Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
3 Research products

  • Publications
  • European Commission
  • EC|H2020
  • K-PLEX
  • Mémoires en Sciences de l'Information et de la Communication
  • Hyper Article en Ligne - Sciences de l'Homme et de la Société
  • Hyper Article en Ligne

Relevance
arrow_drop_down
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Nicola Horsley;

    As one of the major technological concepts driving ICT development today, big data has been touted as offering new forms of analysis of research data. Its application has reached out across disciplines but some research sources and archival practices do not sit comfortably within the computational turn and this has sparked concerns that cultural heritage collections that cannot be structured, represented, or, indeed, digitised accordingly may be excluded and marginalised by this new paradigm. This work-in-progress paper reports on the contribution of the KPLEX project's knowledge complexity approach to understanding the relationship between big data and archival practice.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://hal.archives...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://hal.archives-ouvertes....
    Conference object
    Data sources: UnpayWall
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Hal-Diderot
    Other literature type . 2018
    Data sources: Hal-Diderot
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Hyper Article en Ligne
    Other literature type . 2018
    https://doi.org/10.1109/BigDat...
    Other literature type . Conference object . 2017
    https://doi.org/10.1109/bigdat...
    Conference object . 2017
    Data sources: Sygma; Crossref
    addClaim

    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
    BIP!Powered by BIP!
    more_vert
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://hal.archives...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://hal.archives-ouvertes....
      Conference object
      Data sources: UnpayWall
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Hal-Diderot
      Other literature type . 2018
      Data sources: Hal-Diderot
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Hyper Article en Ligne
      Other literature type . 2018
      https://doi.org/10.1109/BigDat...
      Other literature type . Conference object . 2017
      https://doi.org/10.1109/bigdat...
      Conference object . 2017
      Data sources: Sygma; Crossref
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.
  • Authors: Lehmann, Jörg; Stodulka, Thomas; Huber, Elisabeth;

    This report on Data, Knowledge Organisation, and Epistemic Impact covers the findings of WP 4 of the K-PLEX project. It focuses on data collection, production, and analysis in a broad range of scientific disciplines, on epistemologies and methodologies, and research organisation. The cross-disciplinary research topic “emotions” has been chosen to ensure comparability across disciplines and to investigate different epistemic cultures. Findings are based on a survey with 123 responses and 15 expert interviews. Results show the heterogeneity of research approaches and epistemic dissonances resulting from a broad variety of epistemic cultures in emotion research. Datafication – the rendering of real-world phenomena into data – inevitably leads to a reduction of complexity of the research object “emotions”. This simplification results from the limitations imposed by the epistemologies and the biases inherent to methodological decisions. The dissection into various disciplines and epistemic cultures and the challenges of interdisciplinarity further the marginalisation of complexity. Interdisciplinarity in emotion research was deemed as both beneficial and demanding. While interdisciplinary research projects were seen to be fruitful on a theoretical and conceptual level, the development of research methodologies that enable data structures which can be aggregated into larger data sets proved to be challenging. Data structures are designed according to methodological requirements and not to ensure reusability. Structural factors like the difficulties of research organisation in large-scale interdisciplinary research units, or the lack of high-ranked journals publishing interdisciplinary results further impede such research endeavours. Data cannot be seen independently from the context in which they were constructed and collected. The narrower context of the research setting and of the researcher as well as the wider contexts of the historical, political, social, cultural and linguistic circumstances of data collection have thus to be considered. The omission of contexts and the lack of comprehensive theoretical frameworks form considerable barriers to data aggregation and have consequences for data storage, sharing and reuse. A multiplicity of epistemologies and methodologies leads to a plurality of data and metadata formats and to a reduced acceptance of standard formats like the W3C standard EmotionML. In the case of data on emotions, further barriers are formed by legal restrictions or ethical issues in data sharing. Research participants showed cautiousness with respect to Big Data opening up new research possibilities. Big Data are not collected according to a specific research question or methodology and are thus antecedent to the epistemological process. This can be seen as a major difference between Big Data and research data. Moreover, Big Data are investigated in an exploratory process dominated by serendipitous findings, an approach that runs counter to scientists’ conception of a steered navigation of the research process. Concise recommendations on how these conflicting epistemologies could be combined in terms of integrative datafication standards, infrastructure and methodologies are outlined.

    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
    BIP!Powered by BIP!
    visibility3
    visibilityviews3
    downloaddownloads0
    Powered by Usage counts
    more_vert
  • Authors: Horsley, Nicola; Priddy, Mike;
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
    BIP!Powered by BIP!
    more_vert
Powered by OpenAIRE graph
Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
3 Research products
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Nicola Horsley;

    As one of the major technological concepts driving ICT development today, big data has been touted as offering new forms of analysis of research data. Its application has reached out across disciplines but some research sources and archival practices do not sit comfortably within the computational turn and this has sparked concerns that cultural heritage collections that cannot be structured, represented, or, indeed, digitised accordingly may be excluded and marginalised by this new paradigm. This work-in-progress paper reports on the contribution of the KPLEX project's knowledge complexity approach to understanding the relationship between big data and archival practice.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://hal.archives...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://hal.archives-ouvertes....
    Conference object
    Data sources: UnpayWall
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Hal-Diderot
    Other literature type . 2018
    Data sources: Hal-Diderot
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Hyper Article en Ligne
    Other literature type . 2018
    https://doi.org/10.1109/BigDat...
    Other literature type . Conference object . 2017
    https://doi.org/10.1109/bigdat...
    Conference object . 2017
    Data sources: Sygma; Crossref
    addClaim

    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
    BIP!Powered by BIP!
    more_vert
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://hal.archives...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://hal.archives-ouvertes....
      Conference object
      Data sources: UnpayWall
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Hal-Diderot
      Other literature type . 2018
      Data sources: Hal-Diderot
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Hyper Article en Ligne
      Other literature type . 2018
      https://doi.org/10.1109/BigDat...
      Other literature type . Conference object . 2017
      https://doi.org/10.1109/bigdat...
      Conference object . 2017
      Data sources: Sygma; Crossref
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.
  • Authors: Lehmann, Jörg; Stodulka, Thomas; Huber, Elisabeth;

    This report on Data, Knowledge Organisation, and Epistemic Impact covers the findings of WP 4 of the K-PLEX project. It focuses on data collection, production, and analysis in a broad range of scientific disciplines, on epistemologies and methodologies, and research organisation. The cross-disciplinary research topic “emotions” has been chosen to ensure comparability across disciplines and to investigate different epistemic cultures. Findings are based on a survey with 123 responses and 15 expert interviews. Results show the heterogeneity of research approaches and epistemic dissonances resulting from a broad variety of epistemic cultures in emotion research. Datafication – the rendering of real-world phenomena into data – inevitably leads to a reduction of complexity of the research object “emotions”. This simplification results from the limitations imposed by the epistemologies and the biases inherent to methodological decisions. The dissection into various disciplines and epistemic cultures and the challenges of interdisciplinarity further the marginalisation of complexity. Interdisciplinarity in emotion research was deemed as both beneficial and demanding. While interdisciplinary research projects were seen to be fruitful on a theoretical and conceptual level, the development of research methodologies that enable data structures which can be aggregated into larger data sets proved to be challenging. Data structures are designed according to methodological requirements and not to ensure reusability. Structural factors like the difficulties of research organisation in large-scale interdisciplinary research units, or the lack of high-ranked journals publishing interdisciplinary results further impede such research endeavours. Data cannot be seen independently from the context in which they were constructed and collected. The narrower context of the research setting and of the researcher as well as the wider contexts of the historical, political, social, cultural and linguistic circumstances of data collection have thus to be considered. The omission of contexts and the lack of comprehensive theoretical frameworks form considerable barriers to data aggregation and have consequences for data storage, sharing and reuse. A multiplicity of epistemologies and methodologies leads to a plurality of data and metadata formats and to a reduced acceptance of standard formats like the W3C standard EmotionML. In the case of data on emotions, further barriers are formed by legal restrictions or ethical issues in data sharing. Research participants showed cautiousness with respect to Big Data opening up new research possibilities. Big Data are not collected according to a specific research question or methodology and are thus antecedent to the epistemological process. This can be seen as a major difference between Big Data and research data. Moreover, Big Data are investigated in an exploratory process dominated by serendipitous findings, an approach that runs counter to scientists’ conception of a steered navigation of the research process. Concise recommendations on how these conflicting epistemologies could be combined in terms of integrative datafication standards, infrastructure and methodologies are outlined.

    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
    BIP!Powered by BIP!
    visibility3
    visibilityviews3
    downloaddownloads0
    Powered by Usage counts
    more_vert
  • Authors: Horsley, Nicola; Priddy, Mike;
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
    BIP!Powered by BIP!
    more_vert
Powered by OpenAIRE graph