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.
499
datasets available to search
ShareScore release 0.9.0
Dataset results
499 results for “delineation”
Figure 8 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 8. Examined and verified occurrence records of (A) P. annabelli and (B) P. morti from the Australian Museum dry collection. Main maps showing records in NSW, Queensland and Victoria. Insets showing all of Australia. Source of base map: Hans Braxmeier, maps-for-free.com.
Figure 6 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 6. Boxplots showing comparisons between Paralaoma annabelli and Paralaoma morti in shape metrics: (A) SW/SH, (B) SW/ NOW, (C) SH/NOW, (D) BWH/SH, (E) AW/SW, (F) AH/SH, (G) AH/BWH, (H) AH/AW, and (I) UW/SW. Asterisk above pairwise comparisons of the two species indicate significant difference (p ≤ 0.003) with a Wilcoxon Rank Sum Test with Bonferroni correction.
Figure 5 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 5. Boxplots showing comparisons between Paralaoma annabelli and Paralaoma morti in size and shape metrics: (A) Umbilicus width, (B) Shell width, (C) Shell height, (D) Protoconch width, (E) Body whorl height, (F) Aperture height, (G) Aperture width, (H) Number of whorls, and (I) UW/NOW. Asterisk above pairwise comparisons of the two species indicate significant difference (p ≤ 0.003) with a Wilcoxon Rank Sum Test with Bonferroni correction.
Figure 4 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 4. Best Maximum Likelihood tree based on analysis of ELAV-I8 sequences. Numbers on branches indicate nodal support based on 10,000 ultra-fast bootstrap replicates. Scale bar indicating 2% of modelled sequence divergence. Note that the species for the immature specimens were misidentified (L638 = Iotula microcosmos misidentified as P. morti; L645 = Pseudiotula eurysiana misidentified as P. annabelli).
Figure 3 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 3. Best Maximum Likelihood tree based on analysis of the concatenated dataset of 16S and COI genes. Numbers on branches indicate nodal support by 10,000 ultra-fast bootstrap replicates. Scale bar indicating 10% of modelled sequence divergence. Note that the species for the immature specimens were misidentified (L638 = Iotula microcosmos misidentified as P. morti; L645 = Pseudiotula eurysiana misidentified as P. annabelli).
Figure 2 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 2. Shell dimensions measured. Abbreviations: SW, shell width; AW, aperture width; SH, shell height; BWH, body whorl height; AH, aperture height; UW, umbilicus width; and PW, protoconch width.
Figure 1 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 1. Shells of (A–B) Paralaoma morti and (C–D) Paralaoma annabelli: (A) lectotype, Darling Point (AM C.031181); (B) Woollahra, 1 km from lectotype locality of P. morti (AM C.347565); (C) holotype, Wombeyan Caves (AM C.353384); (D) Jenolan Caves (AM C.466715). Main scale bar = 1 mm. Inset scale bar = 100 µm.
Figure 7 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 7. Scatterplots of shell shape metrics for Paralaoma annabelli and Paralaoma morti: (A) AH/AW, (B) UW/SW, (C) SW/SH, (D) SW/NOW, (E) UW/NOW, (F) BWH/SH, (G) AW/SW, (H) AH/SH, (I) AH/BWH, and (J) SH/NOW.
Genomics‐informed delineation of conservation units in a desert amphibian
Open the record for dataset details and reuse information.
Wetland delineation and characterization layers 2014-2017, Rwanda
<p>Wetlands are highly productive ecosystems and provide a range of goods and services on local to global scales and have become key sites of agricultural development in sub-Saharan Africa. This is the case for East African countries such as Rwanda, where agricultural expansion into wetlands and agricultural intensification are crucial elements of the government's strategy to increase food production and decrease dependence from global food markets. However, spatially explicit information of the location and status of wetlands for informed decision making is lacking or not up to date. In the related research article, we therefore develop a framework comprising the following spatial layers: Delineation, Surface Water Occurrence (SWO), Land Use/Land Cover (LULC) classification and Wetland Use Intensity (WUI). These layers are based on satellite imagery from the European Copernicus Programme and cover the whole of Rwanda. The Delineation is derived from a static Potential Wetlands layer and Sentinel-2 imagery using object-based image analysis (OBIA). The SWO is a per-pixel count of the times a location was detected as flooded using Sentinel-1 imagery. For the LULC classification, an object-based decision tree classifier is applied to Sentinel-2 imagery in Combination with the Delineation layer and the SWO. The WUI layer is based on the Absolute Mean Spectral Dynamics indicator depicting pixel-wise changes in reflectance values in Sentinel-2 bands relevant for wetland vegetation and hydrological dynamics. All layers refer to the year 2017, except for the SWO layer, which covers surface water dynamics from 2014 to 2017. The layers can be used individually or in combination and thus adapted to the different information needs for sustainable wetland management.<br> This dataset contains the Potential Wetlands map based on topographic indices, the Wetland Delineation derived from it, the Surface Water Occurrence layer, the Land Use/Land Cover map, and the Wetland Use Intensity layer. For appropriate map display we provide QGIS layerstyle files associated with these layers.</p>
Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms
<p><strong>Overview </strong></p> <p>This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session.</p> <p> </p> <p><strong>Database</strong></p> <p>The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: <a href="https://pubmed.ncbi.nlm.nih.gov/30094756/">https://pubmed.ncbi.nlm.nih.gov/30094756/</a></p> <p>In addition, PPG signals corrupted by the noise generator at different levels are also included in the database.</p> <p> </p> <p><strong>Realistic noise generator</strong></p> <p>Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper.</p> <p> </p> <p><strong>Data Files</strong></p> <p>The reference PPG signal can be found in <em>Datasets\GoodSignals\PPG</em> and the simultaneously acquired ECG in <em>Datasets\GoodSignals\ECG</em>. The folder <em>Datasets\NoisySignals</em> contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation).</p> <p>Name convention: <em>ppg_stdx_y</em> denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x.</p> <p><em>Datasets\BPMs</em> contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s.</p> <p><strong>Code</strong></p> <p>The folder <em>Code </em>contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator.</p> <p><strong>When referencing this material, please cite:</strong></p> <p>Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 2725. <a href="https://doi.org/10.3390/s21082725">https://doi.org/10.3390/s21082725</a></p>
Data from: Pan-evolutionary and regulatory genome architecture delineated by integrated macro- and microsynteny approach
<p>Based on the published algorithms or tools developed by our and other groups, we introduce a detailed protocol for the most comprehensive and up-to-date genome synteny pipeline (called PanSyn) and provides step-by-step instructions as well as application examples for demonstrating how to use it. PanSyn pipeline includes three major modules (microsynteny analysis, macrosynteny analysis, and integrated micro & macro analysis). PanSyn not only fills a gap of lacking a user-friendly, highly-customized tool for genome macrosynteny analysis but also allows for integrated pan-evolutionary and regulatory analysis of genome microsyntenty and macrosynteny which are not yet available in any public synteny software or tools. PanSyn has been tested under Linux system. PanSyn has multiple subroutines. Users only need to simply modify the configuration file and corresponding command parameters to execute them. Outputs include vector diagrams that are suitable for custom modification. <br> </p>
Data from: Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture
<p>Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.</p>
An Integrated Machine Learning Approach Delineates an Entropic Expansion Mechanism for the Binding of a Small Molecule to α-Synuclein
<p>The <a href="https://zenodo.org/uploads/14177307" target="_blank" rel="noopener noreferrer">TRAJECTORY.zip</a> contains two trajectories. One for the apo simulation (traj_mw_c4.xtc) and the other for the water simulation in .gro format (traj.gro). The run1.tpr is the binary file for running the apo simulation in gromacs. For water simulation, the raw DOSPT files are also included to calculate the entropy.</p> <p>The <a href="https://zenodo.org/uploads/14177307" target="_blank" rel="noopener noreferrer">figure_raw_data.zip</a> file contains the raw data that was used for plotting the figures in the main text and in the supplementary material.</p> <p>For any further inquiries or requests for additional data, please contact <a rel="noopener">jmondal@tifrh.res.in</a>.</p>
GapA, RecA and DnaX sequences used for species delineation and phylogenetic analysis
<p>Survey of soft rot Pectobacteriaceae along a river stream highlights various ecological behaviour among species: GapA, RecA and DnaX sequences used for species delineation and phylogenetic analysis (fasta file format).</p>
Delineating the Controlling Factors of Hydraulic Fracturing-Induced Seismicity in the Northern Montney Play, Northeastern British Columbia, Canada, with Machine Learning
<p>Earthquake catalog for northern Montney Play from 2014-2021.</p>
Data from: When less is more and more is less: the impact of sampling effort on species delineation
Taxonomy is the very first step of most biodiversity studies, but how confident can we be in the taxonomic-systematic exercise? One may hypothesise that the more material, the better the taxonomic delineation, because the more accurate the description of morphological variability. As rarefaction curves assess the degree of knowledge on taxonomic diversity through sampling effort, we aim to test the impact of sampling effort on species delineation by subsampling a given assemblage. To do so, we use an abundant and morphologically diverse conodont fossil record. Within the assemblage, we first recognize four well established morphospecies but about 80% of the specimens share diagnostic characters of these morphospecies. We quantify these diagnostic characters on the sample using geometric morphometrics, and assess the number of morphometric groups, i.e. morphospecies, using ordination and cluster analyses. Then we gradually subsample the assemblage in two ways (randomly and by mimicking taxonomist work) and redo the 'ordination + clustering' protocol to appraise the evolution of the number of clusters related to sampling effort. We observe the number of delineated morphospecies decreasing when increasing the number of specimens, whatever the subsampling method, resulting mostly in less morphospecies than expected. Such rather counter-intuitive influence of sampling effort on species delineation highlights the complexity of taxonomical work. This indicates that new morphotaxa should not be erected based on small samples, and encourages researchers to largely illustrate, measure, and quantitatively compare their material to better constrain the morphological variability of a clade, and so to better characterize and delineate morphospecies. --
Determination of mean effective shore level to delineate periods of emersion and immersion for intertidal rocky shores
<p>This dataset contains data described in the paper entitled "Environmental filtering causes increasingly negative effects towards the range limit of an invasive mussel". Data include: the R script to estimate temperature thresholds and calculate effective shore levels (<strong>esl_function.R</strong>); data files associated with this R script (<strong>material.RData</strong>); mean effective shore levels determined for the five South African rocky-shore sites where submersible temperature data loggers were installed (<strong>esl_overall_results.csv</strong>); measurements of mussel shell lengths, level of endolithic infestation, and counts of epibionts on the basibiont mussel <em>Mytilus galloprovincialis</em> (<strong>mussel_data.csv</strong>); counts of barnacles (<em>Chthamalus dentatus</em>) and mussels (<em>Mytilus galloprovincialis</em> and <em>Perna perna</em>) on emersed rocks using 50 × 50 cm quadrats and counts of mussels (<em>Mytilus galloprovincialis</em> and <em>Perna perna</em>) in mono-layer mixed-species mussel patches using 25 × 25 cm quadrats (<strong>quadrat_data.csv</strong>); hourly temperatures recorded by submersible temperature data loggers (<em>EnvLogger</em> Version 2.4; precision of ≤ 0.1 ºC and accuracy of ≤ 0.2 ºC) from September 2019 to September 2020 at the five South African rocky-shore sites (<strong>temperature_data.csv</strong>); and 2019 and 2020 hourly tidal predictions (courtesy of the South African Navy Hydrographic Office) for the closest maritime port to the sites where the temperature data loggers were installed (<strong>tide_data.csv</strong>).</p>
MMFlood: A Multimodal Dataset for Flood Delineation from Satellite Imagery
<p>MMFlood is remote sensing dataset derived from Sentinel-1 (VV-VH), MapZen (DEM) and OpenStreetMap (Hydrography). It provides a complete and well-rounded set of data specifically designed for flood events, focusing on three main features: worldwide distribution, manually validated annotations and multiple modalities.</p> <ul> <li>1748 pairs of SAR images and pixel-level annotations exclusively based around flooded areas and taking into account 95 flood events in 42 different countries around the world, in a time span ranging from 2014 to 2021.</li> <li>Image size <2000x2000, image resolution of 20m .</li> <li>Manually verified flood events across the world with raw SAR acquisitions derived from Sentinel-1.</li> <li>Includion of Digital Elevation Model (DEM) images.</li> </ul>
Distinct mechanisms of mismatch repair deficiency delineate two modes of response to PD-1 immunotherapy in endometrial carcinoma
<p>Responses to immune checkpoint blockade (ICB) are variable even among mismatch repair deficient (MMRd) cancers. We completed a phase 2 clinical trial of the PD-1 inhibitor pembrolizumab in 24 patients with MMRd endometrial cancer (NCT02899793). Patients with mutational MMRd tumors (6 patients) had higher response rates and longer survival than those with epigenetic MMRd tumors (18 patients). Mutation burden was higher in tumors with mutational MMRd compared to epigenetic MMRd; however, within each category of MMRd, mutation burden was not associated with ICB response. Notably, JAK1 mutations did not confer resistance to pembrolizumab. Longitudinal single-cell RNA-seq of circulating immune cells revealed contrasting modes of anti-tumor immunity against mutational and epigenetic MMRd tumors. Whereas effector CD8+ T cell responses correlated with mutational MMRd, highly active CD16+ NK cells were associated with epigenetic MMRd tumors responsive to ICB. These data highlight factors beyond neoantigen burden that influence ICB response.</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.