cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive23867.20241212.9.2
title: Thalassotalea piscium Zhang et al. 2014

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.23728/b2share.3d6c15da2e5349629a2555d244ff6ae1
title: LTER Zöbelboden, Austria, Litterfall chemistry, 2022

description: Litterfall chemistry data of the LTER station Zöbelboden from the year 2022
DOI: 10.13145/bacdive23868.20241212.9.2
title: Acidovorax wautersii Vaneechoutte et al. 2013

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23871.20241212.9.2
title: Rhodoferax antarcticus Madigan et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23872.20241212.9.2
title: Rhodoferax saidenbachensis Kaden et al. 2014

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23874.20241212.9.2
title: Melaminivora alkalimesophila Wang et al. 2014

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23875.20241212.9.2
title: Corynebacterium uterequi Hoyles et al. 2013 emend. Nouioui et al. 2018

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23876.20241212.9.2
title: Corynebacterium frankenforstense Wiertz et al. 2013 emend. Nouioui et al. 2018

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23877.20241212.9.2
title: Corynebacterium lactis Wiertz et al. 2013 emend. Nouioui et al. 2018

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23879.20241212.9.2
title: Dyadobacter beijingensis Dong et al. 2007 emend. Hahnke et al. 2016

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23880.20241212.9.2
title: Dyadobacter soli Lee et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23883.20241212.9.2
title: Spirosoma endophyticum Fries et al. 2013

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23884.20241212.9.2
title: Pontibacter akesuensis Zhou et al. 2007 emend. Zhang et al. 2013

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23885.20241212.9.2
title: Seleniivibrio woodruffii Rauschenbach et al. 2013

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive23887.20241212.9.2
title: Deinococcus wulumuqiensis Wang et al. 2010 emend. Hong et al. 2015

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15468/dl.brkmbq
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material sample)",
 "GadmGid is CHN.15.12.4_1",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 123 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069678-251120083545085/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.27329727
title: Male Fertility Genomics: Raw Data Download Link for A Blood & Single Sperm WGS

description: ftp download link for male blood and paired 5 single sperms whole genome sequence dataset.
DOI: 10.6084/m9.figshare.27329727.v3
title: Male Fertility Genomics: Raw Data Download Link for A Blood & Single Sperm WGS

description: ftp download link for male blood and paired 5 single sperms whole genome sequence dataset.
DOI: 10.6084/m9.figshare.27329727.v2
title: Male Fertility Genomics: A Blood & Single Sperm WGS Dataset

description: ftp download link for male blood and paired 5 single sperms whole genome sequence dataset.
DOI: 10.34740/kaggle/dsv/14351930
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.15468/dl.zwchwg
title: Occurrence Download

description: A dataset containing 739 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-48.34160 5.30152,100.34160 5.30152,100.34160 64.69848,-48.34160 64.69848,-48.34160 5.30152))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anastatica hierochuntica"
 ]
}

 The dataset includes 739 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069680-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d992s2
title: Occurrence Download

description: A dataset containing 120376 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Megascops kennicottii",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 120376 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069668-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.npyxf6
title: Occurrence Download

description: A dataset containing 8779 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.6.6_1",
 "Year 2000-2024"
 ]
}

 The dataset includes 8779 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069681-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7sy22
title: Occurrence Download

description: A dataset containing 1087325 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Plecoptera"
 ]
}

 The dataset includes 1087325 records from 1122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069670-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dnmyzy
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acipenser sinensis)",
 "HasCoordinate is true",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069683-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4bpggc
title: Occurrence Download

description: A dataset containing 12467 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dipterocarpus"
 ]
}

 The dataset includes 12467 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069684-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.26jfzm
title: Occurrence Download

description: A dataset containing 1392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.6.10_1",
 "Year 2000-2024"
 ]
}

 The dataset includes 1392 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069686-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18105259
title: Dataset for Bounded Dangerous-Word Chains in the Length-64 Accelerated Collatz Map

description: Dataset, visualization, and python experiment for the paper "Bounded Dangerous-Word Chains in theLength-64 Accelerated Collatz Map", 10.5281/zenodo.18105220
DOI: 10.5281/zenodo.18105258
title: Dataset for Bounded Dangerous-Word Chains in the Length-64 Accelerated Collatz Map

description: Dataset, visualization, and python experiment for the paper "Bounded Dangerous-Word Chains in theLength-64 Accelerated Collatz Map", 10.5281/zenodo.18105220
DOI: 10.15468/dl.znppag
title: Occurrence Download

description: A dataset containing 2741 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.6.13_1",
 "Year 2000-2024"
 ]
}

 The dataset includes 2741 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069687-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18103075
title: Annual time series of riparian vegetation and climate variables in the Black Volta Basin, Ghana (2000–2020)

description: This dataset contains annual time series (2000–2020) of key biophysical and climatic variables for the riparian zone of the Black Volta Basin in Ghana’s Savanna Region. It includes:



NDVI_Mean: Mean Normalized Difference Vegetation Index (proxy for vegetation greenness)

MNDWI_Mean: Mean Modified Normalized Difference Water Index (proxy for surface water extent)

Precipitation_Mean: Annual total precipitation (mm), derived from CHIRPS v2.0

Temperature_Mean: Annual mean air temperature (°C), derived from ERA5-Land


The data were used in the study "Shifts and drivers of riparian vegetation in the Black Volta Basin in the Savanna region of Ghana" to assess two decades of riparian vegetation dynamics and identify climatic and hydrological drivers. The dataset supports remote sensing–based environmental monitoring in West African savanna ecosystems and enables replication or extension of trend and regression analyses (e.g., Mann–Kendall, multiple linear regression).
DOI: 10.5281/zenodo.18103076
title: Annual time series of riparian vegetation and climate variables in the Black Volta Basin, Ghana (2000–2020)

description: This dataset contains annual time series (2000–2020) of key biophysical and climatic variables for the riparian zone of the Black Volta Basin in Ghana’s Savanna Region. It includes:



NDVI_Mean: Mean Normalized Difference Vegetation Index (proxy for vegetation greenness)

MNDWI_Mean: Mean Modified Normalized Difference Water Index (proxy for surface water extent)

Precipitation_Mean: Annual total precipitation (mm), derived from CHIRPS v2.0

Temperature_Mean: Annual mean air temperature (°C), derived from ERA5-Land


The data were used in the study "Shifts and drivers of riparian vegetation in the Black Volta Basin in the Savanna region of Ghana" to assess two decades of riparian vegetation dynamics and identify climatic and hydrological drivers. The dataset supports remote sensing–based environmental monitoring in West African savanna ecosystems and enables replication or extension of trend and regression analyses (e.g., Mann–Kendall, multiple linear regression).
DOI: 10.15468/dl.k45p7w
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is Africa",
 "Country is Congo, Democratic Republic of the",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Myrianthus holstii"
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069688-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18105131
title: Code for "Tropical Basin Interactions Reduce Spring Predictability Barrier of ENSO  in a Deep Learning Model"

description: This is the public data and code for the manuscript “Tropical Basin Interactions Reduce Spring Predictability Barrier of ENSO in a Deep Learning Model” by Zhou and Zhang.

README

Due to data size limitations, both training and model output data are stored on Google Drive. You can access them through the following link: https://drive.google.com/drive/folders/1DvASC6Jdtlw-fWl6lkwdrO0Au-S22oOX?usp=sharing. The "train_validation_test_dataset" folder contains the model's training and testing data; the "model_predictions" folder contains prediction datasets made by the GL-Geoformer with monthly predictions initiated from Oct 1980 to Jan 2025 (each prediction predicts 1-24 month ahead).

-> Code Description:The "pretrain" folder contains code for model pretraining. You can modify the training data path in "config.py", then run "trainer.py" to start training the model. The trained model parameters are saved in the "pretrain/model" folder and will be used for subsequent transfer learning.

The "transfer" folder contains code for transfer learning optimization of the pretrained model. Before performing transfer learning, you need to set "configs.TFtrain = True" in "config.py", modify the data path, and set "configs.output_length" to an appropriate value based on your GPU memory. Next, you need to modify the pretrained model parameter path in the "Trainer class" in "transfer_trainer.py". After completing these modifications, you can run "transfer_trainer.py" to perform model transfer learning.

After completing transfer learning and before starting model testing, you need to set "configs.TFtrain = False" in "config.py" and set "configs.output_length" to an appropriate value (e.g., configs.output_length=24). Then modify the model path and test data path in "test_model.py", and you can run this file to execute model testing.

Currently, 10 sets of model parameters have already been saved in the "transfer\model" folder. You can also directly use these parameters for predictions or sensitivity experiments.
DOI: 10.5281/zenodo.18105130
title: Code for "Tropical Basin Interactions Reduce Spring Predictability Barrier of ENSO  in a Deep Learning Model"

description: This is the public data and code for the manuscript “Tropical Basin Interactions Reduce Spring Predictability Barrier of ENSO in a Deep Learning Model” by Zhou and Zhang.

README

Due to data size limitations, both training and model output data are stored on Google Drive. You can access them through the following link: https://drive.google.com/drive/folders/1DvASC6Jdtlw-fWl6lkwdrO0Au-S22oOX?usp=sharing. The "train_validation_test_dataset" folder contains the model's training and testing data; the "model_predictions" folder contains prediction datasets made by the GL-Geoformer with monthly predictions initiated from Oct 1980 to Jan 2025 (each prediction predicts 1-24 month ahead).

-> Code Description:The "pretrain" folder contains code for model pretraining. You can modify the training data path in "config.py", then run "trainer.py" to start training the model. The trained model parameters are saved in the "pretrain/model" folder and will be used for subsequent transfer learning.

The "transfer" folder contains code for transfer learning optimization of the pretrained model. Before performing transfer learning, you need to set "configs.TFtrain = True" in "config.py", modify the data path, and set "configs.output_length" to an appropriate value based on your GPU memory. Next, you need to modify the pretrained model parameter path in the "Trainer class" in "transfer_trainer.py". After completing these modifications, you can run "transfer_trainer.py" to perform model transfer learning.

After completing transfer learning and before starting model testing, you need to set "configs.TFtrain = False" in "config.py" and set "configs.output_length" to an appropriate value (e.g., configs.output_length=24). Then modify the model path and test data path in "test_model.py", and you can run this file to execute model testing.

Currently, 10 sets of model parameters have already been saved in the "transfer\model" folder. You can also directly use these parameters for predictions or sensitivity experiments.
DOI: 10.5281/zenodo.18105217
title: Artificial Intelligence in Contemporary Software Development: An Analytical Study of Practices, Challenges, and Future Directions

description: This research paper explores the integration of Artificial Intelligence (AI) into modern software development processes. It analyzes the applications of AI across the Software Development Life Cycle (SDLC), including requirement analysis, design, implementation, testing, and maintenance. The study identifies the practical benefits of AI, such as improved productivity, higher software quality, and reduced maintenance costs, while also addressing challenges like data quality, ethical concerns, and skill shortages. Additionally, the paper discusses future research directions, including explainable AI models, ethical frameworks, and large-scale empirical validation. This work provides a professional and analytical perspective suitable for academic journals, conferences, and open-access repositories.
DOI: 10.5281/zenodo.18105216
title: Artificial Intelligence in Contemporary Software Development: An Analytical Study of Practices, Challenges, and Future Directions

description: This research paper explores the integration of Artificial Intelligence (AI) into modern software development processes. It analyzes the applications of AI across the Software Development Life Cycle (SDLC), including requirement analysis, design, implementation, testing, and maintenance. The study identifies the practical benefits of AI, such as improved productivity, higher software quality, and reduced maintenance costs, while also addressing challenges like data quality, ethical concerns, and skill shortages. Additionally, the paper discusses future research directions, including explainable AI models, ethical frameworks, and large-scale empirical validation. This work provides a professional and analytical perspective suitable for academic journals, conferences, and open-access repositories.
DOI: 10.15468/dl.ue3z8u
title: Occurrence Download

description: A dataset containing 12467 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dipterocarpus"
 ]
}

 The dataset includes 12467 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069689-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6069/wtxg-z452
title: Absolute quantification of gluten protein groups and their relation to wheat baking quality

description: Wheat gluten proteins are key determinants of baking quality. While untargeted proteomics enable relative quantification, absolute quantities of specific gluten protein groups are not available so far. We developed a targeted liquid chromatography with tandem mass spectrometry (LC-MS/MS) stable isotope dilution assay (SIDA) to quantify eleven individual gluten protein groups based on isotope-labeled internal standards corresponding to the selected marker peptides. The comparison of targeted and untargeted measurements revealed differences in protein composition, likely arising from different MS/MS acquisition strategies and protein assignment. We correlated the absolute protein content with baking quality traits in the Bavarian multiple advanced generation intercross wheat population. None of the individual groups correlated strongly with any baking quality trait. Six groups (α-gliadin 2, γ-gliadin 1, low-molecular-weight glutenin subunit (LMW-GS) 3, and high-molecular-weight glutenin subunits (HMW-GS) 1–4) showed weak to moderate associations (r = 0.32–0.64), mainly with grain protein content, sedimentation value, and wet gluten content. LMW-GS 3 represents the rare i-type containing eight cysteine residues. Loaf volume was only weakly to moderately correlated, primarily with HMW-GS 1 (r = 0.41) and HMW-GS 3 (r = 0.40), supporting the superior effect of Dy10. By contrast, HMW-GS 5 (Dx2, Dx5) showed little effect, consistent with a stronger influence of y-type glutenin subunits. Summing up the protein content across groups increased correlation strengths, yet baking quality remains a complex trait shaped by multiple proteins and non-protein factors.
DOI: 10.5281/zenodo.18061924
title: The Need Analysis Raw Data of Engineering Students

description: This dataset contains responses to the need analysis of the engineering students' questionnaire. The Questionnaire is focused on analysing the requirements and needs of engineering students in the context of virtual reality labs. The questions were Likert-type, open-ended and optional. The questionnaire was distributed to various universities in the participating regions and reached 1009 engineering students. The open-ended question responses were analysed thematically, while the quantitative analysis of the remaining questions was completed by the project consortium members.
DOI: 10.5281/zenodo.18061923
title: The Need Analysis Raw Data of Engineering Students

description: This dataset contains responses to the need analysis of the engineering students' questionnaire. The Questionnaire is focused on analysing the requirements and needs of engineering students in the context of virtual reality labs. The questions were Likert-type, open-ended and optional. The questionnaire was distributed to various universities in the participating regions and reached 1009 engineering students. The open-ended question responses were analysed thematically, while the quantitative analysis of the remaining questions was completed by the project consortium members.
DOI: 10.6084/m9.figshare.28943927
title: Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Investigation of Retrieval-Augmented Generation and Fine-Tuning

description: This project contains the source code and evaluation setup used for comparing fine-tuning and RAG in the context of Austrian VAT law.It includes evaluation data, final results, prompt flow definitions for Azure AI Foundry, and metrics related to the fine-tuning process. The training data used for fine-tuning is not included and will not be published.
DOI: 10.6084/m9.figshare.28943927.v1
title: Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Investigation of Retrieval-Augmented Generation and Fine-Tuning

description: This project contains the source code and evaluation setup used for comparing fine-tuning and RAG in the context of Austrian VAT law.It includes evaluation data, final results, prompt flow definitions for Azure AI Foundry, and metrics related to the fine-tuning process. The training data used for fine-tuning is not included and will not be published.