cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e243841
title: NNDSS - TABLE 1AA. Poliovirus infection, nonparalytic to Psittacosis

description: NNDSS - TABLE 1AA. Poliovirus infection, nonparalytic to Psittacosis - 2020. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

Note:
This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

Footnotes:
U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.
-: No reported cases — The reporting jurisdiction did not submit any cases to CDC.
N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.
NN: Not nationally notifiable — This condition was not designated as being nationally notifiable.
NP: Nationally notifiable but not published.
NC: Not calculated — There is insufficient data available to support the calculation of this statistic.
Cum: Cumulative year-to-date counts.
Max: Maximum — Maximum case count during the previous 52 weeks.
* Case counts for reporting years 2019 and 2020 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf.
†Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).
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description: (Downloaded 1/12/2026)
DOI: 10.15156/bio/sh1412803.10fu
title: SH1412803.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3886/e243841v1
title: NNDSS - TABLE 1AA. Poliovirus infection, nonparalytic to Psittacosis

description: NNDSS - TABLE 1AA. Poliovirus infection, nonparalytic to Psittacosis - 2020. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

Note:
This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

Footnotes:
U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.
-: No reported cases — The reporting jurisdiction did not submit any cases to CDC.
N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.
NN: Not nationally notifiable — This condition was not designated as being nationally notifiable.
NP: Nationally notifiable but not published.
NC: Not calculated — There is insufficient data available to support the calculation of this statistic.
Cum: Cumulative year-to-date counts.
Max: Maximum — Maximum case count during the previous 52 weeks.
* Case counts for reporting years 2019 and 2020 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf.
†Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).
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description: (Downloaded 1/12/2026)
DOI: 10.5066/p146mtrs
title: Random Forest Regression Model Archive for Estimating Low-Streamflow Statistics at Ungaged Locations in New York, excluding Long Island

description: This USGS data release contains R models (R Core Team, 2020) for estimating 7Q10 and 30Q10 [lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years] statistics at ungaged locations in or adjacent to New York State excluding Long Island. Details on model development are available in Stagnitta and others (2025).
 
basin_characteristics.csv - 224 basin characteristics represented by variables that describe basin geometry, climatic conditions, land cover, soils and surficial geology, and other characteristics for 213 unaltered gaged locations determined from (Stagnitta and others, 2024). Basin characteristics were used as predictor variables within the models to estimate low-streamflow statistics for ungaged locations.
 
R scripts used to flag unaltered streamgage datasets for redundant basins, and to train, tune, test, and bias-correct random forest regression models for estimating 7Q10 and 30Q10 are included in model.zip. Users are encouraged to read the readme file in this zipped file for details on the scripts and associated files used to generate the statistics.

References:

R Core Team, 2020, R—A language and environment for statistical computing: R Foundation for Statistical Computing web page, accessed August 2024 at

Stagnitta, T.J., Graziano, A., Woda, J., Glas, R., and Gazoorian, C., 2024, Low-flow statistics for New York State, excluding Long Island, computed through March 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P9NOM6FR.
DOI: 10.17630/c7b0027d-b2d4-4f67-984f-a63834197d5c
title: Toxic Gas Adsorption and Water Stability in Metal-Organic Frameworks (Thesis data)
DOI: 10.15468/dl.94reuz
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Drosophila elegans"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025479-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29712467
title: How are Primary Education Health & Physical Education teachers' best prepared - AITSL study report.pdf

description: This project investigated Primary School Principal's perceptions of a University Pre-service Teacher Education course where the graduate teacher is qualified as a generalist primary classroom teacher and a Health and Physical Education specialist. Surveys were completed in term 4 of the school year, there were 376 principal participants from a cross-section of Australian Government schools representing every state and territory, region and size. Health and Physical Education (HPE) as an Australian curriculum learning area is associated with holistic lifelong health and wellbeing. Research suggests that the optimum time for children to learn and refine their motor skills and to be introduced to positive HPE experiences is during preschool and early primary school years. This project investigates such research through the perceptions of Principals within the context of Government Schools in Australia.
DOI: 10.6084/m9.figshare.29712467.v1
title: How are Primary Education Health & Physical Education teachers' best prepared - AITSL study report.pdf

description: This project investigated Primary School Principal's perceptions of a University Pre-service Teacher Education course where the graduate teacher is qualified as a generalist primary classroom teacher and a Health and Physical Education specialist. Surveys were completed in term 4 of the school year, there were 376 principal participants from a cross-section of Australian Government schools representing every state and territory, region and size. Health and Physical Education (HPE) as an Australian curriculum learning area is associated with holistic lifelong health and wellbeing. Research suggests that the optimum time for children to learn and refine their motor skills and to be introduced to positive HPE experiences is during preschool and early primary school years. This project investigates such research through the perceptions of Principals within the context of Government Schools in Australia.
DOI: 10.15468/dl.rznnaa
title: Occurrence Download

description: A dataset containing 1754 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senecio inaequidens",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1754 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078480-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26182/t89x-x486
title: Fitzroy River ecosystem function data set
DOI: 10.15468/dl.ry7299
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tortula marginata",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010108-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7549t
title: Occurrence Download

description: A dataset containing 3804 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Indonesia",
 "GadmGid is IDN.32_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2015-2024"
 ]
}

 The dataset includes 3804 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007647-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30306631
title: <b>Should we predict maximum oxygen uptake using fat-free mass or body mass in a non-athletic population</b>

description: <b>Background</b>: Our purpose was to assess whether fat-free mass (FFM) or body mass (M) is the more appropriate body-size variable to predict maximum oxygen uptake (VO<sub>2max</sub>) (L⋅min<sup>−</sup><sup>1</sup>) in a non-athletic population.<b>Methods</b>: We adopted the well-known allometric/power function-model VO<sub>2max</sub>=a·X<sup>b</sup>⋅e to predict VO<sub>2max</sub> (L⋅min<sup>−</sup><sup>1</sup>) using either FFM or M as the predictor variable. These allometric models can be linearised with a log-transformation, and linear regression (or ANCOVA) can then be used to estimate the unknown parameters.<b>Results</b>: Initially, ignoring the effect of age and sex, FFM was the strongest predictor of VO<sub>2max</sub>. When we predicted Ln(VO<sub>2max</sub>) using only Ln(FFM) adjusting for the effects of age and sex, the explained variance was R<sup>2</sup>=0.718 (AIC=-1882.5), with the FFM exponent b=0.658. However, when we predicted Ln(VO<sub>2max</sub>) allowing body mass AND bodyfat% as separate covariates also adjusting for age and sex, the explained variance increased further to R<sup>2</sup>=0.733 (AIC=-2077.4), with the M exponent b=0.636. The difference in the explained variances, but more importantly, the difference in the AIC’s was 194.88, confirming the superiority of predicting VO<sub>2max</sub> using separate M and bodyfat% covariates, i.e., adopting FFM alone was less successful compared to using M and bodyfat% separately. Our analyses identified a greater negatively weighted bodyfat% term that improved the prediction of VO<sub>2max</sub>, found by our subsequent/final prediction models, to be due to excess central adiposity (waist circumference). Note that subsequent/final M and FFM exponents were both estimated to be b=0.67, confirming that VO<sub>2max</sub> should be normalized using VO<sub>2max</sub> (mL·M<sup>−2/3</sup>·min<sup>−1</sup>) or VO<sub>2max</sub> (mL·FFM<sup>−2/3</sup>·min<sup>−1</sup>) rather than VO<sub>2max</sub> (mL·FFM<sup>−1</sup>·min<sup>−1</sup>) as recommended by previous research.<b>Conclusion</b>: Incorporating FFM into equations to predict VO<sub>2max</sub> fails to adequately explain the negative effect of excess central adiposity. However, by incorporating body mass (M) and BF% separately into the equations, a greater negatively weighted BF% coefficient is capable of compensating for this apparent omission.
DOI: 10.6084/m9.figshare.30306631.v6
title: <b>Should we predict maximum oxygen uptake using fat-free mass or body mass in a non-athletic population</b>

description: <b>Background</b>: Our purpose was to assess whether fat-free mass (FFM) or body mass (M) is the more appropriate body-size variable to predict maximum oxygen uptake (VO<sub>2max</sub>) (L⋅min<sup>−</sup><sup>1</sup>) in a non-athletic population.<b>Methods</b>: We adopted the well-known allometric/power function-model VO<sub>2max</sub>=a·X<sup>b</sup>⋅e to predict VO<sub>2max</sub> (L⋅min<sup>−</sup><sup>1</sup>) using either FFM or M as the predictor variable. These allometric models can be linearised with a log-transformation, and linear regression (or ANCOVA) can then be used to estimate the unknown parameters.<b>Results</b>: Initially, ignoring the effect of age and sex, FFM was the strongest predictor of VO<sub>2max</sub>. When we predicted Ln(VO<sub>2max</sub>) using only Ln(FFM) adjusting for the effects of age and sex, the explained variance was R<sup>2</sup>=0.718 (AIC=-1882.5), with the FFM exponent b=0.658. However, when we predicted Ln(VO<sub>2max</sub>) allowing body mass AND bodyfat% as separate covariates also adjusting for age and sex, the explained variance increased further to R<sup>2</sup>=0.733 (AIC=-2077.4), with the M exponent b=0.636. The difference in the explained variances, but more importantly, the difference in the AIC’s was 194.88, confirming the superiority of predicting VO<sub>2max</sub> using separate M and bodyfat% covariates, i.e., adopting FFM alone was less successful compared to using M and bodyfat% separately. Our analyses identified a greater negatively weighted bodyfat% term that improved the prediction of VO<sub>2max</sub>, found by our subsequent/final prediction models, to be due to excess central adiposity (waist circumference). Note that subsequent/final M and FFM exponents were both estimated to be b=0.67, confirming that VO<sub>2max</sub> should be normalized using VO<sub>2max</sub> (mL·M<sup>−2/3</sup>·min<sup>−1</sup>) or VO<sub>2max</sub> (mL·FFM<sup>−2/3</sup>·min<sup>−1</sup>) rather than VO<sub>2max</sub> (mL·FFM<sup>−1</sup>·min<sup>−1</sup>) as recommended by previous research.<b>Conclusion</b>: Incorporating FFM into equations to predict VO<sub>2max</sub> fails to adequately explain the negative effect of excess central adiposity. However, by incorporating body mass (M) and BF% separately into the equations, a greater negatively weighted BF% coefficient is capable of compensating for this apparent omission.
DOI: 10.6084/m9.figshare.30306631.v5
title: <b>Should we predict maximum oxygen uptake using fat-free mass or body mass in a non-athletic population</b>

description: <b>Background</b>: Our purpose was to assess whether fat-free mass (FFM) or body mass (M) is the more appropriate body-size variable to predict maximum oxygen uptake (VO<sub>2max</sub>) (L⋅min<sup>−</sup><sup>1</sup>) in a non-athletic population.<b>Methods</b>: We adopted the well-known allometric/power function-model VO<sub>2max</sub>=a·X<sup>b</sup>⋅e to predict VO<sub>2max</sub> (L⋅min<sup>−</sup><sup>1</sup>) using either FFM or M as the predictor variable. These allometric models can be linearised with a log-transformation, and linear regression (or ANCOVA) can then be used to estimate the unknown parameters.<b>Results</b>: Initially, ignoring the effect of age and sex, FFM was the strongest predictor of VO<sub>2max</sub>. When we predicted Ln(VO<sub>2max</sub>) using only Ln(FFM) adjusting for the effects of age and sex, the explained variance was R<sup>2</sup>=0.718 (AIC=-1882.5), with the FFM exponent b=0.658. However, when we predicted Ln(VO<sub>2max</sub>) allowing body mass AND bodyfat% as separate covariates also adjusting for age and sex, the explained variance increased further to R<sup>2</sup>=0.733 (AIC=-2077.4), with the M exponent b=0.636. The difference in the explained variances, but more importantly, the difference in the AIC’s was 194.88, confirming the superiority of predicting VO<sub>2max</sub> using separate M and bodyfat% covariates, i.e., adopting FFM alone was less successful compared to using M and bodyfat% separately. Our analyses identified a greater negatively weighted bodyfat% term that improved the prediction of VO<sub>2max</sub>, found by our subsequent/final prediction models, to be due to excess central adiposity (waist circumference). Note that subsequent/final M and FFM exponents were both estimated to be b=0.67, confirming that VO<sub>2max</sub> should be normalized using VO<sub>2max</sub> (mL·M<sup>−2/3</sup>·min<sup>−1</sup>) or VO<sub>2max</sub> (mL·FFM<sup>−2/3</sup>·min<sup>−1</sup>) rather than VO<sub>2max</sub> (mL·FFM<sup>−1</sup>·min<sup>−1</sup>) as recommended by previous research.<b>Conclusion</b>: Incorporating FFM into equations to predict VO<sub>2max</sub> fails to adequately explain the negative effect of excess central adiposity. However, by incorporating body mass (M) and BF% separately into the equations, a greater negatively weighted BF% coefficient is capable of compensating for this apparent omission.
DOI: 10.60712/si-id44410.1
title: StrainInfo SI-ID 44410.1

description: StrainInfo dataset 44410 about a strain of Arthrobacter. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44508.1
title: StrainInfo SI-ID 44508.1

description: StrainInfo dataset 44508 about a strain of Salmonella enterica subsp. enterica. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44509.1
title: StrainInfo SI-ID 44509.1

description: StrainInfo dataset 44509 about a strain of Salmonella enterica subsp. enterica. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44574.1
title: StrainInfo SI-ID 44574.1

description: StrainInfo dataset 44574 about a strain of Streptomyces fradiae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44575.1
title: StrainInfo SI-ID 44575.1

description: StrainInfo dataset 44575 about a strain of Corynebacterium ammoniagenes. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44576.1
title: StrainInfo SI-ID 44576.1

description: StrainInfo dataset 44576 about a strain of Streptomyces olivochromogenes. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44411.1
title: StrainInfo SI-ID 44411.1

description: StrainInfo dataset 44411 about a strain of Shigella dysenteriae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44412.1
title: StrainInfo SI-ID 44412.1

description: StrainInfo dataset 44412 about a strain of Lactobacillus acidophilus. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44413.1
title: StrainInfo SI-ID 44413.1

description: StrainInfo dataset 44413 about a strain of Lacticaseibacillus rhamnosus. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id44512.1
title: StrainInfo SI-ID 44512.1

description: StrainInfo dataset 44512 about a strain of Unidentified. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.