cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14223005
title: AI4PROFHEALTH - Profession-health status co-occurrence graph statistics

description: This dataset contains the Pointwise Mutual Information (PMI) values for co-occurrence pairs between different mention categories extracted from two distinct clinical datasets: MESINESP2 and the Clinical Case Reports Collection. PMI is a statistical measure used to assess the strength of association between pairs of entities by comparing their observed co-occurrence to the expected frequency under the assumption of independence.

The datasets include PMI values for each co-occurrence pair, derived from the association of professions and clinical concepts, with the aim of identifying potential occupational health risks. By sharing these datasets, we aim to support further research into the relationships between professions and clinical entities, enabling the development of more accurate and targeted occupational health risk models.

There is a separate file for each corpus, and each dataset is provided in CSV format for easy access and analysis. These files include the PMI values for co-occurrence pairs extracted from the respective corpora, making them suitable for further data analysis.

Data Structure:



MESINESP2: mesinesp2_co-occurrence_pmi.zip

Clinical case reports: clinical_cases_co-occurrence_pmi.zip


The repository contains a .zip file for each of the corpus, each containing a .csv file with the co-occurrences between the detected professions and clinical entities. The file has the following columns order:



span_mention_1: Mention string (original): profession

normalized_entity_1: Controlled vocabulary entry for this term

mention1_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 1

span_mention_2: Mention string (original): entity 2 (disease, symptom, species, etc.)

normalized_entity_2: Controlled vocabulary entry for this term

mention2_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 2

co-occurrence: Number of co-occurrences

PMID: PMID value


Notes

This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).

Contact

If you have any questions or suggestions, please contact us at:

- Miguel Rodríguez Ortega (<miguel [dot] rod [at] bsc [dot] com>)- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)

Additional resources and corpora

If you are interested, you might want to check out these corpora and resources:



MEDDOPROF (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)

MESINESP-2 (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)


 
description: This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).
DOI: 10.5281/zenodo.14261964
title: AI4PROFHEALTH - Profession-health status co-occurrence graph statistics

description: This dataset contains the Pointwise Mutual Information (PMI) values for co-occurrence pairs between different mention categories extracted from two distinct clinical datasets: MESINESP2 and the Clinical Case Reports Collection. PMI is a statistical measure used to assess the strength of association between pairs of entities by comparing their observed co-occurrence to the expected frequency under the assumption of independence.

The datasets include PMI values for each co-occurrence pair, derived from the association of professions and clinical concepts, with the aim of identifying potential occupational health risks. By sharing these datasets, we aim to support further research into the relationships between professions and clinical entities, enabling the development of more accurate and targeted occupational health risk models.

There is a separate file for each corpus, and each dataset is provided in CSV format for easy access and analysis. These files include the PMI values for co-occurrence pairs extracted from the respective corpora, making them suitable for further data analysis.

Data Structure:



MESINESP2: mesinesp2_co-occurrence_pmi.zip

Clinical case reports: clinical_cases_co-occurrence_pmi.zip


The repository contains a .zip file for each of the corpus, each containing a .csv file with the co-occurrences between the detected professions and clinical entities. The file has the following columns order:



span_mention_1: Mention string (original): profession

normalized_entity_1: Controlled vocabulary entry for this term

mention1_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 1

span_mention_2: Mention string (original): entity 2 (disease, symptom, species, etc.)

normalized_entity_2: Controlled vocabulary entry for this term

mention2_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 2

co-occurrence: Number of co-occurrences

PMID: PMID value


Notes

This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).

Contact

If you have any questions or suggestions, please contact us at:

- Miguel Rodríguez Ortega (<miguel [dot] rod [at] bsc [dot] com>)- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)

Additional resources and corpora

If you are interested, you might want to check out these corpora and resources:



MEDDOPROF (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)

MESINESP-2 (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)


 
description: This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).
DOI: 10.15468/dl.rs8dku
title: Occurrence Download

description: A dataset containing 49340 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudo-nitzschia delicatissima (Cleve) Heiden, 1928"
 ]
}

 The dataset includes 49340 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013056-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mzw2ue
title: Occurrence Download

description: A dataset containing 866 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Leontodon rigens (Aiton) Paiva & Ormonde)"
 ]
}

 The dataset includes 866 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013061-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr38454.v1
title: Oregon Youth Study Wave 29, 2011-2013
title: Version 1

description: The original Oregon Youth Study began 1983. The goal was to examine the etiology of antisocial behaviors in boys, with a view to designing preventive interventions within the context of the family and the school. This longitudinal study has expanded over the past few decades into an intergenerational study, retaining the original young men and including their partners and children. Wave 29 of the Oregon Youth Study targets males aged 37-38.
description: The goal of this study was to examine the etiology of antisocial behaviors in boys, with a view to designing preventive interventions within the context of the family and the school.
description: Boys and their families were recruited into the Oregon Youth Study (OYS) by inviting the entire fourth grade (ages 9-10 years) of boys from schools in neighborhoods with a higher-than-usual incidence of delinquency within the medium-sized Pacific Northwestern city to participate. The latter was assessed by analyzing the home addresses of youth committing delinquent acts compared with the school-boundary areas. Thus, the boys were at elevated risk for delinquency due to neighborhood characteristics (in a medium-sized city) but were not necessarily showing conduct problems at the time of recruitment. Juvenile court data regarding the frequency of delinquent episodes (by youth home address) reported by the police was used to calculate (for each of the 43 schools in the districts) the prevalence of delinquency in that neighborhood.
A cohort sequential design was employed for the OYS. Two successive birth cohorts of fourth-grade boys were sampled to permit the replication of results, although sample size is such that almost all studies have involved the two combined cohorts (which had highly similar characteristics).
description: The data includes variables on participants' mental health, self reported crimes, employment, sexual history, drug and alcohol use, and suicide history.
description: Datasets: 
DS0: Study-Level Files
DS1: Pain Questionnaire Public Data
DS2: Brief Symptom Inventory Public Data
DS3: Boy's Substance Use Public Data
DS4: CES-Depression Public Data
DS5: Interviewer Ratings Public Data
DS6: Target Interview Restricted Data
DS7: Perceived Stress Scale Public Data
DS8: Self-Esteem (Self- Perception) Public Data
DS9: Young Adult Behavior Checklist Restricted Data
DS10: Self-Report Delinquency Restricted Data
DS11: Self-Report Employment Public Data
DS12: Getting Along with Partner Restricted Data
DS13: Confidant Information Form Public Data
DS14: Michigan Alcohol Screening Test Restricted Data
DS15: Modified Drinking Motives Questionnaire-Revised Restricted Data
DS16: Physical Measures Restricted Data
DS17: Sex Survey Restricted Data
DS18: UPPS-P Impulsive Behavior Scale Public Data
DS19: Young Adult Adjustment Scale Public Data
DS20: Young Adult Self-Report Public Data
description: Males between the ages of 37-38 living in Oregon.
description: For additional information please refer to the study website.
DOI: 10.15468/dl.3rtkcj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Begonia wui-senioris C.I Peng)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013063-241126133413365/datasets/export for details.
DOI: 10.5281/zenodo.14203756
title: AI4PROFHEALTH - Profession-health status knowledge graph

description: This dataset comprises a profession-clinical knowledge graph, derived from the co-occurrence of normalised concepts identified in two distinct corpora: the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labelled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. The application of different NER systems to each corpus has enabled the extraction of clinical mentions related to diseases, drugs, locations, procedures, species, species-human, and symptoms.

The repository contains a .zip file for each of the corpus, each containing a .tsv file for each clinical entity category detected:



mesinesp2_profession_co-occurrences.zip (Mesinesp2)



mesinesp2_occupations_diseases_coocurrence.tsv

mesinesp2_occupations_drugs_coocurrence.tsv

mesinesp2_occupations_locations_coocurrence.tsv

mesinesp2_occupations_symptoms_coocurrence.tsv

mesinesp2_occupations_species_human_coocurrence.tsv

mesinesp2_occupations_species_species_coocurrence.tsv



case_reports_profession_co-occurrences.zip (Case reports)



clinical_cases_occupations_diseases_coocurrence.tsv

clinical_cases_occupations_drugs_coocurrence.tsv

clinical_cases_occupations_procedures_coocurrence.tsv

clinical_cases_occupations_species_coocurrence.tsv

clinical_cases_occupations_symptoms_coocurrence.tsv




Each .tsv file is divided into four columns. The first two columns contain a list of professions and their corresponding clinical concepts, while the third column, named "count," indicates the number of co-occurrences calculated at the document level for each pair of detected entities.

License

 

Contact

If you have any questions or suggestions, please contact us at:

- Miguel Rodríguez Ortega (<miguel [dot] rod [at] bsc [dot] com>)- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)

Additional resources and corpora

If you are interested, you might want to check out these corpora and resources:



MEDDOPROF (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)

MESINESP-2 (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)
DOI: 10.5281/zenodo.14216646
title: AI4PROFHEALTH - Profession-health status knowledge graph

description: This dataset comprises a profession-clinical knowledge graph, derived from the co-occurrence of normalised concepts identified in two distinct corpora: the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labelled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. The application of different NER systems to each corpus has enabled the extraction of clinical mentions related to diseases, drugs, locations, procedures, species, species-human, and symptoms.

The repository contains a .zip file for each of the corpus, each containing a .tsv file with the co-occurrences between the detected professions and clinical entities. The file has the following columns order:



span_mention_1: Mention string (original): profession

normalized_entity_1: Controlled vocabulary entry for this term

code_mention_1: ID terminology for normalization

mention_controlled_vocab: Terminology used for normalization

mention1_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 1

span_mention_2: Mention string (original): entity 2 (disease, symptom, species, etc.)

normalized_entity_2: Controlled vocabulary entry for this term

code_mention_2: ID terminology for normalization

mention_controlled_vocab: Terminology used for normalization

mention2_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 2

co-occurrence: Number of co-occurrences


Notes

This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).

Contact

If you have any questions or suggestions, please contact us at:

- Miguel Rodríguez Ortega (<miguel [dot] rod [at] bsc [dot] com>)- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)

Additional resources and corpora

If you are interested, you might want to check out these corpora and resources:



MEDDOPROF (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)

MESINESP-2 (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)
DOI: 10.5281/zenodo.14222720
title: AI4PROFHEALTH - Profession-health status knowledge graph

description: This dataset comprises a profession-clinical knowledge graph, derived from the co-occurrence of normalised concepts identified in two distinct corpora: the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labelled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. The application of different NER systems to each corpus has enabled the extraction of clinical mentions related to diseases, drugs, locations, procedures, species, species-human, and symptoms.

The repository contains a .zip file for each of the corpus, each containing the following columns order:



span_mention_1: Mention string (original): profession

normalized_entity_1: Controlled vocabulary entry for this term

code_mention_1: ID terminology for normalization

mention_controlled_vocab: Terminology used for normalization

mention1_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 1

span_mention_2: Mention string (original): entity 2 (disease, symptom, species, etc.)

normalized_entity_2: Controlled vocabulary entry for this term

code_mention_2: ID terminology for normalization

mention_controlled_vocab: Terminology used for normalization

mention2_category: Semantic class (i.e., NER label)

mention1_freq: Absolute frequency of this mention entity 2

co-occurrence: Number of co-occurrences


Notes

This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).

Contact

If you have any questions or suggestions, please contact us at:

- Miguel Rodríguez Ortega (<miguel [dot] rod [at] bsc [dot] com>)- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)

Additional resources and corpora

If you are interested, you might want to check out these corpora and resources:



MEDDOPROF (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)

MESINESP-2 (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)


 
description: This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).
DOI: 10.15468/dl.ypsqwk
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperoglyphe japonica (Döderlein, 1884)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 54 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013064-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3bnwsa
title: Occurrence Download

description: A dataset containing 4298 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Saccharum officinarum L."
 ]
}

 The dataset includes 4298 records from 351 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013062-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bg8ppc
title: Occurrence Download

description: A dataset containing 8089 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Mexico, Guatemala, Belize, Honduras, El Salvador, Costa Rica, Panama, Nicaragua)",
 "OccurrenceStatus is Present",
 "TaxonKey is Phlebodium (R.Br.) J.Sm."
 ]
}

 The dataset includes 8089 records from 186 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013059-241126133413365/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.27943062
title: <b>Pituitary gland</b>

description: Glaucoma is characterized by the progressive degeneration of retinal ganglion cells (RGCs) and damage to the optic nerve, with increased intraocular pressure (IOP) being one of the risk factors for the development of the disease. In addition to their role in visual processing, RGCs project to the suprachiasmatic nucleus (SCN) in the hypothalamus, which is responsible for generating and synchronizing biological rhythms with the light-dark (LD) cycle. These rhythms include body temperature, locomotor activity, feeding behavior, and hormonal secretion patterns. We have demonstrated that animals in advanced stages of glaucoma show alterations in the rhythm of spontaneous locomotor activity and core body temperature. Furthermore, humans with glaucoma exhibit sleep disturbances and disruptions in the circadian rhythm of body temperature and blood cortisol levels. Considering that glucocorticoid secretion has an endogenous circadian profile mediated by the SCN, this study aims to evaluate the impact of glaucoma-induced rhythmic alterations on the hypothalamic-pituitary-adrenal (HPA) axis. For this purpose, we will use an animal model that spontaneously develops glaucoma, assessing the molecular components (gene and protein expression) of the HPA axis in glaucomatous (Dba/2j) and control (Dba/2j GPNMB+) animals.
DOI: 10.15468/dl.d9sytw
title: Occurrence Download

description: A dataset containing 3049 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mirabilis linearis (Pursh) Heimerl",
 "Geometry POLYGON((-141 24, -53 24, -53 83, -141 83, -141 24))"
 ]
}

 The dataset includes 3049 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013065-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gt6qgv
title: Occurrence Download

description: A dataset containing 5854 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Leontopodium alpinum Cass.)"
 ]
}

 The dataset includes 5854 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013066-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.