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1. Averages Along the Primes: Improving and Sparse Bounds

   https://doi.org/10.1515/conop-2020-0003  

   Han, Rui; Krause, Ben; Lacey, Michael T.; Yang, Fan (February 2020, Concrete Operators)

   Abstract Consider averages along the prime integers ℙ given by {\mathcal{A}_N}f(x) = {N^{ - 1}}\sum\limits_{p \in \mathbb{P}:p \le N} {(\log p)f(x - p).} These averages satisfy a uniform scale-free ℓ p -improving estimate. For all 1 < p < 2, there is a constant C p so that for all integer N and functions f supported on [0, N ], there holds {N^{ - 1/p'}}{\left\| {{\mathcal{A}_N}f} \right\|_{\ell p'}} \le {C_p}{N^{ - 1/p}}{\left\| f \right\|_{\ell p}}. The maximal function 𝒜 * f = sup N |𝒜 N f | satisfies ( p , p ) sparse bounds for all 1 < p < 2. The latter are the natural variants of the scale-free bounds. As a corollary, 𝒜 * is bounded on ℓ p ( w ), for all weights w in the Muckenhoupt 𝒜 p class. No prior weighted inequalities for 𝒜 * were known. 

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2. Historical road network statistics for core-based statistical areas in the U.S. (1900 - 2010)

   https://doi.org/10.6084/m9.figshare.19584088.v1  

   Burghardt, Keith; Uhl, Johannes H. (January 2022, figshare)

   Tabulated statistics of road networks at the level of intersections and for built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical road networks developed by Johannes Uhl. See Burghardt et al. (2022) for details on the data processing.  Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Keith A. Burghardt, USC Information Sciences Institute, USA Codebook: these CBSA statistics are stratified by degree of aggregation. - CBSA_stats_diffFrom1950: Change in CBSA-aggregated patch statistics between 1950 and 2015 - CBSA_stats_by_decade: CBSA-aggregated patch statistics for each decade from 1900-2010 plus 2015 - CBSA_stats_by_decade: CBSA-aggregated cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics. - Patch_stats_by_decade: Individual patch statistics for each decade from 1900-2010 plus 2015 - Patch_stats_by_decade: Individual cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics. The statistics are the following: msaid: CBSA codeid: (if patch statistics) arbitrary int unique to each patch within the CBSA that yearyear: year of statisticspop: population within all CBSA countiespatch_bupr: built up property records (BUPR) within a patch (or sum of patches within CBSA)patch_bupl: built up property l (BUPL) within a patch (or sum of patches within CBSA)patch_bua: built up area (BUA) within a patch (or sum of patches within CBSA)all_bupr: Same as above but for all data in 2015 regardless of whether properties were in patchesall_bupl: Same as above but for all data in 2015 regardless of whether properties were in patchesall_bua: Same as above but for all data in 2015 regardless of whether properties were in patchesnum_nodes: number of nodes (intersections)num_edges: number of edges (roads between intersections)distance: total road length in kmk_mean: mean number of undirected roads per intersectionk1: fraction of nodes with degree 1k4plus: fraction of nodes with degree 4+bearing: histogram of different bearings between intersectionsentropy: entropy of bearing histogrammean_local_gridness: Griddedness used in textmean_local_gridness_max: Same as griddedness used in text but assumes we can have up to 3 quadrilaterals for degree 3 (maximum possible, although intersections will not necessarily create right angles) Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road Network Evolution in the Urban and Rural  United States Since 1900. Computers, Environment and Urban Systems. 

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3. ic4m2

   https://doi.org/10.5281/zenodo.6213793  

   Cooper, Vincent (January 2022, Zenodo)

   Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

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4. FSD-scatter

   https://doi.org/10.5281/zenodo.6212232  

   Cooper, Vincent (January 2022, Zenodo)

   Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

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5. ic4m3rad1

   https://doi.org/10.5281/zenodo.6214364  

   Cooper, Vincent (January 2022, Zenodo)

   Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

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