An annotated dataset for event-based surveillance of antimicrobial resistance
This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, animal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automatically identify written text on AMR events across the different reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes.
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Subjects: | U10 - Informatique, mathématiques et statistiques, L75 - Pharmacologie et toxicologie, résistance aux antimicrobiens, fouille de textes, analyse de données, épidémiologie, annotation de données, approche Une seule santé, http://aims.fao.org/aos/agrovoc/c_662faf6f, http://aims.fao.org/aos/agrovoc/c_dca12b72, http://aims.fao.org/aos/agrovoc/c_15962, http://aims.fao.org/aos/agrovoc/c_2615, http://aims.fao.org/aos/agrovoc/c_09bf755b, http://aims.fao.org/aos/agrovoc/c_b29a1475, |
Online Access: | http://agritrop.cirad.fr/603290/ http://agritrop.cirad.fr/603290/1/1-s2.0-S2352340922010733-main.pdf |
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dig-cirad-fr-6032902024-01-29T19:04:21Z http://agritrop.cirad.fr/603290/ http://agritrop.cirad.fr/603290/ An annotated dataset for event-based surveillance of antimicrobial resistance. Arinik Nejat, Van Bortel Wim, Boudoua Bahdja, Busani Luca, Decoupes Rémy, Interdonato Roberto, Kafando Rodrique, Van Kleef Esther, Roche Mathieu, Syed Mehtab Alam, Teisseire Maguelonne. 2023. Data in Brief, 46:108870, 8 p.https://doi.org/10.1016/j.dib.2022.108870 <https://doi.org/10.1016/j.dib.2022.108870> An annotated dataset for event-based surveillance of antimicrobial resistance Arinik, Nejat Van Bortel, Wim Boudoua, Bahdja Busani, Luca Decoupes, Rémy Interdonato, Roberto Kafando, Rodrique Van Kleef, Esther Roche, Mathieu Syed, Mehtab Alam Teisseire, Maguelonne eng 2023 Data in Brief U10 - Informatique, mathématiques et statistiques L75 - Pharmacologie et toxicologie résistance aux antimicrobiens fouille de textes analyse de données épidémiologie annotation de données approche Une seule santé http://aims.fao.org/aos/agrovoc/c_662faf6f http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_09bf755b http://aims.fao.org/aos/agrovoc/c_b29a1475 This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, animal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automatically identify written text on AMR events across the different reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/603290/1/1-s2.0-S2352340922010733-main.pdf text cc_by_nc_nd info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ https://doi.org/10.1016/j.dib.2022.108870 10.1016/j.dib.2022.108870 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.dib.2022.108870 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.dib.2022.108870 info:eu-repo/semantics/dataset/purl/https://doi.org/10.57745/MPNSPH info:eu-repo/grantAgreement/EC/H2020/874850//(EU) MOnitoring Outbreak events for Disease surveillance in a data science context/MOOD |
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U10 - Informatique, mathématiques et statistiques L75 - Pharmacologie et toxicologie résistance aux antimicrobiens fouille de textes analyse de données épidémiologie annotation de données approche Une seule santé http://aims.fao.org/aos/agrovoc/c_662faf6f http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_09bf755b http://aims.fao.org/aos/agrovoc/c_b29a1475 U10 - Informatique, mathématiques et statistiques L75 - Pharmacologie et toxicologie résistance aux antimicrobiens fouille de textes analyse de données épidémiologie annotation de données approche Une seule santé http://aims.fao.org/aos/agrovoc/c_662faf6f http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_09bf755b http://aims.fao.org/aos/agrovoc/c_b29a1475 |
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U10 - Informatique, mathématiques et statistiques L75 - Pharmacologie et toxicologie résistance aux antimicrobiens fouille de textes analyse de données épidémiologie annotation de données approche Une seule santé http://aims.fao.org/aos/agrovoc/c_662faf6f http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_09bf755b http://aims.fao.org/aos/agrovoc/c_b29a1475 U10 - Informatique, mathématiques et statistiques L75 - Pharmacologie et toxicologie résistance aux antimicrobiens fouille de textes analyse de données épidémiologie annotation de données approche Une seule santé http://aims.fao.org/aos/agrovoc/c_662faf6f http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_09bf755b http://aims.fao.org/aos/agrovoc/c_b29a1475 Arinik, Nejat Van Bortel, Wim Boudoua, Bahdja Busani, Luca Decoupes, Rémy Interdonato, Roberto Kafando, Rodrique Van Kleef, Esther Roche, Mathieu Syed, Mehtab Alam Teisseire, Maguelonne An annotated dataset for event-based surveillance of antimicrobial resistance |
description |
This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, animal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automatically identify written text on AMR events across the different reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques L75 - Pharmacologie et toxicologie résistance aux antimicrobiens fouille de textes analyse de données épidémiologie annotation de données approche Une seule santé http://aims.fao.org/aos/agrovoc/c_662faf6f http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_09bf755b http://aims.fao.org/aos/agrovoc/c_b29a1475 |
author |
Arinik, Nejat Van Bortel, Wim Boudoua, Bahdja Busani, Luca Decoupes, Rémy Interdonato, Roberto Kafando, Rodrique Van Kleef, Esther Roche, Mathieu Syed, Mehtab Alam Teisseire, Maguelonne |
author_facet |
Arinik, Nejat Van Bortel, Wim Boudoua, Bahdja Busani, Luca Decoupes, Rémy Interdonato, Roberto Kafando, Rodrique Van Kleef, Esther Roche, Mathieu Syed, Mehtab Alam Teisseire, Maguelonne |
author_sort |
Arinik, Nejat |
title |
An annotated dataset for event-based surveillance of antimicrobial resistance |
title_short |
An annotated dataset for event-based surveillance of antimicrobial resistance |
title_full |
An annotated dataset for event-based surveillance of antimicrobial resistance |
title_fullStr |
An annotated dataset for event-based surveillance of antimicrobial resistance |
title_full_unstemmed |
An annotated dataset for event-based surveillance of antimicrobial resistance |
title_sort |
annotated dataset for event-based surveillance of antimicrobial resistance |
url |
http://agritrop.cirad.fr/603290/ http://agritrop.cirad.fr/603290/1/1-s2.0-S2352340922010733-main.pdf |
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