Evidence map›Paper›PMID 37159752›Full record

ArticleIEEE annals of the history of computing

Challenges of Large-Scale Data Processing in the 1990s: The IPUMS Experience.

Diana L Magnuson, Steven Ruggles

Abstract read
In one paragraph

Article in IEEE annals of the history of computing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Diana L MagnusonInstitute for Social Research and Data Innovation, University of Minnesota, Minneapolis, MN, 55455, USA.
Steven RugglesInstitute for Social Research and Data Innovation, University of Minnesota, Minneapolis, MN, 55455, USA.

Funding

Microdata for Population Dynamics and Health ResearchR01HD043392 · NICHD · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Sarah M Flood, Steve Ruggles · 2002 to 2026
$14.8M
Minnesota Population Center Science and Technical CoreP2CHD041023 · NICHD · UNIVERSITY OF MINNESOTA · PI THERESA LOUISE OSYPUK · 2016 to 2026
$4.3M
NICHD NIH HHS P2C HD041023NICHD NIH HHS R01 HD043392
6 · The paper itself

Abstract

When it was launched in 1991, the Integrated Public Use Microdata Series (IPUMS) project faced a challenging environment and limited resources. Few datasets were interoperable and much data collected at great public expense was inaccessible to most researchers. Documentation of datasets was nonstandardized, incomplete, and inadequate for automated processing. With insufficient attention to preservation, valuable scientific data were disappearing (see Bogue et al., 1976). IPUMS was established to address these critical issues. At the outset, IPUMS faced daunting barriers of inadequate data processing, storage, and network capacity. This anecdote describes the improvised computational infrastructure developed in the decade from 1989 to 1999 to process, manage, and disseminate the world's largest population datasets. We use a combination of archival sources, interviews, and our own memories to trace the development of the IPUMS computing environment during a period of explosive technical innovation. The development of IPUMS is part of a larger story of the development of social science infrastructure in the late 20th century and its contribution to democratizing data access.

Identifiers

PMID37159752
PMCPMC10163666

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.