Evidence map›Paper›PMID 33870043›Full record

ArticleFrontiers in research metrics and analytics2020

Tracing Long-Term Outcomes of Basic Research Using Citation Networks.

James Onken, Andrew C Miklos, Richard Aragon

Abstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

3 authors.

James OnkenResearch Enterprise Analytics, LLC, Rockville, MD, United States.
Andrew C MiklosDivision of Data Integration, Modeling, and Analytics, National Institute of General Medical Sciences, Bethesda, MD, United States.
Richard AragonDivision of Data Integration, Modeling, and Analytics, National Institute of General Medical Sciences, Bethesda, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the science of science policy has been facilitated by the greater availability of and access to digital data associated with the science, technology, and innovation enterprise. Historically, most of the studies from which such data are derived have been econometric or "scientometric" in nature, focusing on the development of quantitative data, models, and metrics of the scientific process as well as outputs and outcomes. Broader definitions of research impact, however, necessitate the use of qualitative case-study methods. For many years, U.S. federal science agencies such as the National Institutes of Health have demonstrated the impact of the research they support through tracing studies that document critical events in the development of successful technologies. A significant disadvantage and barrier of such studies is the labor-intensive nature of a case study approach. Currently, however, the same data infrastructures that have been developed to support scientometrics may also facilitate historical tracing studies. In this paper, we describe one approach we used to discover long-term, downstream outcomes of research supported in the late 1970's and early 1980's by the National Institute of General Medical Sciences, a component of the National Institutes of Health.

Indexed as

basic researchcitation networksgovernment fundingknowledge diffusionresearch evaluationresearch outcomesscience of science

Identifiers

PMID33870043
PMCPMC8028394

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.