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Dataset results
13 results for “metadata evaluation”
SciKGTeX Scientific Contribution Metadata LaTeX Package User Evaluation Results & Analysis
<p>The responses and measured variables from 26 participants of the first user test of the SciKGTeX package.</p> <p><a href="https://github.com/Christof93/SciKGTeX">https://github.com/Christof93/SciKGTeX</a></p> <p>Also the raw text source for the evaluation tasks and the result analysis notebook with the results saved as tsv file.</p>
For METADATA evaluation: TSEM image of TiO2 particles from UNITO trials
TSEM (more 'material/thickness' sensitive, see 'Signal A= SE'). Don't be confused by 'SE'. It means in fact 'transmission mode'.
Statistics and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This publication contains aggregated data for the paper. It also contains the evaluation data of all model/hyper-parameter training and test runs.</p>
l-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of large size.</p>
s-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of small size.</p>
Data from: A metadata approach to evaluate the state of ocean knowledge: strengths, limitations, and application to Mexico
Climate change, mismanaged resource extraction, and pollution are reshaping global marine ecosystems with direct consequences on human societies. Sustainable ocean development requires knowledge and data across disciplines, scales and knowledge types. Although several disciplines are generating large amounts of data on marine socio-ecological systems, such information is often underutilized due to fragmentation across institutions or stakeholders, limited standardization across scale, time or disciplines, and the fact that information is often not searchable within existing databases. Compiling metadata, the information which describes existing sets of data, is an effective tool that can address these challenges, particularly when metadata corresponding to multiple datasets can be combined to integrate, organize and classify multidisciplinary data. Here, using Mexico as a case study, we describe the compilation and analysis of a metadatabase of ocean knowledge that aims to improve access to information, facilitate multidisciplinary data sharing and integration, and foster collaboration among stakeholders. We also evaluate the knowledge trends and gaps for informing ocean management. Analysis of the metadatabase highlights that past and current research in Mexico focuses strongly on ecology and fisheries, with biological data more consistent over time and space compared to data on human dimensions. Regional imbalances in available information were also evident, with most available information corresponding to the Gulf of California, Campeche Bank and Caribbean and less available for the central and south Pacific and the western Gulf of Mexico. Despite existing knowledge gaps in Mexico and elsewhere, we argue that systematic efforts such as this can often reveal an abundance of information for decision-makers to develop policies that meet key commitments on ocean sustainability. Surmounting current cross-scale social and ecological challenges for sustainability requires transdisciplinary approaches. Metadatabases are critical tools to make efficient use of existing data, highlight and address strengths and deficiencies, and develop scenarios to inform policies for managing complex marine social-ecological systems.
Trajectories and metadata for Bach et al.: 'Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO2 removal'
<p>Trajectory and metadata files from a Southern Ocean surface particle release experiment using Connectivity Modeling System (Paris et al. 2013, <a href="https://github.com/beatrixparis/connectivity-modeling-system">https://github.com/beatrixparis/connectivity-modeling-system</a>) run offline in the MOM01 model (Stewart et al., 2017; Spence et al., 2017; Morrison et al., 2020), a global 0.1° ocean sea-ice model, based on version 5 of the Modular Ocean Model (MOM) code (mom-ocean.github.io) (Griffies, 2012).</p> <p> Here particle trajectories are are split into 28 netcdf files (numbered traj_file_01.nc through traj_file_28.nc), and uploaded altogether as a single .tar.gz compressed file. Metadata files are also uploaded containing information on the CMS setup and release locations.</p> <p>Netcdf files contain particle trajectory positions, along with temperature, salinity, status in or out of the mixed layer, release date, and particle exit status (see <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a> for details on trajectory files). Other CMS setup files included in each release directory are nest_1.nml, runconf.list, ibm.list, and releaseFile_matchtransport. These input files contain information needed to reproduce the experiment using the MOM01 model output, and are explained in detail in the <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a>.</p> <p> </p> <p>Citation of associated paper: Bach, L., V. Tamsitt, K. Baldry, J. McGee, E. Laurenceau-Cornec, R. Strzepek, Y. Xie, P. W. Boyd, 2021. Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO<sub>2</sub> removal, <em>submitted </em></p> <p> </p> <p>References:</p> <p>Griffies, S. M. (2012). Elements of the modular ocean model (MOM). <em>GFDL Ocean Group Tech. Rep</em>, 7(620), 47.</p> <p> Morrison, A. K., Hogg, A. M., England, M. H., & Spence, P. (2020). Warm Circumpolar Deep Water transport towards Antarctic driven by local dense water export in canyons. <em>Science Advances</em>, 6(18), eaav2516.</p> <p> Paris, C. B., Helgers, J., van Sebille, E., & Srinivasan, A. (2013). Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. <em>Environmental Modelling and Software</em>, 42, 47-54.</p> <p>Spence, P., Holmes, R. M., Hogg, A. M., Gries, S. M., Stewart, K. D., & England, M. H. (2017). Localized rapid warming of West Antarctic subsurface waters by remote winds. <em>Nature Climate Change</em>, 7(8), 595.</p> <p>Stewart, K., Hogg, A. M., Gries, S., Heerdegen, A., Ward, M., Spence, P., & England, M. H. (2017). Vertical resolution of baroclinic modes in global ocean models. <em>Ocean Modelling</em>, 113, 50-65.</p>
Data from: A metadata approach to evaluate the state of ocean knowledge: strengths, limitations, and application to Mexico
Open the record for dataset details and reuse information.
Music indexing and retrieval: evaluating the social production of music metadata and its use
<p>This article will focus on music indexing and retrieval from different points of view. Four elements will be examined: music metadata, indexing and retrieval methods (classic indexing, collaborative indexing and social tagging), tools and users. Regarding the users,we will look at their access modes, their possible participation in indexing, and their music information-seeking behaviors. Do they look for music information in pay-for-music sites (iTunes), radio stations, blogs, or social networks (MySpace, YouTube, etc)? The question we raise is which music information systems would be suitable in a social Web era. An evaluation of the existing indexing and retrieval modes was conducted. It was based on both quantitative and qualitative approaches. Our research methodology used interviews, online questionnaires and semi-directed questionnaires. We believe that the results of our evaluation will be useful for music information indexing in a Socio-Semantic Web context.</p>
Figure 3 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
Figure 3 Schematic diagram of Metadata 2020 Project 6: Metadata Evaluation and Guidance.
Figure 1 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
Figure 1 Metadata dialects, recommendations, and communities.
Figure 2 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
Figure 2 Schematic diagram of Metadata 2020 Project 2: Metadata Recommendations / Element Mappings
m-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used<br> in scientometric research, by repository service providers, and in the context of research data aggregation<br> services. Openly available metadata of the DataCite index for research data were used to compile a large<br> training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of medium size</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.