cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11066698
title: State of Practice and Challenges of Issue Management for Component-based and Service-Oriented Systems: An Empirical Study

description: Modern software systems often consist of independently developed components, e.g., libraries or (micro-)services, that can be combined to form a larger architecture. Many of these components are developed externally, i.e., in other projects by other teams, which is particularly the case in service-oriented architectures. Even though the components are independently developed, bugs may arise that propagate along the call chains, which sometimes negatively affect the calling components. However, as such components are typically managed in separate issue management systems (IMS), identifying that a downstream component is affected by an upstream component's issues is complex and time-consuming. Even though some scientific works and IMS forums discuss how to manage such cross-component issues, there is no systematic study on the state of practice and the challenges of issue management when different components are affected. Therefore, this study aims to determine which types of components are usually included in modern software projects, how developers identify and manage issue propagation between components, and how often they face such propagations. To get these insights, we conducted an empirical study through a questionnaire with 64 industry experts and open-source contributors and interviewed seven of them. Our key takeaways help researchers and software engineers to understand possible impacts and to improve cross-component issue management. We identified, for example, that related issues currently are non-semantically linked via URLs in issue comments.
DOI: 10.5281/zenodo.11066699
title: State of Practice and Challenges of Issue Management for Component-based and Service-Oriented Systems: An Empirical Study

description: Modern software systems often consist of independently developed components, e.g., libraries or (micro-)services, that can be combined to form a larger architecture. Many of these components are developed externally, i.e., in other projects by other teams, which is particularly the case in service-oriented architectures. Even though the components are independently developed, bugs may arise that propagate along the call chains, which sometimes negatively affect the calling components. However, as such components are typically managed in separate issue management systems (IMS), identifying that a downstream component is affected by an upstream component's issues is complex and time-consuming. Even though some scientific works and IMS forums discuss how to manage such cross-component issues, there is no systematic study on the state of practice and the challenges of issue management when different components are affected. Therefore, this study aims to determine which types of components are usually included in modern software projects, how developers identify and manage issue propagation between components, and how often they face such propagations. To get these insights, we conducted an empirical study through a questionnaire with 64 industry experts and open-source contributors and interviewed seven of them. Our key takeaways help researchers and software engineers to understand possible impacts and to improve cross-component issue management. We identified, for example, that related issues currently are non-semantically linked via URLs in issue comments.
DOI: 10.5281/zenodo.11082103
title: State of Practice and Challenges of Issue Management for Component-based and Service-Oriented Systems: An Empirical Study

description: Modern software systems often consist of independently developed components, e.g., libraries or (micro-)services, that can be combined to form a larger architecture. Many of these components are developed externally, i.e., in other projects by other teams, which is particularly the case in service-oriented architectures. Even though the components are independently developed, bugs may arise that propagate along the call chains, which sometimes negatively affect the calling components. However, as such components are typically managed in separate issue management systems (IMS), identifying that a downstream component is affected by an upstream component's issues is complex and time-consuming. Even though some scientific works and IMS forums discuss how to manage such cross-component issues, there is no systematic study on the state of practice and the challenges of issue management when different components are affected. Therefore, this study aims to determine which types of components are usually included in modern software projects, how developers identify and manage issue propagation between components, and how often they face such propagations. To get these insights, we conducted an empirical study through a questionnaire with 64 industry experts and open-source contributors and interviewed seven of them. Our key takeaways help researchers and software engineers to understand possible impacts and to improve cross-component issue management. We identified, for example, that related issues currently are non-semantically linked via URLs in issue comments.
DOI: 10.6084/m9.figshare.27131796
title: Data files supporting the manuscript: <b>Nitisinone’s mosquitocidal properties hold promise for malaria control</b>

description: Created on 29 Sep 2024 - 16:28 by Lee HainesThe project describes a potential method for reducing the spread of diseases carried by insects, specifically mosquitoes that transmit malaria. The method involves using drugs that make the blood of animals or humans toxic to insects that feed on blood. The key point is that a specific enzyme called 4- hydroxyphenylpyruvate dioxygenase, HPPD, which is important for blood-feeding insects like mosquitoes, can be targeted. A drug called nitisinone, which is already approved by the FDA to treat rare human diseases linked to tyrosine metabolism, can inhibit this enzyme.We further characterise nitisinone's activity - when mosquitoes were fed human blood containing nitisinone, it killed both young and old mosquitoes, and those resistant to other insecticides. In side by side comparisons to another similar drug named ivermectin, nitisinone had a better killing profile for mosquitoes. Additionally, people with a rare genetic condition called alkaptonuria, who therapeutically ingest a low daily dose of nitisinone (2 mg/day), have blood that kills mosquitoes. This suggests that using nitisinone to inhibit this enzyme could be a new way to help control malaria.
DOI: 10.18112/openneuro.ds005512.v1.0.0
title: Healthy Brain Network EEG- Release 8
DOI: 10.15468/dl.zsty65
title: Occurrence Download

description: A dataset containing 22712 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Geometry POLYGON((-90 35,-50 35,-50 63,-90 63,-90 35))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Country derived from coordinates, Continent derived from coordinates, Coordinate rounded)",
 "OccurrenceStatus is Present",
 "TaxonKey is Vulpes vulpes (Linnaeus, 1758)"
 ]
}

 The dataset includes 22712 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034597-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3krj3b
title: Occurrence Download

description: A dataset containing 960 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alaria esculenta (L.) Grev.",
 "Year 2013-2023"
 ]
}

 The dataset includes 960 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034598-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18112/openneuro.ds005514.v1.0.0
title: Healthy Brain Network EEG- Release 9
DOI: 10.15468/dl.7rnusc
title: Occurrence Download

description: A dataset listing the 7 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Geometry POLYGON((-90 35,-50 35,-50 63,-90 63,-90 35))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Country derived from coordinates, Continent derived from coordinates, Coordinate rounded)",
 "OccurrenceStatus is Present",
 "TaxonKey is Vulpes vulpes (Linnaeus, 1758)"
 ]
}

 The dataset's 7 records were derived from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034596-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13858293
title: Specific Humidity at 10-m
DOI: 10.5281/zenodo.13858294
title: Specific Humidity at 10-m
DOI: 10.18112/openneuro.ds005516.v1.0.0
title: Healthy Brain Network EEG- Release 11
DOI: 10.18112/openneuro.ds005515.v1.0.0
title: Healthy Brain Network EEG- Release 10
DOI: 10.18112/openneuro.ds005517.v1.0.0
title: Healthy Brain Network EEG- Release 12
DOI: 10.5281/zenodo.13858179
title: LFP from Mice V1

description: Two Neuropixels recording, one vertical and one tangential, in awaka V1 from mice.
DOI: 10.5281/zenodo.13858180
title: LFP from Mice V1

description: Two Neuropixels recording, one vertical and one tangential, in awaka V1 from mice.
DOI: 10.15468/dl.7qxysp
title: Occurrence Download

description: A dataset containing 1372 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Hypanus guttatus (Bloch & Schneider, 1801), Dasyatis americana Hildebrand & Schroeder, 1928)"
 ]
}

 The dataset includes 1372 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034600-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pfu6pn
title: Occurrence Download

description: A dataset containing 22712 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Geometry POLYGON((-90 35,-50 35,-50 63,-90 63,-90 35))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Country derived from coordinates, Continent derived from coordinates, Coordinate rounded)",
 "OccurrenceStatus is Present",
 "TaxonKey is Vulpes vulpes (Linnaeus, 1758)"
 ]
}

 The dataset includes 22712 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034601-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kq6baf
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Specimen, Living Specimen)",
 "Geometry POLYGON((-107.54543 24.88027,-107.4471 24.92159,-107.30943 24.85929,-107.13558 24.52611,-107.04425 24.44527,-107.05822 24.31661,-106.97641 24.19636,-106.59078 23.97719,-106.52475 23.67458,-106.46023 23.60736,-106.39834 23.34463,-106.34796 23.29399,-106.34892 23.18811,-106.34763 23.2941,-106.39801 23.34476,-106.45991 23.60751,-106.52441 23.6747,-106.59048 23.97739,-106.97614 24.19658,-107.05787 24.31669,-107.04391 24.4454,-107.13529 24.5263,-107.30914 24.85949,-107.44711 24.92192,-107.54543 24.88027))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EX, EW, CR, EN, VU, NT, LC, DD, NE)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2004-2024"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034604-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ejysr7
title: Occurrence Download

description: A dataset containing 428 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hypanus guttatus (Bloch & Schneider, 1801)"
 ]
}

 The dataset includes 428 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034605-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13858305
title: Insect Classification

description: The data utilized in our experiments was obtained from the Barcode of Life Data System (BOLD), which is a cloud-based data storage and analysis platform developedat the Centre for Biodiversity Genomics in Canada. The data consisted of 32424 image samples of insect species from four Insecta orders, Diptera, Coleoptera, Lepidoptera and Hymenoptera, each associated with a DNA barcode sequence of that specie.

 

Description of the .mat:

# Insect Dataset

* all_images: vector containing the 32424 64x64x3 images (RGB) pre normalized of the insects* all_dnas: vector containing the 32424 DNA barcodes in one-hot encoding 658x5* all_labels: vector containing the species label for the corresponding DNA and image* all_boldids: vector of strings containing the id from boldsystemsv3 (https://v3.boldsystems.org/) they can be used to download from boldsystems the original DNA barcodes and the full size images and other data related to the sample* train_loc: indices of the training samples in all_dnas, all_images, all_labels, all_boldids* val_seen_loc: indices of the validation samples in all_dnas, all_images, all_labels, all_boldids that contain described(seen) species* val_unseen_loc: indices of the validation samples in all_dnas, all_images, all_labels, all_boldids that contain undescribed(unseen) species* test_seen_loc: indices of the test samples in all_dnas, all_images, all_labels, all_boldids that contain described(seen) species* test_unseen_loc: indices of the test samples in all_dnas, all_images, all_labels, all_boldids that contain undescribed(unseen) species* species2genus: the vector contains at index i the genus label of species with label i (e.g. species i has genus species2genus[i])* described_species_labels_train: vector containing the labels of species that appear in the training set* described_species_labels_trainval: vector containing the labels of species that appear in the training set and/or the validation set* all_dna_features_cnn_original: vector of features extractedfrom DNA nucleotides with the method of Badirli, S., Picard, C. J., Mohler, G.,Richert, F., Akata, Z., & Dundar, M. (2023). Classifying the unknown: Insect identification with deep hierarchical Bayesian learning. Methods in Ecology and Evolution, 14,1515-1530. https://doi.org/10.1111/2041-210X.14104* all_image_features_resnet: vector of features extracted from the insect images with the method of the same paper as the all_dna_features_cnn_original with a pretrained resnet101* all_dna_features_cnn_new: vector of features extracted from DNA nucleotides with our CNN* all_image_features_gan: vector of features extracted from the insect images with out method using a ReACGAN

Note: all arrays and locs are 1-indexed like in MATLAB
DOI: 10.15468/dl.arbdbt
title: Occurrence Download

description: A dataset containing 695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Solanum pseudocapsicum L."
 ]
}

 The dataset includes 695 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034607-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13858315
title: Air-temperature at 10-m
DOI: 10.5281/zenodo.13858316
title: Air-temperature at 10-m
DOI: 10.15468/dl.9dbgyn
title: Occurrence Download

description: A dataset containing 144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Chloraea galeata Lindl."
 ]
}

 The dataset includes 144 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034608-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13858332
title: DESI 2023 details for AI indicator

description: The following data has been selected from EUROSTAT isoc_eb_ai database:



Enterprises don't use any AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use one AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use two AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use three AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use four AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use AI technologies performing analysis of written language (text mining)

Enterprises use AI technologies converting spoken language into machine-readable format (speech recognition)

Enterprises use AI technologies generating written or spoken language (natural language generation)

Enterprises use AI technologies identifying objects or persons based on images (image recognition, image processing)

Enterprises use machine learning (e.g. deep learning) for data analysis

Enterprises use AI technologies automating different workflows or assisting in decision making (AI based software robotic process automation)

Enterprises use AI technologies enabling physical movement of machines via autonomous decisions based on observation of surroundings (autonomous robots, self-driving vehicles, autonomous drones)

Enterprises use at least one of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises don't use any of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises use at least two of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises use at least three of the  AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises use AI technologies for marketing or sales

Enterprises use AI technologies for production processes

Enterprises use AI technologies for organisation of business administration processes

Enterprises use AI technologies for management of enterprises

Enterprises use AI technologies for logistics

Enterprises use AI technologies for ICT security

Enterprises use AI technologies for human resources management or recruiting

Enterprises use AI technologies for at least one of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR

Enterprises use AI technologies for at least two of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR

Enterprises use AI technologies for at least three of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR
DOI: 10.5281/zenodo.13858333
title: DESI 2023 details for AI indicator

description: The following data has been selected from EUROSTAT isoc_eb_ai database:



Enterprises don't use any AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use one AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use two AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use three AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use four AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)

Enterprises use AI technologies performing analysis of written language (text mining)

Enterprises use AI technologies converting spoken language into machine-readable format (speech recognition)

Enterprises use AI technologies generating written or spoken language (natural language generation)

Enterprises use AI technologies identifying objects or persons based on images (image recognition, image processing)

Enterprises use machine learning (e.g. deep learning) for data analysis

Enterprises use AI technologies automating different workflows or assisting in decision making (AI based software robotic process automation)

Enterprises use AI technologies enabling physical movement of machines via autonomous decisions based on observation of surroundings (autonomous robots, self-driving vehicles, autonomous drones)

Enterprises use at least one of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises don't use any of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises use at least two of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises use at least three of the  AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR

Enterprises use AI technologies for marketing or sales

Enterprises use AI technologies for production processes

Enterprises use AI technologies for organisation of business administration processes

Enterprises use AI technologies for management of enterprises

Enterprises use AI technologies for logistics

Enterprises use AI technologies for ICT security

Enterprises use AI technologies for human resources management or recruiting

Enterprises use AI technologies for at least one of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR

Enterprises use AI technologies for at least two of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR

Enterprises use AI technologies for at least three of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR
DOI: 10.5281/zenodo.13858352
title: trachylepis_maculilabris_scaffolds.fasta.gz
DOI: 10.5281/zenodo.13858353
title: trachylepis_maculilabris_scaffolds.fasta.gz
DOI: 10.15468/dl.n7xh6e
title: Occurrence Download

description: A dataset listing the 4 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Specimen, Living Specimen)",
 "Geometry POLYGON((-107.54543 24.88027,-107.4471 24.92159,-107.30943 24.85929,-107.13558 24.52611,-107.04425 24.44527,-107.05822 24.31661,-106.97641 24.19636,-106.59078 23.97719,-106.52475 23.67458,-106.46023 23.60736,-106.39834 23.34463,-106.34796 23.29399,-106.34892 23.18811,-106.34763 23.2941,-106.39801 23.34476,-106.45991 23.60751,-106.52441 23.6747,-106.59048 23.97739,-106.97614 24.19658,-107.05787 24.31669,-107.04391 24.4454,-107.13529 24.5263,-107.30914 24.85949,-107.44711 24.92192,-107.54543 24.88027))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EX, EW, CR, EN, VU, NT, LC, DD, NE)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2004-2024"
 ]
}

 The dataset's 4 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034602-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.