Title: Making Better Numbers through Bioethnographic Collaboration
ABSTRACT
In this article, I describe my ongoing bioethnographic collaboration with a multidisciplinary team of exposure scientists in environmental engineering and health. First, I explain how and why integrating ethnography and number‐based disciplines is such a complex, time‐consuming, and worthwhile process, when ethnography produces a kind of excessive “big data” that is not easily enumerated. Then I describe three of our current bioethnographic projects that seek to make better numbers about how (1) neighborhoods, (2) water distribution, and (3) employment and chemical exposures shape bodily processes in a highly unequal world. To conclude, I reflect on how we might harness ethnographic excess for making better numbers and thus better knowledge, and also how bioethnographic collaboration inevitably transforms ethnography even as we insist on its excess. [collaboration, methodology, ethnography, big data, biomedical science]
Yuan, Ye; Cao, Jan; Wang, Ruotong; Yarosh, Svetlana(
, CHI Conference on Human Factors in Computing Systems)
null
(Ed.)
Prior research has highlighted opportunities for technology to better support the tabletop game experience in offline and online settings, but little work has focused on the social aspect of tabletop gaming. We investigated the social and collaborative aspects of tabletop gaming in the unique context of “social distancing” during the 2020 COVID-19 pandemic to shed light on the experience of remote tabletop gaming. With a multi-method qualitative approach (including digital ethnography and in-depth interviews), we empirically studied how people appropriate existing technologies and adapt their offline practices to play tabletop games remotely. We identify three themes that describe people’s game and social experience during remote play: creating a shared tabletop environment (shared space), enabling a collective understanding (shared information and awareness), and facilitating a communal temporal experience (shared time). We reflect on challenges and design opportunities for a better experience in the age of remote collaboration.
Using the example of Andean archaeology, this article focuses on subtle forms of inequality that arise when academic communities are conceptualized as friendship‐based and egalitarian, rejecting explicit hierarchy. I describe this asperformative informalityand argue that it stems from a meritocratic ideology that inadvertently reproduces Euro‐American white‐male privilege. In a discipline that prides itself on its friendliness, openness, and alcohol‐fueled drinking culture, those who find themselves unable to enact or perform informality appropriately are at a distinct disadvantage. Drawing from a multisited ethnography of Andeanist archaeologists, I make the case that it is the ephemerality and plausible deniability of performative informality that makes it hard to recognize and thus mitigate against it. In doing so, I draw on and contribute to the theorization of gender/class intersectionality in anthropology and science studies, US conceptualizations of meritocracy in academia and higher education, and feminist Jo Freeman's concept of “the tyranny of structurelessness.” [anthropology of science, ethnography of archaeology, class, gender, anthropology of work and education]
Rahman, S.; Miranda, M.; Obeid, I.; Picone, J.(
, IEEE Signal Processing in Medicine and Biology Symposium (SPMB))
Obeid, I.; Selesnick, I.
(Ed.)
The Neural Engineering Data Consortium at Temple University has been providing key data resources to support the development of deep learning technology for electroencephalography (EEG) applications [1-4] since 2012. We currently have over 1,700 subscribers to our resources and have been providing data, software and documentation from our web site [5] since 2012. In this poster, we introduce additions to our resources that have been developed within the past year to facilitate software development and big data machine learning research.
Major resources released in 2019 include:
● Data: The most current release of our open source EEG data is v1.2.0 of TUH EEG and includes the addition of 3,874 sessions and 1,960 patients from mid-2015 through 2016.
● Software: We have recently released a package, PyStream, that demonstrates how to correctly read an EDF file and access samples of the signal. This software demonstrates how to properly decode channels based on their labels and how to implement montages. Most existing open source packages to read EDF files do not directly address the problem of channel labels [6].
● Documentation: We have released two documents that describe our file formats and data representations: (1) electrodes and channels [6]: describes how to map channel labels to physical locations of the electrodes, and includes a description of every channel label appearing in the corpus; (2) annotation standards [7]: describes our annotation file format and how to decode the data structures used to represent the annotations.
Additional significant updates to our resources include:
● NEDC TUH EEG Seizure (v1.6.0): This release includes the expansion of the training dataset from 4,597 files to 4,702. Calibration sequences have been manually annotated and added to our existing documentation. Numerous corrections were made to existing annotations based on user feedback.
● IBM TUSZ Pre-Processed Data (v1.0.0): A preprocessed version of the TUH Seizure Detection Corpus using two methods [8], both of which use an FFT sliding window approach (STFT). In the first method, FFT log magnitudes are used. In the second method, the FFT values are normalized across frequency buckets and correlation coefficients are calculated. The eigenvalues are calculated from this correlation matrix. The eigenvalues and correlation matrix's upper triangle are used to generate feature.
● NEDC TUH EEG Artifact Corpus (v1.0.0): This corpus was developed to support modeling of non-seizure signals for problems such as seizure detection. We have been using the data to build better background models. Five artifact events have been labeled: (1) eye movements (EYEM), (2) chewing (CHEW), (3) shivering (SHIV), (4) electrode pop, electrostatic artifacts, and lead artifacts (ELPP), and (5) muscle artifacts (MUSC). The data is cross-referenced to TUH EEG v1.1.0 so you can match patient numbers, sessions, etc.
● NEDC Eval EEG (v1.3.0): In this release of our standardized scoring software, the False Positive Rate (FPR) definition of the Time-Aligned Event Scoring (TAES) metric has been updated [9]. The standard definition is the number of false positives divided by the number of false positives plus the number of true negatives: #FP / (#FP + #TN).
We also recently introduced the ability to download our data from an anonymous rsync server. The rsync command [10] effectively synchronizes both a remote directory and a local directory and copies the selected folder from the server to the desktop. It is available as part of most, if not all, Linux and Mac distributions (unfortunately, there is not an acceptable port of this command for Windows). To use the rsync command to download the content from our website, both a username and password are needed. An automated registration process on our website grants both. An example of a typical rsync command to access our data on our website is:
rsync -auxv nedc_tuh_eeg@www.isip.piconepress.com:~/data/tuh_eeg/
Rsync is a more robust option for downloading data. We have also experimented with Google Drive and Dropbox, but these types of technology are not suitable for such large amounts of data.
All of the resources described in this poster are open source and freely available at https://www.isip.piconepress.com/projects/tuh_eeg/downloads/. We will demonstrate how to access and utilize these resources during the poster presentation and collect community feedback on the most needed additions to enable significant advances in machine learning performance.
This paper accounts for the influence of social networks on female sex workers (FSWs) (xiaojie) in post‐socialist China, as they interpret cultural values and meaning, create and revise strategies for survival and success, and embody their lived experiences. Drawing upon recent feminist ethnography on sex work and embodiment, I describe the network processes that draw these women into sex work and contribute to both their achievements and challenges as a form of “positional embodiment.” The concept of “positional embodiment” highlights the role of social networks in creating and maintaining a particular inscribed and agentic sex worker body. In identifying this meso‐level process, I delineate the culturally specific and structurally framed ways in which social networks position the women to make daily choices within structural constraints, choices that ultimately lead to the patterned embodiment of their daily experiences. Even as these savvy entrepreneurs tend to exceed gendered moral obligations to family and actively immerse themselves as trendy modern women in contemporary urban lifestyles, they also struggle with the emotional and physical suffering of working within the sex industry. In this sense, their bodies become fields where various challenges and achievements are negotiated, contested, and embodied. This paper intends to contribute to our understanding of intimate labor by developing the concept of “positional embodiment” from the lens of social networks of FSWs.
Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we present the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we describe the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networks and the disrupted access to critical facilities, such as hospitals. Fourth, we introduce how location-based and credit card transaction data were used in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.
Roberts, Elizabeth F. S. Making Better Numbers through Bioethnographic Collaboration. American Anthropologist, 123 (2). https://doi.org/10.1111/aman.13560
@article{osti_10362838,
place = {Country unknown/Code not available},
title = {Making Better Numbers through Bioethnographic Collaboration},
url = {https://par.nsf.gov/biblio/10362838},
DOI = {10.1111/aman.13560},
abstractNote = {ABSTRACT In this article, I describe my ongoing bioethnographic collaboration with a multidisciplinary team of exposure scientists in environmental engineering and health. First, I explain how and why integrating ethnography and number‐based disciplines is such a complex, time‐consuming, and worthwhile process, when ethnography produces a kind of excessive “big data” that is not easily enumerated. Then I describe three of our current bioethnographic projects that seek to make better numbers about how (1) neighborhoods, (2) water distribution, and (3) employment and chemical exposures shape bodily processes in a highly unequal world. To conclude, I reflect on how we might harness ethnographic excess for making better numbers and thus better knowledge, and also how bioethnographic collaboration inevitably transforms ethnography even as we insist on its excess. [collaboration, methodology, ethnography, big data, biomedical science]},
journal = {American Anthropologist},
volume = {123},
number = {2},
publisher = {Wiley-Blackwell},
author = {Roberts, Elizabeth F. S.},
}
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