Big Data and Biomedicine

Levin, Nadine

doi:10.1057/978-1-137-52879-7_28

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…2017; Merry 2016). This study broadly belongs to this budding field that Levin (2019) refers to as an “anthropology of data,” questioning “the norms, politics, and values that get wrapped up in data” (p. 665). We are not against data, but we wish to explore what data work does and to question some common assumptions about the power of data.…”

Section: In Need Of Measurement? the Datafication Of Home Carementioning

confidence: 99%

Weak Data: The Social Biography of a Measurement Instrument and How It Failed to Ensure Accountability in Home Care

Hoeyer

Bødker

2020

Med Anthropol Q

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Contemporary health and social care is saturated by processes of datafication. In many cases, these processes are nested within an ostensibly simple logic of accountability: Define a politically and morally desirable goal, then measure the level of achievement. This logic has come to permeate public health initiatives globally and today it operates in most health care systems in various ways. We explore here a particular instantiation of the logic associated with the introduction of a measurement instrument used in Danish home care. Building on ethnographic fieldwork, interviews, and analysis of policy documents, we show how the instigated processes of datafication—despite hopeful political claims—erode care levels and disempower older people. We believe that these findings can be of relevance for other settings that subscribe to the same accountability logic and to similar forms of measurement instruments.

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Section: In Need Of Measurement? the Datafication Of Home Carementioning

confidence: 99%

Weak Data: The Social Biography of a Measurement Instrument and How It Failed to Ensure Accountability in Home Care

Hoeyer

Bødker

2020

Med Anthropol Q

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“…Big Data technologies, on the other hand, have become increasingly popular, and their usage is not longer restricted to data analytics, but has been successfully used in fields like bioinformatics [7][8][9][10][11]15], chemistry [29,30], or medicine [31,32]. Technologies like Apache Hadoop [4] or Apache Spark [5] offer a scalable way to process enormous amounts of data in large clusters of "cheap" computers or virtual machines in the cloud, using simple programming models.…”

Section: Introductionmentioning

confidence: 99%

Big Data in metagenomics: Apache Spark vs MPI

et al. 2020

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The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when dealing with increasing problems sizes, making in this way the usage of High Performance Computing (HPC) technologies such as the message passing interface (MPI) a promising alternative. Recently, MetaCacheSpark, an Apache Spark based software for detection and quantification of species composition in food samples has been proposed. This tool can be used to analyze high throughput sequencing data sets of metagenomic DNA and allows for dealing with large-scale collections of complex eukaryotic and bacterial reference genome. In this work, we propose MetaCache-MPI, a fast and memory efficient solution for computing clusters which is based on MPI instead of Apache Spark. In order to evaluate its performance a comparison is performed between the original single CPU version of MetaCache, the Spark version and the MPI version we are introducing. Results show that for 32 processes, MetaCache-MPI is 1.65× faster while consuming 48.12% of the RAM memory used by Spark for building a metagenomics database. For querying this database, also with 32 processes, the MPI version is 3.11× faster, while using 55.56% of the memory used by Spark. We conclude that the new MetaCache-MPI version is faster in both building and querying the database and uses less RAM memory, when compared with MetaCacheSpark, while keeping the accuracy of the original implementation.

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Ethnographic Agency in a Data Driven World

Levin

2019

Ethnographic Praxis

Self Cite

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This paper argues that ethnographers can gain increased agency in data-driven corporate environments by increasing their quantitative literacy: their ability to create, understand, and strategically use quantitative data to shape organizations. Drawing on the author's experience conducting strategic user research at a technology company, the paper explores how the ability to engage with quantitative data can increase ethnographers' independence and autonomy within organizations, and can also up-level the role and value of qualitative research. The paper also explores how a deep familiarity with quantitative data can enable ethnographers to imbue quantitative data itself with new forms of agency, and can ultimately give ethnographers the tools to change institutions from within. With a greater understanding of how quantitative data is made and used, ethnographers can ensure that data is collected in representative ways, point out the limitations of existing metrics, and argue for new ways of measuring and understanding social life.

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Big Data and Biomedicine

Cited by 3 publications

References 55 publications

Weak Data: The Social Biography of a Measurement Instrument and How It Failed to Ensure Accountability in Home Care

Weak Data: The Social Biography of a Measurement Instrument and How It Failed to Ensure Accountability in Home Care

Big Data in metagenomics: Apache Spark vs MPI

Ethnographic Agency in a Data Driven World

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