Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Geospatial Dataset of GNSS Anomalies and Political Violence Events

Geospatial Dataset of GNSS Anomalies and Political Violence Events Overview The Geospatial Dataset of GNSS Anomalies and Political Violence Events is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database. Dataset Files The dataset consists of three CSV files: <ol> <li>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv</li> <ul> <li>Description: Contains all grids and dates that had aircraft traffic during 2023.</li> <li>Number of Records: 6,777,228</li> <li>Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.</li> </ul> <li>Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</li> <ul> <li>Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.</li> <li>Number of Records: 718,237</li> <li>Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.</li> </ul> <li>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</li> <ul> <li>Description: Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.</li> <li>Number of Records: 25,770</li> <li>Purpose: Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.</li> </ul> </ol> Data Fields:    Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv <ol> <li>grid_id</li> <ul> <li>Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li>Format: String combining latitude and longitude (e.g., -10.0_-36.0).</li> </ul> <li>day</li> <ul> <li>Description: Date of the recorded data.</li> <li>Format: YYYY-MM-DD (e.g., 2023-03-28).</li> </ul> <li>geometry</li> <ul> <li>Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li>Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).</li> </ul> <li>flights</li> <ul> <li>Description: Number of aircraft flights that passed through the grid on that day.</li> <li>Format: Integer (e.g., 28).</li> </ul> <li>GPS_jumps</li> <ul> <li>Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.</li> <li>Format: Integer (e.g., 1).</li> </ul> <li>GPS_gaps</li> <ul> <li>Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.</li> <li>Format: Integer (e.g., 0).</li> </ul> <li>gaps_density</li> <ul> <li>Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.</li> <li>Format: Decimal (e.g., 0).</li> </ul> <li>jumps_density</li> <ul> <li>Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.</li> <li>Format: Decimal (e.g., 0.035714286).</li> </ul> <li>event_id_cnty</li> <ul> <li>Description: ACLED event ID corresponding to political violence events in the grid on that day.</li> <li>Format: String (e.g., BRA69267).</li> </ul> <li>disorder_type</li> <ul> <li>Description: Type of disorder as classified by ACLED (e.g., "Political violence").</li> <li>Format: String.</li> </ul> <li>event_type</li> <ul> <li>Description: General category of the event according to ACLED (e.g., "Violence against civilians").</li> <li>Format: String.</li> </ul> <li>sub_event_type</li> <ul> <li>Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").</li> <li>Format: String.</li> </ul> <li>acled_count</li> <ul> <li>Description: Number of ACLED events in the grid on that day.</li> <li>Format: Integer (e.g., 1).</li> </ul> <li>acled_flag</li> <ul> <li>Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).</li> <li>Format: Integer (0 or 1).</li> </ul> </ol>   Data Fields: Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below: <ol> <li>grid_id</li> <ul> <li>Description: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.</li> <li>Format: String combining latitude and longitude (e.g., -0.5_-79.0).</li> </ul> <li>year_month</li> <ul> <li>Description: Month and year of the aggregated data.</li> <li>Format: String in Mon-YY format (e.g., Jan-23).</li> </ul> <li>geometry</li> <ul> <li>Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li>Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).</li> </ul> <li>flights</li> <ul> <li>Description: Total number of aircraft flights that passed through the grid cell during the month.</li> <li>Format: Integer (e.g., 1230).</li> </ul> <li>GPS_jumps</li> <ul> <li>Description: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.</li> <li>Format: Integer (e.g., 13).</li> </ul> <li>GPS_gaps</li> <ul> <li>Description: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.</li> <li>Format: Integer (e.g., 0).</li> </ul> <li>event_id_cnty</li> <ul> <li>Description: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.</li> <li>Format: String (e.g., ECU3151;ECU3158;ECU3150).</li> </ul> <li>disorder_type</li> <ul> <li>Description: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.</li> <li>Format: String.</li> </ul> <li>event_type</li> <ul> <li>Description: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").</li> <li>Format: String.</li> </ul> <li>sub_event_type</li> </ol> <ul> <li>Description: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").</li> <li>Format: String.</li> </ul> <ol> <li>acled_count</li> </ol> <ul> <li>Description: Total number of ACLED conflict events in the grid cell during the month.</li> <li>Format: Integer (e.g., 2).</li> </ul> <ol> <li>acled_flag</li> </ol> <ul> <li>Description: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.</li> <li>Format: Integer (0 or 1).</li> </ul> <ol> <li>gaps_density</li> </ol> <ul> <li>Description: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.</li> <li>Format: Decimal (e.g., 0.0).</li> </ul> <ol> <li>jumps_density</li> </ol> <ul> <li>Description: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.</li> <li>Format: Decimal (e.g., 0.0106).</li> </ul>   Data Sources <ul> <li>GNSS Anomalies Data:</li> <ul> <li>Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.</li> <li>GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).</li> </ul> <li>Political Violence Events Data:</li> <ul> <li>Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.</li> </ul> </ul> Temporal and Spatial Coverage <ul> <li>Temporal Coverage:</li> <ul> <li>From January 1, 2023, to December 31, 2023.</li> <li>Daily records provide temporal granularity for time-series analysis.</li> </ul> <li>Spatial Coverage:</li> <ul> <li>Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li>Each grid cell represents an area on Earth's surface, facilitating spatial analysis.</li> </ul> </ul> Usage and Applications <ul> <li>Security Analysis:</li> <ul> <li>Assess potential correlations between GNSS anomalies and political violence events.</li> <li>Identify regions with increased risk of GNSS spoofing or signal disruption.</li> </ul> <li>Research and Development:</li> <ul> <li>Develop models to predict socio-political events based on GNSS anomalies.</li> <li>Study the impact of political instability on aviation safety.</li> </ul> <li>Policy and Decision Making:</li> <ul> <li>Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.</li> <li>Support conflict analysis and monitoring efforts.</li> </ul> </ul>

opencc-by-4.0Oct 2023View details →

Data from: Grand Theft Empathy? Evidence for the absence of effects of violent video games on empathy for pain and emotional reactivity to violence

Abstract:Influential accounts claim that violent video games (VVG) decrease players' emotional empathy by desensitizing them to both virtual and real-life violence. However, scientific evidence for this claim is inconclusive and controversially debated. To assess the causal effect of VVGs on the behavioral and neural correlates of empathy and emotional reactivity to violence, we conducted a prospective experimental study using functional magnetic resonance imaging (fMRI). We recruited eighty-nine male participants without prior VVG experience. Over the course of two weeks, participants played either a highly violent video game, or a non-violent version of the same game. Before and after this period, participants completed an fMRI experiment with paradigms measuring their empathy for pain and emotional reactivity to violent images. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VVGs on the behavioral and neural correlates of empathy. Moreover, participants in the VVG group were not desensitized to images of real-world violence. These results imply that short and controlled exposure to VVGs does not numb empathy nor the responses to real-world violence. We discuss the implications of our findings regarding the potential and limitations of experimental research on the causal effects of VVGs. While VVGs might not have a discernible effect on the investigated subpopulation within our carefully controlled experimental setting, our results cannot preclude that effects could be found in special vulnerable subpopulations, or in settings with higher ecological validity.  Dataset: This dataset contains the fMRI data collected for the study in the BIDS-format (https://bids.neuroimaging.io/)<ul><li>functional neuroimaging (*_bold.nii.gz) data of 89 human participants, collected during two tasks:<ul><li>Empathy-for-Pain paradigm (Session 1 & 2)</li><li>Emotional Reactivity paradigm (Session 2)</li></ul></li><li>associated event files (*_events.tsv) containing event onsets, durations, and behavioral covariates</li><li>metadata</li></ul>FMRI bold timeseries are fully preprocessed, as described in the manuscript.Additional data, such as behavioral data in a simpler format, can be accessed on https://osf.io/yx423/

opencc-by-4.0Dec 2021View details →

Bus Violence: a large-scale benchmark for video violence detection in public transport

Dataset The Bus Violence dataset is a large-scale collection of videos depicting violent and non-violent situations in public transport environments. This benchmark was gathered from multiple cameras located inside a moving bus where several people simulated violent actions, such as stealing an object from another person, fighting between passengers, etc. It contains 1,400 video clips manually annotated as having or not violent scenes, making it one of the biggest benchmarks for video violence detection in the literature. Specifically, videos are recorded from three cameras at 25 Frames Per Second (FPS) --- two cameras located in the corners of the bus (with resolution 960x540 px) and one fisheye in the middle (1280x960 px). The clips have a minimum length of 16 frames and a maximum of 48 frames, capturing a very precise action (either violence or non-violence). The dataset is perfectly balanced, containing 700 videos of violence and 700 videos of non-violence. The Bus Violence dataset is intended as a test data benchmark. However, for researchers interested in using our data also for training purposes, we provide training and test splits. In this repository, we provide <ul> <li> the 1,400 video clips divided into two folders named Violence /NoViolence, containing clips of violent situations and non-violent situations, respectively; </li> <li> two txt files containing the names of the videos belonging to the training and test splits, respectively. </li> </ul>   Citing our work If you found this dataset useful, please cite the following paper <blockquote> <pre>@inproceedings{bus_violence_dataset_2022,   title = {Bus Violence: An Open Benchmark for Video Violence Detection on Public Transport}, doi = {10.3390/s22218345},   url = {https://doi.org/10.3390%2Fs22218345},   year = 2022,   month = {oct},   publisher = {{MDPI} {AG}},   volume = {22},   number = {21},   pages = {8345},   author = {Luca Ciampi and Pawe{\l} Foszner and Nicola Messina and Micha{\l} Staniszewski and Claudio Gennaro and Fabrizio Falchi and Gianluca Serao and Micha{\l} Cogiel and Dominik Golba and Agnieszka Szcz{\k{e}}sna and Giuseppe Amato}, journal = {Sensors} } </pre> </blockquote> and this Zenodo Dataset <blockquote> <pre>@dataset{pawel_bus_violence_zenodo, author = {Paweł Foszner, Michał Staniszewski, Agnieszka Szczęsna, Michał Cogiel, Dominik Golba, Luca Ciampi, Nicola Messina, Claudio Gennaro, Fabrizio Falchi, Giuseppe Amato, Gianluca Serao}, title = {{Bus Violence: a large-scale benchmark for video violence detection in public transport}},   month = sep,   year = 2022,   publisher = {Zenodo},   version = {1.0.0},   doi = {10.5281/zenodo.7044203}, url = {https://doi.org/10.5281/zenodo.7044203} } </pre> </blockquote>   Contact Information Blees Sp. z o.o., Gliwice, Poland mstaniszewski@blees.co   Acknowledgments The presented dataset was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project "AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy" under GA 951911; research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008.   License The Bus Violence dataset was acquired by Blees Sp. z o.o. and is released under a Creative Commons Attribution license for non-commercial use.

opencc-by-4.0Aug 2024View details →

Integration of data sets from different sources for modeling gender violence and perception of insecurity

The dataset is composed of three distinct files which aggregate processed data derived from open datasets of three cities: Dublin, San Francisco, and Valencia. The data has been mapped to a grid of 25m² for Valencia and 50m² for Dublin and San Francisco. The respective files are named DATA_ES_VLC.csv, DATA_IE_DUB.csv, and DATA_US_SFO.csv. Additionally, there is a dataset for tweets named DATA_TWT.csv, which contains tweets collected through web scraping and analysed using natural language processing (NLP) algorithms and neural networks. The aim is to identify and classify tweets that discuss gender-based violence in the city of Valencia. Another file, MAP_ES_VLC.csv, includes points collected during various mapathons conducted by the Polytechnic University of Valencia campus for a science project aimed at identifying potentially insecure locations.

opencc-by-4.0Nov 2022View details →

Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy (Data and Associated Files for Dissertation)

This publication contains files associated with analysis for my dissertation, "Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy." The dissertation explores a potential relationship between political violence and a state leader's choice to use migration as a bargaining chip in pursuit of foreign policy objectives. "Key to datasets.docx" and "dataframes_viz.png" explain the contents of the five datasets used. These five datasets are the five .dta files. There are five log files (.txt) and five do files containing code (.do) corresponding to the five datasets. Finally, each dataset has three associated results tables (.xls) for a total of fifteen .xls files.

opencc-by-4.0Dec 2022View details →

Intimate Partner Violence and Mental Health in Migrant Women Living in Spain

This dataset contains data from a convenience sample in a cross-sectional study of intimate partner violence (IPV) and mental health in migrant women living in the Valencian Community of Spain. IPV was assessed using the Revised Composite Abuse Scale Short Form (CASR-SF), the Revised Scale of Economic Abuse (SEA2) and the Cyber Aggression in Relationship Scale (CARS). Mental health was assessed using the PHQ-9 for symptoms of depression and the GAD-7 for symptoms of anxiety.

opencc-by-4.0Dec 2020View details →

European Study on Risk Factors for Violence in Mental Disorder and Forensic Care: a multicentre project. The EU-Viormed Dataset

The aims of this project, through a series of interlinked work packages, designed by researchers and practicing clinicians, and lead by an experienced management team, is to explore and map these differences, test new risk assessment tools and identify and share best practice where it exists. It aims are fourfold. Firstly to describe forensic psychiatry services as they exist today in 2017 across the European Union. Secondly to identify risk factors for violence in a unique international forensic sample and thirdly to test for the very first time in a related EU sample two contrasting methods of violence risk assessment. Finally it explored what works for these often marginalised patients, their families and their carers, at an operational, clinical and ethical level. These aims were achieved through two studies designs: Study 1: case-control retrospective design in which forensic patients with Schizophrenia Spectrum Disorders (SSDs) who have a history of interpersonal violence and live in forensic units will be compared to non-violent patients with SSDs living in the community. Study 2: prospective cohort study, with 6 and 12 month follow-up, to test the predictive validity of the leading structured professional clinical judgement guide for violence prediction, the HCR-20v3, the Forensic Psychiatry and Violence Tool (FoVOx) and the Mental Illness and Suicide Tool (OXMIS, https://oxrisk.com/)

opencc-zeroMar 2015View details →

A QUESTIONNAIRE FOR THE ASSESSMENT OF VIOLENT BEHAVIORS IN YOUNG COUPLES: THE ITALIAN VERSION OF DATING VIOLENCE QUESTIONNAIRE (DVQ)

In the last years, intimate partner violence (IPV) became a relevant problem for community and for social life, particularly in young people. Its correct assessment and evaluation in the population is mandatory. Our objectives were: Confirm factor structure of Dating Violence Questionnaire (DVQ) and investigate its convergent and divergent validity. The DVQ along with other personality measures were filled by a sample of 418 university students (Females = 310) of average age of 23 y.o. (SD = 4.71). A subsample of participants (223 students) consented in being involved also in retest and filled also the Revised Eysenck Personality Questionnaire (short form) and a brief scale for describing the behavior of the (past) partner after the breaking of the relationship (BRS). The 8-factor structure, with respect to the two other competing models, reported better fit indexes and showed significant correlations with other personality measures. Personality traits, both Neuroticism and Psychoticism, correlated with Sexual Violence, while Detachment correlated only with Neuroticism and Coercion, Humiliation and Physical Violence correlated with only Psychoticism. Extraversion did not report significant relationships with any of the 8 DVQ factors. Also the predictive validity of DVQ was satisfactory with the partner violent reaction to the break of relationship predicted positively predicted by Coercion (b = 0.22) and by Humiliation (b = 0.20) and negatively by Emotional Punishment (b = -0.18). The present results indicate a good factor structure of the questionnaire, and interesting correlations with personality traits, allowing to identify psychological aspects with a predisposing role for anti-social aggressive behaviors. Further studies will be aimed at ascertaining other possible determinants of intimate partner violence and the weight of cultural aspects.

opencc-by-4.0Oct 2023View details →

EMNLP-23-Bootstrapping-a-Violence-Detector-for-Fan-Fiction

Data for the paper `Trigger Warnings: Bootstrapping a Violence Detector for Fan Fiction`. Code: https://github.com/webis-de/emnlp23-bootstrapping-a-violence-detector-for-fan-fictionPublication: tbd. Citation: https://webis.de/publications.html?q=wolska_2023

opencc-by-4.0Oct 2024View details →

Measuring the risk of violence through health surveillance.

database used for the study "Measuring the risk of violence through health surveillance" to be published.

opencc-by-4.0Dec 2021View details →

Classification of unstructured text in types of violence against women using text mining and Machine learning techniques

These are the data used for the development of the investigation. This file was extracted from our mongoDB database. The data set contains real news of violence against women, which were organized with their date, the title and the body of the news.

opencc-by-4.0Aug 2022View details →

A topic modelling application for violence against the women through text mining techniques

This research was focused on a social problem that affects worldwide. Violence Against Women (VAW) and girls occurs in many forms with different levels of aggressiveness. There is a wide amount of data on the internet about real cases of VAW that can be gathered for further study. Many researchers have done studies on structured data sets which facilitates their manipulation and understanding. But in this work digital news (data source large non-structured text) was used. These are the data used for the development of the investigation.This file was extracted from our mongoDB database. The data set contains real news of violence against women, which were organized with their date, the title and the body of the news.

opencc-by-4.0Jun 2023View details →

A Dataset for Violence Trends in the Ancient Middle East between 12,000 and 400 BCE

Thsi archive provides all data generated and analysed and the custom codes produced for the study which will be published in Nature Human Behaviour with the title "Violence Trends in the Ancient Middle East between 12,000 and 400 BCE". In this paper we study violence trends in the very long run by exploiting a novel dataset on cranial trauma and weapon-related wounds from skeletons excavated across the Middle East, spanning the whole pre-Classical period (ca. 12,000-400 BCE). The data set includes more than 3,500 individuals. These data have been collected from existing online digital archives, electronic and print original publications, and from unpublished field reports.

opencc-by-4.0Dec 2022View details →

Sociodemographic predictors of bureaucratic violence in the residency application process: findings from a mixed methods cross-sectional survey of migrant women in Spain.

Dataset contains the data used for the quantitative analyses presented in the paper titled "Sociodemographic predictors of bureaucratic violence in the residency application process: findings from a mixed methods cross-sectional survey of migrant women in Spain.". Data were collected from a cross-sectional online survey of migrant women living in the Valencian Community of Spain between January and March 2023. The dataset contains demographic information about participants, as well as the barriers that the reported facing in the process of applying for residency within Spain.

ClinicalTrials.gov40/100

Exposure to Gun Violence in Video Games Increases Interest in Real Guns

ClinicalTrials.gov study NCT03259139. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

opencc-byAug 2015View details →

SF3D - Violence, sexism, exclusion, fear

From the Sketchfab San Francisco Meetup that took place on Aug. 8, 2015. This was shot in Clarion Alley with my Canon EOS30D (17 photos). Source: Objaverse 1.0 / Sketchfab

dryad36/100

Implementation of guidelines on prevention of coercion and violence (PreVCo) in psychiatry

Fifty-four psychiatric wards in Germany treating voluntary and involuntary patients were randomly allocated to either an intervention or to a waiting list condition. The intervention consisted of the implementation of three out of 12 suggested recommendations as selected by the ward teams, supported by external study workers. As the primary outcome measure, the number of coercive measures used per bed and month in the final 3 months of the intervention period was determined. Secondary outcomes were the cumulative duration of coercive measures used per bed and months and assaults per bed and month. Achieved guideline adherence was measured by a fidelity scale developed for this purpose during a pilot study for the PreVCo Rating Tool. After a 3-month baseline collection period under routine conditions, randomisation was done after matching wards pairwise according to frequency of coercive measures used and scores on the PreVCo Rating Tool at baseline.

opencc-zeroNov 2023View details →

opencc-by-4.0Sep 2022View details →

VIOLENCE AGAINST WOMEN ANALYZED UNDER THE THOUGHT OF EDMUND HUSSERL IN THE CONTEXT OF THE SOCIETY OF APURIMAC

ABSTRACT The purpose of the investigation was to analyze the experiences and perspectives on violence against women in the Apurímac region. The analysis was carried out through interviews with women in each of the provinces of Apurimac. The aim was to learn about the status of women's rights and the effectiveness of provisions and regulations for their protection. It will also delve into the vast cultural and social diversity present in the interviews themselves, in contrast to the current normative system. The results will allow us to address possible future advances for the respect of women's rights. Key words: gender violence, patriarchy, abuse against women, poverty.

opencc-by-4.0Apr 2024View details →

Poor work ability is associated with workplace violence in nurses. A longitudinal study.

Dataset of an article to be published on IJERPH