The Salford Nature Environments Database (SNED): an open-access database of standardized high-quality pictures from natural environments.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Behavior Research Methods Pub Date : 2024-12-19 DOI:10.3758/s13428-024-02556-4
Robert C A Bendall, Sam Royle, James Dodds, Hugh Watmough, Jamie C Gillman, David Beevers, Simon Cassidy, Ben Short, Paige Metcalfe, Michael J Lomas, Draco Graham-Kevan, Samantha E A Gregory
{"title":"The Salford Nature Environments Database (SNED): an open-access database of standardized high-quality pictures from natural environments.","authors":"Robert C A Bendall, Sam Royle, James Dodds, Hugh Watmough, Jamie C Gillman, David Beevers, Simon Cassidy, Ben Short, Paige Metcalfe, Michael J Lomas, Draco Graham-Kevan, Samantha E A Gregory","doi":"10.3758/s13428-024-02556-4","DOIUrl":null,"url":null,"abstract":"<p><p>The growing interest in harnessing natural environments to enhance mental health, including cognitive functioning and mood, has yielded encouraging results in initial studies. Given that images of nature have demonstrated similar benefits, they are frequently employed as proxies for real-world environments. To ensure precision and control, researchers often manipulate images of natural environments. The effectiveness of this approach relies on standardization of imagery, and therefore, inconsistency in methods and stimuli has limited the synthesis of research findings in the area. Responding to these limitations, the current paper introduces the Salford Nature Environments Database (SNED), a standardized database of natural images created to support ongoing research into the benefits of nature exposure. The SNED currently exists as the most comprehensive nature image database available, comprising 500 high-quality, standardized photographs capturing a variety of possible natural environments across the seasons. It also includes normative scores for user-rated (801 participants) characteristics of fascination, refuge and prospect, compatibility, preference, valence, arousal, and approach-avoidance, as well as data on physical properties of the images, specifically luminance, contrast, entropy, CIELAB colour space parameter values, and fractal dimensions. All image ratings and content detail, along with participant details, are freely available online. Researchers are encouraged to use this open-access database in accordance with the specific aims and design of their study. The SNED represents a valuable resource for continued research in areas such as nature-based therapy, social prescribing, and experimental approaches investigating underlying mechanisms that help explain how natural environments improve mental health and wellbeing.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 1","pages":"21"},"PeriodicalIF":4.6000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659377/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior Research Methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13428-024-02556-4","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

The growing interest in harnessing natural environments to enhance mental health, including cognitive functioning and mood, has yielded encouraging results in initial studies. Given that images of nature have demonstrated similar benefits, they are frequently employed as proxies for real-world environments. To ensure precision and control, researchers often manipulate images of natural environments. The effectiveness of this approach relies on standardization of imagery, and therefore, inconsistency in methods and stimuli has limited the synthesis of research findings in the area. Responding to these limitations, the current paper introduces the Salford Nature Environments Database (SNED), a standardized database of natural images created to support ongoing research into the benefits of nature exposure. The SNED currently exists as the most comprehensive nature image database available, comprising 500 high-quality, standardized photographs capturing a variety of possible natural environments across the seasons. It also includes normative scores for user-rated (801 participants) characteristics of fascination, refuge and prospect, compatibility, preference, valence, arousal, and approach-avoidance, as well as data on physical properties of the images, specifically luminance, contrast, entropy, CIELAB colour space parameter values, and fractal dimensions. All image ratings and content detail, along with participant details, are freely available online. Researchers are encouraged to use this open-access database in accordance with the specific aims and design of their study. The SNED represents a valuable resource for continued research in areas such as nature-based therapy, social prescribing, and experimental approaches investigating underlying mechanisms that help explain how natural environments improve mental health and wellbeing.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
索尔福德自然环境数据库 (SNED):一个开放存取的标准化高质量自然环境图片数据库。
人们对利用自然环境来增强心理健康(包括认知功能和情绪)的兴趣日益浓厚,初步研究已经取得了令人鼓舞的结果。鉴于自然图像已经显示出类似的好处,它们经常被用作现实世界环境的代理。为了确保精度和控制,研究人员经常对自然环境的图像进行处理。这种方法的有效性依赖于图像的标准化,因此,方法和刺激的不一致性限制了该领域研究成果的综合。针对这些限制,本文介绍了索尔福德自然环境数据库(SNED),这是一个标准化的自然图像数据库,旨在支持正在进行的关于自然暴露益处的研究。SNED目前是最全面的自然图像数据库,包括500张高质量、标准化的照片,捕捉了各个季节各种可能的自然环境。它还包括用户评价(801名参与者)的魅力、庇护和前景、兼容性、偏好、效价、唤醒和回避方法等特征的标准分数,以及图像物理特性的数据,特别是亮度、对比度、熵、CIELAB色彩空间参数值和分形维数。所有图像评分和内容细节,以及参与者的详细信息,都可以在网上免费获得。鼓励研究人员根据其研究的具体目的和设计使用此开放存取数据库。snd为在自然疗法、社会处方和实验方法等领域的持续研究提供了宝贵的资源,这些领域的研究有助于解释自然环境如何改善心理健康和福祉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
期刊最新文献
Testing for group differences in multilevel vector autoregressive models. Distribution-free Bayesian analyses with the DFBA statistical package. Jiwar: A database and calculator for word neighborhood measures in 40 languages. Open-access network science: Investigating phonological similarity networks based on the SUBTLEX-US lexicon. Survey measures of metacognitive monitoring are often false.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1