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9 results for “Data schema”

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zenodo44/100

Crosswalk between CESSDA Data Catalogue (CDC) Metadata Profile and ECRIN Metadata Schema.

<p>This dataset contains two files: (1) a crosswalk between CESSDA Data Catalogue (CDC) DDI2.5 Metadata Profile (<a href="https://cmv.cessda.eu/profiles/cdc/ddi-2.5/1.0.4/profile.html">https://cmv.cessda.eu/profiles/cdc/ddi-2.5/1.0.4/profile.html</a>)&nbsp; and ECRIN Metadata Schema for Clinical Research Data Objects Version 6.0 (August 2021) (<a href="https://zenodo.org/record/5554961">https://zenodo.org/record/5554961</a>) with an extension to &ldquo;geographical data&rdquo; and (2) vice versa.</p>

opencc-by-4.0Jul 2023View details →
zenodo40/100

D2.1: Artefact, Contributor, and Organisation Relationship Data Schema - Appendix A

<p>Comparison of metadata schema for ORCID, DataCite, Dublin Core, CASRAI, MODS&nbsp;and DDI&nbsp;regarding contributors, organizations and artefacts.</p>

opencc-zeroSep 2015View details →
zenodo40/100

Figure 1. Schema for a data-integration solution-A Proposed Data Driven Architecture for Cardiology Network Application

<p>Data integration has favored loosening the coupling between data. This may involve<br> providing a uniform query interface over a mediated schema (see figure 1), thus transforming<br> a query into specialized queries over the original databases. One can also term this process<br> &quot;view-based query-answering&quot; because each of the data sources functions as a view over the<br> (nonexistent) mediated schema.</p>

opencc-by-4.0Apr 2010View details →
zenodo40/100

Figure 1. Schema of clientside application for semantic browsing.-Browsing Semantic Data in Slovakia

<p>With the aim primary on unstructured information extraction and refining, relationship discovery and visualization, we propose our solution for SBR in the first place. The reason for this is, primary, that HTML formatted results of SBR are very jerky and uncertainty regarding the structure of information is very high. Readers can also be pointed by J. Suchal and P. Vojtek (2009), that care should be taken towards type errors. We discuss that later. In this work, we try to fill&amp;up the gap of visualization and, somehow limited data access offered by SBR, adapting to the problems disclaimed above. We suggest a new client&amp; side paradigm, which does not depend on a particular website like foaf.sk. Figure 1 describes the schema briefly and the key elements are parsers with other tools on the top and structured formats, for datastore, on the bottom.</p>

opencc-by-4.0Nov 2015View details →
zenodo40/100

Stakeholders Access to Data - Standardized Schema

<p>The schema helps to streamline and enhance the understanding of access regimes while facilitating the creation of a machine-readable version for easier automation or embedding into software. By adopting this schema, the authors aim to provide a practical solution for enhancing data access and reuse while improving transparency and accountability in the process.</p>

opencc-by-4.0Sep 2023View details →
zenodo36/100

FAIRmat Tutorial 14: Developing schemas and parsers for FAIR computational data storage using NOMAD-Simulations

<p><a href="https://nomad-lab.eu"><u>NOMAD</u></a> is an open-source, community-driven data infrastructure, focusing on materials science data. Originally built as a repository for data from DFT calculations, the NOMAD software can automatically extract data from the output of a large variety of simulation codes. Our previous computation-focused tutorials (<a href="https://fairmat-nfdi.github.io/AreaC-Tutorial-CECAM-2023/"><u>CECAM workshop</u></a>, <a href="https://fairmat-nfdi.github.io/AreaC-Tutorial10_2023/"><u>Tutorial 10</u></a>, and <a href="https://www.fairmat-nfdi.eu/events/fairmat-tutorial-7/tutorial-7-materials"><u>Tutorial 7</u></a>) have highlighted the extension of NOMAD&rsquo;s functionalities to support advanced many-body calculations, classical molecular dynamics simulations, and complex simulation workflows.&nbsp;<br>But how can you utilize this infrastructure and associated suite of tools if your simulation code or method is not yet supported?&nbsp;<strong>This tutorial will provide foundational knowledge for customizing NOMAD to fit the specific needs of your computational research project</strong>. The following provides an outline of the major topics that will be covered:</p> <ul> <li>Introduction to the NOMAD software and repository</li> <li>Working with the NOMAD-Simulations schema plugin</li> <li>Extending NOMAD-Simulations to support custom methods and outputs</li> <li>Creating parser plugins from scratch</li> <li>Extra: Interfacing complex simulation and analysis workflows with NOMAD</li> </ul> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Processed data for the "Deriving Semantics-Aware Fuzzers from Web API Schemas" paper

<p>Processed data for the &quot;Deriving Semantics-Aware Fuzzers from Web API Schemas&quot; paper.&nbsp; Each directory in the archive consists of:</p> <p>- metadata.json. Metadata about a test run - tested fuzzer name, run duration, etc</p> <p>- fuzzer.json&nbsp;- Structured fuzzer output</p> <p>-&nbsp;deduplicated_cases.json - Deduplicated reported failures, when fuzzers provide it</p> <p>- sentry.json&nbsp;- Cleaned Sentry events for this run</p> <p>- target.json&nbsp;- Parsed stdout for Gitlab &amp; Disease.sh targets that were tested without Sentry integration</p>

opencc-by-4.0Nov 2021View details →
zenodo28/100

Unprocessed data for the "Deriving Semantics-Aware Fuzzers from Web API Schemas" paper.

<p>Unprocessed data for the &quot;Deriving Semantics-Aware Fuzzers from Web API Schemas&quot; paper.</p>

opencc-by-4.0Nov 2021View details →
zenodo20/100

Data from: Premature birth affects visual body representation and body schema in preterm children

<p>Raw data used for the article &quot;Premature birth affects visual body representation and body schema in preterm children&quot;</p> <p>Research has demonstrated that from the first six months of life infants show early sensitivity to body visual features and rely on sensorimotor and&nbsp; proprioceptive inputs in forming representations of their own bodies. Premature birth interferes with typical exposition to visual, sensorimotor and proprioceptive stimulation, thus presumably affecting the development of body representations. Here, we tested this hypothesis by comparing the performance of preterm children with that of age-matched full-term children in two tasks assessing, respectively, visual body processing and body schema. We found that preterm children had spared configural processing but altered holistic processing of others&rsquo; bodies and showed a general difficulty in expressing visuospatial judgements on body stimuli. Furthermore, body-centered visuospatial abilities were associated with specific impairments in operating object-based visuospatial transformations. The findings of this study indicate that preterm birth might interfere with the development of body&nbsp; representations at the levels of body visual perceptual processing and of body schema, with effects even on visuo-spatial abilities for non-bodily stimuli. Body-centered rehabilitative interventions should be proposed to preterm children in order to enhance visuo-spatial abilities and higher-level cognitive functions.</p>

openSep 2020View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record