Evidence map›Paper›PMID 36520791›Full record

ArticleDatabase : the journal of biological databases and curation2022

Reviewing knowledgebase and database grant proposals in the life sciences: the role of innovation.

Peter D Karp

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

1 author.

Peter D KarpSRI International, Artificial Intelligence Center, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA.ORCID 0000-0002-5876-6418

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article offers thoughts on reviewing grant proposals for biological knowledgebases and databases (KDs) in the hope of aiding grant reviewers and applicants in addressing the issue of innovation. Assessing such grant proposals involves a number of subtleties that are worthy of discussion, particularly for new reviewers and applicants. In part, this article is motivated by the release of two funding opportunity announcements by the US National Institutes of Health concerning KDs. We find that the amount of innovation required for different KD projects can vary significantly, particularly depending on where in its life cycle a given project is. Strong innovation is not necessarily required to have an impactful KD project. For example, PubMed has low innovation but high impact. The importance of innovation should be weighted differently for different KD projects depending on the challenges they face and their maturity. The score for the overall impact of a grant proposal might have little dependence on the innovation score, such as for a mature project that is already delivering strong impact.

Indexed as

Biological Science DisciplinesFinancing, OrganizedKnowledge BasesNational Institutes of Health (U.S.)United States

Identifiers

PMID36520791
PMCPMC9753974

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.