Behaviormetrika View Homepage


Ontology type: schema:Periodical     


Journal Info

START YEAR

1974

PUBLISHER

Springer Japan

LANGUAGE

en

HOMEPAGE

http://link.springer.com/journal/41237

Recent publications latest 20 shown

  • 2019-04 Bayesian analysis of happiness with individual heterogeneity
  • 2019-04 Functional logistic discrimination with sparse PCA and its application to the structural MRI
  • 2019-04 Beyond p values: utilizing multiple methods to evaluate evidence
  • 2019-04 A generalized procedure for estimating the multinomial proportions in randomized response sampling using scrambling variables
  • 2019-04 Special feature: functional data analysis and its applications
  • 2019-04 Multivariate functional clustering and its application to typhoon data
  • 2019-04 Exploring the psychometric properties of the mind-map scoring rubric
  • 2019-04 Comparing two maximum likelihood algorithms for mixture Rasch models
  • 2019-04 Logistic regression and Ising networks: prediction and estimation when violating lasso assumptions
  • 2019-04 Introduction to the Vol.46, No.1, 2019
  • 2019-04 Robust curve registration using the t distribution
  • 2019-04 Asymptotic normality of some conditional nonparametric functional parameters in high-dimensional statistics
  • 2019-03-21 An illustrative application of generalized structured component analysis for brain connectivity research
  • 2019-03-11 Clustering and dimension reduction for mixed variables
  • 2018-12-04 Understanding information theoretic measures for comparing clusterings
  • 2018-10 Reasoning with alternative acyclic directed mixed graphs
  • 2018-10 How to conceptualize, represent, and analyze log data from technology-based assessments? A generic framework and an application to questionnaire items
  • 2018-10 Experiments with learning graphical models on text
  • 2018-10 Special feature: advanced methodologies for Bayesian networks 2017
  • 2018-10 Degree of error in Bayesian knowledge tracing estimates from differences in sample sizes
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://scigraph.springernature.com/ontologies/product-market-codes/S11001", 
            "inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/", 
            "name": "Statistical Theory and Methods", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://scigraph.springernature.com/ontologies/product-market-codes/S17010", 
            "inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/", 
            "name": "Statistics for Business, Management, Economics, Finance, Insurance", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://scigraph.springernature.com/ontologies/product-market-codes/S17020", 
            "inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/", 
            "name": "Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "description": "

    Behaviormetrika\u00a0is issued twice a year to provide an international forum for new theoretical and empirical quantitative approaches in data science. When\u00a0Behaviormetrika\u00a0was launched in 1974, the journal advocated data science, as an interdisciplinary field that included the use of statistical methods to extract meaningful knowledge from data in its various forms: structured or unstructured. Behaviormetrika\u00a0is the oldest journal addressing the topic of data science. The first editor-in-chief of\u00a0Behaviormetrika, Dr. Chikio Hayashi, described data science in this way:

    \u201cData science is not only a synthetic concept to unify statistics, data analysis, and their related methods; it also comprises its results. Data science is intended to analyze and understand actual phenomena with \u2018data.\u2019 In other words, the aim of data science is to reveal the features or the hidden structure of complicated natural, human, and social phenomena using data from a different perspective from the established or traditional theory and method.\u201d \u00a0

    Behaviormetrika\u00a0is a fully refereed international journal, which publishes original research papers, notes, and review articles. Subject areas suitable for publication include but are not limited to the following methodologies and fields.\u00a0

    Methodologies

    -Data science

    -Mathematical statistics

    -Survey methodologies

    -Artificial intelligence

    -Information theory

    -Machine learning

    -Knowledge discovery in databases (KDD)

    -Graphical models

    -Computer science

    -Algorithms

    \u00a0

    Fields

    -Medicine\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0

    -Psychology

    -Education

    -Economics

    -Marketing

    -Social science

    -Sociology

    -Political science

    -Policy science

    -Cognitive science

    -Brain science

    ", "editor": [ { "familyName": "Ueno", "givenName": "Maomi", "type": "Person" } ], "id": "sg:journal.1028684", "inLanguage": [ "en" ], "isAccessibleForFree": false, "issn": [ "0385-7417", "1349-6964" ], "license": "Hybrid (Open Choice)", "name": "Behaviormetrika", "productId": [ { "name": "nsd_ids_id", "type": "PropertyValue", "value": [ "471300" ] }, { "name": "springer_id", "type": "PropertyValue", "value": [ "41237" ] }, { "name": "lccn_id", "type": "PropertyValue", "value": [ "83644394" ] }, { "name": "dimensions_id", "type": "PropertyValue", "value": [ "28684" ] }, { "name": "nlm_unique_id", "type": "PropertyValue", "value": [ "101091502" ] } ], "publisher": { "name": "Springer Japan", "type": "Organization" }, "publisherImprint": "Springer", "sameAs": [ "https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1028684" ], "sdDataset": "journals", "sdDatePublished": "2019-03-18T11:05", "sdLicense": "https://scigraph.springernature.com/explorer/license/", "sdPublisher": { "name": "Springer Nature - SN SciGraph project", "type": "Organization" }, "sdSource": "file:///home/ubuntu/piotr/scigraph_export/journals_20190313_sn_only.jsonl", "startYear": "1974", "type": "Periodical", "url": "http://link.springer.com/journal/41237" } ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/journal.1028684'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/journal.1028684'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/journal.1028684'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/journal.1028684'


     

    This table displays all metadata directly associated to this object as RDF triples.

    60 TRIPLES      20 PREDICATES      28 URIs      21 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:journal.1028684 schema:about sg:ontologies/product-market-codes/S11001
    2 sg:ontologies/product-market-codes/S17010
    3 sg:ontologies/product-market-codes/S17020
    4 schema:description <p><i>Behaviormetrika</i> is issued twice a year to provide an international forum for new theoretical and empirical quantitative approaches in data science. When <i>Behaviormetrika</i> was launched in 1974, the journal advocated d<i>ata science</i>, as an interdisciplinary field that included the use of statistical methods to extract meaningful knowledge from data in its various forms: structured or unstructured. <i>Behaviormetrika</i> is the oldest journal addressing the topic of data science. The first editor-in-chief of <i>Behaviormetrika</i>, Dr. Chikio Hayashi, described data science in this way:</p><p>“Data science is not only a synthetic concept to unify statistics, data analysis, and their related methods; it also comprises its results. Data science is intended to analyze and understand actual phenomena with ‘data.’ In other words, the aim of data science is to reveal the features or the hidden structure of complicated natural, human, and social phenomena using data from a different perspective from the established or traditional theory and method.”  </p><p><i>Behaviormetrika</i> is a fully refereed international journal, which publishes original research papers, notes, and review articles. Subject areas suitable for publication include but are not limited to the following methodologies and fields. </p><p><b>Methodologies </b></p><p>-Data science</p><p>-Mathematical statistics</p><p>-Survey methodologies</p><p>-Artificial intelligence </p><p>-Information theory</p><p>-Machine learning </p><p>-Knowledge discovery in databases (KDD)</p><p>-Graphical models</p><p>-Computer science</p><p>-Algorithms</p><p> </p><p><b>Fields</b></p><p>-Medicine         </p><p>-Psychology</p><p>-Education</p><p>-Economics</p><p>-Marketing</p><p>-Social science</p><p>-Sociology</p><p>-Political science</p><p>-Policy science</p><p>-Cognitive science</p><p>-Brain science</p>
    5 schema:editor N6df8682c828c458f84ff00cfbc7a6815
    6 schema:inLanguage en
    7 schema:isAccessibleForFree false
    8 schema:issn 0385-7417
    9 1349-6964
    10 schema:license Hybrid (Open Choice)
    11 schema:name Behaviormetrika
    12 schema:productId N04efaf649d274255a4f8dd2d5b4a4c39
    13 N77398f459db84d38bd5d79ba18de56c9
    14 N860cff072c29403a8be4651487ffcf48
    15 Na7c2710d8e2e487181d3d269d1b7fe78
    16 Nd2a6b0ba9349418e9008be80790bce9f
    17 schema:publisher N756fec450d664baf9244d2381d972bfa
    18 schema:publisherImprint Springer
    19 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1028684
    20 schema:sdDatePublished 2019-03-18T11:05
    21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    22 schema:sdPublisher N9b6cb24f621b4b86b7e561c7f9657e92
    23 schema:startYear 1974
    24 schema:url http://link.springer.com/journal/41237
    25 sgo:license sg:explorer/license/
    26 sgo:sdDataset journals
    27 rdf:type schema:Periodical
    28 N04efaf649d274255a4f8dd2d5b4a4c39 schema:name dimensions_id
    29 schema:value 28684
    30 rdf:type schema:PropertyValue
    31 N5435cf6b0e65444a9b67d7d8cd58e8d2 schema:familyName Ueno
    32 schema:givenName Maomi
    33 rdf:type schema:Person
    34 N6df8682c828c458f84ff00cfbc7a6815 rdf:first N5435cf6b0e65444a9b67d7d8cd58e8d2
    35 rdf:rest rdf:nil
    36 N756fec450d664baf9244d2381d972bfa schema:name Springer Japan
    37 rdf:type schema:Organization
    38 N77398f459db84d38bd5d79ba18de56c9 schema:name nsd_ids_id
    39 schema:value 471300
    40 rdf:type schema:PropertyValue
    41 N860cff072c29403a8be4651487ffcf48 schema:name nlm_unique_id
    42 schema:value 101091502
    43 rdf:type schema:PropertyValue
    44 N9b6cb24f621b4b86b7e561c7f9657e92 schema:name Springer Nature - SN SciGraph project
    45 rdf:type schema:Organization
    46 Na7c2710d8e2e487181d3d269d1b7fe78 schema:name lccn_id
    47 schema:value 83644394
    48 rdf:type schema:PropertyValue
    49 Nd2a6b0ba9349418e9008be80790bce9f schema:name springer_id
    50 schema:value 41237
    51 rdf:type schema:PropertyValue
    52 sg:ontologies/product-market-codes/S11001 schema:inDefinedTermSet sg:ontologies/product-market-codes/
    53 schema:name Statistical Theory and Methods
    54 rdf:type schema:DefinedTerm
    55 sg:ontologies/product-market-codes/S17010 schema:inDefinedTermSet sg:ontologies/product-market-codes/
    56 schema:name Statistics for Business, Management, Economics, Finance, Insurance
    57 rdf:type schema:DefinedTerm
    58 sg:ontologies/product-market-codes/S17020 schema:inDefinedTermSet sg:ontologies/product-market-codes/
    59 schema:name Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
    60 rdf:type schema:DefinedTerm
     




    Preview window. Press ESC to close (or click here)


    ...