The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries: A Collaborative Ethnography of Documentation Work

Computational research and data analytics increasingly relies on complex ecosystems of open source software (OSS) "libraries" -- curated collections of reusable code that programmers import to perform a specific task. Software documentation for these libraries is crucial in helping programmers/analysts know what libraries are available and how to use them. Yet documentation for open source software libraries is widely considered low-quality. This article is a collaboration between CSCW researchers and contributors to data analytics OSS libraries, based on ethnographic fieldwork and qualitative interviews. We examine several issues around the formats, practices, and challenges around documentation in these largely volunteer-based projects. There are many different kinds and formats of documentation that exist around such libraries, which play a variety of educational, promotional, and organizational roles. The work behind documentation is similarly multifaceted, including writing, reviewing, maintaining, and organizing documentation. Different aspects of documentation work require contributors to have different sets of skills and overcome various social and technical barriers. Finally, most of our interviewees do not report high levels of intrinsic enjoyment for doing documentation work (compared to writing code). Their motivation is affected by personal and project-specific factors, such as the perceived level of credit for doing documentation work versus more "technical" tasks like adding new features or fixing bugs. In studying documentation work for data analytics OSS libraries, we gain a new window into the changing practices of data-intensive research, as well as help practitioners better understand how to support this often invisible and infrastructural work in their projects

Simulating social-ecological systems: the Island Digital Ecosystem Avatars (IDEA) consortium

Abstract Systems biology promises to revolutionize medicine, yet human wellbeing is also inherently linked to healthy societies and environments (sustainability). The IDEA Consortium is a systems ecology open science initiative to conduct the basic scientific research needed to build use-oriented simulations (avatars) of entire social-ecological systems. Islands are the most scientifically tractable places for these studies and we begin with one of the best known: Moorea, French Polynesia. The Moorea IDEA will be a sustainability simulator modeling links and feedbacks between climate, environment, biodiversity, and human activities across a coupled marine-terrestrial landscape. As a model system, the resulting knowledge and tools will improve our ability to predict human and natural change on Moorea and elsewhere at scales relevant to management/conservation actions

Modes and Existences in Citizen Science: Thoughts from Earthquake Country

In the Bay Area of San Francisco, the earthquake contours are not easy to define: seismology is still a relatively recent science, and controversies around methods to evaluate the earthquake risk are constant. In this context, the invitation to think about the modes of citizen science is an opportunity to reflect on the modality of hybridized scientific practices as well as the process by which the plurality and complexity of the earthquake characteristics can be articulated, and sometime reconciled. Looking at different existences of the earthquake risk, the paper investigates different assemblages that question the clear-cut distinction between citizen science and science. I'll situate the question of the mode of citizen science within the larger framework of interdisciplinarity knowledge infrastructures and the work on 'mode of existence' initiated by Bruno Latour and Isabelle Stengers (2009). Expanding our understanding with regard to how CS is performed opens the possibility of reconsidering the specific types of assemblages and infrastructures from which these modes emerge and on their distinct trajectories. It is also an invitation to make visible the integration processes, the communities, and the imaginations that "make" science

The making of the Map, the Making of the Risk

Communication consultable sur Internet : http://fukushimaforum.wordpress.com/online-forum-2/second-3-11-virtual-conference-2013/the-making-of-the-map-the-making-of-the-risk

En Attendant le Big One : l'instauration du risque de tremblement de terre dans la baie de San Francisco

The potentiality of disasters forces us to rethink progressive, yet non-linear definitions (“instauration,” in Souriau vocabulary) of risk, space, and expertise. Following a symmetrical approach, this work explores several moving dimensions of the subject and space “at risk” in the San Francisco Bay Area, within the shared experience of an epistemic community waiting for a major earthquake - “the Big One” - to unfold. With a Geography, Science and Technologies Studies perspectives, we will look at the complex system of relations that co-construct the risk of earthquakes and the ways in which this successive instauration convene transformations in the making of space, the definition of risk, and finally, the translation of this scientific work into public policies and the figure of the expert. Drawing from in-depth empirical research of the Bay Area, analyzing the community of “Earthquake Junkies”—as these experts called themselves—and other risk-conscious residents, this work emphasizes the role of experience and emotions in multiple interlaced processes, connecting risk, space, and expertise. Following this exploration will see that the rigid definition that have separated science and experience, rationality and emotion, expertise and lay perception should be recomposed in favor of a more systematic approach that takes into account the role of the different dimensions of knowledge. As a prospect for a better understanding of the complex definition of risk in the public sphere, this research also proposes a framework to think about the definition of the subject “at risk,” as well as allows for reflection on the establishment of closest relation between scientific and non-scientific knowledgeLa possibilité des catastrophes nous oblige à repenser les définitions progressives, non-linéaires ("l'instauration," dans le vocabulaire d'Etienne Souriau) des concepts de risque, d'espace et d'expertise. Suivant une approche symétrique, ce travail explore plusieurs dimensions de l'espace «à risque» dans la Baie de San Francisco, ancrées dans l'expérience partagée d'une communauté épistémique plongée dans l ‘attente d'un séisme majeur - le "Big One". Avec les outils de la géographie et des études des sciences et technologies, nous nous pencherons sur le système complexe de relations qui co-construit le risque de tremblements de terre et regarderons la façon dont son instauration progressive entraine des transformations dans l'aménagement et la pratique de l'espace, la définition du risque, et, finalement, dans la figure de l'expert. A partir d'une recherche empirique approfondie menée dans la baie de San Francisco, l'analyse de la communauté des «Earthquake Junkies» - comme ces experts se présentent eux-mêmes - nous verrons que les différentes existences du tremblement de terre questionnent la séparation rigide entre science et expérience, rationalité et émotion, expertise et savoir profane. En proposant une perspective pragmatique, cette recherche propose également un cadre pour réfléchir à la définition du sujet «à risque

Career Paths and Prospects in Academic Data Science: Report of the Moore-Sloan Data Science Environments Survey

This report is based on a 2016 survey of members and affiliates of three institutes of data science at major U.S. research universities, focusing on career paths for data scientists within academia. After considering how our respondents define data science, we identify various activities, priorities, resources, and concerns around data science in academia, especially with respect to data science careers. We end by providing recommendations about how universities can better support an emerging set of roles and responsibilities around data and computation within and across academic fields