Find research datasets worth reusing

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Fig. 14. PCA plot for standardized morphometric data for H in A new rupicolous species of gecko of the genus Hemidactylus Oken, 1817 from the Satpura Hills, Central India

Fig. 14. PCA plot for standardized morphometric data for H. chipkali sp. nov. (black), H. cf. murrayi (red) and H. treutleri (blue). Circles = male and squares = female.

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Fig. 10 Damage plots for the 3 in On Roth's "human fossil" from Baradero, Buenos Aires Province, Argentina: morphological and genetic analysis

Fig. 10 Damage plots for the 3' (A) and 5ʹ ends (B) for the four libraries [each line corresponds to a different library and treatment (shotgun or capture)], obtained with the built-in version of DamageProfiler (v0.4.9) for nf-core/eager (v. 2.4.3). The number of reads would be sufficient to clearly distinguish the substitution patterns characteristic for aDNA

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Fig. 1. Cynognathia phylogeny plotted onto a in Body mass estimation in Triassic cynodonts from Argentina based on limb variables

Fig. 1. Cynognathia phylogeny plotted onto a stratigraphic scale showing the known observed temporal ranges of taxa. Taxa studied in this contribution are in bold. Modified from Hendrickx et al. (2020). Thick dashed lines indicate separation between periods; thin dotted lines indicate separation between ages.

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figure 5 Lineage through time plot within G. subgutturosa with cytb. The 95 in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling

figure 5 Lineage through time plot within G. subgutturosa with cytb. The 95% highest posterior density interval is shown in blue.

Flower production and water infiltration in LandKlif experimental urban grassland plots

Mixtures of grassland communities (three different compositions varying in composition of grasses and forbs) were sown in field experimental areas (56 plots, 2 x 4 m each) in Munich and Weihenstephan in 2020. In June and August 2021, flower production of dicotyledonous, richness of dicotyledonous species in bloom, and soil water infiltration were measured in the plots, as a proxy to ecosystem functions related to resource offer to pollinators and water regulation. For integrity of the database, all field experimental units of this study are associated to Plot ID 7835_1_U, but the variable PlotID is deprecated.  LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif).  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Aboveground plant biomass in LandKlif experimental plots

Aboveground plant biomass of standing vegetation occurring in three random quadrats of size 20x20 cm located inside of the LandKlif experimental plots. Samples were taken between May and July 2019. LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif).  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Figure 4 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Figure 4. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) with different surrounding habitats (sugarcane + sesame, monoculture, sesame) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.

Figure 3 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Figure 3. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) at different crop developmental stages (crop phenology) of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.

Figure 2 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Figure 2. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.

Figure 1 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Figure 1. Percent numbers of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during 2018 at Layyah, Punjab, Pakistan.

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Fig. 3 – Plots representing the 23-27 Aug 2020 in Calliptamus italicus at 3050 m: a first evidence of dispersal across the Alps? (Orthoptera: Acrididae)

Fig. 3 – Plots representing the 23-27 Aug 2020 trends of the meteorological parameters considered in order to describe the weather conditions in the days just before the observation of the female Calliptamus italicus on Punta Losetta (3050 m, Cottian Alps). The trend of each parameter is better visualized with a Loess curve (blue line; grey area = 95% confi- dence interval): a, atmospheric pressure; b, precipitations; c, wind speed (solid lines) and wind gust (dotted lines); d, wind direction (origin); e, air temperature. The data collected in each weather station are reported in different colours. Sources: ARPA Piemonte and Météo France.

Draft version of poposed images and plotting code of manuscript: Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite

This documentation provides a detailed description of the folder structure and image contents uploaded to the website. It aims to help users understand the purpose and organization of the files. The corresponding manuscript is titled, Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite.

Stem respiration in SAFE intensive GEM carbon plots

<div> <h1>Description</h1> SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/ Stem respiration is measured on approximately 50 stems pre plot (typically two stems per subplot, but varies), using 11 cm diameter PVC collars glued on the stem at 140 cm height. The collars often fall or leak, or the trees die, so the number of measurements per day is almost always <50. The aim is to measure monthly, but in practice the measurement interval is almost always longer (problems with access, staffing and instruments) Data from late 2011 or early 2012, except from the two Danum plots (DC1 and DC2), which were set up in March 2015. The values represent the flux per unit stem area (not scaled to the whole tree). EGM-4 infrared CO2 analyser and SRC-1 respiration chamber (PP Systems). Chamber closure time is 124 seconds, CO2 concentration inside the chamber is recorded every 5 s. Flux is calculated from the linear change in concentration in the chamber headspace, and corrected for collar height (5 cm) and air temperature. The stem respiration data can be linked to other data (census, species, traits) collected from the same stems by the stem tag number. Constants for calculating fluxes 0.106 Collar diameter, m 0.008824734 Collar area, m2 0.05 Collar height, m 0.0012287 Chamber volume, incl. top part of the adapter (as in GEM manual) 0.000441237 Extra airspace of the collar, m3 0.001669937 Total chamber headspace, m3 101,325 pressure 8.314472 R 273.15 Temperature to Kelvins 12.01 Molar mass of carbon 2592000 seconds --> months pV=nRT ideal gas law n=pV/(RT) n=m/M mass mole m=n*M m=MpV/(RT) <h1>Projects</h1> This dataset was collected as part of the following projects: <ul> <li><a href="https://safeproject.net/projects/project_view/113">https://safeproject.net/projects/project_view/113</a> </li> </ul> <h1>Funding</h1> These data were collected as part of research funded by: <ul> <li>Sime Darby Foundation (Grant , SAFE Core data ) </li> <li>European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant , Grant number 321131 ) </li> <li>NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant , NE/K016369/1 ) </li> <li>NERC standard grant: The multi-year impacts of the 2015/2016 El Niño on the carbon cycle of tropical forests worldwide (Grant , NE/P001092/1 ) </li> <li>HSBC Malaysia (Grant ) </li> <li>The University of Zurich (Grant ) </li> </ul> This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. <h1>Permits</h1> These data were collected under permit from the following authorities: <ul> <li>Sabah Biodiversity Centre (SABC) ( Research licence JKM/MBs.1000-2/2 JLD.6 (76))</li> </ul> <h1>Files</h1> This dataset consists of 1 file: SAFE_StemRespiration_SAFEdatabase_2024-07-22.xlsx <h2>SAFE_StemRespiration_SAFEdatabase_2024-07-22.xlsx</h2> This file contains dataset metadata and 1 data tables: <h3>Stem respiration flux data</h3> <ul> <li>Worksheet: data</li> <li>Description: Stem respiration flux data</li> <li>Number of fields: 18</li> <li>Number of data rows: 10414</li> <ul> <li>ForestType: Old-growth or Logged (type: categorical)</li> <li>SAFEPlotName: SAFE plot name, as in the SAFE Gazetteer (type: location)</li> <li>Plot: Plot name used in field wrok (type: id)</li> <li>ForestPlotsCode: Plot code in the ForestPlots database (this should be used in publications, instead of plot name). (type: id)</li> <li>Date: Date of measurement (type: date)</li> <li>Observers: Observers (type: comments)</li> <li>Subplot: Subplot number within each plot, 1-25 (type: id)</li> <li>StemTagNumber: Stem tag number (ID), unique within plot but NOT across plots (type: id)</li> <li>EGM_RecordNumber: EGM record number in raw flux file (type: id)</li> <li>AirT: Air temperature (type: numeric)</li> <li>Remarks: Comments reguarding measurements. This includes collar quality, if measurements are repeated, or if tree is dead (type: comments)</li> <li>Slope: Slope of the linear regression between time (seconds) from the chamber closure and CO2 concentration (parts per million, ppm) in the chamber headspace. (type: numeric)</li> <li>AirT_Use: Air temperature (type: numeric)</li> <li>Flux_g_C_m2ofStemArea_month: Flux corverted from ppm s-1 to Mg carbon per hectare per month. See conversion below. (type: numeric)</li> <li>Quality: 1 - good flux; 0 - missing data or bad measurement; 2 - outlier (type: numeric)</li> <li>Year: Year of measurement (type: id)</li> <li>Month: Month of measurement (type: id)</li> <li>Day: Day of measurement (type: id)</li> </ul> </ul> <h1>Extents</h1> <ul> <li>Date range: 2011-08-26 to 2019-09-11</li> <li>Latitudinal extent: 4.5° to 5.07°</li> <li>Longitudinal extent: 116.75° to 117.82°</li> </ul> </div>

Constraining the dense matter equation of state with new NICER mass-radius measurements and new chiral effective field theory constraints: prior and posterior samples and scripts for generating plots

Full reproduction package accompanying the paper: Constraining the dense matter equation of state with new NICER mass-radius measurements and new chiral effective field theory inputs   *Note, the changes made from version to version are made visible in the CHANGELOG.rst file

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Fig. 1. A in Parasitism of Megacopta cribraria (Hemiptera: Plataspidae) by Paratelenomus saccharalis (Hymenoptera: Platygastridae) in organic soybean plots in Georgia, USA

Fig. 1. A) Density of Megacopta cribraria egg masses (mean ± SE) in organic soybean in 2013. Means with the same lowercase letter are not significantly different between treatments per week, and means with the same uppercase letter are not significantly different among weeks per treatment (Tukey's HSD test, P> 0.05). B) Percentage of parasitism (mean ± SE) of M. cribraria egg masses by Paratelenomus saccharalis over time in organic soybean in 2013. Means with the same uppercase letter are not significantly different (Tukey's HSD test, P> 0.05).

Figure 6. Performance plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 5 we can see that the mse curve reaches the In performance goal but it does not decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these results one can say that RNN is better choice than Feedforward MLP in prediction purpose.

Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can say that Y=T regression is not so good.

Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can say that Y=T regression is totally fit. This paper also comprises of comparative study of performance(mse) plot of both network.