Context

Here is a list that shows the prime number list up to 10000. Source: easycalculation

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

File name definitions:

'...v_50_175_250_300...' - dataset for velocity ranges [50, 175] + [250, 300] m/s

'...v_175_250...' - dataset for velocity range [175, 250] m/s

'ANNdevelop...' - used to perform 9 parametric sub-analyses where, in each one, many ANNs are developed (trained, validated and tested) and the one yielding the best results is selected

'ANNtest...' - used to test the best ANN from each aforementioned parametric sub-analysis, aiming to find the best ANN model; this dataset includes the 'ANNdevelop...' counterpart

Where to find the input (independent) and target (dependent) variable values for each dataset/excel ?

input values in 'IN' sheet

target values in 'TARGET' sheet

Where to find the results from the best ANN model (for each target/output variable and each velocity range)?

open the corresponding excel file and the expected (target) vs ANN (output) results are written in 'TARGET vs OUTPUT' sheet

Check reference below (to be added when the paper is published)

https://www.researchgate.net/publication/328849817_11_Neural_Networks_-_Max_Disp_-_Railway_Beams

The CIFAR-10 and CIFAR-100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR-100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. (Sadly, the 80 million tiny images dataset has been thrown into the memory hole by its authors. Spotting the doublethink which was used to justify its erasure is left as an exercise for the reader.)

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.

Baseline results You can find some baseline replicable results on this dataset on the project page for cuda-convnet. These results were obtained with a convolutional neural network. Briefly, they are 18% test error without data augmentation and 11% with. Additionally, Jasper Snoek has a new paper in which he used Bayesian hyperparameter optimization to find nice settings of the weight decay and other hyperparameters, which allowed him to obtain a test error rate of 15% (without data augmentation) using the architecture of the net that got 18%.

Other results Rodrigo Benenson has collected results on CIFAR-10/100 and other datasets on his website; click here to view.

Dataset layout Python / Matlab versions I will describe the layout of the Python version of the dataset. The layout of the Matlab version is identical.

The archive contains the files data_batch_1, data_batch_2, ..., data_batch_5, as well as test_batch. Each of these files is a Python "pickled" object produced with cPickle. Here is a python2 routine which will open such a file and return a dictionary: python def unpickle(file): import cPickle with open(file, 'rb') as fo: dict = cPickle.load(fo) return dict And a python3 version: def unpickle(file): import pickle with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='bytes') return dict Loaded in this way, each of the batch files contains a dictionary with the following elements: data -- a 10000x3072 numpy array of uint8s. Each row of the array stores a 32x32 colour image. The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image. labels -- a list of 10000 numbers in the range 0-9. The number at index i indicates the label of the ith image in the array data.

The dataset contains another file, called batches.meta. It too contains a Python dictionary object. It has the following entries: label_names -- a 10-element list which gives meaningful names to the numeric labels in the labels array described above. For example, label_names[0] == "airplane", label_names[1] == "automobile", etc. Binary version The binary version contains the files data_batch_1.bin, data_batch_2.bin, ..., data_batch_5.bin, as well as test_batch.bin. Each of these files is formatted as follows: <1 x label><3072 x pixel> ... <1 x label><3072 x pixel> In other words, the first byte is the label of the first image, which is a number in the range 0-9. The next 3072 bytes are the values of the pixels of the image. The first 1024 bytes are the red channel values, the next 1024 the green, and the final 1024 the blue. The values are stored in row-major order, so the first 32 bytes are the red channel values of the first row of the image.

Each file contains 10000 such 3073-byte "rows" of images, although there is nothing delimiting the rows. Therefore each file should be exactly 30730000 bytes long.

There is another file, called batches.meta.txt. This is an ASCII file that maps numeric labels in the range 0-9 to meaningful class names. It is merely a list of the 10 class names, one per row. The class name on row i corresponds to numeric label i.

The CIFAR-100 dataset This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). Her...

This paper addresses the computational methods and challenges associated with prime number generation, a critical component in encryption algorithms for ensuring data security. The generation of prime numbers efficiently is a critical challenge in various domains, including cryptography, number theory, and computer science. The quest to find more effective algorithms for prime number generation is driven by the increasing demand for secure communication and data storage and the need for efficient algorithms to solve complex mathematical problems. Our goal is to address this challenge by presenting two novel algorithms for generating prime numbers: one that generates primes up to a given limit and another that generates primes within a specified range. These innovative algorithms are founded on the formulas of odd-composed numbers, allowing them to achieve remarkable performance improvements compared to existing prime number generation algorithms. Our comprehensive experimental results reveal that our proposed algorithms outperform well-established prime number generation algorithms such as Miller-Rabin, Sieve of Atkin, Sieve of Eratosthenes, and Sieve of Sundaram regarding mean execution time. More notably, our algorithms exhibit the unique ability to provide prime numbers from range to range with a commendable performance. This substantial enhancement in performance and adaptability can significantly impact the effectiveness of various applications that depend on prime numbers, from cryptographic systems to distributed computing. By providing an efficient and flexible method for generating prime numbers, our proposed algorithms can develop more secure and reliable communication systems, enable faster computations in number theory, and support advanced computer science and mathematics research.

Point Cloud MNIST 2D

This is a simple dataset for getting started with Machine Learning for point cloud data. It take the original MNIST and converts each of the non-zero pixels into points in a 2D space. The idea is to classify each collection of point (rather than images) to the same label as in the MNIST. The source for generating this dataset can be found in this repository: cgarciae/point-cloud-mnist-2D

Format

There are 2 files: train.csv and test.csv. Each file has the columns

label,x0,y0,v0,x1,y1,v1,...,x350,y350,v350

where

• label contains the target label in the range [0, 9]
• x{i} contain the x position of the pixel/point as viewed in a Cartesian plane in the range [-1, 27].
• y{i} contain the y position of the pixel/point as viewed in a Cartesian plane in the range [-1, 27].
• v{i} contain the value of the pixel in the range [-1, 255].

Padding

The maximum number of point found on a image was 351, images with less points where padded to this length using the following values:

• x{i} = -1
• y{i} = -1
• v{i} = -1

Subsamples

To make the challenge more interesting you can also try to solve the problem using a subset of points, e.g. the first N. Here are some visualizations of the dataset using different amounts of points:

50

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2Fbbf5393884480e3d24772344e079c898%2F50.png?generation=1579911143877077&alt=media" alt="50">

100

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F5a83f6f5f7c5791e3c1c8e9eba2d052b%2F100.png?generation=1579911238988368&alt=media" alt="100">

200

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F202098ed0da35c41ae45dfc32e865972%2F200.png?generation=1579911264286372&alt=media" alt="200">

351

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F5c733566f8d689c5e0fd300440d04da2%2Fmax.png?generation=1579911289750248&alt=media" alt="">

Distribution

This histogram of the distribution the number of points per image in the dataset can give you a general idea of how difficult each variation can be.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F9eb3b463f77a887dae83a7af0eb08c7d%2Flengths.png?generation=1579911380397412&alt=media" alt="">

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

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These results are from the rail demo of 5G-PICTURE (www.5g-picture-project.eu). For more details see Deliverable D6.3 where there are also plotted figures. Dataset 4-1 This dataset is generated by a computer model. The modulation and coding scheme (MCS) of a mmWave link between an access point (AP) and a station (STA) mounted on the roof of a train is plotted as a function of the distance between AP and STA. The IEEE 802.11ad single-carrier technology is assumed, and typical conditions when the range is approximately 350 m – in other words the lowest MCS, MCS1 can be supported up to this distance. The MCS takes integer values in range 1 to 12. Dataset 4-2 This dataset is generated by the same computer model as dataset 1. In this case we plot the predicted data rate (at the application layer in Gbps) and SNR (in dB). In the simulation we assume SNR requirements of an ideal AWGN channel and adjust the link budget to align with the typical range observed in the field. The SNR is also capped at a maximum value of 25dB commensurate with a real device. Datasets 4-5 to 4-12 This is a measured dataset from field testing of the Rail Demo. In the field test the train drives from one end of the test network to the other (over approximately 2km). Traffic (TCP iperf3) is generated within each trackside mmWave AP and sent to the train STAs when an association has been established. The datasets include measurement performed by the two STA of a single train node (TN), labelled ‘Train-1’. One STA has a radio facing forwards and one is facing backwards (see deliverable D6.3). These form the two datasets for each parameter. When a STA is not associated (i.e. has no mmWave link) the parameter is not recorded since no data packets are received. The following parameters are captured: Datasets 4-5 and 4-6 The modulation and coding scheme (MCS) of a mmWave link between an AP and each STA is logged. Datasets 4-7 and 4-8 The SNR is logged. SNR is measured in dB. Datasets 4-9 and 4-10 The sector ID here indicates which beam has been chosen by the TN radios when receiving packets. A STA maintains a beambook of 13 directional beams, and a beamforming protocol identify the best beam to use. The Sector ID is an integer from 1 to 13. Low beam numbers are close to boresight, whilst the highest numbers (up to 13) imply beam steering up to 45 degrees away from boresight. Odd numbers represent pointing to the left and even number point to the right. Datasets 4-11 and 4-12 This plots the received data rate by each STA at the application layer (TCP iperf3). Unit Mbps.

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The Low, Slow, and Small Target Detection Dataset for Digital Array Surveillance Radar (LSS-DAUR-1.0) includes a total of 154 items of Range-Doppler (RD) complex data and Track (TR) point data collected from 6 types of targets (passenger ships, speedboats, helicopters, rotary-wing UAVs, birds, fixed-wing UAVs). It can support research on detection, classification and recognition of typical maritime targets by digital array radar. 1. Data Collection Process The data collection process mainly includes: Set radar parameters → Detect targets → Collect echo signal data → Record target information → Determine the range bin where the target is located → Extract target Doppler data → Extract target track data. 2. Target Situation The collected typical sea-air targets include 6 categories: passenger ships, speedboats, helicopters, rotary-wing UAVs, birds and fixed-wing UAVs. 3. Range-Doppler (RD) Complex Data By calculating the target range, the echo data of the range bin where the target is located is intercepted. Based on the collected measured data, the Low, Slow, and Small Target RD Dataset for Digital Array Surveillance Radar is constructed, which includes 10 groups of passenger ship (passenger ship) data, 11 groups of speedboat (speedboat) data, 10 groups of helicopter (helicopter) data, 18 groups of rotary-wing UAV (rotary drone) data, 17 groups of bird (bird) data, and 11 groups of fixed-wing UAV (fixed-wing drone) data, totaling 77 groups. Each group of data includes the target's Doppler, GPS time, frame count, etc. The naming method of target RD data is: Start Collection Time_DAUR_RD_Target Type_Serial Number_Target Batch Number.Mat. For example, the file name "20231207093748_DAUR_RD_Passenger Ship_01_2619.mat", where "20231207" represents the date of data collection, "093748" represents the start time of collection which is 09:37:48, "DAUR" represents Digital Array Surveillance Radar, "RD" represents Range-Doppler spectrum complex data, "Passenger Ship_01" represents the target type is passenger ship with serial number 01, and "2619" represents the target track batch number. 4. Track (TR) Data Extract the track data within the time period of the echo data, and construct the Low, Slow, and Small Target TR Dataset for Digital Array Surveillance Radar, which includes 10 groups of passenger ship (passenger ship) data, 11 groups of speedboat (speedboat) data, 10 groups of helicopter (helicopter) data, 18 groups of rotary-wing UAV (rotary drone) data, 17 groups of bird (bird) data, and 11 groups of fixed-wing UAV (fixed-wing drone) data, totaling 77 groups. Each group of data includes target range, target azimuth, elevation angle, target speed, GPS time, signal-to-noise ratio (SNR), etc. The TR data and RD data have the same time and batch number, and they are data of different dimensions for the same target in the same time period. The naming method of target TR data is: Start Collection Time_DAUR_TR_Target Type_Serial Number_Target Batch Number.Mat. For example, the file name "20231207093748_DAUR_TR_Passenger Ship_01_2619.mat", where "20231207" represents the date of data collection, "093748" represents the start time of collection which is 09:37:48, "DAUR" represents Digital Array Surveillance Radar, "TR" represents Range-Doppler spectrum complex data, "Passenger Ship_01" represents the target type is passenger ship with serial number 01, and "2619" represents the target track batch number.

About this dataset

Context

The dataset tabulates the population of South Range by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for South Range. The dataset can be utilized to understand the population distribution of South Range by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in South Range. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for South Range.

Key observations

Largest age group (population): Male # 20-24 years (49) | Female # 20-24 years (50). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the South Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the South Range is shown in the following column.
• Population (Female): The female population in the South Range is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in South Range for each age group.

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 Gender. You can refer the same here

This dataset contains the raw fMRI data accompanying the paper "Overlapping but separate number representations in the intraparietal sulcus – probing format- and modality-independence in sighted Braille readers" Please cite the above paper if you use this data.

The dataset includes:

1. T1-weighted images for each subject
2. functional whole-brain EPI data from 8 runs for each participant
3. event files containing onsets, durations and trial types for all trials in the corresponding run.

Twenty-five healthy individuals (all female; median age = 25, range = 22–32) participated in the experiment. All participants were right-handed and monolingual, with normal or corrected-to-normal vision. Participants were students or graduates with a minimum of 3 years of higher education (median years of higher education = 5; range = 3–12).

In this experiment we used a rapid block design. The numerosities used were: 2,4,6 and 8 and were presented either as visual Arabic digits, visual sets of dots, or tactile Braille numbers. - Each run lasted 402 seconds and contained 6 stimulus sequences separated by fixation - Each sequence contained 12 blocks of stimuli - Each block contained 4–6 trials of the same kind - In each block, participants had to indicate whether the presented numerosity was higher or lower than 5 by pressing one of the two buttons on the response pad

For a full description of the paradigm and the analysis we chose to perform, please see the paper.

Aim: Despite the wide distribution of many parasites around the globe, the range of individual species varies significantly even among phylogenetically related taxa. Since parasites need suitable hosts to complete their development, parasite geographical and environmental ranges should be limited to communities where their hosts are found. Parasites may also suffer from a trade-off between being locally abundant or widely dispersed. We hypothesize that the geographical and environmental ranges of parasites are negatively associated to their host specificity and their local abundance. Location: Worldwide Time period: 2009 to 2021 Major taxa studied: Avian haemosporidian parasites Methods: We tested these hypotheses using a global database which comprises data on avian haemosporidian parasites from across the world. For each parasite lineage, we computed five metrics: phylogenetic host-range, environmental range, geographical range, and their mean local and total number of observations in the database. Phylogenetic generalized least squares models were ran to evaluate the influence of phylogenetic host-range and total and local abundances on geographical and environmental range. In addition, we analysed separately the two regions with the largest amount of available data: Europe and South America. Results: We evaluated 401 lineages from 757 localities and observed that generalism (i.e. phylogenetic host range) associates positively to both the parasites’ geographical and environmental ranges at global and Europe scales. For South America, generalism only associates with geographical range. Finally, mean local abundance (mean local number of parasite occurrences) was negatively related to geographical and environmental range. This pattern was detected worldwide and in South America, but not in Europe. Main Conclusions: We demonstrate that parasite specificity is linked to both their geographical and environmental ranges. The fact that locally abundant parasites present restricted ranges, indicates a trade-off between these two traits. This trade-off, however, only becomes evident when sufficient heterogeneous host communities are considered. Methods We compiled data on haemosporidian lineages from the MalAvi database (http://130.235.244.92/Malavi/ , Bensch et al. 2009) including all the data available from the “Grand Lineage Summary” representing Plasmodium and Haemoproteus genera from wild birds and that contained information regarding location. After checking for duplicated sequences, this dataset comprised a total of ~6200 sequenced parasites representing 1602 distinct lineages (775 Plasmodium and 827 Haemoproteus) collected from 1139 different host species and 757 localities from all continents except Antarctica (Supplementary figure 1, Supplementary Table 1). The parasite lineages deposited in MalAvi are based on a cyt b fragment of 478 bp. This dataset was used to calculate the parasites’ geographical, environmental and phylogenetic ranges. Geographical range All analyses in this study were performed using R version 4.02. In order to estimate the geographical range of each parasite lineage, we applied the R package “GeoRange” (Boyle, 2017) and chose the variable minimum spanning tree distance (i.e., shortest total distance of all lines connecting each locality where a particular lineage has been found). Using the function “create.matrix” from the “fossil” package, we created a matrix of lineages and coordinates and employed the function “GeoRange_MultiTaxa” to calculate the minimum spanning tree distance for each parasite lineage distance (i.e. shortest total distance in kilometers of all lines connecting each locality). Therefore, as at least two distinct sites are necessary to calculate this distance, parasites observed in a single locality could not have their geographical range estimated. For this reason, only parasites observed in two or more localities were considered in our phylogenetically controlled least squares (PGLS) models. Host and Environmental diversity Traditionally, ecologists use Shannon entropy to measure diversity in ecological assemblages (Pielou, 1966). The Shannon entropy of a set of elements is related to the degree of uncertainty someone would have about the identity of a random selected element of that set (Jost, 2006). Thus, Shannon entropy matches our intuitive notion of biodiversity, as the more diverse an assemblage is, the more uncertainty regarding to which species a randomly selected individual belongs. Shannon diversity increases with both the assemblage richness (e.g., the number of species) and evenness (e.g., uniformity in abundance among species). To compare the diversity of assemblages that vary in richness and evenness in a more intuitive manner, we can normalize diversities by Hill numbers (Chao et al., 2014b). The Hill number of an assemblage represents the effective number of species in the assemblage, i.e., the number of equally abundant species that are needed to give the same value of the diversity metric in that assemblage. Hill numbers can be extended to incorporate phylogenetic information. In such case, instead of species, we are measuring the effective number of phylogenetic entities in the assemblage. Here, we computed phylogenetic host-range as the phylogenetic Hill number associated with the assemblage of hosts found infected by a given parasite. Analyses were performed using the function “hill_phylo” from the “hillr” package (Chao et al., 2014a). Hill numbers are parameterized by a parameter “q” that determines the sensitivity of the metric to relative species abundance. Different “q” values produce Hill numbers associated with different diversity metrics. We set q = 1 to compute the Hill number associated with Shannon diversity. Here, low Hill numbers indicate specialization on a narrow phylogenetic range of hosts, whereas a higher Hill number indicates generalism across a broader phylogenetic spectrum of hosts. We also used Hill numbers to compute the environmental range of sites occupied by each parasite lineage. Firstly, we collected the 19 bioclimatic variables from WorldClim version 2 (http://www.worldclim.com/version2) for all sites used in this study (N = 713). Then, we standardized the 19 variables by centering and scaling them by their respective mean and standard deviation. Thereafter, we computed the pairwise Euclidian environmental distance among all sites and used this distance to compute a dissimilarity cluster. Finally, as for the phylogenetic Hill number, we used this dissimilarity cluster to compute the environmental Hill number of the assemblage of sites occupied by each parasite lineage. The environmental Hill number for each parasite can be interpreted as the effective number of environmental conditions in which a parasite lineage occurs. Thus, the higher the environmental Hill number, the more generalist the parasite is regarding the environmental conditions in which it can occur. Parasite phylogenetic tree A Bayesian phylogenetic reconstruction was performed. We built a tree for all parasite sequences for which we were able to estimate the parasite’s geographical, environmental and phylogenetic ranges (see above); this represented 401 distinct parasite lineages. This inference was produced using MrBayes 3.2.2 (Ronquist & Huelsenbeck, 2003) with the GTR + I + G model of nucleotide evolution, as recommended by ModelTest (Posada & Crandall, 1998), which selects the best-fit nucleotide substitution model for a set of genetic sequences. We ran four Markov chains simultaneously for a total of 7.5 million generations that were sampled every 1000 generations. The first 1250 million trees (25%) were discarded as a burn-in step and the remaining trees were used to calculate the posterior probabilities of each estimated node in the final consensus tree. Our final tree obtained a cumulative posterior probability of 0.999. Leucocytozoon caulleryi was used as the outgroup to root the phylogenetic tree as Leucocytozoon spp. represents a basal group within avian haemosporidians (Pacheco et al., 2020).

The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an OOPSLA 2020 paper. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the IDoFT dataset.

The dataset will be presented at the 1st International Flaky Tests Workshop 2024 (FTW 2024). Please refer to our extended abstract for more details about the motivation for and context of this dataset.

The following table provides a summary of the data.

Slug (Module) FIC Hash Tests Commits Av. Commits/Test Flaky Tests Tests w/ Consistent Failures Total Distinct Histories

TooTallNate/Java-WebSocket 822d40 146 75 75 24 1 2.6x10^9

apereo/java-cas-client (cas-client-core) 5e3655 157 65 61.7 3 2 1.0x10^7

eclipse-ee4j/tyrus (tests/e2e/standard-config) ce3b8c 185 16 16 12 0 261

feroult/yawp (yawp-testing/yawp-testing-appengine) abae17 1 191 191 1 1 8

fluent/fluent-logger-java 5fd463 19 131 105.6 11 2 8.0x10^32

fluent/fluent-logger-java 87e957 19 160 122.4 11 3 2.1x10^31

javadelight/delight-nashorn-sandbox d0d651 81 113 100.6 2 5 4.2x10^10

javadelight/delight-nashorn-sandbox d19eee 81 93 83.5 1 5 2.6x10^9

sonatype-nexus-community/nexus-repository-helm 5517c8 18 32 32 0 0 18

spotify/helios (helios-services) 23260 190 448 448 0 37 190

spotify/helios (helios-testing) 78a864 43 474 474 0 7 43

The columns are composed of the following variables:

Slug (Module): The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.

FIC Hash: The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes.

Tests: The number of distinct test class and method combinations over the entire considered commit range.

Commits: The number of commits in the considered commit range

Av. Commits/Test: The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.

Flaky Tests: The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.

Tests w/ Consistent Failures: The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.

Total Distinct Histories: The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.

(1)Vector, human and non-human hosts natural death rates were estimated as 1/individual longevity. The range of variation of longevity (i.e. 1/death rate parameter defined in the model), as those are the raw data found in the literature (see sections ‘Vector local growth rate’ and ‘Human and non-human hosts natural death rates’ in Text S1).(2)Death rates were calculated as the sum of the natural death rate of human or non-human hosts and additional mortality imposed by the pathogen to infectious and ‘recovered’ individuals (as calculated in section ‘Human and non-human hosts mortality induced by the pathogen’ in Text S1).

CWHR species range datasets represent the maximum current geographic extent of each species within California. Ranges were originally delineated at a scale of 1:5,000,000 by species-level experts more than 30 years ago and have gradually been revised at a scale of 1:1,000,000. Species occurrence data are used in defining species ranges, but range polygons may extend beyond the limits of extant occurrence data for a particular species. When drawing range boundaries, CDFW seeks to err on the side of commission rather than omission. This means that CDFW may include areas within a range based on expert knowledge or other available information, despite an absence of confirmed occurrences, which may be due to a lack of survey effort. The degree to which a range polygon is extended beyond occurrence data will vary among species, depending upon each species’ vagility, dispersal patterns, and other ecological and life history factors. The boundary line of a range polygon is drawn with consideration of these factors and is aligned with standardized boundaries including watersheds (NHD), ecoregions (USDA), or other ecologically meaningful delineations such as elevation contour lines. While CWHR ranges are meant to represent the current range, once an area has been designated as part of a species’ range in CWHR, it will remain part of the range even if there have been no documented occurrences within recent decades. An area is not removed from the range polygon unless experts indicate that it has not been occupied for a number of years after repeated surveys or is deemed no longer suitable and unlikely to be recolonized. It is important to note that range polygons typically contain areas in which a species is not expected to be found due to the patchy configuration of suitable habitat within a species’ range. In this regard, range polygons are coarse generalizations of where a species may be found. This data is available for download from the CDFW website: https://www.wildlife.ca.gov/Data/CWHR. The following data sources were collated for the purposes of range mapping and species habitat modeling by RADMAP. Each focal taxon’s location data was extracted (when applicable) from the following list of sources. BIOS datasets are bracketed with their “ds” numbers and can be located on CDFW’s BIOS viewer: https://wildlife.ca.gov/Data/BIOS. California Natural Diversity Database, Terrestrial Species Monitoring [ds2826], North American Bat Monitoring Data Portal, VertNet, Breeding Bird Survey, Wildlife Insights, eBird, iNaturalist, other available CDFW or partner data.

The exercise after this contains questions that are based on the housing dataset.

1. How many houses have a waterfront? a. 21000 b. 21450 c. 163 d. 173

2. How many houses have 2 floors? a. 2692 b. 8241 c. 10680 d. 161

3. How many houses built before 1960 have a waterfront? a. 80 b. 7309 c. 90 d. 92

4. What is the price of the most expensive house having more than 4 bathrooms? a. 7700000 b. 187000 c. 290000 d. 399000

5. For instance, if the ‘price’ column consists of outliers, how can you make the data clean and remove the redundancies? a. Calculate the IQR range and drop the values outside the range. b. Calculate the p-value and remove the values less than 0.05. c. Calculate the correlation coefficient of the price column and remove the values less than the correlation coefficient. d. Calculate the Z-score of the price column and remove the values less than the z-score.

6. What are the various parameters that can be used to determine the dependent variables in the housing data to determine the price of the house? a. Correlation coefficients b. Z-score c. IQR Range d. Range of the Features

7. If we get the r2 score as 0.38, what inferences can we make about the model and its efficiency? a. The model is 38% accurate, and shows poor efficiency. b. The model is showing 0.38% discrepancies in the outcomes. c. Low difference between observed and fitted values. d. High difference between observed and fitted values.

8. If the metrics show that the p-value for the grade column is 0.092, what all inferences can we make about the grade column? a. Significant in presence of other variables. b. Highly significant in presence of other variables c. insignificance in presence of other variables d. None of the above

9. If the Variance Inflation Factor value for a feature is considerably higher than the other features, what can we say about that column/feature? a. High multicollinearity b. Low multicollinearity c. Both A and B d. None of the above

CWHR species range datasets represent the maximum current geographic extent of each species within California. Ranges were originally delineated at a scale of 1:5,000,000 by species-level experts more than 30 years ago and have gradually been revised at a scale of 1:1,000,000. Species occurrence data are used in defining species ranges, but range polygons may extend beyond the limits of extant occurrence data for a particular species. When drawing range boundaries, CDFW seeks to err on the side of commission rather than omission. This means that CDFW may include areas within a range based on expert knowledge or other available information, despite an absence of confirmed occurrences, which may be due to a lack of survey effort. The degree to which a range polygon is extended beyond occurrence data will vary among species, depending upon each species’ vagility, dispersal patterns, and other ecological and life history factors. The boundary line of a range polygon is drawn with consideration of these factors and is aligned with standardized boundaries including watersheds (NHD), ecoregions (USDA), or other ecologically meaningful delineations such as elevation contour lines. While CWHR ranges are meant to represent the current range, once an area has been designated as part of a species’ range in CWHR, it will remain part of the range even if there have been no documented occurrences within recent decades. An area is not removed from the range polygon unless experts indicate that it has not been occupied for a number of years after repeated surveys or is deemed no longer suitable and unlikely to be recolonized. It is important to note that range polygons typically contain areas in which a species is not expected to be found due to the patchy configuration of suitable habitat within a species’ range. In this regard, range polygons are coarse generalizations of where a species may be found. This data is available for download from the CDFW website: https://www.wildlife.ca.gov/Data/CWHR. The following data sources were collated for the purposes of range mapping and species habitat modeling by RADMAP. Each focal taxon’s location data was extracted (when applicable) from the following list of sources. BIOS datasets are bracketed with their “ds” numbers and can be located on CDFW’s BIOS viewer: https://wildlife.ca.gov/Data/BIOS. California Natural Diversity Database, Terrestrial Species Monitoring [ds2826], North American Bat Monitoring Data Portal, VertNet, Breeding Bird Survey, Wildlife Insights, eBird, iNaturalist, other available CDFW or partner data.

About this dataset

Context

The dataset tabulates the population of Grass Range by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Grass Range. The dataset can be utilized to understand the population distribution of Grass Range by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Grass Range. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Grass Range.

Key observations

Largest age group (population): Male # 35-39 years (7) | Female # 70-74 years (36). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Grass Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Grass Range is shown in the following column.
• Population (Female): The female population in the Grass Range is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Grass Range for each age group.

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 Gender. You can refer the same here