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2,359 results for “Online”
Dataset: Global-E Online Ltd. (GLBE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Codere Online Luxembourg, S.A. (CDRO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Codere Online Luxembourg, S.A. (CDROW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: B.O.S. Better Online Solutions Ltd. (BOSC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Yatra Online, Inc. (YTRA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
"Towards Responsible Publishing" - Online researcher survey dataset
<p><a href="https://www.research-consulting.com/" target="_blank" rel="noopener">Research Consulting</a> and the <a href="https://www.cwts.nl/" target="_blank" rel="noopener">Centre for Science and Technology Studies (CWTS)</a> were commissioned to determine to what extent the vision, mission and objectives set out in the TRP proposal serve the needs of the global research community. Through a consultative approach, we sought to assess whether there is appetite for the type of change proposed by cOAlition S in the TRP proposal. In addition, we aimed to understand how the ‘Towards Responsible Publishing’ proposal may be modified or refined to ensure it resonates with the research community and sees broad adoption; identify any showstoppers or unintended consequences and propose proactive measures to mitigate them, ensuring successful implementation; and ascertain whether the existing scholaly communication infrastructure can support this proposal and if not, identify where research funders can strengthen the infrastructure. </p> <p>As part of the consultation, the Centre for Science and Technology Studies and Research Consulting developed a researcher survey to gather researcher feedback on aspects of the scholarly communication system. This deposit provides our anonymised survey data. Please note that country information is available for all countries with at least 50 responses; in all other cases, the country has been replaced with “Other”.</p>
Figure 1. A in Online database "See The Sea" for the Caspian Sea
Figure 1. A satellite view (true color) of the Caspian Sea on 22 August 2019 from MODIS-Terra (©NASA, 2019).
Dataset Thesis of Diskriminasi Gender Berita Kriminalitas Online Di Indonesia
<p>Dataset pada file ZIP berisi data ringkasan berita kriminalitas di indonesia pada 1 Januari - 31 Desember 2023, data hasil pra-pemrosesan, dan data ekstraksi fitur vektor menggunakan word embedding sebelum dan sesudah debiasing.</p>
Video: 50 cool new things you can now do with KB's collection highlights - WikidataCon, Online, 30-10-2021
<div><span><strong>English: </strong></span> This video is a guided tour through the 5 articles of the series <strong><a href="https://kbnlwikimedia.github.io/KBCollectionHighlights/stories/Cool%20new%20things%20you%20can%20now%20do%20with%20the%20KB's%20collection%20highlights/" rel="nofollow">50 cool new things you can now do with KB’s collection highlights</a></strong> <p>You will gain a better understanding of</p> <ol> <li>that the Wikimedia infrastructure (= the combination of Wikidata, Wikimedia Commons and Wikipedia) offers a coherent, free, unlimited, sustainable, publicly accessible and participative 'LEGO box' with which any GLAM can improve the reusability of its collections at very limited costs & resources.</li> <li>how they can use this LEGO box to add new functionalities to their GLAM collections,</li> <li>that these 'new cool things' apply to both human (frond end, GUI) and machine (back end, SPARQL, API) users.</li> </ol> <ul> <li><a title="File:50 cool new things you can now do with KBs collection highlights - WikidataCon 30102021 OlafJanssen Online.pdf" href="https://commons.wikimedia.org/wiki/File:50_cool_new_things_you_can_now_do_with_KBs_collection_highlights_-_WikidataCon_30102021_OlafJanssen_Online.pdf">Presentation used in this video</a> (Wikimedia Commons)</li> <li>Same video of Youtube: <a href="https://www.youtube.com/watch?v=tDsujh1CYUU" rel="nofollow">https://www.youtube.com/watch?v=tDsujh1CYUU</a></li> <li>Outline: <a href="https://web.archive.org/web/20211102112548/https://pretalx.com/wdcon21/talk/review/AB78GBGUTDVY788QHQ3NUPJEEQFLBF9B" rel="nofollow">https://web.archive.org/web/20211102112548/https://pretalx.com/wdcon21/talk/review/AB78GBGUTDVY788QHQ3NUPJEEQFLBF9B</a></li> </ul> </div> <div><span><strong>Nederlands: </strong></span> <a title="File:Nieuwe functionaliteiten voor de topstukken van de Koninklijke Bibliotheek - WikiConNL 13112021 OlafJanssen Online.pdf" href="https://commons.wikimedia.org/wiki/File:Nieuwe_functionaliteiten_voor_de_topstukken_van_de_Koninklijke_Bibliotheek_-_WikiConNL_13112021_OlafJanssen_Online.pdf">Deze gelijkwaardige presentatie in het Nederlands</a> is een rondwandeling door de 5 artikelen in de serie <strong><a href="https://kbnlwikimedia.github.io/KBCollectionHighlights/stories/Cool%20new%20things%20you%20can%20now%20do%20with%20the%20KB's%20collection%20highlights/" rel="nofollow">50 cool new things you can now do with KB’s collection highlights</a></strong> Vorig jaar heeft de Koninklijke Bibliotheek <a href="https://www.kb.nl/galerij/digitale-topstukken" rel="nofollow">haar topstukken</a> in Wikidata, Wikimedia Commons en Wikipedia ondergebracht. Hiermee is dit veelal historische culturele erfgoed beter zichtbaar, vindbaar én herbruikbaar gemaakt, waardoor veel dingen die op de eigen KB-website <a href="https://kbnlwikimedia.github.io/KBCollectionHighlights/stories/Cool%20new%20things%20you%20can%20now%20do%20with%20the%20KB's%20collection%20highlights/Part%201%2C%20Introduction.html" rel="nofollow">niet kunnen</a> nu opeens wél mogelijk zijn geworden. Met andere woorden: door deze collectie te wikificeren zijn er allerlei <a href="https://kbnlwikimedia.github.io/KBCollectionHighlights/stories/Cool%20new%20things%20you%20can%20now%20do%20with%20the%20KB's%20collection%20highlights/" rel="nofollow">nieuwe functionaliteiten</a> voor onze topstukken bijgekomen, waarvan Olaf je tijdens deze presentatie graag een korte indruk wil geven.</div>
A Corpus of Online Drug Usage Guideline Documents Annotated with Type of Advice
<p><strong>Introduction: </strong>The goal of this dataset is to aid NLP research on recognizing safety critical information from drug usage guideline or patient handout data. This dataset contains annotated advice statements from 90 online DUG documents that corresponds to 90 drugs or medications that are used in the prescriptions of patients suffering from one or more chronic diseases. The advice statements are annotated in eight safety-critical categories: activity or lifestyle related, disease or symptom related, drug administration related, exercise related, food or beverage related, other drug related, pregnancy related, and temporal. </p> <p><strong>Data Collection: </strong>The data was collected from <a href="https://www.medscape.com">MedScape</a>. It is one of the most widely used reference for health care providers. At first, 34 real anonymized prescriptions of patients suffering from one or more chronic diseases are collected. These prescriptions contains 165 drugs that are used to treat chronic diseases. Then, MedScape was crawled to collect the drug user guideline (DUG) / patient handout for these 165 drugs. But, MedScape does not have DUG document for all drugs. We found DUG document for 90 drugs in MedScape. </p> <p><strong>Data Annotation tool: </strong>The data annotation tool is developed to ease the annotation process. It allows the user to select a DUG document and select a position from the document in terms of line number. It stores the user log from the annotator and loads the most recent position from the log when the application is launched. It supports annotating multiple files for the same drug, as often there are multiple overlapping sources of drug usage guidelines for a single drug. Often DUG documents contain formatted text. This tool aids annotation of the formatted text as well. The annotation tool is also available upon request.<strong> </strong></p> <p><strong>Annotated Data Description: </strong>The annotated data contains the annotation tag(s) of each advice extracted from the 90 online DUG documents. It also contains the phrases or topics in the advice statement that triggers the annotation tag, such as, activity, exercise, medication name, food or beverage name, disease name, pregnancy condition (gestational, postpartum). Sometimes disease names are not directly mentioned rather mentioned as a condition (e.g., stomach bleeding, alcohol abuse) or state of a parameter (e.g., low blood sugar, low blood pressure). The annotated data is formatted as following:<br> drug name, drug number, line number of the first sentence of the advice in the DUG document, advice Text, advice tag(s), medication, food, activity, exercise, and disease names mentioned in the advice. </p> <p><br> <strong>Unannotated Data Description:</strong><br> The unannotated data contains the raw DUG document for 90 drugs. It also contains the drug interaction information for the 165 drugs. The drug interaction information is categorized in 4 classes, contraindicated, serious, monitor closely, and minor. This information can be utilized to automatically detect potential interaction and effect of interaction among multiple drugs. </p> <p><strong>Citation: </strong>If you use this dataset in your work, please cite the following reference in any publication:</p> <p>@inproceedings{preum2018DUG,<br> title={A Corpus of Drug Usage Guidelines Annotated with Type of Advice},<br> author={Sarah Masud Preum, Md. Rizwan Parvez, Kai-Wei Chang, and John A. Stankovic},<br> booktitle={ Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},<br> publisher = {European Language Resources Association (ELRA)},<br> year={2018}<br> }</p>
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 2. Online presence through a website of Romanian tourism entities with informative role Source: authors
<p>According to research results (Figure 2), almost 68% of the entities with tourist information and promotion role own a proper site for the presentation of the work, while 18.35%, most probably do not realize in pragmatic terms the usefulness of such promotional tools. The situation can be cataloged as quite worrying, especially if we consider that today, due to the fulminant development of smartphones, more and more tourists choose to seek information on the Internet, even during their trip to a new destination (Wang el. al. 2012).</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 6. The general conception of our asynchronous system BCI (offline - online) for reinforcement of a joystick movement
<p>Once the motor imagery is identified, a command may be associated to this mental task in order to control a machine (Prataksita et al., (2014)) (Guger et al., 1999). In this work, we constructed a new Simuhnk/MathWork model to translate on-line the EEG signals into low-level commands. Fig. 6 shows our experimental EEG-based BCI System </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 1. General architecture of an online (BCI)
<p>One major challenge of our BCI system is to describe the signals EEG by a few relevant values called features i.e. step 3 in Fig (1). The success of the mental imagery classification depends on the choice of features used to characterize the raw EEG signals. These features can then be used in step 4 in order to classify the user’s mental state. Several approaches for feature extraction have been proposed in literature. </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach
<p>In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example
<p>Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript. </p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
CIMULACT WP4 online consultation results
<p>The online consultation (consultation.cimulact.eu) is part of the second consultation phase in the CIMULACT project. Its purpose is to create a space where citizens in the 30 countries are exposed to the 48 research scenarios resulting from the project's <a href="http://www.cimulact.eu/scenariococreationworkshop/">Milan co-creation workshop.</a> Within any one country, participants in the online consultations (‘regular citizens’ as well as others with various levels of relevant know-how) engage in depth with eight of these scenarios, according to their interests and/or expertise. Thus, the aim is to prioritize research scenarios and enrich them to some extent.</p> <p>The consultation format relies on a modified online Delphi template (dubbed Dynamic Argumentative Delphi, DAD). This format was tested in the past by the WP4 leader (Institutul de Prospectiva) on several occasions, including three national-scale consultations in Romania, each with thousands of respondents. Technical information on the DAD format is available at <a href="https://goo.gl/8XgY7j">https://goo.gl/8XgY7j</a>.</p>
Effects of community management on user activity in online communities
<p>Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft <a href="https://www.overleaf.com/13551865bzgswwbvkpgq#/52358582/">here</a>.</p> <p>Instructions:</p> <ol> <li>Unzip the files.</li> <li>Start with JSON files obtained from calling platform APIs: each dataset consists of one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.</li> <li>Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.</li> <li>Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file</li> <li>Launch Stata and open each flat csv files with it, then save it in Stata format.</li> <li>Use the provided Stata .do scripts to replicate results.</li> </ol> <p>Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.</p> <p> </p>
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.