{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["006:55"], "contactPoint": {"fn": "Rachael Sexton", "hasEmail": "mailto:rachael.sexton@nist.gov"}, "description": "There is often a large amount of maintenance data already available for use in Smart Manufacturing systems, but in a currently-unusable form: service tickets and maintenance work orders (MWOs). Nestor is a toolkit for using Natural Language Processing (NLP) with efficient user-interaction to perform structured data extraction with minimal annotation time-cost.", "distribution": [{"accessURL": "https://doi.org/10.18434/T4/1502464", "description": "DOI Access for Nestor: a toolkit for quantifying tacit maintenance knowledge, for investigatory analysis in smart manufacturing", "format": "text/html", "title": "DOI Access for Nestor: a toolkit for quantifying tacit maintenance knowledge, for investigatory analysis in smart manufacturing"}], "identifier": "6D99882718D36079E05324570681AAD11932", "keyword": ["CMMS", "communication", "data cleaning", "decision guidance", "diagnostics", "event sequences", "information", "investigations", "machine learning", "maintenance", "manufacturing operations", "manufacturing performance", "nestor", "prognostics", "scheduling", "smart manufacturing", "training", "tribal knowledge", "visualization"], "landingPage": "https://github.com/usnistgov/nestor", "language": ["en"], "license": "https://www.nist.gov/open/license", "modified": "2018-06-01 00:00:00", "programCode": ["006:045"], "publisher": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "references": ["https://dx.doi.org/10.1109/BigData.2017.8258120"], "theme": ["Information Technology:Data and informatics", "Information Technology:Usability and human factors", "Manufacturing:Manufacturing systems design and analysis", "Manufacturing:Work force", "Mathematics and Statistics:Statistical analysis"], "title": "Nestor: a toolkit for quantifying tacit maintenance knowledge, for investigatory analysis in smart manufacturing"}