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

$\mathrm{D}^{0}$ ratio |$y$| < 0.5 / $3 < y < 3.5$ in pp collisions at $\sqrt{s}$ = 13 TeV

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 Circle cross streets in Silverthorne, CO.

A LiDAR-based 3D object detection dataset.

Find details of Haleigh Worthington 1685Rifle Range Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

The Four-Dimensional (4DWX) System is the product of seven years of R&D, sponsored by the US Army Test and Evaluation Command (ATEC) and by the Defense Threat Reduction Agency (DTRA). This dataset contains data from the Dugway Proving Grounds (DPG) in Utah. The DPG site 1 reports every 5 minutes; the other stations all report for 15 minutes. For more information, see www.4dwx.org.

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] (This dataset) 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

Find details of North America Range Hoods Inc Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

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 Four-Dimensional (4DWX) System is the product of seven years of R&D, sponsored by the US Army Test and Evaluation Command (ATEC) and by the Defense Thread Reduction Agency (DTRA). This dataset contains data from the White Sands Missle Range (WSMR) site in New Mexico. For more information, see www.4dwx.org.

There are no missing values in this dataset.

Our mission with this project is to provide an always up-to-date and freely accessible map of the cloud landscape for every major cloud service provider.

We've decided to kick things off with collecting SSL certificate data of AWS EC2 machines, considering the value of this data to security researchers. However, we plan to expand the project to include more data and providers in the near future. Your input and suggestions are incredibly valuable to us, so please don't hesitate to reach out on Twitter or Discord and let us know what areas you think we should prioritize next!

How to find origin IP of any domain or subdomain inside this database?

You can find origin IP for an example: instacart.com, Just search there instacart.com

You can use command as well if you are using linux. Open the dataset using curl or wget and then **cd ** folder now run command: find . -type f -iname "*.csv" -print0 | xargs -0 grep "word"

Like: find . -type f -iname "*.csv" -print0 | xargs -0 grep "instacart.com"

Done, You will see output.

How can SSL certificate data benefit you? The SSL data is organized into CSV files, with the following properties collected for every found certificate:

IP Address Common Name Organization Country Locality Province Subject Alternative DNS Name Subject Alternative IP address Self-signed (boolean)

IP Address Common Name Organization Country Locality Province Subject Alternative DNS Name Subject Alternative IP address Self-signed 1.2.3.4 example.com Example, Inc. US San Francisco California example.com 1.2.3.4 false 5.6.7.8 acme.net Acme, Inc. US Seattle Washington *.acme.net 5.6.7.8 false So what can you do with this data?

Enumerate subdomains of your target domains Search for your target's domain names (e.g. example.com) and find hits in the Common Name and Subject Alternative Name fields of the collected certificates. All IP ranges are scanned daily and the dataset gets updated accordingly so you are very likley to find ephemeral hosts before they are taken down.

Enumerate domains of your target companies Search for your target's company name (e.g. Example, Inc.), find hits in the Organization field, and explore the associated Common Name and Subject Alternative Name fields. The results will probably include subdomains of the domains you're familiar with and if you're in luck you might find new root domains expanding the scope.

Enumerate possible sub-subdomain enumeration target If the certificate is issued for a wildcard (e.g. *.foo.example.com), chances are there are other subdomains you can find by brute-forcing there. And you know how effective of this technique can be. Here are some wordlists to help you with that!

💡 Note: Remeber to monitor the dataset for daily updates to get notified whenever a new asset comes up!

Perform IP lookups Search for an IP address (e.g. 3.122.37.147) to find host names associated with it, and explore the Common Name, Subject Alternative Name, and Organization fields to gain find more information about that address.

Discover origin IP addresses to bypass proxy services When a website is hidden behind security proxy services like Cloudflare, Akamai, Incapsula, and others, it is possible to search for the host name (e.g., example.com) in the dataset. This search may uncover the origin IP address, allowing you to bypass the proxy. We've discussed a similar technique on our blog which you can find here!

Get a fresh dataset of live web servers Each IP address in the dataset corresponds to an HTTPS server running on port 443. You can use this data for large-scale research without needing to spend time collecting it yourself.

Whatever else you can think of If you use this data for a cool project or research, we would love to hear about it!

Additionally, below you will find a detailed explanation of our data collection process and how you can implement the same technique to gather information from your own IP ranges.

TB; DZ (Too big; didn't zoom):

We kick off the workflow with a simple bash script that retrieves AWS's IP ranges. Using a JQ query, we extract the IP ranges of EC2 machines by filtering for .prefixes[] | select(.service=="EC2") | .ip_prefix. Other services are excluded from this workflow since they don't support custom SSL certificates, making their data irrelevant for our dataset.

Then, we use mapcidr to divide the IP ranges obtained in step 1 into smaller ranges, each containing up to 100k hosts (Thanks, ProjectDiscovery team!). This step will be handy in the next step when we run the parallel scanning process.

At the time of writing, the EC2 IP ranges include over 57 million IP addresses, so scanning them all on a single machine would be impractical, which is where our file-splitter node comes into play.

This node iterates through the input from mapcidr and triggers individual jobs for each range. When executing this w...

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

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

AbstractSpecies expanding into new habitats as a result of climate change or human introductions will frequently encounter resident competitors. Theoretical models suggest that such interspecific competition can alter the speed of expansion and the shape of expanding range boundaries. However, competitive interactions are rarely considered when forecasting the success or speed of expansion, in part because there has been no direct experimental evidence that competition affects either expansion speed or boundary shape. Here we demonstrate that interspecific competition alters both expansion speed and range boundary shape. Using a two-species experimental system of the flour beetles Tribolium castaneum and Tribolium confusum, we show that interspecific competition dramatically slows expansion across a landscape over multiple generations. Using a parameterized stochastic model of expansion, we find that this slowdown can persist over the long-term. We also find that the shape of the moving range boundary changes continuously over many generations of expansion, first steepening and then becoming shallower, due to the competitive effect of the resident and density-dependent dispersal of the invader. This dynamic boundary shape suggests that current forecasting approaches assuming a constant shape could be misleading. More broadly, our results demonstrate that interactions between competing species can play a large role during range expansions and thus should be included in models and studies that monitor, forecast, or manage expansions in natural systems. Usage notesThis ZIP archive contains R scripts and data files necessary for reproducing the analysis and figures of the paper. See the README file in the archive for further details.

Many species are undergoing distributional changes in response to climate change. However, wide variability in range shifting rates has been observed across taxa, and even among closely-related species. Attempts to link climate-mediated range shifts to traits has often produced weak or conflicting results. Here we investigate interactive effects of developmental processes and environmental stress on the expression of traits relevant to range shifts. We use an individual-based modelling approach to assess how different developmental strategies affect range shift rates under a range of environmental conditions. We find that under stressful conditions, such as at the margins of the species’ fundamental niche, investment in prolonged development leads to the greatest rates of range shifting, especially when longer time in development leads to of improved fecundity and dispersal-related traits. However, under benign conditions, and when traits are less developmentally plastic, shorter development times are preferred for rapid range shifts, because higher generational frequency increases the number of individual dispersal events occurring over time. Our results suggest that the ability of a species to range shift depends not only on their dispersal and colonisation characteristics but also how these characteristics interact with developmental strategies. Benefits of any trait always depended on the environmental and developmental sensitivity of life history trait combinations, and the environmental conditions under which the range shift takes place. Without considering environmental and developmental sources of variation in the expression of traits relevant to range shifts, there is little hope of developing a general understanding of intrinsic drivers of range shift potential

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

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.