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The 169 Commonwealth electoral divisions as the ABS built them from mesh blocks for 2025, one shape each with its state, in GDA2020.This is the 2025-07-14 version of this dataset on publicdata.au, wit…

The 169 Commonwealth electoral divisions as the ABS built them from mesh blocks for 2025, one shape each with its state, in GDA2020.This is the 2025-07-14 version of this dataset on publicdata.au, with 169 rows and 5 fields. The same version is kept at https://publicdata.au/d/abs-federal-electoral-divisions-2025/v/2025-07-14/ in 12 formats, with every earlier version and a query API. Each Zenodo version of this record is one publicdata.au version.The data is published by Australian Bureau of Statistics under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. The licence requires this attribution:Australian Bureau of Statistics, Australian Statistical Geography Standard (ASGS) Edition 3, CED_2025_AUST_GDA2020.zip, sourced 1 October 2026, https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs/edition-3-july-2021-june-2026/access-and-downloads/digital-boundary-files, licensed under CC BY 4.0.This is an independent republication. The publisher has not endorsed this site.The rows are in data.parquet, data.csv, data.json and data.xlsx. schema.json describes the fields, and publicdata.json names the version, licence, attribution and the SHA-256 of the publisher's file.

The 452 state and territory electoral divisions as the ABS built them from mesh blocks for 2025, one shape each with its state, in GDA2020.This is the 2025-07-14 version of this dataset on publicdata.…

The 452 state and territory electoral divisions as the ABS built them from mesh blocks for 2025, one shape each with its state, in GDA2020.This is the 2025-07-14 version of this dataset on publicdata.au, with 452 rows and 5 fields. The same version is kept at https://publicdata.au/d/abs-state-electoral-divisions-2025/v/2025-07-14/ in 12 formats, with every earlier version and a query API. Each Zenodo version of this record is one publicdata.au version.The data is published by Australian Bureau of Statistics under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. The licence requires this attribution:Australian Bureau of Statistics, Australian Statistical Geography Standard (ASGS) Edition 3, SED_2025_AUST_GDA2020.zip, sourced 1 October 2026, https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs/edition-3-july-2021-june-2026/access-and-downloads/digital-boundary-files, licensed under CC BY 4.0.This is an independent republication. The publisher has not endorsed this site.The rows are in data.parquet, data.csv, data.json and data.xlsx. schema.json describes the fields, and publicdata.json names the version, licence, attribution and the SHA-256 of the publisher's file.

The 2,644 postal areas of Australia, the ABS's census approximation of postcodes for 2021, one shape each, in GDA2020.This is the 2021-09-15 version of this dataset on publicdata.au, with 2,644 r…

The 2,644 postal areas of Australia, the ABS's census approximation of postcodes for 2021, one shape each, in GDA2020.This is the 2021-09-15 version of this dataset on publicdata.au, with 2,644 rows and 4 fields. The same version is kept at https://publicdata.au/d/abs-postal-areas-2021/v/2021-09-15/ in 12 formats, with every earlier version and a query API. Each Zenodo version of this record is one publicdata.au version.The data is published by Australian Bureau of Statistics under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. The licence requires this attribution:Australian Bureau of Statistics, Australian Statistical Geography Standard (ASGS) Edition 3, POA_2021_AUST_GDA2020_SHP.zip, sourced 1 October 2026, https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs/edition-3-july-2021-june-2026/access-and-downloads/digital-boundary-files, licensed under CC BY 4.0.This is an independent republication. The publisher has not endorsed this site.The rows are in data.parquet, data.csv, data.json and data.xlsx. schema.json describes the fields, and publicdata.json names the version, licence, attribution and the SHA-256 of the publisher's file.

The synthetic population for the state of California is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US C…

The synthetic population for the state of California is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US Census on selected demographic variables, which in this case are age (AGEP), household income (HINCP), household size (NP), race (RAC1P) and hispanic (HISP). The population includes people and households with demographic characteristics along with residential locations and general activity locations at parcel-level resolution and accuracy. All individuals are assigned activity sequences and activity locations through statistical matching methods. Finally, a contact network is constructed. The precise way in which this is done depends on the intended scenario. Here the target application is epidemic spread of influenza-like diseases, and the construction of the network is done at each location through a modified Erdos-Renyi random graph. About this Zenodo mirror: this record mirrors the synthetic population for California originally deposited in the UVA Dataverse. It is republished here to provide a stable, programmatically accessible source for the PhyloGeographic Analysis Similars (PhyloGAS) benchmarking framework, which consumes these files as its digital twin layer. The data are unmodified.

El monitoreo de la carga externa mediante sistemas de posicionamiento global permite describir la distribución de las demandas locomotoras y mecánicas durante el entrenamiento y la compe…

El monitoreo de la carga externa mediante sistemas de posicionamiento global permite describir la distribución de las demandas locomotoras y mecánicas durante el entrenamiento y la competición. El objetivo fue analizar la evolución de la carga externa en un microciclo competitivo, comparar los indicadores relativos de cinco entrenamientos con el partido oficial y examinar mediante Pearson las asociaciones entre las variables registradas durante la competición. Se desarrolló un estudio cuantitativo, observacional, no experimental, longitudinal, comparativo y correlacional. Se analizaron 65 registros jugadora-sesión correspondientes a los entrenamientos del 23 al 27 de febrero de 2026 y nueve registros competitivos válidos del partido del 28 de febrero; ocho jugadoras integraron la comparación pareada. Se calcularon estadísticos descriptivos, t de Student para muestras relacionadas o Wilcoxon, ajuste de Holm y correlación producto-momento de Pearson. El partido presentó valores superiores a los entrenamientos en metros por minuto (97,35 frente a 75,31), distancia superior a 13 km/h por minuto (18,62 frente a 9,29), sprints por hora (2,60 frente a 1,10), velocidad máxima (25,13 frente a 22,63 km/h) y PlayerLoad por minuto (10,82 frente a 9,89); todas las diferencias permanecieron significativas después del ajuste de Holm. En el partido, metros por minuto y PlayerLoad por minuto mostraron una correlación positiva muy fuerte (r = ,905; p ajustado = ,008). Se concluye que la competición incrementó la densidad locomotora y mecánica respecto del entrenamiento y que la interpretación aplicada debe integrar volumen, intensidad, tiempo de exposición y variabilidad individual.