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37 results for “science and technology”
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Do extraordinary science and technology scientists balance their publishing and patenting activities?
<p>Data of figures in the article titled "<strong>Do extraordinary science and technology scientists balance their publishing and patenting activities?."</strong></p>
Data for: Torii et al.,Observed Kinetics of Enterovirus Inactivation by Free Chlorine Are Host Cell-Dependent, Environmental Science and Technology, 10.1021/acs.est.2c07048
<p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>- Figure 1 (Inactivation curves for E11 by free chlorine, UV, and heat)</p> <p>- Figure 2 (Inactivation curves for CVA9, CVB1, E7, E9, and E13)</p> <p>- Figure S1 (Loss of attachment and the PCR-target by free chlorine treatment)</p> <p>- Figure S2 (Flow cytometric analysis)</p> <p> </p> <p> </p> <p> </p>
An annotated list of horizon scanned technologies with potential for application in alien species citizen science projects
<p><strong>Context</strong></p> <p>The contribution of volunteers in recording invasive alien species (IAS) has been fostered by technological developments such as social media, apps, low-cost sensors, search engines and predictive analytics. These technology developments, an increased attention to citizen science and a cultural change towards collaboration and openness in research within the policy agenda should increase the contribution of volunteer recording. Within the framework of the COST Action CA17122 <a href="https://www.ceh.ac.uk/our-science/projects/alien-csi"><em>Increasing Understanding of Alien Species through Citizen Science</em></a> (<a href="https://doi.org/10.3897/rio.4.e31412">Roy et al. 2018</a>) a group of researchers explored the value of emerging technologies for citizen science in the context of alien species, recognizing the contribution of volunteers and reviewing their potential to engage broad audiences, motivate volunteers, improve data collection, increase data quality etc.</p> <p><strong>Survey</strong></p> <p>The following criteria were then used to evaluate the potential of these technologies for alien species citizen science through a dedicated <a href="https://forms.gle/9GQJctnAbPLKyxDE7">survey</a>:</p> <p>● <em><strong>Audience</strong></em>: the technology can attract new target audiences for IAS citizen science and/or support more inclusivity in IAS citizen science (can overcome inequalities in participation, attract under-privileged audiences/those underrepresented in the scientific enterprise, allow participation of sensory/cognitive/otherwise impaired...)<br> ● <em><strong>Engagement </strong></em>with others: the technology supports better connections with other participants, helpful in building a community<br> ● <em><strong>Engagement via feedback</strong></em>: the technology increases the quality, amount or rate of feedback (including supporting learning) to participants<br> ● <strong><em>Application</em></strong>: the technology can be embedded in everyday life and therefore has the potential for wide, generic application</p> <p>● <em><strong>New data</strong></em>: the technology yields new types of data that would not be available without the technology (improved the detectability of IAS, new types of data, species interactions, new information sources)<br> ● <strong><em>Extends data</em></strong>: the technology expands the scope of data collection or analysis (e.g. better coverage spatially, temporally)<br> ● <strong><em>Improves data quality</em></strong>: the technology improves species ID, reduces uncertainty, improves validation<br> ● <strong><em>Improves the flow of data</em></strong>: the technology increases the speed of record transmission (e.g. for early warning)<br> ● <strong><em>Improves the curation of data</em></strong>: the technology itself allows for improved data curation (better metadata, sustainability and long term preservation data, open data, tracked provenance of data, FAIR data management, enable to better credit citizen scientists for their data contributions)</p> <p><strong>Dataset description</strong></p> <p>This dataset represents the list of technologies (in the broadest sense, including approaches) that were identified collectively by the experts as being relevant technologies in the framework of (alien species) citizen science. The dataset includes the following fields:</p> <ul> <li><em>Name</em>: name of the approach/technology</li> <li><em>Category</em>: broad categorisation of the approach/technology (Hardware and infrastructure, data collection and analysis tools, tools to improve user experience). If some approaches are combinations this is mentioned in description.</li> <li><em>Description</em>: a definition and/or description of the approach/technology</li> <li><em>Reference</em>: a reference on the approach/technology (e.g. paper, online reference), mostly with a doi</li> <li><em>Example</em>: an example of the approach/technology, mostly with reference to an (alien species) citizen science project that applied it</li> <li><em>Notes: </em>any further remarks</li> </ul>
Improving Pancreatic Cancer Care by the Use of Computational Science and Technology
ClinicalTrials.gov study NCT06055010. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Advanced technologies and data management practices in environmental science: lessons from academia
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Data from: Aversion to playing God predicts negative moral judgments of technology and science
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"INTERNATIONAL SCHOOL OF FINANCE TECHNOLOGY AND SCIENCE"
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THE ROLE OF INFORMATION TECHNOLOGY IN THE INDEPENDENT EDUCATION OF STUDENTS IN THE SCIENCE OF DRAWING
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Data for Olive et al., Removal of waterborne viruses by Tetrahymena pyriformis is virus-specific and coincides with changes in protist swimming speed, Environmental Science and Technology, 2022 (https://doi.org/10.1021/acs.est.1c05518)
<p>This entry contains the data shown in: Olive et al., <em>Removal of waterborne viruses by Tetrahymena pyriformis is virus-specific and coincides with changes in protist swimming speed,</em> Environmental Science and Technology, 2022 (https://doi.org/10.1021/acs.est.1c05518)</p> <p>Net removal values (log10 C/C0 or log10 N/N0) shown in Figures 1 and 4</p> <p>Raw data used to calculate net removal values in Figure 1</p> <p>Raw removal values shown in Figure 2</p> <p>Raw data for protist movement analysis shown in Figure 3</p> <p>R code used for protist movement analysis (as text file)</p> <p>Raw data for all Supporting Figures (S1-S6)</p>
Mapping of Revised Field of Science and Technology (FOS) Classification to Web of Science, Scopus and Dimensions Research Sheames
<p>This data set represents research area classification mapping of Web of Science, Scopus and Dimensions to the OECD Category schema. The OECD Category schema corresponds to the <a href="http://unstats.un.org/unsd/EconStatKB/Attachment332.aspx?AttachmentType=1">Revised Field of Science and Technology (FOS) Classification of the Frascati Manual.</a> </p> <p>Mapping process was conducted in January 2023, utilizing the then-current versions of the All Science Journal Classification Codes (ASJC) for Scopus and the Web of Science Research Area scheme. For Dimensions, the mapping was based on the Australian and New Zealand Standard Research Classification (ANZSRC) 2020.</p>
STEM (Science, Technology, Engineering & Math) Familia Talk
ClinicalTrials.gov study NCT03766906. IPD Sharing: NO. Countries: 1. Publications: 0.
Intervention to Increase Physical Activity in Older Adults Using Citizen-science and Modern Technology
ClinicalTrials.gov study NCT02744924. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Shanghai Meiji Health Science and Technology Co., Ltd
ClinicalTrials.gov study NCT04651023. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Planetary Science Technology Program
Planetary Science Technology Program
Earth Science Technology Programs
Earth Science Technology Programs
The rhizobia strain collection of Latvia University of Life Sciences and Technologies, containing strains from the historical rhizobia collection of Latvia University of Life Sciences and Technologies and newly isolated strains
<p>List of rhizobia and non-rhizobial endophyte bacterial strains, isolated from various legumes in Latvia. These strains are a part of the Rhizobia collection, Insitute of Soil and Plant Sciences, Latvia University of Life Sciences and Technologies. List include rhizobia strain identification, isolation year and host plant.</p>
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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.