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Рис. 9. Δинамика увеΛичения участка обитания моΛоΑых особей гренΛанΑского Λемминга оΑного помета (участок обозначен по крайним точкам уΑаΛения от основной норы): 1 — 13 июΛя (первый выхоΑ из норы); 2 — 16 июΛя; 3 — 21–22 июΛя; 4 — 25 июΛя; 5 — 31 июΛя; 6 — 4 августа. ОстаΛьные обозначения как на рис. 2 Fig. 9. Dynamics of an increase in the habitat area of young Greenland lemmings from the same litter (the site is marked by the extreme points of distance from the main burrow): 1 — 13 July (the first exit from the burrow); 2 — 16 July; 3 — 21–22 July; 4 — 25 July; 5 — 31 July; 6 — 4 August. For other designations see Fig. 2 in Territorial behaviour of the greenlandic lemming (Dicrostonyx groenlandicus Trail, 1823) on Wrangel Island
Рис. 9. Δинамика увеΛичения участка обитания моΛоΑых особей гренΛанΑского Λемминга оΑного помета (участок обозначен по крайним точкам уΑаΛения от основной норы): 1 — 13 июΛя (первый выхоΑ из норы); 2 — 16 июΛя; 3 — 21–22 июΛя; 4 — 25 июΛя; 5 — 31 июΛя; 6 — 4 августа. ОстаΛьные обозначения как на рис. 2 Fig. 9. Dynamics of an increase in the habitat area of young Greenland lemmings from the same litter (the site is marked by the extreme points of distance from the main burrow): 1 — 13 July (the first exit from the burrow); 2 — 16 July; 3 — 21–22 July; 4 — 25 July; 5 — 31 July; 6 — 4 August. For other designations see Fig. 2
РиС. 1. Карта мест сбора в БоΛьшехехцирском заповеΑнике и его окрестностях. Номера точек сбора и их описания см. в тексте Fig. 1. The map of gathering places in the Bolshekhekhtsirsky Nature Reserve and its surroundings. The numbers of collection points and their descriptions are given in the text in Heteroptera (Insecta, Heteroptera) of the Bolshekhetsirsky Nature Reserve, Khabarovsky Krai, Russia
РиС. 1. Карта мест сбора в БоΛьшехехцирском заповеΑнике и его окрестностях. Номера точек сбора и их описания см. в тексте Fig. 1. The map of gathering places in the Bolshekhekhtsirsky Nature Reserve and its surroundings. The numbers of collection points and their descriptions are given in the text
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan) in Free-living protozoa of freshwater and soils of the North-East Azerbaijan
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan)
Рис. 1. Пункты сбора материаΛов в районе Ботчинского заповеΑника. Пункты сбора обозначены красными кружками, наибоΛее крупный из которых соответствует основному месту сбора — корΑону «ТепΛый КΛюч». Номера пунктов сбора соответствуют номерам в тексте при их описании. БΛизко распоΛоженные пункты сборов показаны оΑним симвоΛом Fig. 1. Points of collection of materials in the area of the Botchinsky Nature Reserve. Collection points are marked with red circles, the largest of which corresponds to the main collection point — the cordon "Teply Klyuch". The collection point numbers correspond to the numbers in the text when they are described. Closely located collection points are shown with one symbol in Fauna of the geometrid moths (Lepidoptera, Geometridae) of the eastern Sikhote-Alin in the area of the Botchinsky State Nature Reserve I: History of research and subfamilies Archiearinae, Ennominae, Desmobathrinae, and Geometrinae
Рис. 1. Пункты сбора материаΛов в районе Ботчинского заповеΑника. Пункты сбора обозначены красными кружками, наибоΛее крупный из которых соответствует основному месту сбора — корΑону «ТепΛый КΛюч». Номера пунктов сбора соответствуют номерам в тексте при их описании. БΛизко распоΛоженные пункты сборов показаны оΑним симвоΛом Fig. 1. Points of collection of materials in the area of the Botchinsky Nature Reserve. Collection points are marked with red circles, the largest of which corresponds to the main collection point — the cordon "Teply Klyuch". The collection point numbers correspond to the numbers in the text when they are described. Closely located collection points are shown with one symbol
Рис. 1. Схема точек отбора проб на ООПТ в бассейне Амура Fig. 1. Map of sampling points at the protected areas in the Amur River Basin in Molting feathers and eggshells in monitoring rare bird species populations: Evidence from special protected natural territories
Рис. 1. Схема точек отбора проб на ООПТ в бассейне Амура Fig. 1. Map of sampling points at the protected areas in the Amur River Basin
Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>The number of points and the position of points are not standardized, but it is depending on<br> the features that will be extracted, and used for the classifier. Many researches use various number<br> of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure<br> 3 shows the points we used.</p>
Figure 11: Coding and decoding Cartesian coordinates of geometrical points-Brain Functors: A mathematical model of intentional perception and action
<p>The simplest form of a brain "functor" is just a two-way representation or coding system that constructs and implements a set of codes. Given some set of objects, it is encoded using some isomorphic set of representations or codes for the objects, and then given an instance of the code, it is decoded to determine the object. Coordinatizing is a form of coding. The geometrical plane is a collection of points, and the Cartesian coordinate system represents each point P by a pair (xP, yP) of coordinates. Given a point P , the "coordinate" function selects the coordinates (xP, yP) of the point which is the recognized or coded output, and given the coordinates or code for a point (xP, yP) as an input, the "plot" function designates the point.</p>
BRAIN Journal-Man versus Computer: Difference of the Essences. The Problem of the Scientific Creation-Figure 1. Geometrical figure "right triangle" as a material system. Points are universal joints.
<p>However, the result of the creative activity can be easily tested (verified) by scientists. Example of the creative solution of the Euclid's V-th postulate is as follows (T.Z. Kalanov, 2011a). As is well known, the triangle is one of the most important figures in geometry and trigonometry. This figure as a material system can be constructed and studied as follows. 1. The triangle is constructed as is follows. If the sides of the angle are bound up with the rectilinear segment, then the synthesized system (the constructed geometrical figure) AOB is called triangle (Figure 1). </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 5: Graph Illustrate the new points on the circumference of the circle
<p>Formulas (1) and (2) are used to determine the new points on the circumference of the circle to form the circular formation shown in Figure 5. </p>
Standing repetitive pointing task in individuals with and without Parkinson's disease
<p>[ These data refer to a manuscript currently under revision in PlosOne. In this MS we aimed to determine the effects of levodopa medication on the performance of a repetitive pointing task while standing, and to investigate the optimal trial duration in individuals with Parkinson’s disease, and older adults. Seventeen individuals with Parkinson’s disease (5 freezers) and 9 older adults stood on force platforms for 30 s and 120 s while performing a bilateral repetitive pointing task, tracked by motion capture. Participants with Parkinson’s disease were assessed on and off medication and older adults were also assessed on separate days. The main findings were that: 1) on medication, participants with Parkinson’s exhibited greater center of pressure root mean square in the medial-lateral direction, greater velocity in the medial-lateral and anterior-posterior directions, and greater range in the medial-lateral direction than off medication; 2) longer trial durations resulted in greater center of pressure range in the medial-lateral and anterior-posterior directions and greater coefficient of variation in finger pointing on the least affected side; 3) Parkinson’s participants exhibited larger range in the medial-lateral direction compared to older adults; 4) off medication, freezers presented with less range and root mean square in the anterior-posterior direction than non-freezers; and 5) a correlation emerged between the freezing of gait questionnaire and pointing asymmetry and the coefficient of variation of pointing on the most affected side. Therefore, Parkinson’s medication may increase instability during a repetitive pointing task. Longer trials may provide a better depiction of sway by discriminating between those with and without neurological impairment. Individuals with Parkinson’s were less stable than older adults, supporting that they are at a greater risk for falls. The greater restrictive postural strategy in freezers compared to non-freezers is likely a factor that augments fall-risk. Lastly, the link between freezing of gait and upper-limb movement indicates that freezing may manifest first in the lower-limbs. Add description ]</p>
Diffraction images of a crystal of the complex formed by fragments of the integrin beta4 and the bullous pemphigoid antigen 1 (BP230, BPAG1e). Integrin high affinity point mutant. PDB entry 6GVK.
<p>Data were collected on a single crystal at the beamline XALOC of the ALBA-CELLS synchrotron facility (Cerdanyola del Vallés, Barcelona, Spain) using radiation of 0.97915 Å wavelength and a PILATUS 6M detector. The complete dataset is build up of three sub-sets measured at three different positions of a single crystal. Each sub-set consists of 1800 images (0.2 degree oscillation per image).</p>
Leaf and wood classification framework for terrestrial LiDAR point clouds: Simulated data validation dataset
<p>Set of 200 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of point clouds simulated by a Monte-Carlo ray tracing (librat) using four 3D tree models from the fourth phase RAMI exercise (Widlowski et al, 2015).</p>
Leaf and wood classification framework for terrestrial LiDAR point clouds: Field data validation dataset
<p>Set of 10 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of single trees scanned around the globe, from different biomes (both forest and urban areas), using the Riegl VZ-400 terrestrial laser scanner.</p>
Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm
<p>The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.</p> <p>Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling). </p>
Dose point kernels for 2,174 radionuclides
<p>Dose point kernels for 2,174 radionuclides generated using MCNP v6.2. See accompanying article in Medical Physics for further details. </p>
Supplemental material to the paper: Incorporating epistemic uncertainty in the assessment of an existing masonry building through a Point Estimate Method
<p>This repository contains the meterials used to study numerically the effect of model uncertainties in the seismic assessment of a case-study stone masonry building. The simulations were run in the research version of the software <a href="http://www.tremuri.com/">Tremuri</a>. The repository contains input files, model results and the matlab code to reproduce the figures presented in the paper:</p> <blockquote> <p>Vanin F., Beyer K., "Incorporating epistemic uncertainty in the assessment of an existing masonry building through a Point Estimate Method", submitted for publication (2019)</p> </blockquote>
Avian community survey by point count method in a leafy forest of the Station de Biologie des Laurentides, Université de Montréal
<p>Here we provided a dataset of birds’ communities census in the Station de Biologie des Laurentides (SBL), the Université de Montréal field station. Census was performed as part of the BIO2854 field course. BIO2854 is an undergraduate course in terrestrial animal ecology in biological science at Université de Montréal. Highly qualified teaching assistant and students surveyed forest birds over 4 days in May-June 2011 to 2019 by acoustic point-count method. Abundance counts were made based on bird vital domain available in literature and the assumption that each recorded male was associated with one female from the same species. Data are made available to consult for future research and teaching purpose.</p>
Density-pressure isotherms of the 2D Lennard Jones fluid between the triple point temperature and the critical temperature
<p>Pressure-density isotherms of the 2D truncated-shifted Lennard-Jones fluid, with <span class="math-tex">\(r_{c} = 2.5 \sigma\)</span>:</p> <p><span class="math-tex">\(V\left(r\right) = \begin{cases} U\left(r\right) - U\left(r_{c}\right) & \text{if } 0 < r < r_{c}\\ 0 & \text{if } r \geq r_{c} \end{cases}\)</span> with <span class="math-tex">\(U\left(r\right) = 4 \varepsilon \left(\left(\frac{\sigma}{r}\right)^{12}-\left(\frac{\sigma}{r}\right)^{6}\right)\)</span></p> <p>All thermodynamic quantities are reduced with respect to the Lennard-Jones parameters <span class="math-tex">\(\sigma\)</span> and <span class="math-tex">\(\epsilon\)</span> :</p> <ul> <li>Number 2D density <span class="math-tex">\(\rho^{*} = \sigma^{2}\rho\)</span></li> <li>2D pressure <span class="math-tex">\(P^{*} = \frac{\sigma^{2}}{\varepsilon}P\)</span></li> <li>Temperature <span class="math-tex">\(T^{*} = \frac{k_{B} T}{\varepsilon}\)</span></li> </ul> <p>The temperatures of the isotherms are <span class="math-tex">\(T^{*} = 0.40\)</span>, <span class="math-tex">\(T^{*} = 0.41\)</span>, <span class="math-tex">\(T^{*} = 0.42\)</span>, <span class="math-tex">\(T^{*} = 0.43\)</span>, and <span class="math-tex">\(T^{*} = 0.44\)</span> which corresponds to the range of liquid-gas coexistence, between the triple point temperature (<span class="math-tex">\(T_{t}^{*} \approx 0.40\)</span>) and the critical temperature (<span class="math-tex">\(T_{c}^{*} \approx 0.46\)</span>). Here are reported the isotherms for the gas and liquid phases and the coexistence points.</p> <p>The liquid and gas isotherms are obtained by Molecular Dynamics with the LAMMPS software (<a href="https://lammps.sandia.gov/">https://lammps.sandia.gov/</a>). The density and temperature are imposed (Langevin thermostat) and the pressure is computed with the virial estimate. The simulations are performed for 2D systems of dimensions <span class="math-tex">\(L_{x} = 44.9 \sigma\)</span> and <span class="math-tex">\(L_{y} = 46.7 \sigma\)</span> containing between 1300 and 1900 particles in the liquid phase, and 4 to 200 particles in the gas phase. The systems are equilibrated over <span class="math-tex">\(3 \cdot 10^{7}\)</span> times steps. Then, the computation of the thermodynamic properties is performed over a variable number of time steps in order to reach a targeted accuracy (standard deviation of the pressure). The longest simulations (liquid approaching cavitation) require about <span class="math-tex">\(10^{9}\)</span> time steps of computation. The block averaging method is used to estimate the standard deviation of pressure. The data are provided in csv files named as follows : 'liq_TX.XX.txt' for the liquid at temperature <span class="math-tex">\(T^{*} = X.XX\)</span>, and 'gas_TX.XX.txt' for the gas at temperature <span class="math-tex">\(T^{*} = X.XX\)</span>. The first column is the inverse number density <span class="math-tex">\(1/\rho^{*}\)</span>, the second column is the pressure <span class="math-tex">\(P^{*}\)</span>, and the last column is the standard deviation of the pressure <span class="math-tex">\(\Delta P^{*}\)</span>.</p> <p>The coexistence points are obtained by Gibbs ensemble Monte Carlo with an in house code. The temperature is imposed and the liquid and gas densities and the coexistence pressure (virial estimate) are computed. The csv file 'coexistence.txt' contains the coexistence data in the following order: the first column is the temperature <span class="math-tex">\(T^{*}\)</span>, the second and third columns are the average and standard deviation of the inverse of the gas number density <span class="math-tex">\(1/\rho_{gas}^{*}\)</span> and <span class="math-tex">\(\Delta\left(1/\rho_{gas}^{*}\right)\)</span>, the forth and fifth columns are the average and standard deviation of the inverse of the liquid number density <span class="math-tex">\(1/\rho_{liq}^{*}\)</span> and <span class="math-tex">\(\Delta\left(1/\rho_{liq}^{*}\right)\)</span>, and the sixth and seventh columns are the average and standard deviation of the coexistence pressure <span class="math-tex">\(P^{*}\)</span> and <span class="math-tex">\(\Delta P^{*}\)</span>.</p> <p>The files 'chart_gas.pdf' and 'chart_liq.pdf' provide graphical display of the data, for the gas and liquid phases respectively.</p>
The Locus Algorithm Exoplanet-Search Pointings Catalogue
<p>Presented here is a Catalogue of Pointings for Optimal Differential Photometry for 61,662,376 stars presented in the form of a CSV file. A total of 67,043,579 stars were analysed using the Locus Algorithm (Creaner et al, 2019), and the results of that analysis are presented here. A paper detailing this work is currently in writing. This catalogue is invisaged for use in the search for extrasolar planets by using the pointings presented here to allow for a maximum differential photometry precision and thus aid ground based searches for extrasolar planets.</p> <p>Instructions on how to use the data are contained in readme.md. An SQL script to identify the reference stars is also presented.</p> <p>Also given here are source files containing the fits and csv files generated by the grid jobs in a .zip archive.</p>
The Locus Algorithm Quasar Pointings Catalogue
<p>Presented here is a Catalogue of Pointings for Optimal Differential Photometry for 23,779 Quasars presented in the form of a CSV file. A total of 40,000 quasars were analysed using the Locus Algorithm (Creaner et al, 2019), and the results of that analysis are presented here. A paper detailing this work has been submitted to MNRAS and is available in preprint on arxiv.org.</p> <p>Instructions on how to use the data are contained in readme.md. An SQL script to identify the reference stars is also presented.</p> <p>Also given here are source files containing the fits and csv files generated by the grid jobs in a .zip archive.<br> </p>
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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.