Item talk:Q325375
From geokb
{
"DOI": { "doi": "10.5066/p969tx8f", "identifiers": [], "creators": [ { "name": "Gotthold, Benjamin S", "nameType": "Personal", "givenName": "Benjamin S", "familyName": "Gotthold", "affiliation": [ "United States Geological Survey" ], "nameIdentifiers": [ { "schemeUri": "https://orcid.org", "nameIdentifier": "https://orcid.org/0000-0003-4234-5042", "nameIdentifierScheme": "ORCID" } ] }, { "name": "Khalighifar, Ali", "nameType": "Personal", "givenName": "Ali", "familyName": "Khalighifar", "affiliation": [ "United States Geological Survey" ], "nameIdentifiers": [] }, { "name": "Straw, Bethany R", "nameType": "Personal", "givenName": "Bethany R", "familyName": "Straw", "affiliation": [], "nameIdentifiers": [ { "schemeUri": "https://orcid.org", "nameIdentifier": "https://orcid.org/0000-0001-9086-4600", "nameIdentifierScheme": "ORCID" } ] }, { "name": "Reichert, Brian E", "nameType": "Personal", "givenName": "Brian E", "familyName": "Reichert", "affiliation": [], "nameIdentifiers": [ { "schemeUri": "https://orcid.org", "nameIdentifier": "https://orcid.org/0000-0002-9640-0695", "nameIdentifierScheme": "ORCID" } ] } ], "titles": [ { "title": "Training dataset for NABat Machine Learning V1.0" } ], "publisher": "U.S. Geological Survey", "container": {}, "publicationYear": 2022, "subjects": [ { "subject": "Wildlife Biology, Wildlife Disease" } ], "contributors": [], "dates": [ { "date": "2022", "dateType": "Issued" } ], "language": null, "types": { "ris": "DATA", "bibtex": "misc", "citeproc": "dataset", "schemaOrg": "Dataset", "resourceType": "Dataset", "resourceTypeGeneral": "Dataset" }, "relatedIdentifiers": [], "relatedItems": [], "sizes": [], "formats": [], "version": null, "rightsList": [], "descriptions": [ { "description": "Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to assess status and trends of bat populations while developing innovative and community-driven conservation solutions using its unique data and technology infrastructure. To support scalability and transparency in the NABat acoustic data pipeline, we developed a fully-automated machine-learning algorithm. This dataset includes audio files of bat echolocation calls that were considered to develop V1.0 of the NABat machine-learning algorithm, however the test set (i.e., holdout dataset) has been excluded from this release. These [...]", "descriptionType": "Abstract" } ], "geoLocations": [], "fundingReferences": [], "url": "https://www.sciencebase.gov/catalog/item/627ed4b2d34e3bef0c9a2f30", "contentUrl": null, "metadataVersion": 1, "schemaVersion": "http://datacite.org/schema/kernel-4", "source": "mds", "isActive": true, "state": "findable", "reason": null, "viewCount": 0, "downloadCount": 0, "referenceCount": 0, "citationCount": 0, "partCount": 0, "partOfCount": 0, "versionCount": 0, "versionOfCount": 0, "created": "2022-07-07T14:32:01Z", "registered": "2022-07-07T14:32:02Z", "published": null, "updated": "2022-11-17T12:57:31Z" }
}