A Self-Audit of the NIST Public Data Repository Using the CoreTrustSeal Trustworthy Data Repositories Requirements

ABSTRACT The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a severe international shortage of the nasopharyngeal swabs that are required for collection of optimal specimens, creating a critical bottleneck blocking clinical laboratories’ ability to perform high-sensitivity virological testing for SARS-CoV-2. To address this crisis, we designed and executed an innovative, cooperative, rapid-response translational-research program that brought together health care workers, manufacturers, and scientists to emergently develop and clinically validate new swabs for immediate mass production by 3D printing. We performed a multistep preclinical evaluation of 160 swab designs and 48 materials from 24 companies, laboratories, and individuals, and we shared results and other feedback via a public data repository (http://github.com/rarnaout/Covidswab/). We validated four prototypes through an institutional review board (IRB)-approved clinical trial that involved 276 outpatient volunteers who presented to our hospital’s drive-through testing center with symptoms suspicious for COVID-19. Each participant was swabbed with a reference swab (the control) and a prototype, and SARS-CoV-2 reverse transcriptase PCR (RT-PCR) results were compared. All prototypes displayed excellent concordance with the control (κ = 0.85 to 0.89). Cycle threshold (CT) values were not significantly different between each prototype and the control, supporting the new swabs’ noninferiority (Mann-Whitney U [MWU] test, P > 0.05). Study staff preferred one of the prototypes over the others and preferred the control swab overall. The total time elapsed between identification of the problem and validation of the first prototype was 22 days. Contact information for ordering can be found at http://printedswabs.org. Our experience holds lessons for the rapid development, validation, and deployment of new technology for this pandemic and beyond.

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The Yeast Resource Center Public Data Repository

Nucleic Acids Research ◽

10.1093/nar/gki073 ◽

2004 ◽

Vol 33 (Database issue) ◽

pp. D378-D382 ◽

Cited By ~ 19

Author(s):

M. Riffle

Keyword(s):

Data Repository ◽

Resource Center ◽

Public Data ◽

Public Data Repository

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Targeting TRIB2 expression with oral antidiabetic drugs to overcome tumour malignancy and drug-resistance

European Journal of Public Health ◽

10.1093/eurpub/ckaa040.058 ◽

2020 ◽

Vol 30 (Supplement_2) ◽

Author(s):

A Sousa-Coelho

Keyword(s):

Drug Resistance ◽

Cell Metabolism ◽

Data Repository ◽

Cancer Therapies ◽

Oral Antidiabetic ◽

Advanced Search ◽

Public Data ◽

Obese Rats ◽

Public Data Repository ◽

Marrow Mesenchymal Stem Cells

Abstract Introduction TRIB2 is one of three members of the Tribbles family, which interacts and activates the protein kinase AKT, associated to the suppression of FOXO in melanoma, which mediate insulin action on key functions involved in cell metabolism and growth. TRIB2, recently identified as a druggable protein, has been pointed as a cause of resistance to cancer therapies, and so, modulating its expression might be a suitable strategy to overcome tumour malignancy and drug-resistance. Members from the thiazolidinediones (TZDs) family of oral antidiabetic insulin-sensitizing drugs, available in clinical settings, have been previously reported to be effective in melanoma, however there are still controversial results. Objectives Identifying new therapeutic strategies to overcome therapeutic resistance in TRIB2-positive cancers. Methodology Analyses of TRIB2’s expression levels in different samples in response to TZDs, using GEO profiles available at public data repository with the following advanced search terms: TRIB2 [gene] AND Rosiglitazone OR Pioglitazone OR Troglitazone. Results From a total of 15 independent studies, it was found that TRIB2 was up-regulated in Rosi-treated human dendritic cells, skeletal muscle from obese PCOS women treated with Pioglitazone, and liver from obese rats treated either with Pio or Troglitazone. By contrast, TRIB2 was down-regulated in white adipose tissue (WAT) from obese rats treated either with Pio or Troglitazone, in Rosi-treated mouse stromal vascular cells from inguinal WAT, and in PPARγ–overexpressed marrow mesenchymal stem cells treated with Rosiglitazone. Conclusion Although TRIB2 expression was modulated by the different TZDs in certain samples, depending on the cellular context, it was either up- or down-regulated. While further studies are needed, especially in a malignancy environment, TZDs treatment of TRIB2-positive cancer does not seem to be a valuable therapeutic strategy and might be even counter-productive.

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K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

Indian Journal of Artificial Intelligence and Neural Networking ◽

10.35940/ijainn.b1019.041221 ◽

2021 ◽

Vol 1 (2) ◽

pp. 18-21

Author(s):

Tsehay Admassu Assegie*

Keyword(s):

Nearest Neighbor ◽

Attack Detection ◽

Experimental Result ◽

Data Repository ◽

K Nearest Neighbor ◽

K Nearest Neighbors ◽

K Value ◽

Proposed Model ◽

Public Data ◽

Public Data Repository

Phishing causes many problems in business industry. The electronic commerce and electronic banking such as mobile banking involves a number of online transaction. In such online transactions, we have to discriminate features related to legitimate and phishing websites in order to ensure security of the online transaction. In this study, we have collected data form phish tank public data repository and proposed K-Nearest Neighbors (KNN) based model for phishing attack detection. The proposed model detects phishing attack through URL classification. The performance of the proposed model is tested empirically and result is analyzed. Experimental result on test set reveals that the model is efficient on phishing attack detection. Furthermore, the K value that gives better accuracy is determined to achieve better performance on phishing attack detection. Overall, the average accuracy of the proposed model is 85.08%.

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mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites

Journal of Translational Medicine ◽

10.1186/s12967-021-03127-3 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Ravi Mathur ◽

Megan U. Carnes ◽

Alexander Harding ◽

Amy Moore ◽

Ian Thomas ◽

...

Keyword(s):

Data Integration ◽

Data Sharing ◽

Complex Disease ◽

Chronic Fatigue ◽

Research Community ◽

Research Network ◽

Data Repository ◽

Data Repositories ◽

Public Data ◽

Data Portal

Abstract Background Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease which involves multiple body systems (e.g., immune, nervous, digestive, circulatory) and research domains (e.g., immunology, metabolomics, the gut microbiome, genomics, neurology). Despite several decades of research, there are no established ME/CFS biomarkers available to diagnose and treat ME/CFS. Sharing data and integrating findings across these domains is essential to advance understanding of this complex disease by revealing diagnostic biomarkers and facilitating discovery of novel effective therapies. Methods The National Institutes of Health funded the development of a data sharing portal to support collaborative efforts among an initial group of three funded research centers. This was subsequently expanded to include the global ME/CFS research community. Using the open-source comprehensive knowledge archive network (CKAN) framework as the base, the ME/CFS Data Management and Coordinating Center developed an online portal with metadata collection, smart search capabilities, and domain-agnostic data integration to support data findability and reusability while reducing the barriers to sustainable data sharing. Results We designed the mapMECFS data portal to facilitate data sharing and integration by allowing ME/CFS researchers to browse, share, compare, and download molecular datasets from within one data repository. At the time of publication, mapMECFS contains data curated from public data repositories, peer-reviewed publications, and current ME/CFS Research Network members. Conclusions mapMECFS is a disease-specific data portal to improve data sharing and collaboration among ME/CFS researchers around the world. mapMECFS is accessible to the broader research community with registration. Further development is ongoing to include novel systems biology and data integration methods.

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K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

Indian Journal of Artificial Intelligence and Neural Networking ◽

10.54105/ijainn.b1019.041221 ◽

2021 ◽

pp. 18-21

Author(s):

Tsehay Admassu Assegie ◽

Keyword(s):

Nearest Neighbor ◽

Attack Detection ◽

Experimental Result ◽

Data Repository ◽

K Nearest Neighbor ◽

K Nearest Neighbors ◽

K Value ◽

Proposed Model ◽

Public Data ◽

Public Data Repository

Phishing causes many problems in business industry. The electronic commerce and electronic banking such as mobile banking involves a number of online transaction. In such online transactions, we have to discriminate features related to legitimate and phishing websites in order to ensure security of the online transaction. In this study, we have collected data form phish tank public data repository and proposed K-Nearest Neighbors (KNN) based model for phishing attack detection. The proposed model detects phishing attack through URL classification. The performance of the proposed model is tested empirically and result is analyzed. Experimental result on test set reveals that the model is efficient on phishing attack detection. Furthermore, the K value that gives better accuracy is determined to achieve better performance on phishing attack detection. Overall, the average accuracy of the proposed model is 85.08%.

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mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites

10.21203/rs.3.rs-522847/v1 ◽

2021 ◽

Author(s):

Ravi Mathur ◽

Megan U. Carnes ◽

Alexander Harding ◽

Amy Moore ◽

Ian Thomas ◽

...

Keyword(s):

Data Integration ◽

Data Sharing ◽

Complex Disease ◽

Chronic Fatigue ◽

Research Community ◽

Data Repository ◽

Data Repositories ◽

Fatigue Syndrome ◽

Public Data ◽

Data Portal

Abstract Background Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease which involves multiple body systems (e.g., immune, nervous, digestive, circulatory) and research domains (e.g., immunology, metabolomics, the gut microbiome, genomics, neurology). Despite several decades of research, there are no established ME/CFS biomarkers available to diagnose and treat ME/CFS. Sharing data and integrating findings across these domains is essential to advance understanding of this complex disease by revealing diagnostic biomarkers and facilitating discovery of novel effective therapies. Methods The National Institutes of Health funded the development of a data sharing portal to support collaborative efforts among an initial group of three funded research centers. This was subsequently expanded to include the global ME/CFS research community. Using the open-source comprehensive knowledge archive network (CKAN) framework as the base, the ME/CFS Data Management and Coordinating Center developed targeted metadata collection, smart search capabilities, and domain-agnostic data integration to support data findability and reusability while reducing the barriers to sustainable data sharing. Results We designed the mapMECFS data portal to facilitate data sharing and integration by allowing ME/CFS researchers to browse, share, compare, and download molecular datasets from within one data repository. At the time of publication, mapMECFS contains data curated from public data repositories, peer-reviewed publications, and current ME/CFS network researchers. Conclusions mapMECFS is a disease-specific data portal to improve data sharing and collaboration among ME/CFS researchers around the world. mapMECFS is accessible to the broader research community with registration. Further development is ongoing to include novel systems biology and data integration methods.

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Consolidating Heterogeneous Enterprise Data for Named Entity Linking and Web Intelligence

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015400084 ◽

2015 ◽

Vol 24 (02) ◽

pp. 1540008 ◽

Cited By ~ 7

Author(s):

Albert Weichselbraun ◽

Daniel Streiff ◽

Arno Scharl

Keyword(s):

Linked Data ◽

Data Repository ◽

Special Focus ◽

Data Sets ◽

Entity Linking ◽

Data Repositories ◽

Web Intelligence ◽

Named Entity ◽

Business Information ◽

Public Data

Linking named entities to structured knowledge sources paves the way for state-of-the-art Web intelligence applications which assign sentiment to the correct entities, identify trends, and reveal relations between organizations, persons and products. For this purpose this paper introduces Recognyze, a named entity linking component that uses background knowledge obtained from linked data repositories, and outlines the process of transforming heterogeneous data silos within an organization into a linked enterprise data repository which draws upon popular linked open data vocabularies to foster interoperability with public data sets. The presented examples use comprehensive real-world data sets from Orell Füssli Business Information, Switzerland's largest business information provider. The linked data repository created from these data sets comprises more than nine million triples on companies, the companies' contact information, key people, products and brands. We identify the major challenges of tapping into such sources for named entity linking, and describe required data pre-processing techniques to use and integrate such data sets, with a special focus on disambiguation and ranking algorithms. Finally, we conduct a comprehensive evaluation based on business news from the New Journal of Zurich and AWP Financial News to illustrate how these techniques improve the performance of the Recognyze named entity linking component.

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