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1,453 results for “Outcome research”
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.
Figure 2 from: Haupt TM, Ceasar J, Stefanoudis P, von der Meden C, Payne RP, Adams LA, Anders DR, Bernard AT, Coetzer W, Florence WK, Janson LA, Johnson AS, Juby R, Kock AA, Langenkämper D, Nadjim AM, Parker D, Samaai T, Snyders LB, Upfold L, van der Heever GM, Williams LL (2022) The WIO Regional Benthic Imagery Workshop: Lessons from past IIOE-2 expeditions. Research Ideas and Outcomes 8: e81563. https://doi.org/10.3897/rio.8.e81563
Figure 2 Specimen plate of benthic invertebrates collected via dredge sampling on the IIOE-2 expeditions of 2017/2018. Shown here are a diverse range of taxa - Crustacea, Bryozoa, Brachiopoda, Porifera, Cnidaria, Echinodermata and Mollusca.
Figure 3 from: Haupt TM, Ceasar J, Stefanoudis P, von der Meden C, Payne RP, Adams LA, Anders DR, Bernard AT, Coetzer W, Florence WK, Janson LA, Johnson AS, Juby R, Kock AA, Langenkämper D, Nadjim AM, Parker D, Samaai T, Snyders LB, Upfold L, van der Heever GM, Williams LL (2022) The WIO Regional Benthic Imagery Workshop: Lessons from past IIOE-2 expeditions. Research Ideas and Outcomes 8: e81563. https://doi.org/10.3897/rio.8.e81563
Figure 3 Increasing complexity and cost of visual sampling equipment; the operational requirements for deployment of the equipment, from small craft to large research vessels; and the technical and legal requirements for operators. Photographic credit (Left to Right): Department of Forestry, Fisheries and the Environment (South Africa); Jeanne Mortimer (Island Conservation Society, Seychelles); 3) South African Institute for Aquatic Biodiversity (SAIAB); 4) South African Environmental Observation Network (SAEON); 5) SAIAB; 6) Nekton.
Figure 1 from: Haupt TM, Ceasar J, Stefanoudis P, von der Meden C, Payne RP, Adams LA, Anders DR, Bernard AT, Coetzer W, Florence WK, Janson LA, Johnson AS, Juby R, Kock AA, Langenkämper D, Nadjim AM, Parker D, Samaai T, Snyders LB, Upfold L, van der Heever GM, Williams LL (2022) The WIO Regional Benthic Imagery Workshop: Lessons from past IIOE-2 expeditions. Research Ideas and Outcomes 8: e81563. https://doi.org/10.3897/rio.8.e81563
Figure 1 A map of the representative countries, of which 231 out of the 266 participants (i.e. 87%) were from the Western Indian Ocean. Source: L Williams, DFFE.
Figure 1 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
Figure 1 Solver implementation: Objective cell in F4 (minimize sum of squared errors) by changing values of b1 and b2 in cells F2 and F3 respectively.
Figure 2 from: Scaccia N, Günther T, Lopez de Abechuco E, Filter M (2021) The Glossaryfication Web Service: an automated glossary creation tool to support the One Health community. Research Ideas and Outcomes 7: e70183. https://doi.org/10.3897/rio.7.e70183
Figure 2 A, a screenshot of the GWS KNIME workflow. There are 5 steps within the data processing workflow, which are described in the text. The first and the last green boxes of the workflow, designed with KNIME WebPortal extension, contain so-called "Components" that provide a workflow-specific web user interface that can also be triggered by the KNIME Server. In this way, the GWS KNIME workflow becomes available as a fully functional web service in the KNIME WebPortal. B, a screenshot of the KNIME's Text Processing extension nodes wrapped up into a metanode.
Figure 1 from: Scaccia N, Günther T, Lopez de Abechuco E, Filter M (2021) The Glossaryfication Web Service: an automated glossary creation tool to support the One Health community. Research Ideas and Outcomes 7: e70183. https://doi.org/10.3897/rio.7.e70183
Figure 1 A, a screenshot of the GWS start page. B and C, output sections of the service. Specifically, B, number of exact terms and associated definitions found and, a tag cloud outcome; C, downloadable table of identified terms and definitions with term frequency, sector classification and reference. Through the GWS, end-users can upload their own file (2, A) and select which of the supported glossary(ies) should be searched through (1, A). Clicking "Next" (3, A), the GWS displays the results in an interactive table within the dashboard providing also the number of occurrences for each term in the user's document next to the identified definitions (4, B) and the tag cloud (5, B). The different colours for the terms in the tag cloud refer to different types of matches: exact matches in green, inexact matches in yellow and non-matching terms in grey. Afterwards, scrolling down the dashboard view (image C), the user has the possibility to download directly different tables (6, C) in Excel format. These options are: i) download the table with all the matches found, ii) download the exact-matches table or iii) download the inexact-matches table (6, C). Furthermore, if only a few terms with appropriate definitions are needed, the end-user can download those specific terms checking the corresponding checkboxes (7, C) and then clicking "Next" at the bottom of the dashboard page (8, C).
Supplementary material 1 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
BOD data
Supplementary material 1 from: Weigand AM, Bücs S-L, Deleva S, Lukić Bilela L, Nyssen P, Paragamian K, Ssymank A, Weigand H, Zakšek V, Zagmajster M, Balázs G, Barjadze S, Bürger K, Burn W, Cailhol D, Decrolière A, Didonna F, Doli A, Drazina T, Dreybrodt J, Ðud L, Egri C, Erhard M, Finžgar S, Fröhlich D, Gartrell G, Gazaryan S, Georges M, Godeau J-F, Grunewald R, Gunn J, Hajenga J, Hofmann P, Knight LRFD, Köble H, Kuharic N, Lüthi C, Munteanu CM, Novak R, Ozols D, Petkovic M, Stoch F, Vogel B, Vukovic I, Hall Weberg M, Zaenker C, Zaenker S, Feit U, Thies J-C (2022) Current cave monitoring practices, their variation and recommendations for future improvement in Europe: A synopsis from the 6th EuroSpeleo Protection Symposium. Research Ideas and Outcomes 8: e85859. https://doi.org/10.3897/rio.8.e85859
Questionnaire for the 6th EuroSpeleo Protection Symposium 2022
Supplementary material 5 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Keynote Nature after Europe
Supplementary material 3 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Flyer: Perspectives for the future of conservational institutions and collection practices
Supplementary material 1 from: Niehues A, de Visser C, Hagenbeek FA, Karu N, Kindt ASD, Kulkarni P, Pool R, Boomsma DI, van Dongen J, van Gool AJ, `t Hoen PAC (2022) A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object. Research Ideas and Outcomes 8: e94042. https://doi.org/10.3897/rio.8.e94042
Members of the ACTION Consortium
Supplementary material 1 from: Phillips HRP, Cameron EK, Eisenhauer N (2022) Illuminating biodiversity changes in the 'Black Box'. Research Ideas and Outcomes 8: e87143. https://doi.org/10.3897/rio.8.e87143
SoilFaUNa Sampling Protocol
Supplementary material 1 from: von Rintelen K, Arida E, Häuser C (2017) A review of biodiversity-related issues and challenges in megadiverse Indonesia and other Southeast Asian countries. Research Ideas and Outcomes 3: e20860. https://doi.org/10.3897/rio.3.e20860
Table 1 Biodiversity-related data and economic information for the 10 ASEAN member states
Supplementary material 1 from: Vyshedskiy A, Mahapatra S, Dunn R (2017) Linguistically deprived children: meta-analysis of published research underlines the importance of early syntactic language use for normal brain development. Research Ideas and Outcomes 3: e20696. https://doi.org/10.3897/rio.3.e20696
Linguistic isolates performance in verbal and nonverbal tests
Supplementary material 1 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Appendix 2.
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.