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430 results for “meadows”
Algae, and Seagrass Coverage Relative to Presence of Sargassum in a Seagrass Meadow within Crandon Park, Florida, USA, March 2024 – March 2025
This package contains data on percent coverage of sargassum, seagrass, and algae recorded along 3 transects extending from the shoreline of Crandon Park, Florida from 2024-03-07 to 2025-03-26 as part of the Coastal Ecosystem-Research Experience for Teachers (CE-BIORETS) program. The data also include epiphyte-cover scores and water visibility, temperature, and salinity values. We placed a m^2 quadrat on the seafloor at 5-meter intervals along three 50-meter transects extending from the shoreline. We then recorded percent coverage of seagrass, algae, and sargassum taxa (to genus or species) and the epiphyte load within the quadrat. We also recorded water visibility, temperature, and salinity at the water surface along the transect. We collected these data to investigate the impact sargassum has on native seagrass meadows. Data collection for this project is complete
Dynamics of montane and subalpine meadows in the Three Sisters Wilderness Area and Biosphere Reserve, 1981-1993
The Three Sisters Wilderness Area is a Biosphere Reserve that represents the northern half of the Sierra-Cascade Province. It encompasses nearly 100,000 ha of essentially undisturbed landscape straddling the crest of the central Cascade Range, Oregon. Coniferous forests dominate the Three Sisters, although the Wilderness also supports a diversity of non-forested habitats including montane and subalpine meadows. The Three Sisters was given wilderness status in 1957 and was selected as a UNESCO Biosphere Reserve in 1974, to represent a "control" area for the nearby H. J. Andrews Biosphere Reserve. Integral to the establishment of a Biosphere Reserve is the documentation of existing ecosystems with baseline data. However, basic ecological research in theThree Sisters has been relatively limited. We conducted two types of studies: (1) phytosociological studies to characterize current vegetation patterns and to interpret relationships between community composition and major environmental and edaphic gradients, and (2) retrospective analyses of the invasion of montane and subalpine meadows by trees with a focus on the effects of recent climatic variation, grazing history, and site environment.
Draft genome assembly version 1 of the meadow spittlebug Philaenus spumarius (Linnaeus, 1758) (Hemiptera, Aphrophoridae)
<p>We sequenced the genome of the meadow spittlebug, <em>Philaenus spumarius </em>(Linnaeus, 1758), the main insect vector of <em>Xylella fastidiosa </em>Wells et al. 1987 in Europe (Saponari et al., 2014), using 10x Chromium linked-reads. A single <em>P. spumarius</em> adult female from Portugal (Fontanelas, Sintra; GPS location: 38°50'15.75"N; 9°25'20.77"W), collected in September of 2018, was selected for genome sequencing. This population was initially surveyed for colour polymorphism in 1988 (Quartau & Borges, 1997) and was later included in phylogeographic and population genomic studies of this species (Rodrigues et al., 2014; Seabra et al., unpublished). It is also geographically close to the population from which the individual used for the first partial genome assembly was collected (Rodrigues et al., 2016). The availability of this previous genetic information contributed to the choice of this population as the source of genomic material for whole genome sequencing. A subset of males from the same collection date were analysed for genitalia morphology to confirm species identification, as the best diagnostic characters are the appendages of the aedeagus (Drosopoulos & Quartau, 2002).</p> <p>The genomic DNA of the <em>P. spumarius</em> adult from Sintra was extracted using Illustra Nucleon Phytopure kit according to the manufacturer’s instructions (GE Healthcare). We assessed the quality and concentration of the DNA using Femto fragment analyser (Agilent). 10x Chromium library preparation and Illumina genome sequencing (HiSeq X, 150bp paired-end) were performed by Novogene Bioinformatics Technology Co, Beijing, China, in accordance with standard protocols.</p> <p>To create the <em>de novo</em> 10x Chromium assembly we ran Supernova 2.1.1 (Weisenfeld et al., 2017) on the 10x Chromium linked-read data with default parameters, using 1.0 billion reads corresponding to 56X coverage. To improve the initial supernova assembly, we performed iterative scaffolding using all of the 10x raw data (2.3 billion of reads). We ran two rounds of Scaff10x (https://github.com/wtsi-hpag/Scaff10X), followed by mis-assembly detection and correction with Tigmint (Jackman et al., 2018). This was followed by a final round of scaffolding with ARCS (Yeo et al., 2018). The assembly was checked for contamination using the BlobTools pipeline (version 0.9.19; Laetsch and Blaxter 2017; Kumar et al., 2013) and k-mer content was analysed with the KAT comp tool (Mapleson et al., 2017). In order to perform these analyses, it was necessary to remove the 10x linked barcodes from the reads with the script process_10xReads.py (https://github.com/ucdavis-bioinformatics/proc10xG). We assessed the quality of our draft genome assembly by searching for conserved, single copy, arthropod genes (n=1,066) with Benchmarking Universal Single-Copy Orthologs (BUSCO) v3.0 (Waterhouse et al., 2018).</p> <p>With the above assembly procedure, we obtained a final assembly of 2.7 Gb, having a scaffold N50 length of 116 Kb (contig N50 = 18 Kb) and the longest scaffold was 3.7 Mb. The length of the assembly was consistent with the genome size estimated by flow cytometry (Rodrigues et al., 2016). The k-mer distribution indicated high heterozygosity, estimated at 2.3%. BlobTools analyses revealed the presence of contigs assigned to <em>Sodalis </em>spp. (Enterobacteriaceae), a symbiont in members of tribe Philaenini (Koga et al., 2013). These contigs were filtered from the final assembly. Gene completeness assessment shows that 956 (89.6%) among 1,066 BUSCOs were found as complete copies, with only 26 (2.4%) missing. Of the BUSCOs that were detected, 878 (82.4%) were complete and single-copy, 78 (7.3%) were complete and duplicated and 84 (7.9%) were fragmented.</p> <p>In conclusion, due in part to high (2.3%) heterozygosity levels, the <em>P. spumarius</em> version 1 genome assembly is highly fragmented. Nonetheless, the assembly is considered complete and is likely to contain the majority of the gene content of <em>P. spumarius.</em></p>
Ecosystem metabolism and associated environmental data for a forested, meadow and reforested reach of White Clay Creek, Chester Co., Pennsylvania; 1971-1975 and 1997-2010
Ecosystem metabolism data for a 3rd-order Piedmont stream were collected during two periods: P1- April 1971 – Dec 1975, and P2- May 1997 – January 2010. Measures were made in a meadow and a forested reach during each period and in a reforested (formerly meadow) reach during the latter years of P2. During P1, measures were made by transferring streambed substrata to chambers in water jackets located on the streambank and measuring dissolved oxygen changes over diel periods. During P2, open system measures of dissolved O2 change were made for several days in warm and cold seasons, with reaeration determined from a propane injection experiment. Metabolism estimates were determined from diel curves of dissolved O2 change. Photosynthetically active radiation (PAR) and chlorophyll were measured concurrent with many measurements in P1 and all measures during P2, and temperature with all measures. Water chemistry parameters (NH4-N, NO3-N, PO4-P, SiO2, Cl, SO4, total alkalinity, pH) associated with each run are included in the data set, as are days since storm of various thresholds. Field procedures, analytical methods and data analyses are detailed in Bott, T.L. & J. D. Newbold, 2023. A multi-year analysis of factors affecting ecosystem metabolism in forested and meadow reaches of a Piedmont Stream. Hydrobiologia
Ecology and restoration of montane meadows at Bunchgrass Ridge near the Andrews Experimental Forest, 1999-2013
In a region dominated by coniferous forests, montane meadows contribute greatly to landscape diversity, wildlife habitat, and other important ecological functions and societal values. Throughout the Pacific Northwest, suppression of fire and changes in climate and grazing pressure have led to rapid succession of meadow to forest. Faced by gradual loss of these habitats, land managers are experimenting with tree removal and prescribed fire as tools for restoration. Research at Bunchgrass Ridge explores the history of conifer encroachment, the consequences for meadow vegetation, and the potential to restore native meadows through tree removal and prescribed burning. Spatially explicit reconstructions of the invasion history provide the context for a large-scale restoration experiment testing the efficacy of tree removal, with or without prescribed fire. Measurements from the experimental plots include pre- and post-treatment data on plant species composition for subplots of known invasion history. Data from adjacent, uninvaded meadows serve as targets for assessing restoration success. Our ultimate goal is to determine whether tree removal is sufficient to reverse the effects of encroachment, whether fire is also necessary, and whether the duration or density of invasion pose barriers to meadow reassembly.
Western columbine genetics across HJ Andrews Experimental Forest meadow communities
Woody plant encroachment is diminishing meadow and grassland habitat on a global scale. Increased woody cover influences local conditions such as light/shade environments, local soil characteristics, understory plant community structure, and disturbance regimes. Woody encroachment may also affect landscape-scale biological processes, such as herbaceous plant population structure, through reducing the total cover and continuity of open habitat and eroding mutualistic interactions, such as plant-pollinator interaction networks. A major concern is that habitat fragmentation will have a cascading effect if one or more mutualistic partners is adversely affected. For example, if pollinators are sensitive to disturbance, fragmentation may reduce rates of gene flow among sub-populations of plants, which is predicted to decrease effective population sizes and diminish adaptive potential (i.e. the capacity to respond to selective pressures through the evolution of genetically-based and heritable traits). Alpine meadows of the Cascade Mountains, which support diverse wildflower and pollinator communities, have shrunk dramatically over the last century as a result of forest encroachment. We posited that, as meadows become smaller and less connected, pollinators may abandon the smallest meadows and focus foraging efforts on the largest, most connected meadows with the most resources. This could expedite the decline and ultimate collapse of meadow communities through reducing adaptive potential across sub-populations of plants. We focus on a plant-pollinator interaction between a common, nectar-producing plant, Aquilegia formosa (western columbine), and rufous hummingbird (Selasphorus rufus) pollinators in four montane meadow complexes in the H.J. Andrews experimental forest, Oregon, USA (HJA). Using hummingbird movement data from SA028 (see H.J. Andrews project database), we first ask whether further forest encroachment in the HJA may alter hummingbird movement patterns among me
Thalassia leaf morphology and productivity measurements from arbitrary plots located in a Thalassia seagrass meadow in Rabbit Key Basin, Florida Bay (FCE) from March 2000 to April 2001
Thalassia leaf morphology and productivity were measured from six arbitrary 200 cm2 plots within a Thalassia seagrass meadow in Rabbit Key Basin, Florida Bay.
Co-dominant removal and N and C fertilization experiment for moist meadow tundra, 2002 - 2018.
In 2002 seven experimental sites were set up in areas of moist meadow alpine tundra on Niwot Ridge. At each site, ten 1 m^2 plots were established where there was roughly even cover by two dominant plant species, Geum rossii (forb) and Deschampsia cespitosa (grass). In a factorial design, plots were assigned treatments of 1) removal of G. rossii, D. cespitosa, or control (no plant removal), and 2) nutrient addition of nitrogen (N), carbon (C), or control (no addition). A tenth plot was assigned a treatment of random biomass removal and no nutrient addition. Plots were visited annually to implement removal and fertilization treatments, and to measure plant species composition. Plant productivity was measured every other year and nematode communities once via 18S rRNA metabarcoding, together with soils. Carbon addition plots were dropped from the experiment in 2016.
Mean Seagrass Meadow Metabolism by Time of Day in Virginia, USA
The study examined benthic O2 fluxes over a seagrass meadow using the aquatic eddy covariance technique, while measuring variables that have been shown to affect metabolism (PAR, temperature, O2 concentration, current velocity). Accurate daily metabolic estimates of respiration, gross primary production, and net ecosystem metabolism are widely used to assess ecosystem health and blue carbon sequestration and storage in vegetated coastal ecosystems. Aquatic eddy covariance allows direct measurements under naturally varying in situ conditions of oxygen (O2) fluxes between a benthic substrate and the water above. Here, we used hourly O2 fluxes measured with this approach to examine how respiration for a Zostera marina seagrass meadow varies through night and day, and how this affects commonly performed metabolic estimates. Fitting our database of 2,115 hourly benthic O2 fluxes for a seagrass meadow revealed that respiration decreased linearly by 29% through the night. We primarily attribute this to consumption of highly labile compounds that are formed by photosynthesis and accumulate during daytime. Furthermore, a corresponding linear increase in respiration through the day coupled with photosynthetic production described by a standard photosynthesis irradiance curve provided an accurate prediction of measured O2 fluxes (R2 = 0.993). These results document that night- and daytime respiration vary significantly in a seagrass meadow, and that both can be described accurately by a piecewise linear relationship. Many studies have questioned the widely used assumption in metabolic estimates that night- and daytime respiration are constant and equal. However, if night- and daytime respiration can be approximated as we found here by piecewise linear relationships, these standard means for calculating daily metabolic numbers remain valid. It is, however, important that such estimates are based on full 24-h records of benthic flux data.
Indicative distribution map for Ecosystem Functional Group M1.1 Seagrass meadows
<p>This archive contains indicative distribution maps and profiles for <strong>M1.1 Seagrass meadows</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Long-term studies of secondary succession and community assembly in the prairie-forest ecotone of eastern Kansas, Hay meadow restoration experiment
Local and regional-scale processes interact to govern the assembly, diversity and functioning of ecological communities. Evaluating the interplay of these differently-scaled processes in the regulation of ecological systems is a challenging problem, but is crucial towards understanding and predicting the potential effects of accelerated human activity on biological diversity and ecosystem sustainability. Since 2000, two long-term field experiments have been underway in grasslands of eastern Kansas to investigate the interplay of soil resource availability, species interactions and regional processes governing plant secondary succession, community assembly, biodiversity, and ecosystem functioning. Both experiments involve manipulations of soil nutrients in permanent grassland study plots and employ multi-species seed addition treatments to evaluate the contribution of dispersal limitation and regional constraints on local species pools to the regulation of plant community dynamics. Hay meadow restoration experiment, previously funded by USDA, was established in 2000 in a section of the field that was left unplowed at the start of the experiment. Thus Experiment 2 was initiated in the context of secondary succession on recently abandoned cool-season hayfield where hay grass species were dominant at the start of the study. In this experiment we have been monitoring plant community change annually since 2001 in response to two aspects of hay management important in our area: annual fertilization and annual haying. The experimental design involves factorial manipulations of nutrient supply (two levels of NPK fertilization), annual haying (two levels: hayed; not hayed) and propagule input achieved by adding seeds of 41 native prairie species to half of the plots. Experiment 2 parallels Experiment 1 with manipulations of soil resources and species pools, but does so in the contexts of hay management and native prairie hay meadow restoration.
Hummingbird foraging patterns across alpine meadows with RFID-equipped feeders in the HJ Andrews Experimental Forest, 2014-2017
Landscape changes can alter pollinator movement and foraging patterns which can in turn influence demographic processes of plant populations. In the Cascade Mountains of the Pacific Northwest, USA, forests are encroaching on alpine meadows that harbor diverse plant and pollinator communities. Whether encroachment and isolation of sub-meadows will influence pollinator foraging behaviors is unknown. To help assess those behaviors, subcutaneous Passive Integrated Transponders were implanted into 163 Rufous Hummingbirds (Selasphorus rufus), common avian pollinators in western North America and four arrays of five hummingbird feeders were established equipped with Radio Frequency Identification data loggers to passively relocate individuals at points throughout the landscape. The feeder arrays were established on four peaks along Frizzel Ridge in the H. J. Andrews Experimental Forest (Lookout Mountain, M1, M2, and Carpenter Mountain). A center feeder was established in a large, central alpine meadow and four satellite feeders c.a. 250m from the center. The satellite feeders were positioned such that at least one was in the open and connected to the center feeder by open habitat, one was in the open but separated from the center by coniferous forest canopy, and one was placed under coniferous forest canopy. Feeders were maintained for 1.5-12 weeks per year from 2014-2017.
Stream water chemistry data for Navajo meadow, 1984 - ongoing.
This is a summary of major ion concentrations for stream water samples collected at the flow gauging site at the lower end of the Navajo meadow. Marked by rebar in channel.
A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA
<p>This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: <em>A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA</em> (Bonner et al., 2022).</p> <p>All snow observation and meteorological forcing data are available as both .nc and .mat files.<br> Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files.</p> <p>This dataset will continue to be updated, via this repository, as additional years of data are collected.</p>
Data from: Long-term effects of meadow management on seed bank diversity and composition
<p>Aims: Oligotrophic grasslands are habitats that host among the most diverse plant communities in Europe. Altering management regimes by either intensifying or ceasing management is known to decrease plant diversity. Yet, despite its importance for the recovery of plant communities after disturbances, little is known about whether seed banks are also affected by changes in management. Here, we investigate the effect of management practices on a meadow seed bank using a long-term manipulative experiment. We focus on the response of the seed bank to the treatments, and the relationship between the seed bank and the vegetation response.</p> <p>Methods: The study was conducted in a species-rich wet meadow. The experiment consists of a factorial combination of fertilization, mowing, and removal of the dominant species. After 20 years of management, the seed bank was sampled seasonally at two soil layer depths. Standing vegetation was recorded in June at the peak of vegetation.</p> <p>Results: All seed bank characteristics varied between soil layers. Mowing decreased seed density and diversity, while fertilization significantly affected the species composition. Dominant removal had no effect on the seed bank. While seed bank diversity was not correlated to vegetation diversity, individual species’ responses to mowing and fertilization were positively correlated in the seed bank and the vegetation.</p> <p>Conclusions: Our results show that long-term management influences the seed bank down to 10 cm of soil depth. Whereas mowing apparently reduced seed density and diversity, the effects of fertilization on these characteristics were harder to interpret. After 20 years, most species had concordant responses to both mowing and fertilization, indicating a low legacy of previous management regimes on the seed bank. Our study reveals that the intensification of grassland management has a profound effect on plant diversity by directly affecting plant communities and their seed bank-driven recovery potential.</p>
Jacobaea vulgaris and meadow image classification dataset (binary)
<h3>General Information</h3> <p>Instances in the Jacobaea vulgaris class: 895<br>Instances in the Meadow class: 9141<br>Image sizes from 77x77 to 817x817 pixels on three color channels (RGB)</p> <p> </p> <h3>Data Generation and Source</h3> <p>The images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. <br>In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg. The multicopter flew in a height of circa 11 meters and took pictures with a ground resolution of approximately 3,18 mm/pixel. Additional information about the process of image generation for this dataset are to be found in the relevant papers written by P. Zacharias: 1) <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf" target="_blank" rel="noopener">UAV-basiertes Grünland-Monitoring und Schadpflanzenkartierung mit offenen Geodaten</a> [p. 45–53] and 2) <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf" target="_blank" rel="noopener">UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene</a>.</p> <p>Additionally, to the images of Jacobaea vulgaris taken by the UAV, the dataset includes images of Jacobaea vulgaris plants from the internet (included in the total 895 images; e.g. images 'jkk0523.jpg', 'jkk0527.jpg'). Furthermore, some of the images of the Jacobaea vulgaris plants have been rotated, further cropped or a filter has been applied. The exact number of augmentations made is unknown. As there are augmented images included in the datasets -which makes the dataset useful for training and validation- a use of the dataset for testing purposes is not recommended due to the risk of data leakage.</p> <h3>Data License</h3> <p>The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.</p> <p> </p>
Jacobaea vulgaris and meadow Augmented image classification dataset (binary)
<h3>General Information</h3> <p>Total instances: 117008<br>Instances in the Jacobaea vulgaris class: 58504 <br>Instances in the Meadow class: 58504<br>Image sizes from 224x224 pixels on three color channels (RGB)</p> <p><br>Performance increase training a ResNet50 on the base dataset versus the same architecture on the augmented data set shared here: +3,79 percent points in ROC AUC on an independent test set with 240 instances.<br><br></p> <h3>Data Generation and Source</h3> <p>The initial images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. <br>In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg.</p> <p>In my master's thesis at <a href="https://www.tu.berlin/dams">DAMS Lab</a> at TU Berlin, I evaluated the effect of different augmentation strategies for Jacobaea vulgaris image classification on the several performance metrics (most importantly the ROC AUC score). The identified augmentation strategies are -besides to performance based selection- also selected based on domain knowledge, which I acquired during the research for my master thesis. </p> <p>Additional information about the initial image generation process is to be found <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf">here </a> [p. 45–53] and <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf">here</a>. </p> <h3> </h3> <h3>Augmentations applied</h3> <ul> <li>Gaussian Noise: For the Gaussian noise augmentation, the mean of the added noise is set to zero. The lower and upper bounds for the random variance of the noise are 20.4663 and 54.0395 respectively. The bounds were identified by hyperparameter tuning. The search space for the lower bound was set from 5 to 30 and for the upper bound from 31 to 100. Those two search spaces were defined by visual inspection of the effects of applying Gaussian noise with different variance values to images of both classes. The Gaussian noise is sampled for each color channel individually. </li> <li>Random Brightness and Contrast: The brightness will randomly be increased or decreased by a factor ranging from 0.7010 to 1.2990. The The contrast will also be randomly increased by a factor ranging from 0.5775 to 1.4225. Those two ranges were identified using hyperparameter tuning. The search space for the maximal percentual increase or decrease of brightness and contrast was individually set from 1% to maximally 50% increase or decrease.</li> <li>Cutout Dropout: In this augmentation method a certain percentage of the input image is getting covered by black patches. The patches have a certain size in pixels, the implementation of this technique in this thesis uses square patches. The black patches are then randomly introduced into the image, by randomly alloacting the<br>patches across the image and then setting the corresponding pixel values to zero. The iamge is getting covered with patches until the cover percentage is reached. We<br>set percentage of the image to be randomly covered by black patches to 56.76%. The size of the patches, which randomly cover the image, is set to 4 pixels. A<br>good illustration of this is found in figure 4.2. The augmentation technique is inspired by the research proposed by Devries et al.[8]. Both values were identified by hyperparameter tuning. The search space for the patch size in pixels is categorical and includes the values [1, 2, 4, 7, 8, 14, 16, 28]. Those values all are multiples of 224, which is the image width and height in pixels. The patch size needs to be a multiple of the width and height in order to be suitable for the algorithm implementation. The search space for the cover percentage of the image had been set from 1% to 60%. This search space limits narrows the search down to a space where still a big part of the image is uncovered. The algorithm rearranges the image into a two dimensional grid and randomly masks rows of this grid by setting the pixel values in this row to zero. Then, the image gets rearranged, now with the randomly generated patches included.</li> <li>Random Saturation: The saturation of each pixel is randomly getting shifted. The upper bound for randomly shifting<span> </span>the saturation value of each pixel is set to 231.689%. This value was identified using hyperparameter tuning. An upper limit of the maximal saturation shift had been set to 40% shift in either direction for hyperparameter tuning.</li> <li>Horizontal Flip: The image gets flipped along the horizontal axis. </li> <li>Vertical Flip: The image gets flipped along the vertical axis.</li> <li>Random Rotation 90 degrees: Randomly rotates the image by a k-fold of 90 degrees, whereby k = {0, 1, 2, 3}.</li> </ul> <p> </p> <p>All augmentation methods and with their tuned augmentation hyperparameters (if existent) are applied to an image from the test set in figure 4.2. With the seven identified<br>augmentation techniques a dataset of 800% the size of the original dataset is created. The Augment model is trained on exactly this dataset. Of course next to the augmented images, the dataset still includes the original, unaugmented images. TensorFlow, along with additional libraries including Optuna for hyperparameter optimization and Albumentations for image augmentation, were used in for the implementation of this project.</p> <p> </p> <h3>Rational behind the augmentations applied</h3> <ul> <li>Random Rotation, Vertical and Horizontal Flip: These three augmentation strategies were chosen to make the classifier less sensitive to the orientation of the plant. The goal is to train a model that can classify plants regardless of their orientation. In order to achieve this effectively across different orientations, vertical flips, horizontal flips, and random 90-degree rotations are chosen for evaluation.</li> <li>Random Saturation: The varying saturation of the images simulates different levels of chlorophyll in the leaves, which is responsible for the green color of the<br>leaves and the intensity of this color. The color of the plant parts (leaves, stems, and flowers) is also influenced by factors such as soil, sun, weed density and pressure, location, and water availability. Varying the saturation of the images simulates changes in these factors.</li> <li>Gaussian Noise: By adding noise, in this case Gaussian noise, different lighting conditions are simulated when capturing the images. We specifically chose Gaussian<br>noise because it is common in many real-world scenarios and is based on the Central Limit Theorem, which states that the sum of many independent random variables.<br>tends to be normally distributed. This makes Gaussian noise a logical choice for simulating real-world random noise.</li> <li>Random Brightness Contrast: The Random Brightness and Random Contrast Augmentation uses brightness to mimic varying lighting conditions and contrast to highlight differences between plants by contrasting them more strongly, thereby highlighting their edges. This approach for highlighting edges is of course much more subtle than the canny edge detection augmentation. This augmentation method combines a weak focus on edges with variations in lighting conditions in one approach. The random contrast is a much softer approach for highlighting edges of plants, compared to the Canny edge detection augmentation. The other features in the images do not get changed that much, compared to the changes from edge detection augmentation.</li> <li>Cutout Dropout: The cutout augmentation simulates random occlusion by other plants. These occlusions are common and expected. Jacobaea vulgaris plants may be partially or completely obscured by other plants during image capturing. This augmentation technique makes the models more robust to random occlusion.</li> </ul> <h3> </h3> <h3>Data License</h3> <p>The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.</p>
De novo genome assembly of the meadow brown butterfly, Maniola jurtina
<p>1. Whole-genome GFF file (raw and filtered for min. gene length) [<em>Maniola.jurtina.gff3</em>, <em>Maniola_jurtina_filtered.gff3</em>]</p> <p>2. List of <em>M. jurtina</em> proteins [<em>Mjurtina_proteins.fa</em>].</p> <p>3. Results of spot pattern genes BLAST against <em>M. jurtina</em> proteome [<em>Lepidoptera_MJ_protein_matches.xlsx</em>]. </p> <p>4. Annotations [blast2go_export.txt]</p>
ARTICLE DATASET - ANTHROPOGENIC MICROPARTICLES ACCUMULATION IN SMALL-BODIED SEAGRASS MEADOWS: THE CASE OF TROPICAL ESTUARINE SPECIES IN BRAZIL
<p>This dataset with the data analysis script refers to the publication <a href="https://www.sciencedirect.com/science/article/abs/pii/S0025326X24007768?via%3Dihub"><strong>https://doi.org/10.1016/j.marpolbul.2024.116799</strong></a>.</p> <p>The dataset consists of a spreadsheet containing 9 tabs with survey data described below and their respective captions, found in the first line of each tab.</p> <p>The script for statistical analysis in the R language contains descriptive and statistical analyses, in addition to the functions for creating the graphs displayed in the article.</p> <p><strong>Description of the spreadsheet dataset tabs --------------------------------------------------------------------------------</strong></p> <ol> <li>description - Contains general information about the article</li> <li>meadows - Contains properties related to the characteristics of the multispecific grassland.</li> <li>ap_samples - Contains properties related to the abundances of anthropogenic microparticles, as to classifications by shape, size (mm) and color in units, kg and frequency of occurrence.</li> <li>ap_categories - Contains raw data related to the classifications of anthropogenic microparticles, in terms of shape, size (mm) and color.</li> <li>sediment - Contains raw data related to the classifications of sediment particles.</li> <li>granulometry - Contains properties related to sediment particles in µm, and their classifications by predominance, frequency and kg.</li> <li>shape - Contains raw data related to the classification of the shapes of anthropogenic microparticles in units and kg.</li> <li>size - Contains raw data related to the classification of the sizes of anthropogenic microparticles in units and kg.</li> <li>color - Contains raw data related to the classification of the colors of anthropogenic microparticles in units and kg.</li> </ol>
Data from: Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows
<p>This datasets support the scientific article (submitted) " Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows." It contains detailed data on sedimentary organic carbon content and the abundance of microplastics in both intertidal and subtidal seagrass meadows within the Ria Formosa lagoon (Southern Portugal). The datasets are accompanied by analysis code, available at GitHub repository, allowing for reproducibility and further exploration of the data.</p> <p>The data is composed by 4 datasets with the following variables:</p> <p><strong>data_cores.csv. </strong>Contains properties related to the sampling of the sediment cores.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>replicate [character] - replicate number of the core in each seagrass meadow.</li> <li>core_depth [numeric] - depth sampled with the core (in centimeters).</li> <li>sample_length [numeric] - length of the sampled core measured in the laboratory (in centimeters).</li> <li>compaction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth.</li> <li>compaction_perc [numeric] - core compaction in percentage (%). It is calculated as 100*(1 - compaction_factor).</li> </ul> <p><br><strong>data_samples.csv. </strong>Contains properties of the sediment samples.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>depth_middle [numeric] - middle depth of a sampling increment, calculating as the average of depth_min and depth_max (in centimeters).</li> <li>depth_min [numeric] - minimum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>depth_max [numeric] - maximum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>sample_volume [numeric] - volume of the sediment sample, corrected for compaction (in cubic centimeters).</li> <li>sample_dw [numeric] - dry mass of the sample (in grams of dry weight).</li> <li>percentage_organic_matter [numeric] - mass of organic matter relative to sample dry mass, obtained by loss-on-ignition (in percentage of dry weight).</li> <li>percentage_organic_carbon [numeric] - mass of organic carbon relative to sample dry mass, obtained by a local organic carbon to organic carbon ratio (as a percentage of dry weight).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>weight_sample_mp [numeric] - dry mass of the sample used for the microplastic extraction (in grams of dry weight).</li> <li>dry_bulk_density [numeric] - dry mass per unit volume of the sample. This is calculated as the sample_dw divided by the sample_dw (in grams of dry weight per cubic centimeter). </li> </ul> <p><br><strong>data_particles_visual.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on visual inspection.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>visual_id [character] - unique particle identification code based on visual identification.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>major [numeric] - longest dimension of the particle, analysed in ImageJ (in micrometers).</li> <li>minor [numeric] - Longest dimension perpendicular to major, analysed in ImageJ (in micrometers).</li> </ul> <p><br><strong>data_particles_ftir.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on the FTIR analysis.</p> <ul> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>num_ftir [numeric] - numerical order in which particles were identified within a filter.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>ref_analysis [boolean] - whether the reflection analysis was preformed or not.</li> <li>atr_analysis [boolean] - whether the ATR analysis was preformed or not.</li> <li>ftir_match_ref [character] - name of the polymer with the highest match found using µFTIR for reflection analysis.</li> <li>match_ref [numeric] - percentage of match corresponding to highest match for reflection analysis.</li> <li>ftir_match_atr [character] - name of the polymer with the highest match found using µFTIR for ATR analysis.</li> <li>match_atr [numeric] - percentage of match corresponding to highest match for ATR analysis.</li> <li>plastic_ref [boolean] - whether the particle is classified as having a plastic composition or not, based on the reflection analysis.</li> <li>polymer_group_ref [factor] - polymer group based on the reflection analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>plastic_atr [boolean] - whether the particle is classified as having a plastic composition or not, based on the ATR analysis.</li> <li>polymer_group_atr [factor] - polymer group based on the ATR analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>ftir_match_final [character] - final decision on the polymer composition, including the option "unclear".</li> <li>plastic_final [factor] - whether the particle is classified as having a plastic composition or not, based on final decision "ftir_match_final", includes categories: yes, no, unclear.</li> <li>final_analysis [character] - the analysis performed and used for the final decision, includes categories: ref (reflection analysis), atr (ATR analysis), both-but-atr-more-conclusive, both-but-ref-more-conclusive, both-unclear.</li> <li>polymer_group_final [character] - polymer group based final decision: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> </ul>
ScienceDex guides
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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.