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1,670 results for “FORCE”
Data from: Contributions of subsurface cortical modulations to discrimination of executed and imagined grasp forces through stereoelectroencephalography
Stereoelectroencephalographic (SEEG) depth electrodes have the potential to record neural activity from deep brain structures not easily reached with other intracranial recording technologies. SEEG electrodes were placed through deep cortical structures including central sulcus and insular cortex. In order to observe changes in frequency band modulation, participants performed force matching trials at three distinct force levels using two different grasp configurations: a power grasp and a lateral pinch. Signals from these deeper structures were found to contain information useful for distinguishing force from rest trials as well as different force levels in some participants. High frequency components along with alpha and beta bands recorded from electrodes located near the primary motor cortex wall of central sulcus and electrodes passing through sensory cortex were found to be the most useful for classification of force versus rest although one participant did have significant modulation in the insular cortex. This study electrophysiologically corroborates with previous imaging studies that show force-related modulation occurs inside of central sulcus and insular cortex. The results of this work suggest that depth electrodes could be useful tools for investigating the functions of deeper brain structures as well as showing that central sulcus and insular cortex may contain neural signals that could be used for control of a grasp force BMI.
Data from: Nature of the coupling between neural drive and force-generating capacity in the human quadriceps muscle
The force produced by a muscle depends on both the neural drive it receives and several biomechanical factors. When multiple muscles act on a single joint the nature of the relationship between the neural drive and force-generating capacity of the synergistic muscles is largely unknown. This study aimed to determine the relationship between the ratio of neural drive and the ratio of muscle force-generating capacity between two synergist muscles (vastus lateralis[VL] and medialis[VM]) in humans. Twenty-one participants performed isometric knee extensions at 20% and 50% of maximal voluntary contractions (MVC). Myoelectric activity (surface electromyography [EMG]) provided an index of neural drive. Physiological cross-sectional area (PCSA) was estimated from measurements of muscle volume (magnetic resonance imaging) and muscle fascicle length (3D ultrasound imaging) to represent the muscles' force-generating capacities. Neither PCSA nor neural drive was balanced between VL and VM. There was a large (r=0.68) and moderate (r=0.43) correlation between the ratio of VL/VM EMG amplitude and the ratio of VL/VM PCSA at 20% and 50% of MVC, respectively. This study provides evidence that neural drive is biased by muscle force-generating capacity, the greater the force generating capacity of VL compared to VM, the stronger bias of drive to the VL.
Data from: Adhesion forces and mechanics in mannose-mediated acanthamoeba interactions
The human pathogenic amoeba Acanthamoeba castellanii (A. castellanii) causes severe diseases, including acanthamoeba keratitis and encephalitis. Pathogenicity arises from the killing of target-cells by an extracellular killing mechanism, where the crucial first step is the formation of a close contact between A. castellanii and the target-cell. This process is mediated by the glycocalix of the target-cell and mannose has been identified as key mediator. The aim of the present study was to carry out a detailed biophysical investigation of mannose-mediated adhesion of A. castellanii using force spectroscopy on single trophozoites. In detail, we studied the interaction of a mannose-coated cantilever with an A. castellanii trophozoite, as mannose is the decisive part of the cellular glycocalix in mediating pathogenicity. We observed a clear increase of the force to initiate cantilever detachment from the trophozoite with increasing contact time. This increase is also associated with an increase in the work of detachment. Furthermore, we also analyzed single rupture events during the detachment process and found that single rupture processes are associated with membrane tether formation, suggesting that the cytoskeleton is not involved in mannose binding events during the first few seconds of contact. Our study provides an experimental and conceptual basis for measuring interactions between pathogens and target-cells at different levels of complexity and as a function of interaction time, thus leading to new insights into the biophysical mechanisms of parasite pathogenicity.
Data from: Collective forces of tumor spheroids in three-dimensional biopolymer networks
<p>We describe a method for quantifying the contractile forces that tumor spheroids collectively exert on highly nonlinear three-dimensional collagen networks. While three-dimensional traction force microscopy for single cells in a nonlinear matrix is computationally complex due to the variable cell shape, here we exploit the spherical symmetry of tumor spheroids to derive a scale-invariant relationship between spheroid contractility and the surrounding matrix deformations. This relationship allows us to directly translate the magnitude of matrix deformations to the total contractility of arbitrarily sized spheroids. We show that our method is accurate up to strains of 50% and remains valid even for irregularly shaped tissue samples when considering only the deformations in the far field. Finally, we demonstrate that collective forces of tumor spheroids reflect the contractility of individual cells for up to 1h after seeding, while collective forces on longer timescales are guided by mechanical feedback from the extracellular matrix.</p>
Reduced Neuromuscular Performance in Night Shift Orthopedic Nurses: New Insights From a Combined Electromyographic and Force Signals Approach
<p>The effect of sleep–wake rhythm disruption on neuromuscular control and muscle</p> <p>fatigue has received little attention. Because nurse shift work is so varied, including</p> <p>overnight duty, rotating shift schedules, early awakening, and interrupted nocturnal</p> <p>sleep, it offers an interesting model to study this paradigm. It has been investigated</p> <p>so far using only subjective markers. A combined approach based on the simultaneous</p> <p>analysis of surface electromyographic (sEMG) and force signals can objectively detect</p> <p>possible deficits in neuromuscular control and muscle fatigue. With this study we</p> <p>investigated neuromuscular activation and muscle contraction capacity at submaximum</p> <p>and maximum level in nurses working two night-shift schedules and compared them to</p> <p>levels in nurses working entirely in day shifts. Sleep quality and activity levels were also</p> <p>assessed. The study sample was 71 nurses grouped by their shift work schedule: night</p> <p>shift for 5 days (NS5, n = 46), night shift for 10 days (NS10, n = 9), and only day/swing</p> <p>shift (DS, n = 16). Before and after the shift-work cycle, maximum voluntary contraction</p> <p>(MVC) force and muscle activation, neuromuscular control, and muscle fatigability were</p> <p>measured in the finger flexor muscles. Activity level and sleep quality during the shiftwork</p> <p>cycle were recorded with a wrist actigraph. After the shift-work cycles, MVC force</p> <p>and muscle activation were decreased (11 3% and 33 3%, p < 0.001) as</p> <p>was neuromuscular control (36 8%, p = 0.007), whereas muscle fatigability was</p> <p>increased (C 19 9%, p = 0.006) in the NS5 and the NS10 group. Sleep quality was</p> <p>lower in the NS5 and the NS10 group (8 1.8% and 15%3, respectively, p < 0.001),</p> <p>while the activity level for the three groups was similar. There was a clear reduction</p> <p>in neuromuscular control and an increase in muscle fatigue in the nurses working the</p> <p>night shift. These findings may inform of work schedule planning or recommendations</p> <p>for devising new recovery strategies to counteract neuromuscular alterations in night</p> <p>shift nurses.</p>
Figure 9 in Feeding behaviour and bite force of sabretoothed predators
Figure 9. Mandibular force profiles of scimitar-toothed machairodonts (Homotherini). Values are presented for the canine, P3P4, and post-M1 interdental gaps. Zx/L-values at the canine are lower than those at post-M1, suggesting a shallow canine bite. No increase in dorsoventral buttressing occurs in the vicinity of the cheek teeth, indicating that the mandibular ramus is adapted for slicing meat exclusively. The Zx/Zycanine values are much higher than those of extant felids, suggesting that prey was restrained prior to the sabre bite.
Attractive forces slow contact formation between deformable bodies underwater
<p>Resized images for PDMS in contact with glass surface at different contact angles.</p>
Atmospherically-forced and chaotic interannual variability of the sea level and its components over 1993-2015 from the OCCIPUT ensemble simulations
<p>This data set contains the interannual variability fields for the sea level (ssh_var_inter_1993_2015_annuel.tar.gz) and its steric (hsterica_var_inter_1993_2015_annuel.tar.gz) and manometric (obp_var_inter_1993_2015_annuel.tar.gz) components over 1993-2015 from the OCCIPUT ensemble simulations. It is used in the paper « Atmospherically forced and chaotic interannual variability of regional sea level and its components over 1993-2015 » published in Journal of Geophysical Research - Oceans.</p> <p>This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (Bessières et al., 2017, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (Madec, 2008). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in Brankart, 2013) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis.</p> <p> </p> <p>For each member, the simulated sea surface height (SSH) over 1993-2015 is considered. As NEMO is a Boussinesq model, it conserves volume instead of mass. Therefore, the steric effect is missing into the global mean sea level change (Greatbatch 1994). To overcome this issue, we remove the global mean estimate for the sea level time series at each grid point. Then the sea level anomalies obtained are averaged per year and a linear trend is removed from each member. The same processes are applied to the steric and manometric sea level time series.</p> <p> </p> <p>Here is an example of the file header</p> <p><em><strong>dimensions:</strong></em></p> <p><em><strong>member = UNLIMITED ; // (50 currently)</strong></em></p> <p><em><strong>time = 23 ;</strong></em></p> <p><em><strong>y = 1021 ;</strong></em></p> <p><em><strong>x = 1442 ;</strong></em></p> <p><em><strong>variables:</strong></em></p> <p><em><strong> float ssh(member, time, y, x) ;</strong></em></p> <p><em><strong> ssh:long_name = "sea level interannual variability" ;</strong></em></p> <p><em><strong> ssh:standard_name = "sea_level interannual variability" ;</strong></em></p> <p><em><strong> ssh:units = "m" ;</strong></em></p> <p><em><strong> ssh:FillValue = "nan" ;</strong></em></p> <p><em><strong> float nav_lat(member, y, x) ;</strong></em></p> <p><em><strong> nav_lat:axis = "Y" ;</strong></em></p> <p><em><strong> nav_lat:long_name = "Latitude" ;</strong></em></p> <p><em><strong> nav_lat:standard_name = "latitude" ;</strong></em></p> <p><em><strong> nav_lat:units = "degrees_north" ;</strong></em></p> <p><em><strong> float nav_lon(member, y, x) ;</strong></em></p> <p><em><strong> nav_lon:axis = "Y" ;</strong></em></p> <p><em><strong> nav_lon:long_name = "Longitude" ;</strong></em></p> <p><em><strong> nav_lon:standard_name = "longitude" ;</strong></em></p> <p><em><strong> nav_lon:units = "degrees_east" ;</strong></em></p> <p><em><strong> float time(time) ;</strong></em></p> <p><em><strong> time:long_name = "time" ;</strong></em></p> <p><em><strong> time:standard_name = "time" ;</strong></em></p> <p><em><strong> time:units = "years since 1992" ;</strong></em></p> <p> </p> <p>nav_lat and nav_lon represent the latitude and longitude of the NEMO model whereas var represents the interannual variability time series. </p>
Dataset from: Aggregation of colloidal particles in the presence of hydrophobic anions: Importance of attractive non-DLVO forces
<p>The dataset for the publication "Aggregation of colloidal particles in the presence of hydrophobic anions: Importance of attractive non-DLVO forces". DOI: 10.1021/acs.langmuir.8b03191</p> <p>Files containing data have .dat extension and are in text format. When needed, additional information is reported in xx_Explanation.txt files.</p>
Experiments on the snowfall, temperature, and humidity to the Arctic summer snowstorm using ocean-ice couple model (POP2-CICE5) with JRA55-do and MERRA2 forcing
<p><span>In the Arctic, short-lived summer snowstorms can provide snow cover that can increase surface reflectivity and heat capacity. Despite their potential importance, little research has been done to understand the impact of summer snowstorms on basin-scale Arctic sea ice cover. Our observational analysis shows that a summer snowstorm event is accompanied by cyclonic ice drift, increases in surface albedo and surface air cooling that can persist for up to ~2 weeks, dampening sea ice loss. Specifically, multiple snowstorm events in a summer, on average, results in net increase in sea ice extent of ~0.2×106 km<sup>2</sup> by early September. Experiments with a sophisticated ice-ocean model framework indicate that the initial expansion of sea ice extent is driven by cyclonic wind-driven ice drifts driving sea ice southwards and increasing albedo around the summer ice edge, however the thermal effects from the associated snowfall and atmospheric conditions result in a stronger overall impact on basin-averaged sea ice extent at seasonal scales.</span></p> <p><span>Additional model experiments were carried out to isolate the physical processes contributing to the thermal response of Arctic sea ice to summer snowstorms. Our results show the impact of surface air cooling on sea ice extent is about 3.5 times larger than the snowfall/albedo response. However, our simulated albedo response is weaker than the observed response, likely due to the negligible difference in surface albedo between old snow and freshly fallen snow – a limiting factor in our analysis and a topic worthy of future focus.</span></p>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly RACMO fields, Greenland and Antarctica 1979-2014
<p>These model output are those presented in the manuscript that provides the first description of version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly RACMO output from 1979-2014. </p>
Benzalkonium chlorides MARTINI force field parameters
<p>Benzalkonium chlorides MARTINI force field parameters (BAK8, BAK12, CKC).</p>
Flagellate grazing morphometrics, particle tracking, prey-handling behavior, clearance rates and forces
<p>Heterotrophic nanoflagellates are the main consumers of bacteria and picophytoplankton in the ocean. In their micro-scale world, viscosity impedes predator-prey contact, and the mechanisms that allow flagellates to daily clear a volume of water for prey corresponding to 10<sup>6</sup> times their own volume is unclear. It is also unclear what limits the observed maximum ingestion rates of about 10<sup>4</sup> bacterial prey per day. We used high-speed video-microscopy to describe feeding flows, flagellum kinematics, and prey searching, capture, and handling in four species with different foraging strategies. In three species, prey-handling times limit ingestion rates and account well for their reported maximum values. Similarly, observed feeding flows match reported clearance rates. Simple point-force models allowed us to estimate the forces required to generate the feeding flows, between 4-13 pN, and consistent with the force produced by the hairy (hispid) flagellum, as estimated using resistive force theory. Hispid flagella can produce a force that is much higher than the force produced by a naked flagellum with similar kinematics, and the hairy flagellum is therefore key to foraging in most nanoflagellates. Our findings provide a mechanistic underpinning of observed functional responses of prey ingestion rates in nanoflagellates.</p>
Research and Evaluation of Allosteric Protein Specific Force Field Based on Deep Learning
<p>All trajectories for evaluating APSF force field</p>
SP2 measurement error of BC mixing state and radiative forcing evaluation
<p>The first version of dataset.</p>
Pure POPC Membrane with 650mM NaCl simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 77 NaCl, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC Membrane with 350mM NaCl simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 41 NaCl, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC Membrane with 450mM CaCl2 simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 52 CaCl2, and 6400 SWM4 water molecules. </p> <p>The simulation have been performed using OpenMM 7.4.1 Frames are saved in every 10 ps. Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were centered with VMD command "pbc wrap -center origin -centersel lipid -compound residue -all", concatenated and saved in xtc format with MDAnalysis.</strong></p> <p> </p>
Trapped Solitary Waves in a Periodic External Force: A Numerical Investigation using the Whitham Equation and the Sponge Layer Method
<p>Supplementary Videos.</p>
SNOWPACK simulations forced by MERRA-2 for Antarctic Ice Shelves
<p>This repository contains the SNOWPACK simulations used in the manuscript:<br> Banwell, A., Wever, N., Dunmire, D. and Picard, G. (2023): <em>Quantifying Antarctic-wide ice-shelf surface melt volume using microwave and firn model data: 1980 to 2021.</em> Geophys. Res. Lett. (accepted).</p> <p>For each MERRA-2 grid point that is on an ice shelf, the zip file of the corresponding simulation is named: <latitude>_<longitude>_ICESHELVES.zip</p> <p>Each zip file then contains the following files:</p> <ul> <li><code>output/-72.000_69.375_ICESHELVES.ini</code>: This file contains the model settings as used for the simulation</li> <li><code>output/-72.000_69.375_ICESHELVES.pro</code>: This file contains the detailed, layered SNOWPACK output, with for each layer, for example, temperature, density, liquid water content, grain size, etc. Can be visualized using <a href="https://niviz.org">niViz</a>.</li> <li><code>output/-72.000_69.375_ICESHELVES.smet</code>: This file contains the SNOWPACK time series output, like surface sublimation, snow surface temperature, latent heat flux, etc. Can be visualized using <a href="https://niviz.org">niViz</a>.</li> <li><code>output/-72.000_69.375_ICESHELVES.sno</code>: This file contains the SNOWPACK model state at the end of the simulation.</li> <li><code>output/-72.000_69.375_ICESHELVES.snobackup</code>: This file contains the SNOWPACK model state from the last backup point during the simulation.</li> <li><code>output/-72.000_69.375_ICESHELVES.haz</code>: This file contains very specific SNOWPACK model output and was ignored in the study associated with this dataset.</li> <li><code>output/-72.000_69.375_ICESHELVES.hazbackup</code>: This file contains very specific SNOWPACK model output from the last backup point during the simulation and was ignored in the study associated with this dataset.</li> </ul> <p> </p> <p>Also available are statistics derived from the SNOWPACK model output using the code published at <a href="https://doi.org/10.5281/zenodo.7957639">doi: 10.5281/zenodo.7957639</a> (<a href="https://github.com/nwever/SNOWPACK_iceshelves_melt">GitHub repository</a>):</p> <p> </p> <p><code>statistics.zip</code><br> ==========</p> <p>Contains statistics calculated from the SNOWPACK output. Statistics per region are stored in the folders <code>stats622</code> (for yearly statistics) and <code>stats_per_day622</code> (for daily statistics). Statistics per ice shelf are stored in the folders <code>stats_iceshelves622</code> (for yearly statistics) and <code>stats_iceshelves_per_day622</code> (for daily statistics). For each ice shelf and region, the following files are available:</p> <ul> <li>melt_R<em>X</em>.txt and melt_S<em>X</em>.txt: sum of melt amount (kg/m2) for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> <li>meltdays_<em>A</em>mm_R<em>X</em>.txt and meltdays_<em>A</em>mm_S<em>X</em>.txt: number of days exceeding <em>A</em> mm of melt for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> <li>mswater_<em>A</em>mm_R<em>X</em>.txt and mswater_<em>A</em>mm_S<em>X</em>.txt: number of days with total firn column water content exceeding <em>A</em> kg/m2 for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> <li>mswater<em>A</em>_<em>B</em>_<em>C</em>mm_R<em>X</em>.txt and mswater<em>A</em>_<em>B</em>_<em>C</em>mm_S<em>X</em>.txt: number of days with more than <em>C</em> kg/m2 amount of liquid water in the firn between <em>A</em> and <em>B</em> meters below the surface for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> </ul> <p>Each of the files report the year, respectively the date, in the first column, followed by the statistics for each grid point in the region/ice shelf, followed by the mean and standard deviation over all grid points in the respective region/ice shelf listed in the preceding columns. The first line is a header.</p> <p>To find out the sequence by which MERRA-2 points appear in the output columns, please refer to the list in the file <code>list_points.txt</code>. This list is constructed as follows. First there is a header indicating that what follows are regions. Then, each line contains a comma-delimited list of the region number, latitude, and longitude, respectively. The first line for a region is reported in the column with header <code>1</code> in the statistics file, the second line in the column with header <code>2</code>, and so on. After all regions are listed, there is again a header line denoting that what follows are the ice shelves. Similar to the section for the regions, each following line contains a comma-delimited list of the ice shelf number, latitude, and longitude, respectively. The numbering of regions and ice shelves is listed in the tables below.</p> <p> </p> <p><code>forcingstatistics.zip</code><br> ===============</p> <p>Contains monthly statistics of the forcing variables TA (air temperature, K), QI (specific humidity, -), ILWR (incoming longwave radiation W/m2), ISWR (incoming shortwave radiation, W/m2), PSUM (precipitation, kg/m2/h), and VW (wind speed, m/s). The statistics for each region and ice shelf are stored in <code>monthlystats</code> and <code>monthlystats_iceshelves</code>, respectively. Each file name contains the forcing variable and the region/ice shelf number. The numbering of regions and ice shelves can be found in the tables below. Each file then contains the month (expressed as YYYYMM), followed by means of the specific variable for each MERRA-2 grid point in the region/ice shelves (in the same sequence as for the statistics files discussed above), followed by the mean and standard deviation over all MERRA-2 grid points listed in the preceding columns.</p> <p> </p> <p> </p> <p> </p> <table> <caption>Regions</caption> <tbody> <tr> <td>Number</td> <td> </td> </tr> <tr> <td>R1</td> <td>Amery</td> </tr> <tr> <td>R2</td> <td>Amundsen</td> </tr> <tr> <td>R3</td> <td>Dronning</td> </tr> <tr> <td>R4</td> <td>Peninsula</td> </tr> <tr> <td>R5</td> <td>Ronne</td> </tr> <tr> <td>R6</td> <td>Ross</td> </tr> <tr> <td>R7</td> <td>Victoria</td> </tr> <tr> <td>R8</td> <td>Wilkes</td> </tr> </tbody> </table> <table> <caption> <p> </p> <p>Ice shelves</p> </caption> <tbody> <tr> <td>S115</td> <td>Amery</td> </tr> <tr> <td>S102</td> <td>West</td> </tr> <tr> <td>S104</td> <td>Publications</td> </tr> <tr> <td>S53</td> <td>Venable</td> </tr> <tr> <td>S110</td> <td>Abbot</td> </tr> <tr> <td>S54</td> <td>Cosgrove</td> </tr> <tr> <td>S55</td> <td>PIG</td> </tr> <tr> <td>S56</td> <td>Thwaites</td> </tr> <tr> <td>S57</td> <td>Crosson</td> </tr> <tr> <td>S58</td> <td>Dotson</td> </tr> <tr> <td>S60</td> <td>Getz</td> </tr> <tr> <td>S64</td> <td>Nickerson</td> </tr> <tr> <td>S65</td> <td>Sulzberger</td> </tr> <tr> <td>S36</td> <td>Brunt</td> </tr> <tr> <td>S37</td> <td>Riiser-Larsen</td> </tr> <tr> <td>S38</td> <td>Quar</td> </tr> <tr> <td>S39</td> <td>Ekstrom</td> </tr> <tr> <td>S124</td> <td>Atka</td> </tr> <tr> <td>S125</td> <td>Jelbaert</td> </tr> <tr> <td>S121</td> <td>Fimbil</td> </tr> <tr> <td>S123</td> <td>Vigrid</td> </tr> <tr> <td>S122</td> <td>Nivlisen</td> </tr> <tr> <td>S120</td> <td>Lazarez</td> </tr> <tr> <td>S119</td> <td>Borchgrevink</td> </tr> <tr> <td>S118</td> <td>Baudouin</td> </tr> <tr> <td>S158</td> <td>Prince Herald</td> </tr> <tr> <td>S51</td> <td>Scar Inlet</td> </tr> <tr> <td>S8</td> <td>Larsen C</td> </tr> <tr> <td>S6</td> <td>Larsen D</td> </tr> <tr> <td>S7</td> <td>Larsen E</td> </tr> <tr> <td>S45</td> <td>N GVIIS</td> </tr> <tr> <td>S44</td> <td>S GVIIS</td> </tr> <tr> <td>S46</td> <td>Bach</td> </tr> <tr> <td>S52</td> <td>Stange</td> </tr> <tr> <td>S50</td> <td>Wilkins</td> </tr> <tr> <td>S34</td> <td>Ronne</td> </tr> <tr> <td>S35</td> <td>Filchner</td> </tr> <tr> <td>S70</td> <td>Ross West</td> </tr> <tr> <td>S71</td> <td>Ross East</td> </tr> <tr> <td>S91</td> <td>Mertz</td> </tr> <tr> <td>S162</td> <td>Ninnis</td> </tr> <tr> <td>S90</td> <td>Cook</td> </tr> <tr> <td>S86</td> <td>Rennick</td> </tr> <tr> <td>S83</td> <td>Mariner</td> </tr> <tr> <td>S73</td> <td>Drygalski</td> </tr> <tr> <td>S74</td> <td>Nansen</td> </tr> <tr> <td>S114</td> <td>Dibble</td> </tr> <tr> <td>S100</td> <td>Shackleton</td> </tr> <tr> <td>S24</td> <td>Tracy Tremenchus</td> </tr> <tr> <td>S99</td> <td>Conger Glenzer</td> </tr> <tr> <td>S159</td> <td>Vincennes Bay</td> </tr> <tr> <td>S98</td> <td>Totten</td> </tr> <tr> <td>S157</td> <td>Moscow uni</td> </tr> <tr> <td>S156</td> <td>Holmes</td> </tr> </tbody> </table> <p> </p>
ScienceDex guides
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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.