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2,371 results for “platform”
2013 climate data for eddy flux platform on Toolik Lake, Alaska
Yearly file describing the metological conditions on Toolik Lake adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This location is a floating platform where eddy flux measurements have been made, and should not be confused with either the Toolik Field Station Climate site, which is a land-based station, or the Toolik Lake Climate Station that is lake-based but at a different location (approximately 300 m from the eddy platform). Note that the terrestrial station has been called the "Toolik Main Climate Station", and the station on the lake is located near the main Arctic long term ecological research lake sampling site, and has also been called the Toolik Lake Main Climate Station. Measurements at the eddy climate platform described here include air temperature, relative humidity, barometric pressure, wind direction, wind speed, radiation, and water temperature.
2020 Stakeholder Survey of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) - Quantitative Dataset
<p>This dataset is the outcome of a survey of IPBES stakeholders that was conducted in May-June 2020. The aim of this survey is to better understand stakeholder engagement with IPBES, to improve implementation of the IPBES stakeholder engagement strategy (decision IPBES-3/4 presented in document IPBES/3/18), and to further increase the inclusivity and effectiveness of the IPBES work programme. Results will help, among others, to better align communication and outreach, and to strengthen collaborative processes within the IPBES work programme.</p> <p><br> This dataset presents only the quantitative data of the complete dataset of responses. It has been anonymised and all personal comments in response to open questions have been removed. For information, the full anonymised dataset has been published on Zenodo, with restricted access (see DOI: <a href="http://doi.org/10.5281/zenodo.4121916">10.5281/zenodo.4121916</a>).</p> <p>This dataset is under restricted access and embargoed until after the eighth session of IPBES Plenary. For any inquiry, please contact IPBES Head of Communications (stakeholders@ipbes.net). </p>
Practices and policies of preprint platforms for life and biomedical sciences
<p>Given the increase in the use and profile of preprint servers – and alternative publishing hybrid platforms such as F1000 Research – in the life sciences, it is increasingly important to identify how many such servers and hybrids exist, to describe their scope in terms of the scientific disciplines they cover, and to compare and contrast their characteristics and policies.</p> <p>We surveyed forty-four (44) platforms that host preprints relevant to life and biomedical sciences and that were active online and accepting submissions on 25 June 2019. Information on preprint platform policies, features and practices was collected through online research by the authors and by surveying preprint platform representatives directly. </p> <p>Full data sheets include an additional 5 platforms hosted on OSF Preprints (rows 49-53) to fulfil the wider scope for the ASAPbio project, not in disciplinary scope (biology and medical sciences) for the manuscript with Jamie Kirkham.</p> <p><strong>Tables 1-5: </strong>Data (44 platforms, manuscript) are separated into five main tables of information and a list of preprint platform websites for reference.</p> <p>Table 1: Scope and ownership of each server<br> Table 2: Content-specific characteristics and information relating to submission, journal transfer options, and external discoverability<br> Table 3: Screening, moderation, and permanence of content<br> Table 4: Usage metrics and other features<br> Table 5: Metadata<br> Preprint platform websites</p> <p>Data for each platform are listed as ‘Verified’ in the tables if these tables (V1.0 or V2.0) were seen and approved by a platform representative between January 13 and January 27, 2020.</p> <p><strong>Original online survey:</strong> a blank copy of the original survey form used by online researchers (the authors) and supplied pre-filled (or empty, in some cases) to preprint platform representatives for verification (or completion, in some cases). </p> <p><strong>Final data:</strong> survey data is presented in .txt and .xlsx, as follows:</p> <ul> <li>Row 1: Heading (where field is included in manuscript tables, the heading presented here replaces any heading used in original survey. All columns are presented in the order the information was requested on the original survey form, with some supplementary columns added and columns removed (detailed below).</li> <li>Row 2: Schema or description of field</li> <li>Row 3: Whether and where included in manuscript tables. For supporting information for table data (e.g. source information, URLs), the table location for supported data is indicated in brackets, e.g. (Table 2) and supporting information is not included in tables. Data included in manuscript tables is presented in its final form, which in some cases is simplified from the original survey data. This simplified version of the data was presented to platform representatives for additional verification (v1.0/v2.0 verification). Data not included in manuscript tables is presented here as verified by platform representatives and/or found online. Some columns from the original survey have been removed due to the information not being informative or useful: specifically, Print ISSN (not reported for any platform); End date (no platforms have an end date; although two platforms stopped accepting submissions after survey completed; Personal contact information for platform representative(s) has been removed).</li> <li>Rows 4 onwards: data for each preprint platform (44 included in manuscript (rows 4-47), plus 5 additional OSF platforms (rows 48-52)</li> <li>Columns 3-6 (D-G) report online research and verification information and Column 13 (M) reports an additional data field (number of articles) – these are supplementary to the original survey columns</li> <li>Verification status: Released V1/V2 data applies to data included in manuscript tables (as indicated in row 3); Online survey data applies to data used for manuscript tables and also to original survey data included here but not included in manuscript tables (‘Not included’ in row 3)</li> <li>Note that data fields are presented as individual columns in these sheets, while some entries in Tables 1-5 combine several data fields.</li> </ul> <p>These data were collected in collaboration and as part of:<br> i. An ASAPbio project, led by Dr Naomi Penfold, to develop an online directory of preprint platforms<br> ii. A research project led by Prof Jamie Kirkham<br> These data are supplementary outputs for both projects.</p> <p>Data v1.0 were presented during the ASAPbio January 2020 workshop – see Penfold, Naomi C, & Polka, Jessica. (2020, January). ASAPbio Preprint Platform Directory: 2019 data (presentation) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3626770.<br> <br> <strong>Version 3.0 updates (December 14, 2020): added new files with updated information about servers from the ASAPbio preprint directory (https://asapbio.org/preprint-servers), provided by Jessica Polka (now included as author).</strong></p>
Cross Platform Dataset with Posts Surrounding U.S capitol attack
<p>This is a cross-platform dataset containing the posts around specific hashtags related to U.S. Capitol protests on January 6th 2021. </p>
Top selling games in Steam platform
<p>The dataset is extracted from the list of best-selling games available in Spanish on the Steam platform. It contains information related to the content of the game, to its development and marketing (price and discounts), as well as different metrics derived from user opinions.</p>
(Rawdata) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Rawdata used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Automotive Environment
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the University of Birmingham using distributed radar sensors installed on the mobile laboratory. The data will be used to develop algorithms to extract the information needed for high-resolution multi-modal and multi-perspective sensing.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars.</p> <p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for the data collection campaign.</p> <p>Contact: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com, or m.s.gashinova@bham.ac.uk</p>
Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)
<p>This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.</p> <p>This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.</p> <p> </p> <p>If you want to know more information or obtain complete files, please go to the official website of this platform:</p> <p><a href="https://ccmusic-database.github.io/en/">Music Data Sharing Platform for Academic Research</a></p> <p> </p> <ul> <li> <p><strong>Chinese Traditional Instrument Sound Database (CTIS)</strong></p> </li> </ul> <p>This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.</p> <ul> <li> <p><strong>Multi-functional Music Database for MIR Research</strong></p> </li> </ul> <p>This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.</p> <ul> <li><strong>Midi-wav Bi-directional Database of Pop Music</strong></li> </ul> <p>This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.</p>
2021 5Genesis Berlin Platform Field Trial RTT and Throughput Measurements
<p>RTT and Throughput measurements from the various measurement endpoints of the 2021 5GENESIS Berlin Platform Field Trials at IHP, in Frankfurt (Oder).</p>
Webinar Nuberu: Reliable RAN Virtualization in Shared Platforms @ IMDEA Networks
<p><strong>VIEW IN OUR YOUTUBE CHANNEL:</strong> <a href="https://youtu.be/iAoyuzHuZP0">https://youtu.be/iAoyuzHuZP0</a></p> <p>RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station¿s distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains >95% of the theoretical spectrum efficiency in hostile environments, where state-of-art approaches lose connectivity, and at least 80% resource savings.</p>
SCoRe-LFC: Platform data on crowd collaboration in higher education
<p>SCoRe (short for Student Crowd Research) was a joint research project between the Universities of Bremen (UB), Hamburg (UHH) and Kiel (CAU), the Macromedia University of Applied Sciences (HMM) and the Ghostthinker GmbH (GT). The overall aim of the project was to develop a digital learning and research environment as well as didactic scenarios that foster collaborative processes of research-based learning in large groups of students (crowd). The main subject area was research for sustainable development. Towards this end, the project consortium drew on the partners’ expertise on advanced video-technologies (HHM), virtual collaboration in interdisciplinary and largescale groups (CAU), research-based learning (UHH) and education and research for sustainable development (UB). To achieve its goals, the project adopted a design-based research approach. The Project started in Oct. 2018 and was funded for 3.5 years by the Federal Ministry of Education and Research (BMBF) in a funding scheme on digital higher education.</p> <p>The work in the department of media-pedagogy and educational computer sciences at Kiel University was focused on the sub-project „SCoRe - learning and researching in the crowd“. The sub-project was aimed at the development, implementation and evaluation of pedagogical and organizational measures for the seeding, coordination and orchestration of collaborative research and learning processes in crowd scenarios. Particular emphasis was placed on crowd-specific characteristics of productive knowledge work in large and interdisciplinary groups.</p> <p>This dataset contains interaction data as well as textual content data. As ongoing development of the software platform led to a continuous integration of new features into the platform itself as well as changes to the data collection functions, making this an evolving dataset. Some inconsistencies exist due to software bugs.</p> <p><strong><a href="https://scorelfc.github.io/gestaltungsbericht3/img/datastructure.png">Platform data structure diagram</a> </strong></p> <p>Further Readings to gain an understanding of the platform and its interaction posbilities (in german):</p> <p><a href="https://scorelfc.github.io/gestaltungsbericht2">Design Report Prototype 2</a></p> <p><a href="https://scorelfc.github.io/gestaltungsbericht3">Design Report Prototype 3</a></p> <p> </p> <p><strong>Contained Files</strong></p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>annotations.csv</p> </td> <td> <p>Annotations (comment and/or drawings on the video) of video files</p> </td> </tr> <tr> <td> <p>content.csv</p> </td> <td> <p>Content of <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">sections</a></p> </td> </tr> <tr> <td> <p>events.csv</p> </td> <td> <p>All events triggered by user interaction</p> </td> </tr> <tr> <td> <p>media.csv</p> </td> <td> <p>Uploaded <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">images and videos</a></p> </td> </tr> <tr> <td> <p>messages.csv</p> </td> <td> <p><a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u08/">Chat messages</a></p> </td> </tr> <tr> <td> <p>sequences.csv</p> </td> <td> <p><a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u21/">Sequences</a> of video files</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Columns</strong></p> <p>(not all are present in each file. 0, “null” or “none” might mean not applicable)</p> <table> <tbody> <tr> <td> <p><strong>Column name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Format</strong></p> </td> </tr> <tr> <td> <p>Index (empty column name) </p> </td> <td> <p>unique identifier of the corresponding event in the original dataset</p> </td> <td> <p>UUID (int on rare occasions)</p> </td> </tr> <tr> <td> <p>Actor-Name</p> </td> <td> <p>Unique identifier of an actor – “MA” identifies project staff </p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Annotation-ID</p> </td> <td> <p>Unique identifier of an annotation</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Annotation-Text</p> </td> <td> <p>Label of an annotation</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Version-ID</p> </td> <td> <p>Unique identifier of a version of an auditable object (e.g. a section)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Version-Changelog</p> </td> <td> <p>Changelog message on saving a new version of a section</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Case-ID</p> </td> <td> <p>Unique identifier of a case (if applicable, coded by research team)</p> </td> <td> <p>String </p> </td> </tr> <tr> <td> <p>Media-Caption</p> </td> <td> <p>Title of a media file (image, video)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Media-ID</p> </td> <td> <p>Unique identifier of a media file (image, video)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Media-Timestamp</p> </td> <td> <p>Timestamp in a video</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Message-ID</p> </td> <td> <p>Unique identifier of a chat message</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Message-Text</p> </td> <td> <p>Content of a chat-message</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Object-Type</p> </td> <td> <p>Type of an object an action refers to</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Project-ID</p> </td> <td> <p>Unique identifier of a project</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Research-Task-Type</p> </td> <td> <p>Type of research task (if applicable, coded by research team, see table below)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Section-Content</p> </td> <td> <p>Content of a section (in a specific version) </p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Section-Outline-Level</p> </td> <td> <p>Outline level of a section (in a specific version)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-ID</p> </td> <td> <p>Unique identifier of a section</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Index</p> </td> <td> <p>Position of a section in the project (in a specific version)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Status</p> </td> <td> <p>Status of a section</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Title</p> </td> <td> <p>Title of a section (in a specific version)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Sequence-Description</p> </td> <td> <p>Description of a video sequence</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Sequence-Duration</p> </td> <td> <p>Length of a video sequence</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Sequence-ID</p> </td> <td> <p>Unique identifier of a sequence</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Sequence-Timestamp</p> </td> <td> <p>Timestamp of the start of a sequence in a video</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>timestamp</p> </td> <td> <p>timestamp of an event</p> </td> <td> <p>datetime</p> </td> </tr> <tr> <td> <p>Verb</p> </td> <td> <p>Action type of an event (see table below)</p> </td> <td> <p>string</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Verbs</strong></p> <table> <tbody> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>canceled editing of</p> </td> <td> <p>Actor canceled editing of a section</p> </td> </tr> <tr> <td> <p>clicked</p> </td> <td> <p>Actor clicked a link</p> </td> </tr> <tr> <td> <p>collapsed</p> </td> <td> <p>Actor collapsed a section (hides its content form being viewed)</p> </td> </tr> <tr> <td> <p>compared versions of</p> </td> <td> <p>Actor compared two versions of a section</p> </td> </tr> <tr> <td> <p>created</p> </td> <td> <p>Actor created a new section, video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>deleted</p> </td> <td> <p>Actor deleted a section, video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>ended</p> </td> <td> <p>Actor played a video hitting its end</p> </td> </tr> <tr> <td> <p>expanded</p> </td> <td> <p>Actor expanded a collapsed section</p> </td> </tr> <tr> <td> <p>inserted</p> </td> <td> <p>Actor inserted a video comment (on occasions instead of created)</p> </td> </tr> <tr> <td> <p>left</p> </td> <td> <p>Actor left a context (e.g. a project, a chat window) by e.g. closing it using platform functions, changing a browser tab, etc.</p> </td> </tr> <tr> <td> <p>mentioned</p> </td> <td> <p>Actor mentioned another actor in a chat message</p> </td> </tr> <tr> <td> <p>opened</p> </td> <td> <p>Actor opened a context (e.g. a project, a chat window) by e.g. accessing it using platform functions or changing a browser tab</p> </td> </tr> <tr> <td> <p>paused</p> </td> <td> <p>Actor paused a video</p> </td> </tr> <tr> <td> <p>played</p> </td> <td> <p>Actor played a video</p> </td> </tr> <tr> <td> <p>read</p> </td> <td> <p>Actor read an activity message or news</p> </td> </tr> <tr> <td> <p>read all messages and activities of</p> </td> <td> <p>Actor used switch to mark all chat and activity messages read</p> </td> </tr> <tr> <td> <p>restored</p> </td> <td> <p>Actor restored a deleted section</p> </td> </tr> <tr> <td> <p>reverted</p> </td> <td> <p>Actor restored a deleted section</p> </td> </tr> <tr> <td> <p>reverted version of</p> </td> <td> <p>Actor reverted a section to an earlier version</p> </td> </tr> <tr> <td> <p>seeked</p> </td> <td> <p>Actor seeked on a video timeline</p> </td> </tr> <tr> <td> <p>sent</p> </td> <td> <p>Actor sent a chat message</p> </td> </tr> <tr> <td> <p>started editing of</p> </td> <td> <p>Actor started editing of a section</p> </td> </tr> <tr> <td> <p>switched</p> </td> <td> <p>Actor switched chat focus between project and section chat</p> </td> </tr> <tr> <td> <p>typed</p> </td> <td> <p>Actor typed into the chat</p> </td> </tr> <tr> <td> <p>updated</p> </td> <td> <p>Actor updated an existing section (changing content, heading, heading-depth or status), video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>uploaded</p> </td> <td> <p>Actor uploaded an image or video</p> </td> </tr> <tr> <td> <p>viewed</p> </td> <td> <p>Actor viewed an entity (had it on screen for 5 seconds), e.g. a section or video comment</p> </td> </tr> <tr> <td> <p>viewed history of</p> </td> <td> <p>Actor viewed history of a section</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Project-ID</strong></p> <table> <tbody> <tr> <td> <p><strong>Project-ID</strong></p> </td> <td> <p><strong>Case-IDs</strong></p> </td> <td> <p><strong>Title</strong></p> </td> <td> <p> </p> <p><strong>Type</strong></p> </td> <td> <p><strong>Period</strong></p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>a1-a*</p> </td> <td> <p>Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.11.20-31.3.21</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>b1-b*</p> </td> <td> <p>Nachhaltiger Verkehr</p> </td> <td> <p>Research project</p> </td> <td> <p>1.11.20-31.3.21</p> </td> </tr> <tr> <td> <p>166</p> </td> <td> <p>c1-c*</p> </td> <td> <p>UGF - Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>168</p> </td> <td> <p> </p> </td> <td> <p>LGS - Foyer</p> </td> <td> <p>Onboarding of students in LGS Projects</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>188</p> </td> <td> <p> </p> </td> <td> <p>LGS - Reflexionsraum</p> </td> <td> <p>Reflection project for students in LGS Projects</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>207</p> </td> <td> <p> </p> </td> <td> <p>LGS - Nachhaltiger Konsum</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>210</p> </td> <td> <p> </p> </td> <td> <p>LGS - Bildungsangebote für nachhaltige Entwicklung</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>264</p> </td> <td> <p> </p> </td> <td> <p>LGS - Fahrradmobilität in Städten</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>271</p> </td> <td> <p> </p> </td> <td> <p>Fahrradmobilität in Städten</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>269</p> </td> <td> <p>e1-e*</p> </td> <td> <p>Kaufentscheidung vs. Nachhaltigkeit</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>262</p> </td> <td> <p>d1-d*</p> </td> <td> <p>Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>199</p> </td> <td> <p> </p> </td> <td> <p>Basiskurs</p> </td> <td> <p>Basic course for onboarding of students on the platform</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p> </p> </td> <td> <p>Glossar</p> </td> <td> <p>Glossar of definitions</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p> </p> </td> <td> <p>Erste Schritte</p> </td> <td> <p>How to start using score-docs platform</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p> </p> </td> <td> <p>Testbereich</p> </td> <td> <p>Area for testing score-docs functionalities</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p> </p> </td> <td> <p>Hilfestellungen</p> </td> <td> <p>Helpful links </p> </td> <td> <p>persistent</p> <p> </p> </td> </tr> </tbody> </table> <p>Other Project-IDs refer to personal assessment documents of individual students which are not included in content.csv</p> <p><strong>Research-Task-Type</strong></p> <table> <tbody> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Erheben</p> </td> <td> <p>Data collection</p> </td> </tr> <tr> <td> <p>Analysieren</p> </td> <td> <p>Case-specific data analysis and sensemaking</p> </td> </tr> <tr> <td> <p>Synthetisieren</p> </td> <td> <p>Cross-case data analysis and sensemaking</p> </td> </tr> <tr> <td> <p>Sonstiges</p> </td> <td> <p>Other, e.g. communication between participants</p> </td> </tr> </tbody> </table>
Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform
<p>Food safety and quality assessment mechanisms are unmet needs that industries and countries have been continuously facing in recent years. Our study aimed at developing a platform using Machine Learning algorithms to analyze Mass Spectrometry data for classification of tomatoes on organic and non-organic. Tomato samples were analyzed using silica gel plates and direct-infusion electrospray-ionization mass spectrometry technique. Decision Tree algorithm was tailored for data analysis. This model achieved 92% accuracy, 94% sensitivity and 90% precision in determining to which group each fruit belonged. Potential biomarkers evidenced differences in treatment and production for each group.</p>
Optical tweezer platform for the characterization of pH-triggered colloidal transformations in the oleic acid/water system
<p>Hypothesis: Soft colloidal particles that respond to their environment have innovative potential for many fields ranging from food and health to biotechnology and oil recovery. The in situ characterisation of colloidal transformations that triggers the functional response remain a challenge.</p> <p>Experiments: This study demonstrates the combination of an optical <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/micromanipulation">micromanipulation</a> platform, polarized optical video microscopy and <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/microfluidics">microfluidics</a> in a comprehensive approach for the analysis of pH-driven structural transformations in emulsions. The new platform, together with <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/synchrotron">synchrotron</a> small angle X-ray scattering, was then applied to research the food-relevant, pH-responsive, <a href="https://www.sciencedirect.com/topics/chemistry/oleic-acid">oleic acid</a> in water system.</p> <p>Findings: The experiments demonstrate structural transformations in individual oleic acid particles from micron-sized onion-type multilamellar oleic acid vesicles at pH 8.6, to nanostructured emulsions at pH < 8.0, and eventually oil droplets at pH < 6.5. The smooth particle-water interface of the onion-type vesicles at pH 8.6 was transformed into a rough particle surface at pH below 7.5. The pH-triggered changes of the interfacial tension at the droplet-water interface together with mass transport owing to structural transformations induced a self-propelled motion of the particle. The results of this study contribute to the fundamental understanding of the structure–property relationship in pH-responsive emulsions for nutrient and drug delivery applications.</p>
SCEC Broadband Platform Release 22.4.0 Validation Data
<p>This package includes the set of validation plots generated with the Broadband Platform Release 22.4.</p> <p>Fabio Silva, Kevin Milner, & Philip Maechling. (2022). SCECcode/bbp: Broadband Platform Release v22.4.0 (v22.4.0). Zenodo. https://doi.org/10.5281/zenodo.7062972</p> <p>The following folders include:</p> <p>2022-08-30-bbp-part-a - Broadband validation runs using ground motions from 17 historical events.</p> <p>2022-08-30-bbp-part-a-all - Broadband validation runs using ground motions from 17 historical events (same as above, but includes additional GoF plots such as maps and distance)</p> <p>2022-08-30-bbp-part-b - Broadband verification runs against NGA-West 2 GMPEs.</p> <p>2022-08-30-bbp-converge - Convergence plots for each method and event</p> <p>2022-08-30-bbp-tables - Summary tables for each method, along with aggregate results from all methods/events per distance and period range. Also includes Dreger figure 3 plots (see references below for more information)</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Granular Material Candidates
<p>A database of 14 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Geo-referencing of journal articles and platform design for spatial query capabilities
<p>We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, on the example of New Zealand. We counted place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer in the titles, abstracts and full texts of freely available papers of the New Zealand Journal of Geology and Geophysics, the New Zealand Journal of Marine and Freshwater Research, and the Journal of Hydrology, New Zealand, for the years 1958 to 2015. We generated ISO standard compliant metadata records for each article including the spatial references and make them available in a public catalogue service.</p> <ol> <li><em>articles_georef_count_data.xlsx</em>: The counts and evaluation tracking of the place name occurrences in the journal articles.</li> <li><em>summary_final.xlsx</em>: Summary statistics for evaluation based on the counts data.</li> <li><em>article_template.xml</em>: XML template for ISO 19139 compliant metadata record filled for each article.</li> <li><em>full_article.xm</em>l: Exemplary fully filled ISO 19139 compliant metadata record.<br> </li> </ol>
Macroporous Polymer Monoliths as a Low-Cost Bioanalytical Platform for Biopharmaceutical Glycoproteins
<p>This is data generated during my Marie Curie project. It includes the results for the synthesis of the monomers and polymerised high internal phase emulsions (polyHIPEs).</p> <p>In addition, supplementary data form a publication generated from the co-supervision of a PhD student during the course of the fellowship is also included. </p>
WYRED Platform, the ecosystem for the young people
<p>The WYRED Platform is a technological ecosystem developed as part of WYRED (netWorked Youth Research for Empowerment in the Digital society), a European Project funded by the Horizon2020 programme.</p> <p>As society changes, there is a need to understand how it is changing, to explore what is going on. The young people have a key role to play in our society. They are frequently the drivers of new behaviours and understandings, and since they are part of the future society their views and perceptions should be considered. However, they are not well represented and their voices are unheard, and this makes it hard for research and policy to identify and understand their needs.</p> <p>The aim of the WYRED Platform is to provide the tools to support dialogue and research processes in which children and young people can express and explore the key issues that they consider as important.</p> <p>To design it, different stakeholders were involved, and several questionnaires and social dialogues were carried out.</p> <p>The WYRED Platform is organized in multicultural and interdisciplinary communities where young people can develop research projects with the support of facilitators from different European institutions and associations. The communities have different tools such as forums to establish dialogues and coordinate research cycles, calendars to share dates and organize events or activities, surveys to develop or evaluate the projects, a version control system for files to support the documentation generated during the research processes. Also, it provides a tool to publish the results of the research projects.</p> <p>One of its main innovations is the strongly commitment to user privacy, it is designed as a safe space in which children and young people can be free to express themselves as they wish. Moreover, it design is centred around and driven by children and young people.</p> <p> </p> <p><strong>Video link</strong>: <a href="https://youtu.be/TRDjN5boky8">https://youtu.be/TRDjN5boky8</a> (presented in the student design competition: video presentations of the HCI International 2018, held in Las Vegas, NV, USA, July 15-20, 2018).</p>
Preliminary data collected by 2 prototype Surface Velocity Platform drifters with Barometer and Reference Sensor for Temperature (SVP-BRST)
<p>The SVP-BRST drifter was developed to serve calibration and validation of Sentinel satellite SST retrievals. Two prototypes were deployed in the Mediterranean Sea end of April 2018. Preliminary data collected then until 11 June 2018 are published in this dataset. The drifters were developed and deployed under funding from the European Union's Copernicus Programme. The data are transmitted from the buoy to shore using data format #091 (see References).</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.