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1,970 results for “CONCEPT”
Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data
<p>guided walkthrough of poster at <a href="https://doi.org/10.5281/zenodo.6821815">https://doi.org/10.5281/zenodo.6821815</a></p> <p>which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data.</p> <p>You can also watch the video in the browser on the <a href="https://gerbi-gmb.de/i3dbio/i3dbio-resources/metadata-guide/">I3D:bio website</a>.</p>
Real-time magnetic resonance imaging to study orthostatic intolerance mechanisms in human beings: Proof of concept
<p>This dataset was acquired at DLR, Cologne, Germany. The study complied with the Declaration of Helsinki, and was approved by the local ethics committee. All subjects gave written informed consent.</p> <p> </p> <p><strong>Data acquisition</strong><br> <em>MRI data</em></p> <p>All MR images were acquired on a Siemens 3T mMR Biograph using cardiac, spine and head coils. The scanning protocol consisted of the following sequences.</p> <ul> <li><strong>MPI70_SA20_FOV320_16X6MM_33MS</strong>: cardiac real-time MRI of the short axis (TR=2.56 ms; TE=1.62 ms; FA= 10°; 1.6x1.6 mm; 6 mm slice thickness; 20 slices; FoV320x320 mm; radial spokes 13)</li> <li><strong>MPI70_TP_PCMV100_FOV320_15X6MM_33MS</strong>: blood flow real-time MRI of the pulmonary trunk (TR=3.33 ms; TE=2.24 ms; FA= 10°; 1.5x1.5 mm; 6 mm slice thickness; FoV=320x320 mm; VENC=100 cm/s; radial spokes 5)</li> <li><strong>MPI70_PCMV100_FOV192_075X6MM_80MS:</strong> blood flow real-time MRI of the middle cerebral artery (TR=4.44 ms; TE=3.10 ms; FA= 12°; 0.75x0.75 mm; 6 mm slice thickness; FoV=192x192 mm; VENC=100 cm/s; radial spokes 9)</li> </ul> <p> </p> <p>Cinematic real-time MRI of the short axis view and blood flow measurements of the pulmonary trunk and in the left and right middle cerebral artery were acquired at baseline and during -30mmHg LBNP.</p> <p> </p> <p>Video files:</p> <p><a href="https://zenodo.org/record/7066642/files/Series_040_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1">https://zenodo.org/record/7066642/files/Series_040_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1</a></p> <p><a href="https://zenodo.org/record/7066642/files/Series_103_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1">https://zenodo.org/record/7066642/files/Series_103_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1</a></p> <p> </p> <p> </p>
Datasets for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
<p><strong>Introduction</strong></p> <p>The datasets for training and evaluating our model for explainable depression detection on Twitter aided by metaphor concept mappings proposed in the following paper:</p> <p>Sooji, Han, Rui Mao, and Erik Cambria. "Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings." In Proceedings of the 29th International Conference on Computational Linguistics (COLING), 2022. in press</p> <p>Source code for our model is available at <a href="https://github.com/soojihan/HAN/blob/main/README.md">github.com/soojihan/HAN</a>.</p> <p>These datasets were generated using the dataset proposed in Shen et al., 2017. The original dataset is available at <a href="https://github.com/sunlightsgy/MDDL">github.com/sunlightsgy/MDDL</a>.</p> <p> </p> <p><strong>Description</strong></p> <p>There are three datasets. </p> <p><strong>1. mdl_HAN: </strong>This dataset contains tweets and metaphor concept mappings (MCMs) for 5,899 positive (i.e. depressed) and 4,469 negative users. Tweets in this dataset are extracted from the original MDDL dataset (Shen et al., 2017). MCMs were extracted using MetaPro (Mao et al., 2022). Please refer to our paper for more details.The name of each subfolder under the 'positive' and 'negative' subfolders is tweet userid. Each user's folder contains one or two json files:</p> <ul> <li>[userid].json: This json file contains tweet text objects, each of which is represented by [timestamp, tweet text].</li> <li>[userid]_cm.json: This json file contains MCMs, each of which is represented by [timestamp, MCM]</li> </ul> <p>Note that some users do not have MCMs. There's no [userid]_cm.json file in such users' folders.</p> <p><strong>2. imdl_HAN: </strong>This dataset has the same contents and structure as mdl_HAN except that explicit linguistic cues for depression (i.e., “I’m/I was/I am/I’ve been diagnosed depression” and words containing “depress”, “diagnos”, “anxiety”, “bipolar” and “disorder”) were removed from all tweets.</p> <p><strong>3. sampled_training_eval_data: </strong>This dataset contains 5 randomly sampled cross-validation sets. Each of the five set contains train.csv, test,csv and dev.csv. Each csv file contains user ids.</p> <p> </p> <p><strong>Remarks</strong></p> <p>If you use the datasets, please cite our paper:</p> <p>Sooji, Han, Rui Mao, and Erik Cambria. "Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings." In Proceedings of the 29th International Conference on Computational Linguistics (COLING), 2022. in press</p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any questions about the datasets or source code for our model, please contact <a href="https://soojihan.github.io/">Sooji Han</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Shen, Guangyao, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, and Wenwu Zhu. "Depression detection via harvesting social media: A multimodal dictionary learning solution." In <em>IJCAI</em>, pp. 3838-3844. 2017.</p> <p>Rui Mao, Xiao Li, Mengshi Ge, and Erik Cambria. 2022. MetaPro: A computational metaphor pro- cessing model for text pre-processing. Information Fusion, 86-87:30–43.</p> <p> </p>
Figs 1, 2 in New contribution into concept of the tribe Deveniliini (Lepidoptera: Geometridae, Ennominae) based on skeleton-muscular anatomy of the male genitalia
Figs 1, 2. Devenilia corearia (Leech, 1891), musculature of the male genitala. 1 – the
Figs 3–6 in New contribution into concept of the tribe Deveniliini (Lepidoptera: Geometridae, Ennominae) based on skeleton-muscular anatomy of the male genitalia
Figs 3–6. Musculature of the male genitala. 3, 4 – Platycerota incertaria (Leech, 1891): 3 –
Figs 7–10 in New contribution into concept of the tribe Deveniliini (Lepidoptera: Geometridae, Ennominae) based on skeleton-muscular anatomy of the male genitalia
Figs 7–10. Musculature of the male genitala. 7–9 – Lomographa bimaculata (Fabricius,
HYPERION ARMMOR CONCEPT ART
Concept art of armor HYPERION. If you like it, I'll finish it.Sorry for the quality of the model. I'm just learning. Source: Objaverse 1.0 / Sketchfab
Immaculate Conception
Statue of the Virgin housed inside the Episcopal Palace in Porto, Portugal. Dated to the 17th century and made from polychrome wood, this statue shows Mary as the Immaculate Conception (Imaculada Conceição). Photogrammetry using Nikon Z50 24mm, processed in Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab
Chapel of the Conception, Sigüenza
The Chapel of the Conception was built on the initiative of D. Diego Serrano around 1515, in one of the corners of the cloister of the cathedral of Sigüenza (Spain). Inside, two beautiful balconies stand out, which are accessed through two side stairs. The entire chapel was decorated with mural paintings, including the vault, where we can see the representation of numerous dragons. In 2014, the painting of The Incarnation of Mary, painted by El Greco, was placed in this chapel. 3D model made in Reality Capture using 39 scan positions and 1900 images. Additional processing completed in Geomagic Wrap, Blender, and Meshlab. This work is part of the collaboration agreement signed in 2020 between Junta de Comunidades de Castilla-La Mancha and Global Digital Heritage. We want to thank the Bishopric of Sigüenza-Guadalajara and the cathedral chapter, especially D. Jesús de las Heras, D. Miguel Ángel Ortega and D. Julián García, for the facilities and for the interest they have shown in the project. Source: Objaverse 1.0 / Sketchfab
castle model test concept.
a quick test model i wipped up within a few hours, i know the angle is messed up but i dont have a easy way to fix it, and i dont intend this to reflect my capabilities. Source: Objaverse 1.0 / Sketchfab
Supplementary material 1 from: Humair F, Edwards P, Siegrist M, Kueffer C (2014) Understanding misunderstandings in invasion science: why experts don't agree on common concepts and risk assessments. NeoBiota 20: 1-30. https://doi.org/10.3897/neobiota.20.6043
Interview guideline: perception / valuation of ecosystem change related non-native, invasive plants
Concept spread results for 2 concept environments
<p>Raw data for multiple concepts spreading within a network.</p> <p> </p> <p>We have two concepts spreading, Concept 0, which is our target concept, and Concept 1 which is the concept that we can control.</p> <p>Concept 0 selects its seeds randomly.</p> <p>Concept 1 selects seeds based on chosen heuristic.</p> <p> </p> <p>Heuristics have a number ID for ease of labelling files. The following numbers are used within this data:</p> <p>0 - Random selection</p> <p>2 - Single Discount</p> <p>3 - Degree</p> <p>9 - Degree Discount</p> <p>16 - MPG</p> <p>17 - MoBoo</p> <p> </p> <p>File names follow this format:</p> <p>[number of seeds]_[number of nodes]_[heuristic number]_[Relationship strength * 10]_[average LT threshold * 10]_[IC probability of infection * 100]_[burn in time]_[run number]_[network type]_[network characteristic]_[controllable Concept]Boost[target Concept].txt</p> <p>So, for example, if we are performing run <strong>38</strong> on a <strong>25000</strong> node <strong>small-world</strong> graph, with a seed set size of <strong>100</strong>, clustering exponent of <strong>0.25</strong>, burn in time of <strong>2</strong> time steps, the controllable concept selecting seeds using <strong>degree discount</strong>, both concepts using the<strong> independent cascade model</strong> of spread, with a probability of infection of <strong>0.1</strong>, a LTM average threshold of <strong>0.8</strong> and relationship strength of <strong>0.2 </strong>the file would be:</p> <p>100_25000_9_2_80_10_2_38_SW_25_ICBoostIC.txt</p> <p> </p> <p>Files follow this format:</p> <p>10 line preamble listing the parameters of the run/runs</p> <p>Dashed line break</p> <p>"Run #" where # is the current run number</p> <p>Time in milliseconds of start</p> <p>"Timestep n" - n starting at 0</p> <p>"Infections [conceptType]0: [Number of infected nodes in current time step for target]"<br> "Infections [conceptType]1: [Number of infected nodes in current time step for controllable]"</p> <p>These three lines repeat for each time step. Before a final result heading, with the final infections.</p> <p>Time in milliseconds that run ended.</p> <p>Dashed line divider.</p> <p>Then, if the file contains a single run the file will end or, if it contains multiple runs, will proceed to the next run.</p>
CONCEPT-HF Common Data Model
<p><span>Common Data Model for Cohort Analysis of Patients in the CONCEPT-HF Project.</span></p> <p><span>This data model includes cohort definition information with the specification for selecting hospitalization episodes due to heart failure based on the primary diagnosis (i.e., ICD-9 or ICD-10), and the specification and definition of the minimum set of variables required to accomplish the study's objectives. </span></p> <p><span>A schematic figure is also included to depict the expected sources of the required information within the information systems of different levels of care, according to the sequence of the care process.</span></p> <p><strong><span>Aims of the CONCEPT-HF study</span></strong></p> <p><strong><span>General Objective</span></strong></p> <p><span>The CONCEPT-IC project aims to analyze the effectiveness of the healthcare process experienced by HF patients.</span></p> <p><span>Specific Objectives are to identify and characterize the healthcare trajectories experienced by HF patients within the healthcare system and compare care pathways experienced by HF patients with the theoretical trajectories derived from clinical guidelines, including process indicators, diagnosis, clinical follow-up, and pharmacological treatment recommendations.</span></p> <p><span>A third point of interest is to evaluate the effect of patients' and healthcare providers' adherence to clinical guidelines on health outcomes.</span></p> <p><span>Finally, we want to evaluate the quality improvement strategies based on HF Programs concerning process and outcome indicators, analyzing deviations from observed healthcare trajectories compared to theoretical ones and the differences in HF care and outcomes between three Spanish healthcare systems: Andalusian, Aragonese, and Basque.</span></p>
A Concept for Integrating AI-based Support Systems into Clinical Practice
<p>This repository refers to a contribution entitled "<strong>A Concept for Integrating AI-based Support Systems into Clinical Practice</strong>" submitted to the <strong>34th Medical Informatics Europe Conference</strong>.</p>
Hyperloop CAES Rail Car Concept
<p>Why not make Hyperloop Pods that use railroad infrastructure, and a Compressed Air Energy Storage mechanism?</p>
AI concepts relevant for AI and e-commerce/retail secors
<p><strong>INAIR</strong> (<a href="https://www.ai4retail.eu/en/">Increasing the Uptake of AI in Retail</a>) is a Coordination and Support Action funded by the European Union'’s Horizon Europe Research and Innovation programme - Grant Agreement No. 101133847. The project aims to contribute to reducing the AI skills gap of European MSMEs in Retail, to let them exploit the potential of AI for greening their businesses, support their competitiveness in the global market and ultimately contribute to reaching the digital decade target of 75%+ European companies adopting AI technologies by 2030. </p> <p>These datasets contain AI concepts identified from scientific literature as part of the INAIR Horizon Europe project. It includes keywords for skills and knowledge relevant to AI in the retail and e-commerce sectors. The data supports qualitative and quantitative analyses by providing detailed references to articles discussing each concept. This resource aims to facilitate further research and understanding of the AI-related skills and technologies impacting the retail and e-commerce industries.</p> <p>In our data retrieval process, we used the Scopus API to systematically extract the relevant publications. We collected a diverse range of publications from the database, including conference proceedings, book chapters, and other materials. To ensure the reliability of our analysis, we refined our focus exclusively to peer-reviewed articles, specifically limiting our scope to the categories of articles and reviews. </p> <p>The keywords combinations we used were: </p> <p>TITLE-ABS-KEY ( X AND Y ) AND PUBYEAR > 2017 AND PUBYEAR < 2025</p> <p>Where: X=['sales', 'retail', 'e-commerce'], Y=['AI', 'artificial intelligence']. These keywords were scrutinized within titles, abstracts, author keywords, and ‘topics’ as delineated by the platform.</p> <ul> <li>`concepts_unique.xlsx` lists unique concepts</li> <li>`concepts_master.xlsx` is a master file referencing all articles mentioning each given concept.</li> </ul> <p>The former file allows for browsing skills and knowledge concepts, while the latter serves as a reference to works addressing each concept and enables quantitative analysis, such as identifying the most frequently mentioned concepts.</p> <p> </p>
FAIR Metadata Concepts in DataCite Metadata Schema
<p>Documentation Concepts that support the FAIR Principles are mapped to the DataCite Metadata Schema using json Paths.</p>
Computing integrated activities scored for programming concepts
<p>Educators across disciplines are implementing lessons and activities that integrate computing concepts into their curriculum to broaden participation in computing. Out of myriad important introductory computing skills, it is unknown which—and to what extent—these concepts are included in these integrated experiences, especially when compared to concepts commonly taught in introductory computer science courses. Thus, it is unclear how integrated computing activities serve the goal of broadening participation in computing. To address this deficit, we compiled a database of 81 integrated computing activities, constructed a framework of fundamental programming concepts, and scored each activity in the database for the presence of each concept. The dataset also includes different activity features, including discipline, programming language, student age, and duration of activity. </p>
Data from: What doesn't kill you makes you stronger? Effects of paternal age at conception on fathers and sons
<p>Advancing male age is often hypothesised to reduce both, male fertility and offspring quality due to reproductive senescence. However, the effects of advancing male age on reproductive output and offspring quality are not always deleterious. For example, older fathers might buffer effects of reproductive senescence by terminally investing in reproduction. Similarly, males that survive to reproduce at an old age, might carry alleles that confer high viability (viability selection) which are then inherited by offspring, or might have high reproductive potential (selective disappearance). Differentiating these mechanisms requires an integrated experimental study of paternal survival and reproductive performance, as well as offspring quality, which is currently lacking. Using a cross-sectional study in <em>Drosophila melanogaster</em>, we test the effects of paternal age at conception (PAC) on paternal survival and reproductive success, and on the lifespans of sons. We discover that mating at an old age is linked with decreased future male survival, suggesting that mating-induced mortality is possibly due to old fathers being frail. We find no evidence for terminal investment, and show that reproductive senescence in fathers does not onset until their late-adult life. Additionally, we find that as a father's lifespan increases, his probability of siring offspring increases, for older PAC treatments only. Lastly, we show that sons born to older fathers live longer than those born to younger fathers, due to viability selection. Collectively, our results suggest that advancing paternal age is not necessarily associated with deleterious effects for offspring, and may even lead to older fathers producing longer-lived offspring.</p>
Fig. 5. A in Evidence Supporting the Concept of a Regionalized Distribution of Testate Amoebae in the Arctic
Fig. 5. A hypothetical model for explaining the species diversity in the Arctic.
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.