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5 results for “expert identification”
Dataset of the paper "Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)"
<p>This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species used to test the performance of a CNN in species recognition. All jpg files are 1920 pixels on the long size and additionally available as an archive file to facilitate download of the entire dataset. </p> <p>Bar code labels (unique specimen identifiers or USIs) were attached to all examined specimens used for this study. Further information such as additional photographs of habitus and genitalic structures, georeferenced coordinates of each locality, specimens dissected, notes, collecting method can be obtained from the Heteroptera Species Pages (http://research.amnh.org/pbi/heteropteraspeciespage/) which assembles available data from a specimen database and are also provided as an Excel spreadsheet (file _Adelphocoris_CNN_label_data.xlsx).</p>
Expert review of iNaturalist identifications of Australian millipedes in GBIF
<p>This tab-separated file ("millipede_review.txt") is derived from the "verbatim.txt" file in a Darwin Core archive downloaded 2023-03-24 from GBIF (DOI: <a href="https://doi.org/10.15468/dl.jtpncb">https://doi.org/10.15468/dl.jtpncb</a>). The 2029 records in the download are "Research Grade" <em>iNaturalist</em> observations of millipedes observed in Australia. Encoding is UTF-8.</p> <p>The dataset has the following Darwin Core fields from "verbatim.txt" and my added fields (fieldnames in capitals):</p> <p>gbifID = GBIF occurrence record code; record accessible as https://www.gbif.org/occurrence/[gbifID]</p> <p>catalogNumber = <em>iNaturalist</em> observation number; record accessible as https://www.inaturalist.org/observations/[catalogNumber]</p> <p>scientificName = the taxon identification made on the <em>iNaturalist</em> platform and accepted by GBIF</p> <p>IDCHECK = my assessment of the identification (see below)</p> <p>COMMENT = my explanation for doubting an identification (see below)</p> <p>recordedBy = the name used for copyright assignment by the original observer on <em>iNaturalist</em></p> <p>USERNAME = the<em> iNaturalist</em> username of the original observer</p> <p>RGID1 = the <em>iNaturalist</em> username of the first person to suggest the final, accepted identification</p> <p>RGID2 = the<em> iNaturalist</em> username of the second person to suggest the final, accepted identification</p> <p>RGID3 = the <em>iNaturalist</em> username of the third person to suggest the final, accepted identification</p> <p>RGID4 = the <em>iNaturalist</em> username of the fourth person to suggest the final, accepted identification</p> <p>RGID5 = the<em> iNaturalist</em> username of the fifth person to suggest the final, accepted identification</p> <p>I reviewed the images and image sets on <em>iNaturalist</em> that are referred to in these records between 2023-03-24 and 2023-04-02. I classed the <em>iNaturalist</em> identifications in the IDCHECK field as follows:</p> <p>correct - The image clearly shows the diagnostic characters of the taxon</p> <p>likely - The ID is probably correct, but I can't be sure because diagnostic characters aren't clearly visible in the image</p> <p>possible - The ID might be correct, but the image isn't good enough to distinguish the identified taxon from another, similar taxon</p> <p>unlikely - The ID is probably incorrect because the image appears to show a different taxon, although diagnostic characters aren't clearly visible</p> <p>incorrect - The image clearly shows the diagnostic characters of a different taxon</p>
Supplementary material of the study "Help! I need somebody. A Mapping Study about Expert Identification in Software Development"
<p><strong>Supplementary Material</strong></p> <p><em><strong>Context</strong></em>: Software development is a knowledge-intensive activity, and its success in an organization relies deeply on knowledge sharing. Knowledge management challenges are often increased in agile environments, which involve a lot of tacit knowledge, commonly acquired through experiences and hard to be made explicit. Therefore, knowledge sharing among practitioners is crucial. However, identifying suitable experts to share specific knowledge is not trivial. It involves not only discovering the individuals with the desired knowledge but also considering other factors that may improve the expert responsiveness, such as social connections and availability. <em><strong>Objective</strong></em>: Considering the important role experts play in knowledge sharing, we decided to investigate approaches that help identify experts that can share knowledge in software development. Our goal is to provide a panorama of the existing approaches and shine a light on research opportunities. <em><strong>Method</strong></em>: We carried out a systematic literature mapping and analyzed 17 publications. <em><strong>Results</strong></em>: The results show that most approaches have relied on code repositories as a source of evidence for identifying experts and, consequently, focus on supporting developers and aiding in the codification activity. Additionally, expert identification has been mostly automated, and factors beyond possessing the desired knowledge have often been disregarded. <em><strong>Conclusion</strong></em>: Although there are several expert identification approaches, there has been a lack of concern with factors that influence reaching the most suitable expert for a specific situation (e.g., considering the characteristics of the person seeking knowledge). Moreover, there is a need for deeper reflection on how to better explore different artifacts as sources of expert evidence and how to combine them to improve expert identification.</p> <p>This package contains supplementary material of the study performed to investigate approaches that help identify experts that can share knowledge in software development. It contains:</p> <ul> <li>A spreadsheet containing raw data (research protocol, considered and selected publications, and research questions answers).</li> </ul>
Comparing BlueDop Vascular Expert to Ankle-Brachial Index in the Identification of Peripheral Vascular Disease
ClinicalTrials.gov study NCT06436001. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks
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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.