cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/t38b5fd3tp.1
title: Optimization of Healthcare Infrastructure in Guanajuato: A Comprehensive Unit Planning Model

description: This database was used to address the following problem: Between 2016 and 2020, Guanajuato experienced a marked rise in poverty, as indicated by CONEVAL data. This has led to a substantial surge in demand for public healthcare services. Addressing this challenge, this study focuses on enhancing healthcare access and coverage across the state's 46 municipalities and 630+ medical units. To achieve these objectives and maximize societal benefits, a systematic infrastructure planning approach for healthcare services is proposed. The research undertakes an in-depth analysis of the current healthcare landscape, subsequently presenting an optimization model for locating new medical units. This model factors in service demand and capacity, offering a comprehensive decision-making framework. The practicality of this model is demonstrated through its application in a case study involving Guanajuato. Leveraging Cplex for exact problem-solving, preliminary outcomes showcase the promising potential of this decision-support tool.
DOI: 10.17632/5thfckdhh2.1
title: Unraveling blunt-end RNA binding and ATPase-driven translocation activities of the RIG-I family helicase LGP2 

description: These are the original tif files for all the gels used in this manuscript.
DOI: 10.17632/ydrrbfg7k3.1
title: Third-country effects of the BRI

description: Data and graphs
DOI: 10.34740/kaggle/dsv/6408518
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.24038370
title: bootleg innovation.sav

description: bootleg innovation<br>
DOI: 10.5281/zenodo.8312861
title: falco_chicquera_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8312860
title: falco_chicquera_zanfona_1.fasta.gz

description: .
DOI: 10.17632/r6vvm5jkh6.1
title: Lightweight Dataset for Maize Classification on Resource-Constrained Devices

description: The dataset comprises images of three types of maize seeds: Wang Dataa, Sanzal Sima, and Bihilifa, sourced from Heritage Seeds Ghana. These maize varieties are commonly cultivated in the northern region of Ghana. At the collection point, Heritage Seeds Ghana manually sorted and labeled the images as either 'good' or 'bad' for each of the three varieties. The 'good' category represented high-quality maize seeds suitable for productive yields, while the 'bad' category included damaged, infected, or low-quality seeds not suitable for production. The images were captured using an iPhone 11 Pro Max device with specific technical specifications, resulting in original jpeg images of varying dimensions. Additionally, the augmented images were standardized to a size of 128 by 128. During capture, a blue background was used to ensure consistency and clarity during daylight, with no specific attention to lighting conditions. The images were then organized into their respective classes of 'good' and 'bad.' Overall, the dataset for this study comprises both raw (4,846 images) and augmented (29,094 images) color images.
DOI: 10.34740/kaggle/dsv/6408630
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6408633
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5519/qd.h7ht163i
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (26 records)
DOI: 10.6084/m9.figshare.24078873
title: wyniki wywiadów - kodowanie.xlsx

description: Dataset presents data collected from an experiment and coding
DOI: 10.6084/m9.figshare.24078873.v1
title: wyniki wywiadów - kodowanie.xlsx

description: Dataset presents data collected from an experiment and coding
DOI: 10.6084/m9.figshare.24078885
title: wyniki wywiadow

description: Dataset presents data collected from experiment
DOI: 10.6084/m9.figshare.24078885.v1
title: wyniki wywiadow

description: Dataset presents data collected from experiment
DOI: 10.6084/m9.figshare.22578616
title: Analytical data of Aetokthonostatins

description: NMR and MS raw data of Aetokthonostatins (AEST, MMAEST, DAAEST), dolastatin-like pentapeptide toxins isolated from the cyanobacterium Aetokthonos hydrillicola
DOI: 10.6084/m9.figshare.22578616.v1
title: Analytical data of Aetokthonostatins

description: NMR and MS raw data of Aetokthonostatins (AEST, MMAEST, DAAEST), dolastatin-like pentapeptide toxins isolated from the cyanobacterium Aetokthonos hydrillicola
DOI: 10.34740/kaggle/dsv/6408857
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.19640886
title: Replication Package - Taxonomy of Inline Code Comments Smells

description: Artifacts of our study on the taxonomy of inline code comment smells
DOI: 10.6084/m9.figshare.19640886.v8
title: Replication Package - Taxonomy of Inline Code Comments Smells

description: Artifacts of our study on the taxonomy of inline code comment smells
DOI: 10.5517/ccdc.csd.cc29ygzq
title: CCDC 2143494: Experimental Crystal Structure Determination

description: Related Article: Zhiwei Zhang, Giang N. T. Le, Yang Ge, Xiaowen Tang, Xin Chen, Linda Ejim, Emily Bordeleau, Gerard D. Wright, Darcy C. Burns, Susannah Tran, Peter Axerio-Cilies, Yu Tian Wang, Mingxin Dong, G. Andrew Woolley|2023|Nature Chemistry|15|1285|doi:10.1038/s41557-023-01239-5
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29ygyp
title: CCDC 2143493: Experimental Crystal Structure Determination

description: Related Article: Zhiwei Zhang, Giang N. T. Le, Yang Ge, Xiaowen Tang, Xin Chen, Linda Ejim, Emily Bordeleau, Gerard D. Wright, Darcy C. Burns, Susannah Tran, Peter Axerio-Cilies, Yu Tian Wang, Mingxin Dong, G. Andrew Woolley|2023|Nature Chemistry|15|1285|doi:10.1038/s41557-023-01239-5
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.24078879
title: Annotated file on the structure of the whole genome of Odontamblyopus lacepedii

description: Annotated file on the structure of the whole genome of Odontamblyopus lacepedii
DOI: 10.6084/m9.figshare.24078879.v1
title: Annotated file on the structure of the whole genome of Odontamblyopus lacepedii

description: Annotated file on the structure of the whole genome of Odontamblyopus lacepedii
DOI: 10.6084/m9.figshare.23807889
title: Genome assembly of the spotted sea bass

description: T2T gapless genome assembly of <i>Lateolabrax maculatus</i>
DOI: 10.6084/m9.figshare.24078909
title: DIVFusion-EAS&DIVFusion-FIS.rar

description: DIVFusion-EAS&DIVFusion-FIS.rar
DOI: 10.6084/m9.figshare.24078909.v1
title: DIVFusion-EAS&DIVFusion-FIS.rar

description: DIVFusion-EAS&DIVFusion-FIS.rar
DOI: 10.6084/m9.figshare.24078915
title: DeBertaV3

description: <pre>A tutorial for implementing the DeBERTaV3 model On AWS<br><br>1. Introduction<br>This project implements a Machine Learning Pipeline with DeBERTaV3.<br><br>2. Code files<br>Here, we introduce main files for the ML pipeline of DeBERTaV3.<br>(a) sagemaker-pa-mlops-modelbuild/codebuild-buildspec.yml: it builds the ML continue-integrate pipeline with scripts in the sagemaker-pa-mlops-modelbuild folder.<br>(b) sagemaker-pa-mlops-modelbuild/pipelines: it builds nd register the ML pipeline.<br>(c) sagemaker-pa-mlops-modelbuild/pipelines/run_pipeline.py: it builds nd register the ML pipeline.<br>(d) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/train.py: it trains the DeBERTaV3 model.<br>(e) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/evaluate.py: it evaluates the DeBERTaV3 model to report accuracy and f1-score.<br><br>3. Dataset and preprocess files<br>We provided test datasets for running the proposed MCGCN model, which contains one-day traffic speed, spatial context, and temporal context datasets in the folder “./raw_data/speed_origin_data/”. The detail of each file and its formats are as follows:<br>(a) Data-preprocess/CityofYarra_Verification_Data.xlsx: City of Yarra Dataset.<br>(b) Data-preprocess/Whitehorse_classification.xlsx: City of Whitehorse Dataset.<br>(c) Data-preprocess/high priority.csv: Planning applications with high priority Dataset.<br>(a) Data-preprocess/Split.ipynb: It splits the dataset.</pre>
DOI: 10.6084/m9.figshare.24078915.v1
title: DeBertaV3

description: <pre>A tutorial for implementing the DeBERTaV3 model On AWS<br><br>1. Introduction<br>This project implements a Machine Learning Pipeline with DeBERTaV3.<br><br>2. Code files<br>Here, we introduce main files for the ML pipeline of DeBERTaV3.<br>(a) sagemaker-pa-mlops-modelbuild/codebuild-buildspec.yml: it builds the ML continue-integrate pipeline with scripts in the sagemaker-pa-mlops-modelbuild folder.<br>(b) sagemaker-pa-mlops-modelbuild/pipelines: it builds nd register the ML pipeline.<br>(c) sagemaker-pa-mlops-modelbuild/pipelines/run_pipeline.py: it builds nd register the ML pipeline.<br>(d) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/train.py: it trains the DeBERTaV3 model.<br>(e) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/evaluate.py: it evaluates the DeBERTaV3 model to report accuracy and f1-score.<br><br>3. Dataset and preprocess files<br>We provided test datasets for running the proposed MCGCN model, which contains one-day traffic speed, spatial context, and temporal context datasets in the folder “./raw_data/speed_origin_data/”. The detail of each file and its formats are as follows:<br>(a) Data-preprocess/CityofYarra_Verification_Data.xlsx: City of Yarra Dataset.<br>(b) Data-preprocess/Whitehorse_classification.xlsx: City of Whitehorse Dataset.<br>(c) Data-preprocess/high priority.csv: Planning applications with high priority Dataset.<br>(a) Data-preprocess/Split.ipynb: It splits the dataset.</pre>
DOI: 10.6084/m9.figshare.24078924
title: DeBertaV3

description: <pre>A tutorial for implementing the DeBERTaV3 model On AWS<br><br>1. Introduction<br>This project implements a Machine Learning Pipeline with DeBERTaV3.<br><br>2. Code files<br>Here, we introduce main files for the ML pipeline of DeBERTaV3.<br>(a) sagemaker-pa-mlops-modelbuild/codebuild-buildspec.yml: it builds the ML continue-integrate pipeline with scripts in the sagemaker-pa-mlops-modelbuild folder.<br>(b) sagemaker-pa-mlops-modelbuild/pipelines: it builds nd register the ML pipeline.<br>(c) sagemaker-pa-mlops-modelbuild/pipelines/run_pipeline.py: it builds nd register the ML pipeline.<br>(d) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/train.py: it trains the DeBERTaV3 model.<br>(e) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/evaluate.py: it evaluates the DeBERTaV3 model to report accuracy and f1-score.<br><br>3. Dataset and preprocess files<br>We provided test datasets for running the proposed MCGCN model, which contains one-day traffic speed, spatial context, and temporal context datasets in the folder “./raw_data/speed_origin_data/”. The detail of each file and its formats are as follows:<br>(a) Data-preprocess/CityofYarra_Verification_Data.xlsx: City of Yarra Dataset.<br>(b) Data-preprocess/Whitehorse_classification.xlsx: City of Whitehorse Dataset.<br>(c) Data-preprocess/high priority.csv: Planning applications with high priority Dataset.<br>(a) Data-preprocess/Split.ipynb: It splits the dataset.</pre>
DOI: 10.6084/m9.figshare.24078924.v1
title: DeBertaV3

description: <pre>A tutorial for implementing the DeBERTaV3 model On AWS<br><br>1. Introduction<br>This project implements a Machine Learning Pipeline with DeBERTaV3.<br><br>2. Code files<br>Here, we introduce main files for the ML pipeline of DeBERTaV3.<br>(a) sagemaker-pa-mlops-modelbuild/codebuild-buildspec.yml: it builds the ML continue-integrate pipeline with scripts in the sagemaker-pa-mlops-modelbuild folder.<br>(b) sagemaker-pa-mlops-modelbuild/pipelines: it builds nd register the ML pipeline.<br>(c) sagemaker-pa-mlops-modelbuild/pipelines/run_pipeline.py: it builds nd register the ML pipeline.<br>(d) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/train.py: it trains the DeBERTaV3 model.<br>(e) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/evaluate.py: it evaluates the DeBERTaV3 model to report accuracy and f1-score.<br><br>3. Dataset and preprocess files<br>We provided test datasets for running the proposed MCGCN model, which contains one-day traffic speed, spatial context, and temporal context datasets in the folder “./raw_data/speed_origin_data/”. The detail of each file and its formats are as follows:<br>(a) Data-preprocess/CityofYarra_Verification_Data.xlsx: City of Yarra Dataset.<br>(b) Data-preprocess/Whitehorse_classification.xlsx: City of Whitehorse Dataset.<br>(c) Data-preprocess/high priority.csv: Planning applications with high priority Dataset.<br>(a) Data-preprocess/Split.ipynb: It splits the dataset.</pre>
DOI: 10.14469/hpc/12942
title: Swern oxidation, React1 endo, B3LYP+GD3+BJ/Def2-TZVPP, SCRF=DCM, G = -823.620618 =&gt; 3HCCl3 explicit solvents

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12944
title: Swern oxidation, TS1 endo, B3LYP+GD3+BJ/Def2-TZVPP, SCRF=DCM, G = -823.61242 =&gt; 3HCCl3 explicit solvents

description: Gaussian 16 (C01) calculation
DOI: 10.57967/hf/1068
title: pl-asr-bigos
DOI: 10.5281/zenodo.8312994
title: Intracellular Metabolic Profiling of Drug Resistant Cells by Surface Enhanced Raman Scattering

description: SERS spectra of different cell lysates and pure metabolites enhanced by Ag NPs.
DOI: 10.5281/zenodo.8312993
title: Intracellular Metabolic Profiling of Drug Resistant Cells by Surface Enhanced Raman Scattering

description: SERS spectra of different cell lysates and pure metabolites enhanced by Ag NPs.
DOI: 10.6084/m9.figshare.24078681.v1
title: Supporting information.pdf

description: <b>S1. Analytical methods: </b>Whole-rock major- and trace-element and Sr-Nd-Pb-Mo-B isotope analyses<b>S2. Supplementary Figures: </b>Fig. S1~S3<b>S3. Supplementary Tables: </b>Whole-rock major- and trace-element and Sr-Nd-Pb-Mo-B isotope data
DOI: 10.6084/m9.figshare.24079158
title: kap_covid19

description: Cross sectional study on Covid 19 Knowledge
DOI: 10.34740/kaggle/dsv/6409150
title: Pakistan Employment Dataset 2023
DOI: 10.17909/gs15-rb46
title: HST data from the Star Formation History of the Local Group Galaxy WLM
DOI: 10.17909/t6pm-x063
title: JWST data from The Star Formation History of the Local Group Galaxy WLM
DOI: 10.6084/m9.figshare.24079164
title: Biomimetic nanovaccine-mediated multivalent IL-15 self-transpresentation (MIST) for potent and safe cancer immunotherapy

description: Source data are provided with the article: <b>Biomimetic nanovaccine</b><b>-mediated </b><b>multivalent IL-15 </b><b>self-transpresentation</b><b> (MIST) </b><b>for potent and safe cancer immunotherapy</b>.
DOI: 10.6084/m9.figshare.23995830
title: FoodBar chemical and nutrition content analysis

description: Chemical and nutrition content analysis of Cookies
DOI: 10.6084/m9.figshare.23995830.v1
title: FoodBar chemical and nutrition content analysis

description: Chemical and nutrition content analysis of Cookies
DOI: 10.34740/kaggle/dsv/6409469
title: MoviesDatasets
DOI: 10.5281/zenodo.8313101
title: coracias_cyanogaster_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8313100
title: coracias_cyanogaster_zanfona_1.fasta.gz

description: .
DOI: 10.21979/n9/e2xi2e
title: A Silk-Microneedle Patch to Detect Glucose in the Interstitial Fluid of Skin or Plant Tissue

description: Paper publication
DOI: 10.5281/zenodo.8313123
title: crypturellus_tataupa_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8313122
title: crypturellus_tataupa_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23709810
title: Mental health, school absenteeism and the Education Welfare Service in Ireland

description: Transcript of interviews with case study participants
DOI: 10.34740/kaggle/ds/2878525
title: K-pop girl groups song, album data
DOI: 10.5281/zenodo.7515842
title: Skeletonema marinoi MS2 mzML files in positive mode

description: These are MS2 files in .mzML format acquired in positive mode. The extracted metabolites come from a sample from <em>Skeletonema marinoi.</em>
DOI: 10.5281/zenodo.7515841
title: Skeletonema marinoi MS2 mzML files in positive mode

description: These are MS2 files in .mzML format acquired in positive mode. The extracted metabolites come from a sample from <em>Skeletonema marinoi.</em>
DOI: 10.5281/zenodo.7515829
title: Skeletonema marinoi MS2 mzML files in negative mode

description: These are MS2 files in .mzML format acquired in negative mode. The extracted metabolites come from a sample from <em>Skeletonema marinoi.</em>
DOI: 10.5281/zenodo.7515828
title: Skeletonema marinoi MS2 mzML files in negative mode

description: These are MS2 files in .mzML format acquired in negative mode. The extracted metabolites come from a sample from <em>Skeletonema marinoi.</em>
DOI: 10.5281/zenodo.5772754
title: Skeletonema species Suspect List

description: This is a Suspect List of Metabolites extracted from literature and several open-source Compound, Spectral and Reactions Databases. The Suspect list belongs to two species from the genus <em>Skeletonema</em>, namely <em>Skeletonema costatum</em> and <em>Skeletoname marinoi. </em>A sunburst plot for classification is given as "suspectlist_sunburst.html"
DOI: 10.5281/zenodo.5772755
title: Skeletonema species Suspect List

description: This is a Suspect List of Metabolites extracted from literature and several open-source Compound, Spectral and Reactions Databases. The Suspect list belongs to two species from the genus <em>Skeletonema</em>, namely <em>Skeletonema costatum</em> and <em>Skeletoname marinoi. </em>A sunburst plot for classification is given as "suspectlist_sunburst.html"
DOI: 10.5281/zenodo.7798782
title: Results from MAW - Skeletonema marinoi

description: This dataset contains a list of metabolites annotated from the Mass Spectrometry data from <em>Skeletonema marinoi</em>. 1. The csv file "all_features_candidates_withNamesAndSource.csv" is a list of annotated metabolites obtained from the whole metabolome annotation of <em>S. marinoi</em> using Metabolome Annotation workflow (MAW). (corresponding Sunburst is Smarinoi_sunburst.html) 2. The csv file "intersection.csv" contains metabolites common in the "all_features_candidates_withNamesAndSource.csv" and the suspect list of the <em>S. marinoi</em> with Zenodo DOI: 10.5281/zenodo.5772755. (corresponding Sunburst is intersection_sunburst.html) 3. The csv file "SmarinoiUnion.csv" is a union set from Suspect list Zenodo (DOI: 10.5281/zenodo.5772755) and "all_features_candidates_withNames.csv". (corresponding Sunburst is union_sunburst.html) 4. only_MAW_curated.csv contains previously not known compounds from <em>S. marinoi</em><br> <br> 5. insection_sl_sm_sunburst.html is the sunburst from intersection.csv.
DOI: 10.5281/zenodo.7798781
title: Results from MAW - Skeletonema marinoi

description: This dataset contains a list of metabolites annotated from the Mass Spectrometry data from <em>Skeletonema marinoi</em>. 1. The csv file "all_features_candidates_withNamesAndSource.csv" is a list of annotated metabolites obtained from the whole metabolome annotation of <em>S. marinoi</em> using Metabolome Annotation workflow (MAW). (corresponding Sunburst is Smarinoi_sunburst.html) 2. The csv file "intersection.csv" contains metabolites common in the "all_features_candidates_withNamesAndSource.csv" and the suspect list of the <em>S. marinoi</em> with Zenodo DOI: 10.5281/zenodo.5772755. (corresponding Sunburst is intersection_sunburst.html) 3. The csv file "SmarinoiUnion.csv" is a union set from Suspect list Zenodo (DOI: 10.5281/zenodo.5772755) and "all_features_candidates_withNames.csv". (corresponding Sunburst is union_sunburst.html) 4. only_MAW_curated.csv contains previously not known compounds from <em>S. marinoi</em><br> <br> 5. insection_sl_sm_sunburst.html is the sunburst from intersection.csv.
DOI: 10.5281/zenodo.8313116
title: Studying the association between Gitcoin's issues and resolving outcomes

description: This dataset provides a replication package of our paper submission titled "Studying the association between Gitcoin bounties and issue-addressing outcomes". <strong>Abstract</strong> The development of open-source software (OSS) projects usually have been driven through collaborations among contributors and strongly relies on volunteering. Thus, allocating software practitioners (e.g., contributors) to a particular task is non-trivial and draws attention away from the development. Therefore, a number of bug bounty platforms have emerged to address this problem through bounty rewards. Especially, Gitcoin, a new bounty platform, introduces a bounty reward mechanism that allows individual issue owners (backers) to define a reward value using cryptocurrencies rather than using crowdfunding mechanisms. Although a number of studies have investigated the phenomenon on bounty platforms, those rely on different bounty reward systems. Our study thus investigates the association between the Gitcoin bounties and their outcomes (i.e., success and non-success). We empirically study over 4,000 issues with Gitcoin bounties using statistical analysis and machine learning techniques. We also conducted a comparative study with the Bountysource platform to gain insights into the usage of both platforms. Our study highlights the importance of factors such as the length of the project, issue description, type of bounty issue, and the bounty value, which are found to be highly correlated with the outcome of bounty issues. These findings can provide useful guidance to practitioners.
DOI: 10.5281/zenodo.8313117
title: Studying the association between Gitcoin's issues and resolving outcomes

description: This dataset provides a replication package of our paper submission titled "Studying the association between Gitcoin bounties and issue-addressing outcomes". <strong>Abstract</strong> The development of open-source software (OSS) projects usually have been driven through collaborations among contributors and strongly relies on volunteering. Thus, allocating software practitioners (e.g., contributors) to a particular task is non-trivial and draws attention away from the development. Therefore, a number of bug bounty platforms have emerged to address this problem through bounty rewards. Especially, Gitcoin, a new bounty platform, introduces a bounty reward mechanism that allows individual issue owners (backers) to define a reward value using cryptocurrencies rather than using crowdfunding mechanisms. Although a number of studies have investigated the phenomenon on bounty platforms, those rely on different bounty reward systems. Our study thus investigates the association between the Gitcoin bounties and their outcomes (i.e., success and non-success). We empirically study over 4,000 issues with Gitcoin bounties using statistical analysis and machine learning techniques. We also conducted a comparative study with the Bountysource platform to gain insights into the usage of both platforms. Our study highlights the importance of factors such as the length of the project, issue description, type of bounty issue, and the bounty value, which are found to be highly correlated with the outcome of bounty issues. These findings can provide useful guidance to practitioners.