cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nsa48z
title: Occurrence Download

description: A dataset containing 4621 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Batrachuperus Boulenger, 1878"
 ]
}

 The dataset includes 4621 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056505-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.grz5f8
title: Occurrence Download

description: A dataset containing 757 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Daboia russelii (Shaw & Nodder, 1797)"
 ]
}

 The dataset includes 757 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056506-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7qhcdb
title: Occurrence Download

description: A dataset containing 167620 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Elasmobranchii"
 ]
}

 The dataset includes 167620 records from 338 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056498-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5ks6jh0b
title: MassIVE MSV000096774 - Study of secondary metabolites in Quercus robur leaves
title: Study of secondary metabolites in Quercus robur leaves
DOI: 10.5281/zenodo.14504480
title: RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems

description: RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems

RCAEval benchmark includes three datasets: RE1, RE2, and RE3, designed to comprehensively support benchmarking RCA in microservice systems. Together, our three datasets feature 735 failure cases collected from three microservice systems (Online Boutique, Sock Shop, and Train Ticket) and including 11 fault types. Each failure case also includes annotated root cause service and root cause indicator (e.g., specific metric or log indicating the root cause). The statistics of the datasets are presented below.




File Name
Dataset
Systems
Fault Types
Cases
Metrics
Logs (millions)
Traces (millions)




RE1-*.zip
RE1
3
3 Resource, 2 Network
375
49-212
N/A
N/A


RE2-*.zip
RE2
3
4 Resource, 2 Network
270
77-376
8.6-26.9
39.6-76.7


RE3-*.zip
RE3
3
5 Code-level
90
68-322
1.7-2.7
4.5-4.7




 

RE1 Dataset. The RE1 dataset, introduced in our prior work on metric-based RCA [14], contains 375 failure cases collected from three microservice systems (125 cases per system). These cases combine five fault types across five services, and five repetitions per fault-service pair. The RE1 dataset exclusively contains metrics data, supporting the development of metric-based RCA methods. The fault types in RE1 include CPU, MEM, DISK, DELAY, LOSS. The number of metrics ranges from 49 to 212, depending on the system size, with smaller systems (e.g., Online Boutique, Sock Shop) having fewer metrics compared to larger system (Train Ticket). This dataset does not include logs or traces.

RE2 Dataset. The RE2 dataset, newly collected for this study, supports the development of multi-source RCA methods. It includes 270 failure cases collected from three microservice systems (90 cases per system), combining six fault types across five services, and three repetitions per fault-service pair. RE2 provides multi-source telemetry data, including metrics, logs, and traces. The number of metrics ranges from 77 to 327 per failure case. Each system generates a substantial volume of logs from (8.6 to 26.9 million lines), and traces (39.6 to 76.7 million traces). The fault types include those in RE1 and an additional SOCKET fault.

RE3 Dataset. The RE3 dataset, also newly collected, focuses on supporting multi-source RCA methods with the ability to diagnose code-level faults. It has 90 failure cases (30 per system), involving code-level faults. The fault types in RE3 are F1, F2, F3, F4, F5. Like RE2, RE3 includes multi-source telemetry data (metrics, logs, and traces). This dataset emphasizes diagnosing code-level faults through telemetry data, e.g., leveraging stack traces in logs or response code in traces to pinpoint root causes, making it invaluable for advancing multi-source RCA methods.

The benchmark evaluation framework supports these datasets are in this GitHub repository: https://github.com/phamquiluan/RCAEval
DOI: 10.5281/zenodo.14504481
title: RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems

description: RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems

RCAEval benchmark includes three datasets: RE1, RE2, and RE3, designed to comprehensively support benchmarking RCA in microservice systems. Together, our three datasets feature 735 failure cases collected from three microservice systems (Online Boutique, Sock Shop, and Train Ticket) and including 11 fault types. Each failure case also includes annotated root cause service and root cause indicator (e.g., specific metric or log indicating the root cause). The statistics of the datasets are presented below.




File Name
Dataset
Systems
Fault Types
Cases
Metrics
Logs (millions)
Traces (millions)




RE1.zip
RE1
3
3 Resource, 2 Network
375
49-212
N/A
N/A


RE2.zip
RE2
3
4 Resource, 2 Network
270
77-376
8.6-26.9
39.6-76.7


RE3.zip
RE3
3
5 Code-level
90
68-322
1.7-2.7
4.5-4.7




 

RE1 Dataset. The RE1 dataset, introduced in our prior work on metric-based RCA [14], contains 375 failure cases collected from three microservice systems (125 cases per system). These cases combine five fault types across five services, and five repetitions per fault-service pair. The RE1 dataset exclusively contains metrics data, supporting the development of metric-based RCA methods. The fault types in RE1 include CPU, MEM, DISK, DELAY, LOSS. The number of metrics ranges from 49 to 212, depending on the system size, with smaller systems (e.g., Online Boutique, Sock Shop) having fewer metrics compared to larger system (Train Ticket). This dataset does not include logs or traces.

RE2 Dataset. The RE2 dataset, newly collected for this study, supports the development of multi-source RCA methods. It includes 270 failure cases collected from three microservice systems (90 cases per system), combining six fault types across five services, and three repetitions per fault-service pair. RE2 provides multi-source telemetry data, including metrics, logs, and traces. The number of metrics ranges from 77 to 327 per failure case. Each system generates a substantial volume of logs from (8.6 to 26.9 million lines), and traces (39.6 to 76.7 million traces). The fault types include those in RE1 and an additional SOCKET fault.

RE3 Dataset. The RE3 dataset, also newly collected, focuses on supporting multi-source RCA methods with the ability to diagnose code-level faults. It has 90 failure cases (30 per system), involving code-level faults. The fault types in RE3 are F1, F2, F3, F4, F5. Like RE2, RE3 includes multi-source telemetry data (metrics, logs, and traces). This dataset emphasizes diagnosing code-level faults through telemetry data, e.g., leveraging stack traces in logs or response code in traces to pinpoint root causes, making it invaluable for advancing multi-source RCA methods.

The benchmark evaluation framework supports these datasets are in this GitHub repository: https://github.com/phamquiluan/RCAEval
DOI: 10.5281/zenodo.14590730
title: RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems

description: RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems

RCAEval benchmark includes three datasets: RE1, RE2, and RE3, designed to comprehensively support benchmarking RCA in microservice systems. Together, our three datasets feature 735 failure cases collected from three microservice systems (Online Boutique, Sock Shop, and Train Ticket) and including 11 fault types. Each failure case also includes annotated root cause service and root cause indicator (e.g., specific metric or log indicating the root cause). The statistics of the datasets are presented below.




File Name
Dataset
Systems
Fault Types
Cases
Metrics
Logs (millions)
Traces (millions)




RE1-*.zip
RE1
3
3 Resource, 2 Network
375
49-212
N/A
N/A


RE2-*.zip
RE2
3
4 Resource, 2 Network
270
77-376
8.6-26.9
39.6-76.7


RE3-*.zip
RE3
3
5 Code-level
90
68-322
1.7-2.7
4.5-4.7




 

RE1 Dataset. The RE1 dataset, introduced in our prior work on metric-based RCA [14], contains 375 failure cases collected from three microservice systems (125 cases per system). These cases combine five fault types across five services, and five repetitions per fault-service pair. The RE1 dataset exclusively contains metrics data, supporting the development of metric-based RCA methods. The fault types in RE1 include CPU, MEM, DISK, DELAY, LOSS. The number of metrics ranges from 49 to 212, depending on the system size, with smaller systems (e.g., Online Boutique, Sock Shop) having fewer metrics compared to larger system (Train Ticket). This dataset does not include logs or traces.

RE2 Dataset. The RE2 dataset, newly collected for this study, supports the development of multi-source RCA methods. It includes 270 failure cases collected from three microservice systems (90 cases per system), combining six fault types across five services, and three repetitions per fault-service pair. RE2 provides multi-source telemetry data, including metrics, logs, and traces. The number of metrics ranges from 77 to 327 per failure case. Each system generates a substantial volume of logs from (8.6 to 26.9 million lines), and traces (39.6 to 76.7 million traces). The fault types include those in RE1 and an additional SOCKET fault.

RE3 Dataset. The RE3 dataset, also newly collected, focuses on supporting multi-source RCA methods with the ability to diagnose code-level faults. It has 90 failure cases (30 per system), involving code-level faults. The fault types in RE3 are F1, F2, F3, F4, F5. Like RE2, RE3 includes multi-source telemetry data (metrics, logs, and traces). This dataset emphasizes diagnosing code-level faults through telemetry data, e.g., leveraging stack traces in logs or response code in traces to pinpoint root causes, making it invaluable for advancing multi-source RCA methods.

The benchmark evaluation framework supports these datasets are in this GitHub repository: https://github.com/phamquiluan/RCAEval
DOI: 10.6084/m9.figshare.26021314.v3
title: MSDCW Dataset and Code

description: The MSDCW dataset, its code, and the list of 985 cities. Note that data for Americas, Asia, and Europe are compressed into 7, 11, and 5 files. Users need to download all files corresponding to the same continent to successfully decompress the data.
DOI: 10.34740/kaggle/dsv/10369922
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.5281/zenodo.7380051
title: ECFAS Mapping products, Deliverable 5.5 – Mapping products – ECFAS Project (GA 101004211), www.ecfas.eu

description: The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.
The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. 
Description of the product
The deliverable includes a document and a compressed directory with ready-to-print maps, the associated geospatial datasets and symbology files. All products were developed under Task 5.5 – Mapping products.The document reports on the activities conducted during Task 5.5 – Mapping products that aimed at demonstrating the added value of the ECFAS products and propose adequate representation for communication and integration into the Copernicus Emergency Management Services. Five Demonstration cases, corresponding each to one recent coastal event and one location, were selected to highlight the ECFAS products resulting of the coastal Flood, Impacts and Shoreline position tools developed previously during the ECFAS activities. The Mapping products prepared for each demonstration case are provided along the document in cartographic formats (ready-to-print maps), along with the symbology files and geospatial datasets that were used to create the products.
This dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of this dataset are licensed under the Open Database License: http://opendatacommons.org/licenses/dbcl/1.0/.
This Report is made available under the Creative Commons Attribution 4.0 International License.
Disclaimer:
ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.
This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211
DOI: 10.5281/zenodo.7488750
title: ECFAS Mapping products, Deliverable 5.5 – Mapping products – ECFAS Project (GA 101004211), www.ecfas.eu

description: The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.
The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. 
Description of the product
The deliverable includes a document and a compressed directory with ready-to-print maps, the associated geospatial datasets and symbology files. All products were developed under Task 5.5 – Mapping products.The document reports on the activities conducted during Task 5.5 – Mapping products that aimed at demonstrating the added value of the ECFAS products and propose adequate representation for communication and integration into the Copernicus Emergency Management Services. Five Demonstration cases, corresponding each to one recent coastal event and one location, were selected to highlight the ECFAS products resulting of the coastal Flood, Impacts and Shoreline position tools developed previously during the ECFAS activities. The Mapping products prepared for each demonstration case are provided along the document in cartographic formats (ready-to-print maps), along with the symbology files and geospatial datasets that were used to create the products.
This dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of this dataset are licensed under the Open Database License: http://opendatacommons.org/licenses/dbcl/1.0/.
This Report is made available under the Creative Commons Attribution 4.0 International License.
Disclaimer:
ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.
This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211
DOI: 10.34740/kaggle/dsv/10369970
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.6084/m9.figshare.28090244
title: MSRI_datasets

description: This is a test dataset for geospatial data matching, including simulated urban and rural roads.
DOI: 10.6084/m9.figshare.28090244.v4
title: NewLine_2_rural.ply

description: This is a test dataset for geospatial data matching, including simulated urban and rural roads.
DOI: 10.25345/c5g15tp6w
title: MassIVE MSV000096775 - GNPS_essential oil_molecular networking_4.1.2024
title: Essential oil showing antimicrobial activity against pneumonia causing bacteria and ms spectra file
DOI: 10.5281/zenodo.14598800
title: Pilot data Articles 2013-2015

description: Image of the Belt and Road Initiative in Slovak Media (ViewBRI)

Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V04-00128
DOI: 10.5281/zenodo.14598801
title: Pilot data Articles 2013-2015

description: Image of the Belt and Road Initiative in Slovak Media (ViewBRI)

Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V04-00128
DOI: 10.15468/dl.rmyrst
title: Occurrence Download

description: A dataset containing 9525 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bivalvia"
 ]
}

 The dataset includes 9525 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056507-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8ehkg
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Enteromius manicensis (Pellegrin, 1919)"
 ]
}

 The dataset includes 61 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056508-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/10369992
title: GDP Growth of European Countries
DOI: 10.15468/dl.9xcts7
title: Occurrence Download

description: A dataset containing 705 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Corydalis sheareri S.Moore"
 ]
}

 The dataset includes 705 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056509-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s8czmt
title: Occurrence Download

description: A dataset containing 1421 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Erigeron annuus (L.) Pers."
 ]
}

 The dataset includes 1421 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056510-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ttknxz
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ormosia microphylla Merr."
 ]
}

 The dataset includes 22 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056512-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14598880
title: ptychadena_levenorum_spades_01.fasta.gz
DOI: 10.5281/zenodo.14598881
title: ptychadena_levenorum_spades_01.fasta.gz
DOI: 10.34740/kaggle/dsv/9392095
title: Predict People Personality Types
DOI: 10.6084/m9.figshare.28137290
title: Scallop paper data

description: Full dataset will be uploaded upon acceptance of the paper.
DOI: 10.6084/m9.figshare.28137290.v1
title: Scallop paper data

description: Full dataset will be uploaded upon acceptance of the paper.
DOI: 10.34740/kaggle/dsv/10370142
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.5281/zenodo.14598890
title: syringodium_filiforme_spades_01.fasta.gz
DOI: 10.5281/zenodo.14598891
title: syringodium_filiforme_spades_01.fasta.gz
DOI: 10.5281/zenodo.14598906
title: eriocaulon_aquaticum_spades_01.fasta.gz
DOI: 10.5281/zenodo.14598907
title: eriocaulon_aquaticum_spades_01.fasta.gz
DOI: 10.34740/kaggle/dsv/10370159
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.34740/kaggle/dsv/10370158
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.15468/dl.kuc8xr
title: Occurrence Download

description: A dataset containing 6483 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Living Specimen, Occurrence evidence)",
 "Country is China",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 6483 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056514-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14598924
title: anethum_graveolens_spades_01.fasta.gz
DOI: 10.5281/zenodo.14598925
title: anethum_graveolens_spades_01.fasta.gz
DOI: 10.5281/zenodo.11025748
title: Myopic Maculopathy Analysis Challenge Dataset

description: The dataset used in the Myopic Maculopathy Analysis Challenge (MMAC).

If you use this dataset for your research, please cite our paper:

@article{qian2024competition,title={A competition for the diagnosis of myopic maculopathy by artificial intelligence algorithms},author={Qian, Bo and Sheng, Bin and Chen, Hao and Wang, Xiangning and Li, Tingyao and Jin, Yixiao and Guan, Zhouyu and Jiang, Zehua and Wu, Yilan and Wang, Jinyuan and others},journal={JAMA ophthalmology},volume={142},number={11},pages={1006--1015},year={2024},publisher={American Medical Association}}
DOI: 10.5281/zenodo.11025749
title: Myopic Maculopathy Analysis Challenge Dataset

description: The dataset used in the Myopic Maculopathy Analysis Challenge (MMAC).

If you use this dataset for your research, please cite our paper:

@article{qian2024competition,title={A competition for the diagnosis of myopic maculopathy by artificial intelligence algorithms},author={Qian, Bo and Sheng, Bin and Chen, Hao and Wang, Xiangning and Li, Tingyao and Jin, Yixiao and Guan, Zhouyu and Jiang, Zehua and Wu, Yilan and Wang, Jinyuan and others},journal={JAMA ophthalmology},volume={142},number={11},pages={1006--1015},year={2024},publisher={American Medical Association}}