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Dataset results
22 results for “area of applicability”
Supplementary Dataset for "Extracting near-field seismograms from ocean-bottom pressure gauge inside the focal area: application to the 2011 Mw 9.1 Tohoku-Oki earthquake"
<p>Datasets S1 contains the results obtained by the analysis in this study, such as the spatial and temporal configuration of the basis functions. Dataset S2 contains the ocean-bottom pressure gauge data used in this study.</p> <p>The manuscript is available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091664</p> <p> </p> <p> </p> <div> </div>
Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
<p>We used DSurfTomo (<a href="https://github.com/HongjianFang/DSurfTomo">HongjianFang/DSurfTomo: Direct inversion of surface dispersion data based on ray tracing (github.com)</a>) for the tomography.</p> <p>ABC2_2023.dat is the travel time of C1 and C2 cross-correlation functions, used in our tomography.</p> <p>ManualDSurfTomoV1.3.pdf is the manual of DSurfTomo, including the data format description for ABC2_2023.dat.</p> <p>yunqiVs3D.txt is the interpolated 3D Vs model, including longitude, latitude, depth (meter), Vs (m/s).</p> <p>Previous version error: I forgot to write the Vs value.</p>
Flexible Graphite−Poly(Lactic Acid) Composite Films as Large-Area Conductive Electrodes for Energy Applications
<p>Raw data of energy storage part -author Kalyan Ghosh </p>
Improving the application of Important Plant Areas to conserve threatened habitats: a case study of Uganda
<p><strong>This data set relates to the publication: Richards, S. L., Kalema, J., Ojelel, S., Williams, J., & Darbyshire, I. (2024). Improving the application of Important Plant Areas to conserve threatened habitats: A case study of Uganda. Conservation Science and Practice, e13246. https://doi.org/10.1111/csp2.13246<br></strong></p> <p><strong>Abstract:</strong></p> <p>Important Plant Areas (IPAs) are a successful method of identifying priority areas for plant conservation. Assessment of IPAs, however, often relies on criteria related to species, while incorporation of habitats has been less consistent. Using Uganda as a case study, we test the application of the threatened habitat criterion – criterion C. We identified nationally threatened habitats using Red List of Ecosystems criteria and assess, for the first time, how differing application of thresholds under IPA criterion C can influence IPA network outcomes. Eleven threatened habitats were identified, with declines switching from predominantly forest to savanna after the mid-20<sup>th</sup> century. Significantly, we found current IPA guidance on use of criterion C needlessly limits the number of sites that qualify as IPAs. The “five best sites” IPA threshold is reserved for countries where quantitative data is unavailable, however, the application of the relevant numerical thresholds (site contains ≥10% of national resource or site is among the best quality examples required to collectively prioritisie up to 20% of the national resource) to quantitative data largely generated fewer than five IPAs, comparably limiting conservation opportunities identified. We recommend, therefore, that the “five best” threshold is available for application on both qualitative and quantitative data. This will bolster the value of IPAs in conserving and restoring threatened and ecologically important habitats under the Kunming-Montreal Global Biodiversity Framework.</p> <p><strong>Dataset:</strong></p> <p>Within this dataset is a shapefile of the estimated extent of threatened habitats in Uganda. Each polygon represents a single "site" for each threatened habitat, with methodology for site identification given in the manuscript. Feature area and percentage national resource are given for each site, enabling users to identify those that trigger the different IPA criterion C thresholds.</p> <p><strong>In this study, we have preliminarily identified the threatened habitats and IPAs for Uganda. However, it is important to seek the expertise and views of stakeholders, consider other IPA criteria met and any complementarity between sites when identifying IPAs. In addition, ground-truthing or more localised data could validate the threat status of a vegetation type as well as identifying which sites are best to conserve these habitats. </strong></p>
Key Biodiversity Areas (KBAs) R package, KBAscope, application to Greece
<p>Key Biodiversity Areas (KBAs) represent the largest global network of sites critical to the persistence of biodiversity, which have been identified against standardised quantitative criteria. Sites that hold very high biodiversity value or potential are given specific attention on site-based conservation targets of the Kunming-Montreal Global Biodiversity Framework (GBF), and KBAs are already used in indicators for the GBF and the Sustainable Development Goals. However, most of the species that trigger KBA status are birds and to maximise benefits for biodiversity under the actions taken to fulfil the GBF, countries need to update their KBAs to represent important sites across multiple taxa. Here we introduce KBAscope, an R package to identify potential KBAs using multiple taxonomic groups. KBAscope provides flexible, user-friendly functions to edit species data (population, range maps, area of occupancy, area of habitat and localities); apply KBA criteria; and generate outputs to support the delineation and validation of KBAs. The details of the analysis - such as the spatial units tested or the KBA criteria applied - can be decided according to the scope of the analysis. We demonstrate the functionality of KBAscope by using it to identify potential KBAs in Greece based on multiple terrestrial taxonomic groups and four sizes of grid cells (4 km<sup>2</sup>, 25 km<sup>2</sup>, 100 km<sup>2</sup>, 225 km<sup>2</sup>).</p>
Key Biodiversity Areas (KBAs) R package, KBAscope, application to Greece
Open the record for dataset details and reuse information.
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
The Application Value of Prostate Specific Membrane Antigen PET/CT in Biopsy Free of PSA Grey Area Prostate Cancer
ClinicalTrials.gov study NCT05958004. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Smartphone APPlication for the Transmission of ECGs in the Management of Patients Presenting With Suspected Heart Attacks in the Hamilton, Niagara, Haldimand, and Brant Area
ClinicalTrials.gov study NCT05290389. IPD Sharing: NO. Countries: 1. Publications: 1.
Use of Smartphone Health Applications Among Patients Aged 18 to 69 Years Recruited in Primary Care in Grenoble Area
ClinicalTrials.gov study NCT03351491. IPD Sharing: Not stated. Countries: 1. Publications: 1.
LeafByte: A mobile application that measures leaf area and herbivory quickly and accurately
<ol> <li>In both basic and applied studies, quantification of herbivory on foliage is a key metric in characterizing plant-herbivore interactions, which underpin many ecological, evolutionary, and agricultural processes. Current methods of quantifying herbivory are slow or inaccurate. We present LeafByte, a free iOS application for measuring leaf area and herbivory. LeafByte can save data automatically, read and record barcodes, handle both light and dark colored plant tissue, and be used non-destructively. </li> <li>We evaluate its accuracy and efficiency relative to existing herbivory assessment tools.</li> <li>LeafByte has the same accuracy as ImageJ, the field standard, but is 50% faster. Other tools, such as BioLeaf and grid quantification, are quick and accurate, but limited in the information they can provide. Visual estimation is quickest, but it only provides a coarse measure of leaf damage and tends to overestimate herbivory.</li> <li>LeafByte is a quick and accurate means of measuring leaf area and herbivory, making it a useful tool for research in fields such as ecology, entomology, agronomy, and plant science.</li> </ol> <p> </p>
Social (-ecological) network analysis in environmental governance - central publications, important concepts and areas of application data collected in January 2019
<p>Social and Social-Ecological Network Analysis (S(E)NA) have recently emerged as new methods in the environmental governance (EG) literature. By investigating networks of connections between actors, S(E)NA advances the understanding of who is involved in EG and how. We provide an overview of the EG literature applying S(E)NA and respective metrics of 1) a citation network analysis, 2) topic detection methods and 3) a similarity network analysis. 20 publications occupy central positions as sources, storers or bridges of knowledge in the citation network. While we see S(E)NA applied in diverse resource contexts, these are mainly discussed on the local spatial level, with a focus on “policy” or “collaboration”. We discover that “power structures” and “the production of knowledge” are themes influencing the whole field.</p>
Supplementary material 3 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Setting the context at local level
Supplementary material 1 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Tutorial
Supplementary material 4 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Greenness and changes in vegetation cover in not densely built areas
Supplementary material 2 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Setting the context at European Level
LeafByte: A mobile application that measures leaf area and herbivory quickly and accurately
Open the record for dataset details and reuse information.
Safety and Tolerance of Local Heat Application in the Paralyzed Area of Patients With Complete Paraplegia
ClinicalTrials.gov study NCT03001531. IPD Sharing: NO. Countries: 1. Publications: 0.
Fluoride Application on Preventing and Arresting Root Caries in Fluoridated and Non-fluoridated Areas
ClinicalTrials.gov study NCT02742142. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Moisturizing Effect of Study Product AV0018B and the Persistence of This Effect After Repeated Applications, Compared to an Untreated Control Area.
ClinicalTrials.gov study NCT07271628. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.