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1,970 results for “CONCEPT”
Pregnancy-related diagnosis codelist developed for the ConcePTION pregnancy algorithm
<p><span>The IMI ConcePTION project aims to build an ecosystem to generate Real World Evidence to address the information gap in medication safety in pregnancy. </span></p> <p><span>The ConcePTION pregnancy algorithm was developed as part of the IMI ConcePTION project and aimed at identifying a comprehensive list of pregnancies experienced by the population in European healthcare data sources. To achieve this, the ConcePTION pregnancy algorithm was designed to retrieve any record implying a pregnancy at record date from multiple data provenance, for instance, records of birth registries, congenital anomalies register, hospital admission and discharge, primary care. </span></p> <p><span>The diagnosis code list named “PrA_Codelist” was developed and integrated into the ConcePTION pregnancy algorithm to identify records with a diagnostic code implying that the person is experiencing an ongoing or an end of pregnancy.</span></p>
Concept Description Variation Corpus
<p>Il corpus Concept Description Variation è una risorsa parallela annotata con relazioni di prerequisito tra triple di testi che descrivono in lingua inglese concetti nei domini della matematica, geometria, data mining e fisica. L'annotazione è stata effettuata tramite crowd-sourcing per 10 diverse triple di concetti. Per ogni concetto, la sua descrizione è stata estratta da tre differenti risorse enciclopediche, ciascuna rivolta a un pubblico diverso: Simple Wikipedia, che offre testi semplici pensati per apprendenti di lingua seconda o persone con difficoltà cognitive; Wikipedia, che contiene descrizioni in inglese standard; e enciclopedie per specialisti, che utilizzano un lessico e uno stile di scrittura destinati a esperti del dominio. Pertanto, complessivamente, la risorsa è composta da 90 testi che descrivono 30 diversi concetti. Le annotazioni mirano a valutare se le differenze di stile nella scrittura delle descrizioni dei concetti influiscono sulla capacità dei soggetti di riconoscere e annotare correttamente l'ordine di apprendimento dei concetti all'interno di una tripla. Questa caratteristica rende il corpus parallelo una risorsa originale e unica nel suo genere.</p>
Support data for article "The concept of optimal planning of a linearly oriented segment of the 5G network"
<p>Support data for article</p> <p>V. Kovtun, K. Grochla, E. Zaitseva, and V. Levashenko, “The concept of optimal planning of a linearly oriented segment of the 5G network,” PLOS ONE, vol. 19, no. 4. Public Library of Science (PLoS), p. e0299000, Apr. 17, 2024. doi: 10.1371/journal.pone.0299000.</p> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
Support data for article "The concept of network resource control of a 5G cluster focused on the smart city's critical infrastructure needs"
<p>Support data for article:</p> <div>V. Kovtun, K. Grochla, and K. Połys, “The concept of network resource control of a 5G cluster focused on the smart city’s critical infrastructure needs,” Alexandria Engineering Journal, vol. 94. Elsevier BV, pp. 248–256, May 2024. doi: 10.1016/j.aej.2024.03.038.</div> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
Support data for conference paper "The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices"
<p>upport data for conference paper<br>Kovtun, and O. Kovtun, “The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices.” In Proc. 8th International Conference on Computational Linguistics and Intelligent Systems. Volume I: Machine Learning Workshop, CEUR-WS, vol. 3664, 2024; pp. 273-283.<br>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
OA-Concepts and Wikipedia-Links for "The different AI of Science and Wikipedia"
<p>The files contain the data for the VosViewer analyses in "The Different Artificial Intelligences of Science and Wikipedia" (Korte et al. 2024).</p> <p>OpenAlex:</p> <p>As described in the paper, all works from OpenAlex from 2001 and 2022 with the concept "Artificial Intelligence" and a concept score > 0.3 were downloaded. In August 23 using the OpenAlex API. The files contain for each work all concepts with a concept score > 0.3 in one line each separeted by dots. This allows co-occurrence analyses of concepts in VosViewer. For the analyses in the paper whitespaces and "(" in the concepts were removed.</p> <p>First line for 2001: <br>Random forest. Mathematics. AdaBoost. Statistics. Tree (set theory). Generalization. Support vector machine. Generalization error. Measure (data warehouse). Artificial intelligence. Pattern recognition (psychology). </p> <p>Wikipedia:</p> <p>As described in the paper, all Wikipedia pages of the category "Artificial Intelligence" and of all direct sibling categories were downloaded. In August 23 with the Wikipedia Periodic Revisions tool of El Baff and Hecking (https://github.com/DLR-SC/wikipedia-periodic-revisions): . The files contain for each page all hyperlinks to other Wikipedia pages for the dates of 12.31.2005 and 12.31.2021 in one line each separeted by dots. This allows co-occurrence analyses of links in Wikipedia.</p> <p>First line of 2006: <br>vehicleregistrationplate. corporation. googlesearch. googleplatform. googol. google(disambiguation). menlopark,california. erice.schmidt. sergeybrin. lawrencee.page. georgereyes. internet. [...]</p>
Two document-concept representations of the biomedical literature
<p>These two datasets represent the biomedical literature (Medline abstracts and PubMedCentral articles) in the "document-concept matrix" format produced by <a href="https://github.com/erwanm/tdc-tools">TDC Tools</a>. These datasets can be used in downstream IR applications such as Literature-Based Discovery.</p> <p>Each of the two datasets corresponds to a specific data extraction method, see details <a href="https://erwanm.github.io/tdc-tools/input-data-format/">here</a> and in the paper linked below.</p> <ul> <li>Paper: <em>pending </em></li> <li>Code: <a href="https://github.com/erwanm/tdc-tools">https://github.com/erwanm/tdc-tools</a> <ul> <li>Documentation:<a href="https://erwanm.github.io/tdc-tools/">https://erwanm.github.io/tdc-tools/</a></li> </ul> </li> </ul> <p><strong>Important:</strong> the raw data from which this data is derived was downloaded from <a href="https://www.nlm.nih.gov/medline/medline_overview.html">Medline</a>, <a href="https://www.ncbi.nlm.nih.gov/pmc/">PubMedCentral</a> and <a href="https://www.ncbi.nlm.nih.gov/research/pubtator/">PubTatorCentral</a>, provided <a href="https://www.nlm.nih.gov/databases/download/terms_and_conditions.html">courtesy of the U.S. National Library of Medicine (NLM)</a>. The data was extracted in January 2021 and do not reflect the most current/accurate data available from NLM. See the github repository above in order to generate similar datasets from up to date data.</p>
A concept for FAIR clinical medication data usage - From care to research with OMOP: literature list of OHDSI studies
<p>This list of papers has been reviewed for the usage of drug data and to answer the question on what drug level the study was done. </p> <p>We checked whether drug ingredient level or drug component with dose and unit was required for the studies. </p>
Cardiac patch transplantation instruments for robotic minimally invasive cardiac surgery: initial proof-of-concept designs and surgery in a porcine cadaver
<p>BACKGROUND: Damaged cardiac tissues could potentially be regenerated by transplanting bioengineered cardiac patches to the heart surface. To be fully paradigm-shifting, such patches may need to be transplanted using minimally invasive robotic cardiac surgery (not only traditional open surgery). Here, we present novel robotic designs, initial prototyping and a new surgical operation for instruments to transplant patches <em>via </em>robotic minimally invasive heart surgery. METHODS: Robotic surgical instruments and automated control systems were designed, tested with simulation software and prototyped. Surgical proof-of-concept testing was performed on a pig cadaver. RESULTS: Three robotic instrument designs were developed. The first (called “Claw” for the claw-like patch holder at the tip) operates on a rack and pinion mechanism. The second design (“Shell-Beak”) uses adjustable folding plates and rods with a bevel gear mechanism. The third (“HeartStamp”) utilizes a stamp platform protruding through an adjustable ring. For the HeartStamp, rods run through a cylindrical structure designed to fit a uniportal Video-Assisted Thorascopic Surgery (VATS) surgical port. Designed to work with or without a sterile sheath, the patch is pushed out by the stamp platform as it protrudes. Two instrument robotic control systems were designed, simulated <em>in silico</em> and one of these underwent early ‘sizing and learning’ prototyping as a proof-of-concept. To reflect real surgical conditions, surgery was run “live” and reported exactly (as-it-happened). We successfully picked up, transferred and released a patch onto the heart using the HeartStamp in a pig cadaver model. CONCLUSION: These world-first designs, early prototypes and a novel surgical operation pave the way for robotic instruments for automated keyhole patch transplantation to the heart. Our novel approach is presented for others to build upon free from restrictions or cost – potentially a significant moment in myocardial regeneration surgery which may open a therapeutic avenue for patients unfit for traditional open surgery.</p>
Figure 29 in Expanded concept of the milliped family Spirobolidae Diplopoda: Spirobolida: Spirobolidea): Proposals of Aztecolini n. tribe and Floridobolinae/ini and Tylobolini n. stats.; (re)descriptions of Floridobolus and F. penneri, both Causey, 1957, and F. orini n. sp.; hypotheses on origins and affinities
Figure 29. Distribution of Spirobolini.
FIG. 4 in Evaluation of Linnaeus' concept of Santolina rosmarinifolia L. (Asteraceae, Anthemideae) and its interpretation
FIG. 4. — Lectotype of S. rosmarinifolia L. var. foliosa Sennen & Elías (BC-SENNEN 831809).
FIG. 1 in Evaluation of Linnaeus' concept of Santolina rosmarinifolia L. (Asteraceae, Anthemideae) and its interpretation
FIG. 1. — Specimen of S. rosmarinifolia L. (L 0053126) preserved in the herbarium of A. van Royen.
FIG. 2 in Evaluation of Linnaeus' concept of Santolina rosmarinifolia L. (Asteraceae, Anthemideae) and its interpretation
FIG. 2. — Lectotype of S. linearifolia Jord. & Fourr. (LY-Jordan).
FIGURE 6 in Generic Concept Of The Phytoseiids (Acari: Phytoseiidae) According To Athias-Henriot
FIGURE 6: Some related characters considered for the definition of the genus Neoseiulus and of the species groups inside the genus, according to Athias-Henriot concept (after Ragusa and Athias-Henriot, 1983 modified).
FIGURE 2 in Generic Concept Of The Phytoseiids (Acari: Phytoseiidae) According To Athias-Henriot
FIGURE 2: (a) – Elaboration of Sellnick's and Garman's chaetotactic nomenclature system by Athias-Henriot (1957), using Blattisocius keegani Fox as example: (b) – Schematic position of dorsal setae for the phytoseiid genera Typhlodromus and Amblyseius; (after Athias- Henriot, 1957, 1958 modified).
FIGURE 5 in Generic Concept Of The Phytoseiids (Acari: Phytoseiidae) According To Athias-Henriot
FIGURE 5: Supraspecific grouping of Amblyseiini based on dorsal setal pattern and on the shape of the insemination apparatus (After Athias-Henriot, 1966 modified).
FIGURE 4 in Generic Concept Of The Phytoseiids (Acari: Phytoseiidae) According To Athias-Henriot
FIGURE 4: I – Dorsal chaetotactic nomenclature system adopted by Athias-Henriot for the Amblyseiini after Lindquist & Evans (1965). For more explanation see note 4. II – Position of the solenostome gv3 on ventrianal shield of the protoadenic gamasid Dendrolaelaps sp. (A), and of two phytoseiid mites: Dictydionotus desertus (B) and Cydnodromus fallacoides (C): a - pre-anal sigilla; b - peri-anal sigilla; V4 - para-anal setae (after Athias-Henriot, 1977 and 1978 modified).
FIGURE 3 in Generic Concept Of The Phytoseiids (Acari: Phytoseiidae) According To Athias-Henriot
FIGURE 3: Correspondances between Athias-Henriot's. (a) – and Hirschmann's; (b) – chaetotactic nomenclature systems. The drawing of Amblyseius leucophaeus Athias-Henriot 1959 is considered here as an example (after Athias-Henriot, 1957, 1959 modified).
Concept Recognition as Translation
<p>Large files for the Concept Recognition as Translation project on GitHub with the associated paper titled: "Concept Recognition as a Machine Translation Problem" in BMC Bioinformatics.</p>
Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines - Artefacts DataSet
<p>This Archive contains the Artefact of the paper, submitted at GPCE2021 :<br> <a href="https://doi.org/10.1145/3486609.3487208">Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines</a></p> <p> </p> <p>It contains the dataset and the results of our Feature Location techniques when applied to this dataset.</p>
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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.