cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/zhdxcvcp6k.2
title: Comparative Analysis of Influent and Effluent Measurements of Per- and Polyfluoroalkyl Substances (PFAS) at Wastewater Treatment Facilities Datasets

description: This dataset includes raw data for the publication "Comparative Analysis of Influent and Effluent Measurements of Per- and Polyfluoroalkyl Substances (PFAS) at Wastewater Treatment Facilities".
DOI: 10.17632/528bhp8sgc
title: A Leishmania virulence factor harnesses an allosteric kinase switch to regulate its ubiquitin ligase activity. Fokkens et al.

description: https://doi.org/10.1016/j.molcel.2025.09.002
DOI: 10.17632/528bhp8sgc.1
title: A Leishmania virulence factor harnesses an allosteric kinase switch to regulate its ubiquitin ligase activity. Fokkens et al.

description: https://doi.org/10.1016/j.molcel.2025.09.002
DOI: 10.15468/dl.77tmcx
title: Occurrence Download

description: A dataset containing 12724 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Poland",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Betula pubescens"
 ]
}

 The dataset includes 12724 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016165-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tgx5b2czdf
title: Dataset escuelas rurales en salud

description: Esta base de datos contiene los materiales empleados por el Consejo General para realizar la difusión del proyecto entre los farmacéuticos rurales de Castilla y León con el fin de animarlos a participar en el proyecto (póster, cards para redes sociales, vídeo).
También incluye los materiales utilizados por los farmacéuticos para la preparación e impartición de las cinco sesiones formativas (guiones, presentaciones de PowerPoint, infografías distribuidas entre los asistentes, etc.)
Se pone a disposición, además, las encuestas de satisfacción que tenían que completar los asistentes a las sesiones formativas, así como un Excel donde se recogen el total de respuestas a las preguntas de dichas encuestas.
Por último, tienen a su disposición el informe final y el vídeo-resumen del proyecto.
DOI: 10.17026/ar/byapwy
title: PAN-00172893 - devotional pendant Modern period

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00172893&lt;/p&gt;
description: Modified: 2025-04-28T15:12:56+02:00
DOI: 10.17632/tgx5b2czdf.1
title: Dataset escuelas rurales en salud

description: Esta base de datos contiene los materiales empleados por el Consejo General para realizar la difusión del proyecto entre los farmacéuticos rurales de Castilla y León con el fin de animarlos a participar en el proyecto (póster, cards para redes sociales, vídeo).
También incluye los materiales utilizados por los farmacéuticos para la preparación e impartición de las cinco sesiones formativas (guiones, presentaciones de PowerPoint, infografías distribuidas entre los asistentes, etc.)
Se pone a disposición, además, las encuestas de satisfacción que tenían que completar los asistentes a las sesiones formativas, así como un Excel donde se recogen el total de respuestas a las preguntas de dichas encuestas.
Por último, tienen a su disposición el informe final y el vídeo-resumen del proyecto.
DOI: 10.5281/zenodo.14107593
title: Moisture and density of sediment samples from the Terrebonne Basin, Walker Ridge Block 313, Site H, Expedition UT-GOM2-2

description: These data include wet (bulk) density, water content, water saturation, porosity, and grain density measurements of discrete (sample code MDX) and subsampled whole rounds (sub sample code MAD, whole round sample code MDW) following ASTM International standards. See Expedition UT-GOM2-2 Methods: Index Properties (Flemings et al., 2025a, https://doi.org/10.5281/zenodo.13971228).

The attached readme file, previewed below, describes all the data files in this dataset and their contents. The readme file also contains links to where the data is presented in the expedition proceedings.

Additional data you might expect to find in this dataset can instead be found at the following locations:

The sample depths can be found in the curation dataset (http://doi.org/10.5281/zenodo.14926309). Download the file UT-GOM2-2 CORE LOG depths – sortable.xlsx. For discrete samples, see tab “Sample-measurement log” sample code “MDX” for moisture and density plus minerology and X-ray fluorescence. Minerology and X-ray fluorescence will be measured after the expedition. For subsampled whole rounds, see tab “Section-piece log” sample code “MDW” for moisture and density whole rounds.

Particle size distribution (grain size) measurements can be found in the particle size distribution dataset (http://doi.org/10.5281/zenodo.14109861). Processed classification data can be found in Appendix H, of Expedition UT-GOM2-2 Site H (Flemings et al., 2025b, https://doi.org/10.5281/zenodo.13971276).
DOI: 10.15468/dl.kn4j5t
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parthenocissus vitacea",
 "Country is Ukraine",
 "HasCoordinate is true"
 ]
}

 The dataset includes 21 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016166-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/zfau78
title: PAN-00173113 - devotional pendant Modern period

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00173113&lt;/p&gt;
description: Modified: 2025-04-26T11:33:25+02:00
DOI: 10.17632/wtnjj6smjd
title: Data from release kinetics of curcumin and piperine from starch nanoparticles into simulated gastric media

description: Data from the release kinetics of curcumin and piperine in native and acetylated starch nanoparticles from cassava starch.
DOI: 10.17632/wtnjj6smjd.2
title: Data from release kinetics of curcumin and piperine from starch nanoparticles into simulated gastric media

description: Data from the release kinetics of curcumin and piperine in native and acetylated starch nanoparticles from cassava starch.
DOI: 10.17026/ar/hvx86h
title: PAN-00169541 - coarse ware pot type Niederb. 89

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00169541&lt;/p&gt;
description: Modified: 2025-07-10T17:56:53+02:00
DOI: 10.5281/zenodo.14109861
title: Particle size distribution (grain size) of discrete sediment samples from the Terrebonne Basin, Walker Ridge Block 313, Site H, Expedition UT-GOM2-2

description: This dataset contains particle size distribution assessed using two methods. Discrete samples from split cores (sample code GSL) were measured using a Malvern Mastersizer 3000 particle size analyzer with a Hydro LV dispersion unit. Larger samples (sample code MAD blended material) subsampled from Moisture and Density whole rounds (sample code MDW) were measured using a specific gravity hydrometer in conformance with ASTM D7928. A geoscientific and a geotechnical classification were used to name the sediments. Expedition UT-GOM2-2 Methods: Lithostratigraphy /Grain Size and Index Properties / Particle Size Distribution (Flemings et al., 2025a, https://doi.org/10.5281/zenodo.13971228).

The attached readme file, previewed below, describes all the data files in this dataset and their contents. The readme file also contains links to where the data is presented in the expedition proceedings.

Additional data you might expect to find in this dataset can instead be found at the following locations:

The sample depths can be found in the curation dataset (http://doi.org/10.5281/zenodo.14926309). Download the file UT-GOM2-2 CORE LOG depths – sortable.xlsx. For discrete samples, see tab “Sample-measurement log”, sample code “GSL” for grain size laser. For subsampled whole rounds, see tab “Section-piece log”, sample code “MDW” for moisture and density whole rounds. For hydrometer subsamples of MDW, see the moisture and density dataset (http://doi.org/10.5281/zenodo.14107593), sub-sampling log, “MAD blended material”.

Processed classification data can be found in Appendix H of Expedition UT-GOM2-2 Site H (Flemings et al., 2025b, https://doi.org/10.5281/zenodo.13971276).

Moisture and density measurements can also be found in the moisture and density dataset (http://doi.org/10.5281/zenodo.14107593).
DOI: 10.6084/m9.figshare.30200542.v1
title: Ethical considerations in software development

description: We’re conducting this survey to explore how software developers deal with ethical issues about the software they develop. Ethical issues in software, such as privacy, accessibility flaws, manipulative design, or algorithmic biases, affect individuals and/or society as a whole in a harmful way.
DOI: 10.17632/7t4hg4h8fc
title: Nitrides reinforced AlSi10Mg matrix composites

description: Effect of nitrogen concentration on microstructure and mechanical properties of AlSi10Mg matrix composites fabricated by laser powder bed fusion
DOI: 10.17632/7t4hg4h8fc.1
title: Nitrides reinforced AlSi10Mg matrix composites

description: Effect of nitrogen concentration on microstructure and mechanical properties of AlSi10Mg matrix composites fabricated by laser powder bed fusion
DOI: 10.17632/g2sdzmssgh
title: ML Classifiers, Human-Tagged Datasets, and Validation Code for Structured LLM-Generated Event Messaging: BERT, Keras, XGBoost, and Ensemble Methods

description: This dataset and code package supports the reproducible evaluation of Structured Large Language Model (LLM)-generated event messaging using multiple machine learning classifiers, including BERT (via TensorFlow/Keras), XGBoost, and ensemble methods.

This supports published article: 
C. J. Lynch et al., "AI-Generated Messaging for Life Events Using Structured Prompts: A Comparative Study of GPT With Human Experts and Machine Learning," IEEE Access, vol. 13, 2025, https://ieeexplore.ieee.org/abstract/document/11129070. Abstract: Structured Narrative Prompting was applied to generate life-event messages from LLMs, followed by human annotation, and machine learning validation. This release provides complete transparency for reproducing reported metrics and facilitates further benchmarking in multilingual or domain-specific contexts.

Including:

* Tagged datasets (.csv): human-tagged gold labels for evaluation
* Untagged datasets (.csv): raw data with Prompt matched to corresponding LLM-generated narrative
 - Suitable for inference, semi-automatic labeling, or transfer learning
* Python and R code for preprocessing, model training, evaluation, and visualization
* Configuration files and environment specifications to enable end-to-end reproducibility

Value of the Data:
* Enables direct replication of published results across BERT, Keras-based models, XGBoost, and ensemble classifiers.
* Provides clean, human-tagged datasets suitable for training, evaluation, and bias analysis.
* Offers untagged datasets for new annotation or domain adaptation.
* Contains full preprocessing, training, and visualization code in Python and R for flexibility across workflows.
* Facilitates extension into other domains (e.g., multilingual LLM messaging validation).

Data Description:
* /data/tagged/*.csv – Human-labeled datasets with schema defined in data_dictionary.csv.
* /data/untagged/*.csv – Clean datasets without labels for inference or annotation.
* /code/python/ – Python scripts for preprocessing, model training (BERT, Keras DNN, XGBoost), ensembling, evaluation metrics, and plotting.
* /code/r/ – R scripts for exploratory data analysis, statistical testing, and replication of key figures/tables.

File Formats:
* Data: CSV (UTF-8, RFC 4180)
* Code: .py, .R, .Rproj

Ethics & Licensing
* All data are de-identified and contain no PII.
* Released under CC BY 4.0 (data) and MIT License (code).

Limitations
* Labels reflect annotator interpretations and may encode bias.
* Models trained on English text; generalization to other languages requires adaptation.

Funding Note
* Funding sources provided time in support of human taggers annotating the data sets.


Initial PrePrint available at: 
[Lynch, Christopher, Erik Jensen, Ross Gore, et al. "AI-Generated Messaging for Life Events Using Structured Prompts: A Comparative Study of GPT With Human Experts and Machine Learning." TechRxiv (2025), DOI: https://doi.org/10.36227/techrxiv.174123588.85605769/v1].
DOI: 10.17632/g2sdzmssgh.2
title: ML Classifiers, Human-Tagged Datasets, and Validation Code for Structured LLM-Generated Event Messaging: BERT, Keras, XGBoost, and Ensemble Methods

description: This dataset and code package supports the reproducible evaluation of Structured Large Language Model (LLM)-generated event messaging using multiple machine learning classifiers, including BERT (via TensorFlow/Keras), XGBoost, and ensemble methods.

This supports published article: 
C. J. Lynch et al., "AI-Generated Messaging for Life Events Using Structured Prompts: A Comparative Study of GPT With Human Experts and Machine Learning," IEEE Access, vol. 13, 2025, https://ieeexplore.ieee.org/abstract/document/11129070. Abstract: Structured Narrative Prompting was applied to generate life-event messages from LLMs, followed by human annotation, and machine learning validation. This release provides complete transparency for reproducing reported metrics and facilitates further benchmarking in multilingual or domain-specific contexts.

Including:

* Tagged datasets (.csv): human-tagged gold labels for evaluation
* Untagged datasets (.csv): raw data with Prompt matched to corresponding LLM-generated narrative
 - Suitable for inference, semi-automatic labeling, or transfer learning
* Python and R code for preprocessing, model training, evaluation, and visualization
* Configuration files and environment specifications to enable end-to-end reproducibility

Value of the Data:
* Enables direct replication of published results across BERT, Keras-based models, XGBoost, and ensemble classifiers.
* Provides clean, human-tagged datasets suitable for training, evaluation, and bias analysis.
* Offers untagged datasets for new annotation or domain adaptation.
* Contains full preprocessing, training, and visualization code in Python and R for flexibility across workflows.
* Facilitates extension into other domains (e.g., multilingual LLM messaging validation).

Data Description:
* /data/tagged/*.csv – Human-labeled datasets with schema defined in data_dictionary.csv.
* /data/untagged/*.csv – Clean datasets without labels for inference or annotation.
* /code/python/ – Python scripts for preprocessing, model training (BERT, Keras DNN, XGBoost), ensembling, evaluation metrics, and plotting.
* /code/r/ – R scripts for exploratory data analysis, statistical testing, and replication of key figures/tables.

File Formats:
* Data: CSV (UTF-8, RFC 4180)
* Code: .py, .R, .Rproj

Ethics & Licensing
* All data are de-identified and contain no PII.
* Released under CC BY 4.0 (data) and MIT License (code).

Limitations
* Labels reflect annotator interpretations and may encode bias.
* Models trained on English text; generalization to other languages requires adaptation.

Funding Note
* Funding sources provided time in support of human taggers annotating the data sets.


Initial PrePrint available at: 
[Lynch, Christopher, Erik Jensen, Ross Gore, et al. "AI-Generated Messaging for Life Events Using Structured Prompts: A Comparative Study of GPT With Human Experts and Machine Learning." TechRxiv (2025), DOI: https://doi.org/10.36227/techrxiv.174123588.85605769/v1].
DOI: 10.5281/zenodo.17148157
title: pamflow's beginner tutorial dataset

description: This repository contains a minimal dataset designed to run tests on the passive acoustic data pipeline pamflow.

The repository includes:





Field Deployments Sheet: An Excel file that contains all the metadata associated with the deployments, including equipment, dates, spatial coordinates, and other relevant details.




Audio Data Folder: A folder with all audio data, structured with subfolders named by the deployment ID.


Target species: a csv file with a single column (scientificName) listing one species per row.


The Foundation for Conservation and Sustainable Development (FCDS) has been promoting sustainable forest management practices through community forestry to conserve local forests, reduce deforestation near the Serranía de Chiribiquete National Natural Park (PNN), and improve the quality of life of local communities. To evaluate the effectiveness of these actions, a community monitoring project was carried out in the municipalities of San Jose del Guaviare, El Retorno, and Calamar, located in Guaviare, Colombia.

The field audio recordings and monitoring design were co-created with the Humboldt Institute and the local community. If you use this dataset for testing or for developing other open-source tools, please be sure to acknowledge all participating community members, researchers, and institutions involved. The data cannot be used for machine learning purposes or for training AI models without explicit consent from the data creators.

Moises Romero Franco, Anibal De Jesus Martinez Peña,Daniel Chitiva Diaz, Nubia Quintero Medina, Edgar Gustavo Rodriguez Garzon, Granja Escuela Capricho Granja Escuela Capricho, Maria De Los Angeles Gaitan Muños, Flor Marleni Gaitan Muños, Jefferson Cocinero Garcia, Jose Eduardo Leon Triana, Cesar Augusto Urrego Gonzalez, Anderson Urrea Tacuma, Luz Idalba Restrepo Suarez, Maria De Los Angeles Gaitan Muñoz, Edwin Alcides Reyes Lopez, Juan Sebastián Ulloa, Juan Sebastián Cañas, Marcela González, Raúl Gómez

This data is provided under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
DOI: 10.17632/yf9kpds2ny
title: After-meal blood glucose level prediction data for type-2 prediabetic and diabetic patients

description: This dataset includes data from five prediabetic and diabetic patients. Among them, four are diabetic, and one is pre-diabetic patients. There are 902 samples. For each data sample, there are 23 tabular features: six profile features, nine medication features, six meal features, one exercise feature (duration), one BG reading before taking a meal, and one target variable: the BG reading taken two hours after meal. It contains the meal image names, if the corresponding images are available for different investigation.
DOI: 10.17632/yf9kpds2ny.1
title: After-meal blood glucose level prediction data for type-2 prediabetic and diabetic patients

description: This dataset includes data from five prediabetic and diabetic patients. Among them, four are diabetic, and one is pre-diabetic patients. There are 902 samples. For each data sample, there are 23 tabular features: six profile features, nine medication features, six meal features, one exercise feature (duration), one BG reading before taking a meal, and one target variable: the BG reading taken two hours after meal. It contains the meal image names, if the corresponding images are available for different investigation.
DOI: 10.5281/zenodo.14107263
title: Strength measurement of bulk sediment from the Terrebonne Basin, Walker Ridge Block 313, Site H, Expedition UT-GOM2-2

description: This dataset contains strength measurements of bulk sediment on the ends of whole round core samples including hand vane (sample code VANE), pocket penetrometer (sample code PEN), and fall cone and laboratory table vane measurements (sample code TBVN). Onboard the Helix Q4000, a handheld vane shear and a pocket penetrometer were used to estimate the undrained shear strength. Measurements were made rapidly, typically within 1 hour of cutting core sections. In Salt Lake City, measurements of strength were performed with a fall cone penetrometer and an automated table vane shear device. Both devices were mounted on top of the table frame. Core sections were positioned vertically on an adjustable platform and secured with a vice grip. See Expedition UT-GOM2-2 Methods: Undrained Shear Strength (Flemings et al., 2025a, https://doi.org/10.5281/zenodo.13971228).

The attached readme file, previewed below, describes all the data files in this dataset and their contents. The readme file also contains links to where the data is presented in the expedition proceedings.

Additional data you might expect to find in this dataset can instead be found at the following locations:

Most raw hand vane and pocket penetrometer measurements can be found in scans of the handwritten core curation sheets from the Helix Q4000 in the conventional coring dataset (https://doi.org/10.5281/zenodo.14518080).  Three measurements were made in Salt Lake City and the raw data file is below, PocketPen_HandVane_SLC_handwritten_log.pdf.

Measurement depths can be found in the curation dataset (http://doi.org/10.5281/zenodo.14926309). Download the file UT-GOM2-2 CORE LOG depths – sortable.xlsx. See tab “Sample-measurement log”, measurement codes “VANE” for hand vane, “PEN” for pocket penetrometer, and “TBVN” for table vane and fall cone measurement depths. Some voids were consolidated by moving sediment within the core liner in College Station and sediment depths in section (and resulting mbsf) were changed in the electronic log (See Expedition UT-GOM2-2 Methods: Consolidation of Voids (Flemings et al., 2025a)). Therefore, the depth in section of the VANE and PEN measurements in the electronic log may differ from the depth in the handwritten logs from the Helix Q4000 found in the conventional coring dataset (https://doi.org/10.5281/zenodo.14518080).

Processed data can also be found in Appendix G, of Expedition UT-GOM2-2 Site H (Flemings et al., 2025b, https://doi.org/10.5281/zenodo.13971276).
DOI: 10.17632/8kwp66c9tf
title: Research

description: Track A — No-code clinical workflow
A1) Report Template (paste into Word for each case)
Header: Tooth #: ___ | Visit: Pre-op / Post-op / Follow-up | Date: ___
Rubric items (check/fill):
Lesion size (mm): ___ | Lamina dura: intact / disrupted | PDL: normal / widened
Obturation length: flush / 0–1 mm short / &gt;1 mm short / overextended
Obturation density: adequate / inadequate | Iatrogenic events: none / specify
Notes (optional): _______________________________________
Narrative (edit this block for each case):
“Periapical status is [describe lesion size + PDL + lamina dura]. Root-canal obturation appears [length] with [density] compaction. [No/Any] iatrogenic findings are noted. Compared with prior, [state change or “no significant interval change”]. Clinical symptoms are [if provided in notes]. Overall, findings are [stable/improving/worsening] relative to the recorded parameters. Follow-up is recommended [timeframe if appropriate].”
DOI: 10.17632/8kwp66c9tf.1
title: Research

description: Track A — No-code clinical workflow
A1) Report Template (paste into Word for each case)
Header: Tooth #: ___ | Visit: Pre-op / Post-op / Follow-up | Date: ___
Rubric items (check/fill):
Lesion size (mm): ___ | Lamina dura: intact / disrupted | PDL: normal / widened
Obturation length: flush / 0–1 mm short / &gt;1 mm short / overextended
Obturation density: adequate / inadequate | Iatrogenic events: none / specify
Notes (optional): _______________________________________
Narrative (edit this block for each case):
“Periapical status is [describe lesion size + PDL + lamina dura]. Root-canal obturation appears [length] with [density] compaction. [No/Any] iatrogenic findings are noted. Compared with prior, [state change or “no significant interval change”]. Clinical symptoms are [if provided in notes]. Overall, findings are [stable/improving/worsening] relative to the recorded parameters. Follow-up is recommended [timeframe if appropriate].”
DOI: 10.17026/ar/wimpxg
title: PAN-00164757 - Late Medieval/modern inscription ring

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00164757&lt;/p&gt;
description: Modified: 2025-05-21T14:12:13+02:00