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67 results for “expertise”
Data from: Estimates of observer expertise improve species distributions from citizen science data
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Comparison of different thyroid surgical procedures and their outcomes/complications for benign disease in relation to expertise of the surgeon in a public hospital of a developing country over 2 decades
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Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution
<p>Data for the paper:</p> <p>Ehsan Mirsaeedi and Peter C. Rigby. 2020. Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution. In 42nd International Conference on Software Engineering (ICSE ’20), May 23–29, 2020, Seoul, Republic of Korea. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3377811.3380335</p> <p>Code and tools available at</p> <pre>https://doi.org/10.5281/zenodo.3678570</pre>
Supplementary material 1 from: Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25-37. https://doi.org/10.3897/neobiota.63.55260
List of participants and associated information
HISTORY AND CURRENT PROCESS OF FORENSIC PSYCHOLOGICAL EXPERTISE
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Figure 2 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 2 In the Central Library of Datasets, natural history collection staff will find correctly identified images of their target organisms and download the data for training of an individually customized classifier (photos: Lepidoptera by Entomological Collection of ETH Zürich; Orthoptera by Naturalis Biodiversity Center; Brassicaceae by United Herbaria Z+ZT, ZT-00164967, ZT-00167494, ZT-00171530, CC BY-SA 4.0). The current figure shows a mock-up.
Figure 3 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 3 Sharing of taxonomic knowledge between institutes. (1) Each algorithm contains two basic components: the feature extractor and the classifier. (2) The Central Library of Datasets allows the user to browse through all available images of collection objects; (3) based on all available images, a regularly updated central feature extractor is created and published; (4) custom made algorithms can relatively easily be created by building a classifier based on a selection of taxa from the central library and combining this with the central feature extractor; (5) newly created algorithms together with their metadata (probability & information on content) are published through a web service in the Central Library of Algorithms (6) and can be used through the Identification web services (API) either for batch processing of images or through a mobile app. Models can be easily extended by other institutions by combining data sources (7).
Figure 1 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 1 In the Central Library of Algorithms, natural history collection staff will select algorithms (feature extractors, models, etc.) that are most appropriate for the identification of their target organisms and add them to the workbench. The current figure shows a mock-up.
Figure 6 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 6 Mock-up of an interface for automated taxon identification. Naturalis holds over 500.000 specimens of unmounted, unsorted and often unidentified, papered butterflies and moths that were collected mostly in Europe and Asia over the past 200 years. In early 2016, Naturalis embarked on a 10-year-project to digitally identify all these specimens with the help of dedicated volunteers (Caspers et al. 2019). Specimens are unpacked, photographed, had their label data registered and then repacked, still unmounted, for long-term storage. Specimen images were then dragged and dropped into a web-based interface to get a near-instant response with multiple predictions about the taxonomic identity including probability values.
Figure 5 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 5 Non-expert collection staff easily find and afterwards sort specimens by taxon (line color) and by accuracy of the identification (line type). The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.
Figure 4 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 4 Algorithms recognize and number individual specimens in a drawer of unsorted items. The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.
UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures
<p><strong>Presentation</strong></p> <p>UMONS-TAICHI is a large 3D motion capture dataset of Taijiquan martial art gestures (n = 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a [0-10] scale. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated motion capture system of 11 Oqus cameras that tracked 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless sensor that tracked 25 locations of a person’s skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation. Both segmented and unsegmented data are provided in this database. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by Tits et al. (2017) on a part of the dataset to extract morphology-independent motion features for gesture skill evaluation and presented in: “Morphology Independent Feature Engineering in Motion Capture Database for Gesture Evaluation” (<a href="https://doi.org/10.1145/3077981.3078037">https://doi.org/10.1145/3077981.3078037</a>).</p> <p><strong>Processing</strong></p> <p><em><strong>Qualisys</strong></em></p> <p>Qualisys data were processed manually with <a href="http://www.qualisys.com/software/qualisys-track-manager/">Qualisys Track Manager</a>.</p> <p>Missing data (occluded markers) were then recovered with an automatic recovery method: <a href="https://github.com/titsitits/MocapRecovery">MocapRecovery</a>.</p> <p>Data were annotated for gesture segmentation, using the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework (C++ <a href="http://openframeworks.cc/">openFrameworks</a> addon). The code for annotation can be found <a href="https://github.com/numediart/ofxMotionMachine/tree/master/mmTutorial_4_Annotation">here</a>. Annotations were saved as ".lab" files (see Download section).</p> <p><em><strong>Kinect</strong></em></p> <p>The Kinect data were recorded with <a href="https://msdn.microsoft.com/en-us/library/hh855389.aspx">Kinect Studio</a>. Skeleton data were then extracted with <a href="https://www.microsoft.com/en-us/download/details.aspx?id=44561">Kinect SDK</a> and saved into “.txt” files which contain several lines corresponding to each captured frame. Each line contains one integer number (ms), relative to the moment when the frame was captured, followed by 3 x 25 float numbers corresponding to the 3-dimentional locations of the 25 body joints.</p> <p>For more information please visit <a href="https://github.com/numediart/UMONS-TAICHI">https://github.com/numediart/UMONS-TAICHI</a></p> <p>PS: All files can be used with the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework. Please use the parser provided in this github repository for kinect (.txt) data.</p> <p> </p>
Gaze behavior and decision-making processes of handball referees: Insights from gender differences and expertise levels
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Data from: Perceptual expertise in forensic facial image comparison
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Supporting Data for Integrating Human Factors Expertise into Development of Automated Vehicles
<p>This dataset provides the questionnaires utilized during various stages of data collection for our manuscript titled "Integrating Human Factors Expertise into Development of Automated Vehicles".</p> <p>In addition to the questionnaires, we offer scripts and survey data essential for replicating and evaluating the methodology outlined in our manuscript</p> <p>The dataset includes the following files:</p> <ol> <li>Questionnaire used during Stage 1 of the data collection process.</li> <li>Questionnaire employed in Stage 2 of the data collection.</li> <li>Survey questionnaire used in Stage 3.</li> <li>Zip file providing the dataset and scripts used for quantitative analysis.</li> </ol> <p>For any inquiries or further clarification, please contact <a target="_new" rel="noreferrer">amnap@chalmers.se. </a></p>
Developing Brain Imaging Analysis Expertise for Personalizing Transcranial Electric Stimulation in Anhedonia Treatment of Patients with Bipolar Depression
ClinicalTrials.gov study NCT05240352. IPD Sharing: NO. Countries: 1. Publications: 0.
Tele-expertise in Patients With Diabetes Hospitalized for Covid-19 Infection
ClinicalTrials.gov study NCT04726163. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Computer-Assisted Access to Specialist Expertise
ClinicalTrials.gov study NCT00013117. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Expertise-Based Randomized Controlled Trial of Scrotal Versus Inguinal Orchidopexy on Post-operative Pain
ClinicalTrials.gov study NCT02158780. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of In-Home Versus In-Clinic Administration of Subcutaneous Nivolumab Through Cancer CARE (Connected Access and Remote Expertise) Beyond Walls (CCBW) Program
ClinicalTrials.gov study NCT06265285. 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.