cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15088300
title: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

description: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

This repository includes the data and codes to generate analysis results and figures. 

Data anonymization

To ensure participant confidentiality, we replaced all reported concepts and details—including names mentioned in their personal narratives—with alphanumeric codes. This anonymization process did not compromise the integrity of our analysis, and the results align with our published findings.

Dependencies



CanlabCore

cocoanCORE

spm12

Matlab 2022b

Conda 24.9.2

Docker version 24.0.7, build afdd53b

nvidia-docker2

xz (XZ Utils) 5.2.5, 

liblzma 5.2.5

Python 3.6

Tensorflow 1.15.0


Python library versions are provided by the requirements.txt file.

Docker environment

We trained the Transformer model in a docker container. We provided "fast_nlp_tf1.tar.xz" compressed by xz utils.

To build the environment, follow the command lines below.

$ xz -d -p {# of processes} fast_nlp_tf1.tar.xz

$ docker run --gpus all --name fast_nlp_env -v {path of this repo}:/Projects -itd fast_nlp:tf1.15.0-py3

Abstract

Spontaneous thought plays a crucial role in shaping affective traits and mental health. However, its dynamic and unconstrained nature makes it challenging to quantify and model effectively. To address this, we employed the Free Association Semantic Task (FAST) to obtain self-generated spontaneous thoughts, which were then analyzed using network modeling and natural language processing (NLP) to decode key affective content dimensions of self-generated thought, including valence, self-relevance, and time. In two studies (n = 213 and n = 137), we found that degree centrality and semantic distance between consecutive concepts were associated with the overall self-relevance level of self-generated thought. To capture the trial-by-trial dynamics of content dimensions, we developed a Transformer-based model, with which we extracted dynamic features and were able to predict individual differences in general negative affectivity. These findings highlight the potential of computational linguistic and network models to quantify spontaneous thought and predict affective traits, offering a scalable approach for real-time, automated mental health assessments while reducing reliance on retrospective self-reports.
DOI: 10.5281/zenodo.15088301
title: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method
DOI: 10.5281/zenodo.15201247
title: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

description: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

This repository includes the data and codes to generate analysis results and figures. 

Data anonymization

To ensure participant confidentiality, we replaced all reported concepts and details—including names mentioned in their personal narratives—with alphanumeric codes. This anonymization process did not compromise the integrity of our analysis, and the results align with our published findings.

Dependencies



CanlabCore

cocoanCORE

spm12

Matlab 2022b

Conda 24.9.2

Docker version 24.0.7, build afdd53b

nvidia-docker2

xz (XZ Utils) 5.2.5, 

liblzma 5.2.5

Python 3.6

Tensorflow 1.15.0


Python library versions are provided by the requirements.txt file.

Docker environment

We trained the Transformer model in a docker container. We provided "fast_nlp_tf1.tar.xz" compressed by xz utils.

To build the environment, follow the command lines below.

$ xz -d -p {# of processes} fast_nlp_tf1.tar.xz

$ docker run --gpus all --name fast_nlp_env -v {path of this repo}:/Projects -itd fast_nlp:tf1.15.0-py3

Abstract

Spontaneous thought plays a crucial role in shaping affective traits and mental health. However, its dynamic and unconstrained nature makes it challenging to quantify and model effectively. To address this, we employed the Free Association Semantic Task (FAST) to obtain self-generated spontaneous thoughts, which were then analyzed using network modeling and natural language processing (NLP) to decode key affective content dimensions of self-generated thought, including valence, self-relevance, and time. In two studies (n = 213 and n = 137), we found that degree centrality and semantic distance between consecutive concepts were associated with the overall self-relevance level of self-generated thought. To capture the trial-by-trial dynamics of content dimensions, we developed a Transformer-based model, with which we extracted dynamic features and were able to predict individual differences in general negative affectivity. These findings highlight the potential of computational linguistic and network models to quantify spontaneous thought and predict affective traits, offering a scalable approach for real-time, automated mental health assessments while reducing reliance on retrospective self-reports.
DOI: 10.5281/zenodo.15209264
title: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

description: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

This repository includes the data and codes to generate analysis results and figures. 

Data anonymization

To ensure participant confidentiality, we replaced all reported concepts and details—including names mentioned in their personal narratives—with alphanumeric codes. This anonymization process did not compromise the integrity of our analysis, and the results align with our published findings.

Dependencies



CanlabCore

cocoanCORE

spm12

Matlab 2022b

Conda 24.9.2

Docker version 24.0.7, build afdd53b

nvidia-docker2

xz (XZ Utils) 5.2.5, 

liblzma 5.2.5

Python 3.6

Tensorflow 1.15.0


Python library versions are provided by the requirements.txt file.

Docker environment

We trained the Transformer model in a docker container. We provided "fast_nlp_tf1.tar.xz" compressed by xz utils.

To build the environment, follow the command lines below.

$ xz -d -p {# of processes} fast_nlp_tf1.tar.xz

$ docker run --gpus all --name fast_nlp_env -v {path of this repo}:/Projects -itd fast_nlp:tf1.15.0-py3

Abstract

Spontaneous thought plays a crucial role in shaping affective traits and mental health. However, its dynamic and unconstrained nature makes it challenging to quantify and model effectively. To address this, we employed the Free Association Semantic Task (FAST) to obtain self-generated spontaneous thoughts, which were then analyzed using network modeling and natural language processing (NLP) to decode key affective content dimensions of self-generated thought, including valence, self-relevance, and time. In two studies (n = 213 and n = 137), we found that degree centrality and semantic distance between consecutive concepts were associated with the overall self-relevance level of self-generated thought. To capture the trial-by-trial dynamics of content dimensions, we developed a Transformer-based model, with which we extracted dynamic features and were able to predict individual differences in general negative affectivity. These findings highlight the potential of computational linguistic and network models to quantify spontaneous thought and predict affective traits, offering a scalable approach for real-time, automated mental health assessments while reducing reliance on retrospective self-reports.
DOI: 10.5281/zenodo.15213401
title: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

description: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

This repository includes the data and codes to generate analysis results and figures. 

Data anonymization

To ensure participant confidentiality, we replaced all reported concepts and details—including names mentioned in their personal narratives—with alphanumeric codes. This anonymization process did not compromise the integrity of our analysis, and the results align with our published findings.

Dependencies



CanlabCore

cocoanCORE

spm12

Matlab 2022b

Conda 24.9.2

Docker version 24.0.7, build afdd53b

nvidia-docker2

xz (XZ Utils) 5.2.5, 

liblzma 5.2.5

Python 3.6

Tensorflow 1.15.0


Python library versions are provided by the requirements.txt file.

Docker environment

We trained the Transformer model in a docker container. We provided "fast_nlp_tf1.tar.xz" compressed by xz utils.

To build the environment, follow the command lines below.

$ xz -d -p {# of processes} fast_nlp_tf1.tar.xz

$ docker run --gpus all --name fast_nlp_env -v {path of this repo}:/Projects -itd fast_nlp:tf1.15.0-py3

Abstract

Spontaneous thought plays a crucial role in shaping affective traits and mental health. However, its dynamic and unconstrained nature makes it challenging to quantify and model effectively. To address this, we employed the Free Association Semantic Task (FAST) to obtain self-generated spontaneous thoughts, which were then analyzed using network modeling and natural language processing (NLP) to decode key affective content dimensions of self-generated thought, including valence, self-relevance, and time. In two studies (n = 213 and n = 137), we found that degree centrality and semantic distance between consecutive concepts were associated with the overall self-relevance level of self-generated thought. To capture the trial-by-trial dynamics of content dimensions, we developed a Transformer-based model, with which we extracted dynamic features and were able to predict individual differences in general negative affectivity. These findings highlight the potential of computational linguistic and network models to quantify spontaneous thought and predict affective traits, offering a scalable approach for real-time, automated mental health assessments while reducing reliance on retrospective self-reports.
DOI: 10.5281/zenodo.15213411
title: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

description: Modeling Spontaneous Thought: A Network- and Langauge-based Computational Method

This repository includes the data and codes to generate analysis results and figures. 

Data anonymization

To ensure participant confidentiality, we replaced all reported concepts and details—including names mentioned in their personal narratives—with alphanumeric codes. This anonymization process did not compromise the integrity of our analysis, and the results align with our published findings.

Dependencies



CanlabCore

cocoanCORE

spm12

Matlab 2022b

Conda 24.9.2

Docker version 24.0.7, build afdd53b

nvidia-docker2

xz (XZ Utils) 5.2.5, 

liblzma 5.2.5

Python 3.6

Tensorflow 1.15.0


Python library versions are provided by the requirements.txt file.

Docker environment

We trained the Transformer model in a docker container. We provided "fast_nlp_tf1.tar.xz" compressed by xz utils.

To build the environment, follow the command lines below.

$ xz -d -p {# of processes} fast_nlp_tf1.tar.xz

$ docker run --gpus all --name fast_nlp_env -v {path of this repo}:/Projects -itd fast_nlp:tf1.15.0-py3

Abstract

Spontaneous thought plays a crucial role in shaping affective traits and mental health. However, its dynamic and unconstrained nature makes it challenging to quantify and model effectively. To address this, we employed the Free Association Semantic Task (FAST) to obtain self-generated spontaneous thoughts, which were then analyzed using network modeling and natural language processing (NLP) to decode key affective content dimensions of self-generated thought, including valence, self-relevance, and time. In two studies (n = 213 and n = 137), we found that degree centrality and semantic distance between consecutive concepts were associated with the overall self-relevance level of self-generated thought. To capture the trial-by-trial dynamics of content dimensions, we developed a Transformer-based model, with which we extracted dynamic features and were able to predict individual differences in general negative affectivity. These findings highlight the potential of computational linguistic and network models to quantify spontaneous thought and predict affective traits, offering a scalable approach for real-time, automated mental health assessments while reducing reliance on retrospective self-reports.
DOI: 10.15156/bio/sh1167200.08fu
title: SH1167200.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh614612.07fu
title: SH614612.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh614613.07fu
title: SH614613.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh614614.07fu
title: SH614614.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1167201.08fu
title: SH1167201.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1167202.08fu
title: SH1167202.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh614615.07fu
title: SH614615.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh614616.07fu
title: SH614616.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1167203.08fu
title: SH1167203.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1167204.08fu
title: SH1167204.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.