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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
6,666 Research products, page 1 of 667

  • Digital Humanities and Cultural Heritage
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  • Open Access
    Authors: 
    Andrés Teira-Brión; Xurxo Constela Doce; Miguel Sartal Lorenzo; Dolores Gil Agra; Víctor Rúa Carril;
    Publisher: Elsevier BV
    Project: EC | MILLET (101018935)
  • Open Access
    Authors: 
    Serpico, Davide; Lynch, Kate E.; Porter, Theodore M.;
    Publisher: Elsevier BV
    Country: Poland
    Project: EC | BIOUNCERTAINTY (805498)

    The aim of this virtual special issue is to bring together philosophical and historical perspectives to address long-standing issues in the interpretation, utility, and impacts of quantitative genetics methods and findings. Methodological approaches and the underlying scientific understanding of genetics and heredity have transformed since the field's inception. These advances have brought with them new philosophical issues regarding the interpretation and understanding of quantitative genetic results. The contributions in this issue demonstrate that there is still work to be done integrating old and new methodological and conceptual frameworks. In some cases, new results are interpreted using assumptions based on old concepts and methodologies that need to be explicitly recognised and updated. In other cases, new philosophical tools can be employed to synthesise historical quantitative genetics work with modern methodologies and findings. This introductory article surveys three general themes that have dominated philosophical discussion of quantitative genetics throughout history: (1) how methodologies have changed and transformed our knowledge and interpretations; (2) whether or not quantitative genetics can offer explanations relating to causation and prediction; and (3) the importance of defining the phenotypes under study. We situate the contributions in this virtual special issue within a historical framework addressing these three themes.

  • Open Access
    Authors: 
    Marco Bombieri; Marco Rospocher; Simone Paolo Ponzetto; Paolo Fiorini;
    Publisher: Elsevier BV
    Country: Italy
    Project: EC | ARS (742671)
  • Open Access
    Authors: 
    Carla Kirschbaum; Kevin Pagel;
    Publisher: Wiley
    Project: EC | GlycoSpec (863934)
  • Open Access
    Authors: 
    Dóra Mérai; Loes Veldpaus; John Pendlebury; Markus Kip;
    Publisher: Informa UK Limited
    Project: EC | OpenHeritage (776766)
  • Publication . Other literature type . 2022
    Open Access English
    Authors: 
    Agasøster, Bodil; Arar, Karin; Gyrid Havåg Bergseth; Beuster, Benjamin; Bidargaddi, Archana; Revheim, Sigbjørn; Risnes, Ørnulf; Skjåk, Knut Kalgraff;
    Publisher: Zenodo
    Project: EC | SSHOC (823782)

    Milestone 34, Expose ESS’ interoperable services to external consumers, was achieved on 17 December 2021. The new system gives users access to the data catalogue of the European Social Survey (ESS) from the new data and metadata repositories via https://ess-search.nsd.no/en/all/query/.

  • Open Access
    Authors: 
    Acerbi, A; Snyder, W; Tennie, C;
    Publisher: Springer Science and Business Media LLC
    Country: United Kingdom
    Project: EC | STONECULT (714658)

    The method of exclusion identifies patterns of distributions of behaviours and/or artefact forms among different groups, where these patterns are deemed unlikely to arise from purely genetic and/or ecological factors. The presence of such patterns is often used to establish whether a species is cultural or not—i.e. whether a species uses social learning or not. Researchers using or describing this method have often pointed out that the method cannot pinpoint which specific type(s) of social learning resulted in the observed patterns. However, the literature continues to contain such inferences. In a new attempt to warn against these logically unwarranted conclusions, we illustrate this error using a novel approach. We use an individual-based model, focused on wild ape cultural patterns—as these patterns are the best-known cases of animal culture and as they also contain the most frequent usage of the unwarranted inference for specific social learning mechanisms. We built a model that contained agents unable to copy specifics of behavioural or artefact forms beyond their individual reach (which we define as “copying”). We did so, as some of the previous inference claims related to social learning mechanisms revolve around copying defined in this way. The results of our model however show that non-copying social learning can already reproduce the defining—even iconic—features of observed ape cultural patterns detected by the method of exclusion. This shows, using a novel model approach, that copying processes are not necessary to produce the cultural patterns that are sometimes still used in an attempt to identify copying processes. Additionally, our model could fully control for both environmental and genetic factors (impossible in real life) and thus offers a new validity check for the method of exclusion as related to general cultural claims—a check that the method passed. Our model also led to new and additional findings, which we likewise discuss. European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No 714658; STONECULT project).

  • Publication . Preprint . Article . Other literature type . 2022
    Open Access English
    Authors: 
    Jörg Franke; Michael N. Evans; Andrew Schurer; Gabriele C. Hegerl;
    Publisher: Copernicus Publications
    Countries: United Kingdom, Switzerland
    Project: EC | PALAEO-RA (787574)

    Abstract. The detection and attribution (D&A) of paleoclimatic change to external radiative forcing relies on regression of statistical reconstructions on simulations. However, this procedure may be biased by assumptions of stationarity and univariate linear response of the underlying paleoclimatic observations. Here we perform a D&A study, modeling paleoclimate data observations as a function of paleoclimatic data simulations. Specifically, we detect and attribute tree-ring width (TRW) observations as a linear function of TRW simulations, which are themselves a nonlinear and multivariate TRW simulation driven with singly forced and cumulatively forced climate simulations for the period 1401–2000 CE. Temperature- and moisture-sensitive TRW simulations detect distinct patterns in time and space. Temperature-sensitive TRW observations and simulations are significantly correlated for Northern Hemisphere averages, and their variation is attributed to volcanic forcing. In decadally smoothed temporal fingerprints, we find the observed responses to be significantly larger and/or more persistent than the simulated responses. The pattern of simulated TRW of moisture-limited trees is consistent with the observed anomalies in the 2 years following major volcanic eruptions. We can for the first time attribute this spatiotemporal fingerprint in moisture-limited tree-ring records to volcanic forcing. These results suggest that the use of nonlinear and multivariate proxy system models in paleoclimatic detection and attribution studies may permit more realistic, spatially resolved and multivariate fingerprint detection studies and evaluation of the climate sensitivity to external radiative forcing than has previously been possible.

  • Open Access
    Authors: 
    Debora Cevasco; J. Tautz-Weinert; Athanasios Kolios; Ursula Smolka;
    Publisher: Zenodo
    Project: EC | ROMEO (745625)

    Abstract Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data.

  • Open Access
    Authors: 
    Joshua J. P. Thompson; Dominik Muth; Sebastian Anhäuser; Daniel Bischof; Marina Gerhard; Gregor Witte; Ermin Malic;
    Publisher: Wiley
    Project: EC | GrapheneCore3 (881603)
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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
6,666 Research products, page 1 of 667
  • Open Access
    Authors: 
    Andrés Teira-Brión; Xurxo Constela Doce; Miguel Sartal Lorenzo; Dolores Gil Agra; Víctor Rúa Carril;
    Publisher: Elsevier BV
    Project: EC | MILLET (101018935)
  • Open Access
    Authors: 
    Serpico, Davide; Lynch, Kate E.; Porter, Theodore M.;
    Publisher: Elsevier BV
    Country: Poland
    Project: EC | BIOUNCERTAINTY (805498)

    The aim of this virtual special issue is to bring together philosophical and historical perspectives to address long-standing issues in the interpretation, utility, and impacts of quantitative genetics methods and findings. Methodological approaches and the underlying scientific understanding of genetics and heredity have transformed since the field's inception. These advances have brought with them new philosophical issues regarding the interpretation and understanding of quantitative genetic results. The contributions in this issue demonstrate that there is still work to be done integrating old and new methodological and conceptual frameworks. In some cases, new results are interpreted using assumptions based on old concepts and methodologies that need to be explicitly recognised and updated. In other cases, new philosophical tools can be employed to synthesise historical quantitative genetics work with modern methodologies and findings. This introductory article surveys three general themes that have dominated philosophical discussion of quantitative genetics throughout history: (1) how methodologies have changed and transformed our knowledge and interpretations; (2) whether or not quantitative genetics can offer explanations relating to causation and prediction; and (3) the importance of defining the phenotypes under study. We situate the contributions in this virtual special issue within a historical framework addressing these three themes.

  • Open Access
    Authors: 
    Marco Bombieri; Marco Rospocher; Simone Paolo Ponzetto; Paolo Fiorini;
    Publisher: Elsevier BV
    Country: Italy
    Project: EC | ARS (742671)
  • Open Access
    Authors: 
    Carla Kirschbaum; Kevin Pagel;
    Publisher: Wiley
    Project: EC | GlycoSpec (863934)
  • Open Access
    Authors: 
    Dóra Mérai; Loes Veldpaus; John Pendlebury; Markus Kip;
    Publisher: Informa UK Limited
    Project: EC | OpenHeritage (776766)
  • Publication . Other literature type . 2022
    Open Access English
    Authors: 
    Agasøster, Bodil; Arar, Karin; Gyrid Havåg Bergseth; Beuster, Benjamin; Bidargaddi, Archana; Revheim, Sigbjørn; Risnes, Ørnulf; Skjåk, Knut Kalgraff;
    Publisher: Zenodo
    Project: EC | SSHOC (823782)

    Milestone 34, Expose ESS’ interoperable services to external consumers, was achieved on 17 December 2021. The new system gives users access to the data catalogue of the European Social Survey (ESS) from the new data and metadata repositories via https://ess-search.nsd.no/en/all/query/.

  • Open Access
    Authors: 
    Acerbi, A; Snyder, W; Tennie, C;
    Publisher: Springer Science and Business Media LLC
    Country: United Kingdom
    Project: EC | STONECULT (714658)

    The method of exclusion identifies patterns of distributions of behaviours and/or artefact forms among different groups, where these patterns are deemed unlikely to arise from purely genetic and/or ecological factors. The presence of such patterns is often used to establish whether a species is cultural or not—i.e. whether a species uses social learning or not. Researchers using or describing this method have often pointed out that the method cannot pinpoint which specific type(s) of social learning resulted in the observed patterns. However, the literature continues to contain such inferences. In a new attempt to warn against these logically unwarranted conclusions, we illustrate this error using a novel approach. We use an individual-based model, focused on wild ape cultural patterns—as these patterns are the best-known cases of animal culture and as they also contain the most frequent usage of the unwarranted inference for specific social learning mechanisms. We built a model that contained agents unable to copy specifics of behavioural or artefact forms beyond their individual reach (which we define as “copying”). We did so, as some of the previous inference claims related to social learning mechanisms revolve around copying defined in this way. The results of our model however show that non-copying social learning can already reproduce the defining—even iconic—features of observed ape cultural patterns detected by the method of exclusion. This shows, using a novel model approach, that copying processes are not necessary to produce the cultural patterns that are sometimes still used in an attempt to identify copying processes. Additionally, our model could fully control for both environmental and genetic factors (impossible in real life) and thus offers a new validity check for the method of exclusion as related to general cultural claims—a check that the method passed. Our model also led to new and additional findings, which we likewise discuss. European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No 714658; STONECULT project).

  • Publication . Preprint . Article . Other literature type . 2022
    Open Access English
    Authors: 
    Jörg Franke; Michael N. Evans; Andrew Schurer; Gabriele C. Hegerl;
    Publisher: Copernicus Publications
    Countries: United Kingdom, Switzerland
    Project: EC | PALAEO-RA (787574)

    Abstract. The detection and attribution (D&A) of paleoclimatic change to external radiative forcing relies on regression of statistical reconstructions on simulations. However, this procedure may be biased by assumptions of stationarity and univariate linear response of the underlying paleoclimatic observations. Here we perform a D&A study, modeling paleoclimate data observations as a function of paleoclimatic data simulations. Specifically, we detect and attribute tree-ring width (TRW) observations as a linear function of TRW simulations, which are themselves a nonlinear and multivariate TRW simulation driven with singly forced and cumulatively forced climate simulations for the period 1401–2000 CE. Temperature- and moisture-sensitive TRW simulations detect distinct patterns in time and space. Temperature-sensitive TRW observations and simulations are significantly correlated for Northern Hemisphere averages, and their variation is attributed to volcanic forcing. In decadally smoothed temporal fingerprints, we find the observed responses to be significantly larger and/or more persistent than the simulated responses. The pattern of simulated TRW of moisture-limited trees is consistent with the observed anomalies in the 2 years following major volcanic eruptions. We can for the first time attribute this spatiotemporal fingerprint in moisture-limited tree-ring records to volcanic forcing. These results suggest that the use of nonlinear and multivariate proxy system models in paleoclimatic detection and attribution studies may permit more realistic, spatially resolved and multivariate fingerprint detection studies and evaluation of the climate sensitivity to external radiative forcing than has previously been possible.

  • Open Access
    Authors: 
    Debora Cevasco; J. Tautz-Weinert; Athanasios Kolios; Ursula Smolka;
    Publisher: Zenodo
    Project: EC | ROMEO (745625)

    Abstract Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data.

  • Open Access
    Authors: 
    Joshua J. P. Thompson; Dominik Muth; Sebastian Anhäuser; Daniel Bischof; Marina Gerhard; Gregor Witte; Ermin Malic;
    Publisher: Wiley
    Project: EC | GrapheneCore3 (881603)