cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15131439
title: Inversion recovery NMR data for the paper "Split inversion-recovery experiment by sample shifting" by S. Jopa, D. Gołowicz and K. Kazimierczuk

description: The FID signals from 5 conventional inversion-recovery experiments. The measurements were performed on a 43 MHz Magritek Spinsolve Carbon spectrometer using a formic acid sample (53 mM in D2O).

Folders: 

Conventional_InvRec - five series of FIDs (subfolders 20Dec2024_1...20Dec2024_5) from conventional InvRec experiments, each for tau values 214.3, 355.0, 588.0, 974.2, 1679.9, 2727.8, 4229.3, 6092.2, 9957.5, 12620.5, 20963.5, 26010.2, 30792.4, 38109.2, 50899.0, and 65478.8 ms

SIR_SWAPE - five series of FIDs (subfolders 1,2...5). In each of 5 folders there are four FIDs for short tau values (tau= 214.3, 355.0, 588.0, 974.2 ms, file storage_conv.csv) and 12 for longer tau values ("split" mode, tau=1679.9, 2727.8, 4229.3, 6092.2, 9957.5, 12620.5, 20963.5, 26010.2, 30792.4, 38109.2, 50899.0, and 65478.8 ms, file storage_split.csv)
DOI: 10.25446/oxford.28687268
title: Textbook preparation - SMP

description: Sessions for discussing and testing out an early version of the SMP (middle school) textbook
DOI: 10.25446/oxford.28687268.v1
title: Textbook preparation - SMP

description: Sessions for discussing and testing out an early version of the SMP (middle school) textbook
DOI: 10.6084/m9.figshare.28723529
title: Differences in Soil Carbon Fractions and Microbial Communities between ANR and Plantation Forests

description: This study assessed SOC fractions and microbial communities across four forest management types in southeastern China: secondary forest (SF), assisted natural regeneration (ANR), and two monoculture plantations (Castanopsis carlesii and Cunninghamia lanceolata). Labile and recalcitrant organic carbon fractions, SOC and total nitrogen (TN) were measured. The microbial community structure of the soil was determined using PLFAs.
DOI: 10.71795/q6qg-1p71
title: Survival Condition (Wales) thesaurus
DOI: 10.25345/c5251fz05
title: MassIVE MSV000097506 - Protein contaminants in honey 30-sample dataset A
title: Data set of 90 nanoLC-MS/MS runs on 30 samples. 29 honey samples and 1 royal jelly sample were analyzed.
DOI: 10.60520/ieda/113733
title: Major Elements EMP Analysis Method in Olivine, Clinopyroxene and Spinel

description: The study of the mineral chemistry (olivine, pyroxene and spinel) was conducted by using a JEOL JXA 8230 Electron Probe Microanalyser at the Scientific and Technological Centers of the University of Barcelona. A spot size of 1-3 µm, acceleration voltage of 15 kV and intensity of 15-20 nA were used. Reference minerals were used for equipment calibration (Si: diopside; Al: kyanite; Ti: rutile; Cr: chromite; Fe: Fe2O3; Mg: periclase; Ni: NiO; Mn: rhodonite; Ca: wollastonite; Na: albite; K: orthoclase; Zn: sphalerite; V: V-metallic).
DOI: 10.15468/dl.jdzvk5
title: Occurrence Download

description: A dataset containing 25258 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "DatasetKey is Museum of Comparative Zoology, Harvard University",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Hesperiidae"
 ]
}

 The dataset includes 25258 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002129-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcdc2e
title: Occurrence Download

description: A dataset containing 18636 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chlorostilbon gibsoni (Fraser, 1840)"
 ]
}

 The dataset includes 18636 records from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002128-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6tgypr
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bursera penicillata (Sessé & Moc.) Engl.",
 "Year 2010-2020"
 ]
}

 The dataset includes 55 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002132-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c2gzuh
title: Occurrence Download

description: A dataset containing 25258 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "DatasetKey is Museum of Comparative Zoology, Harvard University",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Hesperiidae"
 ]
}

 The dataset includes 25258 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002130-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/4210008601-eng
title: Centre-based child care providers by licensing status and year of licensing, April 2022, Canada

description: Centre-based child care providers by whether provider is licensed by child care authorities from the province or municipality and the year licensed, Canada.
DOI: 10.15468/dl.zjvgff
title: Occurrence Download

description: A dataset containing 5901 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "OccurrenceStatus is Present",
 "TaxonKey is Tetrax tetrax (Linnaeus, 1758)",
 "Year 1975-2025"
 ]
}

 The dataset includes 5901 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002134-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28303521
title: Partially Observable Online Nonparametric Monitoring of Spatiotemporally Correlated Data Streams

description: Internet of Things sensor networks collect real-time data, characterized by spatial and temporal correlations, for process monitoring, significantly altering daily life and enabling automation. Considering sensor resource constraints due to limited budget of sensor operation and the complexity of capturing spatiotemporal correlation structure among data streams, sensor networks face challenges in monitoring such data streams via a parametric model and distribution, particularly when only subsets of data are available at each acquisition time. This article develops a nonparametric scheme for monitoring such complex spatiotemporally correlated and partially observed data streams. It employs decorrelated rank-based statistics combined with data augmentation over multiple subdata streams, which are derived from the original high-dimensional data using ensemble random projections for dimensionality reduction. Monitoring and sampling decisions are informed by aggregated local statistics of all subdata streams. This method is distribution-free that eschews parametric spatiotemporal models and distributions for real-time monitoring, enhancing its practical applicability to various complex spatiotemporal engineering cases that cannot be accurately characterized by parametric models. The efficacy of the decorrelated rank-based statistics and sampling strategy is substantiated through theoretical analysis. Numerical experiments and case studies focusing on thermal data monitoring in grain storage and solar flare detection affirm robust performance of the proposed method across various scenarios.
DOI: 10.6084/m9.figshare.28303521.v1
title: Partially Observable Online Nonparametric Monitoring of Spatiotemporally Correlated Data Streams

description: Internet of Things sensor networks collect real-time data, characterized by spatial and temporal correlations, for process monitoring, significantly altering daily life and enabling automation. Considering sensor resource constraints due to limited budget of sensor operation and the complexity of capturing spatiotemporal correlation structure among data streams, sensor networks face challenges in monitoring such data streams via a parametric model and distribution, particularly when only subsets of data are available at each acquisition time. This article develops a nonparametric scheme for monitoring such complex spatiotemporally correlated and partially observed data streams. It employs decorrelated rank-based statistics combined with data augmentation over multiple subdata streams, which are derived from the original high-dimensional data using ensemble random projections for dimensionality reduction. Monitoring and sampling decisions are informed by aggregated local statistics of all subdata streams. This method is distribution-free that eschews parametric spatiotemporal models and distributions for real-time monitoring, enhancing its practical applicability to various complex spatiotemporal engineering cases that cannot be accurately characterized by parametric models. The efficacy of the decorrelated rank-based statistics and sampling strategy is substantiated through theoretical analysis. Numerical experiments and case studies focusing on thermal data monitoring in grain storage and solar flare detection affirm robust performance of the proposed method across various scenarios.
DOI: 10.6084/m9.figshare.28723669
title: Regression Recalibration by Learning PIT Map Values

description: In an era dominated by large-scale machine learning models, poor calibration severely limits the trustworthiness of the results. As we increasingly rely on complex systems, recalibration becomes essential, where the objective is to find a mapping that improves the model’s probabilistic predictions. Motivated by the well-known quantile recalibration approach, we explore a broad class of recalibration functions based on learning transformations on the probability integral transform values that includes quantile recalibration as a special case. We derive solutions for the optimal mapping under this class of functions, and we propose a novel recalibration method that outperforms quantile recalibration in both calibration and sharpness in our empirical study. Additionally, we demonstrate the utility of our method in a case study on predicting global stratospheric temperatures following Mt. Pinatubo’s eruption using a convolutional neural network, where we show that our method can be used to adjust the model’s predictions for the post-eruption climate.
DOI: 10.6084/m9.figshare.28723669.v1
title: Regression Recalibration by Learning PIT Map Values

description: In an era dominated by large-scale machine learning models, poor calibration severely limits the trustworthiness of the results. As we increasingly rely on complex systems, recalibration becomes essential, where the objective is to find a mapping that improves the model’s probabilistic predictions. Motivated by the well-known quantile recalibration approach, we explore a broad class of recalibration functions based on learning transformations on the probability integral transform values that includes quantile recalibration as a special case. We derive solutions for the optimal mapping under this class of functions, and we propose a novel recalibration method that outperforms quantile recalibration in both calibration and sharpness in our empirical study. Additionally, we demonstrate the utility of our method in a case study on predicting global stratospheric temperatures following Mt. Pinatubo’s eruption using a convolutional neural network, where we show that our method can be used to adjust the model’s predictions for the post-eruption climate.
DOI: 10.5281/zenodo.14888672
title: Supplementary material for "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks"

description: Overview

This package contains the following files and folders:



analysis.ipynb - Notebook containing the data analysis presented in the paper, as well as some additional inquiries.

data - Data generated from the experiences described in the paper, the extracted Python code, and the results for running said code.

full_non_agg.xlsx - Non-aggregated results extracted from the generated data.

LICENSE_CODE.txt - The license for the included code.

LICENSE_DATA.txt - The license for the included data.

README.md - This file.

requirements.txt- Python dependencies required for running the included notebook.


Reproducibility of data analysis

The data analysis presented in the research paper "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks", published in Electronics, and authored by Daniel Fernandes, João P. Matos-Carvalho, Carlos M. Fernandes, and Nuno Fachada, can be reproduced with the Jupyter notebook included in this package.

Licenses



The code in the Jupyter notebook is made available under the MIT license (see LICENSE_CODE.txt).

The non-code materials are made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).
DOI: 10.5281/zenodo.14888673
title: Supplementary material for "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks"

description: Overview

This package contains the following files and folders:



data - Data generated from the experiences described in the paper, the extracted Python code, and the results for running said code.

LICENSE_CODE.txt - The license for the included code.

LICENSE_DATA.txt - The license for the included data.

README.md - This file.

requirements.txt- Specifies the Python dependencies required for running the included notebook.

analysis.ipynb - Notebook containing the data analysis presented in the paper, as well as some additional inquiries.


Reproducibility of data analysis

The data analysis presented in the research paper "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks", published in TBD, and authored by Daniel Fernandes, João P. Matos-Carvalho, Carlos M. Fernandes, and Nuno Fachada, can be reproduced with the Jupyter notebook included in this package.

Licenses



The code in the Jupyter notebook is made available under the MIT license (see LICENSE_CODE.txt).

The non-code materials are made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).
DOI: 10.5281/zenodo.15085946
title: Supplementary material for "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks"

description: Overview

This package contains the following files and folders:



data - Data generated from the experiences described in the paper, the extracted Python code, and the results for running said code.

LICENSE_CODE.txt - The license for the included code.

LICENSE_DATA.txt - The license for the included data.

README.md - This file.

requirements.txt- Specifies the Python dependencies required for running the included notebook.

analysis.ipynb - Notebook containing the data analysis presented in the paper, as well as some additional inquiries.


Reproducibility of data analysis

The data analysis presented in the research paper "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks", published in TBD, and authored by Daniel Fernandes, João P. Matos-Carvalho, Carlos M. Fernandes, and Nuno Fachada, can be reproduced with the Jupyter notebook included in this package.

Licenses



The code in the Jupyter notebook is made available under the MIT license (see LICENSE_CODE.txt).

The non-code materials are made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).
DOI: 10.5281/zenodo.15131561
title: Supplementary material for "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks"

description: Overview

This package contains the following files and folders:



analysis.ipynb - Notebook containing the data analysis presented in the paper, as well as some additional inquiries.

data - Data generated from the experiences described in the paper, the extracted Python code, and the results for running said code.

full_non_agg.xlsx - Non-aggregated results extracted from the generated data.

LICENSE_CODE.txt - The license for the included code.

LICENSE_DATA.txt - The license for the included data.

README.md - This file.

requirements.txt- Python dependencies required for running the included notebook.


Reproducibility of data analysis

The data analysis presented in the research paper "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks", published in Electronics, and authored by Daniel Fernandes, João P. Matos-Carvalho, Carlos M. Fernandes, and Nuno Fachada, can be reproduced with the Jupyter notebook included in this package.

Licenses



The code in the Jupyter notebook is made available under the MIT license (see LICENSE_CODE.txt).

The non-code materials are made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).
DOI: 10.15468/dl.qaew3c
title: Occurrence Download

description: A dataset containing 8 records in GBIF matching the query:

SELECT 
countrycode,
COUNT(*) AS total_occurrences,
COUNT(DISTINCT publisher) AS total_publishers,
SUM(CASE WHEN kingdom = 'Animalia' THEN 1 ELSE 0 END) AS Animalia_occurrences,
SUM(CASE WHEN kingdom = 'Plantae' THEN 1 ELSE 0 END) AS Plantae_occurrences,
SUM(CASE WHEN kingdom = 'Fungi' THEN 1 ELSE 0 END) AS Fungi_occurrences,
SUM(CASE WHEN kingdom = 'Protista' THEN 1 ELSE 0 END) AS Protista_occurrences,
SUM(CASE WHEN kingdom = 'Bacteria' THEN 1 ELSE 0 END) AS Bacteria_occurrences,
SUM(CASE WHEN kingdom = 'Archaea' THEN 1 ELSE 0 END) AS Archaea_occurrences,
SUM(CASE WHEN kingdom = 'Chromista' THEN 1 ELSE 0 END) AS Chromista_occurrences,
SUM(CASE WHEN kingdom = 'incertae sedis' THEN 1 ELSE 0 END) AS Incertae_sedis_occurrences
FROM occurrence
WHERE countrycode IN ('IR', 'IQ', 'KW', 'SA', 'BH', 'QA', 'AE', 'OM')
GROUP BY countrycode
ORDER BY countrycode;


 The dataset's 8 records were derived from 1952 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002123-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.czcsyl
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is South Africa",
 "Year 1998-2018",
 "TaxonKey is Coelacanthiformes"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029239-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k8rvub
title: Occurrence Download

description: A dataset containing 2233 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sporobolus elongatus R.Br."
 ]
}

 The dataset includes 2233 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0161935-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3d3nr
title: Occurrence Download

description: A dataset containing 62679 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Eleocharis parvula (Roem. & Schult.) Link ex Bluff, Nees & Schauer, Eleocharis quadrangulata (Michx.) Roem. & Schult., Eleocharis quinqueflora (Hartmann) O.Schwarz, Eleocharis exigua (Kunth) Roem. & Schult., Eleocharis retroflexa (Poir.) Urb., Eleocharis reverchonii Svenson, Eleocharis robbinsii Oakes, Eleocharis rostellata (Torr.) Torr., Eleocharis schaffneri Boeckeler, Eleocharis suksdorfiana Beauverd)"
 ]
}

 The dataset includes 62679 records from 729 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007519-240202131308920/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kg43k7
title: Occurrence Download

description: A dataset containing 2664 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aquiloeurycea cephalica (Cope, 1865)"
 ]
}

 The dataset includes 2664 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007604-240202131308920/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h86pwz
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galium mite Boiss. & Hohen."
 ]
}

 The dataset includes 88 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009619-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hxky2a
title: Occurrence Download

description: A dataset containing 1267 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Betula glandulosa Michx.",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Continent is North America"
 ]
}

 The dataset includes 1267 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0161910-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aqz4eh
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Germany",
 "Year 1998-2018",
 "TaxonKey is Coelacanthiformes"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029240-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q5mfdq
title: Occurrence Download

description: A dataset containing 520 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lithospermum canescens (Michx.) Lehm.",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Continent is North America"
 ]
}

 The dataset includes 520 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0161949-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q6w9jq
title: Occurrence Download

description: A dataset listing the 452 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Guyana",
 "DatasetKey is University of Michigan Museum of Zoology, Division of Fishes"
 ]
}

 The dataset's 452 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007524-240202131308920/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cptyfz
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gladiolus mensensis (Schweinf.) Goldblatt"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011834-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.