Find research datasets worth reusing

opencc-by-4.0Dec 2014View details →

Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach

Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters).

opencc-by-4.0May 2024View details →

Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI

<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>

opencc-by-4.0May 2024View details →

Computational Investigation of Co-Aggregation and Cross-Seeding between Aβ and hIAPP Underpinning the Crosstalk in Alzheimer's Disease and Type-2 Diabetes

<p><span>The coexistence of Amyloid-β (Aβ) and human Islet Amyloid Polypeptide (hIAPP) in the brain and pancreas is associated with an increased risk of Alzheimer’s disease (AD) and type-2 diabetes (T2D) due to their co-aggregation and cross-seeding. Despite this, the molecular mechanisms underlying their interaction remain elusive. Here, we systematically investigated the cross-talk between Aβ and hIAPP using atomistic discrete molecular dynamics (DMD) simulations. Our results revealed that the amyloidogenic core regions of both Aβ (Aβ<sub>10–21</sub> and Aβ<sub>30–41</sub>) and hIAPP (hIAPP<sub>8-20</sub> and hIAPP<sub>22-29</sub>), driving their self-aggregation, also exhibited a strong tendency for cross-interaction. This propensity led to the formation of β-sheet-rich hetero-complexes, including potentially toxic β-barrel oligomers. The formation of Aβ and hIAPP hetero-aggregates did not impede the recruitment of additional peptides to grow into larger aggregates. Our cross-seeding simulations demonstrated that both Aβ and hIAPP fibrils could<a name="_Hlk163119646"></a> mutually act as seeds, assisting each other's monomers in converting into β-sheets at the exposed fibril elongation ends. The amyloidogenic core regions of Aβ and hIAPP, in both oligomeric and fibrillar states, exhibited the ability to recruit isolated peptides, thereby extending the β-sheet edges, with limited sensitivity to the amino acid sequence. These findings suggest that targeting these regions by capping them with amyloid-resistant peptide drugs may hold potential as a therapeutic approach for addressing AD, T2D, and their co-pathologies.</span></p>

opencc-by-4.0May 2024View details →

ERA5-Land selected indicators daily aggregates for Africa, 1991

<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1991.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>

Figure 6 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context

Figure 6: Average IJ movement in each of the 3 species when corner placed, in both conspecific and heterospecific conditions. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).

Figure 3 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context

Figure 3: Index of Dispersion for each of the three species when applied alone in the center of dispersal boxes. Values of D> 1 indicate increasing aggregation. Solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).

Figure 2 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context

Figure 2: Pyrex experimental arenas to assess responses when nematodes were added to opposite corners. Arenas filled with approximately 1200 g of sand at 10% moisture. A: Heterospecific experiment arena, where corners have different species of IJs. B: Conspecific test arena, where a single species of IJ was placed at one corner. C: 5 x 5 sampling grid; samples were collected at each circle.

Figure 5 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context

Figure 5: Aggregation shown by each of the 3 species when corner placed, in both conspecific and heterospecific conditions. Increasing values of D indicate increasing aggregation. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).

Figure 1 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context

Figure 1: Polypropylene experimental arenas to assess introduction at a common point. Arenas filled with approximately 1200 g of sand at 10% moisture. Image shows introduction point on 60mm filter paper and sample locations.

Figure 4 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context

Figure 4: Aggregation shown by each of the three species when center placed, in both conspecific (alone) and heterospecific conditions. Increasing values of D indicate increasing aggregation. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range). NOTE that y-axis scale changes significantly across the three panels.

opencc-by-4.0Jun 2024View details →

Synthetic aggregate projected maximum dimensions

<p>These files contain the projected maximum dimensions for the Kuo et al. (2016) aggregates at a variety of particle orientation angles and sensor view angles. The 'aggregate_standard.nc' file contains view angles for standard ice particle probe sensor configurations and the 'aggregate_leb.nc' file contains the view angles for probes following the Lebedev order 7 quadrature rule.</p>

Figure 3 in Response of Invasive Longhorn Beetles (Coleoptera: Lamiinae) to Known Cerambycid Aggregation-Sex Pheromones in the Puna District of Hawaii Island

Figure 3. Mean (± SE) numbers of PLB (Lagocheirus obsoletus) caught in traps baited with solvent control, fuscumol acetate, or monochamol (experiment 1). Means with an asterisk are significantly different than the solvent control (max-t test, p <0.05).

Figure 2 in Response of Invasive Longhorn Beetles (Coleoptera: Lamiinae) to Known Cerambycid Aggregation-Sex Pheromones in the Puna District of Hawaii Island

Figure 2. Mean (± SE) numbers of QLB (Acalolepta aesthetica) caught in traps baited with solvent control, fuscumol, fuscumol acetate, geranylacetone, or a blend of the three compounds (experiment 2). There were no significant differences between the treatments and the solvent control (max-t test, p> 0.05).

Figure 1 in Response of Invasive Longhorn Beetles (Coleoptera: Lamiinae) to Known Cerambycid Aggregation-Sex Pheromones in the Puna District of Hawaii Island

Figure 1. Mean (± SE) numbers of QLB (Acalolepta aesthetica) caught in traps baited with solvent control, fuscumol acetate, or monochamol (experiment 1). There were no significant differences between the treatments and the solvent control (max-t test, p> 0.05).

Figure 4 in Response of Invasive Longhorn Beetles (Coleoptera: Lamiinae) to Known Cerambycid Aggregation-Sex Pheromones in the Puna District of Hawaii Island

Figure 4. Mean (± SE) numbers of PLB (Lagocheirus obsoletus) caught in traps baited with solvent control, fuscumol, fuscumol acetate, geranylacetone, or a blend of the three compounds (experiment 2). Means with an asterisk are significantly different than the solvent control (max-t test, p <0.05).

opencc-zeroJun 2024View details →

Dataset: WisdomTree Interest Rate Hedged U.S. Aggregate Bond Fund (AGZD) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: WisdomTree Interest Rate Hedged U.S. Aggregate Bond Fund (AGZD) Stock Performance

opencc-by-4.0Mar 2018View details →

Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated. in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature

Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated.

opencc-by-4.0Dec 2023View details →

Figure 3. – Average STRUCTURE results aggregated using CLUMPAK for populations 2–6 and 9. K in Landscape biogeography and population structuring of a facultatively amphidromous galaxiid fish, Galaxias brevipinnis

Figure 3. – Average STRUCTURE results aggregated using CLUMPAK for populations 2–6 and 9. K = 6 was selected as the most likely population estimate using Evanno's method. STRUCTURE initially separated the lakes draining to the east coast (L. Wānaka and L. Wakatipu) from all other sites at K = 2. The West Coast lakes were split away next (K = 3), with L. Moeraki and L. Paringa splitting at K = 4 and L. Cristabel at K = 5. East coast L. Wānaka and L. Wakatipu were split at K = 6. L. Paringa and L. Moeraki are split form each other at K = 9.