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Figure 1 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 1. Map of Brazil highlighting the state of Espírito Santo and the fishing port of Anchieta, indicating the sampling points of the seabob shrimp. (P1 = Point 1: 2m; P2 = Point 2: 5m; P3 = Point 3: 10m; blue line = Benevente River).
Figure 5 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 5. Frequency of the carapace size of the Xiphopenaeus spp. shrimp collected from February/2013 to February/2015. M: Male, F: Female, J: Juvenile. The dashed line indicates the LC50 as reference.
Figure 4 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 4. Carapace length (LC) of males (A) and females (B) upon reaching sexual maturity estimated by logistic regression based on the absence (0) or presence (1) of specific morphological sexual characters plotted as a function of carapace length (mm) of Xiphopenaeus spp. in Anchieta, southern coast of Espírito Santo, Brazil (LC50 = Length that 50% of individuals reach in adult size).
Directed sampling datasets
<pre>Datasets:</pre> <pre>easom: optimizer-directed: N16_tol2e6_max1e6_1000SS [strict] N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e6_max1e6_1000SS [strict] 500_tol2e4_max1e4_1000SS [loose] rosen: optimizer-directed: N16_tol2e6_max1e6_1000SS2 [strict] N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e6_max1e6_1000SS [strict] 500_tol2e4_max1e4_1000SS [loose] rast: N16_tol2e6_max1e6_1000SS [strict] N16_tol2e4_max1e4_1000SS2 [loose] random: 500_tol2e6_max1e6_1000SS [strict] 500_tol2e4_max1e4_1000SS [loose] michal: N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e4_max1e4_1000SS [loose] hartmann6: N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e4_max1e4_1000SS [loose] rosen8: N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e4_max1e4_1000SS [loose] </pre>
Data from: Transcriptome analysis of apical meristem enriched bud samples for size dependent flowering commitment in Crocus sativus reveal role of sugar and auxin signalling
<p><strong>Background</strong></p> <p>Cultivation of <em>Crocus sativus</em> (saffron) faces challenges due to inconsistent flowering patterns and variations in yield. Flowering takes place in a graded way with smaller corms unable to produce flowers. Enhancing the productivity requires a comprehensive understanding of the underlying genetic mechanisms that govern this size based flowering initiation and commitment. Therefore, samples enriched with non-flowering and flowering apical buds from small (<6g) and large (>14g) corms were sequenced. </p> <p><strong>Methods and Results</strong></p> <p>Apical bud enriched samples from small and large corms were collected immediately after break of dormancy in July. RNA sequencing was performed using Illumina Novaseq 6000. <em>De-novo</em> transcriptome assembly and analysis using flowering committed buds from large corms at post-dormancy and their comparison with vegetative shoot primordia from small corms pointed out the major role of Auxin and ABA hormonal regulation. Many genes with known dual responses in flowering development and circadian rhythm like Flowering locus T and Cryptochrome 1 along with a transcript showing homology with small auxin upregulated RNA (SAUR) exhibited induced expression in flowering buds. Thorough prediction of <em>Crocus sativus</em> non-coding RNA repertoire has been carried out for the first time. Enolase was found to be acting as a major hub with protein-protein interaction analysis using Arabidopsis counterparts.</p> <p><strong>Conclusion</strong></p> <p>Transcripts belong to key pathways including phenylpropanoid biosynthesis, hormone signaling and carbon metabolism were found significantly modulated. KEGG assessment and protein-protein interaction analysis confirm the expression data. Findings unravel the genetic determinants driving the size-dependent flowering in <em>Crocus sativus</em>.</p>
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)
Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior
<p>Capturing qualitative features of animal behavior requires recording occurrences of behavior over time. Continuous sampling is best for capturing brief behaviors, but can be very time consuming. Instantaneous sampling can reduce the amount of labor required, but can miss short-duration behaviors. We therefore synthesized these techniques by continuously sampling during randomly scattered time intervals; a technique we call piecewise continuous sampling. To optimize and test the efficacy of this technique, we collected a continuous behavioral dataset of harvester ant workers, and then we developed a protocol to estimate the amount of sampling time necessary to reconstruct the proportion of time animals spend in different behavioral states. This protocol finds the sample size needed for the variance of the sample to converge on the variation of the population. We then divided this estimated time into equal-duration intervals that were randomly distributed across the entire continuous dataset. Finally, we calculated both time-dependent and time-independent error from this sample. We found that 4 to 16 sampling intervals minimize both types of error simultaneously. This finding was robust to differences in underlying behavior and was validated with simulations, implying that this method could be used for many types of organisms.</p>
Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a precision level of 10 and 25%.
Figure 2 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 2. Sample sizes required to achieve a given precision level of 10 and 25% at different mean densities of T. urticae per leaf.
Figure 1 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 1. Relationship between variance and mean density (all stages combined per leaf) of T. urticae samples collected from bean fields near Varamin vicinity, Tehran province, Iran. The red lines are the best-fitting lines of Taylor's power law.
Figure 1 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 1. Exemplars of the plant hosts analyzed in this study. (a) Taeniopteris from Mitchell Creek Flats, specimen USNM-612206. (b) Zeilleropteris from Mitchell Creek Flats, specimen USNM-612216. (c) Auritifolia waggoneri from Colwell Creek Pond, specimen USNM-559854. (d) Taeniopteris from Colwell Creek Pond, specimen USNM-559818. (e) Johniphyllum multinerve from South Ash Pasture, specimen USNM-520377. (f) Euparyphoselis gibsonii from South Ash Pasture, specimen USNM-520383.
Figure 4 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 4. Damage type (DT) diversity, the herbivory index (percentage of leaf area removed), and the proportion of specimens excluded, calculated with different specimen area restrictions, for the four primarily and secondarily dominant Permian plant hosts represented by fewer than 400 specimens. The dashed gray line represents the mean value calculated for the complete dataset, and the dotted gray lines represent the 95 % confidence intervals for the complete dataset. For the complete datasets, all specimens with a surface area above 0.5 cm2 were examined. The 95 % confidence interval for each subsampling routine is represented by a light gray rectangle bounded by black lines. The thick black lines represent the mean values for each subsampling routine.
Figure 7 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 7. Surface area of individual specimens ordered by area for the two forms of Johniphyllum multinerve at SAP.
Figure 3 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 3. Damage type (DT) diversity, the herbivory index (percentage of leaf area removed), and the proportion of specimens excluded, calculated with different specimen area restrictions, for the three primarily dominant Permian plant hosts represented by 400 or more specimens. The dashed gray line represents the mean value calculated for the complete dataset, and the dotted gray lines represent the 95 % confidence intervals for the complete dataset. For the complete datasets, all specimens with a surface area above 0.5 cm2 were examined. The 95 % confidence interval for each subsampling routine is represented by a light gray rectangle bounded by black lines. The thick black lines represent the mean values for each subsampling routine.
Figure 6 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 6. Surface area of individual specimens, ordered by area for each plant host, for Auritifolia waggoneri and Taeniopteris spp. of CCP and Johniphyllum multinerve at SAP.
Figure 8 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 8. The DT diversity and the herbivory index of each specimen, plotted against its surface area.
Figure 5 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 5. Sequential increases in sample size, starting with the largest specimens, for the three primarily dominant Permian plant hosts represented by 400 or more specimens.
Figure 2 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 2. Damage type (DT) diversity, the herbivory index (percentage of leaf area removed), and the proportion of specimens excluded, calculated with different subsampling routines for the three primarily dominant Permian plant hosts represented by 400 or more specimens. The dashed gray line represents the mean value calculated from the complete datasets, and the dotted gray lines represent the 95 % confidence intervals calculated from the complete datasets. For the complete datasets, all specimens with a surface area above 0.5 cm2 were examined. The 95 % confidence interval for each subsampling routine is represented by a light gray rectangle bounded by black lines. The thick black lines represent the mean values for each subsampling routine.
Water sample analysis and satellite imagery of a thermo-erosion gully and its surroundings in Adventdalen, Svalbard.
<h2><strong>Data description</strong></h2> <p>This dataset is part of the supplemental information to the paper "Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff" by Parmentier et al. (2024). It includes the analysis of water quality in and around a thermo-erosion gully on the high-Arctic archipelago of Svalbard, and three satellite images that give an overview of the wider area around this gully in the context of a snow fence experiment (Cooper et al. 2011). More details are provided in Parmentier et al. (2024).</p> <h2><strong>Background</strong></h2> <p>Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. The data presented here is part of a study that showed that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially-raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1 to 3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilised aquatic carbon. Our study show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss.</p> <h2><strong>Water samples</strong></h2> <p>The following datafile includes the analysis of several water samples taken in and near a thermo-erosion gully on Svalbard on August 5<sup>th</sup> and 6<sup>th</sup>, 2017. These were analyzed for dissolved organic carbon (DOC), particulate organic carbon (POC), particulate nitrogen (PN) content, and stable carbon isotope ratios δ<sup>13</sup>C-DOC and δ<sup>13</sup>C-POC. In addition, temperature, pH, oxygen, and electrical conductivity were measured in the field on the day of sampling. This data is provided in the following Excel file that also includes the latitude and longitude for each sample point: </p> <ul> <li>Parmentier et al - 2024 - Water Sample Analysis.xlsx</li> </ul> <h3><strong> </strong><strong>Sample analysis</strong></h3> <p>A full description of the analysis is repeated here from the supplemental information in the accompanying publication (Parmentier et al. 2024). The water samples were filtered on the day of collection through a pre-combusted glass fiber filter with pore size of 0.7 µm (Whatman, Grade GF/F). After filtration, the filters were packed in aluminum foil and frozen for later analysis of the collected particulate matter. From the filtrate, three samples of ~50 ml were taken and immediately frozen for transport.</p> <p>The filtered water samples were analyzed for their dissolved organic carbon (DOC) content and their stable carbon isotope ratio δ<sup>13</sup>C-DOC. This combined analysis was carried out at the labs of UCLouvain, Belgium with an Aurora 1030W TOC Carbon Analyzer, from OI Analytical, coupled to an IRMS (Thermo delta V Advantage). In the Aurora 1030W, the water samples were purged with H<sub>3</sub>PO<sub>4</sub>(phosphoric acid) to remove any dissolved inorganic carbon (DIC). Afterwards, Na<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (sodium persulfate) was added to the heated sample (97 °C) to oxidize any DOC to CO<sub>2</sub>. With N<sub>2</sub> as the carrier gas, the CO<sub>2</sub> was transferred to the analyzing units where the total concentration and δ<sup>13</sup>C-DOC of the CO<sub>2</sub> were detected. The δ<sup>13</sup>C-DOC samples were calibrated against the certified standard IAEA-CH-6 (-10.449 ± 0.033 ‰VPDB) and an internal sucrose standard (-26.99 +/- 0.04 ‰). The DOC measurements were calibrated against a concentration range (n=8) of the same standards (Morana et al., 2015).</p> <p> The particulate matter retained on the filters was analyzed for particulate organic carbon (POC) and particulate nitrogen (PN) concentrations, as well as δ<sup>13</sup>C-POC. The glass fiber filters were subsampled and repeatedly acidified with HCl (1.5 M) in pre-combusted Ag capsules to remove carbonates. Analyses were performed at the Stable Isotope Facility of the University of California in Davis using an Elementar Vario EL Cube (Elementar Analysensysteme GmbH, Hanau, Germany) connected to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope ratios of δ<sup>13</sup>C are reported relative to the international standard VPDB (Vienna PeeDee Belemnite).</p> <h2><strong>Satellite imagery</strong></h2> <p>To show the development of the thermo-erosion gully over time, we provide three high resolution satellite images from the Digital Globe constellation of satellites. The areal extent of these images covers the entire snow fence experiment in the valley of Adventdalen on Svalbard. They were acquired on August 5<sup>th</sup>, 2011, August 30<sup>th</sup>, 2013, and July 9<sup>th</sup>, 2015 by the WorldView-2, GeoEye-1 and WorldView-3 satellites, respectively. These images are provided as GeoTiffs – projected in the UTM 33X coordinate system:</p> <ul> <li>SnoEco_2011AUG05_WV2_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2013AUG30_GE1_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2015JUL09_WV3_MUL_Pansharpened_bco_rcs_dobj.tif</li> </ul> <p>Each of these files includes the following color bands: </p> <ul> <li>Band 1: Blue</li> <li>Band 2: Green</li> <li>Band 3: Red</li> <li>Band 4: Near Infrared</li> </ul> <p>In addition, the images are clipped to the following coordinate bounds (in UTM 33X):</p> <ul> <li> <p><span>x<sub>min</sub>, x<sub>max</sub></span><span>: 523740, 524825</span></p> </li> <li> <p><span>y<sub>min</sub>, y<sub>max</sub></span><span>: 8677150, 8678100</span></p> </li> </ul> <p>For full details on these satellite products, we refer to DigitalGlobe/Maxar.<strong> </strong></p> <h3><strong>Image processing</strong></h3> <p>The satellite imagery was processed according to DigitalGlobe guidelines and calibration coefficient adjustment factors. The radiometrically corrected source images were first converted to top-of-the-atmosphere spectral radiance, and thereafter to top-of-the-atmosphere reflectance. Following this processing, each color band of the image was pansharpened (using Bicubic interpolation) with the RCS algorithm in the Orfeo ToolBox of QGIS 2.18 to increase the horizontal resolution to ~50 cm. To reduce haze effects, the images were further corrected through a dark object subtraction (bottom 1 percentile of the blue band) which was applied to each band separately. Subsequent negative values were set to zero.<strong> </strong></p> <h2><strong>Acknowledgments</strong></h2> <p>This research was funded by the Research Council of Norway (RCN; grant agreement 230970), and the FRAM - Terrestrial flagship (362255 and 642018). F.J.W.P. and S.W. received additional funding from the RCN (grant agreement 323945). The high-resolution satellite imagery comes courtesy of the DigitalGlobe Foundation. We thank UCLouvain and the University of California, Davis for assisting in the sample analysis.<strong> </strong></p> <h2><strong>References</strong></h2> <p>Cooper, E. J., Dullinger, S., & Semenchuk, P. (2011). Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. <em>Plant Science</em>, 180(1), 157–167. https://doi.org/10.1016/j.plantsci.2010.09.005</p> <p>Morana, C., Darchambeau, F., Roland, F. A. E., Borges, A. V., Muvundja, F., Kelemen, Z., et al. (2015). Biogeochemistry of a large and deep tropical lake (Lake Kivu, East Africa: insights from a stable isotope study covering an annual cycle. <em>Biogeosciences</em>, 12(16), 4953–4963. https://doi.org/10.5194/bg-12-4953-2015</p> <p>Parmentier, F. J. W., Nilsen, L, Tømmervik, H., Meisel, O. H., Bröder, L., Vonk, J. E., Westermann, S., Semenchuk, P. R., Cooper, E. J., Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff, <em>Geophysical Research Letters</em>, In press</p>
Figure 2. Collector curve for samplings from 1987 in Seasonal analysis of Saturniidae (Insecta: Lepidoptera: Bombycoidea) in a remaining Atlantic Forest in the State of Espírito Santo, Brazil
Figure 2. Collector curve for samplings from 1987 to 1992. The median line is representing the mean and external lines the standard deviation. Vale Nature Reserve, Linhares, ES, Brazil. / Curva colectora para muestreos de 1987 a 1992. La lÍnea mediana representa la media y las lÍneas externas la desviación estándar. Reserva Natural Vale, Linhares, ES, Brasil.
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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)
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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.