Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
252
datasets available to search
ShareScore release 0.9.0
Dataset results
252 results for “platform data”
DNA Origami Raw AFM Data - NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM
<p>The data file is in the original ARIS data format as captured on a Cypher VRS1250 AFM (Oxford Instruments)<br><br><br></p>
AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area,
Data of Benchmarking Elasticity of FaaS Platforms
<p>This data is part of the publication "Benchmarking Elasticity of FaaS Platforms as a Foundation for Objective-driven Design of Serverless Applications", it contains all plots and data used for the assessment of FaaS platform quality under volatile workloads from a client-side perspective. The paper is part of SAC'20, Brno, Czech Republic.</p>
2012 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.
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.
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>
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>
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>
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>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<p>This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. <em>Nanomaterials</em> <strong>2020</strong>, <em>10</em>, 2017.</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.</p>
Codebook - Knowledge-Action Platforms (Data & Policy article) Bream McIntosh et al.,
<p>Codebook used for Round 2 coding against key criteria of a 'Platform' definition that responds to matrix coding of Sustainability Knowledge Action Platforms. To accompany the article in Data & Policy 'The role of sustainability knowledge-action platforms in advancing multi-stakeholder engagement on sustainability'.</p>
Turbulence and flux data from eddy flux platform on Toolik Lake, Alaska 2009-2015.
Yearly file describing the turbulence conditions on Toolik Lake including the CH4, CO2 and H2O fluxes over the 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). These data were used in the Eugster et al. (2020) article that appeared in Environmental Science: Processes and Impacts. Note that the quality flagging system in this file is only for reference, it was not used in the named article. Instead, the 9-level system by Foken et al. was calculated (see Eugster et al. 2020), but no data were eliminated to avoid bias from wrong assumptions made in this approach if measurements are not performed over a terrestrial vegetated land surface.
Data From 'Gallium Phosphide as a Piezoelectric Platform for Quantum Optomechanics'
<p>Source data for figures.</p>
Library Service Platforms (LSPs) characteristics classification via DELPHI method raw data
<p>The purpose of the research that is related to the dataset is to identify the innovative features of the LSPs (Library Service Platforms) that differentiate them from the LMS (Library Management Systems), as well as to evaluate their importance, based on the opinions of the Greek information scientists. The method used is the Delphi 2-round questionnaire. The dataset contains the results of the Delphi method. Each sheet contains the ranking results from a group of 17 experts from two rounds of the DELPHI method.</p>
Research data - taxonomy for revenue models of platform business models (C2E)
<p>Research data to develop a taxonomy of revenue models for platform business models following a conceptual-to-empirical approach.</p>
Research data - taxonomy for platform revenue models (E2C)
<p>Research data to develop a taxonomy of revenue models for platform business models following an empirical-to-conceptual approach.</p>
(Processed data) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Processed data 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>
Argos Platform Transmitter Terminal data for 3 Southern Giant Petrel (Macronectes giganteus) during breeding season in Fildes Peninsula.
<p>Argos Platform Transmitter Terminal raw data for 3 Southern Giant Petrel (Macronectes giganteus) during breeding season in Fildes Peninsula, more specifically in the Islet known as Diomedea or Albatross Islet. All location classes are included. Locations are in EPSG 4326 (WGS 84). All animals had an active nest during data collection.</p> <p> </p> <p>Data generates with funding from INACH Programa Areas Marinhas Protegidas (24 04 052) and Agencia Nacional de Investigación y Desarrollo, Instituto Milénio Base ICN2021_002</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.