Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
74
datasets available to search
ShareScore release 0.9.0
Dataset results
74 results for “surveillance data”
Data from: An investigation of sepsis surveillance and emergency treatment on patient mortality outcomes: an observational cohort study
Objective. To determine the prevalence of initiating the sepsis 3-hour bundle of care and estimate effects of bundle completion on risk-adjusted mortality among ED patients screened-in by electronic surveillance. Materials and Methods. This was a multiple center observational cohort study conducted in 2016. The study population was comprised of patients screened-in by St. John Sepsis Surveillance Agent within four hours of ED arrival, had a sepsis bundle initiated, and admitted to hospital. We built multivariable logistic regression models to estimate impact of a 3-hour bundle completed within three hours of arrival on mortality outcomes. Results. Approximately 3% ED patients were screened-in by electronic surveillance within four hours of arrival and admitted to hospital. Nearly 7 in 10 (69%) patients had a bundle initiated, with most bundles completed within three hours of arrival. The fully-adjusted risk model achieved good discrimination on mortality outcomes (AUROC = .82, 95% CI = .79 to .85) and estimated 34% reduced mortality risk among patients with a bundle completed within three hours of arrival compared to non-completers. Discussion. The sepsis bundle is an effective intervention for many vulnerable patients, and likely to be completed within three hours after arrival when electronic surveillance with reliable alert notifications are integrated into clinical workflow. Beginning at triage, the platform and sepsis program enables identification and management of patients with greater precision, and increases the odds of good outcomes. Conclusion. Sepsis surveillance and clinical decision support accelerate accurate recognition and stratification of patients, and facilitate timely delivery of healthcare.
Data from: Territory surveillance and prey management: wolves keep track of space and time
Identifying behavioral mechanisms that underlie observed movement patterns is difficult when animals employ sophisticated cognitive-based strategies. Such strategies may arise when timing of return visits is important, for instance to allow for resource renewal or territorial patrolling. We fitted spatially explicit random-walk models to GPS movement data of six wolves (Canis lupus; Linnaeus, 1758) from Alberta, Canada to investigate the importance of the following: (1) territorial surveillance likely related to renewal of scent marks along territorial edges, to reduce intraspecific risk among packs, and (2) delay in return to recently hunted areas, which may be related to anti-predator responses of prey under varying prey densities. The movement models incorporated the spatiotemporal variable "time since last visit," which acts as a wolf's memory index of its travel history and is integrated into the movement decision along with its position in relation to territory boundaries and information on local prey densities. We used a model selection framework to test hypotheses about the combined importance of these variables in wolf movement strategies. Time-dependent movement for territory surveillance was supported by all wolf movement tracks. Wolves generally avoided territory edges, but this avoidance was reduced as time since last visit increased. Time-dependent prey management was weak except in one wolf. This wolf selected locations with longer time since last visit and lower prey density, which led to a longer delay in revisiting high prey density sites. Our study shows that we can use spatially explicit random walks to identify behavioral strategies that merge environmental information and explicit spatiotemporal information on past movements (i.e., "when" and "where") to make movement decisions. The approach allows us to better understand cognition-based movement in relation to dynamic environments and resources.
Data from: Application of a Bayesian weighted surveillance approach for detecting chronic wasting disease in white-tailed deer
SUMMARY 1. Surveillance is critical for the early detection of emerging and re-emerging infectious diseases, and weighted surveillance uses heterogeneity in risk of infection to increase the sampling efficiency. 2. We apply a Bayesian approach to estimate weights for 16 surveillance classes of white-tailed deer in Wisconsin, USA, relative to hunter-harvested yearling males. We use these weights to conduct a surveillance program for detecting chronic wasting disease (CWD) in white-tailed deer at Shenandoah National Park (SHEN) in Virginia, USA. 3. Generally, for surveillance, risk of infection increased with age and was greater in males. Clinical suspect deer had the highest risk with weight estimates of 33.33 and 9.09, for community reported and hunter reported suspect deer, respectively, while fawns had the lowest risk with an estimated weight of 0.001. 4. We used surveillance weights for Wisconsin deer to determine sampling effort required to detect a CWD-positive case in SHEN if prevalence in yearling males ≥0.025. The sampling required to detect CWD was 37–91 adult deer, depending on the adult male:female ratio in the surveillance stream. We collected rectal biopsies from 49 and 21 adult female and male deer, respectively, and 10 additional samples from vehicle-killed deer. CWD was not detected and we concluded with 95% probability that prevalence in the reference population (yearling males) was between 0.0 to 3.6%. 5. Synthesis and applications. Our approach allows managers to estimate relative surveillance weights for different host classes and quantify limits of disease detection in real time when only a sample of animals from a population can be tested, resulting in considerable cost savings for agencies performing wildlife disease detection surveillance. Additionally, it provides a rigorous means of estimating prevalence limits when a disease/pathogen is not detected in a sample set, and is generalizable to other wildlife, domestic animal, and human disease systems which can be characterized by surveillance classes with heterogeneous probability of infection. This methodology is also extendable to other disciplines such as invasive species, environmental toxicology, and generally any ecological question seeking to efficiently use scarce financial and human resources to maximize the detection probability of a rare event.
Data from: Incidence and fatality of serious suicide attempts in a predominantly rural population in Shandong, China: a public health surveillance study
Objectives: To estimate the incidence of serious suicide attempts (SSAs, defined as suicide attempts resulting in either death or hospitalisation) and to examine factors associated with fatality among these attempters. Design: A surveillance study of incidence and mortality. Linked data from two public health surveillance systems were analysed. Setting: Three selected counties in Shandong, China. Participants: All residents in the three selected counties. Outcome: measures Incidence rate (per 100 000 person-years) and case fatality rate (%). Methods: Records of suicide deaths and hospitalisations that occurred among residents in selected counties during 2009–2011 (5 623 323 person-years) were extracted from electronic databases of the Disease Surveillance Points (DSP) system and the Injury Surveillance System (ISS) and were linked by name, sex, residence and time of suicide attempt. A multiple logistic regression model was developed to examine the factors associated with a higher or lower fatality rate. Results: The incidence of SSAs was estimated to be 46 (95% CI 44 to 48) per 100 000 person-years, which was 1.5 times higher in rural versus urban areas, slightly higher among females, and increased with age. Among all SSAs, 51% were hospitalised and survived, 9% were hospitalised but later died and 40% died with no hospitalisation. Most suicide deaths (81%) were not hospitalised and most hospitalised SSAs (85%) survived. The fatality rate was 49% overall, but was significantly higher among attempters living in rural areas, who were male, older, with lower education or with a farming occupation. With regard to the method of suicide, fatality was lowest for non-pesticide poisons (7%) and highest for hanging (97%). Conclusions: The incidence of serious suicide attempts is substantially higher in rural areas than in urban areas of China. The risk of death is influenced by the attempter's sex, age, education level, occupation, method used and season of year.
Data from: Not all surveillance data are created equal – a multi‐method dynamic occupancy approach to determine rabies elimination from wildlife
1. A necessary component of elimination programs for wildlife disease is effective surveillance. The ability to distinguish between disease freedom and non‐detection can mean the difference between a successful elimination campaign and new epizootics. Understanding the contribution of different surveillance methods helps to optimize and better allocate effort and develop more effective surveillance programs. 2.We evaluated the probability of rabies virus elimination (disease freedom) in an enzootic area with active management using dynamic occupancy modeling of 10 years of raccoon rabies virus (RABV) surveillance data (2006‐2015) collected from three states in the eastern USA. We estimated detection probability of RABV cases for each surveillance method (e.g., strange acting reports, road‐kill, surveillance‐trapped animals, nuisance animals, and public health samples) used by the USDA National Rabies Management Program. 3.Strange acting, found dead, and public health animals were the most likely to detect RABV when it was present, and generally detectability was higher in the fall‐winter compared to the spring‐summer. Found dead animals in the fall‐winter had the highest detection at 0.33 (95% CI: 0.20, 0.48). Nuisance animals had the lowest detection probabilities (~0.02). 4.Areas with oral rabies vaccination (ORV) management had reduced occurrence probability compared to enzootic areas without ORV management. RABV occurrence was positively associated with deciduous and mixed forests and medium to high developed areas, which are also areas with higher raccoon (Procyon lotor) densities. By combining occupancy and detection estimates we can create a probability of elimination surface that can be updated seasonally to provide guidance on areas managed for wildlife disease. 5.Synthesis and applications. Wildlife disease surveillance is often comprised of a combination of targeted and convenience‐based methods. Using a multi‐method analytical approach allows us to compare the relative strengths of these methods, providing guidance on resource allocation for surveillance actions. Applying this multi‐method approach in conjunction with dynamic occupancy analyses better informs management decisions by understanding ecological drivers of disease occurrence.
Data from: Invasion of two tick-borne diseases across New England: harnessing human surveillance data to capture underlying ecological invasion processes
Modelling the spatial spread of vector-borne zoonotic pathogens maintained in enzootic transmission cycles remains a major challenge. The best available spatio-temporal data on pathogen spread often take the form of human disease surveillance data. By applying a classic ecological approach—occupancy modelling—to an epidemiological question of disease spread, we used surveillance data to examine the latent ecological invasion of tick-borne pathogens. Over the last half-century, previously undescribed tick-borne pathogens including the agents of Lyme disease and human babesiosis have rapidly spread across the northeast United States. Despite their epidemiological importance, the mechanisms of tick-borne pathogen invasion and drivers underlying the distinct invasion trajectories of the co-vectored pathogens remain unresolved. Our approach allowed us to estimate the unobserved ecological processes underlying pathogen spread while accounting for imperfect detection of human cases. Our model predicts that tick-borne diseases spread in a diffusion-like manner with occasional long-distance dispersal and that babesiosis spread exhibits strong dependence on Lyme disease.
Data from: Priority setting for global WASH challenges in the age of wastewater-based epidemiological surveillance
<p>Background: It is not known how the number of deaths due to COVID-19 compare to the number of deaths due to "unsafe water, sanitation, and handwashing" during the COVID-19 global health emergency.</p> <p>Methods: A dataset of deaths due to COVID-19 was downloaded from the World Health Organization. A dataset summarizing deaths due to unsafe water, sanitation, and handwashing was obtained from the Institute for Health Metrics and Evaluation (IHME). </p> <p>Results indicate that COVID-19 deaths in Africa and South East Asia regions exceeded those due to unsafe water, sanitation, and hygiene. </p>
Data set - The design and evaluation of an integrated training load and injury/illness surveillance system in competitive swimming.
<p>Dataset for paper as per title above. </p>
Data for "Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure"
<p>New York City contact network data used in the publication “<a href="https://doi.org/10.1016/j.socnet.2024.06.003">Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure</a>” (LA-UR-23-26868). This contact network comes in the form of a weighted edge list. Each row describes an edge, with the first and second column containing the labels of the nodes connected by the edge, and the third column contains the corresponding weight of the edge. In this network, an edge encodes an interaction between two individuals and the weight describes the duration of the interaction in seconds. In total the edge list describes 6,376,729,847 interactions among 6,813,615 individuals; the first 10 interactions are listed below as an example.</p> <p>2, 1, 84121<br>4, 3, 83654.4<br>5, 3, 79591.4<br>5, 4, 87642<br>6, 3, 79853<br>6, 4, 81604<br>6, 5, 79146<br>8, 7, 80604<br>10, 9, 84259.6<br>12, 11, 68990.8</p> <p> </p> <p>This work is approved for public distribution under LA-UR-24-25046.</p>
FluView 2018 HHS Region 1 Outpatient Illness and Viral Surveillance Data - Epidemiological Week 40 (archived by MIDAS-ISG)
<p><strong>Other(s)</strong></p> <p>MIDAS Informatics Service Group</p> <p><strong>Rights holder(s)</strong></p> <p>Centers for Disease Control and Prevention</p> <p>Description from the FluView Interactive web application (from which this file was downloaded):</p> <p>Viral Surveillance — Data collection from both the U.S. World Health Organization (WHO) Collaborating Laboratories and National Respiratory and Enteric Virus Surveillance System (NREVSS) laboratories began during the 1997-98 season. The volume of tested specimens has greatly increased during this time due to increased participation and increased testing. During the 1997-98 season 43 state public health laboratories participated in surveillance, and by the 2004-05 season all state public health laboratories were participating in surveillance. The addition of NREVSS data during the 1997-98 season roughly doubled the amount of virologic data reported each week. </p> <p>The number of specimens tested and % positive rate vary by region and season based on different testing practices including triaging of specimens by the reporting labs, therefore it is not appropriate to compare the magnitude of positivity rates or the number of positive specimens between regions or seasons. </p> <p>The U.S. WHO and NREVSS collaborating laboratories report the total number of respiratory specimens tested and the number positive for influenza types A and B each week to CDC. Most of the U.S. WHO collaborating laboratories also report the influenza A subtype (H1 or H3) of the viruses they have isolated, but the majority of NREVSS laboratories do not report the influenza A subtype. </p> <p>For more information on virologic surveillance please visit:http://www.cdc.gov/flu/weekly/overview.htm#Viral</p> <p>Outpatient Illness Surveillance — Information on patient visits to health care providers for influenza-like illness is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). This collaborative effort between CDC, state and local health departments, and health care providers started during the 1997-98 influenza season when approximately 250 providers were enrolled. Enrollment in the system has increased over time and there were >3,000 providers enrolled during the 2010-11 season.</p> <p>The number and percent of patients presenting with ILI each week will vary by region and season due to many factors, including having different provider type mixes (children present with higher rates of ILI than adults, and therefore regions with a higher percentage of pediatric practices will have higher numbers of cases). Therefore it is not appropriate to compare the magnitude of the percent of visits due to ILI between regions and seasons.</p> <p>Baseline levels are calculated both nationally and for each region. Percentages at or above the baseline level are considered to be elevated.</p> <p>For more information on ILI surveillance and baselines please visit:http://www.cdc.gov/flu/weekly/overview.htm#Outpatient</p>
FluView 2018 National Outpatient Illness and Viral Surveillance Data - Epidemiological Week 40 (archived by MIDAS-ISG)
<p><strong>Other(s)</strong></p> <p>MIDAS Informatics Service Group</p> <p><strong>Rights holder(s)</strong></p> <p>Centers for Disease Control and Prevention</p> <p>Description from the FluView Interactive web application (from which this file was downloaded):</p> <p>Viral Surveillance — Data collection from both the U.S. World Health Organization (WHO) Collaborating Laboratories and National Respiratory and Enteric Virus Surveillance System (NREVSS) laboratories began during the 1997-98 season. The volume of tested specimens has greatly increased during this time due to increased participation and increased testing. During the 1997-98 season 43 state public health laboratories participated in surveillance, and by the 2004-05 season all state public health laboratories were participating in surveillance. The addition of NREVSS data during the 1997-98 season roughly doubled the amount of virologic data reported each week. </p> <p>The number of specimens tested and % positive rate vary by region and season based on different testing practices including triaging of specimens by the reporting labs, therefore it is not appropriate to compare the magnitude of positivity rates or the number of positive specimens between regions or seasons. </p> <p>The U.S. WHO and NREVSS collaborating laboratories report the total number of respiratory specimens tested and the number positive for influenza types A and B each week to CDC. Most of the U.S. WHO collaborating laboratories also report the influenza A subtype (H1 or H3) of the viruses they have isolated, but the majority of NREVSS laboratories do not report the influenza A subtype. </p> <p>For more information on virologic surveillance please visit:http://www.cdc.gov/flu/weekly/overview.htm#Viral</p> <p>Outpatient Illness Surveillance — Information on patient visits to health care providers for influenza-like illness is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). This collaborative effort between CDC, state and local health departments, and health care providers started during the 1997-98 influenza season when approximately 250 providers were enrolled. Enrollment in the system has increased over time and there were >3,000 providers enrolled during the 2010-11 season.</p> <p>The number and percent of patients presenting with ILI each week will vary by region and season due to many factors, including having different provider type mixes (children present with higher rates of ILI than adults, and therefore regions with a higher percentage of pediatric practices will have higher numbers of cases). Therefore it is not appropriate to compare the magnitude of the percent of visits due to ILI between regions and seasons.</p> <p>Baseline levels are calculated both nationally and for each region. Percentages at or above the baseline level are considered to be elevated.</p> <p>For more information on ILI surveillance and baselines please visit:http://www.cdc.gov/flu/weekly/overview.htm#Outpatient</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Raw data: Validation of a method for surveillance of nanoparticles in mussels using single particle inductively coupled plasma mass spectrometry
<p>The compressed archive contains SP-ICP-MS data to reproduce results for the paper with the working title "Validation of a method for surveillance of nanoparticles in mussels using single particle inductively coupled plasma mass spectrometry"".</p> <p>The remaining data to produce all other results, visualizations and statistics for the paper is included in the supplementary or GitHub. Associated code is deposited in a GitHub repository, github.com/arebruvold/mussel_validation .</p>
E4Warning Mosquito Surveillance Data
<p>Mosquito surveillance data, obtained through various trapping methods, are compiled and shared in Excel (.xlsx) files. The E4Warning dataset template aims to assist field researchers in their data archiving efforts by aligning with the project's Data Management Plan. It consists of two main components: metadata and data.</p> <ol> <li> <p>The <strong>metadata component</strong> includes information about the origin of the dataset, such as study details and licensing for usage. This ensures that all necessary contextual information is accessible. Our metadata component utilizes the template generated by MIReAD (Minimum Information for Reusable Analytical Data) to ensure high standards of data documentation and reusability of arthropod abundance data by establishing a set of guidelines for data reporting. By adopting the MIReAD template for our metadata, we align our data management practices with best practices for data standardization and transparency</p> </li> <li> <p>The <strong>data component </strong>lists and describes the specific data fields that should be included in data collection sheets. This is tailored to capture the essential variables typically collected by academic researchers and surveillance initiatives. The template serves as a comprehensive checklist to help prevent the omission of crucial information.</p> </li> </ol> <p><span>T</span>he mosquito surveillance data template utilized by E4Warning partners is a designed document for recording data from mosquito trapping activities, which is subsequently used for modeling. Each field within the template is structured to ensure a comprehensive understanding of the surveillance efforts and the possible biases introduced by the trapping devices and attractants used.</p> <div><br> <div> <p><a title="" href="#_ftnref1" name="_ftn1"><span>[1]</span></a> Rund et al. 2019. MIReAD, a minimum information standard for reporting arthropod abundance data. Scientific Data. 6: 40.</p> </div> </div>
Linkage of Medicaid Enrollment Information to Surveillance, Epidemiology and End Results Data
ClinicalTrials.gov study NCT02875327. IPD Sharing: Not stated. Countries: 1. Publications: 1.
An Active Surveillance, Post-Authorization Study to Characterize the Safety of Tofacitinib in Patients With Moderately to Severely Active Ulcerative Colitis in the Real-World Setting Using Data From t
ClinicalTrials.gov study NCT06469424. IPD Sharing: NO. Countries: 4. Publications: 28.
Data from: Environmental DNA surveillance for invertebrate species: advantages and technical limitations to detect invasive crayfish Procambarus clarkii in freshwater ponds
Open the record for dataset details and reuse information.
Data from: The impact of resources for clinical surveillance on the control of a hypothetical foot-and-mouth disease epidemic in Denmark
Open the record for dataset details and reuse information.
Data from: A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern US
Open the record for dataset details and reuse information.
Data from: Territory surveillance and prey management: wolves keep track of space and time
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.