Evidence map›Paper›PMID 37516791›Full record

ArticleScientific data2023

A Dataset of 10,000 Situations for Research in Computational Social Sciences Psychology and the Humanities.

Yair Neuman, Yochai Cohen

Abstract readDataset
In one paragraph

Article in Scientific data, 2023. 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. Article
  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

2 authors.

Yair NeumanHead, The Functor Lab, Department of Cognitive and Brain Science, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel. yneuman@bgu.ac.il.
Yochai CohenGilasio coding, Tel-Aviv, Israel.

Funding

Environmental Health Perspectives, NIEHS27302C0031 · NIEHS · 2008 to 2008
$1.2M
2016 SETAC Asia/Pacific ConferenceR13ES027302 · NIEHS · SOCIETY/ENVIRONMENTAL TOXICOLOGY/CHEM · PI MENZIE, CHARLES · 2016 to 2016
$6k
NIEHS NIH HHS 27302C0031
6 · The paper itself

Abstract

It has been realized that situational dimensions, as represented by human beings, are crucial for understanding human behavior. The Riverside Situational Q (RSQ) is a tool that measures the psychological properties of situations. However, the RSQ-4 includes only 90 items and may have limited use for researchers interested in measuring situational dimensions using a computational approach. Here we present a corpus of 10,000 artificially generated situations corresponding mostly with the RSQ-4. The dataset was generated using GPT, the state-of-the-art large language model. The dataset validity is established through inter-judge reliability, and four experiments on large datasets support its quality. The dataset and the code used for generating 100 situational dimensions may be useful for researchers interested in measuring situational dimensions in textual data.

Identifiers

PMID37516791
PMCPMC10387079

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.