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2,359 results for “online”
The motivational factors of AI technology that influence milliennials and members of Generation Z in online transactions
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Kinematics and EMG to show integration of proprioceptive and visual feedback during online control of reaching
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Engaging online students by activating ecological knowledge
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Online Supplementary Data: Deep-time biodiversity patterns and the dinosaurian fossil record of the Late Cretaceous Western Interior, USA
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Dataset with determinants or factors influencing graduate economics student preparation and success in an online environment
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The Threatened Species No-Go Mapping Tool: An online open-access land-use decision support tool that identifies areas of importance for highly sensitive species of conservation concern
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Data from: Comparison of Nottingham Prognostic Index and Adjuvant Online prognostic tools in young women with breast cancer: review of a single-institution experience
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Online program metrics and evaluation for FMNP and NATA
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Data from: Two new species of Limbodessus diving beetles from New Guinea - short verbal descriptions flanked by online content (digital photography, μCT scans, drawings and DNA sequence data)
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Data from: Evolutionary online behaviour learning and adaptation in real robots
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Low- versus standard-dose alteplase in acute lacunar ischemic stroke: the ENCHANTED trial - online supplemental
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Chimpanzee identification and social Network construction through an online citizen science platform
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Coarse-scale online data reveals habitat similarities but weak cross-taxa congruence between insectivorous bats and birds in the eastern U.S.
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Poleward expanding species and non-indigenous species along the Italian, Croatian and Montenegrin coasts: a large‐scale survey based on online questionnaires.
<p>The following dataset contains information from an online survey that was carried out by three different research institutes: the Institute for Environmental Protection and Italy (ISPRA), the IRBIM- CNR, Institute of Biological Resources and Marine Biotechnologies, National Research Council, Ancona, Italy and the Institute of Oceanography and Fisheries, Split, Croatia. The survey was implemented on Google Forms and it was advertised on Facebook groups about recreational spearfishing and sea angling; some questionnaires were also snowballed in the community of recreational spearfishers and sea anglers, on Twitter. Respondents were from Italy, Croatia and Montenegro. The questionnaire was translated in Italian and Croatian. Overall, the questionnaire took approximately 10 minutes to complete.</p>
Virus alignments found in human cancer - Online Table
<p>The file contains three worksheets. The first one (OT1.1-Raw virus alignments) contains a complete list of all viral sequences detected in the study. The second (OT1.2-Filtering rules) describes rules for how raw alignments were filtered. The third (OT1.3-Viruses in cancer table) displays a table of viruses detected across cancers split by library type.</p> <p> </p> <p>OT1.1-Raw virus alignments</p> <p>"reason" (column AF): the value "a10m5" is a specific filter criterion we employed. If the number of alignments (column J) is fewer than 10 or if the MAPQ score (column K) is less than 5, the "keep" column is set to "no" and the "reason" is indicated as "a10m5". </p>
Training Data of Quantitative Online NMR Spectroscopy for Artificial Neural Networks
<p>Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). <sup>1</sup>H spectra (43 MHz) were recorded as single scans.</p> <p> Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (<em>i</em>) Training data based on combinations of measured pure component spectra and (<em>ii</em>) Training data based on a spectral model.</p> <p><strong>Synthetic low-field NMR spectra</strong></p> <p>First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.</p> <p><em>X<sub>i</sub></em> (“pure component spectra dataset”)</p> <p><em>X<sub>ii</sub></em> (“spectral model dataset”)</p> <p><strong>Experimental low-field NMR spectra from MNDPA-Synthesis</strong></p> <p>This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.</p>
Twitter dataset used in the paper: New discourses of masculinity in the context of online misogyny in Spain: the use of the "feminazi" concept on Twitter
<p>Twitter dataset (in spanish) retrieved from 2019-04-04 to 2019-04-09 scraping a sample of two hashtags: #feminazi and #ideologiadegenero</p> <p>File codification:</p> <p>Codification: Western Europe (Windows-1252/WinLatin 1)<br> Field delimiter: ;<br> String delimiter: ""</p>
A Detailed View of the Circumstellar Environment and Disk of the Forming O-star AFGL 4176 - Online Data
<p>These data accompany the ApJ paper Johnston et al. (2020), "A Detailed View of the Circumstellar Environment and Disk of the Forming O-star AFGL 4176" </p> <p>ArXiv link: <a href="https://arxiv.org/abs/2004.13739">https://arxiv.org/abs/2004.13739</a></p> <p>Abstract: We present a detailed analysis of the disk and circumstellar environment of the forming O-type star AFGL 4176 mm1, placing results from the Atacama Large Millimeter/submillimeter Array (ALMA) into context with multiwavelength data. With ALMA, we detect seventeen 1.2 mm continuum sources within 5" (21,000 au) of AFGL 4176 mm1. We find that mm1 has a spectral index of 3.4±0.2 across the ALMA band, with >87% of its 1.2 mm continuum emission from dust. The source mm2, projected 4200 au from mm1, may be a companion or a blueshifted knot in a jet. We also explore the morphological differences between the molecular lines detected with ALMA, finding 203 lines from 25 molecules, which we categorize into several morphological types. Our results show that AFGL 4176 mm1 provides an example of a forming O-star with a large and chemically complex disk, which is mainly traced by nitrogen-bearing molecules. Lines that show strong emission on the blueshifted side of the disk are predominantly oxygen-bearing, which we suggest are tracing a disk accretion shock. The molecules C^{34}S, H2CS and CH3CCN trace a slow wide-angle wind or dense structures in the outflow cavity walls. With the Australia Telescope Compact Array (ATCA), we detect a compact continuum source (<2000 x 760 au) at 1.2 cm, associated with mm1, of which >96% is from ionized gas. The ATCA NH3(1,1) and (2,2) emission traces a large-scale (r ~ 0.5 pc) rotating toroid with the disk source mm1 in the blueshifted part of this structure offset to the northwest.</p> <p>A list and short description of the files in this Zenodo dataset are given in the file <a href="https://zenodo.org/api/files/9c854b6e-4b23-4c32-9322-005b82b9eaf8/README_Johnstonetal2020.txt">README_Johnstonetal2020.txt</a>.</p>
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Dataset on the online cryptocurrency discussion on Twitter, Telegram, and Discord
<p>This Dataset is described in <em><strong>Charting the Landscape of Online Cryptocurrency Manipulation</strong></em>. <strong><em>IEEE Access (2020)</em></strong>, a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.</p> <p>Please, refer to the paper below for more details.</p> <p>Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).</p> <p>This dataset is composed of: </p> <ul> <li>~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API;</li> <li>~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL);</li> <li>~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight).</li> </ul> <p>With such information, one can easily retrieve the content of channels and messages through Twitter, Telegram, and Discord public APIs.</p> <p>Please, refer to the README file for more details about the fields.</p>
ScienceDex guides
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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.