Graphs for all figures are provided along with codes that implement the results described in the paper. We simulate how a spin chain subject to timed local pulses develops long-range entanglement and how timed pulses can also drive a Hubbard chain to a maximally-correlated $\eta$-pairing state. All simulations are performed using exact diagonalization in Mathematica. In Figure 2 we obtain how the central-spin magnetization and the bipartite entanglement in an XY spin-1/2 chain evolves in time. We also obtain the distribution among symmetry sectors with different levels of entanglement and concurrence matrices that show the build-up of long-range Bell pairs. In Figure 3 we show how the result generalizes to larger systems and how the entanglement and preparation time scale with the system size. We also show how the protocol is not sensitive to random timing error of the pulses. In Figure 4 we calculate how the fidelity is affected by several types of imperfections, showing it is relatively robust. In Figure 7 we compute experimentally measurable spin-spin correlations at different stages of the protocol. In Figure 8 we calculate level statistics in the presence of integrability breaking and show that the scaling of entanglement and preparation time are largely unaffected. In Figure 5 we illustrate the protocol for $\eta$-pairing by simulating the evolution of a strongly-interacting, finite Hubbard chain. In Figure 6 we compute signatures of $eta$ pairing, including the average number of $\eta$ pairs, their momentum distribution, and the overlap with the maximally-correlated state as a function of system size.

This dataset provides information about the number of properties, residents, and average property values for Range View Circle cross streets in Rapid City, SD.

GAP species range data are coarse representations of the total areal extent a species occupies, in other words the geographic limits within which a species can be found (Morrison and Hall 2002). These data provide the geographic extent within which the USGS Gap Analysis Project delineates areas of suitable habitat for terrestrial vertebrate species in their species habitat maps. The range maps are created by attributing a vector file derived from the 12-digit Hydrologic Unit Dataset (USDA NRCS 2009). Modifications to that dataset are described here < https://www.sciencebase.gov/catalog/item/56d496eee4b015c306f17a42>. Attribution of the season range for each species was based on the literature and online sources (See Cross Reference section of the metadata). Attribution for each hydrologic unit within the range included values for origin (native, introduced, reintroduced, vagrant), occurrence (extant, possibly present, potentially present, extirpated), reproductive use (breeding, non-breeding, both) and season (year-round, summer, winter, migratory, vagrant). These species range data provide the biological context within which to build our species distribution models. Versioning, Naming Conventions and Codes: A composite version code is employed to allow the user to track the spatial extent, the date of the ground conditions, and the iteration of the data set for that extent/date. For example, CONUS_2001v1 represents the spatial extent of the conterminous US (CONUS), the ground condition year of 2001, and the first iteration (v1) for that extent/date. In many cases, a GAP species code is used in conjunction with the version code to identify specific data sets or files (i.e. Cooper’s Hawk Habitat Map named bCOHAx_CONUS_2001v1_HabMap).

This dataset provides information about the number of properties, residents, and average property values for Range View Drive cross streets in Austin, TX.

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-jrn&identifier=210380001 Webpage with information and links to data files for download

About this dataset

Context

The dataset presents median household incomes for various household sizes in South Range, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, South Range did not include 4, 5, 6, or 7-person households. Across the different household sizes in South Range the mean income is $51,844, and the standard deviation is $18,238. The coefficient of variation (CV) is 35.18%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,226. It then further increased to $65,869 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/south-range-mi-median-household-income-by-household-size.jpeg" alt="South Range, MI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income. You can refer the same here

Technological advances have steadily increased the detail of animal tracking datasets, yet fundamental data limitations exist for many species that cause substantial biases in home‐range estimation. Specifically, the effective sample size of a range estimate is proportional to the number of observed range crossings, not the number of sampled locations. Currently, the most accurate home‐range estimators condition on an autocorrelation model, for which the standard estimation frame‐works are based on likelihood functions, even though these methods are known to underestimate variance—and therefore ranging area—when effective sample sizes are small. Residual maximum likelihood (REML) is a widely used method for reducing bias in maximum‐likelihood (ML) variance estimation at small sample sizes. Unfortunately, we find that REML is too unstable for practical application to continuous‐time movement models. When the effective sample size N is decreased to N ≤ urn:x-wiley:2041210X:media:mee313270:mee313270-math-0001(10), which is common in tracking applications, REML undergoes a sudden divergence in variance estimation. To avoid this issue, while retaining REML’s first‐order bias correction, we derive a family of estimators that leverage REML to make a perturbative correction to ML. We also derive AIC values for REML and our estimators, including cases where model structures differ, which is not generally understood to be possible. Using both simulated data and GPS data from lowland tapir (Tapirus terrestris), we show how our perturbative estimators are more accurate than traditional ML and REML methods. Specifically, when urn:x-wiley:2041210X:media:mee313270:mee313270-math-0002(5) home‐range crossings are observed, REML is unreliable by orders of magnitude, ML home ranges are ~30% underestimated, and our perturbative estimators yield home ranges that are only ~10% underestimated. A parametric bootstrap can then reduce the ML and perturbative home‐range underestimation to ~10% and ~3%, respectively. Home‐range estimation is one of the primary reasons for collecting animal tracking data, and small effective sample sizes are a more common problem than is currently realized. The methods introduced here allow for more accurate movement‐model and home‐range estimation at small effective sample sizes, and thus fill an important role for animal movement analysis. Given REML’s widespread use, our methods may also be useful in other contexts where effective sample sizes are small.

This is the data required for plotting figure 4 and 5 in the paper "Operator Spreading, Duality, and the Noisy Long-Range FKPP Equation"[1]. The folder fig_4a contains data for alpha close to 1.5, and fig_4bc contains data for alpha close to 1 (also used in plotting figure 5).

Files with name *_b and *_r correspond to soft constraint and hard constraind, respectively. These two constraints are defined in the paper.

In each data file, one can find the following variables:

't': time;

'M': he distance to the furthest occupied site;

'Np': the total population;

'Ns': the total number of occupied sites;

'l': typical size of the light cone;

'X_int': integral of X(\{tau})*X(t-\{tau})d \{tau} from 0 to t;

'X_half_t': the value of X at time t/2;

Note 1: X can be one of M, Np, Ns, and l.

Note 2: figure 5 is the first figure in the supplementary material.

[1] Zhou, T., Brunet, É. and Qi, X., 2025. Operator Spreading, Duality, and the Noisy Long-Range FKPP Equation. arXiv preprint arXiv:2505.06353.

Though shorter laser pulses can also be produced, pulses of the 100 fs range are typically used in femtosecond kinetic measurements, which are comparable to characteristic times of the studied processes, making detection of the kinetic response functions inevitably distorted by convolution with the pulses applied. A description of this convolution in terms of experiments and measurable signals is given, followed by a detailed discussion of a large number of available methods to solve the convolution equation to get the undistorted kinetic signal, without any presupposed kinetic or photophysical model of the underlying processes. A thorough numerical test of several deconvolution methods is described, and two iterative time-domain methods (Bayesian and Jansson deconvolution) along with two inverse filtering frequency-domain methods (adaptive Wiener filtering and regularization) are suggested to use for the deconvolution of experimental femtosecond kinetic data sets. Adaptation of these methods to typical kinetic curve shapes is described in detail. We find that the model-free deconvolution gives satisfactory results compared to the classical “reconvolution” method where the knowledge of the kinetic and photophysical mechanism is necessary to perform the deconvolution. In addition, a model-free deconvolution followed by a statistical inference of the parameters of a model function gives less biased results for the relevant parameters of the model than simple reconvolution. We have also analyzed real-life experimental data and found that the model-free deconvolution methods can be successfully used to get undistorted kinetic curves in that case as well. A graphical computer program to perform deconvolution via inverse filtering and additional noise filters is also provided as Supporting Information. Though deconvolution methods described here were optimized for femtosecond kinetic measurements, they can be used for any kind of convolved data where measured experimental shapes are similar.

The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types.

The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance.

If you share or use this dataset, please cite [4] and [5] in any relevant documentation.

In addition, an image dataset for crack classification has also been published at [6].

References:

[1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873

[2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605

[3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434

[4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678

5 Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044

[6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78

About this dataset

Context

The dataset tabulates the South Range population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for South Range. The dataset can be utilized to understand the population distribution of South Range by age. For example, using this dataset, we can identify the largest age group in South Range.

Key observations

The largest age group in South Range, MI was for the group of age 55 to 59 years years with a population of 54 (10.61%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in South Range, MI was the Under 5 years years with a population of 9 (1.77%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the South Range is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of South Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Age. You can refer the same here

Central Plains Experimental Range Study for Long-Term Agroecosystem Research in Nunn, Colorado The Central Plains Experimental Range (CPER) is a site with the The Long-Term Agroecosystem Research (LTAR) Network, which consists of 18 sites across the continental United States (US) sponsored by the US Department of Agriculture, Agricultural Research Service, universities and non-governmental organizations. LTAR scientists seek to determine ways to ensure sustainability and enhance food production (and quality) and ecosystem services at broad regional scales. They are conducting common experiments across the LTAR network to compare traditional production strategies (“business as usual or BAU) with aspirational strategies, which include novel technologies and collaborations with farmers and ranchers. Within- and cross-site network success towards achieving the desired outcomes of enhancing quality food production and reducing environmental impact requires that LTAR scientists and collaborators have well-timed access to various data. We are striving to create opportunities to package and share long-term legacy observations from each site, with new data and metadata in useable, well documented and consistent formats for them. Resources in this dataset:Resource Title: Nunn, CO Central Plains Experimental Range Study (CONUCPER) CSV data. File Name: CONUCPER_csv_data.zipResource Description: CSV format data on Experimental Units, Field Sites, Grazing Plants, Grazing, Persons, Treatments, Weather Daily, Weather Station.

Chondrichthyes, an ancient and diverse class of vertebrates, are crucial to the health of marine ecosystems. Excessive demand for chondrichthyan products has increased fishing pressure, threatening ∼30% of species with extinction in recent decades. India is the second-largest shark landing nation globally and the province of Gujarat, is the largest contributor to its shark exports. Despite their significant contribution to global fish supplies, chondrichthyan fisheries in Gujarat remain understudied and many species, data deficient, posing challenges to the conservation of remaining populations in the region. Here, we report results from taxonomic assessment of elasmobranchs at four key landing sites in Gujarat. We identified thirty-one species of sharks and rays with a significant bias toward capture of females and juveniles by fisheries. Our data indicate the presence of nursery areas for species such as Sphyrna lewini and Rhynchobatus laevis in the neritic areas off Gujarat. Further, we discovered extensions of the current distribution range for three species -Torpedo sinuspersici, Carcharhinus sorrah, and Rhinobatos punctifer. Taxonomic identities for a subset of species were confirmed using genomic analyses conducted with portable DNA sequencing tools. We present assessments for six data deficient species in the region – Rhinobatos annandalei, Rhinoptera jayakari, Maculabatis bineeshi, Pateobatis bleekeri, T. sinuspersici, and Carcharhinus amboinensis. Our investigation underscores species with urgent conservation needs and reduces data deficiencies. These data will inform and pivot future scientific and conservation efforts to protect remaining populations of some of the most vulnerable Chondrichthyes in the Arabian Seas Region.

Transient killers whales inhabit the West Coast of the United States. Their range and movement patterns are difficult to ascertain, but are vital to understanding killer whale population dynamics and abundance trends. Satellite tagging of West Coast transient killer whales to determine range and movement patterns will provide data to assist in understanding transient killer whale populations. Locational data.

About this dataset

Context

The dataset tabulates the Grass Range population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Grass Range. The dataset can be utilized to understand the population distribution of Grass Range by age. For example, using this dataset, we can identify the largest age group in Grass Range.

Key observations

The largest age group in Grass Range, MT was for the group of age 70 to 74 years years with a population of 41 (42.27%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Grass Range, MT was the 25 to 29 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Grass Range is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Grass Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Age. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Range View Road cross streets in Ellensburg, WA.

Supplementary Table 1This file is a table containing data on fossil marine animal genera used in the referenced publication. Fields include: genus name; class; stratigraphic interval; species richness; number of occurrences; and characteristics of the geographic ranges of these genera. Data are derived from the Paleobiology Database, with modification.SupplementaryTable1Final.csv

This dataset contains ERA5 initial release (ERA5t) model level analysis parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Surface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.

This dataset contains ERA5 initial release (ERA5t) surface level forecast parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Surface level and model level analysis data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.