Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps

Mana R Ehlers; Tina B Lonsdorf

doi:10.1016/j.neubiorev.2022.104958

Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps

Standard

Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps. / Ehlers, Mana R; Lonsdorf, Tina B.

In: NEUROSCI BIOBEHAV R, Vol. 143, 104958, 11.2022.

Research output: SCORING: Contribution to journal › SCORING: Review article › Research

Harvard

Ehlers, MR & Lonsdorf, TB 2022, 'Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps', NEUROSCI BIOBEHAV R, vol. 143, 104958. https://doi.org/10.1016/j.neubiorev.2022.104958

APA

Ehlers, M. R., & Lonsdorf, T. B. (2022). Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps. NEUROSCI BIOBEHAV R, 143, [104958]. https://doi.org/10.1016/j.neubiorev.2022.104958

Vancouver

Ehlers MR, Lonsdorf TB. Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps. NEUROSCI BIOBEHAV R. 2022 Nov;143. 104958. https://doi.org/10.1016/j.neubiorev.2022.104958

Bibtex

@article{e3c6c03402aa4eac8876095c9e39488c,

title = "Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps",

abstract = "Data sharing holds promise for advancing and accelerating science by facilitating and fostering collaboration, reproducibility and optimal use of sparse resources. We argue that despite the existence of general data sharing guidelines (e.g, FAIR-principles), their translation and implementation requires field-specific considerations. Here, we addressed this timely question for the field of experimental research on fear and anxiety and showcase the enormous prospects by illustrating the wealth and richness of a curated data collection of publicly available datasets using the fear conditioning paradigm based on 103 studies and 8839 participants. We highlight challenges encountered when aiming to reuse the available data corpus and derive 10 simple steps for making data sharing in the field more efficient and sustainable and hence facilitating collaboration, cumulative knowledge generation and large scale mega-, meta- and psychometric analyses. We share our vision and first steps towards transforming such curated data collections into a homogenized and dynamically growing database allowing for easy contributions and for living analysis tools for the collective benefit of the research community.",

keywords = "Humans, Reproducibility of Results, Fear, Information Dissemination, Anxiety, Anxiety Disorders",

author = "Ehlers, {Mana R} and Lonsdorf, {Tina B}",

year = "2022",

month = nov,

doi = "10.1016/j.neubiorev.2022.104958",

language = "English",

volume = "143",

journal = "NEUROSCI BIOBEHAV R",

issn = "0149-7634",

publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps

AU - Ehlers, Mana R

AU - Lonsdorf, Tina B

PY - 2022/11

Y1 - 2022/11

N2 - Data sharing holds promise for advancing and accelerating science by facilitating and fostering collaboration, reproducibility and optimal use of sparse resources. We argue that despite the existence of general data sharing guidelines (e.g, FAIR-principles), their translation and implementation requires field-specific considerations. Here, we addressed this timely question for the field of experimental research on fear and anxiety and showcase the enormous prospects by illustrating the wealth and richness of a curated data collection of publicly available datasets using the fear conditioning paradigm based on 103 studies and 8839 participants. We highlight challenges encountered when aiming to reuse the available data corpus and derive 10 simple steps for making data sharing in the field more efficient and sustainable and hence facilitating collaboration, cumulative knowledge generation and large scale mega-, meta- and psychometric analyses. We share our vision and first steps towards transforming such curated data collections into a homogenized and dynamically growing database allowing for easy contributions and for living analysis tools for the collective benefit of the research community.

AB - Data sharing holds promise for advancing and accelerating science by facilitating and fostering collaboration, reproducibility and optimal use of sparse resources. We argue that despite the existence of general data sharing guidelines (e.g, FAIR-principles), their translation and implementation requires field-specific considerations. Here, we addressed this timely question for the field of experimental research on fear and anxiety and showcase the enormous prospects by illustrating the wealth and richness of a curated data collection of publicly available datasets using the fear conditioning paradigm based on 103 studies and 8839 participants. We highlight challenges encountered when aiming to reuse the available data corpus and derive 10 simple steps for making data sharing in the field more efficient and sustainable and hence facilitating collaboration, cumulative knowledge generation and large scale mega-, meta- and psychometric analyses. We share our vision and first steps towards transforming such curated data collections into a homogenized and dynamically growing database allowing for easy contributions and for living analysis tools for the collective benefit of the research community.

KW - Humans

KW - Reproducibility of Results

KW - Fear

KW - Information Dissemination

KW - Anxiety

KW - Anxiety Disorders

U2 - 10.1016/j.neubiorev.2022.104958

DO - 10.1016/j.neubiorev.2022.104958

M3 - SCORING: Review article

C2 - 36372236

VL - 143

JO - NEUROSCI BIOBEHAV R

JF - NEUROSCI BIOBEHAV R

SN - 0149-7634

M1 - 104958

ER -