cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15672120
title: Functional Pearl – Truly functional solutions to the longest uptrend problem

description: ICFP 2025 Paper Artifact: Functional Pearl – Truly functional solutions to the longest uptrend problem

The artifact contains the source code as well as facilities to run the benchmarks presented in the paper.
DOI: 10.4121/c3b6bba1-9c3d-44c4-b59a-cbdb33198792
title: Data underlying the publication "Design and evaluation of an MRI-ready, self-propelled needle for prostate interventions"

description: <strong>Supporting information underlying the publication "Design and evaluation of an MRI-ready, self-propelled needle for prostate interventions":</strong><br><strong>S1 Appendix. Support structure design and manufacturing [S1_Appendix.docx]</strong>Description of the developed support structure for the MRI experiment in the 7-Tesla MRI scanner. A support structure for the Ovipositor MRI-Needle and the target tissue was required to facilitate the evaluation of the Ovipositor MRI-Needle performance inside a preclinical MRI system. The preclinical MRI system used in this study is a preclinical 7-T MRI system (MR Solutions, Guildford, United Kingdom). The support structure should provide a steady base for the Ovipositor MRI-Needle that fits within the restricted space available inside the MRI system. Furthermore, the support structure should allow moving the tissue towards the needle with very low friction. <br><strong>S2 Appendix. Magnetic resonance parameters [S2_Appendix.docx]</strong>MR parameters set for the MR images during the <em>ex vivo</em> prostate experiment in the 7-Tesla MRI scanner. This appendix contains the magnetic resonance (MR) parameters set for the <em>ex vivo</em> experiment in this study. Table S2-1 shows the MR parameters set for the MR images of the <em>ex vivo</em> prostate experiment. <br><strong>S3 Appendix. Experimental protocol [S3_Appendix.docx]</strong>Steps for the <em>ex vivo</em> prostate experiment.The experimental procedure for each measurement inside the prostate tissue was executed in the following sequence of ten steps. <br><strong>S4 Appendix. Study data and original MR images [S4_Appendix.docx]</strong>Raw quantitative data and original MR images of the <em>ex vivo</em> prostate experiment. This appendix contains the original MR images of the <em>ex vivo</em> prostate tissue experiment and the measurements performed during the experiment, including the measurements after every set of five cycles. In the “Evaluation” Section, the MR images contain color indications for the needle, tissue, sides of the box, and the reference point in the box to measure the slip ratio. Fig S4-1 shows the original MR images without the color indications. Table S4-1 shows the results of the <em>ex vivo</em> evaluation, including the results after every set of five cycles of actuation. The results from measurement numbers 4 and 5 were removed because the needle moved below or over the tissue instead of through the tissue. <br><strong>S1 Video. Video of the needle tip moving the needle segments subsequently [S1_Video.mp4]</strong>Close-up of the needle tip showing the wasp-inspired motions sequence moving through 5wt% gelatin.
DOI: 10.4121/c3b6bba1-9c3d-44c4-b59a-cbdb33198792.v1
title: Data underlying the publication "Design and evaluation of an MRI-ready, self-propelled needle for prostate interventions"

description: <strong>Supporting information underlying the publication "Design and evaluation of an MRI-ready, self-propelled needle for prostate interventions":</strong><br><strong>S1 Appendix. Support structure design and manufacturing [S1_Appendix.docx]</strong>Description of the developed support structure for the MRI experiment in the 7-Tesla MRI scanner. A support structure for the Ovipositor MRI-Needle and the target tissue was required to facilitate the evaluation of the Ovipositor MRI-Needle performance inside a preclinical MRI system. The preclinical MRI system used in this study is a preclinical 7-T MRI system (MR Solutions, Guildford, United Kingdom). The support structure should provide a steady base for the Ovipositor MRI-Needle that fits within the restricted space available inside the MRI system. Furthermore, the support structure should allow moving the tissue towards the needle with very low friction. <br><strong>S2 Appendix. Magnetic resonance parameters [S2_Appendix.docx]</strong>MR parameters set for the MR images during the <em>ex vivo</em> prostate experiment in the 7-Tesla MRI scanner. This appendix contains the magnetic resonance (MR) parameters set for the <em>ex vivo</em> experiment in this study. Table S2-1 shows the MR parameters set for the MR images of the <em>ex vivo</em> prostate experiment. <br><strong>S3 Appendix. Experimental protocol [S3_Appendix.docx]</strong>Steps for the <em>ex vivo</em> prostate experiment.The experimental procedure for each measurement inside the prostate tissue was executed in the following sequence of ten steps. <br><strong>S4 Appendix. Study data and original MR images [S4_Appendix.docx]</strong>Raw quantitative data and original MR images of the <em>ex vivo</em> prostate experiment. This appendix contains the original MR images of the <em>ex vivo</em> prostate tissue experiment and the measurements performed during the experiment, including the measurements after every set of five cycles. In the “Evaluation” Section, the MR images contain color indications for the needle, tissue, sides of the box, and the reference point in the box to measure the slip ratio. Fig S4-1 shows the original MR images without the color indications. Table S4-1 shows the results of the <em>ex vivo</em> evaluation, including the results after every set of five cycles of actuation. The results from measurement numbers 4 and 5 were removed because the needle moved below or over the tissue instead of through the tissue. <br><strong>S1 Video. Video of the needle tip moving the needle segments subsequently [S1_Video.mp4]</strong>Close-up of the needle tip showing the wasp-inspired motions sequence moving through 5wt% gelatin.
DOI: 10.18150/ioi0rg
title: Dynamics of highly elastic loops under gravity in a viscous fluid

description: Data used in the article "Dynamical modes of highly elastic loops settling under gravity in a viscous fluid" by Y. Melikhov and M.L. Ekiel-Jezewska. The highly elastic loop is modeled as a chain of N identical spherical beads of a diameter d, with the centers of the consecutive beads connected by springs and with elastic resistance to bending. The loop settles under gravity in a very viscous fluid. Time-dependent positions of the bead centers are computed using the precise numerical codes Hydromultipole, based on the multipole expansion of the Stokes equations. Gravity is antiparallel to the z-axis. The diameter of a bead is d=1. Initially, the loop is a flat, inclined circle in elastic equilibrium. The evolution of the loop is monitored until time 3⋅106 τHM, with τHM = π η d2 / (N m g), where η is the viscosity of a fluid, and (N m g) is the gravity corrected for buoyancy acting on the whole loop.
DOI: 10.5281/zenodo.15656889
title: Dataset of "AI-based performance analysis of roof-integrated thin-film CdTe photovoltaic system deployed under long-term outdoor conditions"

description: This paper presents a comprehensive assessment of the long-term performance degradation of a Building-Integrated Photovoltaic (BIPV) system utilizing cadmium telluride (CdTe) modules over a six-year monitoring period. A hybrid methodology combining an Artificial Intelligence (AI)-based predictive model, effective peak power trend analysis, and measured I-V curve evaluations was employed to estimate annual degradation rates (DR) and analyze the PV system behavior. The AI models, developed for each microinverter using historical operational data, showed high accuracy with normalized root mean square error (NRMSE) values between 1.64% and 2.92%. The DR, derived from the comparison between modeled and measured DC energy outputs, ranged from -2.91% to -12.85%, revealing a consistent decline after the third year of operation. These results were corroborated by both the effective peak power method and the I-V flash test measurements. Selected key performance indicators demonstrated seasonal variations and long-term degradation aligned with the estimated DR trend. The findings confirm the reliability of the AI-based approach and provide essential insights for predictive maintenance, inverter placement, and BIPV system optimization in urban settings.
DOI: 10.5281/zenodo.15656888
title: Dataset of "AI-based performance analysis of roof-integrated thin-film CdTe photovoltaic system deployed under long-term outdoor conditions"

description: This paper presents a comprehensive assessment of the long-term performance degradation of a Building-Integrated Photovoltaic (BIPV) system utilizing cadmium telluride (CdTe) modules over a six-year monitoring period. A hybrid methodology combining an Artificial Intelligence (AI)-based predictive model, effective peak power trend analysis, and measured I-V curve evaluations was employed to estimate annual degradation rates (DR) and analyze the PV system behavior. The AI models, developed for each microinverter using historical operational data, showed high accuracy with normalized root mean square error (NRMSE) values between 1.64% and 2.92%. The DR, derived from the comparison between modeled and measured DC energy outputs, ranged from -2.91% to -12.85%, revealing a consistent decline after the third year of operation. These results were corroborated by both the effective peak power method and the I-V flash test measurements. Selected key performance indicators demonstrated seasonal variations and long-term degradation aligned with the estimated DR trend. The findings confirm the reliability of the AI-based approach and provide essential insights for predictive maintenance, inverter placement, and BIPV system optimization in urban settings.
DOI: 10.15468/dl.uubwzj
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, India, Myanmar, Nepal, Bhutan, Bangladesh, Pakistan, Afghanistan)",
 "TaxonKey is Caleta roxus"
 ]
}

 The dataset includes 77 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048606-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r5zpsm
title: Occurrence Download

description: A dataset containing 272 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Matteuccia struthiopteris (L.) Tod."
 ]
}

 The dataset includes 272 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048607-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/uufkoi
title: Partisanship, Conspiracy Beliefs, and Social Media as Drivers of Anti-Ministry Sentiment in Japan

description: 岩波書店・月刊誌『世界』に掲載された「「財務省ヘイト」の論理：党派性・陰謀論・ソーシャルメディアの観点から」で用いたデータです．
DOI: 10.15468/dl.qgzzn7
title: Occurrence Download

description: A dataset containing 383 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera uncia (Schreber, 1775)"
 ]
}

 The dataset includes 383 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048609-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tpm77x
title: Occurrence Download

description: A dataset containing 604 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, India, Myanmar, Nepal, Bhutan, Bangladesh, Pakistan, Afghanistan)",
 "TaxonKey is Discolampa ethion (Westwood, 1851)"
 ]
}

 The dataset includes 604 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048611-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pfya6v
title: Occurrence Download

description: A dataset containing 383 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera uncia (Schreber, 1775)"
 ]
}

 The dataset includes 383 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048610-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15672204
title: Data from "Soft matter mechanics of immune cell aggregates"

description: Data and example of raw experimental movie from "Soft matter mechanics of immune cell aggregates", Askari et al. J. R. Soc. Interface (2025).

Short details about all files:



20231002 11_Fig2C.avi is an example of a raw experimental movie.

20231002 11_Fig2C.txt contains all experimental details needed to analyse the above movie.

CD25_Fig1D.xlsx contains the data needed to replot Fig. 1d in the manuscript.

CD69_Fig1C.xlsx contains the data needed to replot Fig. 1c in the manuscript.

Cells Data FINAL.xlsx contains all experimental data from the stretching experiements.

DATA_Fig2C.txt contains the data needed to replot Fg. 2c in the manuscript (column 1: strain, column 2: stress in Pa).

FigS2.xlsx contains the data needed to replot Fig. S2 in the manuscropt.
DOI: 10.6084/m9.figshare.27636726
title: The annotation of <i>Gypsophila vaccaria </i>(Wang Bu Liu Xing)

description: <i>Gypsophila vaccaria</i> Sm., a member of the Caryophyllaceae family, is known for its dry mature seeds, which are widely used in traditional Chinese medicine as “Wang Bu Liu Xing”. We asssembled and annotated the genome. Here we upload the annotation files to share them widely.
DOI: 10.6084/m9.figshare.27636726.v2
title: The annotation of <i>Gypsophila vaccaria </i>(Wang Bu Liu Xing)

description: <i>Gypsophila vaccaria</i> Sm., a member of the Caryophyllaceae family, is known for its dry mature seeds, which are widely used in traditional Chinese medicine as “Wang Bu Liu Xing”. We asssembled and annotated the genome. Here we upload the annotation files to share them widely.
DOI: 10.6084/m9.figshare.27636726.v1
title: The annotation of <i>Gypsophila vaccaria </i>(Wang Bu Liu Xing)

description: <i>Gypsophila vaccaria</i> Sm., a member of the Caryophyllaceae family, is known for its dry mature seeds, which are widely used in traditional Chinese medicine as “Wang Bu Liu Xing”. We asssembled and annotated the genome. Here we upload the annotation files to share them widely.
DOI: 10.15468/dl.jbnhjh
title: Occurrence Download

description: A dataset containing 6972 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coptodon zillii (Gervais, 1848)"
 ]
}

 The dataset includes 6972 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048612-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gjcghsrgm8
title: Identifying Smiling Depression vai Sentiment Score

description: The dataset comprises self-reported responses from undergraduate students across various colleges and universities in South India, primarily from the states of Telangana, Andhra Pradesh, and Tamil Nadu. Participants are aged between 18 and 22 years and are enrolled in technical (e.g., B.Tech) and traditional undergraduate programs (e.g., B.A., B.Sc., B.Com.). The data captures a diverse mix of students from urban, semi-urban, and rural backgrounds, providing a broad perspective on student experiences.

Each entry includes basic demographic information such as age, gender, place of living, educational qualification, and the name of the institution. The core of the dataset consists of responses to a series of statements designed to probe behaviors and attitudes associated with "smiling depression"—a phenomenon where individuals conceal depressive symptoms behind a façade of happiness. Statements address themes such as hiding negative emotions, reluctance to discuss problems, the use of humour to mask distress, and the perceived need to appear happy or in control around others.

For each statement, students indicate the frequency or degree to which it applies to them, using scales such as "Always," "Often," "Sometimes," "Rarely," and "Never," or levels of agreement from "Strongly Agree" to "Strongly Disagree." These qualitative responses are numerically encoded for analysis.

Additionally, the dataset includes sentiment scoring outputs, where each response is assigned a sentiment value (e.g., -2 for strongly negative, 0 for neutral, +2 for strongly positive). An average sentiment score is calculated for each participant, offering a quantitative measure of their overall emotional expression related to smiling depression behaviors. This scoring allows for the identification of students who may be at risk, despite outward appearances of well-being.

The data is anonymised to protect participant privacy, with personal identifiers removed. The comprehensive structure and sentiment scoring make the dataset suitable for sentiment analysis, enabling researchers to detect patterns and prevalence of smiling depression among undergraduate students and to explore correlations with demographic factors.
DOI: 10.17632/gjcghsrgm8.1
title: Identifying Smiling Depression vai Sentiment Score

description: The dataset comprises self-reported responses from undergraduate students across various colleges and universities in South India, primarily from the states of Telangana, Andhra Pradesh, and Tamil Nadu. Participants are aged between 18 and 22 years and are enrolled in technical (e.g., B.Tech) and traditional undergraduate programs (e.g., B.A., B.Sc., B.Com.). The data captures a diverse mix of students from urban, semi-urban, and rural backgrounds, providing a broad perspective on student experiences.

Each entry includes basic demographic information such as age, gender, place of living, educational qualification, and the name of the institution. The core of the dataset consists of responses to a series of statements designed to probe behaviors and attitudes associated with "smiling depression"—a phenomenon where individuals conceal depressive symptoms behind a façade of happiness. Statements address themes such as hiding negative emotions, reluctance to discuss problems, the use of humour to mask distress, and the perceived need to appear happy or in control around others.

For each statement, students indicate the frequency or degree to which it applies to them, using scales such as "Always," "Often," "Sometimes," "Rarely," and "Never," or levels of agreement from "Strongly Agree" to "Strongly Disagree." These qualitative responses are numerically encoded for analysis.

Additionally, the dataset includes sentiment scoring outputs, where each response is assigned a sentiment value (e.g., -2 for strongly negative, 0 for neutral, +2 for strongly positive). An average sentiment score is calculated for each participant, offering a quantitative measure of their overall emotional expression related to smiling depression behaviors. This scoring allows for the identification of students who may be at risk, despite outward appearances of well-being.

The data is anonymised to protect participant privacy, with personal identifiers removed. The comprehensive structure and sentiment scoring make the dataset suitable for sentiment analysis, enabling researchers to detect patterns and prevalence of smiling depression among undergraduate students and to explore correlations with demographic factors.
DOI: 10.48597/zty9-c4gw
title: Measurement of Aerosol_absorption_coefficient at Whistler Mountain

description: Measurement of Aerosol_absorption_coefficient at Whistler Mountain
DOI: 10.17632/5k25s8r5vf
title: The WB original uncropped image

description: The WB raw data for our research article.
DOI: 10.17632/5k25s8r5vf.1
title: The WB original uncropped image

description: The WB raw data for our research article.
DOI: 10.17632/5zx7sdj7ww
title: Social Influence Impact on User Acceptance, AI, and Customer Behaviour in Smart Home Technology Adoption

description: Social Influence Impact on User Acceptance, AI, and Customer Behaviour in Smart Home Technology Adoption
This dataset supports a quantitative study that investigates how social influence impacts customer behavior in adopting smart home technology in Indonesia, mediated by artificial intelligence (AI) perception and user acceptance. The research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships among the four core constructs: Social Influence, User Acceptance, Artificial Intelligence Perception, and Customer Behaviour.

The data were collected via an online questionnaire distributed across various regions in Indonesia using purposive sampling. A total of 401 valid responses were obtained from individuals with prior experience using smart home technology. Each item in the instrument was measured using a nine-point Likert scale, capturing user attitudes and behavioral patterns.

Indicators include variables related to:

Social Influence (e.g., peer recommendations, community support),
User Acceptance (e.g., perceived usefulness and intention to continue use),
Artificial Intelligence (e.g., trust in AI features, decision-making assistance),
Customer Behaviour (e.g., usage frequency, brand loyalty).
The dataset includes cleaned and anonymized responses and is intended to facilitate further analysis, replication, and model validation in the context of technology adoption. It can be used by researchers interested in smart home adoption, AI perception, behavioral modeling, and digital consumer behavior
DOI: 10.17632/5zx7sdj7ww.1
title: Social Influence Impact on User Acceptance, AI, and Customer Behaviour in Smart Home Technology Adoption

description: Social Influence Impact on User Acceptance, AI, and Customer Behaviour in Smart Home Technology Adoption
This dataset supports a quantitative study that investigates how social influence impacts customer behavior in adopting smart home technology in Indonesia, mediated by artificial intelligence (AI) perception and user acceptance. The research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships among the four core constructs: Social Influence, User Acceptance, Artificial Intelligence Perception, and Customer Behaviour.

The data were collected via an online questionnaire distributed across various regions in Indonesia using purposive sampling. A total of 401 valid responses were obtained from individuals with prior experience using smart home technology. Each item in the instrument was measured using a nine-point Likert scale, capturing user attitudes and behavioral patterns.

Indicators include variables related to:

Social Influence (e.g., peer recommendations, community support),
User Acceptance (e.g., perceived usefulness and intention to continue use),
Artificial Intelligence (e.g., trust in AI features, decision-making assistance),
Customer Behaviour (e.g., usage frequency, brand loyalty).
The dataset includes cleaned and anonymized responses and is intended to facilitate further analysis, replication, and model validation in the context of technology adoption. It can be used by researchers interested in smart home adoption, AI perception, behavioral modeling, and digital consumer behavior