27 datasets found
  1. f

    Data from: S1 Dataset -

    • plos.figshare.com
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    Updated Feb 12, 2025
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    Lukundo Siame; Gift C. Chama; Sepiso K. Masenga (2025). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0312570.s002
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    Dataset updated
    Feb 12, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Lukundo Siame; Gift C. Chama; Sepiso K. Masenga
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundTuberculosis (TB) remains a significant public health challenge, particularly among vulnerable populations like children. This is especially true in Sub-Saharan Africa, where the burden of TB in children is substantial. Zambia ranks 21st among the top 30 high TB endemic countries globally. While studies have explored TB in adults in Zambia, the prevalence and associated factors in children are not well documented. This study aimed to determine the prevalence and sociodemographic, and clinical factors associated with active TB disease in hospitalized children under the age of 15 years at Livingstone University Teaching Hospital (LUTH), the largest referral center in Zambia’s Southern Province.MethodsThis retrospective cross-sectional study of 700 pediatric patients under 15 years old, utilized programmatic data from the Pediatrics Department at LUTH. A systematic sampling method was used to select participants from medical records. Data on demographics, medical conditions, anthropometric measurements, and blood tests were collected. Data analysis included descriptive statistics, chi-square tests, and multivariable logistic regression to identify factors associated with TB.ResultsThe median age was 24 months (interquartile range (IQR): 11, 60) and majority were male (56.7%, n = 397/700). Most participants were from urban areas (59.9%, n = 419/700), and 9.2% (n = 62/675) were living with HIV. Malnutrition and comorbidities were present in a significant portion of the participants (19.0% and 25.1%, respectively). The prevalence of active TB cases was 9.4% (n = 66/700) among hospitalized children. Persons living with HIV (Adjusted odds ratio (AOR) of 6.30; 95% confidence interval (CI) of 2.85, 13.89, p< 0.001), and those who were malnourished (AOR: 10.38, 95% CI: 4.78, 22.55, p< 0.001) had a significantly higher likelihood of developing active TB disease.ConclusionThis study revealed a prevalence 9.4% active TB among hospitalized children under 15 years at LUTH. HIV status and malnutrition emerged as significant factors associated with active TB disease. These findings emphasize the need for pediatric TB control strategies that prioritize addressing associated factors to effectively reduce the burden of tuberculosis in Zambian children.

  2. United States Climate Reference Network (USCRN) Standardized Soil Moisture...

    • catalog.data.gov
    • s.cnmilf.com
    • +1more
    Updated Sep 19, 2023
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    NOAA National Centers for Environmental Information (Point of Contact) (2023). United States Climate Reference Network (USCRN) Standardized Soil Moisture and Soil Moisture Climatology [Dataset]. https://catalog.data.gov/dataset/united-states-climate-reference-network-uscrn-standardized-soil-moisture-and-soil-moisture-clim2
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    Dataset updated
    Sep 19, 2023
    Dataset provided by
    National Centers for Environmental Informationhttps://www.ncei.noaa.gov/
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Area covered
    United States
    Description

    The U.S. Climate Reference Network (USCRN) was designed to monitor the climate of the United States using research quality instrumentation located within representative pristine environments. This Standardized Soil Moisture (SSM) and Soil Moisture Climatology (SMC) product set is derived using the soil moisture observations from the USCRN. The hourly soil moisture anomaly (SMANOM) is derived by subtracting the MEDIAN from the soil moisture volumetric water content (SMVWC) and dividing the difference by the interquartile range (IQR = 75th percentile - 25th percentile) for that hour: SMANOM = (SMVWC - MEDIAN) / (IQR). The soil moisture percentile (SMPERC) is derived by taking all the values that were used to create the empirical cumulative distribution function (ECDF) that yielded the hourly MEDIAN and adding the current observation to the set, recalculating the ECDF, and determining the percentile value of the current observation. Finally, the soil temperature for the individual layers is provided for the dataset user convenience. The SMC files contain the MEAN, MEDIAN, IQR, and decimal fraction of available data that are valid for each hour of the year at 5, 10, 20, 50, and 100 cm depth soil layers as well as for a top soil layer (TOP) and column soil layer (COLUMN). The TOP layer consists of an average of the 5 and 10 cm depths, while the COLUMN layer includes all available depths at a location, either two layers or five layers depending on soil depth. The SSM files contain the mean VWC, SMANOM, SMPERC, and TEMPERATURE for each of the depth layers described above. File names are structured as CRNSSM0101-STATIONNAME.csv and CRNSMC0101-STATIONNAME.csv. SSM stands for Standardized Soil Moisture and SCM represent Soil Moisture Climatology. The first two digits of the trailing integer indicate major version and the second two digits minor version of the product.

  3. 360-info/tracker-seaice: Daily sea ice extent: v2024-07-02

    • zenodo.org
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    Updated Jul 3, 2024
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    James Goldie; James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-07-02 [Dataset]. http://doi.org/10.5281/zenodo.12629789
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    zipAvailable download formats
    Dataset updated
    Jul 3, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    James Goldie; James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

  4. Z

    360-info/tracker-seaice: Daily sea ice extent: v2024-11-28

    • data.niaid.nih.gov
    Updated Nov 29, 2024
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    James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-11-28 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10892561
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    Dataset updated
    Nov 29, 2024
    Dataset authored and provided by
    James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

  5. VLA-COSMOS Survey 324-MHz Continuum Source Catalog - Dataset - NASA Open...

    • data.nasa.gov
    Updated Apr 1, 2025
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    nasa.gov (2025). VLA-COSMOS Survey 324-MHz Continuum Source Catalog - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/vla-cosmos-survey-324-mhz-continuum-source-catalog
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    This table contains a source catalog based on 90-cm (324-MHz) Very Large Array (VLA) imaging of the COSMOS field, comprising a circular area of 3.14 square degrees centered on 10h 00m 28.6s, 02o 12' 21" (J2000.0 RA and Dec). The image from the merger of 3 nights of observations using all 27 VLA antennas had an effective total integration time of ~ 12 hours, an 8.0 arcsecond x 6.0 arcsecond angular resolution, and an average rms of 0.5 mJy beam-1. The extracted catalog contains 182 sources (down to 5.5 sigma), 30 of which are multi-component sources. Using Monte Carlo artificial source simulations, the authors derive the completeness of the catalog, and show that their 90-cm source counts agree very well with those from previous studies. In their paper, the authors use X-ray, NUV-NIR and radio COSMOS data to investigate the population mix of this 90-cm radio sample, and find that the sample is dominated by active galactic nuclei. The average 90-20 cm spectral index (S_nu~ nualpha, where Snu is the flux density at frequency nu and alpha the spectral index) of the 90-cm selected sources is -0.70, with an interquartile range from -0.90 to -0.53. Only a few ultra-steep-spectrum sources are present in this sample, consistent with results in the literature for similar fields. These data do not show clear steepening of the spectral index with redshift. Nevertheless, this sample suggests that sources with spectral indices steeper than -1 all lie at z >~ 1, in agreement with the idea that ultra-steep-spectrum radio sources may trace intermediate-redshift galaxies (z >~ 1). Using both the signal and rms maps (see Figs. 1 and 2 in the reference paper) as input data, the authors ran the AIPS task SAD to obtain a catalog of candidate components above a given local signal-to-noise ratio (S/N) threshold. The task SAD was run four times with search S/N levels of 10, 8, 6 and 5, using the resulting residual image each time. They recovered all the radio components with a local S/N > 5.00. Subsequently, all the selected components were visually inspected, in order to check their reliability, especially for the components near strong side-lobes. After a careful analysis, a S/N threshold of 5.50 was adopted as the best compromise between a deep and a reliable catalog. The procedure yielded a total of 246 components with a local S/N > 5.50. More than one component, identified in the 90-cm map sometimes belongs to a single radio source (e.g. large radio galaxies consist of multiple components). Using the 90-cm COSMOS radio map, the authors combined the various components into single sources based on visual inspection. The final catalog (contained in this HEASARC table) lists 182 radio sources, 30 of which have been classified as multiple, i.e. they are better described by more than a single component. Moreover, in order to ensure a more precise classification, all sources identified as multi-component sources have been also double-checked using the 20-cm radio map. The authors found that all the 26 multiple 90-cm radio sources within the 20-cm map have 20-cm counterpart sources already classified as multiple. The authors have made use of the VLA-COSMOS Large and Deep Projects over 2 square degrees, reaching down to an rms of ~15 µJy beam1 ^ at 1.4 GHz and 1.5 arcsec resolution (Schinnerer et al. 2007, ApJS, 172, 46: the VLACOSMOS table in the HEASARC database). The 90-cm COSMOS radio catalog has, however, been extracted from a larger region of 3.14 square degrees (see Fig. 1 and Section 3.1 of the reference paper). This implies that a certain number of 90-cm sources (48) lie outside the area of the 20-cm COSMOS map used to select the radio catalog. Thus, to identify the 20-cm counterparts of the 90-cm radio sources, the authors used the joint VLA-COSMOS catalog (Schinnerer et al. 2010, ApJS, 188, 384: the VLACOSMJSC table in the HEASARC database) for the 134 sources within the 20-cm VLA-COSMOS area and the VLA- FIRST survey (White et al. 1997, ApJ, 475, 479: the FIRST table in the HEASARC database) for the remaining 48 sources. The 90-cm sources were cross-matched with the 20-cm VLA-COSMOS sources using a search radius of 2.5 arcseconds, while the cross-match with the VLA-FIRST sources has been done using a search radius of 4 arcseconds in order to take into account the larger synthesized beam of the VLA-FIRST survey of ~5 arcseconds. Finally, all the 90 cm - 20 cm associations were visually inspected in order to ensure also the association of the multiple 90-cm radio sources for which the value of the search radius used during the cross-match could be too restrictive. In summary, out of the total of 182 sources in the 90-cm catalog, 168 have counterparts at 20 cm. This table was created by the HEASARC in October 2014 based on an electronic version of Table 1 from the reference paper which was obtained from the COSMOS web site at IRSA, specifically the file vla-cosmos_327_sources_published_version.tbl at http://irsa.ipac.caltech.edu/data/COSMOS/tables/vla/. This is a service provided by NASA HEASARC .

  6. Characteristics of the included medications.

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    Updated Jun 21, 2023
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    Benazir Hodzic-Santor; Chana A. Sacks; Tamara Van Bakel; Michael Fralick (2023). Characteristics of the included medications. [Dataset]. http://doi.org/10.1371/journal.pone.0281076.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Benazir Hodzic-Santor; Chana A. Sacks; Tamara Van Bakel; Michael Fralick
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Characteristics of the included medications.

  7. f

    Minimal dataset.

    • plos.figshare.com
    xlsx
    Updated Sep 6, 2024
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    Emmanuel L. Luwaya; Lackson Mwape; Kaole Bwalya; Chileleko Siakabanze; Benson M. Hamooya; Sepiso K. Masenga (2024). Minimal dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0308869.s002
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    xlsxAvailable download formats
    Dataset updated
    Sep 6, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Emmanuel L. Luwaya; Lackson Mwape; Kaole Bwalya; Chileleko Siakabanze; Benson M. Hamooya; Sepiso K. Masenga
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundAn increase in the prevalence of HIV drug resistance (HIVDR) has been reported in recent years, especially in persons on non-nucleoside reverse transcriptase inhibitors (NNRTIs) due to their low genetic barrier to mutations. However, there is a paucity of epidemiological data quantifying HIVDR in the era of new drugs like dolutegravir (DTG) in sub-Saharan Africa. We, therefore, sought to determine the prevalence and correlates of viral load (VL) suppression in adult people with HIV (PWH) on a fixed-dose combination of tenofovir disoproxil fumarate/lamivudine/dolutegravir (TLD) or tenofovir alafenamide/emtricitabine/dolutegravir (TAFED) and describe patterns of mutations in individuals failing treatment.MethodsWe conducted a cross-sectional study among 384 adults living with HIV aged ≥15 years between 5th June 2023 and 10th August 2023. Demographic, laboratory and clinical data were collected from electronic health records using a data collection form. Viral load suppression was defined as plasma HIV-1 RNA VL of

  8. f

    Raw data (ID PONE-D-24–42713).

    • plos.figshare.com
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    Updated May 16, 2025
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    Feilong Tan; Yanhua Li; Hongying Xia; Wenjie Yin (2025). Raw data (ID PONE-D-24–42713). [Dataset]. http://doi.org/10.1371/journal.pone.0322378.s001
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    zipAvailable download formats
    Dataset updated
    May 16, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Feilong Tan; Yanhua Li; Hongying Xia; Wenjie Yin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ObjectivesBrentuximab Vedotin (BV) is a novel antibody-drug conjugate (ADC) approved for the treatment of classical Hodgkin’s lymphoma and systemic anaplastic large cell lymphoma. However, as a relatively new therapeutic agent, the long-term safety profile and adverse event (AE) profile of BV require further investigation. This study aimed to identify significant and unexpected AEs associated with BV using data from the FDA Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) databases.MethodsData on BV-related AEs were extracted from the FAERS and JADER databases. Signal detection was performed using the reporting odds ratio (ROR) and 95% confidence intervals (95% CI). Risk signals were categorized according to system organ classes (SOCs) and preferred terms (PTs) as defined by the Medical Dictionary for Regulatory Activities (MedDRA) version 26.0. In addition, the onset times of BV-related AEs were analyzed.ResultsBetween 2004 and 2023, a total of 19,279 and 2,561 AEs related to BV were recorded in the FAERS and JADER databases, respectively. At the SOC level, prominent signals in the FAERS database included blood and lymphatic system disorders, benign, malignant, and unspecified neoplasms (including cysts and polyps), as well as congenital, familial, and genetic disorders. In the JADER database, the most notable signals involved benign, malignant, and unspecified neoplasms, blood and lymphatic system disorders, and nervous system disorders. At the PT level, the top five signals in the FAERS database were peripheral motor neuropathy, peripheral sensory neuropathy, pneumocystis jirovecii pneumonia, febrile bone marrow aplasia, and polyneuropathy. Unexpected AEs included febrile bone marrow aplasia and Guillain-Barré syndrome. In the JADER database, the top five signals included peripheral motor neuropathy, peripheral sensory neuropathy, bacterial gastroenteritis, febrile neutropenia and pneumonia, with unexpected AEs such as left ventricular dysfunction, cardiomegaly, retinal detachment, and marasmus. The median onset time of AEs was 22 days (interquartile range [IQR] 7–81 days) in FAERS and 27 days (IQR 7–73 days) in JADER.ConclusionThe signal detection results from the FAERS and JADER databases highlight the importance of monitoring significant and unexpected AEs associated with BV, particularly in the early stages of treatment. These findings contribute to enhancing the post-marketing safety profile of BV and offer valuable insights for clinical risk management strategies.

  9. f

    Patient characteristics by exacerbation count levels. Shown are the...

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    xls
    Updated Jun 23, 2025
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    Alana Schreibman; Kimberly Lactaoen; Jaehyun Joo; Patrick K. Gleeson; Gary E. Weissman; Andrea J. Apter; Rebecca A. Hubbard; Blanca E. Himes (2025). Patient characteristics by exacerbation count levels. Shown are the characteristics of patients according to their number of exacerbations during the study period. For each exacerbation level, the number and percentage of patients are shown for categorical variables, and the Median and Interquartile Range (IQR) are shown for continuous variables. [Dataset]. http://doi.org/10.1371/journal.pdig.0000677.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 23, 2025
    Dataset provided by
    PLOS Digital Health
    Authors
    Alana Schreibman; Kimberly Lactaoen; Jaehyun Joo; Patrick K. Gleeson; Gary E. Weissman; Andrea J. Apter; Rebecca A. Hubbard; Blanca E. Himes
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Patient characteristics by exacerbation count levels. Shown are the characteristics of patients according to their number of exacerbations during the study period. For each exacerbation level, the number and percentage of patients are shown for categorical variables, and the Median and Interquartile Range (IQR) are shown for continuous variables.

  10. f

    Patient characteristics by race. Shown are the number and percentage of...

    • plos.figshare.com
    xls
    Updated Jun 23, 2025
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    Alana Schreibman; Kimberly Lactaoen; Jaehyun Joo; Patrick K. Gleeson; Gary E. Weissman; Andrea J. Apter; Rebecca A. Hubbard; Blanca E. Himes (2025). Patient characteristics by race. Shown are the number and percentage of patients in each level for categorical variables, and the Median and Interquartile Range (IQR) for continuous variables in patients of White race versus Black race. [Dataset]. http://doi.org/10.1371/journal.pdig.0000677.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 23, 2025
    Dataset provided by
    PLOS Digital Health
    Authors
    Alana Schreibman; Kimberly Lactaoen; Jaehyun Joo; Patrick K. Gleeson; Gary E. Weissman; Andrea J. Apter; Rebecca A. Hubbard; Blanca E. Himes
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Patient characteristics by race. Shown are the number and percentage of patients in each level for categorical variables, and the Median and Interquartile Range (IQR) for continuous variables in patients of White race versus Black race.

  11. f

    Characteristics of BV from FAERS and JADER databases.

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 16, 2025
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    Feilong Tan; Yanhua Li; Hongying Xia; Wenjie Yin (2025). Characteristics of BV from FAERS and JADER databases. [Dataset]. http://doi.org/10.1371/journal.pone.0322378.t003
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    xlsAvailable download formats
    Dataset updated
    May 16, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Feilong Tan; Yanhua Li; Hongying Xia; Wenjie Yin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Characteristics of BV from FAERS and JADER databases.

  12. f

    Summary of major algorithms used for signal detection.

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    xls
    Updated May 16, 2025
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    Feilong Tan; Yanhua Li; Hongying Xia; Wenjie Yin (2025). Summary of major algorithms used for signal detection. [Dataset]. http://doi.org/10.1371/journal.pone.0322378.t002
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    xlsAvailable download formats
    Dataset updated
    May 16, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Feilong Tan; Yanhua Li; Hongying Xia; Wenjie Yin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary of major algorithms used for signal detection.

  13. 360-info/tracker-seaice: Daily sea ice extent: v2024-04-30

    • zenodo.org
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    Updated Apr 30, 2024
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    James Goldie; James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-04-30 [Dataset]. http://doi.org/10.5281/zenodo.11089838
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    zipAvailable download formats
    Dataset updated
    Apr 30, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    James Goldie; James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

  14. f

    Subjects’ characteristics during the ISWT (by gender).

    • plos.figshare.com
    • figshare.com
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    Updated Sep 6, 2023
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    Muhammad Zulhaziq Bin Azman; Katherin S. Huang; Wei Jun Koh; Sarah S. Leong; Benjamin Ong; Johanna L. Soon; Sherman W. Tan; Melissa Y. Chan; Mingxing Yang; Meredith T. Yeung (2023). Subjects’ characteristics during the ISWT (by gender). [Dataset]. http://doi.org/10.1371/journal.pone.0291132.t001
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    xlsAvailable download formats
    Dataset updated
    Sep 6, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Muhammad Zulhaziq Bin Azman; Katherin S. Huang; Wei Jun Koh; Sarah S. Leong; Benjamin Ong; Johanna L. Soon; Sherman W. Tan; Melissa Y. Chan; Mingxing Yang; Meredith T. Yeung
    Description

    Subjects’ characteristics during the ISWT (by gender).

  15. 360-info/tracker-seaice: Daily sea ice extent: v2024-05-16

    • zenodo.org
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    Updated May 17, 2024
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    James Goldie; James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-05-16 [Dataset]. http://doi.org/10.5281/zenodo.11206326
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    zipAvailable download formats
    Dataset updated
    May 17, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    James Goldie; James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

  16. 360-info/tracker-seaice: Daily sea ice extent: v2024-10-21

    • zenodo.org
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    Updated Oct 22, 2024
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    James Goldie; James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-10-21 [Dataset]. http://doi.org/10.5281/zenodo.13968011
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    zipAvailable download formats
    Dataset updated
    Oct 22, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    James Goldie; James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

  17. f

    Domain documentation summary statistics pooled across all time periods (n =...

    • plos.figshare.com
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    Updated Jun 13, 2023
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    Timothy Tuti; Jalemba Aluvaala; Daisy Chelangat; George Mbevi; John Wainaina; Livingstone Mumelo; Kefa Wairoto; Dolphine Mochache; Grace Irimu; Michuki Maina; Mike English (2023). Domain documentation summary statistics pooled across all time periods (n = 80060 patients). [Dataset]. http://doi.org/10.1371/journal.pgph.0000673.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOS Global Public Health
    Authors
    Timothy Tuti; Jalemba Aluvaala; Daisy Chelangat; George Mbevi; John Wainaina; Livingstone Mumelo; Kefa Wairoto; Dolphine Mochache; Grace Irimu; Michuki Maina; Mike English
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Domain documentation summary statistics pooled across all time periods (n = 80060 patients).

  18. 360-info/tracker-seaice: Daily sea ice extent: v2024-07-26

    • zenodo.org
    zip
    Updated Jul 27, 2024
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    James Goldie; James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-07-26 [Dataset]. http://doi.org/10.5281/zenodo.13017329
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    zipAvailable download formats
    Dataset updated
    Jul 27, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    James Goldie; James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

  19. 360-info/tracker-seaice: Daily sea ice extent: v2024-06-26

    • zenodo.org
    zip
    Updated Jun 27, 2024
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    James Goldie; James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-06-26 [Dataset]. http://doi.org/10.5281/zenodo.12556905
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 27, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    James Goldie; James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

  20. 360-info/tracker-seaice: Daily sea ice extent: v2024-09-30

    • zenodo.org
    • explore.openaire.eu
    zip
    Updated Oct 1, 2024
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    James Goldie; James Goldie (2024). 360-info/tracker-seaice: Daily sea ice extent: v2024-09-30 [Dataset]. http://doi.org/10.5281/zenodo.13864260
    Explore at:
    zipAvailable download formats
    Dataset updated
    Oct 1, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    James Goldie; James Goldie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

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Lukundo Siame; Gift C. Chama; Sepiso K. Masenga (2025). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0312570.s002

Data from: S1 Dataset -

Related Article
Explore at:
xlsxAvailable download formats
Dataset updated
Feb 12, 2025
Dataset provided by
PLOS ONE
Authors
Lukundo Siame; Gift C. Chama; Sepiso K. Masenga
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

BackgroundTuberculosis (TB) remains a significant public health challenge, particularly among vulnerable populations like children. This is especially true in Sub-Saharan Africa, where the burden of TB in children is substantial. Zambia ranks 21st among the top 30 high TB endemic countries globally. While studies have explored TB in adults in Zambia, the prevalence and associated factors in children are not well documented. This study aimed to determine the prevalence and sociodemographic, and clinical factors associated with active TB disease in hospitalized children under the age of 15 years at Livingstone University Teaching Hospital (LUTH), the largest referral center in Zambia’s Southern Province.MethodsThis retrospective cross-sectional study of 700 pediatric patients under 15 years old, utilized programmatic data from the Pediatrics Department at LUTH. A systematic sampling method was used to select participants from medical records. Data on demographics, medical conditions, anthropometric measurements, and blood tests were collected. Data analysis included descriptive statistics, chi-square tests, and multivariable logistic regression to identify factors associated with TB.ResultsThe median age was 24 months (interquartile range (IQR): 11, 60) and majority were male (56.7%, n = 397/700). Most participants were from urban areas (59.9%, n = 419/700), and 9.2% (n = 62/675) were living with HIV. Malnutrition and comorbidities were present in a significant portion of the participants (19.0% and 25.1%, respectively). The prevalence of active TB cases was 9.4% (n = 66/700) among hospitalized children. Persons living with HIV (Adjusted odds ratio (AOR) of 6.30; 95% confidence interval (CI) of 2.85, 13.89, p< 0.001), and those who were malnourished (AOR: 10.38, 95% CI: 4.78, 22.55, p< 0.001) had a significantly higher likelihood of developing active TB disease.ConclusionThis study revealed a prevalence 9.4% active TB among hospitalized children under 15 years at LUTH. HIV status and malnutrition emerged as significant factors associated with active TB disease. These findings emphasize the need for pediatric TB control strategies that prioritize addressing associated factors to effectively reduce the burden of tuberculosis in Zambian children.

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