The dataset represents the complete, reproducible search strategies for all literature databases searched during the systematic review. The Endnote file and the Endnote import files contain all citations considered for inclusion in the review.
This dataset includes census tract-level data concerning housing in Metropolitan Detroit. The data includes: 1) Total housing units and total mortgages in the tract; 2) Land use; 3) Real estate information (foreclosures, sales transactions, and home values); 4) Vacant housing; 5) Housing age and available facilities; 6) Housing condition; and 7) Spatial measures of subsidized housing in the tract.
Data coverage should say 2006 to 2015.
Health status data includes data about the health of persons within a census tract in Metropolitan Detroit, measured at the census tract level. This includes data about 1) mortality by condition; 2) exposures to toxic substances; and 3) disability.
Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
This systematic review and meta-analysis assesses decision aids in the context of patients considering post-mastectomy breast reconstruction. and NOTE: An updated Read Me file was added to this data set on May 24, 2018 replacing the original.
While collaboration with industry is paramount to innovation, the recent emphasis on industrial relationship transparency has sparked new guidelines, research studies, and standardizations focused on re-defining conflict of interest. There is limited data on defining the specific financial amount wherein a conflict of interest is relevant. This study is the first to assess the potential financial effects on high-quality clinical data, or the “indirect sponsorship”.
Criss CN, MacEachern MP, Matusko N, Dimick JB, Maggard-Gibbons M, Gadepalli SK. The Impact of Corporate Payments on Robotic Surgery Research: A Systematic Review. Ann Surg. 2019 Mar; 269 (3): 389-396. doi: 10.1097/SLA.0000000000003000. PMID: 30067545.
This data is part of a large program to translate detection and interpretation of HFOs into clinical use. A zip file is included which contains hfo detections, metadata, and Matlab scripts. The matlab scripts analyze this input data and produce figures as in the referenced paper (note: the blind source separation method is stochastic, and so the figures may not be exactly the same). A file "README.txt" provides more detail about each individual file within the zip file.
Stephen V. Gliske, Zachary T. Irwin, Cynthia Chestek, Garnett L. Hegeman, Benjamin Brinkmann, Oren Sagher, Hugh J. L. Garton, Greg A. Worrell, William C. Stacey. "Variability in the location of High Frequency Oscillations during prolonged intracranial EEG recordings." Nature Communications. https://doi.org/10.1038/s41467-018-04549-2
Investigating minimum human reaction times is often confounded by the motivation, training, and state of arousal of the subjects. We used the reaction times of athletes competing in the shorter sprint events in the Athletics competitions in recent Olympics (2004-2016) to determine minimum human reaction times because there's little question as to their motivation, training, or state of arousal.
The reaction times of sprinters however are only available on the IAAF web page for each individual heat, in each event, at each Olympic. Therefore we compiled all these data into two separate excel sheets which can be used for further analyses.
Data include variables used to run mixed effects models examining the association between the nose/throat microbiome and influenza virus infection. Certain individual participant data have been excluded due to identifiability concerns. Data also include the oligotype count table and taxonomic classifications. and Curation Notes: Readme updated Nov. 29, 2018 with context for oligotype and taxonomy files, and citation to associated article.
Lee KH, Gordon A, Shedden K, Kuan G, Ng S, Balmaseda A, Foxman B. The respiratory microbiome and susceptibility to influenza virus infection. PloS One. 2019;14:e0207898. doi:10.1371/journal.pone.0207898
Data include variables used to run accelerated failure time models examining the association between the nose/throat microbiome and 1) symptom duration, 2) shedding duration, and 3) time to infection. Certain individual participant data have been excluded due to identifiability concerns. Data also include the oligotype count table and taxonomic classifications.
Lee KH, Gordon A, Shedden K, Kuan G, Ng S, Balmaseda A, Foxman B. The respiratory microbiome and susceptibility to influenza virus infection. PloS One. 2019;14:e0207898. doi:10.1371/journal.pone.0207898.