Dataset Information

Disambiguation of patent inventors and assignees using high-resolution geolocation data.

ABSTRACT: Patent data represent a significant source of information on innovation, knowledge production, and the evolution of technology through networks of citations, co-invention and co-assignment. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in knowledge production and diffusion. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventors and assignees on about 8.5 million patents found in the European Patent Office (EPO), under the Patent Cooperation Treaty (PCT), and in the US Patent and Trademark Office (USPTO). We show this disambiguation is consistent with a number of ground-truth benchmarks of both assignees and inventors, significantly outperforming the use of undisambiguated names to identify unique entities. A significant benefit of this work is the high quality assignee disambiguation with coverage across the world coupled with an inventor disambiguation (that is competitive with other state of the art approaches) in multiple patent offices.

SUBMITTER: Morrison G

PROVIDER: S-EPMC5433392 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Disambiguation of patent inventors and assignees using high-resolution geolocation data.

Morrison Greg G Riccaboni Massimo M Pammolli Fabio F

Scientific data 20170516

Patent data represent a significant source of information on innovation, knowledge production, and the evolution of technology through networks of citations, co-invention and co-assignment. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in knowledge production and diffusion. In this paper, we describe a new algorithm ...[more]

PMID: 28509897

Similar Datasets

Project description:BackgroundRecent epidemics have entailed global discussions on revamping epidemic control and prevention approaches. A general consensus is that all sources of data should be embraced to improve epidemic preparedness. As a disease transmission is inherently governed by individual-level responses, pathogen dynamics within infected hosts posit high potentials to inform population-level phenomena. We propose a multiscale approach showing that individual dynamics were able to reproduce population-level observations.MethodsUsing experimental data, we formulated mathematical models of pathogen infection dynamics from which we simulated mechanistically its transmission parameters. The models were then embedded in our implementation of an age-specific contact network that allows to express individual differences relevant to the transmission processes. This approach is illustrated with an example of Ebola virus (EBOV).ResultsThe results showed that a within-host infection model can reproduce EBOV's transmission parameters obtained from population data. At the same time, population age-structure, contact distribution and patterns can be expressed using network generating algorithm. This framework opens a vast opportunity to investigate individual roles of factors involved in the epidemic processes. Estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting cautions on estimates unadjusted for contact pattern. Assessments of mass vaccination strategies showed that vaccination conducted in a time window from five months before to one week after the start of an epidemic appeared to strongly reduce epidemic size. Noticeably, compared to a non-intervention scenario, a low critical vaccination coverage of 33% cannot ensure epidemic extinction but could reduce the number of cases by ten to hundred times as well as lessen the case-fatality rate.ConclusionsExperimental data on the within-host infection have been able to capture upfront key transmission parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. The population of interest in epidemic assessments could be modelled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios to explore multilevel aspects in infectious diseases epidemics are underway.

Project description:Forensic and clinical laboratories are expected to provide a rapid screening of samples for a wide range of analytes; however, the ever-changing landscape of illicit substances makes analysis complicated. There is a great need for untargeted methods that can aid these laboratories in broad-scope drug screening. Liquid chromatography hyphenated with high-resolution mass spectrometry (LC-HRMS) has become a popular technique for untargeted screening and presumptive identification of drugs of abuse due to its superior sensitivity and detection capabilities in complex matrices. An untargeted extraction and data acquisition method was evaluated for the broad screening of high-priority drugs of abuse in whole blood. A total of 35 forensically relevant target analytes were identified and extracted at biologically relevant low and high (10× low) concentrations from whole blood using supported liquid extraction. Data-independent acquisition was accomplished using ultraperformance liquid chromatography and a quadrupole time-of-flight mass spectrometry. Results were acceptable for screening assays, with limits of detection at or below the recommended low-concentration cutoffs for most analytes. Analyte ionization varied from 30.1 to 267.6% (average: 110.5%) at low concentrations and from 8.6 to 383.5% (average: 93.6%) at high concentrations. Extraction recovery ranged from 8.5 to 330.5% (average: 105.3%) at low concentrations and from 9.4 to 127.5% (average: 82.7%) at high concentrations. This variability was also captured as precision, ranging from 4.7 to 135.2% (average: 36.5%) at low concentrations and from 0.9 to 59.0% (average: 21.7%) at high concentrations. The method described in this work is efficient and effective for qualitative forensic toxicology screening, as demonstrated by analysis of 166 authentic suspected impaired driver and postmortem specimens. That said, it is critical that laboratories establishing untargeted LC-HRMS screening assays be aware of the strengths and limitations across diverse drug categories and chemical structures.

Dataset Information

Disambiguation of patent inventors and assignees using high-resolution geolocation data.

Publications

Disambiguation of patent inventors and assignees using high-resolution geolocation data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets