cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5878/3dtz-9b23
title: Data for: Settling assays testing the substrate preference of larvae of the Cold-Water Coral Desmophyllum pertusum (syn. Lophelia pertusa)
title: Data till: Settling assays testing the substrate preference of larvae of the Cold-Water Coral Desmophyllum pertusum (syn. Lophelia pertusa)

description: The datasets are based on laboratory studies with larvae of the cold-water scleractinian coral Desmophyllum pertusum (syn. Lophelia pertusa) testing larval responses to nine different substrates. The experiment was run in standard micro well plates (6 wells per plate, 34 mm ø), with substrates pressed into a coral epoxy putty to level the surface. Larvae were filmed for 5 minutes as they were added to the wells. There were 3 replicate wells per substrate type and a total of 356 larvae (11–15 per well). The videos were then analyzed in the BORIS software where 8 defined behaviors were annotated to get time spent on each of the behaviors. We also counted the number of attached larvae in the wells.
Dataset publication was supported by Mistra Co-Creating Better Blue (C2B2).
description: Dataseten är baserade på laboratoriestudier med larver av kallvattenkorallen Desmophyllum pertusum (syn. Lophelia pertusa). Vi testade larvernas respons på nio olika substrat. Experimentet utfördes i standard micro well-plattor (6 brunnar per platta, 34 mm ø), med substraten inpressade i en epoxylera för användning i akvarier för att jämna ut ytan. Larverna filmades i 5 minuter efter att de tillsatts i brunnarna. Det var tre replikat per substrat-typ och totalt 356 larver (11–15 per brunn). Videofilmerna analyserades därefter i mjukvaran BORIS där 8 definierade beteenden annoterades för att få ut data på antal sekunder varje larv spenderat på de olika beteendena. Vi räknade även antalet larver som satte sig fast på substraten.
Publiceringen av datasetet stöddes av Mistra Co-Creating Better Blue (C2B2).
DOI: 10.5878/2v40-2c97
title: Data for: Settling assays testing the substrate preference of larvae of the Cold-Water Coral Desmophyllum pertusum (syn. Lophelia pertusa)
title: Data till: Settling assays testing the substrate preference of larvae of the Cold-Water Coral Desmophyllum pertusum (syn. Lophelia pertusa)

description: The datasets are based on laboratory studies with larvae of the cold-water scleractinian coral Desmophyllum pertusum (syn. Lophelia pertusa) testing larval responses to nine different substrates. The experiment was run in standard micro well plates (6 wells per plate, 34 mm ø), with substrates pressed into a coral epoxy putty to level the surface. Larvae were filmed for 5 minutes as they were added to the wells. There were 3 replicate wells per substrate type and a total of 356 larvae (11–15 per well). The videos were then analyzed in the BORIS software where 8 defined behaviors were annotated to get time spent on each of the behaviors. We also counted the number of attached larvae in the wells.
Dataset publication was supported by Mistra Co-Creating Better Blue (C2B2).
description: Dataseten är baserade på laboratoriestudier med larver av kallvattenkorallen Desmophyllum pertusum (syn. Lophelia pertusa). Vi testade larvernas respons på nio olika substrat. Experimentet utfördes i standard micro well-plattor (6 brunnar per platta, 34 mm ø), med substraten inpressade i en epoxylera för användning i akvarier för att jämna ut ytan. Larverna filmades i 5 minuter efter att de tillsatts i brunnarna. Det var tre replikat per substrat-typ och totalt 356 larver (11–15 per brunn). Videofilmerna analyserades därefter i mjukvaran BORIS där 8 definierade beteenden annoterades för att få ut data på antal sekunder varje larv spenderat på de olika beteendena. Vi räknade även antalet larver som satte sig fast på substraten.
Publiceringen av datasetet stöddes av Mistra Co-Creating Better Blue (C2B2).
DOI: 10.5439/1879865
title: nimfr7nchaod1mich.c1

description: nimfr7nchaod1mich.c1
DOI: 10.5281/zenodo.17131609
title: A p-type Cu2O Photoanode for Solar Water Oxidation
DOI: 10.5281/zenodo.17131610
title: A p-type Cu2O Photoanode for Solar Water Oxidation
DOI: 10.25406/wsu.30113140
title: Assessment of the Impact of Early Nutrition on Cardiovascular Risk in 3-to-9-Month-Old Infants, A Cross-Sectional Cohort Study

description: Parent Consent Form and Questionnaire extended data of the study on assessment of the impact of early nutrition on cardiovascular risk in 3-to-9-month-old infants, a cross-sectional cohort study.
DOI: 10.25406/wsu.30113140.v2
title: Assessment of the Impact of Early Nutrition on Cardiovascular Risk in 3-to-9-Month-Old Infants, A Cross-Sectional Cohort Study

description: Parent Consent Form and Questionnaire extended data of the study on assessment of the impact of early nutrition on cardiovascular risk in 3-to-9-month-old infants, a cross-sectional cohort study.
DOI: 10.25406/wsu.30113140.v1
title: Assessment of the Impact of Early Nutrition on Cardiovascular Risk in 3-to-9-Month-Old Infants

description: Parent Consent Form and Questionnaire extended data of the study on assessment of the impact of early nutrition on cardiovascular risk in 3-to-9-month-old infants, a cross-sectional cohort study.
DOI: 10.25375/uct.30112654
title: Micro-CT scans of Mudrock Core Samples containing Trace Fossils

description: Micro-CT scan dataset uploaded as supplementary materials for the manuscript entitled: <i>Marine snow fuels an opportunistic small food web in the Late Ordovician Soom Shale Lagerstätte</i>. Manuscript in press - September 2025. (Nature Ecology and Evolution Manuscript number: NATECOLEVOL-24103054A).Four micro-CT scans of mudrock core from the Cedarberg Formation (Cape Supergroup, South Africa) containing fossilised marine snow, dust aggregates and trace fossils of meiofaunal marine benthic organisms (Nematodes and Foraminifera). Micro-CT scan data consists of TIFF images and associated acquisition files.
DOI: 10.25375/uct.30112654.v1
title: Micro-CT scans of Mudrock Core Samples containing Trace Fossils

description: Micro-CT scan dataset uploaded as supplementary materials for the manuscript entitled: <i>Marine snow fuels an opportunistic small food web in the Late Ordovician Soom Shale Lagerstätte</i>. Manuscript in press - September 2025. (Nature Ecology and Evolution Manuscript number: NATECOLEVOL-24103054A).Four micro-CT scans of mudrock core from the Cedarberg Formation (Cape Supergroup, South Africa) containing fossilised marine snow, dust aggregates and trace fossils of meiofaunal marine benthic organisms (Nematodes and Foraminifera). Micro-CT scan data consists of TIFF images and associated acquisition files.
DOI: 10.5281/zenodo.17100563
title: Data for the manuscript:"Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems"

description: The folders contains all the supporting data for the manuscript "Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems" Here each folder corresponds to the respective figure in the main text and supplementary.Each folder contains the input files to generate the data, output files for the respective analysis and jupyter-notebook (*.ipynb) for visulaization.*.hdf5 contains the phonon frequencies and eigenvectors computed for the respective system using Phonopy.*.pkl contains the VASP-ML predictions on validation and test sets.*.extxyz contains the MACE predictons on validation and test sets.*phonon_freq.txt contain Gamma-point phonon frequencies.*.xyz contains the trajectories for computing vdos for both naphthalene and host-guest system. *.model contains the trained MACE models for respective acene series. VASP_MLmodel_ML_ABN training file for VASP-ML.Data/├── Appendix C│   ├── VASP_MLIP_Phononband.hdf5│   ├── VASP_MLIP_Predictions_Validation_Set.pkl│   └── VASP_MLmodel_ML_ABN├── Fig. 1 - contains the VASP and MACE predictions│   ├── a,b│   └── c,d├── Fig. 2 - contains the phonon frequencies for naphthalene and MD trajecoties for computing vdos│   ├── a│   └── b├── Fig. 3 - contains MACE models and computed phonon frequencies for whole acene series in the manuscript│   ├── b│   ├── c│   ├── d│   ├── e│   └── Plot_Fig_bcde.ipynb├── Fig. 4 - contains the phonon freuquencies for host-guest system│   ├── b│   └── c├── Fig. 5 - contains the MD trajectories and vdos for host-guest system│   ├── a│   ├── b│   ├── MD_Trajectories│   └── Plot_Fig_ab.ipynb├── Fig. S1 - comparision of MACE vs VASP│   ├── a,b│   └── c,d├── Fig. S10 - absolute errors of phonon frequencies in acenes│   ├── a│   └── b├── Fig. S11 - uncertainity propagation to harmonic phonons for acenes│   ├── Anthracene_phonons│   ├── Naphtalene_phonons│   ├── Pentacene_phonons│   └── Tetracene_phonons├── Fig. S12 - phonons for different polymorphs of acenes│   ├── DFT│   └── MACE├── Fig. S14 - uncertainty propagation to the phonons of host and guest molecules│   ├── a│   ├── b│   ├── c│   └── d├── Fig. S6 - unceratinty propagation to naphthalene using AL on NMLIP│   ├── mean│   ├── nn01│   ├── nn02│   ├── nn03│   ├── nn04│   ├── nn05│   ├── nn06│   ├── nn07│   ├── nn08│   ├── phonon_error_cm_1.txt│   └── Plot_Fig_S6.ipynb
DOI: 10.5281/zenodo.17100564
title: Data for the manuscript:"Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems"

description: The folders contains all the supporting data for the manuscript "Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems" Here each folder corresponds to the respective figure in the main text and supplementary.Each folder contains the input files to generate the data, output files for the respective analysis and jupyter-notebook (*.ipynb) for visulaization.*.hdf5 contains the phonon frequencies and eigenvectors computed for the respective system using Phonopy.*.pkl contains the VASP-ML predictions on validation and test sets.*.extxyz contains the MACE predictons on validation and test sets.*phonon_freq.txt contain Gamma-point phonon frequencies.*.xyz contains the trajectories for computing vdos for both naphthalene and host-guest system. *.model contains the trained MACE models for respective acene series. VASP_MLmodel_ML_ABN training file for VASP-ML.Data/├── Appendix C│   ├── VASP_MLIP_Phononband.hdf5│   ├── VASP_MLIP_Predictions_Validation_Set.pkl│   └── VASP_MLmodel_ML_ABN├── Fig. 1 - contains the VASP and MACE predictions│   ├── a,b│   └── c,d├── Fig. 2 - contains the phonon frequencies for naphthalene and MD trajecoties for computing vdos│   ├── a│   └── b├── Fig. 3 - contains MACE models and computed phonon frequencies for whole acene series in the manuscript│   ├── b│   ├── c│   ├── d│   ├── e│   └── Plot_Fig_bcde.ipynb├── Fig. 4 - contains the phonon freuquencies for host-guest system│   ├── b│   └── c├── Fig. 5 - contains the MD trajectories and vdos for host-guest system│   ├── a│   ├── b│   ├── MD_Trajectories│   └── Plot_Fig_ab.ipynb├── Fig. S1 - comparision of MACE vs VASP│   ├── a,b│   └── c,d├── Fig. S10 - absolute errors of phonon frequencies in acenes│   ├── a│   └── b├── Fig. S11 - uncertainity propagation to harmonic phonons for acenes│   ├── Anthracene_phonons│   ├── Naphtalene_phonons│   ├── Pentacene_phonons│   └── Tetracene_phonons├── Fig. S12 - phonons for different polymorphs of acenes│   ├── DFT│   └── MACE├── Fig. S14 - uncertainty propagation to the phonons of host and guest molecules│   ├── a│   ├── b│   ├── c│   └── d├── Fig. S6 - unceratinty propagation to naphthalene using AL on NMLIP│   ├── mean│   ├── nn01│   ├── nn02│   ├── nn03│   ├── nn04│   ├── nn05│   ├── nn06│   ├── nn07│   ├── nn08│   ├── phonon_error_cm_1.txt│   └── Plot_Fig_S6.ipynb
DOI: 10.5519/qd.hctry0o2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (35346 records)
DOI: 10.57745/t4jgwq
title: DATA-DEBORA.zip

description: The DEBORA data
DOI: 10.57745/ggb1gc
title: dataloader.py

description: Minimal working example to load the data in python
DOI: 10.57745/v5fbbk
title: documentation-debora-data.pdf

description: README
DOI: 10.57745/tcamme
title: DEBORA Databank: local measurements of high-pressure convective boiling flows

description: The provided data are radial measures of the flow topology in a boiling pipe produced on the DEBORA facility in CEA at Grenoble. This facility was supported by the Neptune project, a joint collaboration between CEA, EDF, IRSN and Framatome. The fluid is Freon R-12. The data are provided in individual json files with the test conditions and the measurements. A readme file is provided with details about the experiments. A subset of the data was used in the DEBORA benchmark https://doi.org/10.1016/j.ijmultiphaseflow.2024.104920 and is available here https://zenodo.org/records/12551306.
DOI: 10.5281/zenodo.17130056
title: Synchronous atmospheric and oceanic data from a triangle-shaped moored array in the northern South China Sea in 2016

description: This work presents a triangle-shaped moored array dataset comprising three buoys and two moorings with synchronous atmospheric and oceanic data in the northern South China Sea during 2016. The moored array was deployed at late July to early August and recovered at October. The atmospheric data were observed by meteorological sensors and automatic meteorological stations ~2.5 m above sea surface at the buoys. The oceanic data consist temperature and salinity using conductivity, temperature, and depth (CTD) recorders or temperature sensors. It also includes currents observed by acoustic Doppler current profilers (ADCPs) and current meters. The data reveal air–sea interactions and oceanic processes in the upper and bottom ocean. Multiscale processes were recorded, such as air–sea fluxes, tides, internal waves, and low-frequency flows and variations. The data are valuable and may have a lot of potential applications, including analyzing the phenomena and mechanisms of air–sea interactions and ocean dynamics as well as validating and improving numerical model simulations, data reanalysis, and assimilations.
DOI: 10.5281/zenodo.17131666
title: Source Data and Protein/Peptide Summary Reports for the Manuscript "Multicenter Evaluation of Label-Free Quantification in Human Plasma on a High Dynamic Range Benchmark Set"

description: This repository contains the source data underlying the analyses presented in the associated publication. The files are provided to ensure transparency, reproducibility, and reuse by the scientific community.

In addition to the data, all analysis scripts used in the study have been uploaded. If you make use of these scripts or data in your own work, please cite the corresponding publication.

The corresponding preprint is available at: https://doi.org/10.21203/rs.3.rs-5618718/v1

Further information, updates, and documentation can be found on the accompanying GitHub repository: https://github.com/HanYoo1402/LFQ-Bench-Scripts-for-PYE-Multicenter-Study
DOI: 10.5281/zenodo.16948856
title: Integrating Large Language Models in Software Engineering Education: A Pilot Study through GitHub Repository Mining

description: Replication Package

This replication package contains all scripts, datasets, and outputs used in the repository mining study on LLMs in Software Engineering Education. It is intended to allow other researchers to reproduce the results, inspect the intermediate data, and extend the analysis.

Contents:





mine_llm_repos.py: Python script used to query the GitHub API, retrieve repositories with relevant keywords, and extract README files and issue discussions. 




analyze_llm_repos.py: Python script for post-processing. It reads the raw dataset, computes summary statistics for motivators and demotivators, and produces CSV summaries.




final-repos_summary.csv: Filtered list of repositories included in the final analysis, with metadata such as repository name, URL, language, stars, forks, and counts of motivator/demotivator hits.




chi_square_results_400_repos.xlsx: Statistical test results, including Chi-square values and p-values for each motivator and demotivator sub-theme across the 400 repositories.



Usage:





Run mine_llm_repos.py to collect repositories and generate the raw CSV.




Run analyze_llm_repos.py to compute theme-level summaries and produce figures.




The .csv and .xlsx files can be opened in Excel or any statistical software for further inspection.



This package ensures transparency of the analysis pipeline and provides a starting point for future work. Researchers may adapt the keyword sets in mine_llm_repos.py to explore other educational contexts or extend the mining to additional platforms such as GitLab, Bitbucket, or SourceForge.
DOI: 10.5281/zenodo.17131625
title: Integrating Large Language Models in Software Engineering Education: A Pilot Study through GitHub Repository Mining

description: Replication Package

This replication package contains all scripts, datasets, and outputs used in the repository mining study on LLMs in Software Engineering Education. It is intended to allow other researchers to reproduce the results, inspect the intermediate data, and extend the analysis.

Contents:





mine_llm_repos.py: Python script used to query the GitHub API, retrieve repositories with relevant keywords, and extract README files and issue discussions. 




analyze_llm_repos.py: Python script for post-processing. It reads the raw dataset, computes summary statistics for motivators and demotivators, and produces CSV summaries.




final-repos_summary.csv: Filtered list of repositories included in the final analysis, with metadata such as repository name, URL, language, stars, forks, and counts of motivator/demotivator hits.




chi_square_results_400_repos.xlsx: Statistical test results, including Chi-square values and p-values for each motivator and demotivator sub-theme across the 400 repositories.



Usage:





Run mine_llm_repos.py to collect repositories and generate the raw CSV.




Run analyze_llm_repos.py to compute theme-level summaries and produce figures.




The .csv and .xlsx files can be opened in Excel or any statistical software for further inspection.



This package ensures transparency of the analysis pipeline and provides a starting point for future work. Researchers may adapt the keyword sets in mine_llm_repos.py to explore other educational contexts or extend the mining to additional platforms such as GitLab, Bitbucket, or SourceForge.
DOI: 10.15468/dl.k26ke6
title: Occurrence Download

description: A dataset containing 1470 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Vipera berus, Vipera aspis, Hierophis viridiflavus, Lacerta bilineata, Zootoca vivipara, Lacerta agilis, Coronella girondica, Zamenis scalaris, Anguis fragilis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "Geometry POLYGON((4.331 43.435, 4.887 43.344, 4.877 43.364,\n 4.858 43.376, 4.851 43.385, 4.843 43.398, 4.825 43.411, 4.793 43.434,\n 4.766 43.46, 4.733 43.488, 4.697 43.518, 4.654 43.547, 4.606 43.578,\n 4.551 43.608, 4.498 43.633, 4.441 43.655, 4.382 43.67, 4.331 43.677,\n 4.331 43.435))",
 "CoordinateUncertaintyInMeters is greater than or equal to 0",
 "CoordinateUncertaintyInMeters is less than 5000"
 ]
}

 The dataset includes 1470 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006356-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zjvd7g
title: Occurrence Download

description: A dataset containing 92 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stropharia rugosoannulata",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 92 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006354-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f7u75a
title: Occurrence Download

description: A dataset containing 54958 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'NI'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2001 AND occurrence."year" &lt;= 2025)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 54958 records were derived from 219 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006325-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2vf8y8
title: Occurrence Download

description: A dataset containing 91253 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rana arvalis"
 ]
}

 The dataset includes 91253 records from 226 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006287-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.