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.
4,486
datasets available to search
ShareScore release 0.9.0
Dataset results
4,486 results for “emergence”
Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift
<p>Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp, and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management.</p>
Data associated with: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water
<p>Elevated atmospheric CO<sub>2</sub> (eCO<sub>2</sub>) influences the carbon assimilation rate and stomatal conductance of plants and thereby can affect the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO<sub>2</sub> in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO<sub>2</sub> effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO<sub>2</sub> effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO<sub>2</sub> concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO<sub>2</sub> effects. The signal in gross primary production (GPP) emerges at relatively low CO<sub>2</sub> increase (Δ[CO<span>2</span>] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO<sub>2</sub> effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO<sub>2</sub> increase (Δ[CO<sub>2</sub>] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO<sub>2</sub> is detectable earlier for variables of the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO<sub>2</sub> effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.</p>
Emergent Topographies of Vienna
<p>Teaser for the production<br> "Emergent Topographies of Vienna"<br> launched at Ars Electronica Festival 2021 in Vienna "Garden of knowledge / Garden of Vienna"</p> <p> </p> <p>This production was funded by the City of Vienna / Digital Humanism Call, project Algorithm Inventarium<br> and FWF, project PROVIDEDH.</p>
Characterization of data sources for emerging risks identification - Data collection for the identification of emerging risks related to food and feed
<p>The data set presents the results of the quality assessment of data sources by the working group on data collection for the identification of emerging risks related to food and feed. For this assessment, the WG defined text descriptors and quality parameters (i.e. link with indicators, data type, geographic and period coverage, language, edition, timeliness, accessibility, clarity and comparability). These data sources were linked to eleven priority indicators (i.e. the ESCO indicators) and qualitatively assessed and profiled.</p>
Figure 4: Complexity of geographical space with respect of emergent organizations-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>The applications we focus on in the models that we will propose in the<br> following, concerns specifically the multi-center (or multi-organizational) phenomona<br> inside urban development. As an artificial ecosystem, the city development<br> has to deal with many challenges, specifically for sustainable development,<br> mixing economical, social and environmental aspects. The decentralized<br> methodology proposed in the following allows to deal with multi-criteria problems,<br> leading to propose a decision making assistance, based on simulation<br> analysis.</p>
Key dataset used in the paper of "Emergent constraints reveal lower estimates of global river flow"
<p>This dataset includes key data used in the emergent constraint approach for refined partitioning of global water cycle components. </p>
Dataset of chemicals of emerging concern detected in the marine environment in central and northern Patagonia in Chile
<p>This repository encompasses the environmental concentrations of chemicals of emerging concern (CECs), such as pesticides, pharmaceuticals, and industrial chemicals, co-occurring in surface water, porewater, and sediment throughout central and northern Patagonia, Chile.</p>
Quasar dataset: Chronicling the Reionization History at 6 < z < 7 with Emergent Quasar Damping Wings
<p>This is the quasar dataset used in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240110328D/abstract">Ďurovčíková et al. (2024)</a>. Please cite the corresponding paper (https://doi.org/10.3847/1538-4357/ad4888) and the DOI of this dataset (10.5281/zenodo.11402934) if you use this data.</p> <p>This dataset was primarily collected using the FIRE spectrograph on the Magellan telescope, J1030+0524, J159-02, and J1120+0641 are combined Magellan/FIRE and VLT/X-shooter spectra, and J1148+5251 is a Keck/MOSFIRE and Keck/ESI spectrum. We publish the reduced spectral files along with the smoothed spectral fits and the mean continuum predictions computed in this paper. Refer to Table 3 in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240110328D/abstract">Ďurovčíková et al. (2024)</a> for a description of the FITS file extensions.</p>
Figure 5 in Effect of environmental factors on the germination and emergence of drunken horse grass (Achnotherum inebrions)
Figure 5. Effect of osmotic potential on the germination of Achnotherum inebrions seeds at 25 C. Vertical bars represent the standard error of the mean, and a logistic sigmoidal regression model is fit to the data.
Figure 6 in Effect of environmental factors on the germination and emergence of drunken horse grass (Achnotherum inebrions)
Figure 6. Germination of Achnotherum inebrions seeds at low osmotic potential. The vertical bars represent the standard error of the mean. Bars with the same letters indicate that there are no significant differences in the mean values by Fisher's protected LSD test (P ≤ 0.05).
Figure 7 in Effect of environmental factors on the germination and emergence of drunken horse grass (Achnotherum inebrions)
Figure 7. Effect of burial depth on the emergence of A. inebrions seeds at 25 C. Vertical bars represent the standard error of the mean,and a logistic sigmoidal regression model is fit to the data.
Figure 4 in Effect of environmental factors on the germination and emergence of drunken horse grass (Achnotherum inebrions)
Figure 4. Effect of buffered pH solutions on the germination of Achnotherum inebrions seeds at 25 C. The vertical bars represent the standard error of the mean. Bars with the same letters indicate that there are no significant differences in the mean values by Fisher's protected LSD test (P ≤ 0.05).
Figure 3 in Effect of environmental factors on the germination and emergence of drunken horse grass (Achnotherum inebrions)
Figure 3. Effects of different photoperiods on the germination of Achnotherum inebrions seeds under 25 C culture conditions. Bars with the same letters indicate that there are no significant differences in the mean values by Fisher's protected LSD test (P ≤ 0.05).
Figure 2 in Effect of environmental factors on the germination and emergence of drunken horse grass (Achnotherum inebrions)
Figure 2. Effect of rewarming on the germination of Achnotherum inebrions seeds at 30/20 C. Rewarming refers to the transfer of ungerminated seeds kept under a constant temperature of 10, 35, or 40 C to a growth chamber set at the optimal temperature, 25 C (CK). The vertical bars represent the standard error of the mean. Bars with
Figure 1 in Effect of environmental factors on the germination and emergence of drunken horse grass (Achnotherum inebrions)
Figure 1. Effect of rewarming on the germination of Achnotherum inebrions seeds at 25 C. Rewarming refers to the transfer of ungerminated seeds kept under a constant temperature of 10, 35, or 40 C to a growth chamber set at the optimal temperature, 25 C (CK). The vertical bars represent the standard error of the mean. Bars with the same
Figure 1 in Sensitivity to salinity at the emergence and seedling stages of barnyardgrass (EchinochloQ crus-gQlli), weedy rice (OryzQ sQtivQ), and rice with different tolerances to ALS-inhibiting herbicides
Figure 1. Dose–response emergence curve with the average data points of the different Echinochloa crus-galli populations against the salt concentration. Curve parameter estimates (Equation 1): s1 (b = 1.94, d = 55.85, e = 287.76), s2 (b = 1.52, d = 89.91, e = 222.71), s3 (b = 1.13, d = 89.91, e = 282.58), r1(b = 3.96, d = 67.67, e = 196.55), r2 (b = 6.85, d = 94.64, e = 123.58). The salt concentration required to reduce emergence by 50% (EC50) is shown below the graph. Only the significant pairwise comparisons between EC50 (SI index) are shown (Equation 2).
Figure 2 in Sensitivity to salinity at the emergence and seedling stages of barnyardgrass (EchinochloQ crus-gQlli), weedy rice (OryzQ sQtivQ), and rice with different tolerances to ALS-inhibiting herbicides
Figure 2. Dose–response emergence curve with the average data points of the different Oryza sativa (weedy rice) populations and rice varieties against the salt concentration. Curve parameter estimates (Equation 1): wr1 (b = 9.40, d = 87.14, e = 195.80), wr2 (b = 9.40, d = 87.14, e = 160.19), wr3 (b = 8.49, d = 89.73, e = 173.01), Baldo (b = 4.81, d = 87.12, e = 146.49), CL80 (b = 3.08, d = 60.89, e = 140.04). The salt concentration required to reduce the emergence by 50% (EC50) and the significant pairwise comparisons between EC50 (SI index) are shown below the graph (Equation 2).
Fig. 3 in Molecular characterization of the re-emerging West Nile virus in avian species and equids in Israel, 2018, and pathological description of the disease
Fig. 3 Brain histopathology of WNV-infected horses. Perivascular cuffs composed of lymphocytes and plasma cells in the brain of two horses, characteristic of viral encephalitis (marked by arrows). a Horse no. Eq111 (324085). b Horse no. Eq117 (325903). 100× magnification
Fig. 2 in Molecular characterization of the re-emerging West Nile virus in avian species and equids in Israel, 2018, and pathological description of the disease
Fig. 2 Brain histopathology of WNV-infected long-eared owl (Asio otus) AV156. a A glial nodule in the brain stem (marked by an arrow). 100× magnification. b A glial nodule in the brain stem with few adjacent necrotic neurons, 400× magnification
Fig. 1 in Detection of Escherichia fergusonii - an emerging pathogen harbouring drug resistant genes from seafood samples of Tamil Nadu, India
Fig. 1 — Gene specific PCR amplification of Escherichia fergusonii (lane 1 – 100 bp DNA ladder, lane 2 – positive control (clinical E. fergusonii), lane 3 – negative control, lane 4 – E011, lane 5 – E060)
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.