ArticleFrontiers in neuroinformatics2011
A Bottom-up Approach to Data Annotation in Neurophysiology.
Article in Frontiers in neuroinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Understanding the Nature of Metadata: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Metadata practices for simulation workflows.Scientific data · 2025Article
- Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO).Scientific data · 2025Article
- Article
- Patient-Centric Approach to Personalized Electronic Medical Records via QR Code in Japan.Interactive journal of medical research · 2024Article
- A Perspective on Neuroscience Data Standardization with Neurodata Without Borders.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS.Frontiers in neuroinformatics · 2023Article
- Data management strategy for a collaborative research center.GigaScience · 2022Article
- 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state.Scientific data · 2022Article
- A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility.Neuroinformatics · 2022Review
- Workflow for health-related and brain data lifecycle.Frontiers in digital health · 2022Review
- Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics".Frontiers in neuroinformatics · 2022Review
- The Neuroscience Experiments System (NES)-A Software Tool to Manage Experimental Data and Its Provenance.Frontiers in neuroinformatics · 2021Article
- An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology.Brain informatics · 2020Article
- NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format.Frontiers in neuroinformatics · 2020Article
- Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data Analysis.Frontiers in neuroinformatics · 2020Article
- odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments.Frontiers in neuroinformatics · 2019Article
- Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task.Scientific data · 2018Article
- Experimental Directory Structure (Exdir): An Alternative to HDF5 Without Introducing a New File Format.Frontiers in neuroinformatics · 2018Article
- Event-related potential data from a guess the number brain-computer interface experiment on school children.Scientific data · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metadata providing information about the stimulus, data acquisition, and experimental conditions are indispensable for the analysis and management of experimental data within a lab. However, only rarely are metadata available in a structured, comprehensive, and machine-readable form. This poses a severe problem for finding and retrieving data, both in the laboratory and on the various emerging public data bases. Here, we propose a simple format, the "open metaData Markup Language" (odML), for collecting and exchanging metadata in an automated, computer-based fashion. In odML arbitrary metadata information is stored as extended key-value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability. We started to define such terminologies for neurophysiological data, but aim at a community driven extension and refinement of the proposed definitions. By customized terminologies that map to these standard terminologies, metadata can be named and organized as required or preferred without softening the standard. Together with the respective libraries provided for common programming languages, the odML format can be integrated into the laboratory workflow, facilitating automated collection of metadata information where it becomes available. The flexibility of odML also encourages a community driven collection and definition of terms used for annotating data in the neurosciences.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.