Publications by Author: Micah Altman

2009
From Preserving the Past to Preserving the Future: The Data-PASS Project and the Challenges of Preserving Digital Social Science Data
Myron P Gutmann, Mark Abrahamson, Margaret O Adams, Micah Altman, Caroline Arms, Kenneth Bollen, Michael Carlson, Jonathan Crabtree, Darrell Donakowski, Gary King, Jaret Lyle, Marc Maynard, Amy Pienta, Richard Rockwell, Lois Rocms-Ferrara, and Copeland H Young. 2009. “From Preserving the Past to Preserving the Future: The Data-PASS Project and the Challenges of Preserving Digital Social Science Data.” Library Trends, 57: 315–337. Abstract

Social science data are an unusual part of the past, present, and future of digital preservation. They are both an unqualified success, due to long-lived and sustainable archival organizations, and in need of further development because not all digital content is being preserved. This article is about the Data Preservation Alliance for Social Sciences (Data-PASS), a project supported by the National Digital Information Infrastructure and Preservation Program (NDIIPP), which is a partnership of five major U.S. social science data archives. Broadly speaking, Data-PASS has the goal of ensuring that at-risk social science data are identified, acquired, and preserved, and that we have a future-oriented organization that could collaborate on those preservation tasks for the future. Throughout the life of the Data-PASS project we have worked to identify digital materials that have never been systematically archived, and to appraise and acquire them. As the project has progressed, however, it has increasingly turned its attention from identifying and acquiring legacy and at-risk social science data to identifying on going and future research projects that will produce data. This article is about the project's history, with an emphasis on the issues that underlay the transition from looking backward to looking forward.

Article
2007
A Proposed Standard for the Scholarly Citation of Quantitative Data
Micah Altman and Gary King. 2007. “A Proposed Standard for the Scholarly Citation of Quantitative Data.” D-Lib Magazine, 13. Publisher's Version Abstract

An essential aspect of science is a community of scholars cooperating and competing in the pursuit of common goals. A critical component of this community is the common language of and the universal standards for scholarly citation, credit attribution, and the location and retrieval of articles and books. We propose a similar universal standard for citing quantitative data that retains the advantages of print citations, adds other components made possible by, and needed due to, the digital form and systematic nature of quantitative data sets, and is consistent with most existing subfield-specific approaches. Although the digital library field includes numerous creative ideas, we limit ourselves to only those elements that appear ready for easy practical use by scientists, journal editors, publishers, librarians, and archivists.

Article
2003
Numerical Issues Involved in Inverting Hessian Matrices
Jeff Gill and Gary King. 2003. “Numerical Issues Involved in Inverting Hessian Matrices.” In Numerical Issues in Statistical Computing for the Social Scientist, edited by Micah Altman and Michael P. McDonald, 143-176. Hoboken, NJ: John Wiley and Sons, Inc.
Chapter PDF
2001
An Introduction to the Virtual Data Center Project and Software
Micah Altman, Leonid Andreev, Mark Diggory, Gary King, Elizabeth Kolster, M Krot, Sidney Verba, and Daniel L Kiskis. 2001. “An Introduction to the Virtual Data Center Project and Software.” Proceedings of The First ACM+IEEE Joint Conference on Digital Libraries, 203–204.
Article
Micah Altman, Leonid Andreev, Mark Diggory, Gary King, Daniel L Kiskis, Elizabeth Kolster, Michael Krot, and Sidney Verba. 2001. “An Overview of the Virtual Data Center Project and Software.” JCDL ’01: First Joint Conference on Digital Libraries, 203-204. Abstract
In this paper, we present an overview of the Virtual Data Center (VDC) software, an open-source digital library system for the management and dissemination of distributed collections of quantitative data. (see http://TheData.org). The VDC functionality provides everything necessary to maintain and disseminate an individual collection of research studies, including facilities for the storage, archiving, cataloging, translation, and on-line analysis of a particular collection. Moreover, the system provides extensive support for distributed and federated collections including: location-independent naming of objects, distributed authentication and access control, federated metadata harvesting, remote repository caching, and distributed "virtual" collections of remote objects.
A Digital Library for the Dissemination and Replication of Quantitative Social Science Research
Micah Altman, Leonid Andreev, Mark Diggory, Gary King, Daniel L Kiskis, Elizabeth Kolster, Michael Krot, and Sidney Verba. 2001. “A Digital Library for the Dissemination and Replication of Quantitative Social Science Research.” Social Science Computer Review, 19: 458–470. Abstract
The Virtual Data Center (VDC) software is an open-source, digital library system for quantitative data. We discuss what the software does, and how it provides an infrastructure for the management and dissemination of disturbed collections of quantitative data, and the replication of results derived from this data.
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