Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
22,710
datasets available to search
ShareScore release 0.9.0
Dataset results
22,710 results for “Plants for planting”
Figure 4 in Lower Devonian Zosterophyllum-like plants from central Victoria, Australia, and their significance
Figure 4. cf. Zosterophyllum sp. A. from the Wilson Creek Shale Formation on Frenchmans Spur, 10 km west of Matlock: a, line drawing, dotted lines are from faint remains of compression. S=Sporangium; Q=Poorly preserved sporangium or sporangium likely not belonging to same spike; b, part NMV P256740.1. Arrow at stalk, which has been pushed across fertile axis; c, counterpart, NMV P256740.2 with part of basal region of spike missing. Note. Counterpart image reversed to be in the same orientation as the part.
Figure 2 in Lower Devonian Zosterophyllum-like plants from central Victoria, Australia, and their significance
Figure 2. Location of the Wilson Creek Shale outcrop on Frenchmans Spur Track, 10 km west of Matlock. Geological map showing the location of Frenchmans Spur outcrop at the star, north of Springs Creek. Source: Map after Willman et al. (2006).
Figure 1 in Lower Devonian Zosterophyllum-like plants from central Victoria, Australia, and their significance
Figure 1. Location of Matlock in the Mount Easton Province and Yea in the Darraweit Guim Province of the Melbourne Zone in Victoria, Australia. Source: after Moore et al. (1998: fig. 2).
Figure 9 in Lower Devonian Zosterophyllum-like plants from central Victoria, Australia, and their significance
Figure 9. cf. Zosterophyllum sp. A. (part, NMV P256740.1). Proximal region of spike with sporangia one (S1) and two (S2) visible and fertile axis curving into?rhizomatous region (arrow 1) and possibly extending out towards axes (arrows 2 and 3). Isolated sporangium (S) in same orientation as sporangia in spike. Image taken by Rodney Start © Museums Victoria.
Figure 5 in Lower Devonian Zosterophyllum-like plants from central Victoria, Australia, and their significance
Figure 5. cf. Zosterophyllum sp. A. (part, NMV P256740.1): a, two isolated sporangia at arrows (pre-dégagement) with basal region of both sporangia orientated away from the spike; b–d, on the reverse of the slab, isolated sporangia with much similar dimensions and weakly developed sporangial lobes. Image a taken by Rodney Start © Museums Victoria.
Figure 8 in Lower Devonian Zosterophyllum-like plants from central Victoria, Australia, and their significance
Figure 8. cf. Zosterophyllum sp. A. (part, NMV P256740.1): a, proximal region of spike; b, interpretative line drawing. Sporangia one and two (S1–2) appear infolded, with both abaxial (Abx) and partial adaxial (Adx) valves visible in sporangia and two perturbations and depressions (*) possibly representative of additional stalks that were not preserved. Q1, shadowing of possible sporangium, and upper arrow shows change in orientation of stalk beneath sporangium four.
Figure 11 in Lower Devonian Zosterophyllum-like plants from central Victoria, Australia, and their significance
Figure 11. cf. Zosterophyllum sp. B. NMV P256742.1: a, c, (line drawing) sporangium 3 (counterpart) in-folded, with stalk attachment widening out in basal region of sporangium. Poorly defined dehiscence zone (zone) and line; b, sporangium one (counterpart) with border visible; d, basal oval structure (part), with what appears to be a much smaller spike emanating from it (at arrow), and a poorly preserved axis beneath the oval structure.
Dataset for manuscript "Plants as inspiration for material‑based sensing and actuation in soft robots and machines"
<p>The dataset includes data for Figure 2 in the article "Plants as inspiration for material-based sensing and actuation in soft robots and machines<em>" MRS Bulletin</em> (2023). https://doi.org/10.1557/s43577-022-00470-8</p>
Take me for a ride: herbivores can facilitate plant re-invasions
<p>Herbivores shape plant invasions through impacts on demography and dispersal, yet only demographic mechanisms are well understood. Although herbivores negatively impact demography by definition, they can affect dispersal either negatively (e.g. seed consumption), or positively (e.g. caching). Exploring the nuances of how herbivores influence spatial spread will improve forecasting of plant movement on the landscape. Here, we aim to understand how herbivores impact how fast plant populations spread through varying impacts on plant demography and dispersal. We strive to determine if, and under what conditions, we see net positive effects of herbivores, in order to find scenarios where herbivores can help promote spread. We draw on classic invasion theory to develop a stage-structured integrodifference equation model that incorporates herbivore impacts on plant demography and dispersal. We simulate seven herbivore `syndromes' (combinations of demographic and/or dispersal effects) drawn from the literature to understand how increasing herbivore pressure alters plant spreading speed. We find that herbivores with solely negative effects on plant demography or dispersal always slow plant spreading speed, and that the speed slows monotonically as herbivore pressure increases. However, we also find that plant spreading speed can be hump-shaped with respect to herbivore pressure: plants spread faster in the presence of herbivores (for low herbivore pressure) and then slower (for high herbivore pressure). This result is robust, occurring across all syndromes where herbivores have a positive effect on plant dispersal, and is a sign that the positive effects of herbivores on dispersal can outweigh their negative effects on demography. For all syndromes we find that sufficiently high herbivore pressure results in population collapse. Thus, our findings show that herbivores can speed up or slow down plant spread. These insights allow for greater understanding of how to slow invasions, facilitate native species recolonization, and shape range shifts with global change. </p>
Dataset 3 - Mathematical Modeling of Growth for Climbing Plants
<p>This dateset collects some models of climbing plants in the framework of the Task 3.4 of the Growbot project. In particular, it focuses on models describing the climbing plants' secondary growth, emphasizing such a behavior as an optimal way to allocate biomass and maximize climbing plants's reach.</p> <p>The models are described in the following preprints:</p> <table> <tbody> <tr> <td> <ol> <li><em>A 2D Model to describe the mechano-sensory behaviour of self-supporting shoots of climbing plants against gravity </em>(2023), G. Vecchiato; T. Hattermann; M. Palladino; P. Heuret; N. P. Rowe; P. Marcati, <strong>submitted preprint</strong></li> <li><em>Searcher-Shoot: a Reinforcement Learning approach to understand climbing plant behaviour</em> (2023), L. Nasti; G. Vecchiato; T. Hattermann; P. Heuret; N. P. Rowe; M. Palladino; P. Marcati, <strong>preprint</strong></li> <li><em>An optimal control approach to the problem of the longest self-supporting structure</em> (2023), G. Vecchiato; M. Palladino; P. Marcati, <strong>submitted preprint</strong></li> <li><em>Modeling intertwining of growing shoots</em> (2023), O. Giannopoulou; G. Vecchiato; M. Palladino; M. Thielen; T. Speck; P. Marcati, <strong>preprint</strong></li> </ol> </td> </tr> </tbody> </table>
The experience base of the Institute of Soil Science, Agrotechnologies and Plant Protection in the town of Bozhurishte, region Sofia - on an area of 7.2 decares - First experiment
<p>A first field experiment was carried out in the Experimental field Bozhurishte on Leached Smolnitsa with corn for grain (Zea mays, L.) in 2 crop rotations in 2022 under TUdi project. The paper presents the results and short analysis. </p>
Multilevel analysis between Physcomitrium patens and Mortierella explores potential long-standing interaction among land plants and fungi
<p class="MsoNormal"><a name="_Hlk83717523"></a><span>The model moss species <em>Physcomitrium patens</em> has long been used for studying divergence and evolution of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes make it an ideal candidate for modeling plant terrestrialization 500 million years ago. Based on how plants and fungi interact today, it is predicted that early interactions may have aided in overcoming the barriers present for initial plant colonization on land. This may have manifested similar to present day, where fungi enabled easier uptake of nitrogen, phosphorous, micronutrients, and water retention in exchange for a reliable carbon source. However, identifiable fungal symbionts in <em>P. patens</em>, despite mutualistic interaction widespread among all present day embryophyte families, have remained elusive. To test modern representatives of early land fungal lineages, two Mortierella species (<em>Linnemannia elongata</em> and <em>Benniella eriona</em>), with strains lacking and containing endobacterial symbionts, were grown in coculture with <em>P. patens</em>. We illustrate the interaction between <em>P. patens </em>and Mortierella through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other <em>P. patens</em> transcriptomic studies. Our study provides insights into the earliest plant-fungal interactions may have looked like and ways <em>P. patens</em> and Mortierella communicate today.</span></p>
Data from: Bet hedging is not sufficient to explain germination patterns of a winter annual plant
<div> <div> <div> <p>Bet hedging consists of life history strategies that buffer against environmental variability by trading off immediate and long-term fitness. Delayed germination in annual plants is a classic example of bet hedging and is often invoked to explain low germination fractions. We examined whether bet hedging explains low and variable germination fractions among 20 populations of the winter annual plant <em>Clarkia</em> <em>xantiana</em> ssp. <em>xantiana</em> that experience substantial variation in reproductive success among years. Leveraging 15 years of demographic monitoring and 3 years of field germination experiments, we assessed the fitness consequences of seed banks and compared optimal germination fractions from a density-independent bet-hedging model to observed germination fractions. We did not find consistent evidence of bet hedging or the expected trade-off between arithmetic and geometric mean fitness, though delayed germination increased long-term fitness in 7 of 20 populations. Optimal germination fractions were 2 to 5 times higher than observed germination fractions, and among-population variation in germination fractions was not correlated with risks across the life cycle. Our comprehensive test suggests that bet hedging is insufficient to explain the observed germination patterns. Understanding variation in germination strategies will likely require integrating bet hedging with complementary forces shaping the evolution of delayed germination.</p> </div> </div> </div>
Beyond trait distances: Functional distinctiveness captures the outcome of plant competition
<p>Functional trait distances between coexisting organisms reflect not only complementarity in the way they use resources, but also differences in their competitive abilities. Accordingly, absolute and relative trait distances have been widely used to capture the effects of niche dissimilarity and competitive hierarchies, respectively, on the performance of plants in competition. However, multiple dimensions of the plant phenotype are involved in these plant-plant interactions (PPI), challenging the use of relative trait distances to predict their outcomes. Furthermore, estimating the effects of competitive hierarchy on the performance of a group of coexisting plants remains particularly difficult since relative trait distances relate to the effects of a focal plant on another. We argue that trait distinctiveness, an emerging facet of functional diversity that characterizes the eccentric position of a species (or genotype) in a phenotypic space, can reveal the unique role played by a given individual plant in a group of competing plants . We used the model crop species <em>Oryza sativa</em> spp. japonica to evaluate the ability of trait distances and trait distinctiveness to predict the outcome of intraspecific PPI on the performance of single genotype and genotype mixtures. We performed a screening experiment to characterize the phenotypic space of 49 rice genotypes based on 11 aboveground and root traits. We selected nine genotypes with contrasting positions in the phenotypic space and grew them in pots following a complete pairwise interaction design. Relative distances and distinctiveness based on traits associated with light competition were by far the best predictors of the performance of single genotypes - taller genotypes that acquired resource faster being the best competitors - while absolute trait distances had no effect. These results indicate that competitive hierarchy for light dominates PPI in this experiment. Consistently, trait distinctiveness in plant height and age at flowering had the strongest, positive effects on mixture performance, confirming that functional distinctiveness captures the effects of trait hierarchies and asymmetric PPI at this scale. Our findings shed new light on the role of trait diversity in regulating PPI and ecosystem processes and call for a greater consideration of functional distinctiveness in studies of coexistence mechanisms.</p>
Fig. 3 in Vascular plants of Poaceae (Ⅰ) new to Korea: Vulpia bromoides (L.) Gray, Agrostis capillaris L. and Eragrostis pectinacea (Michx.) Nees
Fig. 3. Photograph of Eragrostis pectinacea (Michx.) Nees. A. Habits. B. Inflorescence. C. Ligule. D. Spikelet. E. Maturity spikelet. F. Caryopsis.
Fig. 1 in Vascular plants of Poaceae (Ⅰ) new to Korea: Vulpia bromoides (L.) Gray, Agrostis capillaris L. and Eragrostis pectinacea (Michx.) Nees
Fig. 1. Photograph of Vulpia bromoides (L.) Gray. A. Habit. B. Inflorescence. C. Ligule. D. Spikelet. E. Glumes. F. Lemma and Palea.
Fig. 2 in Vascular plants of Poaceae (Ⅰ) new to Korea: Vulpia bromoides (L.) Gray, Agrostis capillaris L. and Eragrostis pectinacea (Michx.) Nees
Fig. 2. Photograph of Agrostis capillaris L. A. Habits. B. Inflorescence. C. Ligule. D. Rhizome. E. Spikelet. F. Lemma and Palea.
Root mutualistic fungi tailor the induction of direct and indirect antiherbivore defense strategies to enhance plant resistance
<p>GCMS raw data in netCDF format for the manuscript "Root mutualistic fungi tailor the induction of direct and indirect antiherbivore defense strategies to enhance plant resistance" </p> <p>Mutualistic microbes can trigger plant defenses against herbivores. Little is known about how microbes orchestrate multiple induced defense strategies that may trade-off against each other. We investigated how two root mutualistic fungi trigger simultaneously direct and indirect antiherbivore-defenses, and the regulatory mechanisms involved</p>
GCNT-Plume: Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning
<p>This repository contains the relevant code and data for the paper <strong>Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning</strong><strong> </strong>(Wei et al, 2023, <em>Remote Sensing of Environment</em>).</p> <p>Specifically, the <strong>U-Net.zip</strong> file includes the associated codes for segmenting surface thermal plumes from nuclear power plants along the global coasts and the Great Lakes by using the U-Net model integrated with prior knowledge. The <strong>GCNT-Plume.zip</strong> file includes the occurrence footprints of core area plumes (the <strong>occurrence_all </strong>folder), raw water temperature increment (WST) images (the <strong>delta </strong>folder), mixed area plumes and annotations (the <strong>extractWithLocation </strong>folder), model-predicted core area plumes (the <strong>prediction*_*</strong> folders), the mixed/core area plumes and background areas in shapefile format (the <strong>sampleAnnotation* </strong>folders), and location information (the <strong>location.xlsx </strong>table). Please refer to the <strong>README.md </strong>file in the <strong>U-Net.zip</strong> file for more detailed information.</p>
Demontration Activities Data Set on the performances of the Photocatalytic pilot plant In Demosite 4 (Galeb, Omis, Croatia) Deliverable 5.6
<p>Raw data (TOC and emerging contaminants time profiles) concernig the validation experiment of the photocatalytic pilot plant for tertiary water treatment deployed in ProjectO demosite 4. The data are presented in D5.6 and pertain three months of demonstration activities</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.