Google Dataset Search Research Articles

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.

Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview.

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis.

Developing a standardized but extendable framework to increase the findability of infectious disease datasets

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

DataverseNO

DataverseNO (https://dataverse.no/) is a national, generic repository for open research data, owned and operated by UiT The Arctic University of Norway. The technical infrastructure of the repository is based on Dataverse, a community-driven open-source application, which is developed by an international developer and user community led by Harvard University. DataverseNO supports the FAIR Guiding Principles for scientific data management and stewardship, and is CoreTrustSeal certified (Conzett 2020). DataverseNO is based on collaboration between Norwegian research organizations. Datasets from partner institutions are published in institutional collections. Currently, 14 research organizations are partnering with DataverseNO. UiT is responsible for training and support for institutional collection managers, whereas the partner institutions are responsible for the curation of their institutional data and local user support. To ensure that data deposited into DataverseNO comply with the DataverseNO policies and guidelines they are curated by qualified research data service staff before they are published. The DataverseNO curators join forces in the DataverseNO curation network. Datasets in DataverseNO are equipped with persistent identifiers (DOIs, Digital Object Identifiers) at dataset and file level. DOIs serve as unique references to datasets and files and make them searchable in discovery services like B2FIND – EUDAT, Bielefeld Academic Search Engine (BASE), DataCite Search, Google Dataset Search, and Oria/Primo. On December 1, the last day of this year’s Munin Conference, DataverseNO will be turning 5 years!

Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PDF

Growing pains of a data repository: GRIIDC's evolution from environmental disaster rapid response to promoting FAIR data

GRIIDC is a multidisciplinary data repository created in the aftermath of the Deepwater Horizon oil spill. Development of the repository occurred even as researchers collected post-spill data, and as a result, the data management system initially focused on the ingestion of data and metadata. Data sharing was not as prevalent as it is currently, and many researchers were not familiar with data sharing and data organization best practices. Implementation of data management planning, submission, citation, and distribution features required many iterations and occurred while GRIIDC was assisting researchers with managing their rapid response data. From this challenging beginning, over the decade since the Deepwater Horizon oil spill, GRIIDC has improved the data management system and the training of researchers, which has enhanced the ease of submission and quality of data submitted. The GRIIDC system has also evolved to prioritize the implementation of FAIR data principles to ensure the data are findable, accessible, interoperable, and reusable. All data are issued digital object identifiers (DOIs) through DataCite and are findable via GRIIDC's data search page, DataONE, and Google Dataset Search. Each dataset has a landing page where the data and metadata can be accessed. GRIIDC is continuously striving to add FAIR principles to the system. Although there are still many challenges including quality of data and metadata received, funding limitations, and program priorities, GRIIDC must always continue to improve its ability to meet user needs while implementing FAIR data principles.

Findability of UK health datasets available for research: a mixed methods study

ObjectiveHow health researchers find secondary data to analyse is unclear. We sought to describe the approaches that UK organisations take to help researchers find data and to assess the findability...

Schema.org for research data managers: a primer

Data managers are currently investigating the value proposition of Schema.org (SDO) with a lightweight implementation that couples the SDO vocabulary with indexing Google Dataset Search. This pathway essentially 'webifies' dataset search and syndication and enhances dataset discovery. The Primer was created to layout the SDO landscape, where it came from, what is driving its uptake in research data management, and how it works in broad strokes. It was designed to introduce individuals with little technical knowledge to the benefits and importance of Schema.org and aimed to be a 'one-step-back' from the numerous guidance documents that are being produced in the RDM community. This document describes the mark-up process in very simple terms, provides current methods of adoption by the research community, describes related technical areas relevant to data managers and lists what organisations they should follow to keep apprised of this work.

Google dataset search and DOI for data in the ESA space science archives

Reproducibility is at the core of science. For this reason, a common practice for authors is to provide links to the data used in their manuscripts upon submission to peer-reviewed journals. In space science, reference to archives’ websites of major data providers like space agencies are still accepted by most journals. However, even these websites are not persistent links and will eventually not be accepted once journals require full compliance with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. On the contrary, Digital Object Identifiers (DOIs) associated to data could provide persistent links. But so far, the publishing industry is systematically minting DOIs related to their articles or books without requiring DOIs for the data used in their publications. Furthermore, DOIs related to data would not only link refereed publications to the data location. It could also enable a more precise attribution to the data producers and publishers while providing the capability to eventually trace their use. These latter points require that journals eventually request or even enforce the use of these DOIs. For data producers, proper attribution and usage are important in the context of securing research grants or extension of projects. Additionally, structured metadata included in the DOI landing pages offer the possibility for datasets to be discoverable by the new Google Dataset Search (GDS) engine, available since January 2020. This paper describes the on-going activities at the European Space Agency (ESA) to mint DOIs for datasets served across the space science archives, and how a subset of these DOIs are already discoverable by GDS.

Arabic Topic Identification: A Decade Scoping Review

Arabic topic identification is a part of text classification that aims to assign a given text a set of pre-defined classes (i.e., topics) based on its content and extracted features. This task can be performed using rule-based methods or data-driven approaches. These latter gained more popularity since they require much less human effort to accurately classify a large number of documents. Due to the tremendous growth of Web contents primarily in news websites and social media, topic identification had received a great deal of attention over the last years, and has become a cornerstone of both search engines and information retrieval. The Arabic language is the fourth most used language on the web and records the highest growth in the last two decades (2000–2020). Based on these facts currently available, it seems fair to look closer at the advancements in the Arabic topic identification in the last decade. To this end, we performed the first of its kind scoping review that addresses recent studies in the field of Arabic topic identification that follows the PRISMA-ScR guidelines. This review is based on various online bibliographic databases (e.g., Springer, ScienceDirect, and IEEE Xplore) and datasets search engines (e.g., Google Dataset Search).

GOOGLE DATASET SEARCH: descripción general y perspectivas para indexar y poner a disposición conjuntos de datos científicos abiertos

In order to collaborate with scientific production in the field of data science, specifically in tools for storage and retrieval of data sets over the internet, this article aims to obtain an overview of the functioning, standards and perspectives on the Google Dataset Search tool - launched in 2018 with the proposal of identifying, indexing and making available internet datasets (massive sets of data) - essential instruments for the scientific community. The methodology used was descriptive, exploratory and bibliographic. A bibliographic survey was carried out on the platform, identifying internal functioning, standards, guidelines, formats and standardization institutions that guide the platform, in addition to current statistics of indexed data. Then, practical tests of use, usability and operation of the tool were performed, according to available documentation. The results obtained showed a promising platform, with a satisfactory usability score, aligned with international data interoperability standards and with considerable volumes of datasets already available, mostly in the English language. It was also observed, after the tests, that there are already several brazilian data repositories indexed by Google Dataset Search. However, some of them, even adopting the same metadata standards as this tool, are not yet available. The conclusion is that it is a system created by Google, with a high capacity for tracking, identification, indexing, interoperation and making available data sets available on the internet using international standards and, therefore, has significant potential. This work contributes to the large area that is inserted, reducing the scarcity of scientific publications on tools for making data sets available, specifically on the functioning, protocols, mechanisms and interface of this current tool.

Low lung function in the developing world is analogous to stunting: a review of the evidence.

Background: Low vital capacity, one of the consequences of restricted lung growth, is a strong predictor of cardiovascular mortality. Vital capacity is lower in the developing world than the developed world, even after adjusting for height, weight and gender. This difference is typically dismissed as ethnic variation, adjusted for by redefining normal. Whether this is a consequence of stunted lung growth, rather than just genetically smaller lungs, has not been investigated in detail. Therefore, we sought to compare factors implicated in both stunting and lung development, particularly in the developing world. Methods: We conducted a manual screen of articles identified through Google Scholar and assessed risk of bias. No language restrictions were applied, so long as there was an associated English abstract. We queried VizHub (Global Burden of Disease Visualization Tool) and Google Dataset search engines for disease burden and genome wide association studies. The scope of the article and the heterogeneity of the outcome measures reported required a narrative review of available evidence. To the extent possible, the review follows PRISMA reporting guidelines. Results: Early life influences operate in synergism with genetic, environmental and nutritional factors to influence lung growth and development in children. Low lung function and stunting have common anthropometric, environmental and nutritional correlates originating during early development. Similar anthropometric correlates shared chronic inflammatory pathways, indicated that the two conditions were analogous. Conclusion: The analogy between poor lung function and stunting is conspicuous in the developing world, with malnutrition at the center of non -achievement of growth potential, susceptibility to infectious diseases and intrauterine programming for metabolic syndrome. This counter the idea of redefining the normal for lung function measurements, since observed inter-ethnic variations are likely a mix of natural genetic differences as well as differences in nurture such that reduced lung function reflects early life adversities.

IncluSet: A Data Surfacing Repository for Accessibility Datasets.

Datasets and data sharing play an important role for innovation, benchmarking, mitigating bias, and understanding the complexity of real world AI-infused applications. However, there is a scarcity of available data generated by people with disabilities with the potential for training or evaluating machine learning models. This is partially due to smaller populations, disparate characteristics, lack of expertise for data annotation, as well as privacy concerns. Even when data are collected and are publicly available, it is often difficult to locate them. We present a novel data surfacing repository, called IncluSet, that allows researchers and the disability community to discover and link accessibility datasets. The repository is pre-populated with information about 139 existing datasets: 65 made publicly available, 25 available upon request, and 49 not shared by the authors but described in their manuscripts. More importantly, IncluSet is designed to expose existing and new dataset contributions so they may be discoverable through Google Dataset Search.

A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences.

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.

Low lung function in the developing world is analogous to stunting: a review of the evidence

Background: Low vital capacity, one of the consequences of restricted lung growth, is a strong predictor of cardiovascular mortality. Vital capacity is lower in the developing world than the developed world, even after adjusting for height, weight and gender. This difference is typically dismissed as ethnic variation, adjusted for by redefining normal. Whether this is a consequence of stunted lung growth, rather than just genetically smaller lungs, has not been investigated in detail. Therefore, we sought to compare factors implicated in both stunting and lung development, particularly in the developing world. Methods: We conducted a manual screen of articles identified through Google Scholar and assessed risk of bias. No language restrictions were applied, so long as there was an associated English abstract. We queried VizHub (Global Burden of Disease Visualization Tool) and Google Dataset search engines for disease burden and genome wide association studies. The scope of the article and the heterogeneity of the outcome measures reported required a narrative review of available evidence. To the extent possible, the review follows PRISMA reporting guidelines. Results: Early life influences operate in synergism with genetic, environmental and nutritional factors to influence lung growth and development in children. Low lung function and stunting have common anthropometric, environmental and nutritional correlates originating during early development. Similar anthropometric correlates shared chronic inflammatory pathways, indicated that the two conditions were analogous. Conclusion: The analogy between poor lung function and stunting is conspicuous in the developing world, with malnutrition at the center of non -achievement of growth potential, susceptibility to infectious diseases and intrauterine programming for metabolic syndrome. This counter the idea of redefining the normal for lung function measurements, since observed inter-ethnic variations are likely a mix of natural genetic differences as well as differences in nurture such that reduced lung function reflects early life adversities.

Low lung function in the developing world is analogous to stunting: a review of the evidence

Background: Low vital capacity, one of the consequences of restricted lung growth, is a strong predictor of cardiovascular mortality. Vital capacity is lower in the developing world than the developed world, even after adjusting for height, weight and gender. This difference is typically dismissed as ethnic variation, adjusted for by redefining normal. Whether this is a consequence of stunted lung growth, rather than just genetically smaller lungs, has not been investigated in detail. Therefore, we sought to compare factors implicated in both stunting and lung development, particularly in the developing world. Methods: We conducted a manual screen of articles identified through Google Scholar and assessed risk of bias. No language restrictions were applied, so long as there was an associated English abstract. We queried VizHub (Global Burden of Disease Visualization Tool) and Google Dataset search engines for disease burden and genome wide association studies. The scope of the article and the heterogeneity of the outcome measures reported required a narrative review of available evidence. To the extent possible, the review follows PRISMA reporting guidelines. Results: Early life influences operate in synergism with genetic, environmental and nutritional factors to influence lung growth and development in children. Low lung function and stunting have common anthropometric, environmental and nutritional correlates originating during early development. Similar anthropometric correlates shared chronic inflammatory pathways, indicated that the two conditions were analogous. Conclusion: The analogy between poor lung function and stunting is conspicuous in the developing world, with malnutrition at the center of non -achievement of growth potential, susceptibility to infectious diseases and intrauterine programming for metabolic syndrome. This counter the idea of redefining the normal for lung function measurements, since observed inter-ethnic variations are likely a mix of natural genetic differences as well as differences in nurture such that reduced lung function reflects early life adversities.

Machine Learning Models Predicting the Survival of Abrupt Heart Attack in Cardiac Sarcoidosis Disease u sing the Wearable Device

Wearable technology has countless prospects of remodelling healthcare establishment and also medical education. Cardiac sarcoidosis disease (CS) is a sporadic illness in which white blood cells (WBC) clusters known as granulomas, form as heart tissue. Cardiac sarcoidosis disease (CS) Patients are at high threat of ventricular tachycardia or ventricular fibrillation (VT/VF). Wearable cardioverter defibrillator device is introduced which helps to alleviate the abrupt heart attack risk amid patients of cardiac sarcoidosis. A reflective evaluation of the commercial record acknowledged patients of cardiac sarcoidosis disease who sported the wearable cardioverter defibrillator (WCD). ML models are applied to get accurate predictions to motivate WCD wear ability. The wearable device cardioverter defibrillator (WCD) was worn by forty six patients of cardiac sarcoidosis disease in which 22(48%) female, male 24 (52%). The wearable cardioverter defibrillator (WCD) was sported hours about 23.6 median daily. Nearby eleven ventricular tachycardia or ventricular fibrillation (VT/VF) incidents occur in ten patients (22%). Ventricular tachycardia or ventricular fibrillation (VT/VF) happened over a series of (1-79) days, median of twenty-four days. 1st- heart attack success for ventricular tachycardia or ventricular fibrillation (VT/VF) conversion was hundred percent. Survival of Patient in twenty four hours after treatment of attack was hundred percent. To regulate the discontinuing cause for wearable device cardioverter defibrillator (WCD) use specified that among seven attacked patients received ICD, one patient was died two weeks later discontinuing the use of wearable cardioverter defibrillator device (WCD), and two patients were absent to track. Sixteen were not attacked patients, who obtained an implantable cardioverter defibrillator (ICD) while seven of them attained and improved left ventricular ejection fraction (LVEF). Abrupt heart attack (HA) management amongst patients of cardiac sarcoidosis disease (CS) was assisted by wearable device cardioverter defibrillator (WCD) ensuing in positive ventricular tachycardia or ventricular fibrillation (VT/VF) termination upon attack delivery. In this paper, the dataset is retrieved from google dataset search and evaluated on various ML models to predict the survival of the patients Receiving ICD while wearing WCD as well as evaluating the developed model performance and to identify the best applicable model. Dataset is primarily processed and nursed to many machine learning classifiers like KNN, SVM, Perceptron, Random Forest, Decision Trees (DT), Logistic Expression, SGD, and Naïve Basis. Cross-validation is smeared, training is performed so that new machine learning models are established and verified. The outcomes found are assessed on many factors such as Accuracy, Misclassification Rate, True Positive Rate, True Negative Rate, Precision, Prevalence, False Positive rate taken to build the model. Result analysis reveals that among all the classifiers SVM and KNN best model acquiescent high and precise outcomes.

The CHEAR Data Repository

TPS 691: Methods of measurement, design and data analysis, Exhibition Hall, Ground floor, August 28, 2019, 3:00 PM - 4:30 PM Funded by the U.S. National Institute of Environmental Health Sciences, the Children’s Health Exposure Analysis Resource (CHEAR) provides scientific investigators access to laboratory and statistical analyses aimed at incorporating and expanding environmental exposures within their research. To benefit the broader research community, the CHEAR Data Center has created a public data repository that houses deidentified data from studies accepted into the CHEAR program. To date, 26 studies have submitted data containing > 41,000 specimens, > 3,000 mothers and children and 139 environmental chemicals. The goal of this repository is to promote the secondary analysis of pooled CHEAR studies by providing data in a manner that is findable, accessible, interoperable and reusable (FAIR). The repository has been constructed by coupling the open-source Human-aware Data Acquisition Framework with semantic annotation templates that transform CHEAR datasets into machine-readable knowledge graphs. These tools facilitate the ingestion, semantic-mapping, harmonization and accessibility of data (epidemiologic, clinical and biomarker) and metadata across the multiple studies within the CHEAR Program. We demonstrate how users of the public repository have the ability to simultaneously search, view, and download data from multiple CHEAR studies. The repository can be searched based on a number of factors including health outcomes, biological markers of exposure and common covariates. Because data have been harmonized to a common vocabulary (the CHEAR ontology), downloaded datasets automatically contain CHEAR-wide harmonized codes and labels for variables that are present in multiple studies. By selecting common data elements, users can create customized datasets with accompanying codebooks in a format that is easily imported into statistical analysis software. For maximal FAIR impact, we have promoted the CHEAR data, tools and methods through Google dataset search, Github, and Bioportal. The repository will encourage secondary analysis of pooled CHEAR studies, facilitating investigations that leverage larger sample sizes and greater exposure variability.

Deconstructing Google Dataset Search

This essay examines the beta tool from Google, Google Dataset Search. The Google Dataset Search, announced in September 2018, is a search engine specific to finding research data published on the internet. The structure and methods of the search engine are examined, as well as the methods Google recommends to web developers to make it an effective tool across the World Wide Web. The column concludes with a discussion of the pros and cons of this tool in the research information landscape.