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Fig. 3 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 3. Biplot of the uniform components in each direction (UniX and UniY) of morphometrical differences in 80 specimens of Cichla temensis in 4 color variation patterns (CPV) as measured by 9 Thin Plate Spline (TPS) distortion variables (V1-V9). Colored numbers indicate the CPV grade of individuals. The total spread of scores among individuals of each CPV are indicated by an envelope (solid line polygon) calculated as the minimum convex hull for that group. Position in the plot relative to other individuals indicates the degree of similarity in morph. Vectors point in the direction of gradient change for that TPS variable and the magnitude indicates the strength of the gradient. Angles between vectors indicate the TPS interset correlations.
Figure 5 in Assessment of the Renewable Energy Potential in the Republic of Adygeya
Figure 5. Distribution of temperatures of geothermal water (°C) at a depth of 2000 m in the Republic of Adygeya (Butuzov et al. 2009).
Figure 4 in Assessment of the Renewable Energy Potential in the Republic of Adygeya
Figure 4. Estimation of the gross and technical potential of biomass energy in the Republic of Adygeya (a) – from (Guide… 2007) and (b) – According to the Laboratory of Renewable Energy of Lomonosov Moscow State University.
Figure 2 in Assessment of the Renewable Energy Potential in the Republic of Adygeya
Figure 2. Assessment of the wind energy gross and technical potential on the territory of the Republic of Adygeya based on NASA SSE data (Stackhouse et al. 2016).
Figure 1 in Assessment of the Renewable Energy Potential in the Republic of Adygeya
Figure 1. Distribution of the average annual wind speed (m/s) at the height of meteorological measurements on the territory of the Republic of Adygeya (Atlas…, 2005).
Data used in "The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators" by Hunter N. Ratliff et al.
<p>This repository contains the raw experimental and PHITS-simulated data used in the JINST article “The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators” by Hunter N. Ratliff et al., available at <a href="https://doi.org/10.1088/1748-0221/19/07/P07002">https://doi.org/10.1088/1748-0221/19/07/P07002</a> (the accepted manuscript can also be found at <a href="https://hdl.handle.net/11250/3145287">https://hdl.handle.net/11250/3145287</a> and <a href="https://hratliff.com/publications/">https://hratliff.com/publications/</a>).</p> <p> </p> <p>The data consists of three top-level directories, each with various subdirectories. Within the “PHITS-simulated-data” directory are the PHITS simulations (including input and output files) used in producing Figures 4 and 5 in the manuscript, showing energy spectra in the bar for BSG events from a Cs-137 emission for placement of the source on the bar and on the BSG detector and with spacings between the bar and BSG detector of 20, 80, and 150 mm, along with the energy-dependent spatial distribution of these recoil electrons in the bar.</p> <p> </p> <p>The other two top-level directories contain raw data produced by the CAEN CoMPASS software when acquiring data experimentally. In the analysis for each measurement, the contents of the “RAW” folders were used. The “Energy-calibrations-of-CeBr3” directory contains the energy spectra of the CeBr3 detector (used as the BSG detector) when exposed to a variety of radioactive sources, used together to energy-calibrate the CeBr3 detector. The “Backscatter-gating-measurements” directory contains all of the list-mode data acquired with CoMPASS used for all of the other experimental measurements presented in the manuscript. It contains subdirectories for varied sources, source positions along the bar, distances between the bar and BSG detector, measurements with the source affixed to the BSG detector instead, and a few miscellaneous measurements. The “images” directories within each measurement’s directory (at the same level as the “RAW” folders) contain images produced by the analysis script written for this work used for diagnostics and presenting analyzed data.</p> <p> </p>
How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios
<p>This data were presented in the research paper “How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios”, currently accepted in the journal Global Change Biology Bioenergy (https://onlinelibrary.wiley.com/journal/17571707).<br>The study delivers a model-based evaluation of how much energy, in the form of biomethane and bioethanol, can be produced by giant reed and Miscanthus across Italy in 2000, 2055 and 2085. Marginal lands were defined as low profitable non-irrigated lands, without mechanization and/or nature conservation limitations. Our findings offer an estimation of achievable energy yields and related stability under current/future climate, identifying critical spots and opportunities at province and regional level across Italy.<br>This work was conducted by the Council for Agricultural Research and Economics and supported by the Italian Ministry of Agricultural, Food and Forestry Policies (MiPAAF) under i) the AGROENER project (D.D. n. 26329, April 1, 2016, http://agroener.crea.gov.it/) and ii) the AgriDigit-Agromodelli project (DM n. 36502 of 20/12/2018, https://www.progettoagridigit.it/il-progetto).</p> <p><br>The database used was split in two main datasets, one for the national case study and one for the provincial case study (Bologna province).<br>The national dataset consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_National.shp; 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across Italy (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_National.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. USDA soil texture classification: 1= Loamy, 2=Sandy−loam, 3=Silty−loam, 4= Clay−loam, 5= Sandy−clay−loam, 6=Silty−clay−loam, 7=Loamy−sand, 8=Sandy−clay, 9=Silty−clay, 10=Silty, 11=Clay, 12=Heavy−clay, 13=Sandy.<br>b. soil organic carbon (SOC) classification: SOC≤1.5%=low, 1.5%<SOC≤3%,=medium, otherwise=high;<br>c. maximum soil depth (depth) classification: depth≤50 cm=shallow, otherwise=deep;<br>d. absolute values of aboveground biomass (AGB, Mg ha-1) and energy yields (Giga J ha-1) obtainable from bioethanol (ETA) and biomethane (MET) energy carriers simulated for giant reed (GR) and Miscanthus (MI) in the current scenario;<br>e. minimum (Mn) and maximum (Mx) AGB percentage (%) variations (compared to the baseline) estimated in 2055 (55) and 2085 (85) for RCP 4.5 (4.5) and RCP 8.5 (8.5) scenarios;<br>f. potentially assignable marginal lands to Miscanthus (2) and giant reed (1) crop species in Italy based on attainable energy yields under current (C_Base) and future (2085) time slices, considering the more pessimistic (C_8.5_85_MIN) and optimistic (C_4.5_85_MAX) AGB projection for both crops.</p> <p><br>The provincial dataset (case study in the Bologna province) consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_Provincial.shp, 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across the Bologna province (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_Provincial.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. absolute values of simulated energy (EN, Giga J ha-1) from bioethanol (ETA) and biomethane (MET) for giant reed (GR) and Miscanthus (MI) in 1995,<br>b. energy percentage variations (compared to the baseline) estimated in 2085 for more optimistic (EN_Mx, i.e., RCP 4.5_max) and pessimistic (EN_Mn, i.e., RCP 8.5_min) projections for giant reed (GR) and Miscanthus (MI) and<br>c. coefficients of variations (CV, %) computed for the whole 30-year period centred on 1995 (B) and 2085 for RCP 4.5_max (CV_Mx) and RCP 8.5_min (CV_Mn) for giant reed (GR) and Miscanthus (MI) in the Bologna province.</p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 1. The whole-building switch concept for the power lines of standby devices
<p>68 million houses in North America and Europe will be smart by 2019 (Kurkinen, 2016) with a compound annual growth rate of 37 % and 61 %, respectively. The smart equipment is usually installed together with an upgrade (e.g. aluminum wires are replaced by copper ones) of the power grid. In this case, additional power lines for standby devices are cabled, and the WBS concept is applied using one power switch only (see figure 1). For instance, the Songle high-power relay T90 can control the whole building electricity with load up to 30 A using NodeMcu Lua ESP8266 WiFi and/or Arduino Uno / Mega boards.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C
<p>The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 5. An example of smart lighting using NodeMcu Lua ESP8266 ESP-12 WiFi board
<p> for different purposes together with switching on/off relays, e.g. to control the motors, to acquire the data from sensors. It allows developing multifunctional smart systems. For instance, the smart lighting unit is created using NodeMcu Lua ESP8266 ESP-12 WiFi board, Arduino light sensor, and relay SRD-05VDC-SL-C, which controls the power supply of the lamp. Figure 5 shows a simplified example of smart lighting, where the lamp is represented by eight 5 mm light-emitting diodes (LEDs).</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 4. Screen shot of the C# Windows form app
<p>The screen shot of the C# Windows form app is shown in figure 4. The text field on the left side includes numbers from 2 to 7, which are commands to control the states of relays. </p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 2. An example of smart power grid with hierarchical structure
<p> Figure 2 shows an example of smart power grid with hierarchical structure, where every segment equals a room or office. This approach is similar to the idea presented in Alboteanu et al. (2015), where the connecting / disconnecting of renewable energy sources and consumers are made via the appropriate contactors, automatically (or manually) controlled according to the energy consumption/generation. However, the management of micro smart grid is discussed in Alboteanu et al. (2015) only</p>
Spatial Autocorrelation and Entropy for Renewable Energy Forecasting
<p>Additional resources of the paper "Spatial Autocorrelation and Entropy for Renewable Energy Forecasting".</p> <p>The repository includes:</p> <p>- Datasets;</p> <p>- Prediction system and instructions;</p> <p>- Additional experimental results. </p>
EnerGAware monitored energy consumption data
<p>Energy consumption data acquired during the monitoring period, from the houses that were part of the EnerGAware pilot.</p> <p>The data discriminated by dwelling, but it is anonymized.</p>
Time series of detonation velocity for Fickett's model for various values of activation energy
<p>This dataset contains several time series of detonation velocity for Fickett's model.</p> <p>Parameters are: q=4, resolution per unit lenth is 1280.</p> <p>Activation energies (theta) are 0.95, 1, 1.004, 1.055, 1.065, 1.089.</p>
A new and highly robust light-responsive Azo-UiO-66 for highly selective and low energy post-combustion CO2 capture and its application in a mixed matrix membrane for CO2/N2 separation
<p>Supporting information for publication in Journal of Materials Chemistry A, <a href="https://dx.doi.org/10.1039/C8TA03553A">https://dx.doi.org/10.1039/C8TA03553A </a></p>
Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac--Coulomb(--Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= outputs from fits for obtaining spectroscopic constants) results discussed in the paper titled "Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac--Coulomb(--Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states", by Avijit Shee, Trond Saue, Lucas Visscher and Andre Severo Pereira Gomes.</p>
Model results for `Surface pond energy absorption across four Himalayan glaciers accounts for 1/8 of total catchment ice loss'
<p>Model setup (setup.mat) and outputs (allkeyres.mat, postproc.mat) for 5000 runs of Monte Carlo supraglacial pond energy-balance modelling in the Langtang catchment of Nepal. The full set of results are included for the median model run (run_..._n1645.zip).</p> <p>Also included are flux gate results for calculation of emergence velocity (fgates...zip).</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.