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722 results for “use case”
FIG. 3 in First Systematic Study using the Variability of the Residual Colour Patterns: The Case of the Paleogene Seraphsidae (Mollusca, Gastropoda, Stromboidea)
FIG. 3. — Descriptive terminology of the residual colour patterns of the Seraphsidae Jung, 1974: A, B, Paraseraphs placitus Jung, 1974, MNHN A28963 (Ballot coll.), Grignon, Yvelines, France, Lutetian; A, apical part; B, detailed view of the dots; C, D, Seraphs volutatus (Solander in Brander, 1766), MNHN A28873, Grignon, Lutetian; C, apical part; D, detailed view of the dots; E, S. chilophorus (Cossmann, 1889), MNHN A28922 (leg. Pacaud), Fercourt, Oise, France, Lutetian (apical part). Abbreviations: coad, coalescence of dots; d, dot; ds, darker spot; ls, lighter spot; p, patch. All pictures taken under UV light. Scale bars: A, C, E, 5 mm; B, D, 2 mm. Photographs by C. Lemzaouda (MNHN).
Fig. 2 in Assessing diet composition of seahorses in the wild using a non destructive method: Hippocampus reidi (Teleostei: Syngnathidae) as a study-case
Fig. 2. Feeding strategy diagram. Prey-specific abundance plotted against frequency of occurrence of prey items in the diet of the seahorse Hippocampus reidi (n = 280). Prey items: 1. Nematoda, 2. Copepoda, (Harpacticoida), 3. Caridae, 4. Copepoda (nauplii), 5. Copepoda (Calanoida), 6. Copepoda (Cyclopoida), 7. Caridae (chelipods), 8. Teleostei (Gobiidae), 9. Insecta (Hymenoptera), 10. Amphipoda (Gammaridae), 11. Teleostei (scales), 12. Polichaeta (larvae), 13. Amphipoda (Caprellidae), 14. Ostracoda, 15. Eggs (possibly of mollusks or crustaceans), 16. Polichaeta (Nereididae), 17. Brachyura (nauplii), 18. Insecta (Chironomidae), 19. Crustacea (larvae), 20. Gastropoda (larvae), 21. Bivalvia (larvae), 22. Caridae (zoea), 23. Isopoda, 24. Oligochaeta, 25. Foraminifera.
Fig. 1 in Assessing diet composition of seahorses in the wild using a non destructive method: Hippocampus reidi (Teleostei: Syngnathidae) as a study-case
Fig. 1. Mean values of induction (square) and recovery (lozenge) times of Hippocampus reidi (n = 242) in seconds (box = standard error; whisker = standard deviation). Reproduc- tive state: IM = immature, OF = ovipositor region flat, OB = ovipositor region bulging, B = brooding, NB = non-brooding. Sex: U = undetermined, F = female, M = male.
Fig. 3 in Scientific Note Species records, mistaken identifications, and their further use: the case of the diskfish Echeneis naucrates on a spinner dolphin
Fig. 3 Sharksucker (Echeneis naucrates) resting on a reef, showing relative size of sucking disk, body shape, proportions, and color pattern. Bars mark sucking disk and standard lengths. Photo taken at Fernando de Noronha, courtesy D. Brisolla.
Fig. 1 in Scientific Note Species records, mistaken identifications, and their further use: the case of the diskfish Echeneis naucrates on a spinner dolphin
Fig. 1 Detail of the photograph upon which the single and mistaken record of the sharksucker (Echeneis naucrates) on a spinner dolphin (Stenella longirostris) is based on (a); note bicolor pattern – dark and light halves of the fish's ventral side. Photo taken in the Baía dos Golfinhos at Fernando de Noronha, courtesy L. Lodi. Whalesuckers (Remora australis) attached to a spinner dolphin (b); note similarity between shape, proportions, and pattern of the larger foremost remora on both dolphins. Photo taken in the Baía dos Golfinhos at Fernando de Noronha, courtesy J. M. Silva Jr. Bars mark sucking disk and standard lengths; asterisks mark anterior edge of pectoral fin (originally marked on magnified digital photographs). For both pictures the original colors were discarded, since black and white images enhance the diagnostic features in print and allow a better comparison.
Figure 6 in Dental microwear in the orthodentine of the Xenarthra (Mammalia) and its use in reconstructing the palaeodiet of extinct taxa: the case study of Nothrotheriops shastensis (Xenarthra, Tardigrada, Nothrotheriidae)
Figure 6. Hierarchical cluster dendrogram (same method as Fig. 3) including Nothrotheriops shastensis among all extant xenarthran taxa. Note that N. shastensis clusters with extant folivores (Bradypus).
Figure 3 in Dental microwear in the orthodentine of the Xenarthra (Mammalia) and its use in reconstructing the palaeodiet of extinct taxa: the case study of Nothrotheriops shastensis (Xenarthra, Tardigrada, Nothrotheriidae)
Figure 3. Hierarchical cluster dendrogram of microwear variables for all extant xenarthran species in this study. Euclidean distance measure is used. Note that folivores (Bradypus) cluster together (shaded area).
Figure 4 in Dental microwear in the orthodentine of the Xenarthra (Mammalia) and its use in reconstructing the palaeodiet of extinct taxa: the case study of Nothrotheriops shastensis (Xenarthra, Tardigrada, Nothrotheriidae)
Figure 4. Plot of mean scratch and pit values for individual Cabassous centralis specimens (N = 11) in relation to extant xenarthran dietary ecomorphospaces from Fig. 2. The four individuals with highest scratch values are labelled by specimen number for reference within text.
Anesthetic management of a case of pheochromocytoma using bioreactance method with Cheetah-NICOM monitor
<p><strong>Pheochromocytoma is a rare neoplasm originating from the chromaffin cells of the adrenal gland. The number of diagnosed and excised adrenal lesions has steadily increased over the last few decades. Contemporarily, improved monitoring systems and therapeutic advances have reduced mortality associated with this disease. During surgery the anesthesiologist must be ready to face sudden hemodynamic, metabolic and electrolyte fluctuations due to the release of catecholamines. In this Case Report, we describe a particularly complex anesthesiologic management of a large secretory lesion by using Cheetah Non-Invasive Cardiac Output Monitor (Cheetah-NICOM monitor), non-invasive hemodynamic monitoring system. A 72-year-old female patient was subjected to adrenalctomy after a diagnosis of an adrenal mass (5 cm) compatible with pheochromocytoma (highlighted by metanephrine dosage). Patient reports recurrent episodes of hypertensive crisis, sweating and precordial pain and was also affected by Type 2 diabetes mellitus. Adrenal surgery for pheochromocytoma results in a significant increase in heart rate and peripheral vascular resistance, which should therefore be monitored to guide the infusion of medicinal products. Although a preoperative preparation with Alpha and beta blockers was carried out, high doses of short-lived beta-blockers and alphalytic and vasodilator were required during the intervention. This case report shows that Cheetah-NICOM monitor allowed us to manage prompty and optimally the catecholaminergic storm and the volemic filling obtaining a rapid postoperative recovery.</strong></p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
(Dataset) Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America
<p>Dataset for the paper:</p> <p>Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America</p> <p>For more infomation, please look into the README file or contact ljliu@illinois.edu, thank you!</p>
Database of RF fingerprinting on use case IoT devices
<p>This document is a dataset of radiofrequency signals. It is composed of 1000 signals coming emitted by 10 different devices. This dataset was developped for benchmarking machine learning methods on an Internet of Things classification task: recognizing which device emitted a signal.</p> <p>This dataset is in an adaptation of the dataset collected by Basak et al. in “Drone classification from RF fingerprints using deep residual nets” (IEEE COMSNETS conference, 2021).</p> <p>Basak et al. collected signals from six commercial drones, three drone radio-controllers and one WiFi router. The conducted the measurements in an anechoic chamber, using a universal software radio peripheral (USRP X310) placed seven meters apart from the devices . The signals were all in the 2.4 GHz ISM band and the whole 100 MHz band was received instantaneously using a receiving sampling rate of 100 MSps (i.e. the system down-converted the signal frequencies to the 0-100 MHz band to sample them correctly).</p> <p>While the original dataset by Basak et al. consisted in spectrograms of 256 frequency bins by 256 time frames, we have converted in into averaged spectra of 256 frequency bins. Furthermore, while Basak et al. have considered several noise levels, here we only consider the lowest noise level available (-60 dBm).</p> <p>The database is stored in an h5 file, a format adapted to databases. Inside the file there are two datasets: the signals (‘Signals’) and the targets (‘Targets’). The targets correspond to the ten different classes of signals: Parrot Disco (0), Q205 (1), Tello (2), MultiTx (3), Nine Eagles (4), Spektrum DX4e (5), Spectrum DX6i (6), Wltoys (7), S500 (8) and Linkys router (9).</p> <p>This dataset corresponds to the Deliverable D6.2 of the RadioSpin EU funded project.</p>
Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases
<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>
Using geometric morphometrics to determine the 'fittest' floral shape: a case study in large-flowered buzz-pollinated Melastomataceae
<p class="MsoNormal"><span>PREMISE</span></p> <p class="MsoNormal"><span>Floral shape, i.e. the relative arrangement and position of floral organs, is critical in mediating fit with pollinators and maximizing conspecific pollen transfer. This seems particularly true for functionally specialized systems. To date, however, few studies have attempted to quantify flowers as the inherently three-dimensional structures that they are, and determine the effect of<span> </span><span><span>intraspecific</span> </span>shape variation on pollen transfer. We here address this research gap using a functionally specialized system, buzz pollination, where bees extract pollen through vibrations, as a model. Our study species, <em>Meriania hernandoi</em> (Melastomataceae), undergoes a natural floral shape change from pseudo-campanulate corollas with more actinomorphically-arranged stamens (first day) to open corollas with more zygomorphic stamens (second day) over anthesis, providing a natural experiment to test how variation in floral shape affects male and female fitness.</span></p> <p class="MsoNormal"><span>METHODS</span></p> <p class="MsoNormal"><span>In one population of <em>M. hernandoi</em>, we bagged 51 pre-anthetic flowers and exposed half of them to bee pollinators when they were in either st<span>age of their shape transition. We then collected flowers, obtained 3D flower models through X-ray Computed Tomography for 3D geometric morphometrics, and counted the amount of pollen grains remaining per stamen (male fitness) and stigmatic pollen loads (female fitness). </span></span></p> <p class="MsoNormal"><span>KEY RESULTS</span></p> <p class="MsoNormal"><span>We found significantly higher male fitness in open flowers with zygomorphic androecia than in pseudo-campanulate flowers. Female fitness did not differ among floral shapes. </span></p> <p class="MsoNormal"><span>CONCLUSIONS</span></p> <p class="MsoNormal"><span>These results suggest that there is an 'optimal' shape for male fitness, while the movement of bees around the flower when buzzing the spread-out stamens results in sufficient pollen deposition regardless of floral shape.</span></p>
nuMIDAS public use cases data
<p>This data set contains all public data related to the use cases that were deployed in the nuMIDAS Horizon 2020 project during 2022 an 2023.</p> <p>For reasons of privacy, we have only provided data from the following use cases:</p> <ul> <li>UC1 (Pre-planning of shared mobility services)</li> <li>UC2 (Operative areas analysis)</li> <li>UC4 (Planning for parking)</li> <li>UC6 (Assessment of traffic management scenarios)</li> </ul> <p><em>UC3 (Air quality analysis and forecasting) and UC5 (Inflows and outflows in a metropolitan area) are as such not available due to private data.</em></p> <p>For more information, please refer to the public deliverables at <a href="https://www.numidas.eu/.">https://www.numidas.eu/.</a></p>
Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
<p class="MsoNormal"><span>The reconstruction of geographic and demographic scenarios of dissemination for invasive pathogens of crops is a key step towards improving the management of emerging infectious diseases. Nowadays, the reconstruction of biological invasions typically uses the information of both genetic and historical information to test for different hypotheses of colonization. The Approximate Bayesian Computation framework and its recent Random Forest development (ABC-RF) have been successfully used in evolutionary biology to decipher multiple histories of biological invasions. Yet, for some organisms, typically plant pathogens, historical data may not be reliable notably because of the difficulty to identify the organism and the delay between the introduction and the first mention. We investigated the history of the invasion of Africa by the fungal pathogen of banana, <em>Pseudocercospora fijiensis</em>, by testing the historical hypothesis against other plausible hypotheses. We analysed the genetic structure of eight populations from six eastern and western African countries, using 20 microsatellite markers, and tested competing scenarios of population foundation using the ABC-RF methodology. We do find evidence for an invasion front consistent with the historical hypothesis, but also for the existence of another front never mentioned in historical records. We question the historical introduction point of the disease on the continent. Crucially, our results illustrate that even if ABC-RF inferences may sometimes fail to infer a single, well-supported scenario of invasion, they can be helpful in rejecting unlikely scenarios, which can prove much useful to shed light on disease dissemination routes.</span></p>
Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"
<p>The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points. </p> <p>The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.</p>
Reproducibility use cases from ORKG
<p>The dataset offers a selection of use cases from ORKG that serve as noteworthy examples for the reproducibility score. We previously showcased the concept of the reproducibility score during the inaugural symposium organized by <a href="https://zenodo.org/communities/grn/?page=1&size=20">The German Reproducibility Network (GRN)</a>. For detailed information, please refer to our presentation, accessible through the following <a href="https://doi.org/10.5281/zenodo.7974324">link</a>.</p> <p> </p> <p><strong>How to use:</strong></p> <ol> <li>The column "Paper ID" corresponds to a resolve paper within ORKG. To reach the specific resource, please navigate to the following URL: <a href="https://orkg.org/paper/XXX/">https://orkg.org/paper/XXX/</a> , where you should replace the "XXX" with the actual Paper ID.</li> <li>The column "Research Field" symbolizes the domain to which the paper is assigned.</li> <li>The column "Template ID" functions likewise to the "Paper ID," but in this case, you will need to visit the URL: <a href="https://orkg.org/template/XXX/">https://orkg.org/template/XXX/</a> and replace the "XXX" accordingly. Please be aware that a single paper may have multiple templates associated with it.</li> </ol>
Fig. 2 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery
Fig. 2. The composition of active local taxonomists. Blue, black, white, and grey bars indicate marine, faunal, floral, and microbial taxa, respectively (the last three taxa are non-marine). A list of the taxonomists is provided in Appendix 1.
Fig. 3 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery
Fig. 3. Distributional map of Indonesian marine species exclusively described by local taxonomists. Grey and white land areas indicate Indonesia and the neighbouring countries, respectively.
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.