Recent ucla_knowledge_infrastructures_pubs items

From Data Creator to Data Reuser: Distance Matters

Fri, 12 Apr 2024 00:00:00 +0000

From Data Creator to Data Reuser: Distance Matters

New models of privacy for the university

Fri, 12 Apr 2024 00:00:00 +0000

New models of privacy for the university

Data Governance Task Force: Final report and recommendations

Fri, 12 Apr 2024 00:00:00 +0000

Data Governance Task Force: Final report and recommendations

On the Legitimacy of Knowledge Infrastructures

Tue, 16 Feb 2021 00:00:00 +0000

On the Legitimacy of Knowledge Infrastructures

Thought pieces for UCLA KI workshop

Tue, 16 Feb 2021 00:00:00 +0000

Thought pieces for UCLA KI workshop

Thought piece for the Second Knowledge Infrastructures Workshop (Feb 2019)

Tue, 16 Feb 2021 00:00:00 +0000

Thought piece for the Second Knowledge Infrastructures Workshop (Feb 2019)

Thought Pieces for UCLA Knowledge Infrastructure Workshop 2020

Tue, 16 Feb 2021 00:00:00 +0000

Thought Pieces for UCLA Knowledge Infrastructure Workshop 2020

What the Knowledge in Knowledge Infrastructures Does

Tue, 16 Feb 2021 00:00:00 +0000

What the Knowledge in Knowledge Infrastructures Does

2nd Knowledge Infrastructure Workshop – question responses

Tue, 16 Feb 2021 00:00:00 +0000

2nd Knowledge Infrastructure Workshop – question responses

Open online communities as a type of knowledge infrastructure under threat

Tue, 16 Feb 2021 00:00:00 +0000

Open online communities as a type of knowledge infrastructure under threat

Private Platforms, Metadata, and the Enclosure of Data Access: Urgent Issues for Knowledge Infrastructure Research

Tue, 16 Feb 2021 00:00:00 +0000

Private Platforms, Metadata, and the Enclosure of Data Access: Urgent Issues for Knowledge Infrastructure Research

Thought Piece for 2020 Knowledge Infrastructure Workshop

Tue, 16 Feb 2021 00:00:00 +0000

Thought Piece for 2020 Knowledge Infrastructure Workshop

Thought Piece on (Global) Knowledge Infrastructures

Tue, 16 Feb 2021 00:00:00 +0000

Thought Piece on (Global) Knowledge Infrastructures

Collaborative Qualitative Research at Scale:Reflections on 20 years of Acquiring Global Data and Making Data Global

Tue, 15 Dec 2020 00:00:00 +0000

A 5-year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the ‘team science’ approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards’s model of ‘making global data’ – collecting signals via consistent methods, technologies, and policies – to ‘make data global’ – comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations...

Metadata Realities for Cyberinfrastructure: Data Authors as Metadata Creators

Fri, 30 Oct 2020 00:00:00 +0000

As digital data creation technologies become more prevalent, data and metadata management are necessary to make data available, usable, sharable, and storable. Researchers in many scientific settings, however, have little experience or expertise in data and metadata management. In this dissertation, I explore the everyday data and metadata management practices of researchers through a multi-sited ethnographic study of metadata creation by researchers in the Center for Embedded Networked Sensing (CENS). In studying metadata practices, I focused on the ways that researchers document, describe, annotate, organize, and manage their data, both for their own use and the use of researchers outside of their project. This study illustrates how researchers within CENS rarely create documentation that is not directly tied to their own use of their data, and correspondingly, they rarely share data with users from outside of their immediate projects. From these observations, I develop a metadata...

Structure and Evolution of Scientific Collaboration Networks in a Modern Research Collaboratory

Fri, 30 Oct 2020 00:00:00 +0000

This dissertation is a study of scientific collaboration at the Center for Embedded Networked Sensing (CENS), a modern, multi-disciplinary, distributed laboratory involved in sensor network research. By use of survey research and network analysis, this dissertation examines the collaborative ecology of CENS in terms of three networks of interaction: coauthorship of scholarly publications, communication activity on mailing lists, and interpersonal acquaintanceship. This study exposes the topology, structure, and evolution of these networks in relation with the disciplinary and institutional arrangements of CENS. Findings indicate that CENS collaboration networks have fluid, non-cliquish, small-world topologies, and are free of prestige-based mechanisms. Further analysis reveals that structural communities in the coauthorship and acquaintanceship networks overlap considerably. They also exhibit little disciplinary and institutional diversity locally, although CENS becomes more inter-disciplinary...

Building Values into the Design of Pervasive Mobile Technologies

Fri, 30 Oct 2020 00:00:00 +0000

Corporations, governments, and individuals can increasingly collect new forms of personal data using pervasive technologies such as mobile tablets and phones. These always-on, always-present devices carried by billions can capture and transmit users’ location, images, motion, and user input. Mobile technologies could become a platform to document community needs and advocate for civic change, to understand personal habits and routines, or to document health problems and manage chronic illness. Simultaneously, new forms of data collection software utilize techniques traditionally employed by tools of surveillance: granular data gathering, sophisticated modeling, and inferences about a personal behavior and attributes.

There is a shifting and permeable boundary between data collection for individual or social goals, and corporate or government surveillance. This boundary invokes social values in design: the features, principles, or ethics we collectively value in the design...

The Lives and After Lives of Data

Tue, 29 Sep 2020 00:00:00 +0000

The most elusive term in data science is 'data'. While often treated as objects to be computed upon, data is a theory-laden concept with a long history. Data exist within knowledge infrastructures that govern how they are created, managed, and interpreted. By comparing models of data life cycles, implicit assumptions about data become apparent. In linear models, data pass through stages from beginning to end of life, which suggest that data can be recreated as needed. Cyclical models, in which data flow in a virtuous circle of uses and reuses, are better suited for irreplaceable observational data that may retain value indefinitely. In astronomy, for example, observations from one generation of telescopes may become calibration and modeling data for the next generation, whether digital sky surveys or glass plates. The value and reusability of data can be enhanced through investments in knowledge infrastructures, especially digital curation and preservation. Determining what data...

What lies beneath?: Knowledge infrastructures in the subseafloor biosphere and beyond

Thu, 23 Jul 2020 00:00:00 +0000

We present preliminary findings from a three-year research project comprised of longitudinal qualitative case studies of data practices in four large, distributed, highly multidisciplinary scientific collaborations. This project follows a 2 ×× 2 research design: two of the collaborations are big science while two are little science, two have completed data collection activities while two are ramping up data collection. This paper is centered on one of these collaborations, a project bringing together scientists to study subseafloor microbial life. This collaboration is little science, characterized by small teams, using small amounts of data, to address specific questions. Our case study employs participant observation in a laboratory, interviews (n=49n=49 to date) with scientists in the collaboration, and document analysis. We present a data workflow that is typical for many of the scientists working in the observed laboratory. In particular, we show that, although...

Ship space to database: emerging infrastructures for studies of the deep subseafloor biosphere

Thu, 23 Jul 2020 00:00:00 +0000

Background

An increasing array of scientific fields face a “data deluge.” However, in many fields data are scarce, with implications for their epistemic status and ability to command funding. Consequently, they often attempt to develop infrastructure for data production, management, curation, and circulation. A component of a knowledge infrastructure may serve one or more scientific domains. Further, a single domain may rely upon multiple infrastructures simultaneously. Studying how domains negotiate building and accessing scarce infrastructural resources that they share with other domains will shed light on how knowledge infrastructures shape science.

Methods

We conducted an eighteen-month, qualitative study of scientists studying the deep subseafloor biosphere, focusing on the Center for Dark Energy Biosphere Investigations (C-DEBI) and the Integrated Ocean Drilling Program (IODP) and its successor, the International Ocean Discovery Program (IODP2). Our...

Library Cultures of Data Curation: Adventures in Astronomy

Mon, 29 Jun 2020 00:00:00 +0000

University libraries are partnering with disciplinary data producers to provide long-term digital curation of research datasets. Managing dataset producer expectations and guiding future development of library services requires understanding the decisions libraries make about curatorial activities, why they make these decisions, and the effects on future data reuse. We present a study, comprising interviews (n=43) and ethnographic observation, of two university libraries who partnered with the Sloan Digital Sky Survey (SDSS) collaboration to curate a significant astronomy dataset. The two libraries made different choices of the materials to curate and associated services, which resulted in different reuse possibilities. Each of the libraries offered partial solutions to the SDSS leaders’ objectives. The libraries’ approaches to curation diverged due to contextual factors, notably the extant infrastructure at their disposal (including technical infrastructure, staff expertise,...

Our knowledge of knowledge infrastructures: Lessons learned and future directions

Fri, 5 Jun 2020 00:00:00 +0000

The Knowledge Infrastructures Workshop conducted at UCLA in February 2020, and funded by the Alfred P. Sloan Foundation, revisited the goals and findings of the 2012 workshop held at the University of Michigan. Thirty scholars, from a diverse array of disciplines and backgrounds, charted a course for the next decade of KI research. Such infrastructures are increasingly fragile, and often brittle, in the face of open data and open source, the demise of gatekeepers, and shifting public and private boundaries that redistribute power. Participants identified new methods and new opportunities for studying KI. Among the many scholarly products they proposed are publications, grant proposals, conference sessions, and workshops on the role of libraries in data services, the death and afterlives of KI, misinformation and disinformation in KI, KI in the Anthropocene, “N simplish rules” to grow and sustain KI, university capacities for KI, designing sustainable KI, and inclusion of underrepresented...

Knowledge infrastructure workshop thought piece

Fri, 17 Apr 2020 00:00:00 +0000

Knowledge infrastructure workshop thought piece

Whose text, whose mining, and to whose benefit?

Mon, 13 Apr 2020 00:00:00 +0000

Scholarly content has become more difficult to find as information retrieval has devolved from bespoke systems that exploit disciplinary ontologies to keyword search on generic search engines. In parallel, more scholarly content is available through open access mechanisms. These trends have failed to converge in ways that would facilitate text data mining, both for information retrieval and as a research method for the quantitative social sciences. Scholarly content has become open to read without becoming open to mine, due both to constraints by publishers and to lack of attention in scholarly communication. The quantity of available text has grown faster than has the quality. Academic dossier systems are among the means to acquire more quality data for mining. Universities, publishers, and private enterprise may be able to mine these data for strategic purposes, however. On the positive front, changes in copyright may allow more data mining. Privacy, intellectual freedom, and...

Someone to do the work: The skilled trades in the 21st Century

Fri, 10 Apr 2020 00:00:00 +0000

Someone to do the work: The skilled trades in the 21st Century

Space Telescope Science Institute as a knowledge infrastructure

Fri, 10 Apr 2020 00:00:00 +0000

Space Telescope Science Institute as a knowledge infrastructure

Of domains, their knowledge, and their infrastructure

Fri, 10 Apr 2020 00:00:00 +0000

Of domains, their knowledge, and their infrastructure

Knowledge infrastructures in past, present, and future tense

Fri, 10 Apr 2020 00:00:00 +0000

Knowledge infrastructures in past, present, and future tense

Knowledge infrastructures: A research agenda thought piece

Fri, 10 Apr 2020 00:00:00 +0000

Knowledge infrastructures: A research agenda thought piece

Collaborative Ethnography at Scale: Reflections on 20 years of data integration

Thu, 13 Feb 2020 00:00:00 +0000

A 5-year STS project in geography, starting in 1999, evolved into 20 years of data collection about scientific data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the ‘team science’ approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards’s model of ‘making global data’ – collecting signals via consistent methods, technologies, and policies – to ‘make data global’ – comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations...

Maintaining FITS! Some lessons from (and perils of) successful long-term software maintenance

Mon, 4 Nov 2019 00:00:00 +0000

Maintaining FITS! Some lessons from (and perils of) successful long-term software maintenance

Thorny Problems in Data (-Intensive) Science

Mon, 7 Oct 2019 00:00:00 +0000

Thorny Problems in Data (-Intensive) Science

Jupyter notebooks as discovery mechanisms for open science: Citation practices in the astronomy community

Tue, 6 Aug 2019 00:00:00 +0000

Citing data and software is a means to give scholarly credit and to facilitate access to research objects. Citation principles encourage authors to provide full descriptions of objects, with stable links, in their papers. As Jupyter notebooks aggregate data, software, and other objects, they may facilitate or hinder citation, credit, and access to data and software. We report on a study of references to Jupyter notebooks in astronomy over a 5-year period (2014-2018). References increased rapidly, but fewer than half of the references led to Jupyter notebooks that could be located and opened. Jupyter notebooks appear better suited to supporting the research process than to providing access to research objects. We recommend that authors cite individual data and software objects, and that they stabilize any notebooks cited in publications. Publishers should increase the number of citations allowed in papers and employ descriptive metadata-rich citation styles that facilitate credit...

Labor Out of Place: On the Varieties and Valences of (In)visible Labor in Data-Intensive Science

Thu, 9 May 2019 00:00:00 +0000

Labor Out of Place: On the Varieties and Valences of (In)visible Labor in Data-Intensive Science

Once FITS, Always FITS? Astronomical Infrastructure in Transition

Sat, 26 Jan 2019 00:00:00 +0000

The FITS file format has become the de facto standard for sharing, analyzing, and archiving astronomy data over the last four decades. FITS was adopted by astronomers in the early 1980s to overcome incompatibilities between operating systems. On the back of FITS’ success, astronomical data became both backwards compatible and easily shareable. However, new advances in astronomical instrumentation, computational technologies, and analytic techniques have resulted in new data that do not work well within the traditional FITS format. Tensions have arisen between the desire to update the format to meet new analytic challenges and adherence to the original edict for FITS files to be backwards compatible. We examine three inflection points in the governance of FITS: a) initial development and success, b) widespread acceptance and governance by the working group, and c) the challenges to FITS in a new era of increasing data and computational complexity within astronomy.

The principles of tomorrow's university

Thu, 27 Dec 2018 00:00:00 +0000

In the 21st Century, research is increasingly data- and computation-driven. Researchers, funders, and the larger community today emphasize the traits of openness and reproducibility. In March 2017, 13 mostly early-career research leaders who are building their careers around these traits came together with ten university leaders (presidents, vice presidents, and vice provosts), representatives from four funding agencies, and eleven organizers and other stakeholders in an NIH- and NSF-funded one-day, invitation-only workshop titled "Imagining Tomorrow's University." Workshop attendees were charged with launching a new dialog around open research – the current status, opportunities for advancement, and challenges that limit sharing.The workshop examined how the internet-enabled research world has changed, and how universities need to change to adapt commensurately, aiming to understand how universities can and should make themselves competitive and attract the best students, staff,...

PhD Dissertation - From Open Data to Knowledge Production: Biomedical Data Sharing and Unpredictable Data Reuses

Wed, 7 Nov 2018 00:00:00 +0000

Using a US consortium for data sharing as the primary field site, this three-year ethnographic research project examines the socio-technical, epistemic, and ethical challenges of making biomedical research data openly available and reusable. Public policy arguments for releasing scientific data for reuse by others include increasing trust in science and leveraging public investments in research. In most types of scientific research, data release occurs in parallel with associated publications, after peer-review. In the consortium studied for this project, datasets may also be released independently without an associated publication. Such research datasets are conceptualized as “hypothesis free” resources from which novel knowledge can be extracted indefinitely. Among the findings of this project are that biomedical researchers do not download and re-analyze “hypothesis free” research data from open repositories as a regular practice. Data reuse is a complex, delicate, and often...

Open data, grey data, and stewardship: Universities at the privacy frontier

Fri, 28 Sep 2018 00:00:00 +0000

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of “grey data” about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics,...

The Social Embeddedness of Embedded Networked Sensing

Tue, 21 Aug 2018 00:00:00 +0000

The Social Embeddedness of Embedded Networked Sensing

RCUK Review of e-Science 2009: Building a UK foundation for the transformative enhancement of research innovation

Tue, 21 Aug 2018 00:00:00 +0000

RCUK Review of e-Science 2009: Building a UK foundation for the transformative enhancement of research innovation

Social Aspects of Digital Libraries. Final Report to the National Science Foundation

Tue, 21 Aug 2018 00:00:00 +0000

Computer, Information Science, and Engineering Directorate; Division of Information, Robotics, and Intelligent Systems; Information Technology and Organizations Program

Why are the attribution and citation of scientific data important?

Tue, 21 Aug 2018 00:00:00 +0000

Keynote address from the Developing Data Attribution and Citation Practices and Standards: An International Symposium and Workshop in Berkeley, CA on 22 August 2011. Keynote speech is contained within the forthcoming 2012 Report from Developing Data Attribution and Citation Practices and Standards: An International Symposium and Workshop published by the National Academy of Sciences. Preferred Citation: Borgman, C.L. (2012, forthcoming). Why are the attribution and citation of scientific data important? In: Uhlir, Paul and Cohen, Daniel (eds.). Report from Developing Data Attribution and Citation Practices and Standards: An International Symposium and Workshop. National Academy of Sciences’ Board on Research Data and Information. National Academies Press: Washington DC. http://www.nap.edu

Research Data: Who will share what, with whom, when, and why?

Tue, 21 Aug 2018 00:00:00 +0000

The deluge of scientific research data has excited the general public, as well as the scientific community, with the possibilities for better understanding of scientific problems, from climate to culture. For data to be available, researchers must be willing and able to share them. The policies of governments, funding agencies, journals, and university tenure and promotion committees also influence how, when, and whether research data are shared. Data are complex objects. Their purposes and the methods by which they are produced vary widely across scientific fields, as do the criteria for sharing them. To address these challenges, it is necessary to examine the arguments for sharing data and how those arguments match the motivations and interests of the scientific community and the public. Four arguments are examined: to make the results of publicly funded data available to the public, to enable others to ask new questions of extant data, to advance the state of science, and to...

Surf’s Up: Riding the Big Data Wave (Poster)

Tue, 21 Aug 2018 00:00:00 +0000

We use ethnographic methods to follow the builders and users of the Sloan Digital Sky Survey (SDSS), known as “the human genome project of astronomy.” SDSS was designed as an open data project and it is among the most successful big data projects in science. We are spotting the indicators of how well astronomers are riding the big data wave - and when they are wiping out - with consequences for other creators, users, analysts, managers, and funders of big data systems. http://ipp.oii.ox.ac.uk/2012/programme-2012/poster-session/ashley-sands-christine-l-borgman-laura-a

Foreward

Tue, 21 Aug 2018 00:00:00 +0000

Foreward

Data, data use, and scientific inquiry: Two case studies of data practices

Tue, 21 Aug 2018 00:00:00 +0000

Data are proliferating far faster than they can be captured, managed, or stored. What types of data are most likely to be used and reused, by whom, and for what purposes? Answers to these questions will inform information policy and the design of digital libraries. We report findings from semi-structured interviews and field observations to investigate characteristics of data use and reuse and how those characteristics vary within and between scientific communities. The two communities studied are researchers at the Center for Embedded Network Sensing (CENS) and users of the Sloan Digital Sky Survey (SDSS) data. The data practices of CENS and SDSS researchers have implications for data curation, system evaluation, and policy. Some data that are important to the conduct of research are not viewed as sufficiently valuable to keep. Other data of great value may not be mentioned or cited, because those data serve only as background to a given investigation. Metrics to assess the value...

Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge. A 21st Century Agenda for the National Science Foundation

Tue, 21 Aug 2018 00:00:00 +0000

Citizens in all fields need to understand how science and technology affect policy, business, and personal decisions. The shortage of trained scientists and engineers is a small indicator of a much larger problem: insufficient knowledge and understanding about science and technology across our population. The educational system must respond dynamically to prepare our population for the complex, evolving, global challenges of the 21st century. Advances in technology are poised to meet these educational demands. Cyberlearning offers new learning and educational approaches and the possibility of redistributing learning experiences over time and space, beyond the classroom and throughout a lifetime. We believe that cyberlearning has reached a turning point where learning payoffs can be accelerated. We also believe that this moment could be fleeting because, without deliberate efforts to coordinate cyberlearning approaches, we will miss the opportunity to provide effective support...

Data, data use, and inquiry: A new point of view on data curation

Tue, 21 Aug 2018 00:00:00 +0000

Data are proliferating far faster than they can be captured, managed, or stored. What types of data are most likely to be used and reused, by whom, and for what purposes? Answers to these questions will inform information policy and the design of digital libraries. We report findings from semi-structured interviews and field observations to investigate characteristics of data use and reuse and how those characteristics vary within and between scientific communities. The two communities studied are the researchers at the Center for Embedded Network Sensing (CENS) and users of the Sloan Digital Sky Survey (SDSS) data. We found that the interactions between inquiry, data, and use fall into three categories: foreground vs. background, use of the same data for different actions, and sources of data for reuse. The data practices of CENS and SDSS researchers have implications for data curation, system evaluation, and policy. Some data that are important to the conduct of research are...

Toward a definition of user friendliness: A psychological perspective

Tue, 21 Aug 2018 00:00:00 +0000

Toward a definition of user friendliness: A psychological perspective

Is data to knowledge as the wasp is to the fig tree? Reconsidering Licklider’s Intergalactic Network in the days of data deluge.

Tue, 21 Aug 2018 00:00:00 +0000

Is data to knowledge as the wasp is to the fig tree? Reconsidering Licklider’s Intergalactic Network in the days of data deluge.

Research Data, Reproducibility, and Curation

Tue, 21 Aug 2018 00:00:00 +0000

Research Data, Reproducibility, and Curation

The Digital Future is Now: A Call to Action for the Humanities

Tue, 21 Aug 2018 00:00:00 +0000

The digital humanities are at a critical moment in the transition from a specialty area to a full-fledged community with a common set of methods, sources of evidence, and infrastructure – all of which are necessary for achieving academic recognition. As budgets are slashed and marginal programs are eliminated in the current economic crisis, only the most articulate and productive will survive. Digital collections are proliferating, but most remain difficult to use, and digital scholarship remains a backwater in most humanities departments with respect to hiring, promotion, and teaching practices. Only the scholars themselves are in a position to move the field forward. Experiences of the sciences in their initiatives for cyberinfrastructure and eScience offer valuable lessons. Information- and data-intensive, distributed, collaborative, and multi-disciplinary research is now the norm in the sciences, while remaining experimental in the humanities. Discussed here are six factors...

Scholarly Communication and Bibliometrics

Fri, 17 Aug 2018 00:00:00 +0000

Scholarly Communication and Bibliometrics

The Ups and Downs of Knowledge Infrastructures in Science: Implications for Data Management

Fri, 27 Jul 2018 00:00:00 +0000

The promise of technology-enabled, data-intensive scholarship is predicated upon access to knowledge infrastructures that are not yet in place. Scientific data management requires expertise in the scientific domain and in organizing and retrieving complex research objects. The Knowledge Infrastructures project compares data management activities of four large, distributed, multidisciplinary scientific endeavors as they ramp their activities up or down; two are big science and two are small science. Research questions address digital library solutions, knowledge infrastructure concerns, issues specific to individual domains, and common problems across domains. Findings are based on interviews (n=113 to date), ethnography, and other analyses of these four cases, studied since 2002. Based on initial comparisons, we conclude that the roles of digital libraries in scientific data management often depend upon the scale of data, the scientific goals, and the temporal scale of the research...

Knowledge Infrastructures: Intellectual Frameworks and Research Challenges

Fri, 27 Jul 2018 00:00:00 +0000

Knowledge Infrastructures: Intellectual Frameworks and Research Challenges

Ship Space to Database: Motivations to Manage Research Data for the Deep Subseafloor Biosphere

Fri, 27 Jul 2018 00:00:00 +0000

What motivates the building of databases by scientific collaborations? In this paper, we argue that not only are databases being built to support scientific work per se, but also with the intention of performing a variety of social functions. To explore this, we present findings from a longitudinal ethnographic case study of a large, multidisciplinary, distributed scientific project studying subseafloor microbial life. A critical element of this project’s Data Management Plan is the construction of a data portal. We found a range of factors motivating not only the very construction of this portal, but also the inclusion of particular features. In addition to scientific factors relating to improved curation and accessibility of diverse and scarce data, we argue that the building of the portal is also motivated by social factors. One such factor is the attempt to build a community of domain researchers to endure beyond the end of this project in 2020. Another motivation is the possibility...

Data Management in the Long Tail: Science, Software and Service

Wed, 18 Jul 2018 00:00:00 +0000

Scientists in all fields face challenges in managing and sustaining access to their research data. The larger and longer term the research project, the more likely that scientists are to have resources and dedicated staff to manage their technology and data, leaving those scientists whose work is based on smaller and shorter term projects at a disadvantage. The volume and variety of data to be managed varies by many factors, only two of which are the number of collaborators and length of the project. As part of an NSF project to conceptualize the Institute for Empowering Long Tail Research, we explored opportunities offered by Software as a Service (SaaS). These cloud-based services are popular in business because they reduce costs and labor for technology management, and are gaining ground in scientific environments for similar reasons. We studied three settings where scientists conduct research in small and medium-sized laboratories. Two were NSF Science and Technology Centers...

On the Reuse of Scientific Data

Wed, 18 Jul 2018 00:00:00 +0000

While science policy promotes data sharing and open data, these are not ends in themselves. Arguments for data sharing are to reproduce research, to make public assets available to the public, to leverage investments in research, and to advance research and innovation. To achieve these expected benefits of data sharing, data must actually be reused by others. Data sharing practices, especially motivations and incentives, have received far more study than has data reuse, perhaps because of the array of contested concepts on which reuse rests and the disparate contexts in which it occurs. Here we explicate concepts of data, sharing, and open data as a means to examine data reuse. We explore distinctions between use and reuse of data. Lastly we propose six research questions on data reuse worthy of pursuit by the community: How can uses of data be distinguished from reuses? When is reproducibility an essential goal? When is data integration an essential goal? What are the tradeoffs...

Not Fade Away: Social Science Research Data in the Digital Era

Wed, 18 Jul 2018 00:00:00 +0000

Knowledge Rules: Curating Knowledge in the Social Sciences, Social Sciences Research Council Meeting, 2 May 2016, New York Public Library

Open Data in Scientific Settings: From Policy to Practice

Wed, 18 Jul 2018 00:00:00 +0000

Open access to data is commonly required by funding agencies, journals, and public policy, despite the lack of agreement on the concept of “open data.” We present findings from two longitudinal case studies of major scientific collaborations, the Sloan Digital Sky Survey in astronomy and the Center for Dark Energy Biosphere Investigations in deep subseafloor biosphere studies. These sites offer comparisons in rationales and policy interpretations of open data, which are shaped by their differing scientific objectives. While policy rationales and implementations shape infrastructures for scientific data, these rationales also are shaped by pre-existing infrastructure. Meanings of the term “open data” are contingent on project objectives and on the infrastructures to which they have access.

Moving Archival Practices Upstream: An Exploration of the Life Cycle of Ecological Sensing Data in Collaborative Field Research

Mon, 16 Jul 2018 00:00:00 +0000

The success of eScience research depends not only upon effective collaboration between scientists and technologists but also upon the active involvement of data archivists. Archivists rarely receive scientific data until findings are published, by which time important information about their origins, context, and provenance may be lost. Research reported here addresses the life cycle of data from collaborative ecological research with embedded networked sensing technologies. A better understanding of these processes will enable archivists to participate in earlier stages of the life cycle and to improve curation of these types of scientific data. Evidence from our interview study and field research yields a nine-stage life cycle. Among the findings are the cumulative effect of decisions made at each stage of the life cycle; the balance of decision-making between scientific and technology research partners; and the loss of certain types of data that may be essential to later interpretat...

10 Simple Rules for the Care and Feeding of Scientific Data

Wed, 2 May 2018 00:00:00 +0000

This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have been written on questions of data sharing, data provenance, research reproducibility, licensing, attribution, privacy, and more, but our goal here is not to review that literature. Instead, we present a short guide intended for researchers who want to know why it is important to "care for and feed" data, with some practical advice on how to do that.

Syllabus for Data, Data Practices, and Data Curation, Part I, Winter 2014, UCLA Information Studies