cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18405327
title: Cybersecurity dataset of water distribution plant communications under MQTT topic hijacking attacks

description: This dataset captures MQTT traffic from a water distribution control system under topic hijacking attacks, where the communication routing is maliciously altered without modifying message payloads.

The attack consists of manipulating the plant identifier within MQTT topics of the form:



optimizacion/planta_nivelN/parametros

optimizacion/planta_nivelN/consigna


During attack campaigns, messages originally addressed to a given plant are redirected to a different plant (e.g., plant 1 ↔ plant 2), causing control commands and process measurements to be delivered to unintended recipients. This results in logical inconsistencies between the physical process and the supervisory control layer.

Each affected message includes the field nuevo_topic, enabling traceability of the redirection. Campaign durations and cooldown periods are variable and follow a probabilistic model, simulating realistic attacker behavior.

 

Acknowledgements

This activity is carried out in execution of the Strategic Project "Critical infrastructures cybersecure through intelligent modeling of attacks, vulnerabilities and increased security of their IoT devices for the water supply sector" (C061/23), the result of a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of A Coruña. This initiative is carried out within the framework of the funds of the Recovery, Transformation and Resilience Plan, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for the solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.
DOI: 10.5281/zenodo.18405328
title: Cybersecurity dataset of water distribution plant communications under MQTT topic hijacking attacks

description: This dataset captures MQTT traffic from a water distribution control system under topic hijacking attacks, where the communication routing is maliciously altered without modifying message payloads.

The attack consists of manipulating the plant identifier within MQTT topics of the form:



optimizacion/planta_nivelN/parametros

optimizacion/planta_nivelN/consigna


During attack campaigns, messages originally addressed to a given plant are redirected to a different plant (e.g., plant 1 ↔ plant 2), causing control commands and process measurements to be delivered to unintended recipients. This results in logical inconsistencies between the physical process and the supervisory control layer.

Each affected message includes the field nuevo_topic, enabling traceability of the redirection. Campaign durations and cooldown periods are variable and follow a probabilistic model, simulating realistic attacker behavior.

 

Acknowledgements

This activity is carried out in execution of the Strategic Project "Critical infrastructures cybersecure through intelligent modeling of attacks, vulnerabilities and increased security of their IoT devices for the water supply sector" (C061/23), the result of a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of A Coruña. This initiative is carried out within the framework of the funds of the Recovery, Transformation and Resilience Plan, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for the solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.
DOI: 10.15468/dl.u5m263
title: Occurrence Download

description: A dataset containing 2982 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Yucca aloifolia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2982 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014313-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3jmt7h
title: Occurrence Download

description: A dataset containing 2988 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is Italy",
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Myocastor coypus"
 ]
}

 The dataset includes 2988 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014315-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.waz3pb
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "StateProvince is one of (Lazio, Abruzzo)",
 "TaxonKey is Poa annua"
 ]
}

 The dataset includes 21 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014314-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6gmnc9
title: Occurrence Download

description: A dataset containing 490 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Xantusia wigginsi"
 ]
}

 The dataset includes 490 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014319-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ttsu2
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cleistopholis staudtii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 166 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014320-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17662150
title: Data for Lorentz Invariance Violation Study with GRB 221009A

description: Repository for

Title of the paper: Is There New Physics Beyond 30 TeV in the BOAT?  Authors: Filip Rescic, Luis Recabarren Vergara, Michele Doro, Tomislav Terzić  ArXiv: https://arxiv.org/abs/2511.15542

Overview

This repository contains the data tables (.csv) used to generate the figures presented in the above paper.  Each CSV file corresponds to a specific set of points or an attenuation curve from the manuscript and includes the processed numerical values used to produce the results.

The goal is to ensure transparency, reproducibility, and ease of reuse.

Acronyms: SR – Special Relativity, LIV – Lorentz Invariance Violation, GRB – Gamma-Ray Burst.

Repository Contents

Figure 1 data (5 time intervals from GRB 221009A):- boat_data_5_14.csv  - boat_data_14_22.csv  - boat_data_22_100.csv  - boat_data_100_674.csv  - boat_data_674_1774.csv  

Attenuation tables for GRB 221009A:  - boat_sr.csv — SR case  - boat_liv.csv — LIV case  - boat_liv_dom.csv — LIV case with Dominguez EBL 

Attenuation tables for PKS 2155-304 (Fig. 3):- pks2155_sr.csv — SR case  - pks2155_liv.csv — LIV case  

SWGO sensitivity (Fig. 3):- swgo_1yrsensitivity_WP.csv  

LHAASO sensitivity (Fig. 1): - lhaaso_1yrsensitivity.csvCTAO sensitivity (Fig. 3):- ctao_south50h.csv

References for sensitivity curves and EBL model:- SWGO sensitivity: Ref. 33 — https://arxiv.org/abs/2506.01786  - LHAASO sensitivity: Ref. 32 — https://iopscience.iop.org/article/10.1088/1742-6596/718/5/052043  - EBL model: Saldana-Lopez et al. (2021), Ref. 4 — https://academic.oup.com/mnras/article/507/4/5144/6359154    (ArXiv: https://arxiv.org/abs/2012.03035)

Column Descriptions and Units

GRB 221009A spectral points (5 interval files):Files: boat_data_5_14.csv, boat_data_14_22.csv, boat_data_22_100.csv, boat_data_100_674.csv, boat_data_674_1774.csv  Columns:  - E — TeV  - E2dNdE — erg cm⁻² s⁻¹

GRB 221009A attenuation (SR):File: boat_sr.csv  Columns:  - atten_sr — dimensionless attenuation factor  - energy — eV

GRB 221009A attenuation (LIV):File: boat_liv.csv  Columns:  - atten_LIV_lambda_1 … atten_LIV_lambda_7 — dimensionless attenuation factors  - energy — eV

PKS 2155-304 attenuation (SR):File: pks2155_sr.csv  Columns:  - atten_sr — dimensionless  - energy — eV

PKS 2155-304 attenuation (LIV):  File: pks2155_liv.csv  Columns:  - atten_LIV_lambda_1 … atten_LIV_lambda_4 — dimensionless  - energy — eV

SWGO sensitivity curves: Files: swgo_1yrsensitivity_WP.csv, swgo_5yrsensitivity_WP.csv  Columns:  - E — TeV  - E2dNdE — TeV cm⁻² s⁻¹

LHAASO sensitivity: File: lhaaso_1yrsensitivity.csv  Columns:  - E — TeV  - E2dNdE — erg cm⁻² s⁻¹

Note:  The "atten" columns represent attenuation (survival probability) factors that multiply the intrinsic flux in Eq. (2).
DOI: 10.5281/zenodo.18496929
title: Data for Lorentz Invariance Violation Study with GRB 221009A

description: Repository for

Title of the paper: Is There New Physics Beyond 30 TeV in the BOAT?  Authors: Filip Rescic, Luis Recabarren Vergara, Michele Doro, Tomislav Terzić  ArXiv: https://arxiv.org/abs/2511.15542

Overview

This repository contains the data tables (.csv) used to generate the figures presented in the above paper.  Each CSV file corresponds to a specific set of points or an attenuation curve from the manuscript and includes the processed numerical values used to produce the results.

The goal is to ensure transparency, reproducibility, and ease of reuse.

Acronyms: SR – Special Relativity, LIV – Lorentz Invariance Violation, GRB – Gamma-Ray Burst.

Repository Contents

Figure 1 data (5 time intervals from GRB 221009A):- boat_data_5_14.csv  - boat_data_14_22.csv  - boat_data_22_100.csv  - boat_data_100_674.csv  - boat_data_674_1774.csv  

Attenuation tables for GRB 221009A:  - boat_sr.csv — SR case  - boat_liv.csv — LIV case  - boat_liv_dom.csv — LIV case with Dominguez EBL 

Attenuation tables for PKS 2155-304 (Fig. 3):- pks2155_sr.csv — SR case  - pks2155_liv.csv — LIV case  

SWGO sensitivity (Fig. 3):- swgo_1yrsensitivity_WP.csv  

LHAASO sensitivity (Fig. 1): - lhaaso_1yrsensitivity.csvCTAO sensitivity (Fig. 3):- ctao_south50h.csv

References for sensitivity curves and EBL model:- SWGO sensitivity: Ref. 33 — https://arxiv.org/abs/2506.01786  - LHAASO sensitivity: Ref. 32 — https://iopscience.iop.org/article/10.1088/1742-6596/718/5/052043  - EBL model: Saldana-Lopez et al. (2021), Ref. 4 — https://academic.oup.com/mnras/article/507/4/5144/6359154    (ArXiv: https://arxiv.org/abs/2012.03035)

Column Descriptions and Units

GRB 221009A spectral points (5 interval files):Files: boat_data_5_14.csv, boat_data_14_22.csv, boat_data_22_100.csv, boat_data_100_674.csv, boat_data_674_1774.csv  Columns:  - E — TeV  - E2dNdE — erg cm⁻² s⁻¹

GRB 221009A attenuation (SR):File: boat_sr.csv  Columns:  - atten_sr — dimensionless attenuation factor  - energy — eV

GRB 221009A attenuation (LIV):File: boat_liv.csv  Columns:  - atten_LIV_lambda_1 … atten_LIV_lambda_7 — dimensionless attenuation factors  - energy — eV

PKS 2155-304 attenuation (SR):File: pks2155_sr.csv  Columns:  - atten_sr — dimensionless  - energy — eV

PKS 2155-304 attenuation (LIV):  File: pks2155_liv.csv  Columns:  - atten_LIV_lambda_1 … atten_LIV_lambda_4 — dimensionless  - energy — eV

SWGO sensitivity curves: Files: swgo_1yrsensitivity_WP.csv, swgo_5yrsensitivity_WP.csv  Columns:  - E — TeV  - E2dNdE — TeV cm⁻² s⁻¹

LHAASO sensitivity: File: lhaaso_1yrsensitivity.csv  Columns:  - E — TeV  - E2dNdE — erg cm⁻² s⁻¹

Note:  The "atten" columns represent attenuation (survival probability) factors that multiply the intrinsic flux in Eq. (2).
DOI: 10.5281/zenodo.18497030
title: IODP Expedition 399 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18497029
title: IODP Expedition 399 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.60888/epos-gnss-burg00esp
title: GNSS data of BURG00ESP, Burgos, Spain

description: Permanent GNSS station dataset as RINEX files
DOI: 10.5281/zenodo.18496961
title: DIANA's unitigs + blast hits

description: DIANA: Unitig Features and BLAST Annotations

This dataset contains the 107,480 unitig sequences used as features in DIANA (Deep learning for the Identification of Ancient DNA), a multi-task neural network for classifying ancient metagenomic samples. Unitigs are de Bruijn graph sequences (k=31) assembled from 3,070 ancient and modern metagenomic samples.

FILES

unitigs_training_features.fa.gz (5.3 MB)FASTA format containing all 107,480 unitig sequences. Headers include unitig IDs matching the TSV file. Sequences range from 61-184 bp.

unitigs_with_blast_hits.tsv (30 MB)Tab-separated file linking each unitig to its sequence and top BLAST hit against NCBI nt database.
DOI: 10.5281/zenodo.18496960
title: DIANA's unitigs + blast hits

description: DIANA: Unitig Features and BLAST Annotations

This dataset contains the 107,480 unitig sequences used as features in DIANA (Deep learning for the Identification of Ancient DNA), a multi-task neural network for classifying ancient metagenomic samples. Unitigs are de Bruijn graph sequences (k=31) assembled from 3,070 ancient and modern metagenomic samples.

FILES

unitigs_training_features.fa.gz (5.3 MB)FASTA format containing all 107,480 unitig sequences. Headers include unitig IDs matching the TSV file. Sequences range from 61-184 bp.

unitigs_with_blast_hits.tsv (30 MB)Tab-separated file linking each unitig to its sequence and top BLAST hit against NCBI nt database.
DOI: 10.5281/zenodo.18313921
title: Supporting publication for 'Prevalence aggregate guidance for reporting 2025 data'

description: The record contains a list of 26 different examples of zoonoses reportable to EFSA (i.e., the filled-in reporting tools (.xls) and its linked descriptions (.docx)), aiming to support the reporting countries submission of the 2025 aggregated data to the EFSA Data Collection Framework. We include here examples on 



ZOO_FACT_DISEASESTATUS_MAN_Brucella.docx

ZOO_FACT_DISEASESTATUS_MAN_Brucella.xls

ZOO_FACT_DISEASESTATUS_MAN_Mycobacterium.docx

ZOO_FACT_DISEASESTATUS_MAN_Mycobacterium.xls

ZOO_FACT_PREVALENCE_MAN_Brucella.docx

ZOO_FACT_PREVALENCE_MAN_Brucella.xls

ZOO_FACT_PREVALENCE_MAN_Campylobacter_boiler_carcasses.docx

ZOO_FACT_PREVALENCE_MAN_Campylobacter_boiler_carcasses.xls

ZOO_FACT_PREVALENCE_MAN_Cronobacter.docx

ZOO_FACT_PREVALENCE_MAN_Cronobacter.xls

ZOO_FACT_PREVALENCE_MAN_Cysticercus.docx

ZOO_FACT_PREVALENCE_MAN_Cysticercus.xls

ZOO_FACT_PREVALENCE_MAN_Echinococcus.docx

ZOO_FACT_PREVALENCE_MAN_Echinococcus.xls

ZOO_FACT_PREVALENCE_MAN_Histamine.docx

ZOO_FACT_PREVALENCE_MAN_Histamine.xls

ZOO_FACT_PREVALENCE_MAN_Listeria_food.docx

ZOO_FACT_PREVALENCE_MAN_Listeria_food.xls

ZOO_FACT_PREVALENCE_MAN_Mycobacterium.docx

ZOO_FACT_PREVALENCE_MAN_Mycobacterium.xls

ZOO_FACT_PREVALENCE_MAN_Q_fever.docx

ZOO_FACT_PREVALENCE_MAN_Q_fever.xls

ZOO_FACT_PREVALENCE_MAN_Rabies.docx

ZOO_FACT_PREVALENCE_MAN_Rabies.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_special_guarantees.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_special_guarantees.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_broilers.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_broilers.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_food.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_food.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_Gallus_breeding.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_Gallus_breeding.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_laying_hens.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_laying_hens.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_non_special_guarantees.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_non_special_guarantees.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_breeding.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_breeding.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_fattening.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_fattening.xls

ZOO_FACT_PREVALENCE_MAN_Staphylococcal_enterotoxins.docx

ZOO_FACT_PREVALENCE_MAN_Staphylococcal_enterotoxins.xls

ZOO_FACT_PREVALENCE_MAN_STEC.docx

ZOO_FACT_PREVALENCE_MAN_STEC.xls

ZOO_FACT_PREVALENCE_MAN_Toxoplasma.docx

ZOO_FACT_PREVALENCE_MAN_Toxoplasma.xls

ZOO_FACT_PREVALENCE_MAN_Trichinella.docx

ZOO_FACT_PREVALENCE_MAN_Trichinella.xls

ZOO_FACT_PREVALENCE_MAN_WNV.docx

ZOO_FACT_PREVALENCE_MAN_WNV.xls

ZOO_FACT_PREVALENCE_MAN_Yersinia.docx

ZOO_FACT_PREVALENCE_MAN_Yersinia.xls
description: EU; xls; docx; zoonoses.support@efsa.europa.eu
DOI: 10.5281/zenodo.18313922
title: Supporting publication for 'Prevalence aggregate guidance for reporting 2025 data'

description: The record contains a list of 26 different examples of zoonoses reportable to EFSA (i.e., the filled-in reporting tools (.xls) and its linked descriptions (.docx)), aiming to support the reporting countries submission of the 2025 aggregated data to the EFSA Data Collection Framework. We include here examples on 



ZOO_FACT_DISEASESTATUS_MAN_Brucella.docx

ZOO_FACT_DISEASESTATUS_MAN_Brucella.xls

ZOO_FACT_DISEASESTATUS_MAN_Mycobacterium.docx

ZOO_FACT_DISEASESTATUS_MAN_Mycobacterium.xls

ZOO_FACT_PREVALENCE_MAN_Brucella.docx

ZOO_FACT_PREVALENCE_MAN_Brucella.xls

ZOO_FACT_PREVALENCE_MAN_Campylobacter_boiler_carcasses.docx

ZOO_FACT_PREVALENCE_MAN_Campylobacter_boiler_carcasses.xls

ZOO_FACT_PREVALENCE_MAN_Cronobacter.docx

ZOO_FACT_PREVALENCE_MAN_Cronobacter.xls

ZOO_FACT_PREVALENCE_MAN_Cysticercus.docx

ZOO_FACT_PREVALENCE_MAN_Cysticercus.xls

ZOO_FACT_PREVALENCE_MAN_Echinococcus.docx

ZOO_FACT_PREVALENCE_MAN_Echinococcus.xls

ZOO_FACT_PREVALENCE_MAN_Histamine.docx

ZOO_FACT_PREVALENCE_MAN_Histamine.xls

ZOO_FACT_PREVALENCE_MAN_Listeria_food.docx

ZOO_FACT_PREVALENCE_MAN_Listeria_food.xls

ZOO_FACT_PREVALENCE_MAN_Mycobacterium.docx

ZOO_FACT_PREVALENCE_MAN_Mycobacterium.xls

ZOO_FACT_PREVALENCE_MAN_Q_fever.docx

ZOO_FACT_PREVALENCE_MAN_Q_fever.xls

ZOO_FACT_PREVALENCE_MAN_Rabies.docx

ZOO_FACT_PREVALENCE_MAN_Rabies.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_special_guarantees.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_special_guarantees.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_broilers.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_broilers.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_food.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_food.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_Gallus_breeding.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_Gallus_breeding.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_laying_hens.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_laying_hens.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_non_special_guarantees.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_carcasses_non_special_guarantees.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_breeding.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_breeding.xls

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_fattening.docx

ZOO_FACT_PREVALENCE_MAN_Salmonella_turkey_fattening.xls

ZOO_FACT_PREVALENCE_MAN_Staphylococcal_enterotoxins.docx

ZOO_FACT_PREVALENCE_MAN_Staphylococcal_enterotoxins.xls

ZOO_FACT_PREVALENCE_MAN_STEC.docx

ZOO_FACT_PREVALENCE_MAN_STEC.xls

ZOO_FACT_PREVALENCE_MAN_Toxoplasma.docx

ZOO_FACT_PREVALENCE_MAN_Toxoplasma.xls

ZOO_FACT_PREVALENCE_MAN_Trichinella.docx

ZOO_FACT_PREVALENCE_MAN_Trichinella.xls

ZOO_FACT_PREVALENCE_MAN_WNV.docx

ZOO_FACT_PREVALENCE_MAN_WNV.xls

ZOO_FACT_PREVALENCE_MAN_Yersinia.docx

ZOO_FACT_PREVALENCE_MAN_Yersinia.xls
description: EU; xls; docx; zoonoses.support@efsa.europa.eu
DOI: 10.5281/zenodo.18313735
title: Supporting publication for 'Prevalence sample-based guidance for reporting 2025 data

description: The record is aimed at helping the reporting countries to submit the 2025 sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, word documentwe and we provide  below specific information on their use.

The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.

The word document contains the explanation of the examples privided in the Excel and XML and how the aggregation of data reported at sample-based level performed.
description: EU; xlsx; XML; docx; zoonoses.support@efsa.europa.eu
DOI: 10.5281/zenodo.18313736
title: Supporting publication for 'Prevalence sample-based guidance for reporting 2025 data

description: The record is aimed at helping the reporting countries to submit the 2025 sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, word documentwe and we provide  below specific information on their use.

The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.

The word document contains the explanation of the examples privided in the Excel and XML and how the aggregation of data reported at sample-based level performed.
description: EU; xlsx; XML; docx; zoonoses.support@efsa.europa.eu