Statistics in Biosciences View Homepage


Ontology type: schema:Periodical     


Journal Info

START YEAR

2009

PUBLISHER

Springer US

LANGUAGE

en

HOMEPAGE

http://link.springer.com/journal/12561

Recent publications latest 20 shown

  • 2019-04-12 Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis
  • 2019-04-10 Editorial for the Special Issue Challenges in Computational Neuroscience
  • 2019-04-05 Calibrating Variations in Biomarker Measures for Improving Prediction with Time-to-event Outcomes
  • 2019-03-21 Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies
  • 2019-03-06 An Efficient Nonparametric Estimate for Spatially Correlated Functional Data
  • 2019-02-12 Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials
  • 2019-02-11 Binormal Precision–Recall Curves for Optimal Classification of Imbalanced Data
  • 2019-02-09 Organizing and Analyzing the Activity Data in NHANES
  • 2019-01-12 Accelerometry Data in Health Research: Challenges and Opportunities
  • 2019-01-05 Measuring Variability in Rest-Activity Rhythms from Actigraphy with Application to Characterizing Symptoms of Depression
  • 2019-01-05 Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials
  • 2018-12-07 Functional Data Analyses of Gait Data Measured Using In-Shoe Sensors
  • 2018-12 Joint Modeling of Multivariate Longitudinal Data and Competing Risks Using Multiphase Sub-models
  • 2018-12 Estimation of a Concordance Probability for Doubly Censored Time-to-Event Data
  • 2018-12 Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models
  • 2018-12 Tucker Tensor Regression and Neuroimaging Analysis
  • 2018-12 A Joint Modeling Approach for Longitudinal Data with Informative Observation Times and a Terminal Event
  • 2018-12 Assessment of Noninferiority (and Equivalence) for Simple Crossover Trials Using Bayesian Approach
  • 2018-12 Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data
  • 2018-12 Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability
  • 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/S17030", 
            "inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/", 
            "name": "Statistics for Life Sciences, Medicine, Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://scigraph.springernature.com/ontologies/product-market-codes/L15020", 
            "inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/", 
            "name": "Biostatistics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://scigraph.springernature.com/ontologies/product-market-codes/L19147", 
            "inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/", 
            "name": "Theoretical Ecology/Statistics", 
            "type": "DefinedTerm"
          }
        ], 
        "alternateName": "Journal of the International Chinese Statistical Association", 
        "contentRating": [
          {
            "author": "snip", 
            "ratingValue": "0.511", 
            "type": "Rating"
          }, 
          {
            "author": "sjr", 
            "ratingValue": "0.707", 
            "type": "Rating"
          }
        ], 
        "description": "

    Statistics in Biosciences (SIB) is published twice a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science.

    SIB publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIB share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.

    ", "editor": [ { "familyName": "Li", "givenName": "Hongzhe", "type": "Person" } ], "id": "sg:journal.1041137", "inLanguage": [ "en" ], "isAccessibleForFree": false, "issn": [ "1867-1764", "1867-1772" ], "license": "Hybrid (Open Choice)", "name": "Statistics in Biosciences", "productId": [ { "name": "scopus_id", "type": "PropertyValue", "value": [ "19400158517" ] }, { "name": "wos_id", "type": "PropertyValue", "value": [ "1867-1764/STATISTICS IN BIOSCIENCES" ] }, { "name": "nlm_unique_id", "type": "PropertyValue", "value": [ "101498115" ] }, { "name": "springer_id", "type": "PropertyValue", "value": [ "12561" ] }, { "name": "lccn_id", "type": "PropertyValue", "value": [ "2009243675" ] }, { "name": "dimensions_id", "type": "PropertyValue", "value": [ "41137" ] } ], "publisher": { "name": "Springer US", "type": "Organization" }, "publisherImprint": "Springer", "sameAs": [ "https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1041137" ], "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": "2009", "type": "Periodical", "url": "http://link.springer.com/journal/12561" } ]
     

    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.1041137'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/journal.1041137'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/journal.1041137'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/journal.1041137'


     

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

    77 TRIPLES      22 PREDICATES      32 URIs      25 LITERALS      11 BLANK NODES

    Subject Predicate Object
    1 sg:journal.1041137 schema:about sg:ontologies/product-market-codes/L15020
    2 sg:ontologies/product-market-codes/L19147
    3 sg:ontologies/product-market-codes/S17030
    4 schema:alternateName Journal of the International Chinese Statistical Association
    5 schema:contentRating N6618f99a0743465998e1050d3ee3bc39
    6 N6ddbeccd10f146569c01526ff529c71a
    7 schema:description <p><i>Statistics in Biosciences</i> (<i>SIB</i>) is published twice a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science. </p><p/><p><i>SIB</i> publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. <i>Original Articles</i> publish novel statistical and quantitative methods in biosciences. The Bioscience <i>Case Studies and Practice Articles</i> publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. <i>Review Articles</i> publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. <i>Commentaries</i> provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in <i>SIB </i>share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences. </p>
    8 schema:editor N72316bbab5e9480684c307689a75045f
    9 schema:inLanguage en
    10 schema:isAccessibleForFree false
    11 schema:issn 1867-1764
    12 1867-1772
    13 schema:license Hybrid (Open Choice)
    14 schema:name Statistics in Biosciences
    15 schema:productId N538d1a0323f1451180f7325dc05378da
    16 N993b455ea8a04a809017ffe8111b68fa
    17 Nadbc227182e543cfbe374f12a29462e8
    18 Nc644a2123aa5477da6a8ee4ceca09643
    19 Ncff2b7baf3cc4e2e8e638d65ff53be8e
    20 Neac92e81e84845c49fcad4a0f7f1a115
    21 schema:publisher Nc2016bf35ec749cc955454940d241ff7
    22 schema:publisherImprint Springer
    23 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1041137
    24 schema:sdDatePublished 2019-03-18T11:05
    25 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    26 schema:sdPublisher N9f5d311f8e174b12804b0a42e5fea4d0
    27 schema:startYear 2009
    28 schema:url http://link.springer.com/journal/12561
    29 sgo:license sg:explorer/license/
    30 sgo:sdDataset journals
    31 rdf:type schema:Periodical
    32 N538d1a0323f1451180f7325dc05378da schema:name lccn_id
    33 schema:value 2009243675
    34 rdf:type schema:PropertyValue
    35 N6618f99a0743465998e1050d3ee3bc39 schema:author N8b6fd9dece3342f7a4cc591a94a67155
    36 schema:ratingValue 0.511
    37 rdf:type schema:Rating
    38 N6ddbeccd10f146569c01526ff529c71a schema:author Ncfc26d0c43c744c79553488045843c91
    39 schema:ratingValue 0.707
    40 rdf:type schema:Rating
    41 N72316bbab5e9480684c307689a75045f rdf:first Nf9163fdb2ada49c7a0a8336ccfd9d937
    42 rdf:rest rdf:nil
    43 N8b6fd9dece3342f7a4cc591a94a67155 rdf:first snip
    44 rdf:rest rdf:nil
    45 N993b455ea8a04a809017ffe8111b68fa schema:name wos_id
    46 schema:value 1867-1764/STATISTICS IN BIOSCIENCES
    47 rdf:type schema:PropertyValue
    48 N9f5d311f8e174b12804b0a42e5fea4d0 schema:name Springer Nature - SN SciGraph project
    49 rdf:type schema:Organization
    50 Nadbc227182e543cfbe374f12a29462e8 schema:name scopus_id
    51 schema:value 19400158517
    52 rdf:type schema:PropertyValue
    53 Nc2016bf35ec749cc955454940d241ff7 schema:name Springer US
    54 rdf:type schema:Organization
    55 Nc644a2123aa5477da6a8ee4ceca09643 schema:name dimensions_id
    56 schema:value 41137
    57 rdf:type schema:PropertyValue
    58 Ncfc26d0c43c744c79553488045843c91 rdf:first sjr
    59 rdf:rest rdf:nil
    60 Ncff2b7baf3cc4e2e8e638d65ff53be8e schema:name springer_id
    61 schema:value 12561
    62 rdf:type schema:PropertyValue
    63 Neac92e81e84845c49fcad4a0f7f1a115 schema:name nlm_unique_id
    64 schema:value 101498115
    65 rdf:type schema:PropertyValue
    66 Nf9163fdb2ada49c7a0a8336ccfd9d937 schema:familyName Li
    67 schema:givenName Hongzhe
    68 rdf:type schema:Person
    69 sg:ontologies/product-market-codes/L15020 schema:inDefinedTermSet sg:ontologies/product-market-codes/
    70 schema:name Biostatistics
    71 rdf:type schema:DefinedTerm
    72 sg:ontologies/product-market-codes/L19147 schema:inDefinedTermSet sg:ontologies/product-market-codes/
    73 schema:name Theoretical Ecology/Statistics
    74 rdf:type schema:DefinedTerm
    75 sg:ontologies/product-market-codes/S17030 schema:inDefinedTermSet sg:ontologies/product-market-codes/
    76 schema:name Statistics for Life Sciences, Medicine, Health Sciences
    77 rdf:type schema:DefinedTerm
     




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


    ...