cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.kg2mu7
title: Occurrence Download

description: A dataset containing 1775 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dicrocoeliidae"
 ]
}

 The dataset includes 1775 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004464-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qea6me
title: Occurrence Download

description: A dataset containing 28040 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tropiduridae"
 ]
}

 The dataset includes 28040 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0187635-240321170329656/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.28683098
title: MasterThesis_StefanFrey

description: Data for Master thesis, Olfactory perception of GLV and common flower volatilesin bees and wasps: a comparative study
DOI: 10.6084/m9.figshare.28683155
title: Data and Code from Record of Foraminifera Test Composition throughout the Phanerozoic

description: R code and datasets necessary to reproduce the numbers and images in this paper.
DOI: 10.5281/zenodo.15100458
title: TCGA processed datasets for DeConveil case studies
title: TCGA processed data used for DeConveil test
DOI: 10.5281/zenodo.15100511
title: KICK SMART Driver Framework Discourse for Sustainable Livelihoods through Community- Based Tourism Businesses
DOI: 10.5281/zenodo.15100512
title: KICK SMART Driver Framework Discourse for Sustainable Livelihoods through Community- Based Tourism Businesses
DOI: 10.5281/zenodo.13987763
title: Capsaicinoid repository

description: Untargeted-metabolomics of 40 different chili varieties, large-scale etraction and purified capsaicinoids. Included is a capsaicinoid spectral library, all mzmine, GNPS and SIRIUS outputs. Corrospoinding paper's DOI will be added once generated. Updated version also contains all NMR files as well.
DOI: 10.5281/zenodo.13987764
title: Capsaicinoid repository

description: Untargeted-metabolomics of 40 different chili varieties, large-scale etraction and purified capsaicinoids. Included is a capsaicinoid spectral library, all mzmine, GNPS and SIRIUS outputs. Corrospoinding paper's DOI will be added once generated.
DOI: 10.5281/zenodo.15088148
title: Capsaicinoid repository

description: Untargeted-metabolomics of 40 different chili varieties, large-scale etraction and purified capsaicinoids. Included is a capsaicinoid spectral library, all mzmine, GNPS and SIRIUS outputs. Corrospoinding paper's DOI will be added once generated. Updated version also contains all NMR files as well.
DOI: 10.5281/zenodo.14639349
title: Enhancing Trust in Inter-Organisational Data Sharing: Levels of Assurance for Data Trustworthiness - Literature Body

description: The attached literature-body.html file includes all articles which were respected as a resulting final step in the literature review. The structured literature review was conducted following vom Brocke et al. 2015.

Therefore, it started with an unstructured exploration of relevant literature, mainly using Google Scholar, in order to increase the familiarity with the subject. Then, a structured keyword search in the IEEExplore, ACM, and ScienceDirect databases was performed. The search term used was (”Data” AND (”Trustworthiness” OR ”Trustworthy”)), matching titles in research articles only.

The search was executed in December 2024 and returned 318 matches. 148 of these came from IEEExplore, 121 from ACM and 49 from Science Direct. Then, the resulting articles’ titles were screenlined and unequivocal false positives were removed, which resulted in 177 articles that were more thoroughly investigated by reading their abstracts.

Then articles that were not related to measuring, evaluating, assuring or quantifying the trustworthiness of data, and matches that were exclusively focused on securing the integrity of data but did not address any other dimensions of data trustworthiness were excluded. This resulted in a total of 47 articles specifically concerned with data trustworthiness, its purpose, definition, and ensuring and assuring trust.

Using this collection, a backward and forward search was conducted by thoroughly reading each article, looking for key references, and using Google Scholar to execute a targeted forward search. Doing this resulted in additional 35 articles, 20 of which were already part of the set of identified literature. Thus, the literature body ended up to comprise a total of 62 articles.
DOI: 10.5281/zenodo.14639350
title: Enhancing Trust in Inter-Organisational Data Sharing: Levels of Assurance for Data Trustworthiness - Literature Body

description: The attached literature-body.html file includes all articles which were respected as a resulting final step in the literature review. The structured literature review was conducted following vom Brocke et al. 2015.

Therefore, it started with an unstructured exploration of relevant literature, mainly using Google Scholar, in order to increase the familiarity with the subject. Then, a structured keyword search in the IEEExplore, ACM, and ScienceDirect databases was performed. The search term used was (”Data” AND (”Trustworthiness” OR ”Trustworthy”)), matching titles in research articles only.

The search was executed in December 2024 and returned 318 matches. 148 of these came from IEEExplore, 121 from ACM and 49 from Science Direct. Then, the resulting articles’ titles were screenlined and unequivocal false positives were removed, which resulted in 177 articles that were more thoroughly investigated by reading their abstracts.

Then articles that were not related to measuring, evaluating, assuring or quantifying the trustworthiness of data, and matches that were exclusively focused on securing the integrity of data but did not address any other dimensions of data trustworthiness were excluded. This resulted in a total of 47 articles specifically concerned with data trustworthiness, its purpose, definition, and ensuring and assuring trust.

Using this collection, a backward and forward search was conducted by thoroughly reading each article, looking for key references, and using Google Scholar to execute a targeted forward search. Doing this resulted in additional 35 articles, 20 of which were already part of the set of identified literature. Thus, the literature body ended up to comprise a total of 62 articles.
DOI: 10.5281/zenodo.15097726
title: Triaxial compression tests on iron tailings cemented with an alkali activated binder

description: This dataset comprises triaxial compression tests, consolidated isotropically at three different mean effective stresses (50 kPa, 500 kPa and 1200 kPa). The material is an iron tailings cemented with an alkali activated binder made of low calcium fly ash and sodium hydroxide.
DOI: 10.5281/zenodo.15097727
title: Triaxial compression tests on iron tailings cemented with an alkali activated binder

description: This dataset comprises triaxial compression tests, consolidated isotropically at three different mean effective stresses (50 kPa, 500 kPa and 1200 kPa). The material is an iron tailings cemented with an alkali activated binder made of low calcium fly ash and sodium hydroxide.
DOI: 10.60656/260b6cbcecb44c11
title: Custom collection of model data from Granada on 29 Jan 2025
DOI: 10.5281/zenodo.15100520
title: Fruit and Vegetable Disease Dataset

description: Description:

The Fruit and Vegetable Disease Dataset offers an extensive collection of categorize images to aid in building machine learning models designed to identify diseases affecting various fruits and vegetables. This dataset is particularly suited for image classification tasks and deep learning applications, helping to distinguish between healthy and diseased produce.

Dataset Structure

The dataset is organize into 28 distinct directories, with each directory containing images of 14 different fruits and vegetables, divided into healthy and rotten categories. It is specifically structured to assist in the training and testing of machine learning models that aim to detect diseases in fruits and vegetables. The clear categorization and organization of the dataset make it easy for users to implement in image classification and deep learning tasks.

Download Dataset

Data Source and Collection

This dataset was curated from reliable public sources. Every image in the dataset has undergone a manual review to ensure its quality and relevance, making it a valuable resource for developing machine learning models for disease detection in the agricultural sector.

Applications

This dataset is highly adaptable and supports a variety of machine learning tasks, such as:



Image Classification

Transfer Learning

Deep Learning


It is especially well-suited for training models to recognize and differentiate between healthy and diseased produce, making it a valuable tool in agricultural technology and food quality monitoring.

Metadata



Total Classes: 28 (14 fruits and vegetables, with separate categories for healthy and rotten produce)

File Types: JPEG/PNG

Image Dimensions: Varies (It is recommended to standardize image sizes during preprocessing for consistent model training.)


Best Practices

To make the most of this dataset, the following steps are recommend:



Preprocessing: Normalize image sizes to improve model performance.

Augmentation: Apply various data augmentation techniques to increase dataset variability and strengthen model generalization.

Model Evaluation: Reserve a portion of the dataset for validation purposes to better fine-tune and evaluate model accuracy.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15100521
title: Fruit and Vegetable Disease Dataset

description: Description:

The Fruit and Vegetable Disease Dataset offers an extensive collection of categorize images to aid in building machine learning models designed to identify diseases affecting various fruits and vegetables. This dataset is particularly suited for image classification tasks and deep learning applications, helping to distinguish between healthy and diseased produce.

Dataset Structure

The dataset is organize into 28 distinct directories, with each directory containing images of 14 different fruits and vegetables, divided into healthy and rotten categories. It is specifically structured to assist in the training and testing of machine learning models that aim to detect diseases in fruits and vegetables. The clear categorization and organization of the dataset make it easy for users to implement in image classification and deep learning tasks.

Download Dataset

Data Source and Collection

This dataset was curated from reliable public sources. Every image in the dataset has undergone a manual review to ensure its quality and relevance, making it a valuable resource for developing machine learning models for disease detection in the agricultural sector.

Applications

This dataset is highly adaptable and supports a variety of machine learning tasks, such as:



Image Classification

Transfer Learning

Deep Learning


It is especially well-suited for training models to recognize and differentiate between healthy and diseased produce, making it a valuable tool in agricultural technology and food quality monitoring.

Metadata



Total Classes: 28 (14 fruits and vegetables, with separate categories for healthy and rotten produce)

File Types: JPEG/PNG

Image Dimensions: Varies (It is recommended to standardize image sizes during preprocessing for consistent model training.)


Best Practices

To make the most of this dataset, the following steps are recommend:



Preprocessing: Normalize image sizes to improve model performance.

Augmentation: Apply various data augmentation techniques to increase dataset variability and strengthen model generalization.

Model Evaluation: Reserve a portion of the dataset for validation purposes to better fine-tune and evaluate model accuracy.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14535759
title: Sensitivity Prediction on Protein Protein Interaction Networks

description: This repository contains all the necessary data to train the Deep Graph Network model on the sensitivity prediction task from Protein Protein interaction networks task, as presented in the work

The training and prediction code are available in the paper github repository.

The data is structured as follows:



training_data directory contains all the needed data to reproduce the DGN training.



folds contains the k-fold splitting files for each use case presented in the work (UC1, UC2, UC3);

pyg_datalists contains all the subgraphs induced by the DyPPIN dataset, converted in pyg format. There is a datalist for each protein features set, so



io.pkl has only the input and output features;

io+emb.pkl adds the compressed protein sequence embeddings to the I/O features;

io+onehot.pkl adds the onehot encoding of the protein identifiers to the I/O features.





prediction_data directory contains the data necessary to use the trained DGN to predict sensitivity on arbitrary PPIN subgraphs.



biogrid_ppi_with_nodes_having_embedding.pkl contains a PyTorch geometric data object containing the BioGRID interactome restricted to proteins having a precomputed protein sequence embedding, made available via UniProt.

biogrid_graphs_stats.csv contains statistics about the PPIN subgraphs used during training, which are used to estimate prediction reliability.

dyppin_proteins.txt contains a list of all the proteins present in the PPIN subgraphs of the training dataset.

uniprot_embeddings_pca_128.pkl contains the protein sequence embeddings compressed via PCA.

ckpts contains the needed model checkpoints needed for the sensitivity prediction.



case_study_data contains the data relative to the BACH2 case study, presented in Section 4 of the paper.



graphs directory contains the PPIN subgraphs relative to the considered Reactome pathways; here we also provide graphs visualizations in pdf format, where we highlight in green the nodes of interest for the case study (INS, GCG, BACH2, AFF3, CUX2). Node labels refer to the BioGRID identifiers.

case_study_predictions.tsv contains the DGN model predictions with statistics relative to the data samples;
DOI: 10.5281/zenodo.14535760
title: Sensitivity Prediction on Protein Protein Interaction Networks

description: This repository contains all the necessary data to train the Deep Graph Network model on the sensitivity prediction task from Protein Protein interaction networks task, as presented in the work

The training and prediction code are available at .

The data is structured as follows:



training_data directory contains all the needed data to reproduce the DGN training.



folds contains the k-fold splitting files for each use case presented in the work (UC1, UC2, UC3);

pyg_datalists contains all the subgraphs induced by the DyPPIN dataset, converted in pyg format. There is a datalist for each protein features set, so



io.pkl has only the input and output features;

io+emb.pkl adds the compressed protein sequence embeddings to the I/O features;

io+onehot.pkl adds the onehot encoding of the protein identifiers to the I/O features.





prediction_data directory contains the data necessary to use the trained DGN to predict sensitivity on arbitrary PPIN subgraphs.



biogrid_ppi_with_nodes_having_embedding.pkl contains a PyTorch geometric data object containing the BioGRID interactome restricted to proteins having a precomputed protein sequence embedding, made available via UniProt.

biogrid_graphs_stats.csv contains statistics about the PPIN subgraphs used during training, which are used to estimate prediction reliability.

dyppin_proteins.txt contains a list of all the proteins present in the PPIN subgraphs of the training dataset.

uniprot_embeddings_pca_128.pkl contains the protein sequence embeddings compressed via PCA.

ckpts contains the needed model checkpoints needed for the sensitivity prediction.
DOI: 10.5281/zenodo.15081794
title: Sensitivity Prediction on Protein Protein Interaction Networks

description: This repository contains all the necessary data to train the Deep Graph Network model on the sensitivity prediction task from Protein Protein interaction networks task, as presented in the work

The training and prediction code are available in the paper github repository.

The data is structured as follows:



training_data directory contains all the needed data to reproduce the DGN training.



folds contains the k-fold splitting files for each use case presented in the work (UC1, UC2, UC3);

pyg_datalists contains all the subgraphs induced by the DyPPIN dataset, converted in pyg format. There is a datalist for each protein features set, so



io.pkl has only the input and output features;

io+emb.pkl adds the compressed protein sequence embeddings to the I/O features;

io+onehot.pkl adds the onehot encoding of the protein identifiers to the I/O features.





prediction_data directory contains the data necessary to use the trained DGN to predict sensitivity on arbitrary PPIN subgraphs.



biogrid_ppi_with_nodes_having_embedding.pkl contains a PyTorch geometric data object containing the BioGRID interactome restricted to proteins having a precomputed protein sequence embedding, made available via UniProt.

biogrid_graphs_stats.csv contains statistics about the PPIN subgraphs used during training, which are used to estimate prediction reliability.

dyppin_proteins.txt contains a list of all the proteins present in the PPIN subgraphs of the training dataset.

uniprot_embeddings_pca_128.pkl contains the protein sequence embeddings compressed via PCA.

ckpts contains the needed model checkpoints needed for the sensitivity prediction.



case_study_data contains the data relative to the BACH2 case study, presented in Section 4 of the paper.



graphs directory contains the PPIN subgraphs relative to the considered Reactome pathways; here we also provide graphs visualizations in pdf format, where we highlight in green the nodes of interest for the case study (INS, GCG, BACH2, AFF3, CUX2). Node labels refer to the BioGRID identifiers.

case_study_predictions.tsv contains the DGN model predictions with statistics relative to the data samples;
DOI: 10.5281/zenodo.14671343
title: Host plant dataset of Curculionidae Scolytinae of the world: miscellaneous tribes (Part 3)

description: This dataset includes a complete list of host plants, with economic categorization, for 829 scolytine species, belonging to 5 tribes: Hylastini, Hylurgini, Ipini, Phloeosinini, and Polygraphini.
DOI: 10.5281/zenodo.14671342
title: Host plant dataset of Curculionidae Scolytinae of the world: miscellaneous tribes (Part 3)

description: This dataset includes a complete list of host plants, with economic categorization, for 829 scolytine species, belonging to 5 tribes: Hylastini, Hylurgini, Ipini, Phloeosinini, and Polygraphini.
DOI: 10.5281/zenodo.15100558
title: Host plant dataset of Curculionidae Scolytinae of the world: miscellaneous tribes (Part 3)

description: This dataset includes a complete list of host plants, with economic categorization, for 829 scolytine species, belonging to 5 tribes: Hylastini, Hylurgini, Ipini, Phloeosinini, and Polygraphini.
DOI: 10.5281/zenodo.15100256
title: A Test for DTM Generated by MRO CTX Stereo Pair
DOI: 10.5281/zenodo.15100257
title: A Test for DTM Generated by MRO CTX Stereo Pair
DOI: 10.5281/zenodo.15100500
title: List of submitted ideas by citizen scientists in the amai! program (wave 4)

description: List of submitted ideas by citizen scientists in the amai! program (wave 4). The ideas are aggregated by theme (climate, health, mobility and work). Only available in Dutch.
DOI: 10.5281/zenodo.15100501
title: List of submitted ideas by citizen scientists in the amai! program (wave 4)

description: List of submitted ideas by citizen scientists in the amai! program (wave 4). The ideas are aggregated by theme (climate, health, mobility and work). Only available in Dutch.
DOI: 10.25934/pr00005580.6
title: Available IPD datapackage for study 'Randomized, Multicenter, Phase III, Open-Label Study of Alectinib Versus Crizotinib in Treatment-Naive Anaplastic Lymphoma Kinase-Positive Advanced Non-Small Cell Lung Cancer'

description: This randomized, active controlled, multicenter phase III open-label study is designed to evaluate the efficacy and safety of alectinib compared with crizotinib treatment in participants with treatment-naive anaplastic lymphoma kinase-positive (ALK-positive) advanced non-small cell lung cancer (NSCLC). Participants will be randomized in a 1:1 ratio to receive either alectinib, 600 milligrams (mg) orally twice daily (BID), or crizotinib, 250 mg orally BID. Participants will receive treatment until disease progression, unacceptable toxicity, consent withdrawal or death. The study is expected to last approximately 42 months.
DOI: 10.6084/m9.figshare.28683206
title: Original data for soil and water conservation monitoring experiment records

description: This is the original data from the soil and water conservation monitoring experiment record.
DOI: 10.34740/kaggle/dsv/11194970
title: World Weather Repository ( Daily Updating )
DOI: 10.5281/zenodo.14711001
title: Test - carbon footprint

description: This is a test dataset. Please do not use it.
DOI: 10.5281/zenodo.14711002
title: Test - carbon footprint

description: This is a test dataset. Please do not use it.
DOI: 10.5281/zenodo.14711521
title: Test - carbon footprint

description: This is a test dataset. Please do not use it.
DOI: 10.5281/zenodo.14711805
title: Test - carbon footprint

description: This is a test dataset. Please do not use it.
DOI: 10.5281/zenodo.15058811
title: Test - carbon footprint

description: This is a test dataset. Please do not use it.
DOI: 10.5281/zenodo.15059267
title: Test - carbon footprint

description: This is a test dataset. Please do not use it.
DOI: 10.5281/zenodo.15100590
title: Test - carbon footprint

description: This is a test dataset. Please do not use it.
DOI: 10.18712/nsd-md1998-v6
title: Kirkelig årsstatistikk 1998

description: Sikts Kirkedatabase er et resultat av at Sikt på oppdrag fra Kirkerådet registrerer og tilrettelegger menighetenes årsstatistikk. Tallene som presenteres her er Den norske kirkes offisielle kirkestatistikk.

Kirkedatabasen inneholder data fra menighetenes årsstatistikk som bygger på kirkebøkene, menighetenes dagregister og tellinger utført av ansatte i menighetene. Årsstatistikken inneholder blant annet opplysninger om antall døpte, konfirmerte, innmeldte og utmeldte, tall på avholdte gudstjenester, gudstjenestedeltakere, nattverdsdeltakere, innsamlet kollekt, ulike aktiviteter for barn og unge, diakonale og økumeniske tiltak i menighetene m.m. 

Datasettet Kirkelig årsstatistikk 1998 presenterer en oversikt over Den norske kirkes virksomhet slik den utfolder seg på lokalplanet dette året. Statistikken omfatter 1312 enheter: 1311 enkeltsokn, pluss Bjarkøy sokn og Sandsøy sokn, som har levert felles (ble slått sammen i 2000).

Versjon 5.2:
 - La til tidsperiode.

Versjon 5.1:
 - Nøyere utfylling av metadata.

Versjon 5.0:
 - Anonymisering av små tall er justert, er nå mer nøyaktige.

Versjon 4:
- Rett navn på Stavanger domkirke sokn.

Versjon 3:
- Justerte anonymisering av små tall: 1-4 settes til 2,5 for sensitive variabler. For øvrige persondata settes 1-2 til 1,5.

Versjon 2:
- Anonymiserte små tall: 1-5 settes til 3