Evidence map›Paper›PMID 31804487›Full record

ArticleScientific data2019

Comparative dataset of experimental and computational attributes of UV/vis absorption spectra.

Edward J Beard, Ganesh Sivaraman, Álvaro Vázquez-Mayagoitia, Venkatram Vishwanath, Jacqueline M Cole

Abstract read
In one paragraph

Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

  1. Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. A database overview of metal-coordination distances in metalloproteins.Acta crystallographica. Section D, Structural biology · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.Journal of chemical information and modeling · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.Chemistry of materials : a publication of the American Chemical Society · 2023
    Article
  18. Article
  19. Article
  20. 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

5 authors.

Edward J BeardCavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, UK.
Ganesh SivaramanArgonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA.ORCID http://orcid.org/0000-0001-9056-9855
Álvaro Vázquez-MayagoitiaArgonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA.
Venkatram VishwanathArgonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, USA.
Jacqueline M ColeCavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, UK. jmc61@cam.ac.uk.

Funding

RCUK | Science and Technology Facilities Council (STFC) ISIS FacilityRoyal Academy of Engineering RCSRF1819\7\10Royal Commission for the Exhibition of 1851 2014 Design Fellowship
6 · The paper itself

Abstract

The ability to auto-generate databases of optical properties holds great prospects in data-driven materials discovery for optoelectronic applications. We present a cognate set of experimental and computational data that describes key features of optical absorption spectra. This includes an auto-generated database of 18,309 records of experimentally determined UV/vis absorption maxima, λ

Identifiers

PMID31804487
PMCPMC6895184

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.