities in hybridizing populations
<p>Species pairs often become genetically incompatible during divergence, which is an important source of reproductive isolation. An idealized picture is often painted where incompatibility alleles accumulate and fix between diverging species. However, recent studies have shown both that incompatibilities can collapse with ongoing hybridization, and that incompatibility loci can be polymorphic within species. This paper suggests some general rules for the behavior of incompatibilities under hybridization. In particular, we argue that redundancy of genetic pathways can strongly affect the dynamics of intrinsic incompatibilities. Since fitness in genetically redundant systems is unaffected by introducing a few foreign alleles, higher redundancy decreases the stability of incompatibilities during hybridization, but also increases tolerance of incompatibility polymorphism within species. We use simulations and theories to show that this principle leads to two types of collapse: in redundant systems, exemplified by classical Dobzhansky-Muller incompatibilities, collapse is continuous and approaches a quasi-neutral polymorphism between broadly sympatric species, often as a result of isolation-by-distance. In non-redundant systems, exemplified by coevolution among genetic elements, incompatibilities are often stable, but can collapse abruptly with spatial traveling waves. As both types are common, the proposed principle may be useful in understanding the abundance of genetic incompatibilities in natural populations.</p>
Trajectories and Code from "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022
<p>Trajectories, GROMACS input files, and analysis code from the manuscript "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022 (Nature Communications, In Press)</p> <p>https://www.biorxiv.org/content/10.1101/2021.12.23.474012v1.abstract</p>
Text-fig. 14. Photomicrographs of thin sections of specimen BP/16/1732 Palmoxylon dutoitii from Mhengere Hill, Gorongosa, Mozambique. a: transverse section (TS) with four fibre vascular bundles (fvb) and poorly preserved parenchyma between; b: diagram of one of the fvbs in (a) of the reniform type (f – fibres, mx – metaxylum, p – phloem, px – protoxylum); c: close up of the vascular part of a fvb with 2 metaxylem elements and the collapsed cells to the lower left represents the phloem; d: fvb with 2 metaxylem elements, fibrous part to the left and parencymarous ground tissue to the right; e: lower magnification of fvb in (c); f: fvb with three metaxylem elements and phloem patch below; g: longitudinal section (LS) showing the vascular sections alternating with the fibrous sections; h: LS showing the horizontal thickening on the walls of the metaxylem vessels and a patch of parenchyma to the right (darker cells); i: spheroid echinate phytoliths that are typical of Hyphaene and Borassus. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 14. Photomicrographs of thin sections of specimen BP/16/1732 Palmoxylon dutoitii from Mhengere Hill, Gorongosa, Mozambique. a: transverse section (TS) with four fibre vascular bundles (fvb) and poorly preserved parenchyma between; b: diagram of one of the fvbs in (a) of the reniform type (f – fibres, mx – metaxylum, p – phloem, px – protoxylum); c: close up of the vascular part of a fvb with 2 metaxylem elements and the collapsed cells to the lower left represents the phloem; d: fvb with 2 metaxylem elements, fibrous part to the left and parencymarous ground tissue to the right; e: lower magnification of fvb in (c); f: fvb with three metaxylem elements and phloem patch below; g: longitudinal section (LS) showing the vascular sections alternating with the fibrous sections; h: LS showing the horizontal thickening on the walls of the metaxylem vessels and a patch of parenchyma to the right (darker cells); i: spheroid echinate phytoliths that are typical of Hyphaene and Borassus.
Data from: Island-wide characterization of agricultural production challenges the demographic collapse hypothesis for Rapa Nui (Easter Island)
<p>Communities in resource-poor areas face health, food production, sustainability, and overall survival challenges. Consequently, they are commonly featured in global debates surrounding societal collapse. Rapa Nui (Easter Island) is often used as an example of how over-exploitation of limited resources resulted in a catastrophic population collapse. A vital component of this narrative is that the rapid rise and fall of pre-contact Rapanui population growth rates was driven by the construction and overexploitation of once extensive rock gardens. However, the extent of island-wide rock gardening, while key for understanding food systems and demography, must be better understood. Here, we use shortwave infrared (SWIR) satellite imagery and machine learning to generate an islandwide estimate of rock gardening and re-evaluate prior population size models for Rapa Nui. We show that the extent of this agricultural infrastructure is substantially less than previously claimed and likely could not have supported the large population sizes that have been assumed.</p>
Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface
<h3>Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface</h3> <p>We ran simulations for four different systems:</p> <ul> <li>WT: A1-LCD WT (N=137), wild-type (WT) sequence of the low-complexity domain (LCD) of the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), with electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T260: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T290: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=290K</li> <li>HP: homopolymer consisting of prolines (N=137), at temperature T=550</li> </ul> <p>For every system, we ran five independent simulations over 5µs (1000 frames) and used the last 900 frames (4.5µs) for our analysis.</p> <p>This data repository consists of<br> (1) folders containing the data for every seperate run (*_i, i=1,2,3,4,5) in simulation units<br> (2) folders containing the averaged data of all five runs (*_AVG), converted to SI units<br> (3) a droplet folder, containing the data (square radius of gyration and asphericity) for the whole droplet (for all four systems, all five runs)<br>Units are also clarified in each file's header.</p> <p>The simulation units can be converted to SI units via:</p> <ul> <li>Distance: D = 0.45nm</li> <li>Mass: M = 57.05amu</li> <li>Energy: epsilon = 0.2 kcal/mol</li> </ul> <p> </p> <p>Details for (1) and (2):<br>Each folder (*_i, i=1,2,3,4,5, and *_AVG) contains the following subfolders and files:</p> <p><strong>Ree:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ_{ee}) of the whole chains, where θ_{ee} is the angle between the polymer's center r_c and the chain’s end-to-end vector Ree [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_{ee,s}) of segment seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_{ee,s}) of all segments seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos.dat: distribution of the polymer end positions (whole chains) [Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos_segment_i.dat: distribution of the polymer segment end positions of seg_i</li> <li>distribPolymerEndPos_segment_all.dat: distribution of the polymer segment end positions of all segments</li> <li>distribPolymerRee2_all.dat: distribution of Ree^2 (whole chains), binned via polymer center position r_c</li> <li>distribPolymerRee_segment_i.dat: distribution of Ree^2 of segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerRee_segments_all.dat: distribution of Ree^2 of all segments, binned via segment center position r_{c,s}</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all polymer segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>Rg:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ) of the whole chains, where θ is the angle between the polymer's center r_c and the eigenvector belonging to the largest eigenvalue of the chain’s gyration tensor [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_s) of segment seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_s) of all segments seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribMonomerRg_all.dat: distribution of monomer weighted Rg^2 (whole chains), referred to as R_{g,mono}^2 (following Farag et. al) [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_all.dat: distribution of Rg^2 (whole chains), binned via polymer center position r_c [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_segment_i.dat: distribution of Rg^2 of segment seg_i, referred to as R_{g,s}^2, binned via segment center position r_{c,s} [Fig. S4a, Fig. S7a, Fig. S10a, Fig. S13a]</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>resDist:</strong></p> <ul> <li>distribPolymerRee2_base_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRee2_center_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRg2_base_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> </ul> <p>distribPolymerRg2_center_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14] </p> <p> </p> <p>Details for (3):<br>The folder '<strong>droplet</strong>' contains four system folders (HP, WT, WT_noEL_T260, WT_noEL_T290). Each of those folders contains the following files:</p> <ul> <li>runX_cluster_Rg2_Rg2Normal_kappa2.dat: for every run X, one finds the time evolution (in simulation units, with 1e8 timesteps = 1µs) of the square radius of gyration Rg2 of the full droplet, its x-, y- and z-components, its three eigenvalues and the droplet asphericity A (referred to as kappa2 in the header) [Fig.S1c, Fig.S1d]</li> <li>AVG_cluster_Rg2_Rg2Normal_kappa2.dat: average of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> <li>STD_cluster_Rg2_Rg2Normal_kappa2.dat: standard deviation of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> </ul>
Collapse and recovery of livestock systems shape fire regimes on the Eurasian steppe: a review of ecosystem and biodiversity implications
<p>This file contains bibliographic data from the literature research; livestock and fire data as well as Google Earth Engine and R-scripts to reproduce all analyses and figures.</p>
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Allen Brain Atlas
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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
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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.