Historical Research in a Digital Age: Reflections from the Mapping the Republic of Letters Project

Edelstein, Dan; Findlen, Paula; Ceserani, Giovanna; Winterer, Caroline; Coleman, Nicole

doi:10.1093/ahr/122.2.400

Cited by 59 publications

(16 citation statements)

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“…Nesse âmbito, vários grupos de investigação internacionais que têm dados digitalizados em larga escala têm aplicado essas metodologias. Mencione-se, por exemplo, o projeto Mapping the Republic of Letters (Edelstein et al, 2017), desenvolvido em parceria pela Universidade de Stanford. Entre vários estudos de caso, é possível constatar como muitos projetos têm-se beneficiado das capacidades analíticas da análise de redes sociais, recorrendo aos metadados e repensando os limites das histórias nacionais.…”

Section: Métodos E Instrumentos Digitais Para a História Da Construçãounclassified

Portugal Builds: Uma Plataforma Digital Para a História Da Construção Em Portugal Nos Séculos Xix E Xx

Mascarenhas-Mateus

Veiga

2020

Estud. hist. (Rio J.)

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RESUMO São apresentados os procedimentos da criação de uma plataforma digital de acesso gratuito destinada à disseminação dos resultados da investigação no campo da história da construção em Portugal, durante os séculos XIX e XX. Depois da descrição das fontes das coleções digitais, procede-se à análise da metodologia usada em sua estruturação e nas adaptações às singularidades desse campo de conhecimento de discussão recente. O estudo é ilustrado com a primeira exposição virtual, avaliando potencialidades e limites da plataforma para diferentes níveis de pesquisa no espaço e no tempo, relativas a atores individuais e coletivos e a objetos concretos e abstratos.

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Section: Métodos E Instrumentos Digitais Para a História Da Construçãounclassified

Portugal Builds: Uma Plataforma Digital Para a História Da Construção Em Portugal Nos Séculos Xix E Xx

Mascarenhas-Mateus

Veiga

2020

Estud. hist. (Rio J.)

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“…Visualization designers in all areas commonly build on the technique of multiple views, when it comes to complex interface design. Exemplarily, the Palladio interface-a widely used open tool for cultural collection visualization-uses (among other views) (i) a geo-spatial and (ii) a relational visualization in combination with (iii) a timeline visualization [54]. Building on this standard practice, the PolyCube framework aims to connect, combine, and intertwine its multiple views for the sake of a better integrated "bigger picture" of complex collections.…”

Section: The Polycube Projectmentioning

confidence: 99%

Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

2019

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Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups.

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“…This soar can be explained, at least in part, by our new-found ability to generate and store ever larger amounts of data (Big Data) with which to train ML models coupled with unprecedented computational speed and power. It is becoming difficult to find a field that hasn't yet adopted ML approaches in one way or another, with applications ranging all the way from the humanities (see [2][3][4] for some examples) to paleontology [5] or health care [6]. The biological sciences are indeed no exception [7][8][9][10], having entered into the era of Big Data thanks to technological advances such as next generation sequencing and multi-omics approaches [11].…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for staging embryonic tissue isolates with small data

Pond

Hwang

Verd

et al. 2020

Preprint

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Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches.Author summaryThe application of machine learning approaches currently hinges on the availability of large data sets to train the models with. However, recent research has shown that large data sets might not always be required. In this work we set out to see whether we could use small confocal microscopy image data sets to train a convolutional neural network (CNN) to stage zebrafish tail buds at four different stages in their development. We found that high test accuracies can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a CNN. This work also shows that we can robustly stage the embryonic development of isolated structures, without the need to refer back to landmarks in the tail bud. This constitutes an important methodological advance for staging organoids and other 3D culture in vitro systems. This work proves that prohibitively large data sets are not always required to train CNNs, and we hope will encourage others to apply the power of machine learning to their areas of study even if data are scarce.

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Historical Research in a Digital Age: Reflections from the Mapping the Republic of Letters Project

Cited by 59 publications

References 0 publications

Portugal Builds: Uma Plataforma Digital Para a História Da Construção Em Portugal Nos Séculos Xix E Xx

Portugal Builds: Uma Plataforma Digital Para a História Da Construção Em Portugal Nos Séculos Xix E Xx

Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

A deep learning approach for staging embryonic tissue isolates with small data

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